text
stringlengths 5
22M
| id
stringlengths 12
177
| metadata
dict | __index_level_0__
int64 0
1.37k
|
|---|---|---|---|
import React from 'react';
import { IContextualMenuProps, Persona, PersonaSize, PrimaryButton } from '@fluentui/react';
import { useAccount, useMsal } from '@azure/msal-react';
export const UserMenu: React.FunctionComponent = () => {
const { instance, accounts } = useMsal();
const account = useAccount(accounts[0] || {});
const menuProps: IContextualMenuProps = {
shouldFocusOnMount: true,
directionalHint: 6, // bottom right edge
items: [
{
key: 'logout',
text: 'Logout',
iconProps: { iconName: 'SignOut' },
onClick: () => {
instance.logout(); // will use MSAL to logout and redirect to the /logout page
}
}
]
};
return (
<div className='tre-user-menu'>
<PrimaryButton menuProps={menuProps} style={{background:'none', border:'none'}}>
<Persona
text={account?.name}
size={PersonaSize.size32}
imageAlt={account?.name}
/>
</PrimaryButton>
</div>
);
};
|
AzureTRE/ui/app/src/components/shared/UserMenu.tsx/0
|
{
"file_path": "AzureTRE/ui/app/src/components/shared/UserMenu.tsx",
"repo_id": "AzureTRE",
"token_count": 417
}
| 141 |
import React, { useContext, useEffect, useState } from 'react';
import { useNavigate, useParams } from 'react-router-dom';
import { ApiEndpoint } from '../../models/apiEndpoints';
import { useAuthApiCall, HttpMethod } from '../../hooks/useAuthApiCall';
import { UserResource } from '../../models/userResource';
import { WorkspaceContext } from '../../contexts/WorkspaceContext';
import { ResourceHeader } from '../shared/ResourceHeader';
import { Resource } from '../../models/resource';
import { useComponentManager } from '../../hooks/useComponentManager';
import { ResourceBody } from '../shared/ResourceBody';
interface UserResourceItemProps {
userResource?: UserResource;
updateUserResource: (u: UserResource) => void;
removeUserResource: (u: UserResource) => void;
}
export const UserResourceItem: React.FunctionComponent<UserResourceItemProps> = (props: UserResourceItemProps) => {
const { workspaceServiceId, userResourceId } = useParams();
const [userResource, setUserResource] = useState({} as UserResource);
const apiCall = useAuthApiCall();
const workspaceCtx = useContext(WorkspaceContext);
const navigate = useNavigate();
const latestUpdate = useComponentManager(
userResource,
(r: Resource) => { props.updateUserResource(r as UserResource); setUserResource(r as UserResource); },
(r: Resource) => {
props.removeUserResource(r as UserResource);
if (workspaceCtx.workspace.id)
navigate(`/${ApiEndpoint.Workspaces}/${workspaceCtx.workspace.id}/${ApiEndpoint.WorkspaceServices}/${workspaceServiceId}`);
}
);
useEffect(() => {
const getData = async () => {
// did we get passed the workspace service, or shall we get it from the api?
if (props.userResource && props.userResource.id) {
setUserResource(props.userResource);
} else if (workspaceCtx.workspace.id) {
let ur = await apiCall(`${ApiEndpoint.Workspaces}/${workspaceCtx.workspace.id}/${ApiEndpoint.WorkspaceServices}/${workspaceServiceId}/${ApiEndpoint.UserResources}/${userResourceId}`, HttpMethod.Get, workspaceCtx.workspaceApplicationIdURI);
setUserResource(ur.userResource);
}
};
getData();
}, [apiCall, props.userResource, workspaceCtx.workspaceApplicationIdURI, userResourceId, workspaceServiceId, workspaceCtx.workspace.id]);
return (
userResource && userResource.id ?
<>
<ResourceHeader resource={userResource} latestUpdate={latestUpdate} />
<ResourceBody resource={userResource} />
</>
: <></>
);
};
|
AzureTRE/ui/app/src/components/workspaces/UserResourceItem.tsx/0
|
{
"file_path": "AzureTRE/ui/app/src/components/workspaces/UserResourceItem.tsx",
"repo_id": "AzureTRE",
"token_count": 838
}
| 142 |
import { User } from "./user";
export interface AirlockRequest {
id: string;
resourceVersion: number;
createdBy: User;
createdWhen: number;
updatedBy: User;
updatedWhen: number;
history: Array<AirlockRequestHistoryItem>;
workspaceId: string;
type: AirlockRequestType;
files: Array<{name: string, size: number}>;
title: string;
businessJustification: string;
status: AirlockRequestStatus;
reviewUserResources: {[key: string]: AirlockReviewUserResource};
allowedUserActions: Array<AirlockRequestAction>;
reviews?: Array<AirlockReview>;
statusMessage?: string;
etag?: string
}
export interface AirlockRequestHistoryItem {
resourceVersion: number;
updatedWhen: number;
updatedBy: User;
properties: {};
}
export enum AirlockRequestType {
Import = 'import',
Export = 'export'
}
export enum AirlockRequestStatus {
Draft = 'draft',
InReview = 'in_review',
Approved = 'approved',
ApprovalInProgress = 'approval_in_progress',
RejectionInProgress = 'rejection_in_progress',
Rejected = 'rejected',
Blocked = 'blocked',
BlockingInProgress = 'blocking_in_progress',
Submitted = 'submitted',
Cancelled = 'cancelled',
Failed = 'failed'
}
export interface NewAirlockRequest {
type: AirlockRequestType;
title: string;
businessJustification: string;
}
export enum AirlockRequestAction {
Cancel = 'cancel',
Submit = 'submit',
Review = 'review'
}
export const AirlockFilesLinkValidStatus = [
AirlockRequestStatus.Draft,
AirlockRequestStatus.Approved,
]
export enum AirlockReviewDecision {
Approved = 'approved',
Rejected = 'rejected'
}
export interface AirlockReview {
id: string,
dateCreated: number,
reviewDecision: AirlockReviewDecision,
decisionExplanation: string,
reviewer: User
}
export interface AirlockReviewUserResource {
workspaceId: string,
workspaceServiceId: string,
userResourceId: string
}
|
AzureTRE/ui/app/src/models/airlock.ts/0
|
{
"file_path": "AzureTRE/ui/app/src/models/airlock.ts",
"repo_id": "AzureTRE",
"token_count": 592
}
| 143 |
/// <reference types="react-scripts" />
|
AzureTRE/ui/app/src/react-app-env.d.ts/0
|
{
"file_path": "AzureTRE/ui/app/src/react-app-env.d.ts",
"repo_id": "AzureTRE",
"token_count": 11
}
| 144 |
# coding: utf-8
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from ast import Global
import os
import sys
from sklearn.metrics import f1_score
from sklearn.preprocessing import MultiLabelBinarizer
pred_file = sys.argv[1]
gold_file = sys.argv[2]
def convert_hoc_labels(lines):
labels = []
classes = ['tumor promoting inflammation', 'inducing angiogenesis', 'evading growth suppressors', 'resisting cell death', 'cellular energetics', 'empty', 'genomic instability and mutation', 'sustaining proliferative signaling', 'avoiding immune destruction', 'activating invasion and metastasis', 'enabling replicative immortality']
for line in lines:
labels.append([w.strip() for w in line.strip().split('|')])
return MultiLabelBinarizer(classes=classes).fit_transform(labels)
def do_eval(preds, golden):
preds = convert_hoc_labels(preds)
golden = convert_hoc_labels(golden)
score = f1_score(golden, preds, average='micro')
print(score)
return
def main():
preds = []
with open(pred_file) as reader:
for line in reader:
preds.append(line.strip())
golden = []
with open(gold_file) as reader:
for line in reader:
line = line.strip()
if line != '' and len(line) > 0:
golden.append(line.strip().split('\t')[-1])
assert len(preds) == len(golden), f"{len(preds)} {len(golden)}"
print("\n====File: ", os.path.basename(pred_file))
do_eval(preds, golden)
if __name__ == "__main__":
main()
|
BioGPT/examples/DC-HoC/hard_match_evaluation.py/0
|
{
"file_path": "BioGPT/examples/DC-HoC/hard_match_evaluation.py",
"repo_id": "BioGPT",
"token_count": 585
}
| 145 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import sys
import re
import json
out_file = sys.argv[1]
entity_file=sys.argv[2]
pmids_file = sys.argv[3]
prefix = [
'(learned[0-9]+ )+',
'in conclusion ,',
'we can conclude that',
'we have that',
]
def strip_prefix(line):
for p in prefix:
res = re.search(p, line)
if res is not None:
line = re.split(p, line)[-1].strip()
break
return line
def split_sentence(line):
sentences = re.split(r";", line)
return sentences
def convert_relis_sentence(sentence):
ans = None
segs = re.match(r"the relation between (.*) and (.*) exists", sentence.strip())
if segs is not None:
segs = segs.groups()
chemical = segs[0].strip()
disease = segs[1].strip()
ans = (chemical, disease)
return ans
all_lines = []
with open(out_file, "r", encoding="utf8") as fr:
for line in fr:
e = line.strip()
if e[-1] == ".":
all_lines.append(e[:-1])
else:
all_lines.append(e)
with open(entity_file, "r", encoding="utf8") as fr:
ent2id = json.load(fr)
with open(pmids_file, "r") as reader:
if '.json' in pmids_file:
pmids = json.load(reader)
else:
pmids = []
for line in reader:
pmids.append(line.strip())
hypothesis = []
cnt = 0
fail_cnt = 0
for i, line in enumerate(all_lines):
cnt += 1
strip_line = strip_prefix(line)
ret = []
sentences = split_sentence(strip_line)
for sen in sentences:
ans = convert_relis_sentence(sen)
if ans is not None:
chemical, disease = ans
chemicalID = ent2id['chemical2id'].get(chemical.strip(), "-1")
diseaseID = ent2id['disease2id'].get(disease.strip(), "-1")
ret.append(f"{pmids[i]}\tCID\t{chemicalID}\t{diseaseID}\t1.0")
if len(ret) > 0:
hypothesis.extend(ret)
else:
fail_cnt += 1
print("Failed:id:{}, line:{}".format(i+1, line))
with open(f"{out_file}.extracted.PubTator", "w", encoding="utf8") as fw:
for line in hypothesis:
print(line, file=fw)
print(f"failed = {fail_cnt}, total = {cnt}")
|
BioGPT/examples/RE-BC5CDR/postprocess.py/0
|
{
"file_path": "BioGPT/examples/RE-BC5CDR/postprocess.py",
"repo_id": "BioGPT",
"token_count": 1034
}
| 146 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import sys
import json
import re
data_dir=sys.argv[1]
def map_relation_to_verb(relation):
special_mapping = {
"product of": "is the product of",
"negative modulator": "negatively modulates",
"other/unknown": "randomly works with",
"other": "randomly works with",
"incorporation into and destabilization": "incorporates into and destabilizates",
"cross-linking/alkylation": "cross lines / alkylates",
"antibody": "is the antibody of",
"downregulator": "downregulates",
"desensitize the target": "desensitizes",
"protector": "protects",
"inhibitor": "inhibits",
"weak inhibitor": "weakly inhibits",
"blocker": "blocks"
}
if relation in special_mapping:
return special_mapping[relation]
if relation.endswith("agonist") or relation.endswith("antagonist"):
return relation + "s"
if relation.endswith("or") or relation.endswith("er"):
return relation[:-2] + "es"
if relation.endswith("tion"):
return relation[:-3] + "es"
if relation.endswith("ing"):
return relation[:-3] + "s"
return relation + "s"
def sort_triples(triples, text):
sorted_triples = sorted(triples, key=lambda x:text.find(x['drug']))
return sorted_triples
def build_target_seq_svo(triples):
answer = ""
for z in triples:
drug = z["drug"].lower()
target = z["target"].lower()
rel = map_relation_to_verb(z["interaction"].lower())
answer += f"{drug} {rel} {target}; "
return answer[:-2] + "."
def build_target_seq_isof(triples):
answer = ""
for z in triples:
drug = z["drug"].lower()
target = z["target"].lower()
rel = z["interaction"].lower()
answer += f"{drug} is the {rel} of {target}; "
return answer[:-2] + "."
def build_target_seq_htr(triples):
answer = ""
for z in triples:
drug = z["drug"].lower()
target = z["target"].lower()
rel = z["interaction"].lower()
answer += f"<h> {drug} <t> {target} <r> {rel} "
return answer[:-1] + "."
def build_target_seq_relis(triples):
answer = ""
for z in triples:
drug = z["drug"].lower()
target = z["target"].lower()
rel = z["interaction"].lower()
answer += f"the interaction between {drug} and {target} is {rel}; "
return answer[:-2] + "."
def loader(fname, fn):
ret = []
null_cnt = 0
suc_cnt = 0
null_flag = False
with open(fname, "r", encoding="utf8") as fr:
data = json.load(fr)
for pmid, v in data.items():
if re.search(r"\W$", v["title"]):
content = v["title"] + " " + v["abstract"]
else:
content = v["title"] + ". " + v["abstract"]
content = content.lower()
if v["triples"] is None or len(v["triples"]) == 0:
if not null_flag:
print(f"Following PMID in {fname} has no extracted triples:")
null_flag = True
print(f"{pmid} ", end="")
null_cnt += 1
else:
triples = v['triples']
triples = sort_triples(triples, content)
answer = fn(triples)
ret.append((pmid, content, answer))
suc_cnt += 1
if null_flag:
print("")
print(f"{len(data)} samples in {fname} has been processed with {null_cnt} samples has no triples extracted.")
return ret
def dumper(content_list, prefix):
fw_pmid = open(prefix + ".pmid", "w")
fw_content = open(prefix + ".x", "w")
fw_label = open(prefix + ".y", "w")
for ele in content_list:
print(ele[0], file=fw_pmid)
print(ele[1], file=fw_content)
print(ele[2], file=fw_label)
fw_pmid.close()
fw_content.close()
fw_label.close()
def worker(fname, prefix, fn):
ret = loader(fname, fn)
dumper(ret, prefix)
for split in ['train', 'valid', 'test']:
worker(os.path.join(f"{data_dir}", f"{split}.json"), os.path.join(f"{data_dir}", f"relis_{split}"), build_target_seq_relis)
#worker(os.path.join(f"{data_dir}", f"{split}.json"), os.path.join(f"{data_dir}", f"isof_{split}"), build_target_seq_isof)
#worker(os.path.join(f"{data_dir}", f"{split}.json"), os.path.join(f"{data_dir}", f"svo_{split}"), build_target_seq_svo)
#worker(os.path.join(f"{data_dir}", f"{split}.json"), os.path.join(f"{data_dir}", f"htr_{split}"), build_target_seq_htr)
|
BioGPT/examples/RE-DTI/rebuild_data.py/0
|
{
"file_path": "BioGPT/examples/RE-DTI/rebuild_data.py",
"repo_id": "BioGPT",
"token_count": 2091
}
| 147 |
## Matmul
`Matmul` is an operator class that performs matrix multiplication, supporting various optimizations and quantization strategies.
### MatmulConfig:
`MatmulConfig` is a configuration class for the `Matmul` operator, specifying the matrix multiplication operation's parameters and behaviors.
### Parameters:
- **M** *(Union[int, Tuple[int]])*: The size of the first dimension of the matrix A, or a range of sizes if dynamic shape support is needed. Can be an integer or a tuple representing the dynamic range.
- If `int`, the bitblas matmul will generate a static shape kernel, which can only be used for the input shape of the specified value.
- If `List[int]`, the bitblas matmul will generate a dynamic shape kernel, which can be used for the input shape of the specified values. While the input shape represents the target optimized range.
- If `None`, the bitblas matmul will use a default value [1, 16, 32, 64, 128, 256, 512, 1024].
- **N** *(int)*: The size of the second dimension of matrix W and the output matrix.
- **K** *(int)*: The common dimension of matrices A and W.
- **A_dtype** *(str, default='float16')*: The data type of matrix A.
- Choices: `'float16'`, `'int8'`.
- **W_dtype** *(str, optional)*: Data type of the weights. Default: `'float16'`.
- Choices: `'float16'`, `'int8'`, `'int4'`, `'int2'`, `'int1'`, `'uint4'`,`'uint2'`, `'uint1'`, `'fp4_e2m1'`, `'nf4'`.
- The Range of the INT Format:
- `'int4'`: [-8, 7]
- `'int2'`: [-2, 1]
- `'int1'`: -1 and 1
- **accum_dtype** *(str, default='float16')*: The data type used for accumulation during the matrix multiplication.
- Choices: `'float16'`, `'int32'`.
- **out_dtype** *(str, default='float16')*: The data type of the output matrix.
- Choices: `'float32'`, `'float16'`, `'int8'`, `'int32'`.
- **layout** *(Literal['nn', 'nt', 'tn', 'tt'], default='nt')*: The layout of the matrix multiplication operation. The matrix is stored in row-major.
- `'nn'`: Both matrices are non-transposed.
- `'nt'`: Matrix A is non-transposed, and matrix W is transposed.
- **with_bias** *(bool, default=False)*: Indicates whether a bias vector is added to the output.
- **group_size** *(int, default=-1)*: The group size for quantization, -1 indicates no grouping.
- **with_scaling** *(bool, default=False)*: Indicates whether scaling is applied during quantization.
- **with_zeros** *(bool, default=False)*: Indicates whether zero optimization is applied.
- **zeros_mode** *(Literal['original', 'rescale', 'quantized'], default='original')*: The mode of zero optimization.
- Choices: `None`, `'original'`, `'rescale'`, `'quantized'`.
- `'original'`: Subtract zero-point before scaling. Formula: `target = (dequantize_weight - zero_point) * scale`. where `zero_point` has the same datatype with scale.
- `'rescale'`: Apply scale factor directly to dequantized weight and then subtract zero-point. Formula: `target = dequantize_weight * scale - zero_point`.
- `'quantized'`: Apply zero-point adjustment after dequantization and additional dequantization of zero values. Formula: `target = (dequantize_weight - dequantize_qzeros) * scale`, where `dequantize_zeros` represents the dequantized representation of zero values, which can be adapted to qzeros params.
### Initialization:
```python
Matmul(config: MatmulConfig)
```
- **config** *(MatmulConfig)*: The configuration for the matrix multiplication operation.
### Methods:
#### `forward(A, W, scale=None, seros=None, bias=None, output=None) -> Any`
Performs the matrix multiplication operation with the given input tensors and optional scaling, zeros, and bias.
- **A** *(Tensor)*: The input tensor A.
- **W** *(Tensor)*: The input tensor W.
- **scale** *(Optional[Tensor], default=None)*: The scaling tensor.
- **zeros** *(Optional[Tensor], default=None)*: The zeros tensor.
- **bias** *(Optional[Tensor], default=None)*: The bias tensor.
- **output** *(Optional[Tensor], default=None)*: The pre-allocated output tensor.
#### `transform_weight(weight, scale=None, zeros=None, bias=None)`
Transforms the given weight tensor based on the specified quantization parameters.
- **weight** *(Tensor)*: The input weight tensor to be transformed.
- **scale** *(Optional[Tensor], default=None)*: Scaling factor for the weight tensor.
- **zeros** *(Optional[Tensor], default=None)*: Zero-point adjustment for the weight tensor.
- **bias** *(Optional[Tensor], default=None)*: Bias to be added to the weight tensor.
#### `__call__(*args: Any) -> Any`
Allows the object to be called like a function, forwarding the call to the `forward` method.
### Properties:
- **M**, **N**, **K**, **A_dtype**, **W_dtype**, **out_dtype**, **accum_dtype**, **storage_dtype**, **with_scaling**, **with_zeros**, **group_size**, **fast_decoding**, **with_bias**, **layout**, **zeros_mode**: These properties correspond to the parameters defined in `MatmulConfig`, providing easy access to the configuration details.
## Linear
`Linear(in_features: int, out_features: int, bias: bool = False, A_dtype: str = 'float16', W_dtype: str = 'float16', accum_dtype: str = 'float16', out_dtype: str = 'float16', group_size: int = -1, with_scaling: bool = None, with_zeros: bool = False, zeros_mode: str = None, opt_M: Union[int, List[int]] = [1, 16, 32, 64, 128, 256, 512])`
Applies a linear transformation to the incoming data: $out[M, N] = A[M, K] \times W[N, K]$ . This module supports quantization and optimization for NVIDIA GPUs using the BitBLAS library.
### Parameters:
- **in_features** *(int)*: size of each input sample.
- **out_features** *(int)*: size of each output sample.
- **bias** *(bool, optional)*: If set to `False`, the layer will not learn an additive bias. Default: `False`.
- **A_dtype** *(str, optional)*: Data type of the input tensor. Default: `'float16'`.
- Choices: `'float16'`, `'int8'`.
- **W_dtype** *(str, optional)*: Data type of the weights. Default: `'float16'`.
- Choices: `'float16'`, `'int8'`, `'int4'`, `'int2'`, `'int1'`, `'uint4'`,`'uint2'`, `'uint1'`, `'fp4_e2m1'`, `'nf4'`.
- The Range of the INT Format:
- `'int4'`: [-8, 7]
- `'int2'`: [-2, 1]
- `'int1'`: -1 and 1
- **accum_dtype** *(str, optional)*: Data type for accumulation. Default: `'float16'`.
- Choices: `'float16'`, `'int32'`.
- **out_dtype** *(str, optional)*: Data type of the output tensor. Default: `'float16'`.
- Choices: `'float32'`, `'float16'`, `'int8'`, `'int32'`.
- **group_size** *(int, optional)*: Group size for quantization. Default: `-1` (no grouping).
- **with_scaling** *(bool, optional)*: Whether to use scaling during quantization. Default: `False`.
- **with_zeros** *(bool, optional)*: Whether to use zeropoints . Default: `False`.
- **zeros_mode** *(str, optional)*: Mode for zero zeropoints. Default: `None`.
- Choices: `None`, `'original'`, `'rescale'`, `'quantized'`.
- `'original'`: Subtract zero-point before scaling. Formula: `target = (dequantize_weight - zero_point) * scale`. where `zero_point` has the same datatype with scale.
- `'rescale'`: Apply scale factor directly to dequantized weight and then subtract zero-point. Formula: `target = dequantize_weight * scale - zero_point`.
- `'quantized'`: Apply zero-point adjustment after dequantization and additional dequantization of zero values. Formula: `target = (dequantize_weight - dequantize_qzeros) * scale`, where `dequantize_zeros` represents the dequantized representation of zero values, which can be adapted to qzeros params.
- **opt_M** *(Union[int, List[int]], optional)*: Optimize range of the input shape for dynamic symbolic. Default: `[1, 16, 32, 64, 128, 256, 512]`.
- If `int`, the bitblas matmul will generate a static shape kernel, which can only be used for the input shape of the specified value.
- If `List[int]`, the bitblas matmul will generate a dynamic shape kernel, which can be used for the input shape of the specified values. While the input shape represents the target optimized range. It is important to note that if an input size is provided that is not explicitly listed, such as 15, bitblas matmul will select the nearest larger kernel available. In the case where opt_M is `[1, 16, 32, 64, 128, 256, 512]`, an input size of 15 would utilize the kernel optimized for size 16. T
### Methods:
#### `forward(A, output=None)`
Defines the computation performed at every call.
- **A** *(Tensor)*: Input tensor.
- **Output** *(Tensor, optional)*: Pre-allocated output tensor. Default: `None`.
- If `None`, the module will allocate a new tensor for the output.
- If not `None`, the module will use the pre-allocated tensor for the output.
Returns: The output tensor.
#### `init_params()`
Initializes parameters handles (convert constant params into ctypes void pointer) for the computation. We currently put this fuction in the forward function, so you do not need to call it manually. But if you lift this function out of the forward function, you can call it manually to aoid the transformation.
#### `load_and_transform_weight(weight, scales=None, zeros=None, bias=None)`
This method is designed to load and optionally transform the weight matrix along with scales, zeros, and bias for use in quantized computations. It is particularly useful when transitioning from a floating-point model to a quantized model, allowing for the adjustment of model parameters to fit the requirements of quantization and optimization processes.
- **Parameters:**
- **weight** *(Tensor)*: The weight tensor to be loaded into the layer. This tensor should have dimensions that match the expected input features and output features of the layer. The method will also apply any necessary transformations to the weight tensor to align with the quantization and optimization configurations of the layer.
- **scales** *(Tensor, optional)*: A tensor containing scale factors for quantization. These scales are used to adjust the weight values during the quantization process, ensuring that the dynamic range of the weights is appropriately represented in the quantized format. If not provided, the method assumes that either scaling is not required or has already been applied to the weights.
- **zeros** *(Tensor, optional)*: A tensor indicating the optimized representation of zeros, particularly useful in sparse models where zero values can be efficiently encoded. This parameter is only relevant if zero points (`with_zeros`) is enabled for the layer. Providing this tensor allows for further memory and computation optimizations during the forward pass.
- **bias** *(Tensor, optional)*: The bias tensor to be loaded into the layer. If the layer is configured to use a bias (`bias=True` during initialization), this tensor provides the bias values for each output feature. If `None`, it is assumed that the layer does not use a bias or that the bias is already incorporated into another parameter.
`load_and_transform_weight(weight, scales=None, zeros=None, bias=None)`
Loads and transforms the weight matrix and optional scales, zeros, and bias for quantized computation.
- **weight** *(Tensor)*: Weight tensor.
- **scales** *(Tensor, optional)*: Scales tensor for quantization. Default: `None`.
- **zeros** *(Tensor, optional)*: Zeros tensor for zeropoints. Default: `None`.
- **bias** *(Tensor, optional)*: Bias tensor. Default: `None`.
### `repack_from_gptq(gptq_module)`
This method facilitates the integration of parameters from a module that has undergone Generalized Post Training Quantization (GPTQ), repacking and transforming these parameters as necessary for compatibility with the BitBLAS-optimized `Linear` layer. The `gptq_module` must have its parameters in a format that is compatible with the expectations of the `Linear` layer's quantization and optimization configurations. This includes the shape and data type of the quantized weights, scales, and zeros. The method automatically handles the transformation and repacking of these parameters, including transposing weights if necessary, converting quantized zeros into the expected format, and adjusting scales and biases for direct use in the optimized forward pass of the `Linear` layer.
- **Parameters:**
- **gptq_module** *(Module)*: A module that contains quantized parameters following the GPTQ process. This module should have attributes corresponding to quantized weights (`qweight`), scales (`scales`), optimized zeros (`qzeros`), and optionally biases (`bias`). The method extracts these parameters, applies any necessary transformations for compatibility with the BitBLAS optimizations, and loads them into the `Linear` layer.
|
BitBLAS/docs/PythonAPI.md/0
|
{
"file_path": "BitBLAS/docs/PythonAPI.md",
"repo_id": "BitBLAS",
"token_count": 3821
}
| 148 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import argparse
import torch
from modeling_bitnet import BitnetForCausalLM
torch.set_grad_enabled(False)
parser = argparse.ArgumentParser()
parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str)
def profile(model, input_data):
import time
import numpy as np
model = model.cuda()
model.eval()
def get_runtime(num_repeats=1):
tic = time.time()
for _ in range(num_repeats):
_ = model(input_data)
torch.cuda.synchronize()
return (time.time() - tic) * 1000 / num_repeats
with torch.no_grad():
st = time.time()
while time.time() - st < 1.0:
get_runtime() # warmup
warmup_runtime = get_runtime()
num_repeats = max(1, int(1000 / warmup_runtime))
times = get_runtime(num_repeats)
return np.mean(times)
def main():
model = BitnetForCausalLM.from_pretrained(
'1bitLLM/bitnet_b1_58-3B',
device_map='auto',
low_cpu_mem_usage=True,
use_flash_attention_2=True,
torch_dtype=torch.float16,
).half()
print(
f"gpu memory: {torch.cuda.memory_allocated() / 1024 ** 3} GB"
)
with torch.no_grad():
model._post_process_weights()
print(
f"gpu memory BitBLAS: {torch.cuda.memory_allocated() / 1024 ** 3} GB"
)
if __name__ == '__main__':
main()
|
BitBLAS/integration/BitNet/eval_gpu_memory.py/0
|
{
"file_path": "BitBLAS/integration/BitNet/eval_gpu_memory.py",
"repo_id": "BitBLAS",
"token_count": 652
}
| 149 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
"""Policy for cuda core schedule"""
import functools
import math
from queue import PriorityQueue
from typing import Iterable, Dict, List, Optional
import numpy as np
import tvm
from ..arch import TileDevice
from ..bestfit import BestFit
from ..hint import Hint, Stride, TileDict
from .common import coalesced_factor, coalesced_tensor_shape, factorize, get_all_factors
from ..node import PrimFuncNode
from ..rasterization import NoRasterization
class DefaultPolicy:
"""
Default Policy for fastdlight, a heuristic plan that tries to
minimize memory traffic and maximize parallelism.for BitBLAS Schedule.
"""
def __init__(self,
func: tvm.tir.PrimFunc,
arch: TileDevice,
tags: Optional[Dict] = None) -> None:
if tags is None:
tags = {}
self.arch = arch
self.prim_func_node = PrimFuncNode(func, tags)
self.ordered_nodes = [self.prim_func_node]
self.output_nodes = [self.prim_func_node]
def emit_config(self, topk: int) -> List[Hint]:
base_tile = self.get_base_tile()
if base_tile is None:
return []
rstep_map = self._assign_reduce_step(self.prim_func_node)
smem_tile_condidates = self.dfs_smem_tile(base_tile, rstep_map)
results = []
for td in smem_tile_condidates:
if not self.check_tile_shape_isvalid(td):
continue
self._expand_reduce_axis(td)
for codegen_dicts in self.assign_block_size(td):
results.append(codegen_dicts)
if len(results) >= topk:
break
if len(results) >= topk:
break
return results
def dfs_smem_tile(self, init_tile, rstep_map) -> Iterable[TileDict]:
_steps = [get_all_factors(n) for n in self.prim_func_node.get_space_dim()]
steps = [step[step.index(t):] for step, t in zip(_steps, init_tile)]
for i in range(len(steps)):
added = list(
filter(
lambda s: s < steps[i][-1] and s > steps[i][0] and s not in steps[i],
[2, 4, 8, 16, 32],
))
steps[i].extend(added)
steps[i] = sorted(steps[i])
visited_tiles = {}
queue = PriorityQueue()
def prio(td: TileDict):
return (td.traffic + 1) * td.num_wave
def add_to_queue(tile):
if tuple(tile) in visited_tiles:
return
td = self.compute_tile_dict(tile, rstep_map)
visited_tiles[tuple(tile)] = td
if td.valid:
queue.put([prio(td), tile])
add_to_queue(init_tile)
while not (queue.empty() or len(visited_tiles) > 2000):
_, tile = queue.get()
dim_ids = [step.index(t) for step, t in zip(steps, tile)]
for i in reversed(range(len(dim_ids))):
if dim_ids[i] + 1 < len(steps[i]):
new_tile = tile.copy()
new_tile[i] = steps[i][dim_ids[i] + 1]
add_to_queue(new_tile)
visited_tiles = filter(lambda td: td.valid, visited_tiles.values())
sorted_tiles = sorted(visited_tiles, key=lambda td: prio(td))
return sorted_tiles
def get_base_tile(self):
"""
Gets the minimum tile configuration that satisfies no redundancy in computation.
Returns
-------
List[int]
The base tile configuration, which is a list of 1s equal in length to the space dimensions
of the primary function node.
"""
shape = self.prim_func_node.get_space_dim()
base_tile = [1 for _ in shape]
return base_tile
# handles multiple output cases
def _get_output_tile_map(self, tile):
"""
Handles multiple output cases by mapping output nodes to their respective tile configurations.
Parameters
----------
tile : List[int]
The tile configuration.
Returns
-------
Dict
A dictionary mapping the primary function node to its corresponding tile configuration
based on the output nodes' space dimensions.
"""
tile_map = {}
tile_map[self.prim_func_node] = [
tile[i] * self.prim_func_node.get_space_dim()[i] //
self.output_nodes[0].get_space_dim()[i] for i in range(len(tile))
]
return tile_map
def score_block_size(self, n):
"""
Scores a block size based on its efficiency and fit relative to the architecture's warp size and SM partition.
Parameters
----------
n : int
The block size to score.
Returns
-------
Tuple[float, float]
A tuple containing two scores representing efficiency and fit, respectively.
"""
num_wrap = (n + self.arch.warp_size - 1) // self.arch.warp_size
r1 = max(num_wrap / self.arch.sm_partition, self.arch.sm_partition / num_wrap)
r2 = (num_wrap * self.arch.warp_size - n) / n
return (r1, r2)
def get_block_size(self, n):
"""
Determines the optimal block size for a given constraint, based on scoring various factors.
Parameters
----------
n : int
The constraint size.
Returns
-------
int
The optimal block size chosen from the factors of n, constrained by a maximum of 1024 and
scored by the `score_block_size` method.
"""
factors = get_all_factors(n)
factors = list(filter(lambda x: x <= 1024, factors))
factor_ordered = sorted(factors, key=self.score_block_size)
return factor_ordered[0]
def get_node_reduce_step_candidates(self, node: PrimFuncNode):
"""
Calculates reduction step candidates for each reduction axis in a PrimFuncNode. General idea : use factor first, since it does not require extra boundary check. for large prime number, which is rare case, use power of 2.
Parameters
----------
node : PrimFuncNode
The node for which to calculate reduction step candidates. It contains reduction axes (raxis)
with their domains (dom.extent).
Returns
-------
Dict[str, List[int]]
A dictionary mapping axis variable names to lists of step candidates. For each axis in the node,
this function calculates possible step sizes. For axes with a large prime domain, it uses powers of 2
as step candidates; for others, it uses all factors of the domain.
"""
results = {}
for k_iter in node.raxis:
all_factors = get_all_factors(int(k_iter.dom.extent))
if len(all_factors) == 2 and int(k_iter.dom.extent) > 64:
all_factors = [1]
while all_factors[-1] * 2 < int(k_iter.dom.extent):
all_factors.append(all_factors[-1] * 2)
results[k_iter.var.name] = all_factors
return results
def _assign_reduce_step(self, node: PrimFuncNode):
"""
Assigns an optimal reduction step for the given PrimFuncNode.
Parameters
----------
node : PrimFuncNode
The node for which the reduction step is to be assigned.
Returns
-------
Dict
A dictionary mapping reduction axis variable names to their optimal reduction steps.
"""
if node.reduction_block is None:
return {}
raxis = node.raxis
tile = [1] * len(node.get_space_dim())
all_steps = self.get_node_reduce_step_candidates(node)
def sim(a: int, b: int):
return (2 * a * b) / (a * a + b * b)
def _score(rstep_id):
rstep = {k: all_steps[k][rstep_id[k]] for k in rstep_id}
score = 0
shape = node.propagate_inputs(tile, rstep=rstep)
for i, input_buffer in enumerate(node.input_buffers):
read_transaction_elements = self.arch.transaction_size[1] // (
(node.get_buffer_dtype(input_buffer).bits + 7) // 8)
score += sim(
int(coalesced_factor(shape[i], input_buffer.shape)),
read_transaction_elements,
)
return score
def _enlarge(rstep_id):
candidates = []
candidates.append((rstep_id, _score(rstep_id)))
for ax in rstep_id:
if rstep_id[ax] + 1 == len(all_steps[ax]):
continue
r = rstep_id.copy()
r[ax] += 1
candidates.append((r, _score(r)))
best = max(candidates, key=lambda x: x[1])
return best
# enlarge rstep to ensure read is coaleased
cur_rstep_id = {ax.var.name: 0 for ax in raxis}
cur_score = _score(cur_rstep_id)
while True:
if cur_score == 0:
break
new_rstep, new_score = _enlarge(cur_rstep_id)
if new_score <= cur_score:
break
else:
cur_rstep_id, cur_score = new_rstep, new_score
rstep = {k: all_steps[k][cur_rstep_id[k]] for k in cur_rstep_id}
return rstep
def _expand_reduce_axis(self, td: TileDict):
"""
Expands the reduction axis in the TileDict based on shared memory limits.
Parameters
----------
td : TileDict
The TileDict object to be optimized.
Returns
-------
None
This function modifies the TileDict in place.
"""
smem_limit = min(self.arch.max_smem_usage // td.block_per_SM, self.arch.smem_cap)
rstep_map = td.rstep_map.copy()
def _optimize(node, rstep):
all_steps = self.get_node_reduce_step_candidates(node)
for k in all_steps:
all_steps[k] = list(filter(lambda x: x % rstep[k] == 0, all_steps[k]))
def _score(rstep_id):
rstep = {
k.var.name: all_steps[k.var.name][rstep_id[k.var.name]] for k in node.raxis
}
score = 0
shape = node.propagate_inputs(td.get_tile(node), rstep=rstep)
for i, input_buffer in enumerate(node.input_buffers):
score += coalesced_factor(shape[i], input_buffer.shape)
return score
def _enlarge(rstep_id):
candidates = []
for ax in rstep_id:
if rstep_id[ax] + 1 == len(all_steps[ax]):
continue
r = rstep_id.copy()
r[ax] += 1
candidates.append((r, _score(r)))
if len(candidates) == 0:
return None
return max(candidates, key=lambda x: x[1])[0]
cur_rstep_id = {
k.var.name: all_steps[k.var.name].index(rstep[k.var.name]) for k in node.raxis
}
new_rstep_map = rstep_map.copy()
while True:
new_rstep_id = _enlarge(cur_rstep_id)
if new_rstep_id is None:
break
new_rstep_map = {
k.var.name: all_steps[k.var.name][new_rstep_id[k.var.name]] for k in node.raxis
}
old_rstep_map = td.rstep_map
td.rstep_map = new_rstep_map
smem_usage, _ = self._compute_shared_memory_usage(td)
td.rstep_map = old_rstep_map
if smem_usage > smem_limit:
break
else:
cur_rstep_id = new_rstep_id
rstep = {
k.var.name: all_steps[k.var.name][cur_rstep_id[k.var.name]] for k in node.raxis
}
return rstep
for node in self.ordered_nodes:
if len(node.raxis) > 0:
rstep = _optimize(node, rstep_map)
rstep_map = rstep
td.rstep_map = rstep_map
td.smem_cost, td.cached_tensors_map = self._compute_shared_memory_usage(td)
def _compute_memory_traffic(self, output_tile):
"""
Computes the memory traffic for a given output tile configuration.
Parameters
----------
output_tile : List[int]
The output tile configuration.
Returns
-------
Tuple[int, Dict]
The total memory traffic and a map of operation tiles.
"""
op_tile_map = self._get_output_tile_map(output_tile)
traffic = 0
for node in reversed(self.ordered_nodes):
tile = op_tile_map[node]
input_shapes = node.propagate_inputs(tile)
output_shapes = node.propagate_outputs(tile)
for i, buffer in enumerate(node.input_buffers):
nbytes = (node.get_buffer_dtype(buffer).bits + 7) // 8
read_transaction_elements = self.arch.transaction_size[1] // nbytes
traffic += (
coalesced_tensor_shape(input_shapes[i], buffer.shape, read_transaction_elements)
* nbytes)
for i, buffer in enumerate(node.output_buffers):
nbytes = (node.get_buffer_dtype(buffer).bits + 7) // 8
write_transaction_elements = self.arch.transaction_size[0] // nbytes
traffic += (
coalesced_tensor_shape(output_shapes[i], buffer.shape,
write_transaction_elements) * nbytes)
return traffic, op_tile_map
def infer_node_smem_usage(self, td: TileDict, node: PrimFuncNode):
"""
Infers the shared memory usage of a node given a TileDict configuration.
Parameters
----------
td : TileDict
The TileDict object containing the tile configuration.
node : PrimFuncNode
The node for which to infer the shared memory usage.
Returns
-------
int
The estimated amount of shared memory used by the node.
"""
return node.footprint(td.get_tile(node), td.get_rstep(node), td.tensor_strides_map[node])
def _compute_shared_memory_usage(self, td: TileDict):
"""
Computes the stride map for a given node and TileDict configuration.
Parameters
----------
node : PrimFuncNode
The node for which to compute the stride map.
td : TileDict
The TileDict object containing the tile configuration.
Returns
-------
Tuple[Dict, Dict]
The output strides and tensor strides.
"""
self._compute_stride_map(td)
allocator = BestFit()
block_map = {}
cached_tensors_map = {}
node_internal_bytes, cached_tensors_map[self.prim_func_node] = self.infer_node_smem_usage(
td, self.prim_func_node)
block = allocator.malloc(node_internal_bytes)
allocator.free(block)
assert len(block_map) == 0
return allocator.limit, cached_tensors_map
def compute_node_stride_map(self, node: PrimFuncNode, td: TileDict):
"""
Computes the stride map for a given node based on the TileDict configuration.
Parameters
----------
node : PrimFuncNode
The node for which to compute the stride map.
td : TileDict
The TileDict object containing the tile configuration.
Returns
-------
Tuple[Dict, Dict]
A tuple of dictionaries containing the output strides and tensor strides.
"""
output_strides = {
int(i + len(node.input_buffers)): Stride() for i, _ in enumerate(node.output_buffers)
}
tensor_strides = {}
return output_strides, tensor_strides
def _compute_stride_map(self, td: TileDict):
"""
Computes the stride map for all nodes in a TileDict.
Parameters
----------
td : TileDict
The TileDict object for which to compute the stride maps.
Returns
-------
None
This function updates the TileDict object in place with the computed stride maps.
"""
output_strides_map = {}
tensor_strides_map = {}
for node in self.ordered_nodes:
output_strides_map[node], tensor_strides_map[node] = self.compute_node_stride_map(
node, td)
td.output_strides_map, td.tensor_strides_map = output_strides_map, tensor_strides_map
def compute_tile_dict(self, output_tile: List[int], rstep_map) -> TileDict:
"""
Computes and returns a TileDict object for a given output tile configuration and reduction step map.
Parameters
----------
output_tile : List[int]
The output tile configuration.
rstep_map : Dict
The reduction step map.
Returns
-------
TileDict
A TileDict object containing the computed tile configuration, memory traffic, shared memory cost,
grid size, and other related parameters.
"""
td = TileDict(output_tile)
td.rstep_map = rstep_map
td.traffic, td.tile_map = self._compute_memory_traffic(output_tile)
td.smem_cost, td.cached_tensors_map = self._compute_shared_memory_usage(td)
if td.smem_cost > self.arch.smem_cap:
td.valid = False
return td
output_shape = self.output_nodes[0].get_space_dim()
td.grid_size = int(np.prod([(y + x - 1) // x for x, y in zip(output_tile, output_shape)]))
# estimated reg usage
reg_usage = int(2 * max([
np.prod(td.get_tile(node)) * node.get_dtype().bits / 32 for node in self.ordered_nodes
]))
if reg_usage > self.arch.reg_cap:
td.valid = False
return td
td.block_per_SM = min(
self.arch.max_smem_usage // max(td.smem_cost, 1),
self.arch.reg_cap // max(reg_usage, 1),
self.arch.sm_partition,
)
td.num_wave = int(np.ceil(td.grid_size / int(td.block_per_SM * self.arch.compute_max_core)))
return td
def check_tile_shape_isvalid(self, td: TileDict) -> bool:
"""
Checks if the tile shapes in the TileDict are valid for the nodes in this context.
Parameters:
- td (TileDict): The TileDict object containing tile shapes and other configurations.
Returns:
- bool: True if all tile shapes are valid, False otherwise.
"""
for node in self.ordered_nodes:
if np.prod(td.get_tile(node)) == 0:
return False
node_grid_size = np.prod([
(y + x - 1) // x for x, y in zip(td.get_tile(node), node.get_space_dim())
])
if node_grid_size != td.grid_size:
return False
if (hasattr(node, "reduce_op") and node.reduce_op is not None and
len(node.reduce_op.axis) == len(td.output_tile)):
for i, tile_extent in enumerate(td.output_tile):
if node.reduce_op.axis[i].dom.extent % tile_extent:
return False
return True
def recommend_block_size(self, td: TileDict) -> List[int]:
"""
Recommends optimal block sizes based on the TileDict configuration.
Parameters
----------
td : TileDict
The TileDict object containing the tile configuration.
Returns
-------
List[int]
A list of recommended block sizes sorted based on their score.
"""
node_space_sizes = [int(np.prod(td.get_tile(node))) for node in self.ordered_nodes]
max_block_size = functools.reduce(math.gcd, node_space_sizes)
if max_block_size < self.arch.warp_size * self.arch.sm_partition and max_block_size == min(
node_space_sizes):
node_reduce_sizes = [
int(np.prod(list(td.get_rstep(node).values()))) for node in self.ordered_nodes
]
total_sizes = [x * y for x, y in zip(node_space_sizes, node_reduce_sizes)]
max_possible_size = functools.reduce(math.gcd, total_sizes)
possible_block_sizes = list(
filter(
lambda x: x % max_block_size == 0 and x <= 1024,
get_all_factors(max_possible_size),
))
possible_block_sizes = list(
filter( # either be a factor of space or cover fully cover the space
lambda x: all([x % s == 0 or s % x == 0 for s in node_space_sizes]),
possible_block_sizes,
))
factor_ordered = sorted(possible_block_sizes, key=self.score_block_size)
return factor_ordered
else:
possible_block_sizes = get_all_factors(max_block_size)
possible_block_sizes = list(filter(lambda x: x <= 1024, possible_block_sizes))
factor_ordered = sorted(possible_block_sizes, key=self.score_block_size)
return factor_ordered
def assign_block_size(self, td: TileDict, topk=1):
"""
Assigns block sizes to the TileDict based on the recommended block sizes.
Parameters
----------
td : TileDict
The TileDict object to assign block sizes to.
topk : int, optional
The number of top block sizes to consider.
Yields
-------
Dict
The block size assignment for the primary function node.
"""
block_size_ordered = self.recommend_block_size(td)
for block_size in block_size_ordered:
result = {}
failed = False
result = self._assign_block_size(self.prim_func_node, td, block_size)
if result is None:
failed = True
break
if failed:
continue
else:
yield result
topk -= 1
if topk == 0:
break
def _assign_block_size(self, node: PrimFuncNode, td: TileDict, block_size: int):
"""
Assigns a block size to a given PrimFuncNode based on the TileDict configuration and the specified block size.
Parameters
----------
node : PrimFuncNode
The node to assign the block size to.
td : TileDict
The TileDict object containing the tile configuration.
block_size : int
The block size to be assigned.
Returns
-------
Hint
A Hint object containing the assigned block size and other related settings.
"""
tile, rsteps = td.get_tile(node), td.get_rstep(node)
factors = factorize(block_size)
cur_threads = [1 for _ in tile]
reduce_thread = {k: 1 for k in rsteps}
ndim = len(tile)
def _score(node, thread): # small is better
score = 0
block_tile = [int(np.ceil(tile[i] / thread[i])) for i in range(ndim)]
shape = node.propagate_inputs(block_tile)
for i, _ in enumerate(node.input_buffers):
score += np.prod(shape[i]) / self.arch.bandwidth[1]
for buffer in node.output_buffers:
score += coalesced_tensor_shape(thread, buffer.shape, 8) / self.arch.bandwidth[0]
return score
for factor in reversed(factors):
score_map = {}
for i in range(ndim):
if cur_threads[i] >= tile[i]:
continue
if (tile[i] % (cur_threads[i] * factor)) != 0:
continue
cur_threads[i] *= factor
score_map[i] = (_score(node, cur_threads), i)
cur_threads[i] //= factor
if len(score_map) > 0:
# assign to space axis
dim_order = sorted(score_map.keys(), key=lambda x: score_map[x])
cur_threads[dim_order[0]] *= factor
else:
# assign to reduce axis
target_ax = None
for ax, ax_len in reversed(list(rsteps.items())):
if ax_len % (reduce_thread[ax] * factor) == 0:
target_ax = ax
break
assert target_ax
reduce_thread[target_ax] *= factor
codegen_dict = Hint()
codegen_dict.block = tile
codegen_dict.thread = cur_threads
codegen_dict.rstep = [rsteps[ax.var.name] for ax in node.raxis]
codegen_dict.reduce_thread = [reduce_thread[ax.var.name] for ax in node.raxis]
codegen_dict.cached_tensors = td.cached_tensors_map[node]
codegen_dict.rasterization_plan = self.plan_rasterization(td)
if node.get_dtype().bits == 16: # set step=2 for 16bit case to ensure coalesced access
codegen_dict._step = [1 for _ in range(ndim)]
for i in reversed(range(ndim)):
if codegen_dict.block[i] // codegen_dict.thread[i] % 2 == 0:
codegen_dict._step[i] = 2
break
elif node.get_dtype().bits == 8: # set step=4 for 8bit case to ensure coalesced access
codegen_dict._step = [1 for _ in range(ndim)]
for i in reversed(range(ndim)):
if codegen_dict.block[i] // codegen_dict.thread[i] % 4 == 0:
codegen_dict._step[i] = 4
break
# Plan vectorize
codegen_dict.vectorize = self._plan_vectorize(node, td, block_size)
codegen_dict.arch = self.arch
codegen_dict.opt_shapes = self.prim_func_node.get_tag("opt_shapes")
return codegen_dict
def _plan_vectorize(self, node: PrimFuncNode, td: TileDict, block_size: int):
"""
Plans vectorization for a given PrimFuncNode based on the TileDict configuration and block size.
Parameters
----------
node : PrimFuncNode
The node for which to plan vectorization.
td : TileDict
The TileDict object containing the tile configuration.
block_size : int
The block size used for vectorization planning.
Returns
-------
Dict
A dictionary mapping tensors to their vectorization size.
"""
def is_cont(shape, vec):
if len(shape) == 0:
return vec == 1
last = shape[-1]
if last == 1:
return is_cont(shape[0:-1], vec // last)
else:
return last % vec == 0
def is_shape_aligned(shape, factor):
return int(np.prod(shape)) % factor == 0
def is_type_allowed(dtype, vec):
return dtype.bits * vec <= 128
vectorize_sizes = [16, 8, 4, 2]
dtypes = node.get_reduce_inputs_dtype()
shapes = node.propagate_reduction_inputs(td.get_tile(node), td.get_rstep(node))
vectorize_result = {}
for tensor, shape in shapes.items():
for v in vectorize_sizes:
if (is_shape_aligned(shape, block_size * v) and is_cont(shape, v) and
is_type_allowed(dtypes[tensor], v)):
vectorize_result[tensor] = v
break
return vectorize_result
def plan_rasterization(self, td: TileDict): # pylint: disable=unused-argument
"""
Plans the rasterization for the given TileDict. This function is not implemented yet.
Parameters
----------
td : TileDict
The TileDict object to plan rasterization for.
Raises
-------
RasterRationPlan
This function is not implemented yet.
"""
return NoRasterization()
|
BitBLAS/python/bitblas/base/roller/policy/default.py/0
|
{
"file_path": "BitBLAS/python/bitblas/base/roller/policy/default.py",
"repo_id": "BitBLAS",
"token_count": 13708
}
| 150 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# Copyright 2018 The apache/tvm Authors. All Rights Reserved.
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
# Modifications Copyright (c) Microsoft.
# The code below is mostly copied from apache/tvm gemv.py in dlight.
"""A rule for GEMV and DecodeGEMV."""
import re
from functools import reduce
from typing import List, Optional, Union, Dict
from tvm import DataType, arith, ir, tir
from tvm.target import Target
from ..base import (
BlockInfo,
collect_block_iter_vars_used_in_access_region,
collect_vars_used_in_prim_expr,
detect_dominant_read,
is_broadcast_epilogue,
normalize_prim_func,
try_inline_contiguous_spatial,
get_output_blocks,
)
from .base import GPUScheduleRule
from .gemv_dequantize import GEMVWithDequantizeInfo
def _get_reduction_expr(block: tir.Block) -> Optional[tir.PrimExpr]:
# Detect and return `Y` in `X[...] = X[...] + Y`
buffer_store = block.body
if not isinstance(buffer_store, tir.BufferStore):
return None
if not isinstance(buffer_store.value, tir.Add):
return None
if not ir.structural_equal(
buffer_store.value.a,
tir.BufferLoad(buffer_store.buffer, block.body.indices),
map_free_vars=True,
):
return None
return buffer_store.value.b
def get_extent(sch: tir.Schedule, loop_rv: tir.schedule.LoopRV):
loop: tir.For = sch.get(loop_rv)
return loop.extent.value if isinstance(loop.extent, tir.IntImm) else loop.extent
def get_bytes(dtype: Union[DataType, str]) -> int:
if isinstance(dtype, str):
dtype = DataType(dtype)
return int(dtype.bits) // 8
def is_gemv(sch: tir.Schedule, block_info: BlockInfo) -> Optional[List[tir.Buffer]]:
"""Check if the block is a GEMV.
Parameters
----------
sch : tir.Schedule
The schedule
block_info : BlockInfo
The block info to be checked
Returns
-------
ret : Optional[List[tir.Buffer]]
The vector buffers used in the GEMV if it is a GEMV, otherwise None.
"""
block = block_info.block_rv
block_stmt = sch.get(block)
conditions = []
conditions.append(block_info.is_reduction())
conditions.append(len(block_stmt.reads) >= 2)
conditions.append(len(block_stmt.writes) == 1)
conditions.append(_get_reduction_expr(block_stmt) is not None)
conditions.append(
len(collect_block_iter_vars_used_in_access_region(block_stmt, block_stmt.writes[0].region))
> 0)
if not all(conditions):
return None
iter_num = len(block_stmt.iter_vars)
ret = [
read.buffer
for read in block_stmt.reads
if len(collect_block_iter_vars_used_in_access_region(block_stmt, read.region)) < iter_num
and len(collect_block_iter_vars_used_in_access_region(block_stmt, read.region)) > 0
]
if len(ret) == len(block_stmt.reads):
func = sch.mod["main"]
opt_shapes: Dict = {}
if "opt_shapes" in func.attrs:
opt_shapes = func.attrs["opt_shapes"]
# check with dynamic symbolic and at least one is unit
if not all([opt_shapes.get(buf.name, (1,))[0] == 1 for buf in ret]):
return None
elif len(ret) == 0:
return None
return ret
def normalize(
sch: tir.Schedule,
block_info: BlockInfo,
) -> Optional[bool]:
"""Normalize the main block."""
block_stmt: tir.Block = sch.get(block_info.block_rv)
access = arith.normalize_to_iter_sum(
detect_dominant_read(block_stmt),
input_iters={i.var: i.dom for i in block_stmt.iter_vars},
)
buffers_use_vars = [
collect_block_iter_vars_used_in_access_region(block_stmt, buf.region)
for buf in block_stmt.writes
]
buffers_use_vars.extend([
collect_block_iter_vars_used_in_access_region(block_stmt, buf.region)
for buf in block_stmt.reads
])
if collect_vars_used_in_prim_expr(access.base) & set(
iter_var.var for iter_var in block_stmt.iter_vars):
return None
iter_to_info = {i.var: i for i in block_info.iters}
batch_loops, s_loops, r_loops, c_loops = [], [], [], []
inner_axis = access.args[-1].source.source
is_inner_reduction = iter_to_info[inner_axis].kind == "R"
for split_expr in access.args:
var = split_expr.source.source
info = iter_to_info.get(var)
loop = info.loop_rv
is_reduction = info.kind == "R"
if split_expr.lower_factor > 1:
if c_loops:
return None
loop, c_loop = sch.split(loop, factors=[None, split_expr.lower_factor])
# we only support the reduction dim being grouped atm
if not is_reduction:
return None
c_loops.append(c_loop)
if is_reduction:
r_loops.append(loop)
elif all([var in buf_vars for buf_vars in buffers_use_vars]):
batch_loops.append(loop)
else:
s_loops.append(loop)
assert s_loops
assert r_loops
if not c_loops:
c_loops = [sch.add_unit_loop(block_info.block_rv)]
if not batch_loops:
batch_loops = [sch.add_unit_loop(block_info.block_rv)]
sch.reorder(*batch_loops, *s_loops, *r_loops, *c_loops)
sch.fuse(*batch_loops)
sch.fuse(*s_loops)
sch.fuse(*r_loops)
return is_inner_reduction
class GEMV(GPUScheduleRule):
"""A rule for GEMV and DecodeGEMV."""
def apply( # pylint: disable=too-many-locals,too-many-branches,too-many-return-statements
self,
func: tir.PrimFunc,
target: Target,
_: bool,
) -> Union[None, tir.Schedule, List[tir.Schedule]]:
if not isinstance(func, tir.PrimFunc) or not self.is_target_available(target):
return None
if "dequantize_info" in func.attrs:
dequantize_rule = GEMVWithDequantizeInfo()
return dequantize_rule.apply(func, target, False)
sch = tir.Schedule(func)
block_infos = normalize_prim_func(sch)
block_infos = try_inline_contiguous_spatial(sch, block_infos)
if len(block_infos) == 1:
epilogue = None
elif len(block_infos) == 2:
epilogue = block_infos[1]
if not epilogue.is_injective():
return None
else:
return None
block_info = block_infos[0]
if len(block_info.iters) not in [2, 3]:
# either [B, S, R] = [B, S, R] * [B, R]
# or [S, R] = [S, R] * [R]
return None
block = block_info.block_rv
vector_input_buffers = is_gemv(sch, block_info)
if vector_input_buffers is None:
return None
# Step 1. Normalize the block, merge spatial and reduction iters
is_inner_reduction = normalize(sch, block_info)
# Step 2. Do the scheduling
if is_inner_reduction is None:
return None
elif is_inner_reduction:
self.sch_inner_reduction(sch, target, block, vector_input_buffers, epilogue)
return sch
else:
return self.sch_outer_reduction(sch, target, block, vector_input_buffers, epilogue)
def sch_inner_reduction( # pylint: disable=too-many-arguments, invalid-name, unused-argument
self,
sch: tir.Schedule,
target: Target,
block: tir.schedule.BlockRV,
vector_input_buffers: List[tir.Buffer],
epilogue_info: Optional[BlockInfo],
):
"""Schedule the inner reduction block."""
def get_max_factor(n, factors):
factors = sorted(factors, reverse=True)
for factor in factors:
if n % factor == 0:
return factor
return 1
def apply(
sch: tir.Schedule,
gemv,
TAG_S,
TAG_R,
TS,
TR,
TILE_S,
TILE_R,
VEC_LOAD,
VEC_C,
LOAD_V_SHARED,
LOAD_V_VEC,
UNROLL,
):
# rfactor: reduce to tx * vec_c
_, s, r, c = sch.get_loops(block=gemv)
s = sch.fuse(_, s)
r = sch.fuse(r, c)
bx, ts, tile_s = sch.split(s, factors=[None, TS, TILE_S], preserve_unit_iters=True)
r, tr, tile_r_vec_n, vec_c = sch.split(
r, factors=[None, TR, TILE_R // VEC_C, VEC_C], preserve_unit_iters=True)
sch.reorder(r, tile_r_vec_n, tr, vec_c)
tr_vec_c = sch.fuse(tr, vec_c)
rf = sch.rfactor(tr_vec_c, 0)
# rfactor: reduce to tx
bx, ts, tile_s, tr_vec_c = sch.get_loops(block=gemv)
tr, vec_c = sch.split(tr_vec_c, factors=[TR, None], preserve_unit_iters=True)
rf2 = sch.rfactor(tr, 0)
# bind, vectorize compute
bx, ts, tile_s, r, tile_r_vec_n, tr_vec_c = sch.get_loops(block=rf)
tr, vec_c = sch.split(tr_vec_c, factors=[TR, None], preserve_unit_iters=True)
sch.reorder(bx, ts, tr, r, tile_s, tile_r_vec_n, vec_c)
sch.bind(bx, "blockIdx.x")
sch.bind(ts, TAG_S)
sch.bind(tr, TAG_R)
sch.vectorize(vec_c)
shared_mem_usage = 0
for buf in vector_input_buffers:
buf_size = reduce(lambda x, y: x * y, buf.shape, tir.IntImm(
buf.shape[0].dtype, 1)) * get_bytes(buf.dtype)
shared_mem_usage += buf_size
try:
max_shared_memory_per_block = target.max_shared_memory_per_block
except Exception:
max_shared_memory_per_block = 49152
LOAD_V_SHARED = (
LOAD_V_SHARED and isinstance(shared_mem_usage, tir.IntImm) and
shared_mem_usage.value <= max_shared_memory_per_block)
# vectorize load A
# (TODO) this is now actually problematic since the number of loops is dependent on the
# number of dimensions of A_q
Aq_local = sch.cache_read(rf, read_buffer_index=1, storage_scope="local")
sch.compute_at(Aq_local, r, preserve_unit_loops=True)
s_local, r_local = sch.get_loops(block=Aq_local)[-2:]
s_local, vec_load = sch.split(
s_local, factors=[None, VEC_LOAD], preserve_unit_iters=True)
sch.reorder(s_local, r_local, vec_load) # either s_local or r_local should be 1
sch.vectorize(vec_load)
# load vector into shared memory, shape should be the whole vector
if LOAD_V_SHARED:
V_shared = sch.cache_read(rf, read_buffer_index=0, storage_scope="shared")
sch.compute_at(V_shared, tr, preserve_unit_loops=True)
l = sch.get_loops(block=V_shared)[-1] # noqa: E741
loop: tir.For = sch.get(l)
if isinstance(loop.extent, tir.IntImm):
# avoid introducing predicates when vector length is too large
vec_length = max(
min(
get_max_factor(
(int)(loop.extent),
[TS * TR * 1, TS * TR * 2, TS * TR * 4, TS * TR * 8],
) // TS // TR,
LOAD_V_VEC,
),
1,
)
else:
vec_length = LOAD_V_VEC
if TAG_R == "threadIdx.x":
_, ty, tx, vec = sch.split(
l, factors=[None, TS, TR, vec_length], preserve_unit_iters=True)
else:
_, ty, tx, vec = sch.split(
l, factors=[None, TR, TS, vec_length], preserve_unit_iters=True)
sch.bind(ty, "threadIdx.y")
sch.bind(tx, "threadIdx.x")
sch.vectorize(vec)
# reduce tile_s * tr * vec to tile_s * tr
sch.reverse_compute_at(rf2, loop=bx, preserve_unit_loops=True)
tr, vec_c, *ts_tile_s = sch.get_loops(block=rf2)[1:]
ts_tile_s = sch.fuse(*ts_tile_s)
ts, tile_s = sch.split(ts_tile_s, factors=[TS, None], preserve_unit_iters=True)
tile_s, vec_s = sch.split(
tile_s,
factors=[None, get_max_factor(TILE_S, [1, 2, 4, 8])],
preserve_unit_iters=True,
)
sch.reorder(ts, tr, tile_s, vec_s, vec_c)
sch.bind(ts, TAG_S)
sch.bind(tr, TAG_R)
sch.vectorize(vec_s)
# reduce tile_s * tr to tile_s
sch.reverse_compute_at(gemv, loop=bx, preserve_unit_loops=True)
tr, *ts_tile_s = sch.get_loops(block=gemv)[1:]
ts_tile_s = sch.fuse(*ts_tile_s)
ts, tile_s = sch.split(ts_tile_s, factors=[TS, None], preserve_unit_iters=True)
sch.reorder(tile_s, ts, tr)
sch.bind(ts, TAG_S)
sch.bind(tr, TAG_R)
sch.decompose_reduction(rf, loop=sch.get_loops(block=rf)[3])
sch.decompose_reduction(rf2, loop=sch.get_loops(block=rf2)[-1])
sch.set_scope(rf, buffer_index=0, storage_scope="local")
sch.set_scope(rf2, buffer_index=0, storage_scope="local")
unroll_factor = UNROLL
sch.annotate(
block_or_loop=sch.get_loops(rf)[3],
ann_key="pragma_auto_unroll_max_step",
ann_val=unroll_factor,
)
sch.annotate(
block_or_loop=sch.get_loops(rf)[3],
ann_key="pragma_unroll_explicit",
ann_val=1,
)
sch.annotate(
block_or_loop=sch.get_loops(rf2)[3],
ann_key="pragma_auto_unroll_max_step",
ann_val=unroll_factor,
)
sch.annotate(
block_or_loop=sch.get_loops(rf2)[3],
ann_key="pragma_unroll_explicit",
ann_val=1,
)
if LOAD_V_SHARED:
sch.annotate(
block_or_loop=sch.get_loops(V_shared)[-4],
ann_key="pragma_unroll_explicit",
ann_val=unroll_factor,
)
sch.annotate(
block_or_loop=sch.get_loops(V_shared)[-4],
ann_key="pragma_vectorize",
ann_val=1,
)
# Schedule epilogue
if epilogue_info is not None:
epilogue = epilogue_info.block_rv
if is_broadcast_epilogue(sch, block, epilogue):
sch.reverse_compute_at(epilogue, bx)
sch.set_scope(block, 0, "shared")
_, _, *s = sch.get_loops(epilogue) # pylint: disable=invalid-name
_, tx = sch.split(sch.fuse(*s), factors=[None, TS])
sch.bind(tx, "threadIdx.x")
else:
sch.reverse_compute_at(epilogue, bx, preserve_unit_loops=True)
ts_tile_s = sch.fuse(*sch.get_loops(epilogue)[1:])
ts_tile_s = sch.get_loops(epilogue)[-1]
ts, tile_s = sch.split(ts_tile_s, factors=[TS, None], preserve_unit_iters=True)
sch.bind(ts, TAG_S)
sch.set_scope(block, 0, "local")
# pylint: enable=invalid-name
return sch
# Specify the `len_tx` and `len_ty` according to the loop extent
batch, s, r, c = sch.get_loops(block=block)
len_batch, len_s, len_r, len_c = (
get_extent(sch, batch),
get_extent(sch, s),
get_extent(sch, r),
get_extent(sch, c),
)
len_S = len_batch * len_s
len_R = len_r * len_c
TAG_S, TAG_R = "threadIdx.y", "threadIdx.x"
if target.kind.name == "cuda":
VEC_C = 4
LOAD_V_SHARED = True
LOAD_V_VEC = 8
UNROLL = 256
if isinstance(len_S, int):
if len_S > len_R:
TS, TR = 4, 64
else:
TS, TR = 16, 32
elif target.kind.name == "metal":
# Note that the following tile size is tuned on M2 Ultra for 7B
TAG_S, TAG_R = "threadIdx.x", "threadIdx.y"
VEC_C = 1
LOAD_V_SHARED = False
LOAD_V_VEC = -1
UNROLL = 256
if isinstance(len_S, int):
if len_S > len_R:
TS, TR = 4, 16
else:
TS, TR = 2, 64
elif target.kind.name == "rocm":
VEC_C = 4
LOAD_V_SHARED = True
LOAD_V_VEC = 8
UNROLL = 256
if isinstance(len_S, int):
if len_S > len_R:
TS, TR = 1, 128
else:
TS, TR = 8, 64
elif target.kind.name == "opencl" and "android" in str(target.host):
TAG_S, TAG_R = "threadIdx.x", "threadIdx.y"
VEC_C = 8
LOAD_V_SHARED = False
LOAD_V_VEC = -1
UNROLL = 8
TS, TR = 2, 32
elif target.kind.name == "vulkan":
VEC_C = 4
LOAD_V_SHARED = True
LOAD_V_VEC = 4
UNROLL = 256
if isinstance(len_S, int):
if len_S > len_R:
TS, TR = 4, 32
else:
TS, TR = 16, 32
elif target.kind.name == "opencl" and "mali" in str(target.attrs):
VEC_C = 8
LOAD_V_SHARED = False
LOAD_V_VEC = -1
UNROLL = 64
TS, TR = 1, 64
else:
VEC_C = 1
LOAD_V_SHARED = False
LOAD_V_VEC = -1
UNROLL = 64
TS, TR = 1, 64
if not isinstance(len_S, int):
TS, TR = 1, 64
while TS * TR > target.max_num_threads:
if TS > 1:
TS //= 2
else:
TR //= 2
TILE_S, TILE_R = (
1,
(len_c if len_c > 1 else max(
get_max_factor(len_r, [TR * 1, TR * 2, TR * 4, TR * 8]) // TR, 1)),
)
VEC_C = min(get_max_factor(TILE_R, [1, 2, 4, 8]), VEC_C)
VEC_LOAD = 1
return apply(
sch,
gemv=block,
TAG_S=TAG_S,
TAG_R=TAG_R,
TS=TS,
TR=TR,
TILE_S=TILE_S,
TILE_R=TILE_R,
VEC_LOAD=VEC_LOAD,
VEC_C=VEC_C,
LOAD_V_SHARED=LOAD_V_SHARED,
LOAD_V_VEC=LOAD_V_VEC,
UNROLL=UNROLL,
)
def sch_outer_reduction( # pylint: disable=too-many-arguments, invalid-name, unused-argument
self,
sch: tir.Schedule,
target: Target,
block: tir.schedule.BlockRV,
vector_input_buffers: List[tir.Buffer],
epilogue_info: Optional[BlockInfo],
):
"""Schedule the outer reduction block."""
# NOTE: Only Android is supported so far
if not (target.kind.name == "opencl" and "android" in str(target.host)):
return None
batch, s, r, c = sch.get_loops(block)
len_s = get_extent(sch, s)
# The config is designed for Adreno
tx_len = 64
vec_len = (4 if len_s > 4096 else 2) if isinstance(len_s, int) else 1
inner_r = 4
bx, tx, vec = sch.split(s, factors=[None, tx_len, vec_len])
r0, r1 = sch.split(r, factors=[None, inner_r])
sch.bind(batch, "blockIdx.y")
sch.bind(bx, "blockIdx.x")
sch.bind(tx, "threadIdx.x")
sch.reorder(bx, tx, r0, r1, c, vec)
sch.annotate(tx, ann_key="pragma_auto_unroll_max_step", ann_val=8)
sch.annotate(tx, ann_key="pragma_unroll_explicit", ann_val=1)
cache_v = sch.cache_read(block, vector_input_buffers[0], "local")
sch.compute_at(cache_v, r1, preserve_unit_loops=True)
sch.vectorize(sch.get_loops(cache_v)[-1])
sch.vectorize(vec)
# Schedule epilogue
if epilogue_info is not None:
sch.reverse_compute_at(epilogue_info.block_rv, tx)
sch.set_scope(block, 0, "local")
sch.decompose_reduction(block, r0)
return sch
def sch_inner_reduction_with_config( # pylint: disable=too-many-locals,too-many-branches,too-many-return-statements
self,
func: tir.PrimFunc,
config,
):
sch = tir.Schedule(func)
block_infos = normalize_prim_func(sch)
if block_infos is None:
return None
reduction_block: tir.schedule.BlockRV = None
for block in block_infos:
s_loops: List[tir.schedule.LoopRV] = []
r_loops: List[tir.schedule.LoopRV] = []
o_loops: List[tir.schedule.LoopRV] = []
dom_kind = block.dom_kind()
block = block.block_rv
if (any([
sch.get(loop_rv).thread_binding is not None for loop_rv in sch.get_loops(block)
]) or len(sch.get_loops(block)) == 0):
continue
for loop, iter_type in zip(sch.get_loops(block), dom_kind):
{"S": s_loops, "R": r_loops, "O": o_loops}[iter_type].append(loop)
if not s_loops:
s_loops.append(sch.add_unit_loop(block))
if len(r_loops) > 0:
reduction_block = block
# skip analysis for following blocks
break
def prod(iterable):
return reduce(lambda x, y: x * y, iterable, 1)
vec = 1
if len(config.vectorize):
vec = list(config.vectorize.values())[-1]
num_warps = int(prod(config.thread))
warp_size = int(prod(config.reduce_thread))
block_b = reduction_block
output_blocks = get_output_blocks(sch, block_infos)
# compute inline
for block_info in reversed(block_infos):
block = block_info.block_rv
if block not in (reduction_block, *output_blocks):
sch.compute_inline(block)
try:
i, j, k = sch.get_loops(block_b)
except Exception:
j, k = sch.get_loops(block_b)
block_local_A = sch.cache_read(block_b, 0, "local")
block_local_B = sch.cache_read(block_b, 1, "local")
block_local_C = sch.cache_write(block_b, 0, "local")
# reverse inline
if reduction_block is not None and reduction_block != output_blocks[0]:
sch.reverse_compute_inline(output_blocks[0])
bx, j = sch.split(j, factors=[None, num_warps])
k, tx, vk = sch.split(k, factors=[None, warp_size, vec])
sch.reorder(bx, j, k, tx)
sch.bind(bx, "blockIdx.x")
sch.bind(tx, "threadIdx.x")
sch.bind(j, "threadIdx.y")
self.block_size = [sch.get(tx).extent, sch.get(j).extent, 1]
self.grid_size = [sch.get(bx).extent, 1, 1]
sch.compute_at(block_local_A, tx, preserve_unit_loops=True)
sch.compute_at(block_local_B, tx, preserve_unit_loops=True)
sch.reverse_compute_at(block_local_C, j, preserve_unit_loops=True)
block_local_a_v = sch.get_loops(block_local_A)[-1]
sch.vectorize(block_local_a_v)
block_local_b_v = sch.get_loops(block_local_B)[-1]
sch.vectorize(block_local_b_v)
return sch
def sch_outer_reduction_with_config( # pylint: disable=too-many-locals,too-many-branches,too-many-return-statements
self,
func: tir.PrimFunc,
config,
):
sch = tir.Schedule(func)
block_infos = normalize_prim_func(sch)
if block_infos is None:
return None
reduction_block: tir.schedule.BlockRV = None
for block in block_infos:
s_loops: List[tir.schedule.LoopRV] = []
r_loops: List[tir.schedule.LoopRV] = []
o_loops: List[tir.schedule.LoopRV] = []
dom_kind = block.dom_kind()
block = block.block_rv
if (any([
sch.get(loop_rv).thread_binding is not None for loop_rv in sch.get_loops(block)
]) or len(sch.get_loops(block)) == 0):
continue
for loop, iter_type in zip(sch.get_loops(block), dom_kind):
{"S": s_loops, "R": r_loops, "O": o_loops}[iter_type].append(loop)
if not s_loops:
s_loops.append(sch.add_unit_loop(block))
if len(r_loops) > 0:
reduction_block = block
# skip analysis for following blocks
break
C = reduction_block
CL = sch.cache_write(reduction_block, 0, "local")
blck_axis = []
vthd_axis = []
thrd_axis = []
tile_axis = []
# for gemv, we should skip dynamic symbolic in s_loops
s_loops = [loop for loop in s_loops if isinstance(sch.get(loop).extent, tir.IntImm)]
assert len(s_loops) == len(config.block), f"{len(s_loops)} != {len(config.block)}"
for i, loop in enumerate(s_loops):
if sch.get(loop).extent % config.block[i]:
raise NotImplementedError("Undivisible block in TIR schedule is still buggy.")
bx, _t = sch.split(loop, factors=[None, config.block[i]])
blck_axis.append(bx)
if config.step[i] > 1:
_t, tn = sch.split(_t, factors=[None, config.step[i]])
tile_axis.append(tn)
if config.block[i] <= config.thread[i] * config.step[i]:
tx = _t
else:
vx, tx = sch.split(_t, factors=[None, config.thread[i]])
vthd_axis.append(vx)
thrd_axis.append(tx)
reduce_outer_axis, reduce_inner_axis = [], []
for i in config.raxis_order:
loop = r_loops[i]
ro, ri = sch.split(loop, factors=[None, config.rstep[i]])
reduce_outer_axis.append(ro)
reduce_inner_axis.append(ri)
vthd_axis = list(reversed(vthd_axis)) # inner virtual thread first
axis_order = (
blck_axis + vthd_axis + thrd_axis + reduce_outer_axis + reduce_inner_axis + tile_axis)
sch.reorder(*axis_order)
blck_fused = sch.fuse(*blck_axis)
thrd_fused = sch.fuse(*thrd_axis)
sch.bind(blck_fused, "blockIdx.x")
sch.bind(thrd_fused, "threadIdx.x")
if len(vthd_axis) > 3:
vthd_axis = vthd_axis[0:2] + [sch.fuse(*vthd_axis[2:])]
for i, ax in enumerate(vthd_axis):
sch.bind(ax, "vthread" + [".x", ".y", ".z"][i])
for ax in tile_axis:
sch.unroll(ax)
sch.reverse_compute_at(CL, thrd_fused)
if len(tile_axis) > 0:
for ax in sch.get_loops(CL)[-len(tile_axis):]:
sch.unroll(ax)
sch.decompose_reduction(C, reduce_outer_axis[0])
try_inline_contiguous_spatial(sch, block_infos)
return sch
def apply_config( # pylint: disable=too-many-locals,missing-docstring
self,
func: tir.PrimFunc,
config,
) -> tir.Schedule:
if not isinstance(func, tir.PrimFunc):
return None
sch = tir.Schedule(func)
block_infos = normalize_prim_func(sch)
block_infos = try_inline_contiguous_spatial(sch, block_infos)
if len(block_infos) == 1:
epilogue = None
elif len(block_infos) == 2:
epilogue = block_infos[1]
if not epilogue.is_injective():
return None
else:
return None
block_info = block_infos[0]
if len(block_info.iters) not in [2, 3]:
# either [B, S, R] = [B, S, R] * [B, R]
# or [S, R] = [S, R] * [R]
return None
if is_gemv(sch, block_info) is None:
return None
if "dequantize_info" in func.attrs:
dequantize_rule = GEMVWithDequantizeInfo()
return dequantize_rule.apply_config(func, config)
if any([t > 1 for t in config.reduce_thread]):
return self.sch_inner_reduction_with_config(func, config)
return self.sch_outer_reduction_with_config(func, config)
|
BitBLAS/python/bitblas/gpu/gemv.py/0
|
{
"file_path": "BitBLAS/python/bitblas/gpu/gemv.py",
"repo_id": "BitBLAS",
"token_count": 15458
}
| 151 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import tvm
from tvm.target import Target
import operator
from functools import reduce
from bitblas.base.roller.arch.cuda import CUDA
from typing import Any, List, Literal, Optional, Tuple, Union
from .operator import Operator, TransformKind
from .impl.matmul_dequantize_impl import (
select_implementation as weight_dequantize_implementation,)
from .impl.matmul_impl import select_implementation as consistent_implementation
from ..base.utils import tensor_replace_dp4a, tensor_remove_make_int4
from bitblas.utils.target_detector import auto_detect_nvidia_target
from bitblas.utils.tensor_adapter import tvm_tensor_to_torch
from dataclasses import dataclass
from .ladder_permutate import LadderPermutate, LadderPermutateConfig
from .lop3_permutate import LOP3Permutate, LOP3PermutateConfig
import logging
import torch
logger = logging.getLogger(__name__)
WORKSPACE_SIZE = 1024 * 1024 * 256
# TODO(lei): This should be improved into a general
# Method to get the consistent compute patterns.
NATIVE_COMPUTE_PATTERNS = [
# A_dtype, W_dtype
("float64", "float64"),
("float32", "float32"),
("float16", "float16"),
("int8", "int8"),
("e4m3_float8", "e4m3_float8"),
("e4m3_float8", "e5m2_float8"),
("e5m2_float8", "e4m3_float8"),
("e5m2_float8", "e5m2_float8"),
]
def is_native_compute(A_dtype, W_dtype) -> bool:
return (A_dtype, W_dtype) in NATIVE_COMPUTE_PATTERNS
class OPExecutorCPU:
def __init__(self, operators: Optional[List[Operator]] = None):
if operators is None:
operators = []
self.operators = operators
def append(self, op):
self.operators.append(op)
def is_none(self):
return len(self.operators) == 0
def forward(self, weight):
inputs = [weight]
for op in self.operators:
inputs.append(tvm_tensor_to_torch(op.get_profile_tensors()[-1]).cpu())
inputs = [op.forward(*inputs)]
return inputs[-1]
def __call__(self, *args: Any, **kwds: Any) -> Any:
return self.forward(*args, **kwds)
@property
def size(self):
return len(self.operators)
@dataclass(frozen=True)
class MatmulConfig:
M: Union[int, Tuple[int]] = None
N: int = None
K: int = None
A_dtype: str = "float16"
# is a wrapper for source_format and bit
W_dtype: str = A_dtype # W_dtype is the same as A_dtype by default
out_dtype: str = "float16"
accum_dtype: str = "float16"
layout: Literal["nn", "nt", "tn", "tt"] = "nt"
with_bias: bool = False
group_size: int = -1
with_scaling: bool = False
with_zeros: bool = False
# documents for zeros_mode:
# original: target = (dequantize_weight - zero_point) * scale
# rescale: target = dequantize_weight * scale - zero_point
# quantized: target = (dequantize_weight - dequantize_zeros) * scale
# The auto-gptq framework prefer "quantized" and "original" for alignment with cuda.
zeros_mode: Literal["original", "rescale", "quantized"] = "original"
storage_dtype: str = "int8"
# weight transform related flags
fast_decoding: Optional[bool] = None # enable fast decoding by default, if not specified, it is enabled by a rule.
propagate_a: Optional[TransformKind] = None # propagate_a is a flag to control the ladder permutation.
propagate_b: Optional[TransformKind] = None # propagate_b is a flag to control the ladder permutation
def __legalize_dynamic_symbolic(self, M):
return tuple(self.M) if isinstance(self.M, list) else self.M
def __legalize_propagate(self, propagate):
if isinstance(propagate, bool):
return (TransformKind.IntraWarpTransform
if propagate else TransformKind.NonTransform)
elif isinstance(propagate, int):
return TransformKind(propagate)
return propagate
def __initialize_propagate(self, propagate_a: Optional[TransformKind], propagate_b: Optional[TransformKind]):
MICRO_KERNEL_SIZE = 16
if isinstance(
self.M,
int) and (self.M % MICRO_KERNEL_SIZE) == 0 and (self.K % MICRO_KERNEL_SIZE) == 0:
object.__setattr__(self, "propagate_a", TransformKind.IntraWarpTransform)
else:
object.__setattr__(self, "propagate_a", TransformKind.NonTransform)
if self.M == 1 or (
self.N % MICRO_KERNEL_SIZE) != 0 or (self.K % MICRO_KERNEL_SIZE) != 0 or isinstance(
self.M, Tuple) or (self.with_zeros and self.zeros_mode == "quantized"):
object.__setattr__(self, "propagate_a", TransformKind.NonTransform)
object.__setattr__(self, "propagate_b", TransformKind.NonTransform)
else:
object.__setattr__(self, "propagate_b", TransformKind.IntraWarpTransform)
# set a and b value if is not None
if propagate_a is not None:
object.__setattr__(self, "propagate_a", propagate_a)
if propagate_b is not None:
object.__setattr__(self, "propagate_b", propagate_b)
# TODO(lei): This is a limitation arose by pytorch and llvm
# Should be removed in the future.
if self.A_dtype in ["e4m3_float8", "e5m2_float8"]:
object.__setattr__(self, "propagate_a", TransformKind.NonTransform)
object.__setattr__(self, "propagate_b", TransformKind.NonTransform)
def __initialize_zeros_mode(self, zeros_mode: Optional[str]):
if zeros_mode is None:
object.__setattr__(self, "zeros_mode", "original")
def __initialize_fast_decoding(self, fast_decoding: Optional[bool]):
if "int" not in self.W_dtype or self.W_dtype == self.A_dtype:
object.__setattr__(self, "fast_decoding", False)
else:
object.__setattr__(self, "fast_decoding", self.fast_decoding)
if fast_decoding is not None:
object.__setattr__(self, "fast_decoding", fast_decoding)
def __post_init__(self):
# set M to default dynamic range if it is None
if self.M is None:
object.__setattr__(self, "M", [1, 16, 32, 64, 128, 256, 512, 1024])
if self.N is None:
raise ValueError("N should be specified currently.")
if self.K is None:
raise ValueError("K should be specified currently.")
# set M to tuple if it is list
# otherwise, M is not hashable
object.__setattr__(self, "M", self.__legalize_dynamic_symbolic(self.M))
# set propagate_a and propagate_b to default value if it is None
object.__setattr__(self, "propagate_a", self.__legalize_propagate(self.propagate_a))
object.__setattr__(self, "propagate_b", self.__legalize_propagate(self.propagate_b))
# This is hack to legalize propagate_a and b
# TODO(lei): should be removed in the future when tc+br template is ready.
self.__initialize_propagate(self.propagate_a, self.propagate_b)
self.__initialize_zeros_mode(self.zeros_mode)
self.__initialize_fast_decoding(self.fast_decoding)
if self.with_bias is None:
object.__setattr__(self, "with_bias", False)
if self.group_size is None:
object.__setattr__(self, "group_size", -1)
if self.with_scaling is None:
object.__setattr__(self, "with_scaling", False)
if self.with_zeros is None:
object.__setattr__(self, "with_zeros", False)
if self.A_dtype == self.W_dtype and self.W_dtype in [
"float16", "int8", "e4m3_float8", "e5m2_float8"
]:
object.__setattr__(self, "storage_dtype", self.W_dtype)
class Matmul(Operator):
# TODO(lei): This should be improved into a general datatype.
BITBLAS_TRICK_DTYPE_MAP = {
"float64": ("fp", 64),
"float32": ("fp", 32),
"float16": ("fp", 16),
"int32": ("int", 32),
"uint32": ("uint", 32),
"int16": ("int", 16),
"uint16": ("uint", 16),
"int8": ("int", 8),
"uint8": ("uint", 8),
"int4": ("int", 4),
"uint4": ("uint", 4),
"int2": ("int", 2),
"uint2": ("uint", 2),
"int1": ("int", 1),
"uint1": ("uint", 1),
"nf4": ("nf", 4),
"fp8_e5m2": ("fp", 8),
"fp4_e2m1": ("fp", 4),
"e4m3_float8": ("fp", 8), # "e4m3_float8" is a trick for "float8_e4m3fn"
"e5m2_float8": ("fp", 8),
}
def __init__(
self,
config: MatmulConfig,
name: str = "matmul",
target: Optional[Union[str, Target]] = None,
enable_tuning: bool = True,
from_database: bool = False,
):
# if from database, we should disable default schedule
# to save compilation time
if target is None:
target = auto_detect_nvidia_target()
logger.info(f"Auto detected target: {target}")
assert (config.A_dtype
in self.BITBLAS_TRICK_DTYPE_MAP), f"Unsupported input dtype {config.A_dtype}"
source_format, bit = self.BITBLAS_TRICK_DTYPE_MAP[config.W_dtype]
self.source_format = source_format
self.bit = bit
super().__init__(name, config, target)
if source_format == "int" and self.with_zeros:
logger.warning(
"[BitBLAS][Warning] with_zeros is not supported for int source format as int has a constant zeropoints already."
)
target = self.target
if target.kind.name != "cuda":
raise ValueError("Currently only support cuda target")
self.arch = CUDA(target)
if isinstance(self.M, Tuple):
self.dynamic_range = {"m": self.M}
self.prim_func_mod["main"] = self.prim_func_mod["main"].with_attrs(
{"opt_shapes": self.dynamic_range})
else:
self.dynamic_range = None
if not from_database:
self._build_default_module(target)
self.workspace = None
if self.propagate_a:
# for general purpose, we use propagate_a to control the ladder permutation.
ladder_permutate_config = LadderPermutateConfig(
M=self.M,
N=self.K,
datatype=self.A_dtype,
storage_dtype=self.A_dtype,
propagate_kind="A",
transpose_matrix=False,
transform_kind=self.propagate_a,
)
self.ladder_permutate_a = LadderPermutate(
config=ladder_permutate_config,
target=target,
enable_tuning=enable_tuning,
)
self.workspace = torch.empty(WORKSPACE_SIZE, dtype=torch.float16).cuda()
else:
self.ladder_permutate_a = None
if self.propagate_b:
ladder_permutate_config = LadderPermutateConfig(
M=self.N,
N=self.K,
datatype=self.A_dtype,
dequantize_bits=self.bit,
storage_dtype=self.storage_dtype,
propagate_kind="B",
transpose_matrix=self.layout == "nt",
transform_kind=self.propagate_b,
)
self.ladder_permutate_b = LadderPermutate(
config=ladder_permutate_config,
target=tvm.target.Target("llvm"),
)
else:
self.ladder_permutate_b = None
if self.fast_decoding:
lop3_permutate_config = LOP3PermutateConfig(
M=self.N,
N=self.K,
datatype=self.A_dtype,
dequantize_bits=self.bit,
storage_dtype=self.storage_dtype,
)
self.lop3_permutate = LOP3Permutate(
config=lop3_permutate_config,
target=tvm.target.Target("llvm"),
)
else:
self.lop3_permutate = None
input_executors = OPExecutorCPU()
if self.ladder_permutate_a is not None:
input_executors.append(self.ladder_permutate_a)
self.input_executors = input_executors
weight_executors = OPExecutorCPU()
if self.lop3_permutate is not None:
weight_executors.append(self.lop3_permutate)
if self.ladder_permutate_b is not None:
weight_executors.append(self.ladder_permutate_b)
self.weight_executors = weight_executors
if enable_tuning:
self.hardware_aware_finetune()
if source_format == "nf":
self.lut = torch.Tensor(([
-1.0, -0.6961928009986877, -0.5250730514526367, -0.39491748809814453,
-0.28444138169288635, -0.18477343022823334, -0.09105003625154495, 0.0,
0.07958029955625534, 0.16093020141124725, 0.24611230194568634, 0.33791524171829224,
0.44070982933044434, 0.5626170039176941, 0.7229568362236023, 1.0
]),
dtype=getattr(torch, self.A_dtype)).cuda()
else:
self.lut = None
# output data type
self.torch_output_dtype = getattr(torch, self.out_dtype)
def _build_default_module(self, target: Target):
try:
self.optimized_func = self.apply_default_schedule(self.prim_func_mod, target)
except Exception:
self.optimized_func = None
logger.warning(
"[BitBLAS][Warning] Apply default schedule failed, should do hardware-aware optimization manually."
)
self._build_runtime_module(target)
def _select_implementation(self):
if is_native_compute(self.A_dtype, self.W_dtype):
return consistent_implementation(
M=self.M,
N=self.N,
K=self.K,
in_dtype=self.A_dtype,
out_dtype=self.out_dtype,
accum_dtype=self.accum_dtype,
with_bias=self.with_bias,
layout=self.layout,
propagate_a=self.propagate_a,
propagate_b=self.propagate_b,
)
else:
return weight_dequantize_implementation(
M=self.M,
N=self.N,
K=self.K,
in_dtype=self.A_dtype,
out_dtype=self.out_dtype,
accum_dtype=self.accum_dtype,
bit=self.bit,
storage_dtype=self.storage_dtype,
source_format=self.source_format,
with_scaling=self.with_scaling,
with_zeros=self.with_zeros,
group_size=self.group_size,
fast_decoding=self.fast_decoding,
with_bias=self.with_bias,
layout=self.layout,
zeros_mode=self.zeros_mode,
propagate_a=self.propagate_a,
propagate_b=self.propagate_b,
)
def post_process(self, code: str) -> str:
code = tensor_replace_dp4a(code)
code = tensor_remove_make_int4(code)
return code
def retrieve_weight_shape(self):
return [int(i) for i in self.prim_func.buffer_map[self.prim_func.params[1]].shape]
def transform_weight(self, weight, scale=None, zeros=None, bias=None):
"""
Transforms the given weight tensor based on the specified quantization parameters and
returns the transformed weight along with optional scale, zeros, and bias.
Parameters:
- weight: The input weight tensor to be transformed.
- scale: Optional scaling factor for the weight tensor.
- zeros: Optional zero-point adjustment for the weight tensor.
- bias: Optional bias to be added to the weight tensor.
Returns:
A list containing the transformed weight tensor and optionally the scale, zeros, and bias.
"""
weight = weight.contiguous()
if self.W_dtype == self.A_dtype:
if self.weight_transform is not None:
return self.weight_transform(weight.cpu()).cuda().contiguous()
return weight
from bitblas.quantization import general_compress
import torch
import numpy as np
source_format, bit = self.source_format, self.bit
# Process integer source format
if source_format == "int":
assert not self.with_scaling, "scale should be False for int source format"
assert not self.with_zeros, "zeros should be False for int source format"
maxq = 2**(bit - 1)
# Clamp weight values to be within the quantizable range and adjust
weight = torch.clamp(weight, -maxq, maxq).int() + maxq
else:
# For non-integer formats, simply convert weights to integers
weight = weight.int()
np_storage_dtype = getattr(np, self.storage_dtype)
weight = general_compress(
weight.cpu().numpy(), source_bits=bit, storage_dtype=np_storage_dtype)
weight = torch.from_numpy(weight).cuda().contiguous()
# Apply an optional weight transformation if specified
if self.weight_transform is not None:
weight = self.weight_transform(weight.cpu()).cuda().contiguous()
# Prepare the return list with the transformed weight and optionally include scale, zeros, and bias
result = [weight]
if scale is not None:
result.append(scale)
if zeros is not None:
result.append(zeros)
if bias is not None:
result.append(bias)
return next(iter(result), result)
def transform_input(self, input_tensor):
if self.propagate_a is not TransformKind.NonTransform:
# check workspace size
if input_tensor.numel() > WORKSPACE_SIZE:
raise ValueError(
f"Input size {input_tensor.numel()} is larger than the workspace size {WORKSPACE_SIZE}, please increase the workspace size."
)
self.ladder_permutate_a._forward_from_prebuild_lib(input_tensor, self.workspace)
return self.workspace
return input_tensor
def forward(self, A, W, scale=None, zeros=None, bias=None, output=None) -> Any:
args = []
args.append(self.transform_input(A))
if self.lut is not None:
args.append(self.lut)
args.append(W)
if output is None:
output = torch.empty(
A.shape[:-1] + (self.N,), dtype=self.torch_output_dtype, device=A.device)
if scale is not None:
args.append(scale)
if zeros is not None:
args.append(zeros)
if bias is not None:
args.append(bias)
args.append(output)
if self.dynamic_range is not None:
m = reduce(operator.mul, A.shape[:-1], 1)
args.append(m)
if self.lib is None:
self._forward_from_torch_func(*args)
self._forward_from_prebuild_lib(*args)
return output
def __call__(self, *args: Any, **kwds: Any) -> Any:
return self.forward(*args, **kwds)
@property
def M(self):
return self.config.M
@property
def N(self):
return self.config.N
@property
def K(self):
return self.config.K
@property
def A_dtype(self):
return self.config.A_dtype
@property
def W_dtype(self):
return self.config.W_dtype
@property
def out_dtype(self):
return self.config.out_dtype
@property
def accum_dtype(self):
return self.config.accum_dtype
@property
def storage_dtype(self):
return self.config.storage_dtype
@property
def with_scaling(self):
return self.config.with_scaling
@property
def with_zeros(self):
return self.config.with_zeros
@property
def group_size(self):
return self.config.group_size
@property
def fast_decoding(self):
return self.config.fast_decoding
@property
def with_bias(self):
return self.config.with_bias
@property
def propagate_a(self):
return self.config.propagate_a
@property
def propagate_b(self):
return self.config.propagate_b
@property
def layout(self):
return self.config.layout
@property
def zeros_mode(self):
return self.config.zeros_mode
@property
def input_transform(self):
return self.input_executors if self.input_executors.size else None
@property
def weight_transform(self):
return self.weight_executors if self.weight_executors.size else None
|
BitBLAS/python/bitblas/ops/general_matmul.py/0
|
{
"file_path": "BitBLAS/python/bitblas/ops/general_matmul.py",
"repo_id": "BitBLAS",
"token_count": 9731
}
| 152 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import numpy as np
import torch
import torch.nn as nn
def gen_quant4(k, n, groupsize=-1):
maxq = 2**4
w = torch.randn((k, n), dtype=torch.half, device="cpu")
original_w = w.clone()
if groupsize == -1:
groupsize = k
if groupsize != -1:
w = w.reshape((-1, groupsize, n))
w = w.permute(1, 0, 2)
w = w.reshape((groupsize, -1))
s = torch.max(torch.abs(w), 0, keepdim=True)[0]
s *= 2 / maxq
# Quantize.
w = torch.round(w / s).int()
# Unsigned storage.
w += (maxq) // 2
w = torch.clamp(w, 0, maxq)
# Dequantize.
ref = (w - (maxq) // 2).half() * s
if groupsize != -1:
def reshape(w):
w = w.reshape((groupsize, -1, n))
w = w.permute(1, 0, 2)
w = w.reshape((k, n)).contiguous()
return w
ref = reshape(ref)
w = reshape(w)
s = s.reshape((-1, n)).contiguous()
linear = nn.Linear(k, n, bias=False)
linear.weight.data = ref.t()
return original_w, linear, s, (w - (maxq) // 2)
def general_compress(lowprecision_weight, source_bits=4, storage_dtype=np.int8):
elems_per_byte = 8 // source_bits
if lowprecision_weight.dtype == np.float16:
lowprecision_weight = lowprecision_weight.astype(dtype=np.int8)
int8_weight = np.zeros(
(
*lowprecision_weight.shape[:-1],
lowprecision_weight.shape[-1] // elems_per_byte,
),
dtype=np.int8,
)
for j in range(lowprecision_weight.shape[-1] // elems_per_byte):
for k in range(elems_per_byte):
int8_weight[:, j] |= lowprecision_weight[:, j * elems_per_byte + k] << (source_bits * k)
return int8_weight.view(storage_dtype)
# interleave weight numpy implementation
def interleave_weight(qweight, nbits=4, target_dtype="float16"):
assert target_dtype in ["float16", "int8"]
# reinterpret the data type of qweight to int32
qweight = qweight.view(np.int32)
new_qweight = np.zeros_like(qweight)
bits_stride = 8 if target_dtype == "int8" else 16
mask = (1 << nbits) - 1 # for 4bit the val is 0x0000000f
num_groups = 32 // bits_stride
elems_per_group = bits_stride // nbits
for i in range(num_groups):
for j in range(elems_per_group):
offset = i * elems_per_group + j
shift = (offset % num_groups) * bits_stride + (offset // num_groups) * nbits
new_qweight |= ((qweight >> (nbits * offset)) & mask) << shift
if nbits == 1 and target_dtype == "int8":
# special handling for 1b interleave
n16_weight = new_qweight & np.int32(0xF0F00F0F)
n16_weight |= ((new_qweight & np.int32(0x000000F0)) >> 4) << 16
n16_weight |= ((new_qweight & np.int32(0x0000F000)) >> 12) << 24
n16_weight |= ((new_qweight & np.int32(0x000F0000)) >> 16) << 4
n16_weight |= ((new_qweight & np.int32(0x0F000000)) >> 24) << 12
return n16_weight.view(np.int8)
elif nbits == 2 and target_dtype == "float16":
n8_weight = new_qweight & np.int32(0xFF0000FF)
n8_weight |= ((new_qweight & np.int32(0x0000FF00)) >> 8) << 16
n8_weight |= ((new_qweight & np.int32(0x00FF0000)) >> 16) << 8
return n8_weight.view(np.int8)
elif nbits == 1 and target_dtype == "float16":
n8_weight = new_qweight & 0xF000000F
n8_weight |= ((new_qweight & 0x000000F0) >> 4) << 8
n8_weight |= ((new_qweight & 0x00000F00) >> 8) << 16
n8_weight |= ((new_qweight & 0x0000F000) >> 12) << 24
n8_weight |= ((new_qweight & 0x000F0000) >> 16) << 4
n8_weight |= ((new_qweight & 0x00F00000) >> 20) << 12
n8_weight |= ((new_qweight & 0x0F000000) >> 24) << 20
return new_qweight.view(np.int8)
|
BitBLAS/python/bitblas/quantization/utils.py/0
|
{
"file_path": "BitBLAS/python/bitblas/quantization/utils.py",
"repo_id": "BitBLAS",
"token_count": 1799
}
| 153 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from tvm.script import ir as I
from tvm.script import tir as T
from tvm.script import relax as R
import tvm
import tvm.testing
from tvm import relax
from tvm.script import ir as I, relax as R, tir as T
from tvm import tir
from tvm.ir import IRModule
from tvm.ir.transform import PassContext, module_pass
from bitblas.base.utils import get_dummy_input_arrays
from copy import deepcopy
import bitblas
from bitblas.relax.transform.annotate_decode_block import AnnotateDecodeInformation
from bitblas.relax.transform.weight_only_propagate import WeightOnlyLayoutPropagation
import numpy as np
np.random.seed(0)
def get_ref_result(ref_mod, input_tensors):
# input_tensors to cpu
device = tvm.cpu(0)
target = tvm.target.Target("llvm")
input_tensors = [tvm.nd.array(x, device) for x in input_tensors]
ref_mod = tvm.tir.transform.MakePackedAPI()(ref_mod)
ex = relax.build(ref_mod, target)
vm = relax.VirtualMachine(ex, device)
res = vm["main"](*input_tensors)
return res
def get_default_result(ref_mod, input_tensors, target, device):
with target:
ref_mod = bitblas.ApplyDefaultSchedule( # pylint: disable=not-callable
bitblas.gpu.GEMV(),
bitblas.gpu.Matmul(),
bitblas.gpu.Reduction(),
bitblas.gpu.GeneralReduction(),
bitblas.gpu.Fallback(),
)(ref_mod)
ref_mod = tvm.tir.transform.MakePackedAPI()(ref_mod)
ex = relax.build(ref_mod, target)
vm = relax.VirtualMachine(ex, device)
res = vm["main"](*input_tensors)
return res
def get_fast_tune_result(ref_mod, input_tensors, target, device):
ref_mod = bitblas.ApplyFastTuning(target=target)(ref_mod)
ref_mod = tvm.tir.transform.MakePackedAPI()(ref_mod)
print(ref_mod)
ex = relax.build(ref_mod, target)
vm = relax.VirtualMachine(ex, device)
res = vm["main"](*input_tensors)
return res
def test_lop3_transform():
@I.ir_module
class Before:
@T.prim_func(private=True)
def fused_fused_decode3_fused_NT_matmul8_add1(
lv47: T.Buffer((T.int64(4096), T.int64(512)), "uint32"),
lv48: T.Buffer((T.int64(4096), T.int64(128)), "float16"),
p_lv41: T.handle,
p_output0: T.handle,
):
T.func_attr({"tir.noalias": T.bool(True)})
n = T.int64()
lv41 = T.match_buffer(p_lv41, (T.int64(1), 1, T.int64(4096)), "float16")
NT_matmul_intermediate = T.match_buffer(
p_output0, (T.int64(1), 1, T.int64(4096)), "float16"
)
# with T.block("root"):
decode_intermediate_intermediate = T.alloc_buffer(
(T.int64(4096), T.int64(4096)), "float16"
)
for i, j in T.grid(T.int64(4096), T.int64(4096)):
with T.block("decode"):
v_i, v_j = T.axis.remap("SS", [i, j])
T.reads(lv47[v_i, v_j // T.int64(8)], lv48[v_i, v_j // T.int64(32)])
T.writes(decode_intermediate_intermediate[v_i, v_j])
decode_intermediate_intermediate[v_i, v_j] = (
T.Cast(
"float16",
T.bitwise_and(
T.shift_right(
lv47[v_i, v_j // T.int64(8)],
T.Cast("uint32", v_j % T.int64(8)) * T.uint32(4),
),
T.uint32(15),
),
)
- T.float16(7)
) * lv48[v_i, v_j // T.int64(32)]
for i0, i1, i2, k in T.grid(T.int64(1), 1, T.int64(4096), T.int64(4096)):
with T.block("NT_matmul"):
v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
T.reads(
lv41[v_i0, v_i1, v_k],
decode_intermediate_intermediate[v_i2, v_k],
)
T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2])
with T.init():
NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0)
NT_matmul_intermediate[v_i0, v_i1, v_i2] = (
NT_matmul_intermediate[v_i0, v_i1, v_i2]
+ lv41[v_i0, v_i1, v_k]
* decode_intermediate_intermediate[v_i2, v_k]
)
@R.function
def main(
lv47: R.Tensor((T.int64(4096), T.int64(512)), dtype="uint32"),
lv48: R.Tensor((T.int64(4096), T.int64(128)), dtype="float16"), # type: ignore
p_lv41: R.Tensor((T.int64(1), 1, T.int64(4096)), dtype="float16"),
) -> R.Tensor((1, 4096), dtype="float16"):
R.func_attr({"Primitive": 1})
# n = T.int64()
cls = Before
with R.dataflow():
gv = R.call_tir(
cls.fused_fused_decode3_fused_NT_matmul8_add1,
(lv47, lv48, p_lv41),
out_sinfo=R.Tensor((1, 1, 4096), dtype="float16"),
)
R.output(gv)
return gv
relax_mod = Before
ref_mod = deepcopy(relax_mod)
dispatch_target = tvm.target.Target("cuda")
# input_arrays = get_dummy_input_arrays(relax_mod)
relax_mod = AnnotateDecodeInformation()(relax_mod)
with dispatch_target:
relax_mod = WeightOnlyLayoutPropagation(
transform_level=0, faster_conversion=False
)(relax_mod)
input_tensors = get_dummy_input_arrays(ref_mod["main"], tvm.cpu())
ref_mod = tvm.tir.transform.MakePackedAPI()(ref_mod)
ex = relax.build(ref_mod, "llvm")
device = tvm.cpu(0)
vm = relax.VirtualMachine(ex, device)
res = vm["main"](*input_tensors)
print("ref ", res)
print(relax_mod)
relax_mod = tvm.tir.transform.MakePackedAPI()(relax_mod)
ex = relax.build(relax_mod, "llvm")
device = tvm.cpu(0)
vm = relax.VirtualMachine(ex, device)
res = vm["main"](*input_tensors)
print("relax ", res)
def test_matmul_transform(transform_level = 2):
@I.ir_module
class Before:
@T.prim_func(private=True)
def fused_fused_decode3_fused_NT_matmul8_add1(
p_lv41: T.handle,
lv47: T.Buffer((T.int64(4096), T.int64(4096)), "float16"),
p_output0: T.handle,
):
T.func_attr({"tir.noalias": T.bool(True)})
n = T.int64()
lv41 = T.match_buffer(p_lv41, (T.int64(1), 1, T.int64(4096)), "float16")
NT_matmul_intermediate = T.match_buffer(
p_output0, (T.int64(1), 1, T.int64(4096)), "float16"
)
for i0, i1, i2, k in T.grid(T.int64(1), 1, T.int64(4096), T.int64(4096)):
with T.block("NT_matmul"):
v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
T.reads(lv41[v_i0, v_i1, v_k], lv47[v_i2, v_k])
T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2])
with T.init():
NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0)
NT_matmul_intermediate[v_i0, v_i1, v_i2] = (
NT_matmul_intermediate[v_i0, v_i1, v_i2]
+ lv41[v_i0, v_i1, v_k] * lv47[v_i2, v_k]
)
@R.function
def main(
lv47: R.Tensor((T.int64(4096), T.int64(4096)), dtype="float16"),
p_lv41: R.Tensor((T.int64(1), 1, T.int64(4096)), dtype="float16"),
) -> R.Tensor((1, 4096), dtype="float16"):
R.func_attr({"Primitive": 1})
# n = T.int64()
cls = Before
with R.dataflow():
gv = R.call_tir(
cls.fused_fused_decode3_fused_NT_matmul8_add1,
(p_lv41, lv47),
out_sinfo=R.Tensor((1, 1, 4096), dtype="float16"),
)
R.output(gv)
return gv
relax_mod = Before
ref_mod = deepcopy(relax_mod)
dispatch_target = tvm.target.Target("cuda")
relax_mod = AnnotateDecodeInformation()(relax_mod)
with dispatch_target:
relax_mod = WeightOnlyLayoutPropagation(
transform_level=transform_level, faster_conversion=False
)(relax_mod)
input_tensors = get_dummy_input_arrays(ref_mod["main"], tvm.cpu())
ref_mod = tvm.tir.transform.MakePackedAPI()(ref_mod)
ex = relax.build(ref_mod, "llvm")
device = tvm.cpu(0)
vm = relax.VirtualMachine(ex, device)
res = vm["main"](*input_tensors)
print("ref ", res)
print(relax_mod)
relax_mod = tvm.tir.transform.MakePackedAPI()(relax_mod)
ex = relax.build(relax_mod, "llvm")
device = tvm.cpu(0)
vm = relax.VirtualMachine(ex, device)
res = vm["main"](*input_tensors)
print("relax ", res)
def test_dequantize_matmul_transform(transform_level=2):
@I.ir_module
class Before:
@T.prim_func(private=True)
def fused_fused_decode3_fused_NT_matmul8_add1(
lv47: T.Buffer((T.int64(4096), T.int64(512)), "uint32"),
lv48: T.Buffer((T.int64(4096), T.int64(128)), "float16"),
p_lv41: T.handle,
p_output0: T.handle,
):
T.func_attr({"tir.noalias": T.bool(True)})
n = T.int64()
lv41 = T.match_buffer(
p_lv41, (T.int64(1), T.int64(1), T.int64(4096)), "float16"
)
NT_matmul_intermediate = T.match_buffer(
p_output0, (T.int64(1), T.int64(1), T.int64(4096)), "float16"
)
# with T.block("root"):
decode_intermediate_intermediate = T.alloc_buffer(
(T.int64(4096), T.int64(4096)), "float16"
)
for i, j in T.grid(T.int64(4096), T.int64(4096)):
with T.block("decode"):
v_i, v_j = T.axis.remap("SS", [i, j])
T.reads(lv47[v_i, v_j // T.int64(8)], lv48[v_i, v_j // T.int64(32)])
T.writes(decode_intermediate_intermediate[v_i, v_j])
decode_intermediate_intermediate[v_i, v_j] = (
T.Cast(
"float16",
T.bitwise_and(
T.shift_right(
lv47[v_i, v_j // T.int64(8)],
T.Cast("uint32", v_j % T.int64(8)) * T.uint32(4),
),
T.uint32(15),
),
)
- T.float16(7)
) * lv48[v_i, v_j // T.int64(32)]
for i0, i1, i2, k in T.grid(T.int64(1), 1, T.int64(4096), T.int64(4096)):
with T.block("NT_matmul"):
v_i0, v_i1, v_i2, v_k = T.axis.remap("SSSR", [i0, i1, i2, k])
T.reads(
lv41[v_i0, v_i1, v_k],
decode_intermediate_intermediate[v_i2, v_k],
)
T.writes(NT_matmul_intermediate[v_i0, v_i1, v_i2])
with T.init():
NT_matmul_intermediate[v_i0, v_i1, v_i2] = T.float16(0)
NT_matmul_intermediate[v_i0, v_i1, v_i2] = (
NT_matmul_intermediate[v_i0, v_i1, v_i2]
+ lv41[v_i0, v_i1, v_k]
* decode_intermediate_intermediate[v_i2, v_k]
)
@R.function
def main(
lv47: R.Tensor((T.int64(4096), T.int64(512)), dtype="uint32"),
lv48: R.Tensor((T.int64(4096), T.int64(128)), dtype="float16"), # type: ignore
p_lv41: R.Tensor((T.int64(1), T.int64(1), T.int64(4096)), dtype="float16"),
) -> R.Tensor((1, 4096), dtype="float16"):
R.func_attr({"Primitive": 1})
# n = T.int64()
cls = Before
with R.dataflow():
gv = R.call_tir(
cls.fused_fused_decode3_fused_NT_matmul8_add1,
(lv47, lv48, p_lv41),
out_sinfo=R.Tensor((1, 1, 4096), dtype="float16"),
)
R.output(gv)
return gv
relax_mod = Before
ref_mod = deepcopy(relax_mod)
dispatch_target = tvm.target.Target("cuda")
# input_arrays = get_dummy_input_arrays(relax_mod)
device = tvm.cpu(0)
if dispatch_target.kind.name == "cuda":
device = tvm.cuda(0)
relax_mod = AnnotateDecodeInformation()(relax_mod)
with dispatch_target:
relax_mod = WeightOnlyLayoutPropagation(
transform_level=transform_level, faster_conversion=False
)(relax_mod)
input_tensors = get_dummy_input_arrays(ref_mod["main"], device)
print(relax_mod)
print("=======================ref llvm result=======================")
# ref_res = get_ref_result(ref_mod, input_tensors)
# print("ref_mod", ref_res)
# bitblas_res = get_ref_result(relax_mod, input_tensors)
# print("bitblas_res", bitblas_res)
print("=======================default gpu result=======================")
# ref_res = get_default_result(ref_mod, input_tensors, dispatch_target, device)
# print("ref_mod", ref_res)
# bitblas_res = get_default_result(relax_mod, input_tensors, dispatch_target, device)
# print("bitblas_res", bitblas_res)
# print("=======================fast tune gpu result=======================")
ref_res = get_fast_tune_result(ref_mod, input_tensors, dispatch_target, device)
print("ref_mod", ref_res)
print(relax_mod)
bitblas_res = get_fast_tune_result(
relax_mod, input_tensors, dispatch_target, device
)
print("bitblas_res", bitblas_res)
# test_lop3_transform()
# test_matmul_transform()
test_dequantize_matmul_transform()
|
BitBLAS/testing/python/transform/test_weight_only_transform.py/0
|
{
"file_path": "BitBLAS/testing/python/transform/test_weight_only_transform.py",
"repo_id": "BitBLAS",
"token_count": 8023
}
| 154 |
# Based on https://pytorch-lightning.readthedocs.io/en/stable/notebooks/lightning_examples/text-transformers.html
import copy
import os
from datetime import datetime
from typing import Optional
from pytorch_lightning.loggers import WandbLogger
import datasets
import torch
import pytorch_lightning as pl
from pytorch_lightning import LightningDataModule, LightningModule
from torch.utils.data import DataLoader
from transformers import (
AdamW,
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
get_linear_schedule_with_warmup,
)
from sacred import Experiment
ex = Experiment("GLUE")
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (20480, rlimit[1]))
glue_the_metric = {
'cola': 'matthews_correlation',
'sst2': 'accuracy',
'mrpc': 'f1',
'qqp': 'f1',
'stsb': 'spearmanr',
'mnli': 'accuracy',
'qnli': 'accuracy',
'rte': 'accuracy',
'wnli': 'accuracy',
}
# # get metric name ...
# import datasets
# task_text_field_map = {
# "cola": ["sentence"],
# "sst2": ["sentence"],
# "mrpc": ["sentence1", "sentence2"],
# "qqp": ["question1", "question2"],
# "stsb": ["sentence1", "sentence2"],
# "mnli": ["premise", "hypothesis"],
# "qnli": ["question", "sentence"],
# "rte": ["sentence1", "sentence2"],
# "wnli": ["sentence1", "sentence2"],
# "ax": ["premise", "hypothesis"],
# }
# for task_name in task_text_field_map:
# print(task_name)
# glue_metric = datasets.load_metric('glue', task_name)
# references = [0, 1]
# predictions = [0, 1]
# results = glue_metric.compute(predictions=predictions, references=references)
# print(results)
class GLUEDataModule(LightningDataModule):
task_text_field_map = {
"cola": ["sentence"],
"sst2": ["sentence"],
"mrpc": ["sentence1", "sentence2"],
"qqp": ["question1", "question2"],
"stsb": ["sentence1", "sentence2"],
"mnli": ["premise", "hypothesis"],
"qnli": ["question", "sentence"],
"rte": ["sentence1", "sentence2"],
"wnli": ["sentence1", "sentence2"],
"ax": ["premise", "hypothesis"],
}
glue_task_num_labels = {
"cola": 2,
"sst2": 2,
"mrpc": 2,
"qqp": 2,
"stsb": 1,
"mnli": 3,
"qnli": 2,
"rte": 2,
"wnli": 2,
"ax": 3,
}
loader_columns = [
"datasets_idx",
"input_ids",
"token_type_ids",
"attention_mask",
"start_positions",
"end_positions",
"labels",
]
def __init__(
self,
model_name_or_path: str,
task_name: str = "mrpc",
max_seq_length: int = 128,
train_batch_size: int = 32,
eval_batch_size: int = 32,
**kwargs,
):
super().__init__()
self.model_name_or_path = model_name_or_path
self.task_name = task_name
self.max_seq_length = max_seq_length
self.train_batch_size = train_batch_size
self.eval_batch_size = eval_batch_size
self.text_fields = self.task_text_field_map[task_name]
self.num_labels = self.glue_task_num_labels[task_name]
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True)
def setup(self, stage: str):
self.dataset = datasets.load_dataset("glue", self.task_name)
for split in self.dataset.keys():
self.dataset[split] = self.dataset[split].map(
self.convert_to_features,
batched=True,
remove_columns=["label"],
)
self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]
self.dataset[split].set_format(type="torch", columns=self.columns)
self.eval_splits = [x for x in self.dataset.keys() if "validation" in x]
def prepare_data(self):
datasets.load_dataset("glue", self.task_name)
AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True)
def train_dataloader(self):
return DataLoader(self.dataset["train"], batch_size=self.train_batch_size, shuffle=True)
def val_dataloader(self):
if len(self.eval_splits) == 1:
return DataLoader(self.dataset["validation"], batch_size=self.eval_batch_size, shuffle=False)
elif len(self.eval_splits) > 1:
return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size, shuffle=False) for x in self.eval_splits]
def test_dataloader(self):
if len(self.eval_splits) == 1:
return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size, shuffle=False)
elif len(self.eval_splits) > 1:
return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size, shuffle=False) for x in self.eval_splits]
def convert_to_features(self, example_batch, indices=None):
# Either encode single sentence or sentence pairs
if len(self.text_fields) > 1:
texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]))
else:
texts_or_text_pairs = example_batch[self.text_fields[0]]
# Tokenize the text/text pairs
features = self.tokenizer.batch_encode_plus(
texts_or_text_pairs, max_length=self.max_seq_length, pad_to_max_length=True, truncation=True
)
# Rename label to labels to make it easier to pass to model forward
features["labels"] = example_batch["label"]
return features
class GLUETransformer(LightningModule):
def __init__(
self,
model_name_or_path: str,
num_labels: int,
task_name: str,
learning_rate: float = 2e-5,
adam_epsilon: float = 1e-8,
warmup_steps: int = 0,
weight_decay: float = 0.0,
train_batch_size: int = 32,
eval_batch_size: int = 32,
load_path: str = None,
eval_splits: Optional[list] = None,
**kwargs,
):
super().__init__()
self.save_hyperparameters()
self.config = AutoConfig.from_pretrained(model_name_or_path, num_labels=num_labels)
self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=self.config)
self.the_metric = -1
if load_path is not None:
ckpt = torch.load(load_path, map_location="cpu")
state_dict = ckpt["state_dict"]
state_dict = {k.replace('text_transformer.', ''): v for k, v in state_dict.items() if k.startswith("text_transformer")}
self.model.roberta.load_state_dict(state_dict, strict=False)
self.metric = datasets.load_metric(
"glue", self.hparams.task_name, experiment_id=datetime.now().strftime("%d-%m-%Y_%H-%M-%S")
)
def forward(self, **inputs):
return self.model(**inputs)
def training_step(self, batch, batch_idx):
outputs = self(**batch)
loss = outputs[0]
return loss
def validation_step(self, batch, batch_idx, dataloader_idx=0):
outputs = self(**batch)
val_loss, logits = outputs[:2]
if self.hparams.num_labels > 1:
preds = torch.argmax(logits, axis=1)
elif self.hparams.num_labels == 1:
preds = logits.squeeze()
labels = batch["labels"]
return {"loss": val_loss, "preds": preds, "labels": labels}
def validation_epoch_end(self, outputs):
if self.hparams.task_name == "mnli":
accumulate_the_metric = 0
accumulate_counts = 0
for i, output in enumerate(outputs):
# matched or mismatched
split = self.hparams.eval_splits[i].split("_")[-1]
preds = torch.cat([x["preds"] for x in output]).detach().cpu().numpy()
labels = torch.cat([x["labels"] for x in output]).detach().cpu().numpy()
loss = torch.stack([x["loss"] for x in output]).mean()
self.log(f"val_loss_{split}", loss, prog_bar=True)
split_metrics = {
f"{k}_{split}": v for k, v in self.metric.compute(predictions=preds, references=labels).items()
}
self.log_dict(split_metrics, prog_bar=True)
accumulate_the_metric += list(split_metrics.values())[0]
accumulate_counts += 1
self.the_metric = max(self.the_metric, accumulate_the_metric / accumulate_counts)
self.log("the_metric", self.the_metric)
return loss
preds = torch.cat([x["preds"] for x in outputs]).detach().cpu().numpy()
labels = torch.cat([x["labels"] for x in outputs]).detach().cpu().numpy()
loss = torch.stack([x["loss"] for x in outputs]).mean()
self.log("val_loss", loss, prog_bar=True)
metrics_results = self.metric.compute(predictions=preds, references=labels)
self.log_dict(metrics_results, prog_bar=True)
the_metric_name = glue_the_metric[self.hparams.task_name]
self.the_metric = max(self.the_metric, metrics_results[the_metric_name])
self.log("the_metric", self.the_metric)
return loss
def setup(self, stage=None) -> None:
if stage != "fit":
return
# Get dataloader by calling it - train_dataloader() is called after setup() by default
train_loader = self.trainer.datamodule.train_dataloader()
# Calculate total steps
self.total_steps = len(train_loader.dataset) * self.trainer.max_epochs // self.hparams.train_batch_size // self.trainer.accumulate_grad_batches // max(1, self.trainer.gpus)
def configure_optimizers(self):
"""Prepare optimizer and schedule (linear warmup and decay)"""
model = self.model
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": self.hparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon, betas=(0.9, 0.98))
print(self.total_steps)
print(self.hparams.warmup_steps if type(self.hparams.warmup_steps) is int else self.hparams.warmup_steps * self.total_steps)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=self.hparams.warmup_steps if type(self.hparams.warmup_steps) is int else self.hparams.warmup_steps * self.total_steps,
num_training_steps=self.total_steps,
)
scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1}
return [optimizer], [scheduler]
@ex.config
def config():
root_dir = "."
data_root = f"{root_dir}/dataset/glue"
log_dir = f"{root_dir}/logs"
output_dir = f"{root_dir}/checkpoints"
load_path = f""
load_flag = False # load from load_path or roberta
num_gpus = 1
num_nodes = 1
precision = 32
per_gpu_batchsize = 32 # you should define this manually with per_gpu_batch_size=#
per_gpu_eval_batchsize = 128
# Wandb Logger Setting
exp_name = "Uni-Modal"
group_name = "cola"
run_name = "finetune"
# PL Trainer Setting
resume_from = None
fast_dev_run = False
val_check_interval = 1.0
log_every_n_steps = 50
# Experiment Setting
seed = 0
batch_size = 32 # this is a desired batch size; pl trainer will accumulate gradients when per step batch is smaller.
# Text Setting
max_seq_length = 512
tokenizer = "roberta-base"
# Optimizer Setting
learning_rate = 1e-5
weight_decay = 0.1
adam_epsilon = 1e-6
max_epoch = 10
max_steps = -1
warmup_steps = 0.06
patience = 3
@ex.automain
def main(_config):
_config = copy.deepcopy(_config)
# pl.seed_everything(_config["seed"])
dm = GLUEDataModule(
model_name_or_path=_config["tokenizer"],
task_name=_config["group_name"],
max_seq_length=_config["max_seq_length"],
train_batch_size=_config["per_gpu_batchsize"],
eval_batch_size=_config["per_gpu_eval_batchsize"],
)
dm.setup("fit")
model = GLUETransformer(
model_name_or_path=_config["tokenizer"],
load_path=_config["load_path"] if _config["load_flag"] else None,
num_labels=dm.num_labels,
learning_rate=_config["learning_rate"],
warmup_steps=_config["warmup_steps"],
weight_decay=_config["weight_decay"],
adam_epsilon=_config["adam_epsilon"],
train_batch_size=_config["per_gpu_batchsize"],
eval_batch_size=_config["per_gpu_eval_batchsize"],
eval_splits=dm.eval_splits,
task_name=dm.task_name,
)
exp_name = _config["exp_name"]
group_name = _config["group_name"]
run_name = _config["run_name"]
output_dir = f'{_config["output_dir"]}/{exp_name}_{group_name}_{run_name}'
os.makedirs(_config["log_dir"], exist_ok=True)
os.makedirs(output_dir, exist_ok=True)
logger = WandbLogger(save_dir=_config["log_dir"], project=exp_name, name=f'{exp_name}_{group_name}_{run_name}', group=group_name)
lr_callback = pl.callbacks.LearningRateMonitor(logging_interval="step")
early_stop_callback = pl.callbacks.EarlyStopping(
monitor='the_metric',
patience=_config["patience"],
strict=True,
verbose=True,
mode='max'
)
callbacks = [lr_callback, early_stop_callback]
logger.log_hyperparams(_config)
num_gpus = (
_config["num_gpus"]
if isinstance(_config["num_gpus"], int)
else len(_config["num_gpus"])
)
grad_steps = max(_config["batch_size"] // (
_config["per_gpu_batchsize"] * num_gpus * _config["num_nodes"]
), 1)
trainer = pl.Trainer(
gpus=_config["num_gpus"],
num_nodes=_config["num_nodes"],
precision=_config["precision"],
strategy="ddp",
benchmark=True,
deterministic=True,
max_epochs=_config["max_epoch"] if _config["max_steps"] == -1 else 1000,
max_steps=_config["max_steps"],
logger=logger,
accumulate_grad_batches=grad_steps,
log_every_n_steps=_config["log_every_n_steps"],
resume_from_checkpoint=_config["resume_from"],
weights_summary="top",
callbacks=callbacks,
fast_dev_run=_config["fast_dev_run"],
val_check_interval=_config["val_check_interval"],
)
trainer.fit(model, datamodule=dm)
# trainer.validate(model, datamodule=dm)
# trainer.test(model, datamodule=dm)
|
BridgeTower/run_glue.py/0
|
{
"file_path": "BridgeTower/run_glue.py",
"repo_id": "BridgeTower",
"token_count": 6891
}
| 155 |
#!/bin/bash
# General
## root dir, you can change it to your specified dir, but remember to change the path in the following script, root_dir in the config.py and src/utils/write_xxx.py
sudo mkdir -p ~/BT
sudo chmod -R 777 ~/BT
cd ~/BT
## plugins
sudo apt-get install software-properties-common tmux net-tools
# BridgeTower
## update Python
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update
sudo apt install python3.8 python3.8-venv
## create virtual environment
cd ~/BT
python3.8 -m venv venv_BridgeTower
# echo "alias venv_BridgeTower='source ~/BT/venv_BridgeTower/bin/activate'" >> ~/.bashrc
# echo "source ~/BT/venv_BridgeTower/bin/activate" >> ~/.bashrc
source ~/.bashrc
## git clone
cd ~/BT
mkdir BridgeTower
# Please put here our code
cd ~/BT/BridgeTower
## dependency
source ~/BT/venv_BridgeTower/bin/activate
pip install --upgrade pip
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
pip install -r requirements.txt
pip install evalai
pip install --upgrade requests click python-dateutil
## mkdir
sudo mkdir -p ~/BT
sudo mkdir -p ~/BT/dataset/
sudo mkdir -p ~/BT/best_checkpoints/
sudo mkdir -p ~/BT/checkpoints/
sudo mkdir -p ~/BT/logs/
sudo chmod -R 777 ~/BT
## download data and checkpoints, and put them in ~/BT/dataset/ and ~/BT/best_checkpoints/
## the final file structure of ~/BT/dataset/ should be like this:
# root
# └── dataset
# ├── pre-train
# ├── fine-tune
# ├── sbu
# ├── cc
# ├── nlvr
# │ ├── dev
# │ ├── images
# │ ├── nlvr
# │ ├── nlvr2
# │ ├── test1
# │ └── README.md
# ├── vg
# │ ├── annotations
# │ ├── coco_splits
# │ ├── images
# │ ├── vgqa
# │ └── image_data.json
# └── mscoco_flickr30k_vqav2_snli_ve
# ├── flickr30k-images
# ├── karpathy
# ├── snli_ve
# ├── test2015
# ├── train2014
# ├── val2014
# └── vqav2
## then run the src/utils/write_xxx.py to convert the dataset to pyarrow binary file.
# python src/utils/write_coco_karpathy.py
# python src/utils/write_conceptual_caption.py
# python src/utils/write_f30k_karpathy.py
# python src/utils/write_nlvr2.py
# python src/utils/write_sbu.py
# python src/utils/write_vg.py
# python src/utils/write_vqa.py
# python src/utils/vgqa_split.py
# python src/utils/write_vgqa.py
# cp ~/BT/dataset/pre-train/coco_caption_karpathy_* ~/BT/dataset/fine-tune
|
BridgeTower/setup.sh/0
|
{
"file_path": "BridgeTower/setup.sh",
"repo_id": "BridgeTower",
"token_count": 1045
}
| 156 |
from .base_dataset import BaseDataset
import io
from PIL import Image
class CocoCaptionKarpathyDataset(BaseDataset):
def __init__(self, *args, split="", **kwargs):
assert split in ["train", "val", "test"]
self.split = split
if split == "train":
names = ["coco_caption_karpathy_train", "coco_caption_karpathy_restval"] # ViLT
elif split == "val":
# names = ["coco_caption_karpathy_val"]
names = ["coco_caption_karpathy_test"] # ViLT, METER
elif split == "test":
names = ["coco_caption_karpathy_test"]
super().__init__(*args, **kwargs, names=names, text_column_name="caption")
def __getitem__(self, index):
suite = self.get_suite(index)
if "test" in self.split:
_index, _question_index = self.index_mapper[index]
iid = self.table["image_id"][_index].as_py()
iid = int(iid.split(".")[0].split("_")[-1])
suite.update({"iid": iid})
return suite
|
BridgeTower/src/datasets/coco_caption_karpathy_dataset.py/0
|
{
"file_path": "BridgeTower/src/datasets/coco_caption_karpathy_dataset.py",
"repo_id": "BridgeTower",
"token_count": 481
}
| 157 |
import torch
import torch.nn as nn
import pytorch_lightning as pl
import torch.nn.functional as F
from .bert_model import BertConfig, BertModel, BertCrossLayer
from . import swin_transformer as swin
from . import vit_model as vit
from .vit_model import resize_pos_embed
from . import heads, objectives, meter_utils
from .clip_model import build_model, adapt_position_encoding
from .swin_helpers import swin_adapt_position_encoding
from transformers import RobertaConfig, RobertaModel
class METERTransformerSS(pl.LightningModule):
def __init__(self, config):
super().__init__()
self.save_hyperparameters()
self.prepare_data_per_node = False
self.is_clip= ('CLIP' in config["vit"])
self.is_swin= ('swin' in config["vit"])
self.is_vit= ('vit' in config["vit"])
self.jump_val_first_for_irtr_itm_irc = True
if 'roberta' in config['tokenizer']:
bert_config = RobertaConfig(
vocab_size=config["vocab_size"],
hidden_size=config["hidden_size"],
num_hidden_layers=config["num_layers"],
num_attention_heads=config["num_heads"],
intermediate_size=config["hidden_size"] * config["mlp_ratio"],
max_position_embeddings=config["max_text_len"],
hidden_dropout_prob=config["drop_rate"],
attention_probs_dropout_prob=config["drop_rate"],
)
else:
bert_config = BertConfig(
vocab_size=config["vocab_size"],
hidden_size=config["hidden_size"],
num_hidden_layers=config["num_layers"],
num_attention_heads=config["num_heads"],
intermediate_size=config["hidden_size"] * config["mlp_ratio"],
max_position_embeddings=config["max_text_len"],
hidden_dropout_prob=config["drop_rate"],
attention_probs_dropout_prob=config["drop_rate"],
)
resolution_after = config['image_size']
self.cross_modal_text_transform = nn.Linear(config['input_text_embed_size'], config['hidden_size'])
self.cross_modal_text_transform.apply(objectives.init_weights)
self.cross_modal_image_transform = nn.Linear(config['input_image_embed_size'], config['hidden_size'])
self.cross_modal_image_transform.apply(objectives.init_weights)
self.token_type_embeddings = nn.Embedding(2, config["hidden_size"])
self.token_type_embeddings.apply(objectives.init_weights)
if torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
if self.is_clip:
build_model(config["vit"], resolution_after=resolution_after, model_type="BT" if config["meter_fusion"] else config["model_type"], vit_remove_last=config["vit_remove_last"])
elif self.is_swin:
getattr(swin, config["vit"])(pretrained=True, config=config,)
else:
getattr(vit, config["vit"])(pretrained=True, img_size=resolution_after, model_type=config["model_type"],)
if 'roberta' in config['tokenizer']:
RobertaModel.from_pretrained(config['tokenizer'])
else:
BertModel.from_pretrained(config['tokenizer'])
torch.distributed.barrier()
if self.is_clip:
self.vit_model = build_model(config["vit"], resolution_after=resolution_after, model_type="BT" if config["meter_fusion"] else config["model_type"], vit_remove_last=config["vit_remove_last"])
elif self.is_swin:
self.vit_model = getattr(swin, config["vit"])(
pretrained=True, config=config,
)
self.avgpool = nn.AdaptiveAvgPool1d(1)
else:
self.vit_model = getattr(vit, config["vit"])(pretrained=True, img_size=resolution_after, model_type=config["model_type"],)
if 'roberta' in config['tokenizer']:
self.text_transformer = RobertaModel.from_pretrained(config['tokenizer'])
else:
self.text_transformer = BertModel.from_pretrained(config['tokenizer'])
self.cross_modal_image_layers = nn.ModuleList([BertCrossLayer(bert_config) for _ in range(config['num_layers'])])
self.cross_modal_image_layers.apply(objectives.init_weights)
self.cross_modal_text_layers = nn.ModuleList([BertCrossLayer(bert_config) for _ in range(config['num_layers'])])
self.cross_modal_text_layers.apply(objectives.init_weights)
# Class token => Linear => Tanh
self.cross_modal_image_pooler = heads.Pooler(config["hidden_size"])
self.cross_modal_image_pooler.apply(objectives.init_weights)
self.cross_modal_text_pooler = heads.Pooler(config["hidden_size"])
self.cross_modal_text_pooler.apply(objectives.init_weights)
# Temperature for image text contrastive learning
self.temperature = nn.Parameter(torch.ones([]) * config['temperature'])
if config["loss_names"]["mlm"] > 0:
# MLM Head weights don't tie with BERT Embedding weights. Train from scratch.
self.mlm_score = heads.MLMHead(bert_config)
self.mlm_score.apply(objectives.init_weights)
if config["loss_names"]["itm"] > 0 or config["loss_names"]["itm_itc"] > 0 or config["loss_names"]["irtr_itm_itc"] > 0:
self.itm_score = heads.ITMHead(config["hidden_size"] * 2)
self.itm_score.apply(objectives.init_weights)
if config["loss_names"]["itc"] > 0 or config["loss_names"]["itm_itc"] > 0 or config["loss_names"]["irtr_itm_itc"] > 0:
self.itc_text_head = heads.ITCHead(config['hidden_size'], config['contrastive_hidden_size'])
self.itc_text_head.apply(objectives.init_weights)
self.itc_image_head = heads.ITCHead(config['hidden_size'], config['contrastive_hidden_size'])
self.itc_image_head.apply(objectives.init_weights)
hs = config["hidden_size"]
if self.hparams.config["meter_fusion"]:
self.text_gate = nn.Linear(hs, hs)
self.text_gate.apply(objectives.init_weights)
self.image_gate = nn.Linear(hs, hs)
self.image_gate.apply(objectives.init_weights)
# ===================== Load Pretrained METER Weights =====================
if (config["load_path"] != "" and not config["test_only"]):
ckpt = torch.load(config["load_path"], map_location="cpu")
state_dict = ckpt["state_dict"]
if self.is_clip:
state_dict = adapt_position_encoding(state_dict, after=resolution_after, patch_size=self.hparams.config['patch_size'])
elif self.is_swin:
state_dict = swin_adapt_position_encoding(state_dict, after=resolution_after, before=config['resolution_before'])
else:
state_dict['vit_model.pos_embed'] = resize_pos_embed(state_dict['vit_model.pos_embed'], self.vit_model.pos_embed, getattr(self.vit_model, 'num_tokens', 1), self.vit_model.patch_embed.grid_size)
self.load_state_dict(state_dict, strict=False)
# ===================== Downstream ===================== #
if self.hparams.config["loss_names"]["vqa"] > 0:
vs = self.hparams.config["vqav2_label_size"]
self.vqa_classifier = nn.Sequential(
nn.Linear(hs * 2, hs * 2),
nn.LayerNorm(hs * 2),
nn.GELU(),
nn.Linear(hs * 2, vs),
)
self.vqa_classifier.apply(objectives.init_weights)
if self.hparams.config["loss_names"]["nlvr2"] > 0:
self.nlvr2_classifier = nn.Sequential(
nn.Linear(hs * 4, hs * 2),
nn.LayerNorm(hs * 2),
nn.GELU(),
nn.Linear(hs * 2, 2),
)
self.nlvr2_classifier.apply(objectives.init_weights)
emb_data = self.token_type_embeddings.weight.data
self.token_type_embeddings = nn.Embedding(3, hs)
self.token_type_embeddings.apply(objectives.init_weights)
self.token_type_embeddings.weight.data[0, :] = emb_data[0, :]
self.token_type_embeddings.weight.data[1, :] = emb_data[1, :]
self.token_type_embeddings.weight.data[2, :] = emb_data[1, :]
if self.hparams.config["loss_names"]["snli"] > 0:
self.snli_classifier = nn.Sequential(
nn.Linear(hs * 2, hs * 2),
nn.LayerNorm(hs * 2),
nn.GELU(),
nn.Linear(hs * 2, 3),
)
self.snli_classifier.apply(objectives.init_weights)
if config["loss_names"]["irtr"] > 0:
self.rank_output = nn.Linear(hs * 2, 1)
self.rank_output.weight.data = self.itm_score.fc.weight.data[1:]
self.rank_output.bias.data = self.itm_score.fc.bias.data[1:]
for p in self.itm_score.parameters():
p.requires_grad = False
# ===================== load downstream (test_only) ======================
if config["load_path"] != "" and config["test_only"]:
ckpt = torch.load(config["load_path"], map_location="cpu")
state_dict = ckpt["state_dict"]
if self.is_clip:
state_dict = adapt_position_encoding(state_dict, after=resolution_after, patch_size=config['patch_size'])
elif self.is_swin:
state_dict = swin_adapt_position_encoding(state_dict, after=resolution_after, before=config['resolution_before'])
else:
state_dict['vit_model.pos_embed'] = resize_pos_embed(state_dict['vit_model.pos_embed'], self.vit_model.pos_embed, getattr(self.vit_model, 'num_tokens', 1), self.vit_model.patch_embed.grid_size)
self.load_state_dict(state_dict, strict=False)
meter_utils.set_metrics(self)
self.current_tasks = list()
def get_cls_feats(self, text_feats, image_feats):
cls_feats_text = self.cross_modal_text_pooler(text_feats)
if self.is_clip:
cls_feats_image = self.cross_modal_image_pooler(image_feats)
elif self.is_swin:
avg_image_feats = self.avgpool(image_feats.transpose(1, 2)).view(image_feats.size(0), 1, -1)
cls_feats_image = self.cross_modal_image_pooler(avg_image_feats)
else:
cls_feats_image = self.cross_modal_image_pooler(image_feats)
return torch.cat([cls_feats_text, cls_feats_image], dim=-1)
def meter_fusion(self, func, feats):
layer_gate = F.softmax(func(feats[:-1]), dim=0)
feats = feats[-1] + torch.sum(layer_gate * feats[:-1], dim=0)
return feats
def get_uni_modal_features(self, batch, fusion_features=False, itc=False):
img = batch["image"][0]
text_ids = batch[f"text_ids"]
text_labels = batch[f"text_labels"]
text_masks = batch[f"text_masks"]
text_embeds = self.text_transformer.embeddings(input_ids=text_ids)
input_shape = text_masks.size()
extend_text_masks = self.text_transformer.get_extended_attention_mask(text_masks, input_shape, self.device)
if self.hparams.config["meter_fusion"]:
text_embedss = []
for layer in self.text_transformer.encoder.layer:
text_embeds = layer(text_embeds, extend_text_masks)[0]
text_embedss.append(text_embeds)
text_embedss = torch.stack(text_embedss, dim=0)
image_embedss = self.vit_model(img)
text_embeds = self.meter_fusion(self.text_gate, text_embedss)
image_embeds = self.meter_fusion(self.image_gate, image_embedss)
else:
for layer in self.text_transformer.encoder.layer:
text_embeds = layer(text_embeds, extend_text_masks)[0]
image_embeds = self.vit_model(img)
if itc:
if self.hparams.config["meter_fusion"]:
unimodal_feats_text = F.normalize(self.itc_text_head(text_embedss[-1][:, 0, :]), dim=-1, p=2)
unimodal_feats_image = F.normalize(self.itc_image_head(image_embedss[-1][:, 0, :]), dim=-1, p=2)
else:
unimodal_feats_text = F.normalize(self.itc_text_head(text_embeds[:, 0, :]), dim=-1, p=2)
unimodal_feats_image = F.normalize(self.itc_image_head(image_embeds[:, 0, :]), dim=-1, p=2)
if not fusion_features:
ret = {
'unimodal_feats_text': unimodal_feats_text,
'unimodal_feats_image': unimodal_feats_image,
}
return ret
text_embeds = self.cross_modal_text_transform(text_embeds)
image_embeds = self.cross_modal_image_transform(image_embeds)
if not itc:
ret = {
"extend_text_masks": extend_text_masks,
"text_embeds": text_embeds,
"image_embeds": image_embeds,
"text_labels": text_labels,
"text_ids": text_ids,
"text_masks": text_masks,
}
else:
if fusion_features:
ret = {
'unimodal_feats_text': unimodal_feats_text,
'unimodal_feats_image': unimodal_feats_image,
"extend_text_masks": extend_text_masks,
"text_embeds": text_embeds,
"image_embeds": image_embeds,
"text_labels": text_labels,
"text_ids": text_ids,
"text_masks": text_masks,
}
return ret
def infer_text(
self,
batch,
mask_text=False,
itc=False,
):
text_ids = batch[f"text_ids"]
text_masks = batch[f"text_masks"]
text_embeds = self.text_transformer.embeddings(input_ids=text_ids)
input_shape = text_masks.size()
extend_text_masks = self.text_transformer.get_extended_attention_mask(text_masks, input_shape, self.device)
if self.hparams.config["meter_fusion"]:
text_embedss = []
for layer in self.text_transformer.encoder.layer:
text_embeds = layer(text_embeds, extend_text_masks)[0]
text_embedss.append(text_embeds)
text_embedss = torch.stack(text_embedss, dim=0)
text_embeds = self.meter_fusion(self.text_gate, text_embedss)
else:
for layer in self.text_transformer.encoder.layer:
text_embeds = layer(text_embeds, extend_text_masks)[0]
if itc:
if self.hparams.config["meter_fusion"]:
unimodal_feats_text = F.normalize(self.itc_text_head(text_embedss[-1][:, 0, :]), dim=-1, p=2)
else:
unimodal_feats_text = F.normalize(self.itc_text_head(text_embeds[:, 0, :]), dim=-1, p=2)
text_embeds = self.cross_modal_text_transform(text_embeds)
if itc:
return text_embeds, extend_text_masks, unimodal_feats_text
else:
return text_embeds, extend_text_masks
def infer_image(
self,
img,
itc=False,
):
if self.hparams.config["meter_fusion"]:
image_embedss = self.vit_model(img)
image_embeds = self.meter_fusion(self.image_gate, image_embedss)
else:
image_embeds = self.vit_model(img)
if itc:
if self.hparams.config["meter_fusion"]:
unimodal_feats_image = F.normalize(self.itc_image_head(image_embedss[-1][:, 0, :]), dim=-1, p=2)
else:
unimodal_feats_image = F.normalize(self.itc_image_head(image_embeds[:, 0, :]), dim=-1, p=2)
image_embeds = self.cross_modal_image_transform(image_embeds)
if itc:
return image_embeds, unimodal_feats_image
else:
return image_embeds
def infer_fusion(
self,
image_embeds,
text_embeds,
extend_text_masks,
image_token_type_idx=1,
irtr_len_image=0,
irtr_len_text=0,
):
if irtr_len_image > 0:
image_masks = torch.ones((irtr_len_image, image_embeds.size(0)), dtype=torch.long, device=self.device)
else:
image_masks = torch.ones((image_embeds.size(0), image_embeds.size(1)), dtype=torch.long, device=self.device)
extend_image_masks = self.text_transformer.get_extended_attention_mask(image_masks, image_masks.size(), self.device)
text_embeds, image_embeds = (
text_embeds + self.token_type_embeddings(torch.zeros(1).long().to(self.device)).expand_as(text_embeds),
image_embeds + self.token_type_embeddings(torch.zeros(1).long().to(self.device).fill_(image_token_type_idx)).expand_as(image_embeds),
)
x, y = text_embeds, image_embeds
if irtr_len_text > 0:
_L, _D = text_embeds.size(0), text_embeds.size(1)
x = x.unsqueeze(0).expand(irtr_len_text, _L, _D)
if irtr_len_image > 0:
_L, _D = image_embeds.size(0), image_embeds.size(1)
y = y.unsqueeze(0).expand(irtr_len_image, _L, _D)
for text_layer, image_layer in zip(self.cross_modal_text_layers, self.cross_modal_image_layers):
x1 = text_layer(x, y, extend_text_masks, extend_image_masks)
y1 = image_layer(y, x, extend_image_masks, extend_text_masks)
x, y = x1[0], y1[0]
text_feats, image_feats = x, y
cls_feats = self.get_cls_feats(text_feats, image_feats)
ret = {
"text_feats": text_feats,
"image_feats": image_feats,
"cls_feats": cls_feats,
}
return ret
def infer(
self,
batch,
mask_text=False,
mask_image=False,
image_token_type_idx=1,
img=None,
):
if img is None:
if f"image_{image_token_type_idx - 1}" in batch:
imgkey = f"image_{image_token_type_idx - 1}"
else:
imgkey = "image"
img = batch[imgkey][0]
do_mlm = "_mlm" if mask_text else ""
text_ids = batch[f"text_ids{do_mlm}"]
text_labels = batch[f"text_labels{do_mlm}"]
text_masks = batch[f"text_masks"]
text_embeds = self.text_transformer.embeddings(input_ids=text_ids)
input_shape = text_masks.size()
extend_text_masks = self.text_transformer.get_extended_attention_mask(text_masks, input_shape, self.device)
if self.hparams.config["meter_fusion"]:
text_embedss = []
for layer in self.text_transformer.encoder.layer:
text_embeds = layer(text_embeds, extend_text_masks)[0]
text_embedss.append(text_embeds)
text_embedss = torch.stack(text_embedss, dim=0)
image_embedss = self.vit_model(img)
text_embeds = self.meter_fusion(self.text_gate, text_embedss)
image_embeds = self.meter_fusion(self.image_gate, image_embedss)
else:
for layer in self.text_transformer.encoder.layer:
text_embeds = layer(text_embeds, extend_text_masks)[0]
image_embeds = self.vit_model(img)
if self.hparams.config["num_layers"] == 0:
cls_feats = self.get_cls_feats(text_embeds, image_embeds)
ret = {
"text_feats": text_embeds,
"image_feats": image_embeds,
"cls_feats": cls_feats,
"text_labels": text_labels,
"text_ids": text_ids,
"text_masks": text_masks,
}
return ret
text_embeds = self.cross_modal_text_transform(text_embeds)
image_embeds = self.cross_modal_image_transform(image_embeds)
image_masks = torch.ones((image_embeds.size(0), image_embeds.size(1)), dtype=torch.long, device=self.device)
extend_image_masks = self.text_transformer.get_extended_attention_mask(image_masks, image_masks.size(), self.device)
text_embeds, image_embeds = (
text_embeds + self.token_type_embeddings(torch.zeros(1).long().to(self.device)).expand_as(text_embeds),
image_embeds + self.token_type_embeddings(torch.zeros(1).long().to(self.device).fill_(image_token_type_idx)).expand_as(image_embeds),
)
x, y = text_embeds, image_embeds
for text_layer, image_layer in zip(self.cross_modal_text_layers, self.cross_modal_image_layers):
x1 = text_layer(x, y, extend_text_masks, extend_image_masks)
y1 = image_layer(y, x, extend_image_masks, extend_text_masks)
x, y = x1[0], y1[0]
text_feats, image_feats = x, y
cls_feats = self.get_cls_feats(text_feats, image_feats)
ret = {
"text_feats": text_feats,
"image_feats": image_feats,
"cls_feats": cls_feats,
"text_labels": text_labels,
"text_ids": text_ids,
"text_masks": text_masks,
}
return ret
def forward(self, batch, split):
ret = dict()
if len(self.current_tasks) == 0:
ret.update(self.infer(batch))
return ret
# Masked Language Modeling
if "mlm" in self.current_tasks:
ret.update(objectives.compute_mlm(self, batch, split))
# Image Text Matching
if "itm" in self.current_tasks:
ret.update(objectives.compute_itm(self, batch, split))
# Image Text Contrastive Learning
if "itc" in self.current_tasks:
ret.update(objectives.compute_itc(self, batch, split))
if "itm_itc" in self.current_tasks:
ret.update(objectives.compute_itm_itc(self, batch, split, pretrain=True))
if "irtr_itm_itc" in self.current_tasks:
if self.hparams.config["num_layers"] == 0:
ret.update(objectives.compute_itc(self, batch, split))
else:
ret.update(objectives.compute_itm_itc_meter(self, batch, split, pretrain=False))
# Visual Question Answering
if "vqa" in self.current_tasks:
ret.update(objectives.compute_vqa(self, batch, split))
# Natural Language for Visual Reasoning 2
if "nlvr2" in self.current_tasks:
ret.update(objectives.compute_nlvr2(self, batch, split))
# SNLI Visual Entailment
if "snli" in self.current_tasks:
ret.update(objectives.compute_snli(self, batch, split))
# Image Retrieval and Text Retrieval
if "irtr" in self.current_tasks:
ret.update(objectives.compute_irtr(self, batch, split))
return ret
def training_step(self, batch, batch_idx):
meter_utils.set_task(self)
output = self(batch, 'train')
total_loss = sum([v for k, v in output.items() if "loss" in k])
return total_loss
def training_epoch_end(self, outs):
meter_utils.epoch_wrapup(self, 'train')
def validation_step(self, batch, batch_idx):
meter_utils.set_task(self)
output = self(batch, 'val')
def validation_epoch_end(self, outs):
if self.jump_val_first_for_irtr_itm_irc and "irtr_itm_itc" in self.hparams.config["group_name"]:
old_get_recall_metric = self.hparams.config["get_recall_metric"]
self.hparams.config["get_recall_metric"] = False
meter_utils.epoch_wrapup(self, 'val')
self.hparams.config["get_recall_metric"] = old_get_recall_metric
self.jump_val_first_for_irtr_itm_irc = False
else:
meter_utils.epoch_wrapup(self, 'val')
def test_step(self, batch, batch_idx):
meter_utils.set_task(self)
output = self(batch, 'test')
ret = dict()
if self.hparams.config["loss_names"]["vqa"] > 0:
ret.update(objectives.vqa_test_step(self, batch, output))
return ret
def test_epoch_end(self, outs):
model_name = self.hparams.config["load_path"].split("/")[-2]
checkpoint_name = self.hparams.config["load_path"].split("/")[-1][:-5]
if self.hparams.config["loss_names"]["vqa"] > 0:
objectives.vqa_test_wrapup(outs, f"{model_name}_{checkpoint_name}", self.hparams.config["log_dir"])
meter_utils.epoch_wrapup(self, 'test')
def configure_optimizers(self):
# Optimizer: AdamW; Scheduler: linear_schedule_with_warmup
# Parameters for cross-modal and each task head will be multiply by lr_mult_cross_modal or lr_mult_head
# New task heads need to enroll here.
return meter_utils.set_schedule(self)
|
BridgeTower/src/modules/meter_module.py/0
|
{
"file_path": "BridgeTower/src/modules/meter_module.py",
"repo_id": "BridgeTower",
"token_count": 12305
}
| 158 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torch.nn.utils.spectral_norm as spectral_norm
from models.networks.normalization import SPADE
# ResNet block that uses SPADE.
# It differs from the ResNet block of pix2pixHD in that
# it takes in the segmentation map as input, learns the skip connection if necessary,
# and applies normalization first and then convolution.
# This architecture seemed like a standard architecture for unconditional or
# class-conditional GAN architecture using residual block.
# The code was inspired from https://github.com/LMescheder/GAN_stability.
class SPADEResnetBlock(nn.Module):
def __init__(self, fin, fout, opt):
super().__init__()
# Attributes
self.learned_shortcut = fin != fout
fmiddle = min(fin, fout)
self.opt = opt
# create conv layers
self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1)
self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1)
if self.learned_shortcut:
self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False)
# apply spectral norm if specified
if "spectral" in opt.norm_G:
self.conv_0 = spectral_norm(self.conv_0)
self.conv_1 = spectral_norm(self.conv_1)
if self.learned_shortcut:
self.conv_s = spectral_norm(self.conv_s)
# define normalization layers
spade_config_str = opt.norm_G.replace("spectral", "")
self.norm_0 = SPADE(spade_config_str, fin, opt.semantic_nc, opt)
self.norm_1 = SPADE(spade_config_str, fmiddle, opt.semantic_nc, opt)
if self.learned_shortcut:
self.norm_s = SPADE(spade_config_str, fin, opt.semantic_nc, opt)
# note the resnet block with SPADE also takes in |seg|,
# the semantic segmentation map as input
def forward(self, x, seg, degraded_image):
x_s = self.shortcut(x, seg, degraded_image)
dx = self.conv_0(self.actvn(self.norm_0(x, seg, degraded_image)))
dx = self.conv_1(self.actvn(self.norm_1(dx, seg, degraded_image)))
out = x_s + dx
return out
def shortcut(self, x, seg, degraded_image):
if self.learned_shortcut:
x_s = self.conv_s(self.norm_s(x, seg, degraded_image))
else:
x_s = x
return x_s
def actvn(self, x):
return F.leaky_relu(x, 2e-1)
# ResNet block used in pix2pixHD
# We keep the same architecture as pix2pixHD.
class ResnetBlock(nn.Module):
def __init__(self, dim, norm_layer, activation=nn.ReLU(False), kernel_size=3):
super().__init__()
pw = (kernel_size - 1) // 2
self.conv_block = nn.Sequential(
nn.ReflectionPad2d(pw),
norm_layer(nn.Conv2d(dim, dim, kernel_size=kernel_size)),
activation,
nn.ReflectionPad2d(pw),
norm_layer(nn.Conv2d(dim, dim, kernel_size=kernel_size)),
)
def forward(self, x):
y = self.conv_block(x)
out = x + y
return out
# VGG architecter, used for the perceptual loss using a pretrained VGG network
class VGG19(torch.nn.Module):
def __init__(self, requires_grad=False):
super().__init__()
vgg_pretrained_features = torchvision.models.vgg19(pretrained=True).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
self.slice5 = torch.nn.Sequential()
for x in range(2):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(2, 7):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(7, 12):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(12, 21):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
for x in range(21, 30):
self.slice5.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, X):
h_relu1 = self.slice1(X)
h_relu2 = self.slice2(h_relu1)
h_relu3 = self.slice3(h_relu2)
h_relu4 = self.slice4(h_relu3)
h_relu5 = self.slice5(h_relu4)
out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
return out
class SPADEResnetBlock_non_spade(nn.Module):
def __init__(self, fin, fout, opt):
super().__init__()
# Attributes
self.learned_shortcut = fin != fout
fmiddle = min(fin, fout)
self.opt = opt
# create conv layers
self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1)
self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1)
if self.learned_shortcut:
self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False)
# apply spectral norm if specified
if "spectral" in opt.norm_G:
self.conv_0 = spectral_norm(self.conv_0)
self.conv_1 = spectral_norm(self.conv_1)
if self.learned_shortcut:
self.conv_s = spectral_norm(self.conv_s)
# define normalization layers
spade_config_str = opt.norm_G.replace("spectral", "")
self.norm_0 = SPADE(spade_config_str, fin, opt.semantic_nc, opt)
self.norm_1 = SPADE(spade_config_str, fmiddle, opt.semantic_nc, opt)
if self.learned_shortcut:
self.norm_s = SPADE(spade_config_str, fin, opt.semantic_nc, opt)
# note the resnet block with SPADE also takes in |seg|,
# the semantic segmentation map as input
def forward(self, x, seg, degraded_image):
x_s = self.shortcut(x, seg, degraded_image)
dx = self.conv_0(self.actvn(x))
dx = self.conv_1(self.actvn(dx))
out = x_s + dx
return out
def shortcut(self, x, seg, degraded_image):
if self.learned_shortcut:
x_s = self.conv_s(x)
else:
x_s = x
return x_s
def actvn(self, x):
return F.leaky_relu(x, 2e-1)
|
Bringing-Old-Photos-Back-to-Life/Face_Enhancement/models/networks/architecture.py/0
|
{
"file_path": "Bringing-Old-Photos-Back-to-Life/Face_Enhancement/models/networks/architecture.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 2928
}
| 159 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import struct
from PIL import Image
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def make_dataset(dir):
images = []
assert os.path.isdir(dir), '%s is not a valid directory' % dir
for root, _, fnames in sorted(os.walk(dir)):
for fname in fnames:
if is_image_file(fname):
#print(fname)
path = os.path.join(root, fname)
images.append(path)
return images
### Modify these 3 lines in your own environment
indir="/home/ziyuwan/workspace/data/temp_old"
target_folders=['VOC','Real_L_old','Real_RGB_old']
out_dir ="/home/ziyuwan/workspace/data/temp_old"
###
if os.path.exists(out_dir) is False:
os.makedirs(out_dir)
#
for target_folder in target_folders:
curr_indir = os.path.join(indir, target_folder)
curr_out_file = os.path.join(os.path.join(out_dir, '%s.bigfile'%(target_folder)))
image_lists = make_dataset(curr_indir)
image_lists.sort()
with open(curr_out_file, 'wb') as wfid:
# write total image number
wfid.write(struct.pack('i', len(image_lists)))
for i, img_path in enumerate(image_lists):
# write file name first
img_name = os.path.basename(img_path)
img_name_bytes = img_name.encode('utf-8')
wfid.write(struct.pack('i', len(img_name_bytes)))
wfid.write(img_name_bytes)
#
# # write image data in
with open(img_path, 'rb') as img_fid:
img_bytes = img_fid.read()
wfid.write(struct.pack('i', len(img_bytes)))
wfid.write(img_bytes)
if i % 1000 == 0:
print('write %d images done' % i)
|
Bringing-Old-Photos-Back-to-Life/Global/data/Create_Bigfile.py/0
|
{
"file_path": "Bringing-Old-Photos-Back-to-Life/Global/data/Create_Bigfile.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 924
}
| 160 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import torch
import sys
class BaseModel(torch.nn.Module):
def name(self):
return "BaseModel"
def initialize(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
def set_input(self, input):
self.input = input
def forward(self):
pass
# used in test time, no backprop
def test(self):
pass
def get_image_paths(self):
pass
def optimize_parameters(self):
pass
def get_current_visuals(self):
return self.input
def get_current_errors(self):
return {}
def save(self, label):
pass
# helper saving function that can be used by subclasses
def save_network(self, network, network_label, epoch_label, gpu_ids):
save_filename = "%s_net_%s.pth" % (epoch_label, network_label)
save_path = os.path.join(self.save_dir, save_filename)
torch.save(network.cpu().state_dict(), save_path)
if len(gpu_ids) and torch.cuda.is_available():
network.cuda()
def save_optimizer(self, optimizer, optimizer_label, epoch_label):
save_filename = "%s_optimizer_%s.pth" % (epoch_label, optimizer_label)
save_path = os.path.join(self.save_dir, save_filename)
torch.save(optimizer.state_dict(), save_path)
def load_optimizer(self, optimizer, optimizer_label, epoch_label, save_dir=""):
save_filename = "%s_optimizer_%s.pth" % (epoch_label, optimizer_label)
if not save_dir:
save_dir = self.save_dir
save_path = os.path.join(save_dir, save_filename)
if not os.path.isfile(save_path):
print("%s not exists yet!" % save_path)
else:
optimizer.load_state_dict(torch.load(save_path))
# helper loading function that can be used by subclasses
def load_network(self, network, network_label, epoch_label, save_dir=""):
save_filename = "%s_net_%s.pth" % (epoch_label, network_label)
if not save_dir:
save_dir = self.save_dir
# print(save_dir)
# print(self.save_dir)
save_path = os.path.join(save_dir, save_filename)
if not os.path.isfile(save_path):
print("%s not exists yet!" % save_path)
# if network_label == 'G':
# raise('Generator must exist!')
else:
# network.load_state_dict(torch.load(save_path))
try:
# print(save_path)
network.load_state_dict(torch.load(save_path))
except:
pretrained_dict = torch.load(save_path)
model_dict = network.state_dict()
try:
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
network.load_state_dict(pretrained_dict)
# if self.opt.verbose:
print(
"Pretrained network %s has excessive layers; Only loading layers that are used"
% network_label
)
except:
print(
"Pretrained network %s has fewer layers; The following are not initialized:"
% network_label
)
for k, v in pretrained_dict.items():
if v.size() == model_dict[k].size():
model_dict[k] = v
if sys.version_info >= (3, 0):
not_initialized = set()
else:
from sets import Set
not_initialized = Set()
for k, v in model_dict.items():
if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
not_initialized.add(k.split(".")[0])
print(sorted(not_initialized))
network.load_state_dict(model_dict)
def update_learning_rate():
pass
|
Bringing-Old-Photos-Back-to-Life/Global/models/base_model.py/0
|
{
"file_path": "Bringing-Old-Photos-Back-to-Life/Global/models/base_model.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 2124
}
| 161 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import numpy as np
import os
import torch.nn as nn
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
if isinstance(image_tensor, list):
image_numpy = []
for i in range(len(image_tensor)):
image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
return image_numpy
image_numpy = image_tensor.cpu().float().numpy()
if normalize:
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
else:
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
image_numpy = np.clip(image_numpy, 0, 255)
if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:
image_numpy = image_numpy[:, :, 0]
return image_numpy.astype(imtype)
# Converts a one-hot tensor into a colorful label map
def tensor2label(label_tensor, n_label, imtype=np.uint8):
if n_label == 0:
return tensor2im(label_tensor, imtype)
label_tensor = label_tensor.cpu().float()
if label_tensor.size()[0] > 1:
label_tensor = label_tensor.max(0, keepdim=True)[1]
label_tensor = Colorize(n_label)(label_tensor)
label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
return label_numpy.astype(imtype)
def save_image(image_numpy, image_path):
image_pil = Image.fromarray(image_numpy)
image_pil.save(image_path)
def mkdirs(paths):
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
|
Bringing-Old-Photos-Back-to-Life/Global/util/util.py/0
|
{
"file_path": "Bringing-Old-Photos-Back-to-Life/Global/util/util.py",
"repo_id": "Bringing-Old-Photos-Back-to-Life",
"token_count": 790
}
| 162 |
###### [Overview](#CLAP) | [Setup](#Setup) | [CLAP weights](#CLAP-weights) | [Usage](#Usage) | [Examples](#Examples) | [Citation](#Citation)
# CLAP
CLAP (Contrastive Language-Audio Pretraining) is a model that learns acoustic concepts from natural language supervision and enables “Zero-Shot” inference. The model has been extensively evaluated in 26 audio downstream tasks achieving SoTA in several of them including classification, retrieval, and captioning.
<img width="832" alt="clap_diagrams" src="docs/clap2_diagram.png">
## Setup
First, install python 3.8 or higher (3.11 recommended). Then, install CLAP using either of the following:
```shell
# Install pypi pacakge
pip install msclap
# Or Install latest (unstable) git source
pip install git+https://github.com/microsoft/CLAP.git
```
## CLAP weights
CLAP weights are downloaded automatically (choose between versions _2022_, _2023_, and _clapcap_), but are also available at: [Zenodo](https://zenodo.org/record/8378278) or [HuggingFace](https://huggingface.co/microsoft/msclap)
_clapcap_ is the audio captioning model that uses the 2023 encoders.
## Usage
- Zero-Shot Classification and Retrieval
```python
from msclap import CLAP
# Load model (Choose between versions '2022' or '2023')
# The model weight will be downloaded automatically if `model_fp` is not specified
clap_model = CLAP(version = '2023', use_cuda=False)
# Extract text embeddings
text_embeddings = clap_model.get_text_embeddings(class_labels: List[str])
# Extract audio embeddings
audio_embeddings = clap_model.get_audio_embeddings(file_paths: List[str])
# Compute similarity between audio and text embeddings
similarities = clap_model.compute_similarity(audio_embeddings, text_embeddings)
```
- Audio Captioning
```python
from msclap import CLAP
# Load model (Choose version 'clapcap')
clap_model = CLAP(version = 'clapcap', use_cuda=False)
# Generate audio captions
captions = clap_model.generate_caption(file_paths: List[str])
```
## Examples
Take a look at [examples](./examples/) for usage examples.
To run Zero-Shot Classification on the ESC50 dataset try the following:
```bash
> cd examples && python zero_shot_classification.py
```
Output (version 2023)
```bash
ESC50 Accuracy: 93.9%
```
## Citation
Kindly cite our work if you find it useful.
[CLAP: Learning Audio Concepts from Natural Language Supervision](https://ieeexplore.ieee.org/abstract/document/10095889)
```
@inproceedings{CLAP2022,
title={Clap learning audio concepts from natural language supervision},
author={Elizalde, Benjamin and Deshmukh, Soham and Al Ismail, Mahmoud and Wang, Huaming},
booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2023},
organization={IEEE}
}
```
[Natural Language Supervision for General-Purpose Audio Representations](https://arxiv.org/abs/2309.05767)
```
@misc{CLAP2023,
title={Natural Language Supervision for General-Purpose Audio Representations},
author={Benjamin Elizalde and Soham Deshmukh and Huaming Wang},
year={2023},
eprint={2309.05767},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2309.05767}
}
```
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
|
CLAP/README.md/0
|
{
"file_path": "CLAP/README.md",
"repo_id": "CLAP",
"token_count": 1399
}
| 163 |
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from transformers import AutoModel
from .audio import get_audio_encoder
class Projection(nn.Module):
def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None:
super().__init__()
self.linear1 = nn.Linear(d_in, d_out, bias=False)
self.linear2 = nn.Linear(d_out, d_out, bias=False)
self.layer_norm = nn.LayerNorm(d_out)
self.drop = nn.Dropout(p)
def forward(self, x: torch.Tensor) -> torch.Tensor:
embed1 = self.linear1(x)
embed2 = self.drop(self.linear2(F.gelu(embed1)))
embeds = self.layer_norm(embed1 + embed2)
return embeds
class AudioEncoder(nn.Module):
def __init__(self, audioenc_name:str, d_in: int, d_out: int, sample_rate: int, window_size: int,
hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int) -> None:
super().__init__()
audio_encoder = get_audio_encoder(audioenc_name)
self.base = audio_encoder(
sample_rate, window_size,
hop_size, mel_bins, fmin, fmax,
classes_num, d_in)
self.projection = Projection(d_in, d_out)
def forward(self, x):
out_dict = self.base(x)
audio_features, audio_classification_output = out_dict['embedding'], out_dict['clipwise_output']
projected_vec = self.projection(audio_features)
return projected_vec, audio_classification_output
class TextEncoder(nn.Module):
def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None:
super().__init__()
self.text_model = text_model
self.base = AutoModel.from_pretrained(text_model)
if 'clip' in text_model:
self.clip_text_projection = self.base.text_projection
self.base = self.base.text_model
if 'base' in text_model:
transformer_embed_dim = 512
self.projection = Projection(transformer_embed_dim, d_out)
def forward(self, x):
if 'clip' in self.text_model:
pooled_output = self.base(**x)[1] # get pooled output
out = self.clip_text_projection(pooled_output) # get CLS token output
elif 'gpt' in self.text_model:
batch_size = x['input_ids'].shape[0]
hidden_states = self.base(**x)[0] # (batch_size=4, seq_len, 768)
sequence_lengths = torch.ne(x['input_ids'], 0).sum(-1) - 1 # tensor([13, 14, 18, 17])
out = hidden_states[torch.arange(batch_size, device=hidden_states.device), sequence_lengths] # [batch_size, 768] = [4, 768]
else:
out = self.base(**x)[0]
out = out[:, 0, :] # get CLS token output
projected_vec = self.projection(out)
return projected_vec
class CLAP(nn.Module):
def __init__(self,
# audio
audioenc_name: str,
sample_rate: int,
window_size: int,
hop_size: int,
mel_bins: int,
fmin: int,
fmax: int,
classes_num: int,
out_emb: int,
# text
text_model: str,
transformer_embed_dim: int,
# common
d_proj: int,
):
super().__init__()
self.audio_encoder = AudioEncoder(
audioenc_name, out_emb, d_proj,
sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num)
self.caption_encoder = TextEncoder(
d_proj, text_model, transformer_embed_dim
)
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
def forward(self, audio, text):
audio_embed, _ = self.audio_encoder(audio)
caption_embed = self.caption_encoder(text)
return caption_embed, audio_embed, self.logit_scale.exp()
|
CLAP/msclap/models/clap.py/0
|
{
"file_path": "CLAP/msclap/models/clap.py",
"repo_id": "CLAP",
"token_count": 1937
}
| 164 |
# BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
[https://arxiv.org/abs/1910.13461](https://arxiv.org/abs/1910.13461)
## Introduction
BART is sequence-to-sequence model trained with denoising as pretraining objective. We show that this pretraining objective is more generic and show that we can match [RoBERTa](../roberta) results on SQuAD and GLUE and gain state-of-the-art results on summarization (XSum, CNN dataset), long form generative question answering (ELI5) and dialog response genration (ConvAI2). See the associated paper for more details.
## Pre-trained models
Model | Description | # params | Download
---|---|---|---
`bart.base` | BART model with 6 encoder and decoder layers | 140M | [bart.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz)
`bart.large` | BART model with 12 encoder and decoder layers | 400M | [bart.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz)
`bart.large.mnli` | `bart.large` finetuned on `MNLI` | 400M | [bart.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz)
`bart.large.cnn` | `bart.large` finetuned on `CNN-DM` | 400M | [bart.large.cnn.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz)
`bart.large.xsum` | `bart.large` finetuned on `Xsum` | 400M | [bart.large.xsum.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz)
## Results
**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)**
_(dev set, single model, single-task finetuning)_
Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B
---|---|---|---|---|---|---|---|---
`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4
`bart.large` | 89.9 | 94.9 | 92.5 | 87.0 | 96.6 | 90.4 | 62.8 | 91.2
**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)**
_(dev set, no additional data used)_
Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1
---|---|---
`roberta.large` | 88.9/94.6 | 86.5/89.4
`bart.large` | 88.8/94.6 | 86.1/89.2
**[CNN/Daily Mail](http://nlpprogress.com/english/summarization.html)**
_(test set, no additional data used)_
Model | R1 | R2 | RL
---|---|---|---
`BERTSUMEXTABS` | 42.13 | 19.60 | 39.18
`bart.large` | 44.16 | 21.28 | 40.90
## Example usage
##### Load BART from torch.hub (PyTorch >= 1.1):
```python
import torch
bart = torch.hub.load('pytorch/fairseq', 'bart.large')
bart.eval() # disable dropout (or leave in train mode to finetune)
```
##### Load BART (for PyTorch 1.0 or custom models):
```python
# Download bart.large model
wget https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz
tar -xzvf bart.large.tar.gz
# Load the model in fairseq
from fairseq.models.bart import BARTModel
bart = BARTModel.from_pretrained('/path/to/bart.large', checkpoint_file='model.pt')
bart.eval() # disable dropout (or leave in train mode to finetune)
```
##### Apply Byte-Pair Encoding (BPE) to input text:
```python
tokens = bart.encode('Hello world!')
assert tokens.tolist() == [0, 31414, 232, 328, 2]
bart.decode(tokens) # 'Hello world!'
```
##### Extract features from BART:
```python
# Extract the last layer's features
last_layer_features = bart.extract_features(tokens)
assert last_layer_features.size() == torch.Size([1, 5, 1024])
# Extract all layer's features from decoder (layer 0 is the embedding layer)
all_layers = bart.extract_features(tokens, return_all_hiddens=True)
assert len(all_layers) == 13
assert torch.all(all_layers[-1] == last_layer_features)
```
##### Use BART for sentence-pair classification tasks:
```python
# Download BART already finetuned for MNLI
bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli')
bart.eval() # disable dropout for evaluation
# Encode a pair of sentences and make a prediction
tokens = bart.encode('BART is a seq2seq model.', 'BART is not sequence to sequence.')
bart.predict('mnli', tokens).argmax() # 0: contradiction
# Encode another pair of sentences
tokens = bart.encode('BART is denoising autoencoder.', 'BART is version of autoencoder.')
bart.predict('mnli', tokens).argmax() # 2: entailment
```
##### Register a new (randomly initialized) classification head:
```python
bart.register_classification_head('new_task', num_classes=3)
logprobs = bart.predict('new_task', tokens)
```
##### Batched prediction:
```python
import torch
from fairseq.data.data_utils import collate_tokens
bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli')
bart.eval()
batch_of_pairs = [
['BART is a seq2seq model.', 'BART is not sequence to sequence.'],
['BART is denoising autoencoder.', 'BART is version of autoencoder.'],
]
batch = collate_tokens(
[bart.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1
)
logprobs = bart.predict('mnli', batch)
print(logprobs.argmax(dim=1))
# tensor([0, 2])
```
##### Using the GPU:
```python
bart.cuda()
bart.predict('new_task', tokens)
```
#### Filling masks:
BART can be used to fill multiple `<mask>` tokens in the input.
```python
bart = torch.hub.load('pytorch/fairseq', 'bart.base')
bart.eval()
bart.fill_mask(['The cat <mask> on the <mask>.'], topk=3, beam=10)
# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))]]
```
Note that by default we enforce the output length to match the input length.
This can be disabled by setting ``match_source_len=False``:
```
bart.fill_mask(['The cat <mask> on the <mask>.'], topk=3, beam=10, match_source_len=False)
# [[('The cat was on the ground.', tensor(-0.6185)), ('The cat was asleep on the couch.', tensor(-0.6276)), ('The cat was on the floor.', tensor(-0.6800))]]
```
Example code to fill masks for a batch of sentences using GPU
```
bart.cuda()
bart.fill_mask(['The cat <mask> on the <mask>.', 'The dog <mask> on the <mask>.'], topk=3, beam=10)
# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))], [('The dog was on the ground.', tensor(-0.6190)), ('The dog lay on the ground.', tensor(-0.6711)),
('The dog was asleep on the couch', tensor(-0.6796))]]
```
#### Evaluating the `bart.large.mnli` model:
Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set.
```python
label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'}
ncorrect, nsamples = 0, 0
bart.cuda()
bart.eval()
with open('glue_data/MNLI/dev_matched.tsv') as fin:
fin.readline()
for index, line in enumerate(fin):
tokens = line.strip().split('\t')
sent1, sent2, target = tokens[8], tokens[9], tokens[-1]
tokens = bart.encode(sent1, sent2)
prediction = bart.predict('mnli', tokens).argmax().item()
prediction_label = label_map[prediction]
ncorrect += int(prediction_label == target)
nsamples += 1
print('| Accuracy: ', float(ncorrect)/float(nsamples))
# Expected output: 0.9010
```
#### Evaluating the `bart.large.cnn` model:
- Follow instructions [here](https://github.com/abisee/cnn-dailymail) to download and process into data-files such that `test.source` and `test.target` has one line for each non-tokenized sample.
- For simpler preprocessing, you can also `wget https://cdn-datasets.huggingface.co/summarization/cnn_dm_v2.tgz`, although there is no guarantee of identical scores
- `huggingface/transformers` has a simpler interface that supports [single-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_eval.py) and [multi-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_distributed_eval.py) beam search.
In `huggingface/transformers`, the BART models' paths are `facebook/bart-large-cnn` and `facebook/bart-large-xsum`.
In `fairseq`, summaries can be generated using:
```bash
cp data-bin/cnn_dm/dict.source.txt checkpoints/
python examples/bart/summarize.py \
--model-dir pytorch/fairseq \
--model-file bart.large.cnn \
--src cnn_dm/test.source \
--out cnn_dm/test.hypo
```
For calculating rouge, install `files2rouge` from [here](https://github.com/pltrdy/files2rouge).
```bash
export CLASSPATH=/path/to/stanford-corenlp-full-2016-10-31/stanford-corenlp-3.7.0.jar
# Tokenize hypothesis and target files.
cat test.hypo | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.tokenized
cat test.target | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.target
files2rouge test.hypo.tokenized test.hypo.target
# Expected output: (ROUGE-2 Average_F: 0.21238)
```
## Finetuning
- [Finetuning on GLUE](README.glue.md)
- [Finetuning on CNN-DM](README.summarization.md)
## Citation
```bibtex
@article{lewis2019bart,
title = {BART: Denoising Sequence-to-Sequence Pre-training for Natural
Language Generation, Translation, and Comprehension},
author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and
Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov
and Luke Zettlemoyer },
journal={arXiv preprint arXiv:1910.13461},
year = {2019},
}
```
|
COCO-LM/fairseq/examples/bart/README.md/0
|
{
"file_path": "COCO-LM/fairseq/examples/bart/README.md",
"repo_id": "COCO-LM",
"token_count": 3401
}
| 165 |
#!/bin/bash
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
source_lang=kk_KZ
target_lang=en_XX
MODEL=criss_checkpoints/criss.3rd.pt
SPM=criss_checkpoints/sentence.bpe.model
SPLIT=test
LANG_DICT=criss_checkpoints/lang_dict.txt
SPM_ENCODE=flores/scripts/spm_encode.py
SAVE_ENCODER=save_encoder.py
ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL
DICT=criss_checkpoints/dict.txt
THRESHOLD=1.02
MIN_COUNT=500
DATA_DIR=data_tmp
SAVE_DIR=mining/${source_lang}_${target_lang}_mined
ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang}
INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba
mkdir -p $ENCODER_SAVE_DIR/${target_lang}
mkdir -p $ENCODER_SAVE_DIR/${source_lang}
mkdir -p $SAVE_DIR
## Save encoder outputs
# Save encoder outputs for source sentences
python $SAVE_ENCODER \
${INPUT_DIR} \
--path ${MODEL} \
--task translation_multi_simple_epoch \
--lang-pairs ${source_lang}-${target_lang} \
--lang-dict ${LANG_DICT} \
--gen-subset ${SPLIT} \
--bpe 'sentencepiece' \
-s ${source_lang} -t ${target_lang} \
--sentencepiece-model ${SPM} \
--remove-bpe 'sentencepiece' \
--beam 1 \
--lang-tok-style mbart \
--encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang}
## Save encoder outputs for target sentences
python $SAVE_ENCODER \
${INPUT_DIR} \
--path ${MODEL} \
--lang-pairs ${source_lang}-${target_lang} \
--lang-dict ${LANG_DICT} \
--task translation_multi_simple_epoch \
--gen-subset ${SPLIT} \
--bpe 'sentencepiece' \
-t ${source_lang} -s ${target_lang} \
--sentencepiece-model ${SPM} \
--remove-bpe 'sentencepiece' \
--beam 1 \
--lang-tok-style mbart \
--encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang}
## Mining
python mining/mine.py \
--src-lang ${source_lang} \
--tgt-lang ${target_lang} \
--dim 1024 \
--mem 10 \
--neighborhood 4 \
--src-dir ${ENCODER_SAVE_DIR}/${source_lang} \
--tgt-dir ${ENCODER_SAVE_DIR}/${target_lang} \
--output $SAVE_DIR \
--threshold ${THRESHOLD} \
--min-count ${MIN_COUNT} \
--valid-size 100 \
--dict-path ${DICT} \
--spm-path ${SPM} \
## Process and binarize mined data
python $SPM_ENCODE \
--model ${SPM} \
--output_format=piece \
--inputs mining/${source_lang}_${target_lang}_mined/train.${source_lang} mining/${source_lang}_${target_lang}_mined/train.${target_lang} \
--outputs mining/${source_lang}_${target_lang}_mined/train.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/train.bpe.${target_lang}
python $SPM_ENCODE \
--model ${SPM} \
--output_format=piece \
--inputs mining/${source_lang}_${target_lang}_mined/valid.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.${target_lang} \
--outputs mining/${source_lang}_${target_lang}_mined/valid.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.bpe.${target_lang}
fairseq-preprocess \
--source-lang ${source_lang} \
--target-lang ${target_lang} \
--trainpref mining/${source_lang}_${target_lang}_mined/train.bpe \
--validpref mining/${source_lang}_${target_lang}_mined/valid.bpe \
--destdir mining/${source_lang}_${target_lang}_mined \
--srcdict ${DICT} \
--joined-dictionary \
--workers 8
|
COCO-LM/fairseq/examples/criss/mining/mine_example.sh/0
|
{
"file_path": "COCO-LM/fairseq/examples/criss/mining/mine_example.sh",
"repo_id": "COCO-LM",
"token_count": 1345
}
| 166 |
# Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
## Example usage
First download and preprocess the data following the main [language modeling README](README.md).
Then to train a convolutional LM using the `fconv_lm_dauphin_wikitext103`
architecture:
```bash
fairseq-train --task language_modeling \
data-bin/wikitext-103 \
--save-dir checkpoints/fconv_wikitext-103 \
--arch fconv_lm_dauphin_wikitext103 \
--adaptive-softmax-cutoff 10000,20000,200000 \
--dropout 0.2 \
--criterion adaptive_loss \
--optimizer nag --clip-norm 0.1 --weight-decay 5e-06 \
--lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 \
--max-tokens 1024 --tokens-per-sample 1024 \
--ddp-backend legacy_ddp \
--max-epoch 35
```
And evaluate with:
```bash
fairseq-eval-lm data-bin/wikitext-103 --path checkpoints/fconv_wiki103/checkpoint_best.pt
```
## Citation
```bibtex
@inproceedings{dauphin2017language,
title={Language Modeling with Gated Convolutional Networks},
author={Dauphin, Yann N and Fan, Angela and Auli, Michael and Grangier, David},
booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
pages={933--941},
year={2017},
organization={JMLR}
}
```
|
COCO-LM/fairseq/examples/language_model/README.conv.md/0
|
{
"file_path": "COCO-LM/fairseq/examples/language_model/README.conv.md",
"repo_id": "COCO-LM",
"token_count": 468
}
| 167 |
import gzip
import argparse
from string import punctuation
def len_no_punc(s, punc):
return len([ch for ch in s if ch in punc])
def filter_overpunc(len_npunc, len_sen):
return len_npunc < 0.5*len_sen
def main(args):
punc = punctuation + "—|–"
print('Processing file {}'.format(args.input))
with gzip.open(args.input, 'rt', encoding=args.encoding) as tsv:
with open(args.bitext + '.' + args.src_lang, 'wt', encoding=args.encoding) as fsrc:
with open(args.bitext + '.' + args.tgt_lang, 'wt', encoding=args.encoding) as ftgt:
line = tsv.readline()
fields = line.split('\t')
src, tgt = fields[1], fields[2]
nchar_npunc_src = len_no_punc(src, punc)
nchar_npunc_tgt = len_no_punc(tgt, punc)
if filter_overpunc(nchar_npunc_src, len(src)) and filter_overpunc(nchar_npunc_tgt, len(tgt)):
fsrc.write(src.strip() + '\n')
ftgt.write(tgt.strip() + '\n')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True, type=str)
parser.add_argument('--encoding', default='utf-8', help='character encoding for input/output')
parser.add_argument('--bitext', type=str, required=True, help='language direction')
parser.add_argument('--src-lang', type=str, required=True, help='Source language')
parser.add_argument('--tgt-lang', type=str, required=True, help='Target language')
main(parser.parse_args())
|
COCO-LM/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py/0
|
{
"file_path": "COCO-LM/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py",
"repo_id": "COCO-LM",
"token_count": 681
}
| 168 |
import shutil
import os, sys
from subprocess import check_call, check_output
import glob
import argparse
import shutil
import pathlib
import itertools
def call_output(cmd):
print(f"Executing: {cmd}")
ret = check_output(cmd, shell=True)
print(ret)
return ret
def call(cmd):
print(cmd)
check_call(cmd, shell=True)
WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
sys.exit(-1)
SPM_PATH = os.environ.get('SPM_PATH', None)
if SPM_PATH is None or not SPM_PATH.strip():
print("Please install sentence piecence from https://github.com/google/sentencepiece and set SPM_PATH pointing to the installed spm_encode.py. Exitting...")
sys.exit(-1)
SPM_MODEL = f'{WORKDIR_ROOT}/sentence.bpe.model'
SPM_VOCAB = f'{WORKDIR_ROOT}/dict_250k.txt'
SPM_ENCODE = f'{SPM_PATH}'
if not os.path.exists(SPM_MODEL):
call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/sentence.bpe.model -O {SPM_MODEL}")
if not os.path.exists(SPM_VOCAB):
call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/dict_250k.txt -O {SPM_VOCAB}")
def get_data_size(raw):
cmd = f'wc -l {raw}'
ret = call_output(cmd)
return int(ret.split()[0])
def encode_spm(model, direction, prefix='', splits=['train', 'test', 'valid'], pairs_per_shard=None):
src, tgt = direction.split('-')
for split in splits:
src_raw, tgt_raw = f'{RAW_DIR}/{split}{prefix}.{direction}.{src}', f'{RAW_DIR}/{split}{prefix}.{direction}.{tgt}'
if os.path.exists(src_raw) and os.path.exists(tgt_raw):
cmd = f"""python {SPM_ENCODE} \
--model {model}\
--output_format=piece \
--inputs {src_raw} {tgt_raw} \
--outputs {BPE_DIR}/{direction}{prefix}/{split}.bpe.{src} {BPE_DIR}/{direction}{prefix}/{split}.bpe.{tgt} """
print(cmd)
call(cmd)
def binarize_(
bpe_dir,
databin_dir,
direction, spm_vocab=SPM_VOCAB,
splits=['train', 'test', 'valid'],
):
src, tgt = direction.split('-')
try:
shutil.rmtree(f'{databin_dir}', ignore_errors=True)
os.mkdir(f'{databin_dir}')
except OSError as error:
print(error)
cmds = [
"fairseq-preprocess",
f"--source-lang {src} --target-lang {tgt}",
f"--destdir {databin_dir}/",
f"--workers 8",
]
if isinstance(spm_vocab, tuple):
src_vocab, tgt_vocab = spm_vocab
cmds.extend(
[
f"--srcdict {src_vocab}",
f"--tgtdict {tgt_vocab}",
]
)
else:
cmds.extend(
[
f"--joined-dictionary",
f"--srcdict {spm_vocab}",
]
)
input_options = []
if 'train' in splits and glob.glob(f"{bpe_dir}/train.bpe*"):
input_options.append(
f"--trainpref {bpe_dir}/train.bpe",
)
if 'valid' in splits and glob.glob(f"{bpe_dir}/valid.bpe*"):
input_options.append(f"--validpref {bpe_dir}/valid.bpe")
if 'test' in splits and glob.glob(f"{bpe_dir}/test.bpe*"):
input_options.append(f"--testpref {bpe_dir}/test.bpe")
if len(input_options) > 0:
cmd = " ".join(cmds + input_options)
print(cmd)
call(cmd)
def binarize(
databin_dir,
direction, spm_vocab=SPM_VOCAB, prefix='',
splits=['train', 'test', 'valid'],
pairs_per_shard=None,
):
def move_databin_files(from_folder, to_folder):
for bin_file in glob.glob(f"{from_folder}/*.bin") \
+ glob.glob(f"{from_folder}/*.idx") \
+ glob.glob(f"{from_folder}/dict*"):
try:
shutil.move(bin_file, to_folder)
except OSError as error:
print(error)
bpe_databin_dir = f"{BPE_DIR}/{direction}{prefix}_databin"
bpe_dir = f"{BPE_DIR}/{direction}{prefix}"
if pairs_per_shard is None:
binarize_(bpe_dir, bpe_databin_dir, direction, spm_vocab=spm_vocab, splits=splits)
move_databin_files(bpe_databin_dir, databin_dir)
else:
# binarize valid and test which will not be sharded
binarize_(
bpe_dir, bpe_databin_dir, direction,
spm_vocab=spm_vocab, splits=[s for s in splits if s != "train"])
for shard_bpe_dir in glob.glob(f"{bpe_dir}/shard*"):
path_strs = os.path.split(shard_bpe_dir)
shard_str = path_strs[-1]
shard_folder = f"{bpe_databin_dir}/{shard_str}"
databin_shard_folder = f"{databin_dir}/{shard_str}"
print(f'working from {shard_folder} to {databin_shard_folder}')
os.makedirs(databin_shard_folder, exist_ok=True)
binarize_(
shard_bpe_dir, shard_folder, direction,
spm_vocab=spm_vocab, splits=["train"])
for test_data in glob.glob(f"{bpe_databin_dir}/valid.*") + glob.glob(f"{bpe_databin_dir}/test.*"):
filename = os.path.split(test_data)[-1]
try:
os.symlink(test_data, f"{databin_shard_folder}/{filename}")
except OSError as error:
print(error)
move_databin_files(shard_folder, databin_shard_folder)
def load_langs(path):
with open(path) as fr:
langs = [l.strip() for l in fr]
return langs
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--data_root", default=f"{WORKDIR_ROOT}/ML50")
parser.add_argument("--raw-folder", default='raw')
parser.add_argument("--bpe-folder", default='bpe')
parser.add_argument("--databin-folder", default='databin')
args = parser.parse_args()
DATA_PATH = args.data_root #'/private/home/yuqtang/public_data/ML50'
RAW_DIR = f'{DATA_PATH}/{args.raw_folder}'
BPE_DIR = f'{DATA_PATH}/{args.bpe_folder}'
DATABIN_DIR = f'{DATA_PATH}/{args.databin_folder}'
os.makedirs(BPE_DIR, exist_ok=True)
raw_files = itertools.chain(
glob.glob(f'{RAW_DIR}/train*'),
glob.glob(f'{RAW_DIR}/valid*'),
glob.glob(f'{RAW_DIR}/test*'),
)
directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files]
for direction in directions:
prefix = ""
splits = ['train', 'valid', 'test']
try:
shutil.rmtree(f'{BPE_DIR}/{direction}{prefix}', ignore_errors=True)
os.mkdir(f'{BPE_DIR}/{direction}{prefix}')
os.makedirs(DATABIN_DIR, exist_ok=True)
except OSError as error:
print(error)
spm_model, spm_vocab = SPM_MODEL, SPM_VOCAB
encode_spm(spm_model, direction=direction, splits=splits)
binarize(DATABIN_DIR, direction, spm_vocab=spm_vocab, splits=splits)
|
COCO-LM/fairseq/examples/multilingual/data_scripts/binarize.py/0
|
{
"file_path": "COCO-LM/fairseq/examples/multilingual/data_scripts/binarize.py",
"repo_id": "COCO-LM",
"token_count": 3488
}
| 169 |
import os, sys
import glob, itertools
import pandas as pd
WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
sys.exit(-1)
def load_langs(path):
with open(path) as fr:
langs = [l.strip() for l in fr]
return langs
def load_sentences(raw_data, split, direction):
src, tgt = direction.split('-')
src_path = f"{raw_data}/{split}.{direction}.{src}"
tgt_path = f"{raw_data}/{split}.{direction}.{tgt}"
if os.path.exists(src_path) and os.path.exists(tgt_path):
return [(src, open(src_path).read().splitlines()), (tgt, open(tgt_path).read().splitlines())]
else:
return []
def swap_direction(d):
src, tgt = d.split('-')
return f'{tgt}-{src}'
def get_all_test_data(raw_data, directions, split='test'):
test_data = [
x
for dd in directions
for d in [dd, swap_direction(dd)]
for x in load_sentences(raw_data, split, d)
]
# all_test_data = {s for _, d in test_data for s in d}
all_test_data = {}
for lang, d in test_data:
for s in d:
s = s.strip()
lgs = all_test_data.get(s, set())
lgs.add(lang)
all_test_data[s] = lgs
return all_test_data, test_data
def check_train_sentences(raw_data, direction, all_test_data, mess_up_train={}):
src, tgt = direction.split('-')
tgt_path = f"{raw_data}/train.{direction}.{tgt}"
src_path = f"{raw_data}/train.{direction}.{src}"
print(f'check training data in {raw_data}/train.{direction}')
size = 0
if not os.path.exists(tgt_path) or not os.path.exists(src_path):
return mess_up_train, size
with open(src_path) as f, open(tgt_path) as g:
for src_line, tgt_line in zip(f, g):
s = src_line.strip()
t = tgt_line.strip()
size += 1
if s in all_test_data:
langs = mess_up_train.get(s, set())
langs.add(direction)
mess_up_train[s] = langs
if t in all_test_data:
langs = mess_up_train.get(t, set())
langs.add(direction)
mess_up_train[t] = langs
return mess_up_train, size
def check_train_all(raw_data, directions, all_test_data):
mess_up_train = {}
data_sizes = {}
for direction in directions:
_, size = check_train_sentences(raw_data, direction, all_test_data, mess_up_train)
data_sizes[direction] = size
return mess_up_train, data_sizes
def count_train_in_other_set(mess_up_train):
train_in_others = [(direction, s) for s, directions in mess_up_train.items() for direction in directions]
counts = {}
for direction, s in train_in_others:
counts[direction] = counts.get(direction, 0) + 1
return counts
def train_size_if_remove_in_otherset(data_sizes, mess_up_train):
counts_in_other = count_train_in_other_set(mess_up_train)
remain_sizes = []
for direction, count in counts_in_other.items():
remain_sizes.append((direction, data_sizes[direction] - count, data_sizes[direction], count, 100 * count / data_sizes[direction] ))
return remain_sizes
def remove_messed_up_sentences(raw_data, direction, mess_up_train, mess_up_train_pairs, corrected_langs):
split = 'train'
src_lang, tgt_lang = direction.split('-')
tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}"
src = f"{raw_data}/{split}.{direction}.{src_lang}"
print(f'working on {direction}: ', src, tgt)
if not os.path.exists(tgt) or not os.path.exists(src) :
return
corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}"
corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}"
line_num = 0
keep_num = 0
with open(src, encoding='utf8',) as fsrc, \
open(tgt, encoding='utf8',) as ftgt, \
open(corrected_src, 'w', encoding='utf8') as fsrc_corrected, \
open(corrected_tgt, 'w', encoding='utf8') as ftgt_corrected:
for s, t in zip(fsrc, ftgt):
s = s.strip()
t = t.strip()
if t not in mess_up_train \
and s not in mess_up_train \
and (s, t) not in mess_up_train_pairs \
and (t, s) not in mess_up_train_pairs:
corrected_langs.add(direction)
print(s, file=fsrc_corrected)
print(t, file=ftgt_corrected)
keep_num += 1
line_num += 1
if line_num % 1000 == 0:
print(f'completed {line_num} lines', end='\r')
return line_num, keep_num
##########
def merge_valid_test_messup(mess_up_train_valid, mess_up_train_test):
merged_mess = []
for s in set(list(mess_up_train_valid.keys()) + list(mess_up_train_test.keys())):
if not s:
continue
valid = mess_up_train_valid.get(s, set())
test = mess_up_train_test.get(s, set())
merged_mess.append((s, valid | test))
return dict(merged_mess)
#########
def check_train_pairs(raw_data, direction, all_test_data, mess_up_train={}):
src, tgt = direction.split('-')
#a hack; TODO: check the reversed directions
path1 = f"{raw_data}/train.{src}-{tgt}.{src}"
path2 = f"{raw_data}/train.{src}-{tgt}.{tgt}"
if not os.path.exists(path1) or not os.path.exists(path2) :
return
with open(path1) as f1, open(path2) as f2:
for src_line, tgt_line in zip(f1, f2):
s = src_line.strip()
t = tgt_line.strip()
if (s, t) in all_test_data or (t, s) in all_test_data:
langs = mess_up_train.get( (s, t), set())
langs.add(src)
langs.add(tgt)
mess_up_train[(s, t)] = langs
def load_pairs(raw_data, split, direction):
src, tgt = direction.split('-')
src_f = f"{raw_data}/{split}.{direction}.{src}"
tgt_f = f"{raw_data}/{split}.{direction}.{tgt}"
if tgt != 'en_XX':
src_f, tgt_f = tgt_f, src_f
if os.path.exists(src_f) and os.path.exists(tgt_f):
return list(zip(open(src_f).read().splitlines(),
open(tgt_f).read().splitlines(),
))
else:
return []
# skip_langs = ['cs_CZ', 'en_XX', 'tl_XX', 'tr_TR']
def get_messed_up_test_pairs(split, directions):
test_pairs = [
(d, load_pairs(raw_data, split, d))
for d in directions
]
# all_test_data = {s for _, d in test_data for s in d}
all_test_pairs = {}
for direction, d in test_pairs:
src, tgt = direction.split('-')
for s in d:
langs = all_test_pairs.get(s, set())
langs.add(src)
langs.add(tgt)
all_test_pairs[s] = langs
mess_up_train_pairs = {}
for direction in directions:
check_train_pairs(raw_data, direction, all_test_pairs, mess_up_train_pairs)
return all_test_pairs, mess_up_train_pairs
if __name__ == "__main__":
#######
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
'--from-folder',
required=True,
type=str)
parser.add_argument(
'--to-folder',
required=True,
type=str)
parser.add_argument(
'--directions',
default=None,
type=str)
args = parser.parse_args()
raw_data = args.from_folder
to_folder = args.to_folder
os.makedirs(to_folder, exist_ok=True)
if args.directions:
directions = args.directions.split(',')
else:
raw_files = itertools.chain(
glob.glob(f'{raw_data}/train*'),
glob.glob(f'{raw_data}/valid*'),
glob.glob(f'{raw_data}/test*'),
)
directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files]
print('working on directions: ', directions)
##########
all_test_data, test_data = get_all_test_data(raw_data, directions, 'test')
print('==loaded test data==')
all_valid_data, valid_data = get_all_test_data(raw_data, directions, 'valid')
print('==loaded valid data==')
all_valid_test_data = merge_valid_test_messup(all_test_data, all_valid_data)
mess_up_train, data_sizes = check_train_all(raw_data, directions, all_valid_test_data)
print('training messing up with valid, test data:', len(mess_up_train))
data_situation = train_size_if_remove_in_otherset(data_sizes, mess_up_train)
df = pd.DataFrame(data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent'])
df.sort_values('remove_percent', ascending=False)
df.to_csv(f'{raw_data}/clean_summary.tsv', sep='\t')
print(f'projected data clean summary in: {raw_data}/clean_summary.tsv')
# correct the dataset:
all_test_pairs, mess_up_test_train_pairs = get_messed_up_test_pairs('test', directions)
all_valid_pairs, mess_up_valid_train_pairs = get_messed_up_test_pairs('valid', directions)
all_messed_pairs = set(mess_up_test_train_pairs.keys()).union(set(mess_up_valid_train_pairs.keys()))
corrected_directions = set()
real_data_situation = []
for direction in directions:
org_size, new_size = remove_messed_up_sentences(raw_data, direction, mess_up_train, all_messed_pairs, corrected_directions)
if org_size == 0:
print(f"{direction} has size 0")
continue
real_data_situation.append(
(direction, new_size, org_size, org_size - new_size, (org_size - new_size) / org_size * 100)
)
print('corrected directions: ', corrected_directions)
df = pd.DataFrame(real_data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent'])
df.sort_values('remove_percent', ascending=False)
df.to_csv(f'{raw_data}/actual_clean_summary.tsv', sep='\t')
print(f'actual data clean summary (which can be different from the projected one because of duplications) in: {raw_data}/actual_clean_summary.tsv')
import shutil
for direction in directions:
src_lang, tgt_lang = direction.split('-')
for split in ['train', 'valid', 'test']:
# copying valid, test and uncorrected train
if direction in corrected_directions and split == 'train':
continue
tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}"
src = f"{raw_data}/{split}.{direction}.{src_lang}"
if not (os.path.exists(src) and os.path.exists(tgt)):
continue
corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}"
corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}"
print(f'copying {src} to {corrected_src}')
shutil.copyfile(src, corrected_src)
print(f'copying {tgt} to {corrected_tgt}')
shutil.copyfile(tgt, corrected_tgt)
print('completed')
|
COCO-LM/fairseq/examples/multilingual/data_scripts/remove_valid_test_in_train.py/0
|
{
"file_path": "COCO-LM/fairseq/examples/multilingual/data_scripts/remove_valid_test_in_train.py",
"repo_id": "COCO-LM",
"token_count": 5231
}
| 170 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import os
import re
import subprocess
from contextlib import redirect_stdout
from fairseq import options
from fairseq_cli import eval_lm, preprocess
def reprocess(fle):
# takes in a file of generate.py translation generate_output
# returns a source dict and hypothesis dict, where keys are the ID num (as a string)
# and values and the corresponding source and translation. There may be several translations
# per source, so the values for hypothesis_dict are lists.
# parses output of generate.py
with open(fle, "r") as f:
txt = f.read()
"""reprocess generate.py output"""
p = re.compile(r"[STHP][-]\d+\s*")
hp = re.compile(r"(\s*[-]?\d+[.]?\d+\s*)|(\s*(-inf)\s*)")
source_dict = {}
hypothesis_dict = {}
score_dict = {}
target_dict = {}
pos_score_dict = {}
lines = txt.split("\n")
for line in lines:
line += "\n"
prefix = re.search(p, line)
if prefix is not None:
assert len(prefix.group()) > 2, "prefix id not found"
_, j = prefix.span()
id_num = prefix.group()[2:]
id_num = int(id_num)
line_type = prefix.group()[0]
if line_type == "H":
h_txt = line[j:]
hypo = re.search(hp, h_txt)
assert (
hypo is not None
), "regular expression failed to find the hypothesis scoring"
_, i = hypo.span()
score = hypo.group()
if id_num in hypothesis_dict:
hypothesis_dict[id_num].append(h_txt[i:])
score_dict[id_num].append(float(score))
else:
hypothesis_dict[id_num] = [h_txt[i:]]
score_dict[id_num] = [float(score)]
elif line_type == "S":
source_dict[id_num] = line[j:]
elif line_type == "T":
target_dict[id_num] = line[j:]
elif line_type == "P":
pos_scores = (line[j:]).split()
pos_scores = [float(x) for x in pos_scores]
if id_num in pos_score_dict:
pos_score_dict[id_num].append(pos_scores)
else:
pos_score_dict[id_num] = [pos_scores]
return source_dict, hypothesis_dict, score_dict, target_dict, pos_score_dict
def reprocess_nbest(fle):
"""reprocess interactive.py output"""
with open(fle, "r") as f:
txt = f.read()
source_dict = {}
hypothesis_dict = {}
score_dict = {}
target_dict = {}
pos_score_dict = {}
lines = txt.split("\n")
hp = re.compile(r"[-]?\d+[.]?\d+")
j = -1
for _i, line in enumerate(lines):
line += "\n"
line_type = line[0]
if line_type == "H":
hypo = re.search(hp, line)
_, start_index = hypo.span()
score = hypo.group()
if j in score_dict:
score_dict[j].append(float(score))
hypothesis_dict[j].append(line[start_index:].strip("\t"))
else:
score_dict[j] = [float(score)]
hypothesis_dict[j] = [line[start_index:].strip("\t")]
elif line_type == "O":
j += 1
source_dict[j] = line[2:]
# we don't have the targets for interactive.py
target_dict[j] = "filler"
elif line_type == "P":
pos_scores = [float(pos_score) for pos_score in line.split()[1:]]
if j in pos_score_dict:
pos_score_dict[j].append(pos_scores)
else:
pos_score_dict[j] = [pos_scores]
assert source_dict.keys() == hypothesis_dict.keys()
assert source_dict.keys() == pos_score_dict.keys()
assert source_dict.keys() == score_dict.keys()
return source_dict, hypothesis_dict, score_dict, target_dict, pos_score_dict
def write_reprocessed(
sources,
hypos,
targets,
source_outfile,
hypo_outfile,
target_outfile,
right_to_left=False,
prefix_len=None,
bpe_symbol=None,
target_prefix_frac=None,
source_prefix_frac=None,
):
"""writes nbest hypothesis for rescoring"""
assert not (
prefix_len is not None and target_prefix_frac is not None
), "in writing reprocessed, only one type of prefix may be used"
assert not (
prefix_len is not None and source_prefix_frac is not None
), "in writing reprocessed, only one type of prefix may be used"
assert not (
target_prefix_frac is not None and source_prefix_frac is not None
), "in writing reprocessed, only one type of prefix may be used"
with open(source_outfile, "w") as source_file, open(
hypo_outfile, "w"
) as hypo_file, open(target_outfile, "w") as target_file:
assert len(sources) == len(hypos), "sources and hypos list length mismatch"
if right_to_left:
for i in range(len(sources)):
for j in range(len(hypos[i])):
if prefix_len is None:
hypo_file.write(make_right_to_left(hypos[i][j]) + "\n")
else:
raise NotImplementedError()
source_file.write(make_right_to_left(sources[i]) + "\n")
target_file.write(make_right_to_left(targets[i]) + "\n")
else:
for i in sorted(sources.keys()):
for j in range(len(hypos[i])):
if prefix_len is not None:
shortened = (
get_prefix_no_bpe(hypos[i][j], bpe_symbol, prefix_len)
+ "\n"
)
hypo_file.write(shortened)
source_file.write(sources[i])
target_file.write(targets[i])
elif target_prefix_frac is not None:
num_words, shortened, num_bpe_tokens = calc_length_from_frac(
hypos[i][j], target_prefix_frac, bpe_symbol
)
shortened += "\n"
hypo_file.write(shortened)
source_file.write(sources[i])
target_file.write(targets[i])
elif source_prefix_frac is not None:
num_words, shortened, num_bpe_tokensn = calc_length_from_frac(
sources[i], source_prefix_frac, bpe_symbol
)
shortened += "\n"
hypo_file.write(hypos[i][j])
source_file.write(shortened)
target_file.write(targets[i])
else:
hypo_file.write(hypos[i][j])
source_file.write(sources[i])
target_file.write(targets[i])
def calc_length_from_frac(bpe_sentence, prefix_frac, bpe_symbol):
# return number of words, (not bpe tokens) that we want
no_bpe_sen = remove_bpe(bpe_sentence, bpe_symbol)
len_sen = len(no_bpe_sen.split())
num_words = math.ceil(len_sen * prefix_frac)
prefix = get_prefix_no_bpe(bpe_sentence, bpe_symbol, num_words)
num_bpe_tokens = len(prefix.split())
return num_words, prefix, num_bpe_tokens
def get_prefix(sentence, prefix_len):
"""assuming no bpe, gets the prefix of the sentence with prefix_len words"""
tokens = sentence.strip("\n").split()
if prefix_len >= len(tokens):
return sentence.strip("\n")
else:
return " ".join(tokens[:prefix_len])
def get_prefix_no_bpe(sentence, bpe_symbol, prefix_len):
if bpe_symbol is None:
return get_prefix(sentence, prefix_len)
else:
return " ".join(get_prefix_from_len(sentence.split(), bpe_symbol, prefix_len))
def get_prefix_from_len(sentence, bpe_symbol, prefix_len):
"""get the prefix of sentence with bpe, with prefix len in terms of words, not bpe tokens"""
bpe_count = sum([bpe_symbol.strip(" ") in t for t in sentence[:prefix_len]])
if bpe_count == 0:
return sentence[:prefix_len]
else:
return sentence[:prefix_len] + get_prefix_from_len(
sentence[prefix_len:], bpe_symbol, bpe_count
)
def get_num_bpe_tokens_from_len(sentence, bpe_symbol, prefix_len):
"""given a prefix length in terms of words, return the number of bpe tokens"""
prefix = get_prefix_no_bpe(sentence, bpe_symbol, prefix_len)
assert len(remove_bpe(prefix, bpe_symbol).split()) <= prefix_len
return len(prefix.split(" "))
def make_right_to_left(line):
tokens = line.split()
tokens.reverse()
new_line = " ".join(tokens)
return new_line
def remove_bpe(line, bpe_symbol):
line = line.replace("\n", "")
line = (line + " ").replace(bpe_symbol, "").rstrip()
return line + ("\n")
def remove_bpe_dict(pred_dict, bpe_symbol):
new_dict = {}
for i in pred_dict:
if type(pred_dict[i]) == list:
new_list = [remove_bpe(elem, bpe_symbol) for elem in pred_dict[i]]
new_dict[i] = new_list
else:
new_dict[i] = remove_bpe(pred_dict[i], bpe_symbol)
return new_dict
def parse_bleu_scoring(line):
p = re.compile(r"(BLEU4 = )\d+[.]\d+")
res = re.search(p, line)
assert res is not None, line
return float(res.group()[8:])
def get_full_from_prefix(hypo_prefix, hypos):
"""given a hypo prefix, recover the first hypo from the list of complete hypos beginning with that prefix"""
for hypo in hypos:
hypo_prefix = hypo_prefix.strip("\n")
len_prefix = len(hypo_prefix)
if hypo[:len_prefix] == hypo_prefix:
return hypo
# no match found
raise Exception()
def get_score(
a,
b,
c,
target_len,
bitext_score1,
bitext_score2=None,
lm_score=None,
lenpen=None,
src_len=None,
tgt_len=None,
bitext1_backwards=False,
bitext2_backwards=False,
normalize=False,
):
if bitext1_backwards:
bitext1_norm = src_len
else:
bitext1_norm = tgt_len
if bitext_score2 is not None:
if bitext2_backwards:
bitext2_norm = src_len
else:
bitext2_norm = tgt_len
else:
bitext2_norm = 1
bitext_score2 = 0
if normalize:
score = (
a * bitext_score1 / bitext1_norm
+ b * bitext_score2 / bitext2_norm
+ c * lm_score / src_len
)
else:
score = a * bitext_score1 + b * bitext_score2 + c * lm_score
if lenpen is not None:
score /= (target_len) ** float(lenpen)
return score
class BitextOutput(object):
def __init__(
self,
output_file,
backwards,
right_to_left,
bpe_symbol,
prefix_len=None,
target_prefix_frac=None,
source_prefix_frac=None,
):
"""process output from rescoring"""
source, hypo, score, target, pos_score = reprocess(output_file)
if backwards:
self.hypo_fracs = source_prefix_frac
else:
self.hypo_fracs = target_prefix_frac
# remove length penalty so we can use raw scores
score, num_bpe_tokens = get_score_from_pos(
pos_score, prefix_len, hypo, bpe_symbol, self.hypo_fracs, backwards
)
source_lengths = {}
target_lengths = {}
assert hypo.keys() == source.keys(), "key mismatch"
if backwards:
tmp = hypo
hypo = source
source = tmp
for i in source:
# since we are reranking, there should only be one hypo per source sentence
if backwards:
len_src = len(source[i][0].split())
# record length without <eos>
if len_src == num_bpe_tokens[i][0] - 1:
source_lengths[i] = num_bpe_tokens[i][0] - 1
else:
source_lengths[i] = num_bpe_tokens[i][0]
target_lengths[i] = len(hypo[i].split())
source[i] = remove_bpe(source[i][0], bpe_symbol)
target[i] = remove_bpe(target[i], bpe_symbol)
hypo[i] = remove_bpe(hypo[i], bpe_symbol)
score[i] = float(score[i][0])
pos_score[i] = pos_score[i][0]
else:
len_tgt = len(hypo[i][0].split())
# record length without <eos>
if len_tgt == num_bpe_tokens[i][0] - 1:
target_lengths[i] = num_bpe_tokens[i][0] - 1
else:
target_lengths[i] = num_bpe_tokens[i][0]
source_lengths[i] = len(source[i].split())
if right_to_left:
source[i] = remove_bpe(make_right_to_left(source[i]), bpe_symbol)
target[i] = remove_bpe(make_right_to_left(target[i]), bpe_symbol)
hypo[i] = remove_bpe(make_right_to_left(hypo[i][0]), bpe_symbol)
score[i] = float(score[i][0])
pos_score[i] = pos_score[i][0]
else:
assert (
len(hypo[i]) == 1
), "expected only one hypothesis per source sentence"
source[i] = remove_bpe(source[i], bpe_symbol)
target[i] = remove_bpe(target[i], bpe_symbol)
hypo[i] = remove_bpe(hypo[i][0], bpe_symbol)
score[i] = float(score[i][0])
pos_score[i] = pos_score[i][0]
self.rescore_source = source
self.rescore_hypo = hypo
self.rescore_score = score
self.rescore_target = target
self.rescore_pos_score = pos_score
self.backwards = backwards
self.right_to_left = right_to_left
self.target_lengths = target_lengths
self.source_lengths = source_lengths
class BitextOutputFromGen(object):
def __init__(
self,
predictions_bpe_file,
bpe_symbol=None,
nbest=False,
prefix_len=None,
target_prefix_frac=None,
):
if nbest:
(
pred_source,
pred_hypo,
pred_score,
pred_target,
pred_pos_score,
) = reprocess_nbest(predictions_bpe_file)
else:
pred_source, pred_hypo, pred_score, pred_target, pred_pos_score = reprocess(
predictions_bpe_file
)
assert len(pred_source) == len(pred_hypo)
assert len(pred_source) == len(pred_score)
assert len(pred_source) == len(pred_target)
assert len(pred_source) == len(pred_pos_score)
# remove length penalty so we can use raw scores
pred_score, num_bpe_tokens = get_score_from_pos(
pred_pos_score, prefix_len, pred_hypo, bpe_symbol, target_prefix_frac, False
)
self.source = pred_source
self.target = pred_target
self.score = pred_score
self.pos_score = pred_pos_score
self.hypo = pred_hypo
self.target_lengths = {}
self.source_lengths = {}
self.no_bpe_source = remove_bpe_dict(pred_source.copy(), bpe_symbol)
self.no_bpe_hypo = remove_bpe_dict(pred_hypo.copy(), bpe_symbol)
self.no_bpe_target = remove_bpe_dict(pred_target.copy(), bpe_symbol)
# indexes to match those from the rescoring models
self.rescore_source = {}
self.rescore_target = {}
self.rescore_pos_score = {}
self.rescore_hypo = {}
self.rescore_score = {}
self.num_hypos = {}
self.backwards = False
self.right_to_left = False
index = 0
for i in sorted(pred_source.keys()):
for j in range(len(pred_hypo[i])):
self.target_lengths[index] = len(self.hypo[i][j].split())
self.source_lengths[index] = len(self.source[i].split())
self.rescore_source[index] = self.no_bpe_source[i]
self.rescore_target[index] = self.no_bpe_target[i]
self.rescore_hypo[index] = self.no_bpe_hypo[i][j]
self.rescore_score[index] = float(pred_score[i][j])
self.rescore_pos_score[index] = pred_pos_score[i][j]
self.num_hypos[index] = len(pred_hypo[i])
index += 1
def get_score_from_pos(
pos_score_dict, prefix_len, hypo_dict, bpe_symbol, hypo_frac, backwards
):
score_dict = {}
num_bpe_tokens_dict = {}
assert prefix_len is None or hypo_frac is None
for key in pos_score_dict:
score_dict[key] = []
num_bpe_tokens_dict[key] = []
for i in range(len(pos_score_dict[key])):
if prefix_len is not None and not backwards:
num_bpe_tokens = get_num_bpe_tokens_from_len(
hypo_dict[key][i], bpe_symbol, prefix_len
)
score_dict[key].append(sum(pos_score_dict[key][i][:num_bpe_tokens]))
num_bpe_tokens_dict[key].append(num_bpe_tokens)
elif hypo_frac is not None:
num_words, shortened, hypo_prefix_len = calc_length_from_frac(
hypo_dict[key][i], hypo_frac, bpe_symbol
)
score_dict[key].append(sum(pos_score_dict[key][i][:hypo_prefix_len]))
num_bpe_tokens_dict[key].append(hypo_prefix_len)
else:
score_dict[key].append(sum(pos_score_dict[key][i]))
num_bpe_tokens_dict[key].append(len(pos_score_dict[key][i]))
return score_dict, num_bpe_tokens_dict
class LMOutput(object):
def __init__(
self,
lm_score_file,
lm_dict=None,
prefix_len=None,
bpe_symbol=None,
target_prefix_frac=None,
):
(
lm_sentences,
lm_sen_scores,
lm_sen_pos_scores,
lm_no_bpe_sentences,
lm_bpe_tokens,
) = parse_lm(
lm_score_file,
prefix_len=prefix_len,
bpe_symbol=bpe_symbol,
target_prefix_frac=target_prefix_frac,
)
self.sentences = lm_sentences
self.score = lm_sen_scores
self.pos_score = lm_sen_pos_scores
self.lm_dict = lm_dict
self.no_bpe_sentences = lm_no_bpe_sentences
self.bpe_tokens = lm_bpe_tokens
def parse_lm(input_file, prefix_len=None, bpe_symbol=None, target_prefix_frac=None):
"""parse output of eval_lm"""
with open(input_file, "r") as f:
text = f.readlines()
text = text[7:]
cleaned_text = text[:-2]
sentences = {}
sen_scores = {}
sen_pos_scores = {}
no_bpe_sentences = {}
num_bpe_tokens_dict = {}
for _i, line in enumerate(cleaned_text):
tokens = line.split()
if tokens[0].isdigit():
line_id = int(tokens[0])
scores = [float(x[1:-1]) for x in tokens[2::2]]
sentences[line_id] = " ".join(tokens[1::2][:-1]) + "\n"
if bpe_symbol is not None:
# exclude <eos> symbol to match output from generate.py
bpe_sen = " ".join(tokens[1::2][:-1]) + "\n"
no_bpe_sen = remove_bpe(bpe_sen, bpe_symbol)
no_bpe_sentences[line_id] = no_bpe_sen
if prefix_len is not None:
num_bpe_tokens = get_num_bpe_tokens_from_len(
bpe_sen, bpe_symbol, prefix_len
)
sen_scores[line_id] = sum(scores[:num_bpe_tokens])
num_bpe_tokens_dict[line_id] = num_bpe_tokens
elif target_prefix_frac is not None:
num_words, shortened, target_prefix_len = calc_length_from_frac(
bpe_sen, target_prefix_frac, bpe_symbol
)
sen_scores[line_id] = sum(scores[:target_prefix_len])
num_bpe_tokens_dict[line_id] = target_prefix_len
else:
sen_scores[line_id] = sum(scores)
num_bpe_tokens_dict[line_id] = len(scores)
sen_pos_scores[line_id] = scores
return sentences, sen_scores, sen_pos_scores, no_bpe_sentences, num_bpe_tokens_dict
def get_directories(
data_dir_name,
num_rescore,
gen_subset,
fw_name,
shard_id,
num_shards,
sampling=False,
prefix_len=None,
target_prefix_frac=None,
source_prefix_frac=None,
):
nbest_file_id = (
"nbest_"
+ str(num_rescore)
+ "_subset_"
+ gen_subset
+ "_fw_name_"
+ fw_name
+ "_shard_"
+ str(shard_id)
+ "_of_"
+ str(num_shards)
)
if sampling:
nbest_file_id += "_sampling"
# the directory containing all information for this nbest list
pre_gen = (
os.path.join(os.path.dirname(__file__))
+ "/rerank_data/"
+ data_dir_name
+ "/"
+ nbest_file_id
)
# the directory to store the preprocessed nbest list, for left to right rescoring
left_to_right_preprocessed_dir = pre_gen + "/left_to_right_preprocessed"
if source_prefix_frac is not None:
left_to_right_preprocessed_dir = (
left_to_right_preprocessed_dir + "/prefix_frac" + str(source_prefix_frac)
)
# the directory to store the preprocessed nbest list, for right to left rescoring
right_to_left_preprocessed_dir = pre_gen + "/right_to_left_preprocessed"
# the directory to store the preprocessed nbest list, for backwards rescoring
backwards_preprocessed_dir = pre_gen + "/backwards"
if target_prefix_frac is not None:
backwards_preprocessed_dir = (
backwards_preprocessed_dir + "/prefix_frac" + str(target_prefix_frac)
)
elif prefix_len is not None:
backwards_preprocessed_dir = (
backwards_preprocessed_dir + "/prefix_" + str(prefix_len)
)
# the directory to store the preprocessed nbest list, for rescoring with P(T)
lm_preprocessed_dir = pre_gen + "/lm_preprocessed"
return (
pre_gen,
left_to_right_preprocessed_dir,
right_to_left_preprocessed_dir,
backwards_preprocessed_dir,
lm_preprocessed_dir,
)
def lm_scoring(
preprocess_directory,
bpe_status,
gen_output,
pre_gen,
cur_lm_dict,
cur_lm_name,
cur_language_model,
cur_lm_bpe_code,
batch_size,
lm_score_file,
target_lang,
source_lang,
prefix_len=None,
):
if prefix_len is not None:
assert (
bpe_status == "different"
), "bpe status must be different to use prefix len"
if bpe_status == "no bpe":
# run lm on output without bpe
write_reprocessed(
gen_output.no_bpe_source,
gen_output.no_bpe_hypo,
gen_output.no_bpe_target,
pre_gen + "/rescore_data_no_bpe.de",
pre_gen + "/rescore_data_no_bpe.en",
pre_gen + "/reference_file_no_bpe",
)
preprocess_lm_param = [
"--only-source",
"--trainpref",
pre_gen + "/rescore_data_no_bpe." + target_lang,
"--srcdict",
cur_lm_dict,
"--destdir",
preprocess_directory,
]
preprocess_parser = options.get_preprocessing_parser()
input_args = preprocess_parser.parse_args(preprocess_lm_param)
preprocess.main(input_args)
eval_lm_param = [
preprocess_directory,
"--path",
cur_language_model,
"--output-word-probs",
"--batch-size",
str(batch_size),
"--max-tokens",
"1024",
"--sample-break-mode",
"eos",
"--gen-subset",
"train",
]
eval_lm_parser = options.get_eval_lm_parser()
input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param)
with open(lm_score_file, "w") as f:
with redirect_stdout(f):
eval_lm.main(input_args)
elif bpe_status == "shared":
preprocess_lm_param = [
"--only-source",
"--trainpref",
pre_gen + "/rescore_data." + target_lang,
"--srcdict",
cur_lm_dict,
"--destdir",
preprocess_directory,
]
preprocess_parser = options.get_preprocessing_parser()
input_args = preprocess_parser.parse_args(preprocess_lm_param)
preprocess.main(input_args)
eval_lm_param = [
preprocess_directory,
"--path",
cur_language_model,
"--output-word-probs",
"--batch-size",
str(batch_size),
"--sample-break-mode",
"eos",
"--gen-subset",
"train",
]
eval_lm_parser = options.get_eval_lm_parser()
input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param)
with open(lm_score_file, "w") as f:
with redirect_stdout(f):
eval_lm.main(input_args)
elif bpe_status == "different":
rescore_file = pre_gen + "/rescore_data_no_bpe"
rescore_bpe = pre_gen + "/rescore_data_new_bpe"
rescore_file += "."
rescore_bpe += "."
write_reprocessed(
gen_output.no_bpe_source,
gen_output.no_bpe_hypo,
gen_output.no_bpe_target,
rescore_file + source_lang,
rescore_file + target_lang,
pre_gen + "/reference_file_no_bpe",
bpe_symbol=None,
)
# apply LM bpe to nbest list
bpe_src_param = [
"-c",
cur_lm_bpe_code,
"--input",
rescore_file + target_lang,
"--output",
rescore_bpe + target_lang,
]
subprocess.call(
[
"python",
os.path.join(
os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py"
),
]
+ bpe_src_param,
shell=False,
)
# uncomment to use fastbpe instead of subword-nmt bpe
# bpe_src_param = [rescore_bpe+target_lang, rescore_file+target_lang, cur_lm_bpe_code]
# subprocess.call(["/private/home/edunov/fastBPE/fast", "applybpe"] + bpe_src_param, shell=False)
preprocess_dir = preprocess_directory
preprocess_lm_param = [
"--only-source",
"--trainpref",
rescore_bpe + target_lang,
"--srcdict",
cur_lm_dict,
"--destdir",
preprocess_dir,
]
preprocess_parser = options.get_preprocessing_parser()
input_args = preprocess_parser.parse_args(preprocess_lm_param)
preprocess.main(input_args)
eval_lm_param = [
preprocess_dir,
"--path",
cur_language_model,
"--output-word-probs",
"--batch-size",
str(batch_size),
"--max-tokens",
"1024",
"--sample-break-mode",
"eos",
"--gen-subset",
"train",
]
eval_lm_parser = options.get_eval_lm_parser()
input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param)
with open(lm_score_file, "w") as f:
with redirect_stdout(f):
eval_lm.main(input_args)
def rescore_file_name(
nbest_dir,
prefix_len,
scorer_name,
lm_file=False,
target_prefix_frac=None,
source_prefix_frac=None,
backwards=None,
):
if lm_file:
score_file = nbest_dir + "/lm_score_translations_model_" + scorer_name + ".txt"
else:
score_file = nbest_dir + "/" + scorer_name + "_score_translations.txt"
if backwards:
if prefix_len is not None:
score_file += "prefix_len" + str(prefix_len)
elif target_prefix_frac is not None:
score_file += "target_prefix_frac" + str(target_prefix_frac)
else:
if source_prefix_frac is not None:
score_file += "source_prefix_frac" + str(source_prefix_frac)
return score_file
|
COCO-LM/fairseq/examples/noisychannel/rerank_utils.py/0
|
{
"file_path": "COCO-LM/fairseq/examples/noisychannel/rerank_utils.py",
"repo_id": "COCO-LM",
"token_count": 14992
}
| 171 |
# RoBERTa: A Robustly Optimized BERT Pretraining Approach
https://arxiv.org/abs/1907.11692
## Introduction
RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. See the associated paper for more details.
### What's New:
- December 2020: German model (GottBERT) is available: [GottBERT](https://github.com/pytorch/fairseq/tree/master/examples/gottbert).
- January 2020: Italian model (UmBERTo) is available from Musixmatch Research: [UmBERTo](https://github.com/musixmatchresearch/umberto).
- November 2019: French model (CamemBERT) is available: [CamemBERT](https://github.com/pytorch/fairseq/tree/master/examples/camembert).
- November 2019: Multilingual encoder (XLM-RoBERTa) is available: [XLM-R](https://github.com/pytorch/fairseq/tree/master/examples/xlmr).
- September 2019: TensorFlow and TPU support via the [transformers library](https://github.com/huggingface/transformers).
- August 2019: RoBERTa is now supported in the [pytorch-transformers library](https://github.com/huggingface/pytorch-transformers).
- August 2019: Added [tutorial for finetuning on WinoGrande](https://github.com/pytorch/fairseq/tree/master/examples/roberta/wsc#roberta-training-on-winogrande-dataset).
- August 2019: Added [tutorial for pretraining RoBERTa using your own data](README.pretraining.md).
## Pre-trained models
Model | Description | # params | Download
---|---|---|---
`roberta.base` | RoBERTa using the BERT-base architecture | 125M | [roberta.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz)
`roberta.large` | RoBERTa using the BERT-large architecture | 355M | [roberta.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz)
`roberta.large.mnli` | `roberta.large` finetuned on [MNLI](http://www.nyu.edu/projects/bowman/multinli) | 355M | [roberta.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz)
`roberta.large.wsc` | `roberta.large` finetuned on [WSC](wsc/README.md) | 355M | [roberta.large.wsc.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz)
## Results
**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)**
_(dev set, single model, single-task finetuning)_
Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B
---|---|---|---|---|---|---|---|---
`roberta.base` | 87.6 | 92.8 | 91.9 | 78.7 | 94.8 | 90.2 | 63.6 | 91.2
`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4
`roberta.large.mnli` | 90.2 | - | - | - | - | - | - | -
**[SuperGLUE (Wang et al., 2019)](https://super.gluebenchmark.com/)**
_(dev set, single model, single-task finetuning)_
Model | BoolQ | CB | COPA | MultiRC | RTE | WiC | WSC
---|---|---|---|---|---|---|---
`roberta.large` | 86.9 | 98.2 | 94.0 | 85.7 | 89.5 | 75.6 | -
`roberta.large.wsc` | - | - | - | - | - | - | 91.3
**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)**
_(dev set, no additional data used)_
Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1
---|---|---
`roberta.large` | 88.9/94.6 | 86.5/89.4
**[RACE (Lai et al., 2017)](http://www.qizhexie.com/data/RACE_leaderboard.html)**
_(test set)_
Model | Accuracy | Middle | High
---|---|---|---
`roberta.large` | 83.2 | 86.5 | 81.3
**[HellaSwag (Zellers et al., 2019)](https://rowanzellers.com/hellaswag/)**
_(test set)_
Model | Overall | In-domain | Zero-shot | ActivityNet | WikiHow
---|---|---|---|---|---
`roberta.large` | 85.2 | 87.3 | 83.1 | 74.6 | 90.9
**[Commonsense QA (Talmor et al., 2019)](https://www.tau-nlp.org/commonsenseqa)**
_(test set)_
Model | Accuracy
---|---
`roberta.large` (single model) | 72.1
`roberta.large` (ensemble) | 72.5
**[Winogrande (Sakaguchi et al., 2019)](https://arxiv.org/abs/1907.10641)**
_(test set)_
Model | Accuracy
---|---
`roberta.large` | 78.1
**[XNLI (Conneau et al., 2018)](https://arxiv.org/abs/1809.05053)**
_(TRANSLATE-TEST)_
Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
`roberta.large.mnli` | 91.3 | 82.91 | 84.27 | 81.24 | 81.74 | 83.13 | 78.28 | 76.79 | 76.64 | 74.17 | 74.05 | 77.5 | 70.9 | 66.65 | 66.81
## Example usage
##### Load RoBERTa from torch.hub (PyTorch >= 1.1):
```python
import torch
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
roberta.eval() # disable dropout (or leave in train mode to finetune)
```
##### Load RoBERTa (for PyTorch 1.0 or custom models):
```python
# Download roberta.large model
wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
tar -xzvf roberta.large.tar.gz
# Load the model in fairseq
from fairseq.models.roberta import RobertaModel
roberta = RobertaModel.from_pretrained('/path/to/roberta.large', checkpoint_file='model.pt')
roberta.eval() # disable dropout (or leave in train mode to finetune)
```
##### Apply Byte-Pair Encoding (BPE) to input text:
```python
tokens = roberta.encode('Hello world!')
assert tokens.tolist() == [0, 31414, 232, 328, 2]
roberta.decode(tokens) # 'Hello world!'
```
##### Extract features from RoBERTa:
```python
# Extract the last layer's features
last_layer_features = roberta.extract_features(tokens)
assert last_layer_features.size() == torch.Size([1, 5, 1024])
# Extract all layer's features (layer 0 is the embedding layer)
all_layers = roberta.extract_features(tokens, return_all_hiddens=True)
assert len(all_layers) == 25
assert torch.all(all_layers[-1] == last_layer_features)
```
##### Use RoBERTa for sentence-pair classification tasks:
```python
# Download RoBERTa already finetuned for MNLI
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
roberta.eval() # disable dropout for evaluation
# Encode a pair of sentences and make a prediction
tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.')
roberta.predict('mnli', tokens).argmax() # 0: contradiction
# Encode another pair of sentences
tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.')
roberta.predict('mnli', tokens).argmax() # 2: entailment
```
##### Register a new (randomly initialized) classification head:
```python
roberta.register_classification_head('new_task', num_classes=3)
logprobs = roberta.predict('new_task', tokens) # tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>)
```
##### Batched prediction:
```python
import torch
from fairseq.data.data_utils import collate_tokens
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
roberta.eval()
batch_of_pairs = [
['Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.'],
['Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.'],
['potatoes are awesome.', 'I like to run.'],
['Mars is very far from earth.', 'Mars is very close.'],
]
batch = collate_tokens(
[roberta.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1
)
logprobs = roberta.predict('mnli', batch)
print(logprobs.argmax(dim=1))
# tensor([0, 2, 1, 0])
```
##### Using the GPU:
```python
roberta.cuda()
roberta.predict('new_task', tokens) # tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
```
## Advanced usage
#### Filling masks:
RoBERTa can be used to fill `<mask>` tokens in the input. Some examples from the
[Natural Questions dataset](https://ai.google.com/research/NaturalQuestions/):
```python
roberta.fill_mask('The first Star wars movie came out in <mask>', topk=3)
# [('The first Star wars movie came out in 1977', 0.9504708051681519, ' 1977'), ('The first Star wars movie came out in 1978', 0.009986862540245056, ' 1978'), ('The first Star wars movie came out in 1979', 0.009574787691235542, ' 1979')]
roberta.fill_mask('Vikram samvat calender is official in <mask>', topk=3)
# [('Vikram samvat calender is official in India', 0.21878819167613983, ' India'), ('Vikram samvat calender is official in Delhi', 0.08547237515449524, ' Delhi'), ('Vikram samvat calender is official in Gujarat', 0.07556215673685074, ' Gujarat')]
roberta.fill_mask('<mask> is the common currency of the European Union', topk=3)
# [('Euro is the common currency of the European Union', 0.9456493854522705, 'Euro'), ('euro is the common currency of the European Union', 0.025748178362846375, 'euro'), ('€ is the common currency of the European Union', 0.011183084920048714, '€')]
```
#### Pronoun disambiguation (Winograd Schema Challenge):
RoBERTa can be used to disambiguate pronouns. First install spaCy and download the English-language model:
```bash
pip install spacy
python -m spacy download en_core_web_lg
```
Next load the `roberta.large.wsc` model and call the `disambiguate_pronoun`
function. The pronoun should be surrounded by square brackets (`[]`) and the
query referent surrounded by underscores (`_`), or left blank to return the
predicted candidate text directly:
```python
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.wsc', user_dir='examples/roberta/wsc')
roberta.cuda() # use the GPU (optional)
roberta.disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.')
# True
roberta.disambiguate_pronoun('The trophy would not fit in the brown _suitcase_ because [it] was too big.')
# False
roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] feared violence.')
# 'The city councilmen'
roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] advocated violence.')
# 'demonstrators'
```
See the [RoBERTA Winograd Schema Challenge (WSC) README](wsc/README.md) for more details on how to train this model.
#### Extract features aligned to words:
By default RoBERTa outputs one feature vector per BPE token. You can instead
realign the features to match [spaCy's word-level tokenization](https://spacy.io/usage/linguistic-features#tokenization)
with the `extract_features_aligned_to_words` method. This will compute a
weighted average of the BPE-level features for each word and expose them in
spaCy's `Token.vector` attribute:
```python
doc = roberta.extract_features_aligned_to_words('I said, "hello RoBERTa."')
assert len(doc) == 10
for tok in doc:
print('{:10}{} (...)'.format(str(tok), tok.vector[:5]))
# <s> tensor([-0.1316, -0.0386, -0.0832, -0.0477, 0.1943], grad_fn=<SliceBackward>) (...)
# I tensor([ 0.0559, 0.1541, -0.4832, 0.0880, 0.0120], grad_fn=<SliceBackward>) (...)
# said tensor([-0.1565, -0.0069, -0.8915, 0.0501, -0.0647], grad_fn=<SliceBackward>) (...)
# , tensor([-0.1318, -0.0387, -0.0834, -0.0477, 0.1944], grad_fn=<SliceBackward>) (...)
# " tensor([-0.0486, 0.1818, -0.3946, -0.0553, 0.0981], grad_fn=<SliceBackward>) (...)
# hello tensor([ 0.0079, 0.1799, -0.6204, -0.0777, -0.0923], grad_fn=<SliceBackward>) (...)
# RoBERTa tensor([-0.2339, -0.1184, -0.7343, -0.0492, 0.5829], grad_fn=<SliceBackward>) (...)
# . tensor([-0.1341, -0.1203, -0.1012, -0.0621, 0.1892], grad_fn=<SliceBackward>) (...)
# " tensor([-0.1341, -0.1203, -0.1012, -0.0621, 0.1892], grad_fn=<SliceBackward>) (...)
# </s> tensor([-0.0930, -0.0392, -0.0821, 0.0158, 0.0649], grad_fn=<SliceBackward>) (...)
```
#### Evaluating the `roberta.large.mnli` model:
Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set.
```python
label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'}
ncorrect, nsamples = 0, 0
roberta.cuda()
roberta.eval()
with open('glue_data/MNLI/dev_matched.tsv') as fin:
fin.readline()
for index, line in enumerate(fin):
tokens = line.strip().split('\t')
sent1, sent2, target = tokens[8], tokens[9], tokens[-1]
tokens = roberta.encode(sent1, sent2)
prediction = roberta.predict('mnli', tokens).argmax().item()
prediction_label = label_map[prediction]
ncorrect += int(prediction_label == target)
nsamples += 1
print('| Accuracy: ', float(ncorrect)/float(nsamples))
# Expected output: 0.9060
```
## Finetuning
- [Finetuning on GLUE](README.glue.md)
- [Finetuning on custom classification tasks (e.g., IMDB)](README.custom_classification.md)
- [Finetuning on Winograd Schema Challenge (WSC)](wsc/README.md)
- [Finetuning on Commonsense QA (CQA)](commonsense_qa/README.md)
## Pretraining using your own data
See the [tutorial for pretraining RoBERTa using your own data](README.pretraining.md).
## Citation
```bibtex
@article{liu2019roberta,
title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach},
author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and
Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and
Luke Zettlemoyer and Veselin Stoyanov},
journal={arXiv preprint arXiv:1907.11692},
year = {2019},
}
```
|
COCO-LM/fairseq/examples/roberta/README.md/0
|
{
"file_path": "COCO-LM/fairseq/examples/roberta/README.md",
"repo_id": "COCO-LM",
"token_count": 5008
}
| 172 |
[Better Fine-Tuning by Reducing Representational Collapse](https://arxiv.org/abs/2008.03156)
=====================
This repo contains the code to replicate all experiments from the _Better Fine-Tuning by Reducing Representational Collapse_ paper excluding the probing results.
The R3F sentence prediction criterion is registered as `sentence_prediction_r3f` while the label smoothing version of it is implemented as `label_smoothed_cross_entropy_r3f`. The R4F version of the sentence prediction criterion can be achieved by applying spectral norm to the classification head via the `--spectral-norm-classification-head` parameter.
## Hyper-parameters
Our methods introduce 3 new hyper-parameters; `--eps` which sets the standard deviation or range of the distribution we're sampling from, `--r3f-lambda` which controls the combining of logistic loss and noisy KL loss and `--noise-type` which controls which parametric distribution we use ('normal', 'uniform').
For example to run R3F on RTE from GLUE
```
TOTAL_NUM_UPDATES=3120
WARMUP_UPDATES=187
LR=1e-05
NUM_CLASSES=2
MAX_SENTENCES=8 # Batch size.
ROBERTA_PATH=/path/to/roberta/model.pt
CUDA_VISIBLE_DEVICES=0 fairseq-train RTE-bin \
--restore-file $ROBERTA_PATH \
--max-positions 512 \
--max-sentences $MAX_SENTENCES \
--max-tokens 4400 \
--task sentence_prediction \
--reset-optimizer --reset-dataloader --reset-meters \
--required-batch-size-multiple 1 \
--init-token 0 --separator-token 2 \
--arch roberta_large \
--criterion sentence_prediction_r3f \
--num-classes $NUM_CLASSES \
--dropout 0.1 --attention-dropout 0.1 \
--weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \
--clip-norm 0.0 \
--lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
--fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \
--max-epoch 10 \
--find-unused-parameters \
--best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \
--noise-type uniform --r3f-lambda 0.7 \
--user-dir examples/rxf/rxf_src
```
## Citation
```bibtex
@article{aghajanyan2020better,
title={Better Fine-Tuning by Reducing Representational Collapse},
author={Aghajanyan, Armen and Shrivastava, Akshat and Gupta, Anchit and Goyal, Naman and Zettlemoyer, Luke and Gupta, Sonal},
journal={arXiv preprint arXiv:2008.03156},
year={2020}
}
```
|
COCO-LM/fairseq/examples/rxf/README.md/0
|
{
"file_path": "COCO-LM/fairseq/examples/rxf/README.md",
"repo_id": "COCO-LM",
"token_count": 867
}
| 173 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
from agents import build_agent
from client import SimulSTEvaluationService, SimulSTLocalEvaluationService
from fairseq.registry import REGISTRIES
DEFAULT_HOSTNAME = "localhost"
DEFAULT_PORT = 12321
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--hostname", type=str, default=DEFAULT_HOSTNAME, help="server hostname"
)
parser.add_argument(
"--port", type=int, default=DEFAULT_PORT, help="server port number"
)
parser.add_argument("--agent-type", default="simul_trans_text", help="Agent type")
parser.add_argument("--scorer-type", default="text", help="Scorer type")
parser.add_argument(
"--start-idx",
type=int,
default=0,
help="Start index of the sentence to evaluate",
)
parser.add_argument(
"--end-idx",
type=int,
default=float("inf"),
help="End index of the sentence to evaluate",
)
parser.add_argument(
"--scores", action="store_true", help="Request scores from server"
)
parser.add_argument("--reset-server", action="store_true", help="Reset the server")
parser.add_argument(
"--num-threads", type=int, default=10, help="Number of threads used by agent"
)
parser.add_argument(
"--local", action="store_true", default=False, help="Local evaluation"
)
args, _ = parser.parse_known_args()
for registry_name, REGISTRY in REGISTRIES.items():
choice = getattr(args, registry_name, None)
if choice is not None:
cls = REGISTRY["registry"][choice]
if hasattr(cls, "add_args"):
cls.add_args(parser)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = get_args()
if args.local:
session = SimulSTLocalEvaluationService(args)
else:
session = SimulSTEvaluationService(args.hostname, args.port)
if args.reset_server:
session.new_session()
if args.agent_type is not None:
agent = build_agent(args)
agent.decode(session, args.start_idx, args.end_idx, args.num_threads)
if args.scores:
session.get_scores()
print(session.get_scores())
|
COCO-LM/fairseq/examples/simultaneous_translation/eval/evaluate.py/0
|
{
"file_path": "COCO-LM/fairseq/examples/simultaneous_translation/eval/evaluate.py",
"repo_id": "COCO-LM",
"token_count": 955
}
| 174 |
### 2021 Update: We are merging this example into the [S2T framework](../speech_to_text), which supports more generic speech-to-text tasks (e.g. speech translation) and more flexible data processing pipelines. Please stay tuned.
# Speech Recognition
`examples/speech_recognition` is implementing ASR task in Fairseq, along with needed features, datasets, models and loss functions to train and infer model described in [Transformers with convolutional context for ASR (Abdelrahman Mohamed et al., 2019)](https://arxiv.org/abs/1904.11660).
## Additional dependencies
On top of main fairseq dependencies there are couple more additional requirements.
1) Please follow the instructions to install [torchaudio](https://github.com/pytorch/audio). This is required to compute audio fbank features.
2) [Sclite](http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sclite.htm#sclite_name_0) is used to measure WER. Sclite can be downloaded and installed from source from sctk package [here](http://www.openslr.org/4/). Training and inference doesn't require Sclite dependency.
3) [sentencepiece](https://github.com/google/sentencepiece) is required in order to create dataset with word-piece targets.
## Preparing librispeech data
```
./examples/speech_recognition/datasets/prepare-librispeech.sh $DIR_TO_SAVE_RAW_DATA $DIR_FOR_PREPROCESSED_DATA
```
## Training librispeech data
```
python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 80 --task speech_recognition --arch vggtransformer_2 --optimizer adadelta --lr 1.0 --adadelta-eps 1e-8 --adadelta-rho 0.95 --clip-norm 10.0 --max-tokens 5000 --log-format json --log-interval 1 --criterion cross_entropy_acc --user-dir examples/speech_recognition/
```
## Inference for librispeech
`$SET` can be `test_clean` or `test_other`
Any checkpoint in `$MODEL_PATH` can be selected. In this example we are working with `checkpoint_last.pt`
```
python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --max-tokens 25000 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --beam 20 --results-path $RES_DIR --batch-size 40 --gen-subset $SET --user-dir examples/speech_recognition/
```
## Inference for librispeech
```
sclite -r ${RES_DIR}/ref.word-checkpoint_last.pt-${SET}.txt -h ${RES_DIR}/hypo.word-checkpoint_last.pt-${SET}.txt -i rm -o all stdout > $RES_REPORT
```
`Sum/Avg` row from first table of the report has WER
## Using flashlight (previously called [wav2letter](https://github.com/facebookresearch/wav2letter)) components
[flashlight](https://github.com/facebookresearch/flashlight) now has integration with fairseq. Currently this includes:
* AutoSegmentationCriterion (ASG)
* flashlight-style Conv/GLU model
* flashlight's beam search decoder
To use these, follow the instructions on [this page](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) to install python bindings.
## Training librispeech data (flashlight style, Conv/GLU + ASG loss)
Training command:
```
python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 100 --task speech_recognition --arch w2l_conv_glu_enc --batch-size 4 --optimizer sgd --lr 0.3,0.8 --momentum 0.8 --clip-norm 0.2 --max-tokens 50000 --log-format json --log-interval 100 --num-workers 0 --sentence-avg --criterion asg_loss --asg-transitions-init 5 --max-replabel 2 --linseg-updates 8789 --user-dir examples/speech_recognition
```
Note that ASG loss currently doesn't do well with word-pieces. You should prepare a dataset with character targets by setting `nbpe=31` in `prepare-librispeech.sh`.
## Inference for librispeech (flashlight decoder, n-gram LM)
Inference command:
```
python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder kenlm --kenlm-model $KENLM_MODEL_PATH --lexicon $LEXICON_PATH --beam 200 --beam-threshold 15 --lm-weight 1.5 --word-score 1.5 --sil-weight -0.3 --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
```
`$KENLM_MODEL_PATH` should be a standard n-gram language model file. `$LEXICON_PATH` should be a flashlight-style lexicon (list of known words and their spellings). For ASG inference, a lexicon line should look like this (note the repetition labels):
```
doorbell D O 1 R B E L 1 ▁
```
For CTC inference with word-pieces, repetition labels are not used and the lexicon should have most common spellings for each word (one can use sentencepiece's `NBestEncodeAsPieces` for this):
```
doorbell ▁DOOR BE LL
doorbell ▁DOOR B E LL
doorbell ▁DO OR BE LL
doorbell ▁DOOR B EL L
doorbell ▁DOOR BE L L
doorbell ▁DO OR B E LL
doorbell ▁DOOR B E L L
doorbell ▁DO OR B EL L
doorbell ▁DO O R BE LL
doorbell ▁DO OR BE L L
```
Lowercase vs. uppercase matters: the *word* should match the case of the n-gram language model (i.e. `$KENLM_MODEL_PATH`), while the *spelling* should match the case of the token dictionary (i.e. `$DIR_FOR_PREPROCESSED_DATA/dict.txt`).
## Inference for librispeech (flashlight decoder, viterbi only)
Inference command:
```
python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder viterbi --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
```
|
COCO-LM/fairseq/examples/speech_recognition/README.md/0
|
{
"file_path": "COCO-LM/fairseq/examples/speech_recognition/README.md",
"repo_id": "COCO-LM",
"token_count": 1780
}
| 175 |
#!/usr/bin/env python -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import ast
import hashlib
import logging
import os
import shutil
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
import editdistance
import torch
import torch.distributed as dist
from examples.speech_recognition.hydra.decoder import Decoder, DecoderConfig
from fairseq import (checkpoint_utils, distributed_utils, progress_bar, tasks,
utils)
from fairseq.data.data_utils import post_process
from fairseq.dataclass.configs import (CheckpointConfig, CommonConfig,
CommonEvalConfig, DatasetConfig,
DistributedTrainingConfig,
FairseqDataclass, GenerationConfig)
from fairseq.logging.meters import StopwatchMeter, TimeMeter
from fairseq.logging.progress_bar import BaseProgressBar
from fairseq.models.fairseq_model import FairseqModel
from omegaconf import MISSING, OmegaConf
import hydra
from hydra.core.config_store import ConfigStore
logging.root.setLevel(logging.INFO)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
config_path = Path(__file__).resolve().parent / "conf"
@dataclass
class DecodingConfig(FairseqDataclass):
exp_dir: str = field(
default=MISSING,
metadata={"help": "Path to the experiment directory"},
)
unique_wer_file: bool = field(
default=False,
metadata={"help": "If set, use a unique file for storing WER"},
)
write_sentences: bool = field(
default=True,
metadata={"help": "If set, write hypothesis and reference sentences"},
)
decoder: DecoderConfig = DecoderConfig()
@dataclass
class InferConfig(FairseqDataclass):
task: Any = None
decoding: DecodingConfig = DecodingConfig()
common: CommonConfig = CommonConfig()
common_eval: CommonEvalConfig = CommonEvalConfig()
checkpoint: CheckpointConfig = CheckpointConfig()
generation: GenerationConfig = GenerationConfig()
distributed_training: DistributedTrainingConfig = DistributedTrainingConfig()
dataset: DatasetConfig = DatasetConfig()
def reset_logging():
root = logging.getLogger()
for handler in root.handlers:
root.removeHandler(handler)
root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper())
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(
logging.Formatter(
fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
)
root.addHandler(handler)
class InferenceProcessor:
def __init__(self, cfg: InferConfig) -> None:
self.cfg = cfg
self.task = tasks.setup_task(cfg.task)
self.tgt_dict = self.task.target_dictionary
models, saved_cfg = self.load_model_ensemble()
self.models = models
self.saved_cfg = saved_cfg
self.task.load_dataset(
self.cfg.dataset.gen_subset,
task_cfg=saved_cfg.task,
)
self.generator = Decoder(cfg.decoding.decoder, self.tgt_dict)
self.gen_timer = StopwatchMeter()
self.wps_meter = TimeMeter()
self.num_sentences = 0
self.total_errors = 0
self.total_length = 0
self.hypo_words_file = None
self.hypo_units_file = None
self.ref_words_file = None
self.ref_units_file = None
self.progress_bar = self.build_progress_bar()
def __enter__(self) -> "InferenceProcessor":
if self.cfg.decoding.write_sentences:
self.hypo_words_file = self.get_res_file("hypo.word")
self.hypo_units_file = self.get_res_file("hypo.units")
self.ref_words_file = self.get_res_file("ref.word")
self.ref_units_file = self.get_res_file("ref.units")
return self
def __exit__(self, *exc) -> bool:
if self.cfg.decoding.write_sentences:
self.hypo_words_file.close()
self.hypo_units_file.close()
self.ref_words_file.close()
self.ref_units_file.close()
return False
def __iter__(self) -> Any:
for sample in self.progress_bar:
if not self.cfg.common.cpu:
sample = utils.move_to_cuda(sample)
# Happens on the last batch.
if "net_input" not in sample:
continue
yield sample
def log(self, *args, **kwargs):
self.progress_bar.log(*args, **kwargs)
def print(self, *args, **kwargs):
self.progress_bar.print(*args, **kwargs)
def get_res_file(self, fname: str) -> None:
if self.data_parallel_world_size > 1:
fname = f"{fname}.{self.data_parallel_rank}"
return open(fname, "w", buffering=1)
def merge_shards(self) -> None:
"""Merges all shard files into shard 0, then removes shard suffix."""
shard_id = self.data_parallel_rank
num_shards = self.data_parallel_world_size
def merge_shards_with_root(fname: str) -> None:
logger.info("Merging %s on shard %d", fname, shard_id)
base_fpath = Path(f"{fname}.0")
with open(base_fpath, "a") as out_file:
for s in range(1, num_shards):
shard_fpath = Path(f"{fname}.{s}")
with open(shard_fpath, "r") as in_file:
for line in in_file:
out_file.write(line)
shard_fpath.unlink()
shutil.move(f"{fname}.0", fname)
if shard_id == (0 % num_shards):
merge_shards_with_root("hypo.word")
if shard_id == (1 % num_shards):
merge_shards_with_root("hypo.units")
if shard_id == (2 % num_shards):
merge_shards_with_root("ref.word")
if shard_id == (3 % num_shards):
merge_shards_with_root("ref.units")
dist.barrier()
def optimize_model(self, model: FairseqModel) -> None:
gcfg = self.cfg.generation
model.make_generation_fast_(
beamable_mm_beam_size=None if gcfg.no_beamable_mm else gcfg.beam,
need_attn=gcfg.print_alignment,
)
if self.cfg.common.fp16:
model.half()
if not self.cfg.common.cpu:
model.cuda()
def load_model_ensemble(self) -> Tuple[List[FairseqModel], FairseqDataclass]:
arg_overrides = ast.literal_eval(self.cfg.common_eval.model_overrides)
models, saved_cfg = checkpoint_utils.load_model_ensemble(
utils.split_paths(self.cfg.common_eval.path),
arg_overrides=arg_overrides,
task=self.task,
suffix=self.cfg.checkpoint.checkpoint_suffix,
strict=(self.cfg.checkpoint.checkpoint_shard_count == 1),
num_shards=self.cfg.checkpoint.checkpoint_shard_count,
)
for model in models:
self.optimize_model(model)
return models, saved_cfg
def get_dataset_itr(self, disable_iterator_cache: bool = False) -> None:
return self.task.get_batch_iterator(
dataset=self.task.dataset(self.cfg.dataset.gen_subset),
max_tokens=self.cfg.dataset.max_tokens,
max_sentences=self.cfg.dataset.batch_size,
max_positions=(sys.maxsize, sys.maxsize),
ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple,
seed=self.cfg.common.seed,
num_shards=self.data_parallel_world_size,
shard_id=self.data_parallel_rank,
num_workers=self.cfg.dataset.num_workers,
data_buffer_size=self.cfg.dataset.data_buffer_size,
disable_iterator_cache=disable_iterator_cache,
).next_epoch_itr(shuffle=False)
def build_progress_bar(
self,
epoch: Optional[int] = None,
prefix: Optional[str] = None,
default_log_format: str = "tqdm",
) -> BaseProgressBar:
return progress_bar.progress_bar(
iterator=self.get_dataset_itr(),
log_format=self.cfg.common.log_format,
log_interval=self.cfg.common.log_interval,
epoch=epoch,
prefix=prefix,
tensorboard_logdir=self.cfg.common.tensorboard_logdir,
default_log_format=default_log_format,
)
@property
def data_parallel_world_size(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 1
return distributed_utils.get_data_parallel_world_size()
@property
def data_parallel_rank(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 0
return distributed_utils.get_data_parallel_rank()
def process_sentence(
self,
sample: Dict[str, Any],
hypo: Dict[str, Any],
sid: int,
batch_id: int,
) -> Tuple[int, int]:
speaker = None # Speaker can't be parsed from dataset.
if "target_label" in sample:
toks = sample["target_label"]
else:
toks = sample["target"]
toks = toks[batch_id, :]
# Processes hypothesis.
hyp_pieces = self.tgt_dict.string(hypo["tokens"].int().cpu())
if "words" in hypo:
hyp_words = " ".join(hypo["words"])
else:
hyp_words = post_process(hyp_pieces,
self.cfg.common_eval.post_process)
# Processes target.
target_tokens = utils.strip_pad(toks, self.tgt_dict.pad())
tgt_pieces = self.tgt_dict.string(target_tokens.int().cpu())
tgt_words = post_process(tgt_pieces,
self.cfg.common_eval.post_process)
if self.cfg.decoding.write_sentences:
print(f"{hyp_pieces} ({speaker}-{sid})", file=self.hypo_units_file)
print(f"{hyp_words} ({speaker}-{sid})", file=self.hypo_words_file)
print(f"{tgt_pieces} ({speaker}-{sid})", file=self.ref_units_file)
print(f"{tgt_words} ({speaker}-{sid})", file=self.ref_words_file)
hyp_words, tgt_words = hyp_words.split(), tgt_words.split()
return editdistance.eval(hyp_words, tgt_words), len(tgt_words)
def process_sample(self, sample: Dict[str, Any]) -> None:
self.gen_timer.start()
hypos = self.task.inference_step(
generator=self.generator,
models=self.models,
sample=sample,
)
num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
self.gen_timer.stop(num_generated_tokens)
self.wps_meter.update(num_generated_tokens)
for batch_id, sample_id in enumerate(sample["id"].tolist()):
errs, length = self.process_sentence(
sample=sample,
sid=sample_id,
batch_id=batch_id,
hypo=hypos[batch_id][0],
)
self.total_errors += errs
self.total_length += length
self.log({"wps": round(self.wps_meter.avg)})
if "nsentences" in sample:
self.num_sentences += sample["nsentences"]
else:
self.num_sentences += sample["id"].numel()
def log_generation_time(self) -> None:
logger.info("Processed %d sentences (%d tokens) in %.1fs %.2f "
"sentences per second, %.2f tokens per second)",
self.num_sentences, self.gen_timer.n, self.gen_timer.sum,
self.num_sentences / self.gen_timer.sum,
1.0 / self.gen_timer.avg)
def parse_wer(wer_file: Path) -> float:
with open(wer_file, "r") as f:
return float(f.readline().strip().split(" ")[1])
def get_wer_file(cfg: InferConfig) -> Path:
"""Hashes the decoding parameters to a unique file ID."""
if cfg.decoding.unique_wer_file:
yaml_str = OmegaConf.to_yaml(cfg.decoding)
fid = int(hashlib.md5(yaml_str.encode("utf-8")).hexdigest(), 16)
return Path(f"wer.{fid % 1000000}")
else:
return Path("wer")
def main(cfg: InferConfig) -> float:
"""Entry point for main processing logic.
Args:
cfg: The inferance configuration to use.
wer: Optional shared memory pointer for returning the WER. If not None,
the final WER value will be written here instead of being returned.
Returns:
The final WER if `wer` is None, otherwise None.
"""
yaml_str, wer_file = OmegaConf.to_yaml(cfg.decoding), get_wer_file(cfg)
# Validates the provided configuration.
if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
cfg.dataset.max_tokens = 4000000
if not cfg.common.cpu and not torch.cuda.is_available():
raise ValueError("CUDA not found; set `cpu=True` to run without CUDA")
if cfg.generation.nbest > 1:
raise ValueError("`nbest > 1` not implemented yet")
with InferenceProcessor(cfg) as processor:
for sample in processor:
processor.process_sample(sample)
processor.log_generation_time()
if cfg.decoding.write_sentences:
processor.merge_shards()
errs_t, leng_t = processor.total_errors, processor.total_length
if cfg.common.cpu:
logger.warning("Merging WER requires CUDA.")
else:
stats = torch.LongTensor([errs_t, leng_t]).cuda()
dist.all_reduce(stats, op=dist.ReduceOp.SUM)
errs_t, leng_t = stats[0].item(), stats[1].item()
wer = errs_t * 100.0 / leng_t
if distributed_utils.is_master(cfg.distributed_training):
with open(wer_file, "w") as f:
f.write(f"WER: {wer}\n\n{yaml_str}")
return wer
@hydra.main(config_path=config_path, config_name="infer")
def hydra_main(cfg: InferConfig) -> None:
container = OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)
cfg = OmegaConf.create(container)
OmegaConf.set_struct(cfg, True)
if cfg.common.reset_logging:
reset_logging()
logger.info("Config:\n%s", OmegaConf.to_yaml(cfg))
logger.info("Working directory: %s", Path.cwd())
wer = float("inf")
try:
if cfg.common.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, main)
else:
distributed_utils.call_main(cfg, main)
wer = parse_wer(get_wer_file(cfg))
except BaseException as e: # pylint: disable=broad-except
if not cfg.common.suppress_crashes:
raise
else:
logger.error("Crashed! %s", str(e))
logger.info("Word error rate: %.4f", wer)
return wer
def cli_main() -> None:
try:
from hydra._internal.utils import \
get_args # pylint: disable=import-outside-toplevel
cfg_name = get_args().config_name or "infer"
except ImportError:
logger.warning("Failed to get config name from hydra args")
cfg_name = "infer"
cs = ConfigStore.instance()
cs.store(name=cfg_name, node=InferConfig)
for k in InferConfig.__dataclass_fields__:
v = InferConfig.__dataclass_fields__[k].default
try:
cs.store(name=k, node=v)
except BaseException:
logger.error(f"{k} - {v}")
raise
hydra_main() # pylint: disable=no-value-for-parameter
if __name__ == "__main__":
cli_main()
|
COCO-LM/fairseq/examples/speech_recognition/hydra/infer.py/0
|
{
"file_path": "COCO-LM/fairseq/examples/speech_recognition/hydra/infer.py",
"repo_id": "COCO-LM",
"token_count": 7328
}
| 176 |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
from pathlib import Path
import shutil
from tempfile import NamedTemporaryFile
from typing import Optional, Tuple
import pandas as pd
import torchaudio
from examples.speech_to_text.data_utils import (
create_zip,
extract_fbank_features,
filter_manifest_df,
gen_config_yaml,
gen_vocab,
get_zip_manifest,
load_df_from_tsv,
save_df_to_tsv,
)
from torch import Tensor
from torch.utils.data import Dataset
from torchaudio.datasets.utils import download_url, extract_archive
from tqdm import tqdm
log = logging.getLogger(__name__)
MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"]
class CoVoST(Dataset):
"""Create a Dataset for CoVoST (https://github.com/facebookresearch/covost).
Args:
root (str): root path to the dataset and generated manifests/features
source_language (str): source (audio) language
target_language (str, optional): target (text) language,
None for no translation (default: None)
version (int, optional): CoVoST version. (default: 2)
download (bool, optional): Whether to download the dataset if it is not
found at root path. (default: ``False``).
"""
COVOST_URL_TEMPLATE = (
"https://dl.fbaipublicfiles.com/covost/"
"covost_v2.{src_lang}_{tgt_lang}.tsv.tar.gz"
)
VERSIONS = {2}
SPLITS = ["train", "dev", "test"]
XX_EN_LANGUAGES = {
1: ["fr", "de", "nl", "ru", "es", "it", "tr", "fa", "sv-SE", "mn", "zh-CN"],
2: [
"fr",
"de",
"es",
"ca",
"it",
"ru",
"zh-CN",
"pt",
"fa",
"et",
"mn",
"nl",
"tr",
"ar",
"sv-SE",
"lv",
"sl",
"ta",
"ja",
"id",
"cy",
],
}
EN_XX_LANGUAGES = {
1: [],
2: [
"de",
"tr",
"fa",
"sv-SE",
"mn",
"zh-CN",
"cy",
"ca",
"sl",
"et",
"id",
"ar",
"ta",
"lv",
"ja",
],
}
def __init__(
self,
root: str,
split: str,
source_language: str,
target_language: Optional[str] = None,
version: int = 2,
) -> None:
assert version in self.VERSIONS and split in self.SPLITS
assert source_language is not None
self.no_translation = target_language is None
if not self.no_translation:
assert "en" in {source_language, target_language}
if source_language == "en":
assert target_language in self.EN_XX_LANGUAGES[version]
else:
assert source_language in self.XX_EN_LANGUAGES[version]
else:
# Hack here so that we can get "split" column from CoVoST TSV.
# Note that we use CoVoST train split for ASR which is an extension
# to Common Voice train split.
target_language = "de" if source_language == "en" else "en"
self.root: Path = Path(root)
cv_tsv_path = self.root / "validated.tsv"
assert cv_tsv_path.is_file()
covost_url = self.COVOST_URL_TEMPLATE.format(
src_lang=source_language, tgt_lang=target_language
)
covost_archive = self.root / Path(covost_url).name
if not covost_archive.is_file():
download_url(covost_url, self.root.as_posix(), hash_value=None)
extract_archive(covost_archive.as_posix())
cv_tsv = load_df_from_tsv(cv_tsv_path)
covost_tsv = load_df_from_tsv(
self.root / Path(covost_url).name.replace(".tar.gz", "")
)
df = pd.merge(
left=cv_tsv[["path", "sentence", "client_id"]],
right=covost_tsv[["path", "translation", "split"]],
how="inner",
on="path",
)
if split == "train":
df = df[(df["split"] == split) | (df["split"] == f"{split}_covost")]
else:
df = df[df["split"] == split]
data = df.to_dict(orient="index").items()
data = [v for k, v in sorted(data, key=lambda x: x[0])]
self.data = []
for e in data:
try:
path = self.root / "clips" / e["path"]
_ = torchaudio.info(path.as_posix())
self.data.append(e)
except RuntimeError:
pass
def __getitem__(
self, n: int
) -> Tuple[Tensor, int, str, str, Optional[str], str, str]:
"""Load the n-th sample from the dataset.
Args:
n (int): The index of the sample to be loaded
Returns:
tuple: ``(waveform, sample_rate, sentence, translation, speaker_id,
sample_id)``
"""
data = self.data[n]
path = self.root / "clips" / data["path"]
waveform, sample_rate = torchaudio.load(path)
sentence = data["sentence"]
translation = None if self.no_translation else data["translation"]
speaker_id = data["client_id"]
_id = data["path"].replace(".mp3", "")
return waveform, sample_rate, sentence, translation, speaker_id, _id
def __len__(self) -> int:
return len(self.data)
def process(args):
root = Path(args.data_root).absolute() / args.src_lang
if not root.is_dir():
raise NotADirectoryError(f"{root} does not exist")
# Extract features
feature_root = root / "fbank80"
feature_root.mkdir(exist_ok=True)
for split in CoVoST.SPLITS:
print(f"Fetching split {split}...")
dataset = CoVoST(root, split, args.src_lang, args.tgt_lang)
print("Extracting log mel filter bank features...")
for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset):
extract_fbank_features(
waveform, sample_rate, feature_root / f"{utt_id}.npy"
)
# Pack features into ZIP
zip_path = root / "fbank80.zip"
print("ZIPing features...")
create_zip(feature_root, zip_path)
print("Fetching ZIP manifest...")
zip_manifest = get_zip_manifest(zip_path)
# Generate TSV manifest
print("Generating manifest...")
train_text = []
task = f"asr_{args.src_lang}"
if args.tgt_lang is not None:
task = f"st_{args.src_lang}_{args.tgt_lang}"
for split in CoVoST.SPLITS:
manifest = {c: [] for c in MANIFEST_COLUMNS}
dataset = CoVoST(root, split, args.src_lang, args.tgt_lang)
for wav, sr, src_utt, tgt_utt, speaker_id, utt_id in tqdm(dataset):
manifest["id"].append(utt_id)
manifest["audio"].append(zip_manifest[utt_id])
duration_ms = int(wav.size(1) / sr * 1000)
manifest["n_frames"].append(int(1 + (duration_ms - 25) / 10))
manifest["tgt_text"].append(src_utt if args.tgt_lang is None else tgt_utt)
manifest["speaker"].append(speaker_id)
is_train_split = split.startswith("train")
if is_train_split:
train_text.extend(manifest["tgt_text"])
df = pd.DataFrame.from_dict(manifest)
df = filter_manifest_df(df, is_train_split=is_train_split)
save_df_to_tsv(df, root / f"{split}_{task}.tsv")
# Generate vocab
vocab_size_str = "" if args.vocab_type == "char" else str(args.vocab_size)
spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size_str}_{task}"
with NamedTemporaryFile(mode="w") as f:
for t in train_text:
f.write(t + "\n")
gen_vocab(
Path(f.name),
root / spm_filename_prefix,
args.vocab_type,
args.vocab_size
)
# Generate config YAML
gen_config_yaml(
root,
spm_filename_prefix + ".model",
yaml_filename=f"config_{task}.yaml",
specaugment_policy="lb",
)
# Clean up
shutil.rmtree(feature_root)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--data-root", "-d", required=True, type=str,
help="data root with sub-folders for each language <root>/<src_lang>"
)
parser.add_argument(
"--vocab-type",
default="unigram",
required=True,
type=str,
choices=["bpe", "unigram", "char"],
),
parser.add_argument("--vocab-size", default=1000, type=int)
parser.add_argument("--src-lang", "-s", required=True, type=str)
parser.add_argument("--tgt-lang", "-t", type=str)
args = parser.parse_args()
process(args)
if __name__ == "__main__":
main()
|
COCO-LM/fairseq/examples/speech_to_text/prep_covost_data.py/0
|
{
"file_path": "COCO-LM/fairseq/examples/speech_to_text/prep_covost_data.py",
"repo_id": "COCO-LM",
"token_count": 4360
}
| 177 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
class MeanPoolGatingNetwork(torch.nn.Module):
"""A simple mean-pooling gating network for selecting experts.
This module applies mean pooling over an encoder's output and returns
reponsibilities for each expert. The encoder format is expected to match
:class:`fairseq.models.transformer.TransformerEncoder`.
"""
def __init__(self, embed_dim, num_experts, dropout=None):
super().__init__()
self.embed_dim = embed_dim
self.num_experts = num_experts
self.fc1 = torch.nn.Linear(embed_dim, embed_dim)
self.dropout = torch.nn.Dropout(dropout) if dropout is not None else None
self.fc2 = torch.nn.Linear(embed_dim, num_experts)
def forward(self, encoder_out):
if not (
"encoder_out" in encoder_out
and "encoder_padding_mask" in encoder_out
and encoder_out["encoder_out"][0].size(2) == self.embed_dim
):
raise ValueError("Unexpected format for encoder_out")
# mean pooling over time
encoder_padding_mask = encoder_out["encoder_padding_mask"][0] # B x T
encoder_out = encoder_out["encoder_out"][0].transpose(0, 1) # B x T x C
if encoder_padding_mask is not None:
encoder_out = encoder_out.clone() # required because of transpose above
encoder_out[encoder_padding_mask] = 0
ntokens = torch.sum(~encoder_padding_mask, dim=1, keepdim=True)
x = torch.sum(encoder_out, dim=1) / ntokens.type_as(encoder_out)
else:
x = torch.mean(encoder_out, dim=1)
x = torch.tanh(self.fc1(x))
if self.dropout is not None:
x = self.dropout(x)
x = self.fc2(x)
return F.log_softmax(x, dim=-1, dtype=torch.float32).type_as(x)
|
COCO-LM/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py/0
|
{
"file_path": "COCO-LM/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py",
"repo_id": "COCO-LM",
"token_count": 860
}
| 178 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
import os
import sys
try:
from .version import __version__ # noqa
except ImportError:
version_txt = os.path.join(os.path.dirname(__file__), "version.txt")
with open(version_txt) as f:
__version__ = f.read().strip()
__all__ = ["pdb"]
# backwards compatibility to support `from fairseq.X import Y`
from fairseq.distributed import utils as distributed_utils
from fairseq.logging import meters, metrics, progress_bar # noqa
sys.modules["fairseq.distributed_utils"] = distributed_utils
sys.modules["fairseq.meters"] = meters
sys.modules["fairseq.metrics"] = metrics
sys.modules["fairseq.progress_bar"] = progress_bar
# initialize hydra
from fairseq.dataclass.initialize import hydra_init
hydra_init()
import fairseq.criterions # noqa
import fairseq.distributed # noqa
import fairseq.models # noqa
import fairseq.modules # noqa
import fairseq.optim # noqa
import fairseq.optim.lr_scheduler # noqa
import fairseq.pdb # noqa
import fairseq.scoring # noqa
import fairseq.tasks # noqa
import fairseq.token_generation_constraints # noqa
import fairseq.benchmark # noqa
import fairseq.model_parallel # noqa
|
COCO-LM/fairseq/fairseq/__init__.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/__init__.py",
"repo_id": "COCO-LM",
"token_count": 434
}
| 179 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq import utils
from fairseq.criterions import LegacyFairseqCriterion, register_criterion
from torch import nn
@register_criterion("composite_loss")
class CompositeLoss(LegacyFairseqCriterion):
"""This is a composite loss that, given a list of model outputs and a list of targets,
computes an average of losses for each output-target pair"""
def __init__(self, args, task):
super().__init__(args, task)
self.underlying_criterion = args.underlying_criterion
@staticmethod
def add_args(parser):
"""Add criterion-specific arguments to the parser."""
# fmt: off
parser.add_argument('--underlying-criterion', type=str, metavar='VAL', required=True,
help='underlying criterion to use for the composite loss')
# fmt: on
@staticmethod
def build_underlying_criterion(args, task):
saved_criterion = args.criterion
args.criterion = args.underlying_criterion
assert saved_criterion != args.underlying_criterion
underlying_criterion = task.build_criterion(args)
args.criterion = saved_criterion
return underlying_criterion
@classmethod
def build_criterion(cls, args, task):
underlying_criterion = CompositeLoss.build_underlying_criterion(args, task)
class FakeModel(nn.Module):
def __init__(self, model, net_out, target):
super().__init__()
self.model = model
self.net_out = net_out
self.target = target
def forward(self, **unused):
return self.net_out
def get_normalized_probs(self, net_output, log_probs, sample=None):
return self.model.get_normalized_probs(
net_output, log_probs, sample=sample
)
def get_targets(self, *unused):
return self.target
@property
def decoder(self):
return self.model.decoder
class _CompositeLoss(LegacyFairseqCriterion):
def __init__(self, args, task, underlying_criterion):
super().__init__(args, task)
self.underlying_criterion = underlying_criterion
def forward(self, model, sample, reduce=True):
net_outputs = model(**sample["net_input"])
targets = sample["target"]
bsz = targets[0].size(0)
loss = net_outputs[0][0].new(1 if reduce else bsz).float().zero_()
sample_size = 0
logging_output = {}
for o, t in zip(net_outputs[0], targets):
m = FakeModel(model, (o, net_outputs[1]), t)
sample["target"] = t
l, ss, logging_output = self.underlying_criterion(m, sample, reduce)
loss += l
sample_size += ss
loss.div_(len(targets))
sample_size /= len(targets)
logging_output["loss"] = utils.item(loss.data) if reduce else loss.data
return loss, sample_size, logging_output
@staticmethod
def aggregate_logging_outputs(logging_outputs):
return underlying_criterion.__class__.aggregate_logging_outputs(
logging_outputs
)
@staticmethod
def reduce_metrics(logging_outputs) -> None:
underlying_criterion.__class__.reduce_metrics(logging_outputs)
return _CompositeLoss(args, task, underlying_criterion)
|
COCO-LM/fairseq/fairseq/criterions/composite_loss.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/criterions/composite_loss.py",
"repo_id": "COCO-LM",
"token_count": 1734
}
| 180 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import BaseWrapperDataset, data_utils
class AddTargetDataset(BaseWrapperDataset):
def __init__(
self,
dataset,
labels,
pad,
eos,
batch_targets,
process_label=None,
add_to_input=False,
):
super().__init__(dataset)
self.labels = labels
self.batch_targets = batch_targets
self.pad = pad
self.eos = eos
self.process_label = process_label
self.add_to_input = add_to_input
def get_label(self, index):
return (
self.labels[index]
if self.process_label is None
else self.process_label(self.labels[index])
)
def __getitem__(self, index):
item = self.dataset[index]
item["label"] = self.get_label(index)
return item
def size(self, index):
sz = self.dataset.size(index)
own_sz = len(self.get_label(index))
return (sz, own_sz)
def collater(self, samples):
collated = self.dataset.collater(samples)
if len(collated) == 0:
return collated
indices = set(collated["id"].tolist())
target = [s["label"] for s in samples if s["id"] in indices]
if self.batch_targets:
collated["target_lengths"] = torch.LongTensor([len(t) for t in target])
target = data_utils.collate_tokens(target, pad_idx=self.pad, left_pad=False)
collated["ntokens"] = collated["target_lengths"].sum().item()
else:
collated["ntokens"] = sum([len(t) for t in target])
collated["target"] = target
if self.add_to_input:
eos = target.new_full((target.size(0), 1), self.eos)
collated["target"] = torch.cat([target, eos], dim=-1).long()
collated["net_input"]["prev_output_tokens"] = torch.cat(
[eos, target], dim=-1
).long()
collated["ntokens"] += target.size(0)
return collated
|
COCO-LM/fairseq/fairseq/data/add_target_dataset.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/data/add_target_dataset.py",
"repo_id": "COCO-LM",
"token_count": 1035
}
| 181 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
try:
from collections.abc import Iterable
except ImportError:
from collections import Iterable
import contextlib
import itertools
import logging
import os
import warnings
from typing import Optional, Tuple
import numpy as np
import torch
from fairseq.file_io import PathManager
logger = logging.getLogger(__name__)
def infer_language_pair(path):
"""Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
src, dst = None, None
for filename in PathManager.ls(path):
parts = filename.split(".")
if len(parts) >= 3 and len(parts[1].split("-")) == 2:
return parts[1].split("-")
return src, dst
def collate_tokens(
values,
pad_idx,
eos_idx=None,
left_pad=False,
move_eos_to_beginning=False,
pad_to_length=None,
pad_to_multiple=1,
):
"""Convert a list of 1d tensors into a padded 2d tensor."""
size = max(v.size(0) for v in values)
size = size if pad_to_length is None else max(size, pad_to_length)
if pad_to_multiple != 1 and size % pad_to_multiple != 0:
size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
res = values[0].new(len(values), size).fill_(pad_idx)
def copy_tensor(src, dst):
assert dst.numel() == src.numel()
if move_eos_to_beginning:
if eos_idx is None:
# if no eos_idx is specified, then use the last token in src
dst[0] = src[-1]
else:
dst[0] = eos_idx
dst[1:] = src[:-1]
else:
dst.copy_(src)
for i, v in enumerate(values):
copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)])
return res
def load_indexed_dataset(
path, dictionary=None, dataset_impl=None, combine=False, default="cached"
):
"""A helper function for loading indexed datasets.
Args:
path (str): path to indexed dataset (e.g., 'data-bin/train')
dictionary (~fairseq.data.Dictionary): data dictionary
dataset_impl (str, optional): which dataset implementation to use. If
not provided, it will be inferred automatically. For legacy indexed
data we use the 'cached' implementation by default.
combine (bool, optional): automatically load and combine multiple
datasets. For example, if *path* is 'data-bin/train', then we will
combine 'data-bin/train', 'data-bin/train1', ... and return a
single ConcatDataset instance.
"""
import fairseq.data.indexed_dataset as indexed_dataset
from fairseq.data.concat_dataset import ConcatDataset
datasets = []
for k in itertools.count():
path_k = path + (str(k) if k > 0 else "")
try:
path_k = indexed_dataset.get_indexed_dataset_to_local(path_k)
except Exception as e:
if "StorageException: [404] Path not found" in str(e):
logger.warning(f"path_k: {e} not found")
else:
raise e
dataset_impl_k = dataset_impl
if dataset_impl_k is None:
dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
dataset = indexed_dataset.make_dataset(
path_k,
impl=dataset_impl_k or default,
fix_lua_indexing=True,
dictionary=dictionary,
)
if dataset is None:
break
logger.info("loaded {:,} examples from: {}".format(len(dataset), path_k))
datasets.append(dataset)
if not combine:
break
if len(datasets) == 0:
return None
elif len(datasets) == 1:
return datasets[0]
else:
return ConcatDataset(datasets)
@contextlib.contextmanager
def numpy_seed(seed, *addl_seeds):
"""Context manager which seeds the NumPy PRNG with the specified seed and
restores the state afterward"""
if seed is None:
yield
return
if len(addl_seeds) > 0:
seed = int(hash((seed, *addl_seeds)) % 1e6)
state = np.random.get_state()
np.random.seed(seed)
try:
yield
finally:
np.random.set_state(state)
def collect_filtered(function, iterable, filtered):
"""
Similar to :func:`filter` but collects filtered elements in ``filtered``.
Args:
function (callable): function that returns ``False`` for elements that
should be filtered
iterable (iterable): iterable to filter
filtered (list): list to store filtered elements
"""
for el in iterable:
if function(el):
yield el
else:
filtered.append(el)
def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False):
def compare_leq(a, b):
return a <= b if not isinstance(a, tuple) else max(a) <= b
def check_size(idx):
if isinstance(max_positions, float) or isinstance(max_positions, int):
return size_fn(idx) <= max_positions
elif isinstance(max_positions, dict):
idx_size = size_fn(idx)
assert isinstance(idx_size, dict)
intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
return all(
all(
a is None or b is None or a <= b
for a, b in zip(idx_size[key], max_positions[key])
)
for key in intersect_keys
)
else:
# For MultiCorpusSampledDataset, will generalize it later
if not isinstance(size_fn(idx), Iterable):
return all(size_fn(idx) <= b for b in max_positions)
return all(
a is None or b is None or a <= b
for a, b in zip(size_fn(idx), max_positions)
)
ignored = []
itr = collect_filtered(check_size, indices, ignored)
indices = np.fromiter(itr, dtype=np.int64, count=-1)
return indices, ignored
def filter_by_size(indices, dataset, max_positions, raise_exception=False):
"""
[deprecated] Filter indices based on their size.
Use `FairseqDataset::filter_indices_by_size` instead.
Args:
indices (List[int]): ordered list of dataset indices
dataset (FairseqDataset): fairseq dataset instance
max_positions (tuple): filter elements larger than this size.
Comparisons are done component-wise.
raise_exception (bool, optional): if ``True``, raise an exception if
any elements are filtered (default: False).
"""
warnings.warn(
"data_utils.filter_by_size is deprecated. "
"Use `FairseqDataset::filter_indices_by_size` instead.",
stacklevel=2,
)
if isinstance(max_positions, float) or isinstance(max_positions, int):
if hasattr(dataset, "sizes") and isinstance(dataset.sizes, np.ndarray):
ignored = indices[dataset.sizes[indices] > max_positions].tolist()
indices = indices[dataset.sizes[indices] <= max_positions]
elif (
hasattr(dataset, "sizes")
and isinstance(dataset.sizes, list)
and len(dataset.sizes) == 1
):
ignored = indices[dataset.sizes[0][indices] > max_positions].tolist()
indices = indices[dataset.sizes[0][indices] <= max_positions]
else:
indices, ignored = _filter_by_size_dynamic(
indices, dataset.size, max_positions
)
else:
indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)
if len(ignored) > 0 and raise_exception:
raise Exception(
(
"Size of sample #{} is invalid (={}) since max_positions={}, "
"skip this example with --skip-invalid-size-inputs-valid-test"
).format(ignored[0], dataset.size(ignored[0]), max_positions)
)
if len(ignored) > 0:
logger.warning(
(
"{} samples have invalid sizes and will be skipped, "
"max_positions={}, first few sample ids={}"
).format(len(ignored), max_positions, ignored[:10])
)
return indices
def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes):
"""Filter a list of sample indices. Remove those that are longer
than specified in max_sizes.
Args:
indices (np.array): original array of sample indices
max_sizes (int or list[int] or tuple[int]): max sample size,
can be defined separately for src and tgt (then list or tuple)
Returns:
np.array: filtered sample array
list: list of removed indices
"""
if max_sizes is None:
return indices, []
if type(max_sizes) in (int, float):
max_src_size, max_tgt_size = max_sizes, max_sizes
else:
max_src_size, max_tgt_size = max_sizes
if tgt_sizes is None:
ignored = indices[src_sizes[indices] > max_src_size]
else:
ignored = indices[
(src_sizes[indices] > max_src_size) | (tgt_sizes[indices] > max_tgt_size)
]
if len(ignored) > 0:
if tgt_sizes is None:
indices = indices[src_sizes[indices] <= max_src_size]
else:
indices = indices[
(src_sizes[indices] <= max_src_size)
& (tgt_sizes[indices] <= max_tgt_size)
]
return indices, ignored.tolist()
def batch_by_size(
indices,
num_tokens_fn,
num_tokens_vec=None,
max_tokens=None,
max_sentences=None,
required_batch_size_multiple=1,
fixed_shapes=None,
):
"""
Yield mini-batches of indices bucketed by size. Batches may contain
sequences of different lengths.
Args:
indices (List[int]): ordered list of dataset indices
num_tokens_fn (callable): function that returns the number of tokens at
a given index
num_tokens_vec (List[int], optional): precomputed vector of the number
of tokens for each index in indices (to enable faster batch generation)
max_tokens (int, optional): max number of tokens in each batch
(default: None).
max_sentences (int, optional): max number of sentences in each
batch (default: None).
required_batch_size_multiple (int, optional): require batch size to
be less than N or a multiple of N (default: 1).
fixed_shapes (List[Tuple[int, int]], optional): if given, batches will
only be created with the given shapes. *max_sentences* and
*required_batch_size_multiple* will be ignored (default: None).
"""
try:
from fairseq.data.data_utils_fast import (
batch_by_size_fn,
batch_by_size_vec,
batch_fixed_shapes_fast,
)
except ImportError:
raise ImportError(
"Please build Cython components with: `pip install --editable .` "
"or `python setup.py build_ext --inplace`"
)
except ValueError:
raise ValueError(
"Please build (or rebuild) Cython components with: `pip install "
" --editable .` or `python setup.py build_ext --inplace`."
)
# added int() to avoid TypeError: an integer is required
max_tokens = (
int(max_tokens) if max_tokens is not None else -1
)
max_sentences = max_sentences if max_sentences is not None else -1
bsz_mult = required_batch_size_multiple
if not isinstance(indices, np.ndarray):
indices = np.fromiter(indices, dtype=np.int64, count=-1)
if num_tokens_vec is not None and not isinstance(num_tokens_vec, np.ndarray):
num_tokens_vec = np.fromiter(num_tokens_vec, dtype=np.int64, count=-1)
if fixed_shapes is None:
if num_tokens_vec is None:
return batch_by_size_fn(
indices,
num_tokens_fn,
max_tokens,
max_sentences,
bsz_mult,
)
else:
return batch_by_size_vec(
indices,
num_tokens_vec,
max_tokens,
max_sentences,
bsz_mult,
)
else:
fixed_shapes = np.array(fixed_shapes, dtype=np.int64)
sort_order = np.lexsort(
[
fixed_shapes[:, 1].argsort(), # length
fixed_shapes[:, 0].argsort(), # bsz
]
)
fixed_shapes_sorted = fixed_shapes[sort_order]
return batch_fixed_shapes_fast(indices, num_tokens_fn, fixed_shapes_sorted)
def post_process(sentence: str, symbol: str):
if symbol == "sentencepiece":
sentence = sentence.replace(" ", "").replace("\u2581", " ").strip()
elif symbol == "wordpiece":
sentence = sentence.replace(" ", "").replace("_", " ").strip()
elif symbol == "letter":
sentence = sentence.replace(" ", "").replace("|", " ").strip()
elif symbol == "_EOW":
sentence = sentence.replace(" ", "").replace("_EOW", " ").strip()
elif symbol in {"subword_nmt", "@@ ", "@@"}:
if symbol == "subword_nmt":
symbol = "@@ "
sentence = (sentence + " ").replace(symbol, "").rstrip()
elif symbol == "none":
pass
elif symbol is not None:
raise NotImplementedError(f"Unknown post_process option: {symbol}")
return sentence
def compute_mask_indices(
shape: Tuple[int, int],
padding_mask: Optional[torch.Tensor],
mask_prob: float,
mask_length: int,
mask_type: str = "static",
mask_other: float = 0.0,
min_masks: int = 0,
no_overlap: bool = False,
min_space: int = 0,
) -> np.ndarray:
"""
Computes random mask spans for a given shape
Args:
shape: the the shape for which to compute masks.
should be of size 2 where first element is batch size and 2nd is timesteps
padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
however due to overlaps, the actual number will be smaller (unless no_overlap is True)
mask_type: how to compute mask lengths
static = fixed size
uniform = sample from uniform distribution [mask_other, mask_length*2]
normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
poisson = sample from possion distribution with lambda = mask length
min_masks: minimum number of masked spans
no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
"""
bsz, all_sz = shape
mask = np.full((bsz, all_sz), False)
all_num_mask = int(
# add a random number for probabilistic rounding
mask_prob * all_sz / float(mask_length)
+ np.random.rand()
)
all_num_mask = max(min_masks, all_num_mask)
mask_idcs = []
for i in range(bsz):
if padding_mask is not None:
sz = all_sz - padding_mask[i].long().sum().item()
num_mask = int(
# add a random number for probabilistic rounding
mask_prob * sz / float(mask_length)
+ np.random.rand()
)
num_mask = max(min_masks, num_mask)
else:
sz = all_sz
num_mask = all_num_mask
if mask_type == "static":
lengths = np.full(num_mask, mask_length)
elif mask_type == "uniform":
lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
elif mask_type == "normal":
lengths = np.random.normal(mask_length, mask_other, size=num_mask)
lengths = [max(1, int(round(x))) for x in lengths]
elif mask_type == "poisson":
lengths = np.random.poisson(mask_length, size=num_mask)
lengths = [int(round(x)) for x in lengths]
else:
raise Exception("unknown mask selection " + mask_type)
if sum(lengths) == 0:
lengths[0] = min(mask_length, sz - 1)
if no_overlap:
mask_idc = []
def arrange(s, e, length, keep_length):
span_start = np.random.randint(s, e - length)
mask_idc.extend(span_start + i for i in range(length))
new_parts = []
if span_start - s - min_space >= keep_length:
new_parts.append((s, span_start - min_space + 1))
if e - span_start - keep_length - min_space > keep_length:
new_parts.append((span_start + length + min_space, e))
return new_parts
parts = [(0, sz)]
min_length = min(lengths)
for length in sorted(lengths, reverse=True):
lens = np.fromiter(
(e - s if e - s >= length + min_space else 0 for s, e in parts),
np.int,
)
l_sum = np.sum(lens)
if l_sum == 0:
break
probs = lens / np.sum(lens)
c = np.random.choice(len(parts), p=probs)
s, e = parts.pop(c)
parts.extend(arrange(s, e, length, min_length))
mask_idc = np.asarray(mask_idc)
else:
min_len = min(lengths)
if sz - min_len <= num_mask:
min_len = sz - num_mask - 1
mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
mask_idc = np.asarray(
[
mask_idc[j] + offset
for j in range(len(mask_idc))
for offset in range(lengths[j])
]
)
mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
min_len = min([len(m) for m in mask_idcs])
for i, mask_idc in enumerate(mask_idcs):
if len(mask_idc) > min_len:
mask_idc = np.random.choice(mask_idc, min_len, replace=False)
mask[i, mask_idc] = True
return mask
def get_mem_usage():
try:
import psutil
mb = 1024 * 1024
return f"used={psutil.virtual_memory().used / mb}Mb; avail={psutil.virtual_memory().available / mb}Mb"
except ImportError:
return "N/A"
def lengths_to_padding_mask(lens: torch.LongTensor) -> torch.BoolTensor:
bsz, max_lens = lens.size(0), torch.max(lens).item()
mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
return mask
def lengths_to_mask(lens: torch.LongTensor) -> torch.BoolTensor:
return ~lengths_to_padding_mask(lens)
def get_buckets(sizes, num_buckets):
buckets = np.unique(
np.percentile(
sizes,
np.linspace(0, 100, num_buckets + 1),
interpolation='lower',
)[1:]
)
return buckets
def get_bucketed_sizes(orig_sizes, buckets):
sizes = np.copy(orig_sizes)
assert np.min(sizes) >= 0
start_val = -1
for end_val in buckets:
mask = (sizes > start_val) & (sizes <= end_val)
sizes[mask] = end_val
start_val = end_val
return sizes
|
COCO-LM/fairseq/fairseq/data/data_utils.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/data/data_utils.py",
"repo_id": "COCO-LM",
"token_count": 9067
}
| 182 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.data.encoders import register_tokenizer
from fairseq.dataclass import FairseqDataclass
@register_tokenizer("nltk", dataclass=FairseqDataclass)
class NLTKTokenizer(object):
def __init__(self, *unused):
try:
from nltk.tokenize import word_tokenize
self.word_tokenize = word_tokenize
except ImportError:
raise ImportError("Please install nltk with: pip install nltk")
def encode(self, x: str) -> str:
return " ".join(self.word_tokenize(x))
def decode(self, x: str) -> str:
return x
|
COCO-LM/fairseq/fairseq/data/encoders/nltk_tokenizer.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/data/encoders/nltk_tokenizer.py",
"repo_id": "COCO-LM",
"token_count": 283
}
| 183 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from typing import Dict
from fairseq.data.monolingual_dataset import MonolingualDataset
from . import FairseqDataset
class LMContextWindowDataset(FairseqDataset):
"""
Wraps a MonolingualDataset and provides more context for evaluation.
Each item in the new dataset will have a maximum size of
``tokens_per_sample + context_window``.
Args:
dataset: dataset to wrap
tokens_per_sample (int): the max number of tokens in each dataset item
context_window (int): the number of accumulated tokens to add to each
dataset item
pad_idx (int): padding symbol
"""
def __init__(
self,
dataset: MonolingualDataset,
tokens_per_sample: int,
context_window: int,
pad_idx: int,
):
assert context_window > 0
self.dataset = dataset
self.tokens_per_sample = tokens_per_sample
self.context_window = context_window
self.pad_idx = pad_idx
self.prev_tokens = np.empty([0])
def __getitem__(self, index):
return self.dataset[index]
def __len__(self):
return len(self.dataset)
def collater(self, samples) -> Dict:
sample = self.dataset.collater(samples)
pad = self.pad_idx
max_sample_len = self.tokens_per_sample + self.context_window
bsz, tsz = sample["net_input"]["src_tokens"].shape
start_idxs = [0] * bsz
toks = sample["net_input"]["src_tokens"]
lengths = sample["net_input"]["src_lengths"]
tgt = sample["target"]
new_toks = np.empty([bsz, tsz + self.context_window], dtype=np.int64)
new_tgt = np.full([bsz, tsz + self.context_window], pad, dtype=np.int64)
sample_lens = toks.ne(pad).long().sum(dim=1).cpu()
for i in range(bsz):
sample_len = sample_lens[i]
extra = len(self.prev_tokens) + sample_len - max_sample_len
if extra > 0:
self.prev_tokens = self.prev_tokens[extra:]
pads = np.full(self.context_window - len(self.prev_tokens), pad)
new_toks[i] = np.concatenate([self.prev_tokens, toks[i].numpy(), pads])
new_tgt[
i, len(self.prev_tokens) : len(self.prev_tokens) + len(tgt[i])
] = tgt[i]
start_idxs[i] = len(self.prev_tokens)
lengths[i] += len(self.prev_tokens)
self.prev_tokens = new_toks[i][new_toks[i] != pad][-self.context_window :]
sample["net_input"]["src_tokens"] = torch.from_numpy(new_toks)
sample["target"] = torch.from_numpy(new_tgt)
sample["start_indices"] = start_idxs
return sample
def num_tokens(self, index):
return self.dataset.num_tokens(index)
def size(self, index):
return self.dataset.size(index)
def ordered_indices(self):
# NOTE we don't shuffle the data to retain access to the previous dataset elements
return np.arange(len(self.dataset))
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
return self.dataset.prefetch(indices)
|
COCO-LM/fairseq/fairseq/data/lm_context_window_dataset.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/data/lm_context_window_dataset.py",
"repo_id": "COCO-LM",
"token_count": 1516
}
| 184 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Very heavily inspired by the official evaluation script for SQuAD version 2.0 which was modified by XLNet authors to
update `find_best_threshold` scripts for SQuAD V2.0
In addition to basic functionality, we also compute additional statistics and plot precision-recall curves if an
additional na_prob.json file is provided. This file is expected to map question ID's to the model's predicted
probability that a question is unanswerable.
"""
import collections
import json
import math
import re
import string
import logging
logger = logging.getLogger(__name__)
from . import BasicTokenizer
#todo, check xl-net implementation https://github.com/google-research/albert/blob/master/squad_utils.py
class SquadResult:
"""
Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
Args:
unique_id: The unique identifier corresponding to that example.
start_logits: The logits corresponding to the start of the answer
end_logits: The logits corresponding to the end of the answer
"""
def __init__(self, unique_id, start_logits, end_logits):
self.start_logits = start_logits
self.end_logits = end_logits
self.unique_id = unique_id
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
return re.sub(regex, " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def get_tokens(s):
if not s:
return []
return normalize_answer(s).split()
def compute_exact(a_gold, a_pred):
return int(normalize_answer(a_gold) == normalize_answer(a_pred))
def compute_f1(a_gold, a_pred):
gold_toks = get_tokens(a_gold)
pred_toks = get_tokens(a_pred)
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if len(gold_toks) == 0 or len(pred_toks) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def get_raw_scores(examples, preds):
"""
Computes the exact and f1 scores from the examples and the model predictions
"""
exact_scores = {}
f1_scores = {}
for example in examples:
qas_id = example.qas_id
gold_answers = [answer["text"] for answer in example.answers if normalize_answer(answer["text"])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = [""]
if qas_id not in preds:
print("Missing prediction for %s" % qas_id)
continue
prediction = preds[qas_id]
exact_scores[qas_id] = max(compute_exact(a, prediction) for a in gold_answers)
f1_scores[qas_id] = max(compute_f1(a, prediction) for a in gold_answers)
return exact_scores, f1_scores
def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
new_scores = {}
for qid, s in scores.items():
pred_na = na_probs[qid] > na_prob_thresh
if pred_na:
new_scores[qid] = float(not qid_to_has_ans[qid])
else:
new_scores[qid] = s
return new_scores
def make_eval_dict(exact_scores, f1_scores, qid_list=None):
if not qid_list:
total = len(exact_scores)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values()) / total),
("f1", 100.0 * sum(f1_scores.values()) / total),
("total", total),
]
)
else:
total = len(qid_list)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total),
("f1", 100.0 * sum(f1_scores[k] for k in qid_list) / total),
("total", total),
]
)
def merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval["%s_%s" % (prefix, k)] = new_eval[k]
def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
has_ans_score, has_ans_cnt = 0, 0
for qid in qid_list:
if not qid_to_has_ans[qid]:
continue
has_ans_cnt += 1
if qid not in scores:
continue
has_ans_score += scores[qid]
return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval["best_exact"] = best_exact
main_eval["best_exact_thresh"] = exact_thresh
main_eval["best_f1"] = best_f1
main_eval["best_f1_thresh"] = f1_thresh
main_eval["has_ans_exact"] = has_ans_exact
main_eval["has_ans_f1"] = has_ans_f1
def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for _, qid in enumerate(qid_list):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
return 100.0 * best_score / len(scores), best_thresh
def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval["best_exact"] = best_exact
main_eval["best_exact_thresh"] = exact_thresh
main_eval["best_f1"] = best_f1
main_eval["best_f1_thresh"] = f1_thresh
def squad_evaluate(examples, preds, no_answer_probs=None, no_answer_probability_threshold=1.0):
qas_id_to_has_answer = {example.qas_id: bool(example.answers) for example in examples}
has_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if has_answer]
no_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if not has_answer]
if not no_answer_probs:
no_answer_probs = {k: 0.0 for k in preds}
exact, f1 = get_raw_scores(examples, preds)
exact_threshold = apply_no_ans_threshold(
exact, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold
)
f1_threshold = apply_no_ans_threshold(f1, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold)
evaluation = make_eval_dict(exact_threshold, f1_threshold)
if has_answer_qids:
has_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=has_answer_qids)
merge_eval(evaluation, has_ans_eval, "HasAns")
if no_answer_qids:
no_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=no_answer_qids)
merge_eval(evaluation, no_ans_eval, "NoAns")
if no_answer_probs:
find_all_best_thresh(evaluation, preds, exact, f1, no_answer_probs, qas_id_to_has_answer)
return evaluation
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heuristic between
# `pred_text` and `orig_text` to get a character-to-character alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for (i, c) in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose_logging:
logger.info("Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose_logging:
logger.info("Length not equal after stripping spaces: '%s' vs '%s'", orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in tok_ns_to_s_map.items():
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose_logging:
logger.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose_logging:
logger.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position : (orig_end_position + 1)]
return output_text
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
def compute_predictions_logits(
all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
verbose_logging,
version_2_with_negative,
null_score_diff_threshold,
tokenizer,
):
"""Write final predictions to the json file and log-odds of null if needed."""
if output_prediction_file:
logger.info(f"Writing predictions to: {output_prediction_file}")
if output_nbest_file:
logger.info(f"Writing nbest to: {output_nbest_file}")
if output_null_log_odds_file and version_2_with_negative:
logger.info(f"Writing null_log_odds to: {output_null_log_odds_file}")
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_logit", "end_logit"]
)
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
all_null_scores = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min null score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
feature_null_score = result.start_logits[0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
doc_offset = feature.doc_offset
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(feature.tokens) or start_index < doc_offset:
continue
if end_index >= len(feature.tokens) or end_index < doc_offset:
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index],
)
)
if version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(
feature_index=min_null_feature_index,
start_index=0,
end_index=0,
start_logit=null_start_logit,
end_logit=null_end_logit,
)
)
prelim_predictions = sorted(prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"]
)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
# tok_text = " ".join(tok_tokens)
#
# # De-tokenize WordPieces that have been split off.
# tok_text = tok_text.replace(" ##", "")
# tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(_NbestPrediction(text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit))
# if we didn't include the empty option in the n-best, include it
if version_2_with_negative:
if "" not in seen_predictions:
nbest.append(_NbestPrediction(text="", start_logit=null_start_logit, end_logit=null_end_logit))
# In very rare edge cases we could only have single null prediction.
# So we just create a nonce prediction in this case to avoid failure.
if len(nbest) == 1:
nbest.insert(0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
assert len(nbest) >= 1, "No valid predictions"
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1, "No valid predictions"
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
all_predictions[example.qas_id] = best_non_null_entry.text
all_null_scores[example.qas_id] = score_diff
all_nbest_json[example.qas_id] = nbest_json
if output_prediction_file:
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
if output_nbest_file:
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if output_null_log_odds_file and version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions, all_null_scores
|
COCO-LM/fairseq/fairseq/data/squad/squad_metrics.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/data/squad/squad_metrics.py",
"repo_id": "COCO-LM",
"token_count": 10472
}
| 185 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
A modified version of the legacy DistributedDataParallel module that uses c10d
communication primitives. This version is simpler than the latest PyTorch
version and is useful for debugging. Notably it does not overlap gradient
communication with the backward pass, which makes it slower but more robust
than the PyTorch version.
This version also supports the *no_sync* context manager, which allows faster
training with `--update-freq`.
"""
from collections import OrderedDict
from contextlib import contextmanager
import torch
from torch import nn
from fairseq.distributed import utils
class LegacyDistributedDataParallel(nn.Module):
"""Implements distributed data parallelism at the module level.
A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`.
This version uses a c10d process group for communication and does not
broadcast buffers.
Args:
module (~torch.nn.Module): module to be parallelized
process_group: the c10d process group to be used for distributed data
parallel all-reduction.
buffer_size (int, optional): number of elements to buffer before
performing all-reduce (default: 256M).
"""
def __init__(self, module, process_group, buffer_size=2 ** 28):
super().__init__()
self.module = module
self.process_group = process_group
self.world_size = utils.get_world_size(self.process_group)
# Never use a bigger buffer than the number of model params
self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters()))
self.buffer = None
# We can also forcibly accumulate grads locally and only do the
# all-reduce at some later time
self.accumulate_grads = False
# make per-device lists of parameters
paramlists = OrderedDict()
for param in self.module.parameters():
device = param.device
if paramlists.get(device) is None:
paramlists[device] = []
paramlists[device] += [param]
self.per_device_params = list(paramlists.values())
@contextmanager
def no_sync(self):
"""A context manager to disable gradient synchronization."""
old_accumulate_grads = self.accumulate_grads
self.accumulate_grads = True
yield
self.accumulate_grads = old_accumulate_grads
def forward(self, *inputs, **kwargs):
return self.module(*inputs, **kwargs)
def all_reduce_grads(self):
"""
This function must be called explicitly after backward to reduce
gradients. There is no automatic hook like c10d.
"""
def all_reduce_params(params):
buffer = self.buffer
nonzero_buffer = False
if len(params) > 1:
offset = 0
for p in params:
sz = p.numel()
if p.grad is not None:
buffer[offset : offset + sz].copy_(p.grad.data.view(-1))
nonzero_buffer = True
else:
buffer[offset : offset + sz].zero_()
offset += sz
else:
# we only have a single grad to all-reduce
p = params[0]
if p.grad is not None:
buffer = p.grad.data
nonzero_buffer = True
elif p.numel() <= self.buffer.numel():
buffer = buffer[: p.numel()]
buffer.zero_()
else:
buffer = torch.zeros_like(p)
if nonzero_buffer:
buffer.div_(self.world_size)
utils.all_reduce(buffer, self.process_group)
# copy all-reduced grads back into their original place
offset = 0
for p in params:
sz = p.numel()
if p.grad is not None:
p.grad.data.copy_(buffer[offset : offset + sz].view_as(p))
else:
p.grad = buffer[offset : offset + sz].view_as(p).clone()
offset += sz
def reduction_fn():
# This function only needs to be called once
if self.accumulate_grads:
return
if self.buffer is None:
self.buffer = next(self.module.parameters()).new(self.buffer_size)
for params in self.per_device_params:
# All-reduce the gradients in buckets
offset = 0
buffered_params = []
for param in params:
if not param.requires_grad:
continue
if param.grad is None:
param.grad = torch.zeros_like(param)
if param.grad.requires_grad:
raise RuntimeError(
"DistributedDataParallel only works "
"with gradients that don't require "
"grad"
)
sz = param.numel()
if sz > self.buffer.numel():
# all-reduce big params directly
all_reduce_params([param])
else:
if offset + sz > self.buffer.numel():
all_reduce_params(buffered_params)
offset = 0
buffered_params.clear()
buffered_params.append(param)
offset += sz
if len(buffered_params) > 0:
all_reduce_params(buffered_params)
reduction_fn()
|
COCO-LM/fairseq/fairseq/distributed/legacy_distributed_data_parallel.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/distributed/legacy_distributed_data_parallel.py",
"repo_id": "COCO-LM",
"token_count": 2856
}
| 186 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a network across multiple GPUs.
"""
from fairseq.dataclass.configs import FairseqConfig
from fairseq.distributed import utils as distributed_utils
from fairseq.trainer import Trainer
try:
from fairseq.model_parallel.megatron.mpu import (
get_data_parallel_rank,
get_data_parallel_world_size,
get_model_parallel_src_rank,
get_cuda_rng_tracker,
)
has_megatron_submodule = True
except (ImportError, ModuleNotFoundError):
has_megatron_submodule = False
class MegatronTrainer(Trainer):
"""Main class for model parallel with data parallel training."""
def __init__(self, cfg: FairseqConfig, task, model, criterion, **kwargs):
if not has_megatron_submodule:
raise ImportError(
"\n\nPlease install the megatron submodule:"
"\n\n git submodule update --init "
"fairseq/model_parallel/megatron"
)
super().__init__(cfg, task, model, criterion, **kwargs)
def clip_grad_norm(self, clip_norm):
def _aggregate_model_parallel_grad_norm(total_norm):
total_norm = total_norm ** 2
distributed_utils.all_reduce(
total_norm, group=distributed_utils.get_model_parallel_group()
)
total_norm = total_norm ** 0.5
return total_norm
return self.optimizer.clip_grad_norm(
clip_norm,
aggregate_norm_fn=_aggregate_model_parallel_grad_norm,
)
def save_checkpoint(self, filename, extra_state):
"""Save all training state in a checkpoint file."""
extra_state['rng_tracker_states'] \
= get_cuda_rng_tracker().get_states()
super().save_checkpoint(filename, extra_state)
def load_checkpoint(
self,
filename,
reset_optimizer=False,
reset_lr_scheduler=False,
optimizer_overrides=None,
reset_meters=False,
):
extra_state = super().load_checkpoint(filename, reset_optimizer=reset_optimizer, reset_lr_scheduler=reset_lr_scheduler, optimizer_overrides=optimizer_overrides, reset_meters=reset_meters)
if extra_state is not None and 'rng_tracker_states' in extra_state:
get_cuda_rng_tracker().set_states(
extra_state['rng_tracker_states'])
return extra_state
|
COCO-LM/fairseq/fairseq/model_parallel/megatron_trainer.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/model_parallel/megatron_trainer.py",
"repo_id": "COCO-LM",
"token_count": 1073
}
| 187 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq import utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.lstm import Embedding, LSTMDecoder
DEFAULT_MAX_TARGET_POSITIONS = 1e5
@register_model("lstm_lm")
class LSTMLanguageModel(FairseqLanguageModel):
def __init__(self, decoder):
super().__init__(decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
help='path to pre-trained decoder embedding')
parser.add_argument('--decoder-hidden-size', type=int, metavar='N',
help='decoder hidden size')
parser.add_argument('--decoder-layers', type=int, metavar='N',
help='number of decoder layers')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='BOOL',
help='decoder attention')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion')
parser.add_argument('--residuals', default=False,
action='store_true',
help='applying residuals between LSTM layers')
# Granular dropout settings (if not specified these default to --dropout)
parser.add_argument('--decoder-dropout-in', type=float, metavar='D',
help='dropout probability for decoder input embedding')
parser.add_argument('--decoder-dropout-out', type=float, metavar='D',
help='dropout probability for decoder output')
parser.add_argument('--share-decoder-input-output-embed', default=False,
action='store_true',
help='share decoder input and output embeddings')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if getattr(args, "max_target_positions", None) is not None:
max_target_positions = args.max_target_positions
else:
max_target_positions = getattr(
args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS
)
def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim
)
if args.share_decoder_input_output_embed:
# double check all parameters combinations are valid
if task.source_dictionary != task.target_dictionary:
raise ValueError(
"--share-decoder-input-output-embeddings requires a joint dictionary"
)
if args.decoder_embed_dim != args.decoder_out_embed_dim:
raise ValueError(
"--share-decoder-input-output-embeddings requires "
"--decoder-embed-dim to match --decoder-out-embed-dim"
)
decoder = LSTMDecoder(
dictionary=task.dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
attention=False, # decoder-only language model doesn't support attention
encoder_output_units=0,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion == "adaptive_loss"
else None
),
max_target_positions=max_target_positions,
residuals=args.residuals,
)
return cls(decoder)
@register_model_architecture("lstm_lm", "lstm_lm")
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.1)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_hidden_size = getattr(
args, "decoder_hidden_size", args.decoder_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 1)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
args.decoder_attention = getattr(args, "decoder_attention", "0")
args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout)
args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.adaptive_softmax_cutoff = getattr(
args, "adaptive_softmax_cutoff", "10000,50000,200000"
)
args.residuals = getattr(args, "residuals", False)
|
COCO-LM/fairseq/fairseq/models/lstm_lm.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/models/lstm_lm.py",
"repo_id": "COCO-LM",
"token_count": 2957
}
| 188 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.data import encoders
class RobertaHubInterface(nn.Module):
"""A simple PyTorch Hub interface to RoBERTa.
Usage: https://github.com/pytorch/fairseq/tree/master/examples/roberta
"""
def __init__(self, cfg, task, model):
super().__init__()
self.cfg = cfg
self.task = task
self.model = model
self.bpe = encoders.build_bpe(cfg.bpe)
# this is useful for determining the device
self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float))
@property
def device(self):
return self._float_tensor.device
def encode(
self, sentence: str, *addl_sentences, no_separator=False
) -> torch.LongTensor:
"""
BPE-encode a sentence (or multiple sentences).
Every sequence begins with a beginning-of-sentence (`<s>`) symbol.
Every sentence ends with an end-of-sentence (`</s>`) and we use an
extra end-of-sentence (`</s>`) as a separator.
Example (single sentence): `<s> a b c </s>`
Example (sentence pair): `<s> d e f </s> </s> 1 2 3 </s>`
The BPE encoding follows GPT-2. One subtle detail is that the GPT-2 BPE
requires leading spaces. For example::
>>> roberta.encode('Hello world').tolist()
[0, 31414, 232, 2]
>>> roberta.encode(' world').tolist()
[0, 232, 2]
>>> roberta.encode('world').tolist()
[0, 8331, 2]
"""
bpe_sentence = "<s> " + self.bpe.encode(sentence) + " </s>"
for s in addl_sentences:
bpe_sentence += " </s>" if not no_separator else ""
bpe_sentence += " " + self.bpe.encode(s) + " </s>"
tokens = self.task.source_dictionary.encode_line(
bpe_sentence, append_eos=False, add_if_not_exist=False
)
return tokens.long()
def decode(self, tokens: torch.LongTensor):
assert tokens.dim() == 1
tokens = tokens.numpy()
if tokens[0] == self.task.source_dictionary.bos():
tokens = tokens[1:] # remove <s>
eos_mask = tokens == self.task.source_dictionary.eos()
doc_mask = eos_mask[1:] & eos_mask[:-1]
sentences = np.split(tokens, doc_mask.nonzero()[0] + 1)
sentences = [
self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences
]
if len(sentences) == 1:
return sentences[0]
return sentences
def extract_features(
self, tokens: torch.LongTensor, return_all_hiddens: bool = False
) -> torch.Tensor:
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
if tokens.size(-1) > self.model.max_positions():
raise ValueError(
"tokens exceeds maximum length: {} > {}".format(
tokens.size(-1), self.model.max_positions()
)
)
features, extra = self.model(
tokens.to(device=self.device),
features_only=True,
return_all_hiddens=return_all_hiddens,
)
if return_all_hiddens:
# convert from T x B x C -> B x T x C
inner_states = extra["inner_states"]
return [inner_state.transpose(0, 1) for inner_state in inner_states]
else:
return features # just the last layer's features
def register_classification_head(
self, name: str, num_classes: int = None, embedding_size: int = None, **kwargs
):
self.model.register_classification_head(
name, num_classes=num_classes, embedding_size=embedding_size, **kwargs
)
def predict(self, head: str, tokens: torch.LongTensor, return_logits: bool = False):
features = self.extract_features(tokens.to(device=self.device))
logits = self.model.classification_heads[head](features)
if return_logits:
return logits
return F.log_softmax(logits, dim=-1)
def extract_features_aligned_to_words(
self, sentence: str, return_all_hiddens: bool = False
) -> torch.Tensor:
"""Extract RoBERTa features, aligned to spaCy's word-level tokenizer."""
from fairseq.models.roberta import alignment_utils
from spacy.tokens import Doc
nlp = alignment_utils.spacy_nlp()
tokenizer = alignment_utils.spacy_tokenizer()
# tokenize both with GPT-2 BPE and spaCy
bpe_toks = self.encode(sentence)
spacy_toks = tokenizer(sentence)
spacy_toks_ws = [t.text_with_ws for t in tokenizer(sentence)]
alignment = alignment_utils.align_bpe_to_words(self, bpe_toks, spacy_toks_ws)
# extract features and align them
features = self.extract_features(
bpe_toks, return_all_hiddens=return_all_hiddens
)
features = features.squeeze(0)
aligned_feats = alignment_utils.align_features_to_words(
self, features, alignment
)
# wrap in spaCy Doc
doc = Doc(
nlp.vocab,
words=["<s>"] + [x.text for x in spacy_toks] + ["</s>"],
spaces=[True]
+ [x.endswith(" ") for x in spacy_toks_ws[:-1]]
+ [True, False],
)
assert len(doc) == aligned_feats.size(0)
doc.user_token_hooks["vector"] = lambda token: aligned_feats[token.i]
return doc
def fill_mask(self, masked_input: str, topk: int = 5):
masked_token = "<mask>"
assert (
masked_token in masked_input and masked_input.count(masked_token) == 1
), "Please add one {0} token for the input, eg: 'He is a {0} guy'".format(
masked_token
)
text_spans = masked_input.split(masked_token)
text_spans_bpe = (
(" {0} ".format(masked_token))
.join([self.bpe.encode(text_span.rstrip()) for text_span in text_spans])
.strip()
)
tokens = self.task.source_dictionary.encode_line(
"<s> " + text_spans_bpe + " </s>",
append_eos=False,
add_if_not_exist=False,
)
masked_index = (tokens == self.task.mask_idx).nonzero(as_tuple=False)
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
with utils.model_eval(self.model):
features, extra = self.model(
tokens.long().to(device=self.device),
features_only=False,
return_all_hiddens=False,
)
logits = features[0, masked_index, :].squeeze()
prob = logits.softmax(dim=0)
values, index = prob.topk(k=topk, dim=0)
topk_predicted_token_bpe = self.task.source_dictionary.string(index)
topk_filled_outputs = []
for index, predicted_token_bpe in enumerate(
topk_predicted_token_bpe.split(" ")
):
predicted_token = self.bpe.decode(predicted_token_bpe)
# Quick hack to fix https://github.com/pytorch/fairseq/issues/1306
if predicted_token_bpe.startswith("\u2581"):
predicted_token = " " + predicted_token
if " {0}".format(masked_token) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(
" {0}".format(masked_token), predicted_token
),
values[index].item(),
predicted_token,
)
)
else:
topk_filled_outputs.append(
(
masked_input.replace(masked_token, predicted_token),
values[index].item(),
predicted_token,
)
)
return topk_filled_outputs
def disambiguate_pronoun(self, sentence: str) -> bool:
"""
Usage::
>>> disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.')
True
>>> disambiguate_pronoun('The trophy would not fit in the brown suitcase because [it] was too big.')
'The trophy'
"""
assert hasattr(
self.task, "disambiguate_pronoun"
), "roberta.disambiguate_pronoun() requires a model trained with the WSC task."
with utils.model_eval(self.model):
return self.task.disambiguate_pronoun(
self.model, sentence, use_cuda=self.device.type == "cuda"
)
|
COCO-LM/fairseq/fairseq/models/roberta/hub_interface.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/models/roberta/hub_interface.py",
"repo_id": "COCO-LM",
"token_count": 4271
}
| 189 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from typing import Any, Dict
from fairseq import checkpoint_utils
from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerDecoder,
TransformerEncoder,
TransformerModel,
base_architecture as transformer_base_architecture,
)
@register_model("transformer_from_pretrained_xlm")
class TransformerFromPretrainedXLMModel(TransformerModel):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
TransformerModel.add_args(parser)
parser.add_argument(
"--pretrained-xlm-checkpoint",
type=str,
metavar="STR",
help="XLM model to use for initializing transformer encoder and/or decoder",
)
parser.add_argument(
"--init-encoder-only",
action="store_true",
help="if set, don't load the XLM weights and embeddings into decoder",
)
parser.add_argument(
"--init-decoder-only",
action="store_true",
help="if set, don't load the XLM weights and embeddings into encoder",
)
@classmethod
def build_model(self, args, task, cls_dictionary=MaskedLMDictionary):
assert hasattr(args, "pretrained_xlm_checkpoint"), (
"You must specify a path for --pretrained-xlm-checkpoint to use "
"--arch transformer_from_pretrained_xlm"
)
assert isinstance(task.source_dictionary, cls_dictionary) and isinstance(
task.target_dictionary, cls_dictionary
), (
"You should use a MaskedLMDictionary when using --arch "
"transformer_from_pretrained_xlm because the pretrained XLM model "
"was trained using data binarized with MaskedLMDictionary. "
"For translation, you may want to use --task "
"translation_from_pretrained_xlm"
)
assert not (
getattr(args, "init_encoder_only", False)
and getattr(args, "init_decoder_only", False)
), "Only one of --init-encoder-only and --init-decoder-only can be set."
return super().build_model(args, task)
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
return TransformerEncoderFromPretrainedXLM(args, src_dict, embed_tokens)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
return TransformerDecoderFromPretrainedXLM(args, tgt_dict, embed_tokens)
def upgrade_state_dict_with_xlm_weights(
state_dict: Dict[str, Any], pretrained_xlm_checkpoint: str
) -> Dict[str, Any]:
"""
Load XLM weights into a Transformer encoder or decoder model.
Args:
state_dict: state dict for either TransformerEncoder or
TransformerDecoder
pretrained_xlm_checkpoint: checkpoint to load XLM weights from
Raises:
AssertionError: If architecture (num layers, attention heads, etc.)
does not match between the current Transformer encoder or
decoder and the pretrained_xlm_checkpoint
"""
if not os.path.exists(pretrained_xlm_checkpoint):
raise IOError("Model file not found: {}".format(pretrained_xlm_checkpoint))
state = checkpoint_utils.load_checkpoint_to_cpu(pretrained_xlm_checkpoint)
xlm_state_dict = state["model"]
for key in xlm_state_dict.keys():
for search_key in ["embed_tokens", "embed_positions", "layers"]:
if search_key in key:
subkey = key[key.find(search_key) :]
assert subkey in state_dict, (
"{} Transformer encoder / decoder "
"state_dict does not contain {}. Cannot "
"load {} from pretrained XLM checkpoint "
"{} into Transformer.".format(
str(state_dict.keys()), subkey, key, pretrained_xlm_checkpoint
)
)
state_dict[subkey] = xlm_state_dict[key]
return state_dict
class TransformerEncoderFromPretrainedXLM(TransformerEncoder):
def __init__(self, args, dictionary, embed_tokens):
super().__init__(args, dictionary, embed_tokens)
if getattr(args, "init_decoder_only", False):
# Don't load XLM weights for encoder if --init-decoder-only
return
assert hasattr(args, "pretrained_xlm_checkpoint"), (
"--pretrained-xlm-checkpoint must be specified to load Transformer "
"encoder from pretrained XLM"
)
xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights(
state_dict=self.state_dict(),
pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint,
)
self.load_state_dict(xlm_loaded_state_dict, strict=True)
class TransformerDecoderFromPretrainedXLM(TransformerDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(args, dictionary, embed_tokens, no_encoder_attn)
if getattr(args, "init_encoder_only", False):
# Don't load XLM weights for decoder if --init-encoder-only
return
assert hasattr(args, "pretrained_xlm_checkpoint"), (
"--pretrained-xlm-checkpoint must be specified to load Transformer "
"decoder from pretrained XLM"
)
xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights(
state_dict=self.state_dict(),
pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint,
)
self.load_state_dict(xlm_loaded_state_dict, strict=True)
@register_model_architecture(
"transformer_from_pretrained_xlm", "transformer_from_pretrained_xlm"
)
def base_architecture(args):
transformer_base_architecture(args)
|
COCO-LM/fairseq/fairseq/models/transformer_from_pretrained_xlm.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/models/transformer_from_pretrained_xlm.py",
"repo_id": "COCO-LM",
"token_count": 2609
}
| 190 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.modules.fairseq_dropout import FairseqDropout
from .unfold import unfold1d
def DynamicConv(
input_size,
kernel_size=1,
padding_l=None,
num_heads=1,
weight_dropout=0.0,
weight_softmax=False,
renorm_padding=False,
bias=False,
conv_bias=False,
query_size=None,
in_proj=False,
):
if torch.cuda.is_available():
try:
from fairseq.modules.dynamicconv_layer import DynamicconvLayer
return DynamicconvLayer(
input_size,
kernel_size=kernel_size,
padding_l=padding_l,
num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax,
renorm_padding=renorm_padding,
bias=bias,
conv_bias=conv_bias,
query_size=query_size,
)
except ImportError as e:
print(e)
return DynamicConv1dTBC(
input_size,
kernel_size=kernel_size,
padding_l=padding_l,
num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax,
renorm_padding=renorm_padding,
bias=bias,
conv_bias=conv_bias,
query_size=query_size,
)
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
@with_incremental_state
class DynamicConv1dTBC(nn.Module):
"""Dynamic lightweight convolution taking T x B x C inputs
Args:
input_size: # of channels of the input
kernel_size: convolution channels
padding_l: padding to the left when using "same" padding
num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size)
weight_dropout: the drop rate of the DropConnect to drop the weight
weight_softmax: normalize the weight with softmax before the convolution
renorm_padding: re-normalize the filters to ignore the padded part (only the non-padding parts sum up to 1)
bias: use bias
conv_bias: bias of the convolution
query_size: specified when feeding a different input as the query
in_proj: project the input and generate the filter together
Shape:
Input: TxBxC, i.e. (timesteps, batch_size, input_size)
Output: TxBxC, i.e. (timesteps, batch_size, input_size)
Attributes:
weight: the learnable weights of the module of shape
`(num_heads, 1, kernel_size)`
bias: the learnable bias of the module of shape `(input_size)`
"""
def __init__(
self,
input_size,
kernel_size=1,
padding_l=None,
num_heads=1,
weight_dropout=0.0,
weight_softmax=False,
renorm_padding=False,
bias=False,
conv_bias=False,
query_size=None,
in_proj=False,
):
super().__init__()
self.input_size = input_size
self.query_size = input_size if query_size is None else query_size
self.kernel_size = kernel_size
self.padding_l = padding_l
self.num_heads = num_heads
self.weight_dropout_module = FairseqDropout(
weight_dropout, module_name=self.__class__.__name__
)
self.weight_softmax = weight_softmax
self.renorm_padding = renorm_padding
if in_proj:
self.weight_linear = Linear(
self.input_size, self.input_size + num_heads * kernel_size * 1
)
else:
self.weight_linear = Linear(
self.query_size, num_heads * kernel_size * 1, bias=bias
)
if conv_bias:
self.conv_bias = nn.Parameter(torch.Tensor(input_size))
else:
self.conv_bias = None
self.reset_parameters()
@property
def in_proj(self):
return (
self.weight_linear.out_features
== self.input_size + self.num_heads * self.kernel_size
)
def reset_parameters(self):
self.weight_linear.reset_parameters()
if self.conv_bias is not None:
nn.init.constant_(self.conv_bias, 0.0)
def forward(self, x, incremental_state=None, query=None, unfold=None):
"""Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
args:
x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
incremental_state: A dict to keep the state
unfold: unfold the input or not. If not, we use the matrix trick instead
query: use the specified query to predict the conv filters
"""
unfold = (
x.size(0) > 512 if unfold is None else unfold
) # use unfold mode as default for long sequence to save memory
unfold = unfold or (incremental_state is not None)
assert query is None or not self.in_proj
if query is None:
query = x
if unfold:
output = self._forward_unfolded(x, incremental_state, query)
else:
output = self._forward_expanded(x, incremental_state, query)
if self.conv_bias is not None:
output = output + self.conv_bias.view(1, 1, -1)
return output
def _forward_unfolded(self, x, incremental_state, query):
"""The conventional implementation of convolutions.
Unfolding the input by having a window shifting to the right."""
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
if self.in_proj:
proj = self.weight_linear(x)
x = proj.narrow(2, 0, self.input_size).contiguous()
weight = (
proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1)
)
else:
weight = self.weight_linear(query).view(T * B * H, -1)
# renorm_padding is only implemented in _forward_expanded
assert not self.renorm_padding or incremental_state is not None
if incremental_state is not None:
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = x.new()
x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
if self.kernel_size > 1:
self._set_input_buffer(
incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :]
)
x_unfold = x_unfold.view(T * B * H, R, -1)
else:
padding_l = self.padding_l
if K > T and padding_l == K - 1:
weight = weight.narrow(1, K - T, T)
K, padding_l = T, T - 1
# unfold the input: T x B x C --> T' x B x C x K
x_unfold = unfold1d(x, K, padding_l, 0)
x_unfold = x_unfold.view(T * B * H, R, K)
if self.weight_softmax and not self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = weight.narrow(1, 0, K)
if incremental_state is not None:
weight = weight[:, -x_unfold.size(2) :]
K = weight.size(1)
if self.weight_softmax and self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = self.weight_dropout_module(weight, inplace=False)
output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1
output = output.view(T, B, C)
return output
def _forward_expanded(self, x, incremental_stat, query):
"""Turn the convolution filters into band matrices and do matrix multiplication.
This is faster when the sequence is short, but less memory efficient.
This is not used in the decoder during inference.
"""
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
if self.in_proj:
proj = self.weight_linear(x)
x = proj.narrow(2, 0, self.input_size).contiguous()
weight = (
proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1)
)
else:
weight = self.weight_linear(query).view(T * B * H, -1)
if not self.renorm_padding:
if self.weight_softmax:
weight = F.softmax(weight, dim=1)
weight = self.weight_dropout_module(weight, inplace=False)
weight = weight.narrow(1, 0, K).contiguous()
weight = weight.view(T, B * H, K).transpose(0, 1)
x = x.view(T, B * H, R).transpose(0, 1)
if self.weight_softmax and self.renorm_padding:
# turn the convolution filters into band matrices
weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf"))
weight_expanded.as_strided(
(B * H, T, K), (T * (T + K - 1), T + K, 1)
).copy_(weight)
weight_expanded = weight_expanded.narrow(2, self.padding_l, T)
# normalize the weight over valid positions like self-attention
weight_expanded = F.softmax(weight_expanded, dim=2)
weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False)
else:
P = self.padding_l
# For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length
if K > T and P == K - 1:
weight = weight.narrow(2, K - T, T)
K, P = T, T - 1
# turn the convolution filters into band matrices
weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False)
weight_expanded.as_strided(
(B * H, T, K), (T * (T + K - 1), T + K, 1)
).copy_(weight)
weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T
output = torch.bmm(weight_expanded, x)
output = output.transpose(0, 1).contiguous().view(T, B, C)
return output
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, "input_buffer")
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(
self, incremental_state, "input_buffer", new_buffer
)
def extra_repr(self):
s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, conv_bias={}, renorm_padding={}, in_proj={}".format(
self.input_size,
self.kernel_size,
self.padding_l,
self.num_heads,
self.weight_softmax,
self.conv_bias is not None,
self.renorm_padding,
self.in_proj,
)
if self.query_size != self.input_size:
s += ", query_size={}".format(self.query_size)
if self.weight_dropout_module.p > 0.0:
s += ", weight_dropout={}".format(self.weight_dropout_module.p)
return s
|
COCO-LM/fairseq/fairseq/modules/dynamic_convolution.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/modules/dynamic_convolution.py",
"repo_id": "COCO-LM",
"token_count": 5524
}
| 191 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
LayerDrop as described in https://arxiv.org/abs/1909.11556.
"""
import torch
import torch.nn as nn
class LayerDropModuleList(nn.ModuleList):
"""
A LayerDrop implementation based on :class:`torch.nn.ModuleList`.
We refresh the choice of which layers to drop every time we iterate
over the LayerDropModuleList instance. During evaluation we always
iterate over all layers.
Usage::
layers = LayerDropList(p=0.5, modules=[layer1, layer2, layer3])
for layer in layers: # this might iterate over layers 1 and 3
x = layer(x)
for layer in layers: # this might iterate over all layers
x = layer(x)
for layer in layers: # this might not iterate over any layers
x = layer(x)
Args:
p (float): probability of dropping out each layer
modules (iterable, optional): an iterable of modules to add
"""
def __init__(self, p, modules=None):
super().__init__(modules)
self.p = p
def __iter__(self):
dropout_probs = torch.empty(len(self)).uniform_()
for i, m in enumerate(super().__iter__()):
if not self.training or (dropout_probs[i] > self.p):
yield m
|
COCO-LM/fairseq/fairseq/modules/layer_drop.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/modules/layer_drop.py",
"repo_id": "COCO-LM",
"token_count": 534
}
| 192 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from operator import attrgetter
import torch.distributed as dist
import torch.nn as nn
from ..pq.utils import attrsetter, get_layers
from .modules import ActivationQuantizer, IntConv2d, IntEmbedding, IntLinear
MAPPING = {nn.Linear: IntLinear, nn.Embedding: IntEmbedding, nn.Conv2d: IntConv2d}
def quantize_model_(model, p=0.2, bits=8, update_step=3000):
"""
Replaces all modules with their scalar quantized counterpart and
registers hooks to quantize the post-ativations of those modules.
Args:
- model: a nn.Module
- p: amount of noise (0 for no noise, 1 to quantize all the weights/activations)
- bits: number of bits
- update_step: update quantization parameters every update_step steps
"""
# quantize all layers
quantized_layers = get_layers(model, "(.*?)")
for layer in quantized_layers:
# book-keeping
is_master_process = (not dist.is_initialized()) or (
dist.is_initialized() and dist.get_rank() == 0
)
# recover module
module = attrgetter(layer)(model)
if is_master_process:
logging.info(
f"Quantizing layer {layer} with bits={bits} and QuantNoise={p}"
)
# quantization params
q_params = {
"p": p,
"update_step": update_step,
"bits": bits,
"method": "histogram",
"counter": 0,
}
# instantiate the quantized counterpart
if isinstance(module, tuple(MAPPING.keys())):
QuantizedModule = MAPPING[module.__class__]
quantized_module = QuantizedModule.__new__(QuantizedModule)
params = module.__dict__
params.update(q_params)
quantized_module.__dict__.update(params)
else:
if is_master_process:
logging.info(f"Module {module} not yet supported for quantization")
continue
# activation quantization
a_q = ActivationQuantizer(quantized_module, p=0, bits=bits, method="histogram")
# replace layer by its quantized counterpart
attrsetter(layer)(model, quantized_module)
# return name of quantized layers
return quantized_layers
|
COCO-LM/fairseq/fairseq/modules/quantization/scalar/utils.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/modules/quantization/scalar/utils.py",
"repo_id": "COCO-LM",
"token_count": 1006
}
| 193 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.optim
from . import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("adadelta")
class Adadelta(LegacyFairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config)
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--adadelta-rho', type=float, default=0.9, metavar='RHO',
help='coefficient used for computing a running average of squared gradients')
parser.add_argument('--adadelta-eps', type=float, default=1e-6, metavar='EPS',
help='term added to the denominator to improve numerical stability')
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
parser.add_argument('--anneal-eps', action='store_true', help='flag to anneal eps')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
"lr": self.args.lr[0],
"rho": self.args.adadelta_rho,
"eps": self.args.adadelta_eps,
"weight_decay": self.args.weight_decay,
}
@property
def supports_flat_params(self):
return True
|
COCO-LM/fairseq/fairseq/optim/adadelta.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/optim/adadelta.py",
"repo_id": "COCO-LM",
"token_count": 753
}
| 194 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from typing import Optional, List
from omegaconf import II
from fairseq.dataclass import FairseqDataclass
from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
@dataclass
class FixedLRScheduleConfig(FairseqDataclass):
force_anneal: Optional[int] = field(
default=None,
metadata={"help": "force annealing at specified epoch"},
)
lr_shrink: float = field(
default=0.1,
metadata={"help": "shrink factor for annealing, lr_new = (lr * lr_shrink)"},
)
warmup_updates: int = field(
default=0,
metadata={"help": "warmup the learning rate linearly for the first N updates"},
)
lr: List[float] = II("optimization.lr")
@register_lr_scheduler("fixed", dataclass=FixedLRScheduleConfig)
class FixedLRSchedule(FairseqLRScheduler):
"""Decay the LR on a fixed schedule."""
def __init__(self, cfg: FixedLRScheduleConfig, optimizer):
super().__init__(cfg, optimizer)
self.lr = cfg.lr[0]
if cfg.warmup_updates > 0:
self.warmup_factor = 1.0 / cfg.warmup_updates
else:
self.warmup_factor = 1
def state_dict(self):
return {"lr": self.lr}
def load_state_dict(self, state_dict):
if "lr" in state_dict:
self.lr = state_dict["lr"]
def get_next_lr(self, epoch):
lrs = self.cfg.lr
if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal:
# use fixed LR schedule
next_lr = lrs[min(epoch - 1, len(lrs) - 1)]
else:
# annneal based on lr_shrink
next_lr = lrs[-1] * self.cfg.lr_shrink ** (
epoch + 1 - self.cfg.force_anneal
)
return next_lr
def step_begin_epoch(self, epoch):
"""Update the learning rate at the beginning of the given epoch."""
self.lr = self.get_next_lr(epoch)
self.optimizer.set_lr(self.warmup_factor * self.lr)
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if self.cfg.warmup_updates > 0 and num_updates < self.cfg.warmup_updates:
self.warmup_factor = (num_updates + 1) / float(self.cfg.warmup_updates)
self.optimizer.set_lr(self.warmup_factor * self.lr)
else:
self.optimizer.set_lr(self.lr)
return self.optimizer.get_lr()
|
COCO-LM/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py",
"repo_id": "COCO-LM",
"token_count": 1150
}
| 195 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import ctypes
import math
import sys
from dataclasses import dataclass, field
import torch
from fairseq.dataclass import FairseqDataclass
from fairseq.scoring import BaseScorer, register_scorer
from fairseq.scoring.tokenizer import EvaluationTokenizer
class BleuStat(ctypes.Structure):
_fields_ = [
("reflen", ctypes.c_size_t),
("predlen", ctypes.c_size_t),
("match1", ctypes.c_size_t),
("count1", ctypes.c_size_t),
("match2", ctypes.c_size_t),
("count2", ctypes.c_size_t),
("match3", ctypes.c_size_t),
("count3", ctypes.c_size_t),
("match4", ctypes.c_size_t),
("count4", ctypes.c_size_t),
]
@dataclass
class SacrebleuConfig(FairseqDataclass):
sacrebleu_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field(
default="13a", metadata={"help": "tokenizer"}
)
sacrebleu_lowercase: bool = field(
default=False, metadata={"help": "apply lowercasing"}
)
sacrebleu_char_level: bool = field(
default=False, metadata={"help": "evaluate at character level"}
)
@register_scorer("sacrebleu", dataclass=SacrebleuConfig)
class SacrebleuScorer(BaseScorer):
def __init__(self, cfg):
super(SacrebleuScorer, self).__init__(cfg)
import sacrebleu
self.sacrebleu = sacrebleu
self.tokenizer = EvaluationTokenizer(
tokenizer_type=cfg.sacrebleu_tokenizer,
lowercase=cfg.sacrebleu_lowercase,
character_tokenization=cfg.sacrebleu_char_level,
)
def add_string(self, ref, pred):
self.ref.append(self.tokenizer.tokenize(ref))
self.pred.append(self.tokenizer.tokenize(pred))
def score(self, order=4):
return self.result_string(order).score
def result_string(self, order=4):
if order != 4:
raise NotImplementedError
# tokenization and lowercasing are performed by self.tokenizer instead.
return self.sacrebleu.corpus_bleu(
self.pred, [self.ref], tokenize="none"
).format()
@dataclass
class BleuConfig(FairseqDataclass):
pad: int = field(default=1, metadata={"help": "padding index"})
eos: int = field(default=2, metadata={"help": "eos index"})
unk: int = field(default=3, metadata={"help": "unk index"})
@register_scorer("bleu", dataclass=BleuConfig)
class Scorer(object):
def __init__(self, cfg):
self.stat = BleuStat()
self.pad = cfg.pad
self.eos = cfg.eos
self.unk = cfg.unk
try:
from fairseq import libbleu
except ImportError as e:
sys.stderr.write(
"ERROR: missing libbleu.so. run `pip install --editable .`\n"
)
raise e
self.C = ctypes.cdll.LoadLibrary(libbleu.__file__)
self.reset()
def reset(self, one_init=False):
if one_init:
self.C.bleu_one_init(ctypes.byref(self.stat))
else:
self.C.bleu_zero_init(ctypes.byref(self.stat))
def add(self, ref, pred):
if not isinstance(ref, torch.IntTensor):
raise TypeError("ref must be a torch.IntTensor (got {})".format(type(ref)))
if not isinstance(pred, torch.IntTensor):
raise TypeError("pred must be a torch.IntTensor(got {})".format(type(pred)))
# don't match unknown words
rref = ref.clone()
assert not rref.lt(0).any()
rref[rref.eq(self.unk)] = -999
rref = rref.contiguous().view(-1)
pred = pred.contiguous().view(-1)
self.C.bleu_add(
ctypes.byref(self.stat),
ctypes.c_size_t(rref.size(0)),
ctypes.c_void_p(rref.data_ptr()),
ctypes.c_size_t(pred.size(0)),
ctypes.c_void_p(pred.data_ptr()),
ctypes.c_int(self.pad),
ctypes.c_int(self.eos),
)
def score(self, order=4):
psum = sum(
math.log(p) if p > 0 else float("-Inf") for p in self.precision()[:order]
)
return self.brevity() * math.exp(psum / order) * 100
def precision(self):
def ratio(a, b):
return a / b if b > 0 else 0
return [
ratio(self.stat.match1, self.stat.count1),
ratio(self.stat.match2, self.stat.count2),
ratio(self.stat.match3, self.stat.count3),
ratio(self.stat.match4, self.stat.count4),
]
def brevity(self):
r = self.stat.reflen / self.stat.predlen
return min(1, math.exp(1 - r))
def result_string(self, order=4):
assert order <= 4, "BLEU scores for order > 4 aren't supported"
fmt = "BLEU{} = {:2.2f}, {:2.1f}"
for _ in range(1, order):
fmt += "/{:2.1f}"
fmt += " (BP={:.3f}, ratio={:.3f}, syslen={}, reflen={})"
bleup = [p * 100 for p in self.precision()[:order]]
return fmt.format(
order,
self.score(order=order),
*bleup,
self.brevity(),
self.stat.predlen / self.stat.reflen,
self.stat.predlen,
self.stat.reflen
)
|
COCO-LM/fairseq/fairseq/scoring/bleu.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/scoring/bleu.py",
"repo_id": "COCO-LM",
"token_count": 2527
}
| 196 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import numpy as np
import torch
from fairseq import utils
from fairseq.data import (
ConcatDataset,
Dictionary,
IdDataset,
MaskTokensDataset,
NestedDictionaryDataset,
NumelDataset,
NumSamplesDataset,
PadDataset,
PrependTokenDataset,
RawLabelDataset,
ResamplingDataset,
SortDataset,
TokenBlockDataset,
data_utils,
encoders,
)
from fairseq.tasks import LegacyFairseqTask, register_task
logger = logging.getLogger(__name__)
@register_task("multilingual_masked_lm")
class MultiLingualMaskedLMTask(LegacyFairseqTask):
"""Task for training masked language models (e.g., BERT, RoBERTa)."""
@staticmethod
def add_args(parser):
"""Add task-specific arguments to the parser."""
parser.add_argument(
"data",
help="colon separated path to data directories list, \
will be iterated upon during epochs in round-robin manner",
)
parser.add_argument(
"--sample-break-mode",
default="complete",
choices=["none", "complete", "complete_doc", "eos"],
help='If omitted or "none", fills each sample with tokens-per-sample '
'tokens. If set to "complete", splits samples only at the end '
"of sentence, but may include multiple sentences per sample. "
'"complete_doc" is similar but respects doc boundaries. '
'If set to "eos", includes only one sentence per sample.',
)
parser.add_argument(
"--tokens-per-sample",
default=512,
type=int,
help="max number of total tokens over all segments "
"per sample for BERT dataset",
)
parser.add_argument(
"--mask-prob",
default=0.15,
type=float,
help="probability of replacing a token with mask",
)
parser.add_argument(
"--leave-unmasked-prob",
default=0.1,
type=float,
help="probability that a masked token is unmasked",
)
parser.add_argument(
"--random-token-prob",
default=0.1,
type=float,
help="probability of replacing a token with a random token",
)
parser.add_argument(
"--freq-weighted-replacement",
action="store_true",
help="sample random replacement words based on word frequencies",
)
parser.add_argument(
"--mask-whole-words",
default=False,
action="store_true",
help="mask whole words; you may also want to set --bpe",
)
parser.add_argument(
"--multilang-sampling-alpha",
type=float,
default=1.0,
help="smoothing alpha for sample rations across multiple datasets",
)
def __init__(self, args, dictionary):
super().__init__(args)
self.dictionary = dictionary
self.seed = args.seed
# add mask token
self.mask_idx = dictionary.add_symbol("<mask>")
@classmethod
def setup_task(cls, args, **kwargs):
paths = utils.split_paths(args.data)
assert len(paths) > 0
dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt"))
logger.info("dictionary: {} types".format(len(dictionary)))
return cls(args, dictionary)
def _get_whole_word_mask(self):
# create masked input and targets
if self.args.mask_whole_words:
bpe = encoders.build_bpe(self.args)
if bpe is not None:
def is_beginning_of_word(i):
if i < self.source_dictionary.nspecial:
# special elements are always considered beginnings
return True
tok = self.source_dictionary[i]
if tok.startswith("madeupword"):
return True
try:
return bpe.is_beginning_of_word(tok)
except ValueError:
return True
mask_whole_words = torch.ByteTensor(
list(map(is_beginning_of_word, range(len(self.source_dictionary))))
)
else:
mask_whole_words = None
return mask_whole_words
def _get_sample_prob(self, dataset_lens):
"""
Get smoothed sampling porbability by languages. This helps low resource
languages by upsampling them.
"""
prob = dataset_lens / dataset_lens.sum()
smoothed_prob = prob ** self.args.multilang_sampling_alpha
smoothed_prob = smoothed_prob / smoothed_prob.sum()
return smoothed_prob
def load_dataset(self, split, epoch=1, combine=False, **kwargs):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
paths = utils.split_paths(self.args.data)
assert len(paths) > 0
data_path = paths[(epoch - 1) % len(paths)]
languages = sorted(
name
for name in os.listdir(data_path)
if os.path.isdir(os.path.join(data_path, name))
)
logger.info("Training on {0} languages: {1}".format(len(languages), languages))
logger.info(
"Language to id mapping: ", {lang: id for id, lang in enumerate(languages)}
)
mask_whole_words = self._get_whole_word_mask()
lang_datasets = []
for lang_id, language in enumerate(languages):
split_path = os.path.join(data_path, language, split)
dataset = data_utils.load_indexed_dataset(
split_path,
self.source_dictionary,
self.args.dataset_impl,
combine=combine,
)
if dataset is None:
raise FileNotFoundError(
"Dataset not found: {} ({})".format(split, split_path)
)
# create continuous blocks of tokens
dataset = TokenBlockDataset(
dataset,
dataset.sizes,
self.args.tokens_per_sample - 1, # one less for <s>
pad=self.source_dictionary.pad(),
eos=self.source_dictionary.eos(),
break_mode=self.args.sample_break_mode,
)
logger.info("loaded {} blocks from: {}".format(len(dataset), split_path))
# prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT)
dataset = PrependTokenDataset(dataset, self.source_dictionary.bos())
src_dataset, tgt_dataset = MaskTokensDataset.apply_mask(
dataset,
self.source_dictionary,
pad_idx=self.source_dictionary.pad(),
mask_idx=self.mask_idx,
seed=self.args.seed,
mask_prob=self.args.mask_prob,
leave_unmasked_prob=self.args.leave_unmasked_prob,
random_token_prob=self.args.random_token_prob,
freq_weighted_replacement=self.args.freq_weighted_replacement,
mask_whole_words=mask_whole_words,
)
lang_dataset = NestedDictionaryDataset(
{
"net_input": {
"src_tokens": PadDataset(
src_dataset,
pad_idx=self.source_dictionary.pad(),
left_pad=False,
),
"src_lengths": NumelDataset(src_dataset, reduce=False),
},
"target": PadDataset(
tgt_dataset,
pad_idx=self.source_dictionary.pad(),
left_pad=False,
),
"nsentences": NumSamplesDataset(),
"ntokens": NumelDataset(src_dataset, reduce=True),
"lang_id": RawLabelDataset([lang_id] * src_dataset.sizes.shape[0]),
},
sizes=[src_dataset.sizes],
)
lang_datasets.append(lang_dataset)
dataset_lengths = np.array(
[len(d) for d in lang_datasets],
dtype=float,
)
logger.info(
"loaded total {} blocks for all languages".format(
dataset_lengths.sum(),
)
)
if split == self.args.train_subset:
# For train subset, additionally up or down sample languages.
sample_probs = self._get_sample_prob(dataset_lengths)
logger.info(
"Sample probability by language: ",
{
lang: "{0:.4f}".format(sample_probs[id])
for id, lang in enumerate(languages)
},
)
size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths
logger.info(
"Up/Down Sampling ratio by language: ",
{
lang: "{0:.2f}".format(size_ratio[id])
for id, lang in enumerate(languages)
},
)
resampled_lang_datasets = [
ResamplingDataset(
lang_datasets[i],
size_ratio=size_ratio[i],
seed=self.args.seed,
epoch=epoch,
replace=size_ratio[i] >= 1.0,
)
for i, d in enumerate(lang_datasets)
]
dataset = ConcatDataset(resampled_lang_datasets)
else:
dataset = ConcatDataset(lang_datasets)
lang_splits = [split]
for lang_id, lang_dataset in enumerate(lang_datasets):
split_name = split + "_" + languages[lang_id]
lang_splits.append(split_name)
self.datasets[split_name] = lang_dataset
# [TODO]: This is hacky for now to print validation ppl for each
# language individually. Maybe need task API changes to allow it
# in more generic ways.
if split in self.args.valid_subset:
self.args.valid_subset = self.args.valid_subset.replace(
split, ",".join(lang_splits)
)
with data_utils.numpy_seed(self.args.seed + epoch):
shuffle = np.random.permutation(len(dataset))
self.datasets[split] = SortDataset(
dataset,
sort_order=[
shuffle,
dataset.sizes,
],
)
def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True):
src_dataset = PadDataset(
TokenBlockDataset(
src_tokens,
src_lengths,
self.args.tokens_per_sample - 1, # one less for <s>
pad=self.source_dictionary.pad(),
eos=self.source_dictionary.eos(),
break_mode="eos",
),
pad_idx=self.source_dictionary.pad(),
left_pad=False,
)
src_dataset = PrependTokenDataset(src_dataset, self.source_dictionary.bos())
src_dataset = NestedDictionaryDataset(
{
"id": IdDataset(),
"net_input": {
"src_tokens": src_dataset,
"src_lengths": NumelDataset(src_dataset, reduce=False),
},
},
sizes=src_lengths,
)
if sort:
src_dataset = SortDataset(src_dataset, sort_order=[src_lengths])
return src_dataset
@property
def source_dictionary(self):
return self.dictionary
@property
def target_dictionary(self):
return self.dictionary
|
COCO-LM/fairseq/fairseq/tasks/multilingual_masked_lm.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/tasks/multilingual_masked_lm.py",
"repo_id": "COCO-LM",
"token_count": 6393
}
| 197 |
__version__ = "1.0.0a0+6c15ee7"
|
COCO-LM/fairseq/fairseq/version.py/0
|
{
"file_path": "COCO-LM/fairseq/fairseq/version.py",
"repo_id": "COCO-LM",
"token_count": 20
}
| 198 |
#include <torch/extension.h>
#include <vector>
std::vector<torch::Tensor> fwd_cuda(
bool is_training,
int heads,
torch::Tensor const& input,
float dropout_prob
);
torch::Tensor bwd_cuda(
int heads,
torch::Tensor const& output_grads,
torch::Tensor const& softmax_results,
torch::Tensor const& dropout_mask,
float dropout_prob
);
// C++ interface
#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
std::vector<torch::Tensor> fwd(
bool is_training,
int heads,
torch::Tensor const& input,
float dropout_prob
)
{
AT_ASSERTM(input.dim() == 3, "expected 3D tensor");
AT_ASSERTM(input.type().scalarType() == at::ScalarType::Half || input.type().scalarType() == at::ScalarType::BFloat16 || input.type().scalarType() == at::ScalarType::Float, "Only HALF/BFloat16/Float is supported");
return fwd_cuda(
is_training,
heads,
input,
dropout_prob
);
}
torch::Tensor bwd(int heads,
torch::Tensor const& output_grads,
torch::Tensor const& softmax_results,
torch::Tensor const& dropout_mask,
float dropout_prob
)
{
AT_ASSERTM(output_grads.dim() == 3, "expected 3D tensor");
AT_ASSERTM(softmax_results.dim() == 3, "expected 3D tensor");
AT_ASSERTM(dropout_mask.dim() == 3, "expected 3D tensor");
AT_ASSERTM(output_grads.type().scalarType() == at::ScalarType::Half || output_grads.type().scalarType() == at::ScalarType::BFloat16 || output_grads.type().scalarType() == at::ScalarType::Float, "Only HALF/BFloat16/Float is supported");
AT_ASSERTM(softmax_results.type().scalarType() == at::ScalarType::Half || softmax_results.type().scalarType() == at::ScalarType::BFloat16 || softmax_results.type().scalarType() == at::ScalarType::Float, "Only HALF/BFloat16/Float is supported");
return bwd_cuda(
heads,
output_grads,
softmax_results,
dropout_mask,
dropout_prob
);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &fwd, "softmax dropout -- Forward.");
m.def("backward", &bwd, "softmax dropout -- Backward.");
}
|
COCO-LM/fairseq/fused_ops/csrc/softmax_dropout/interface.cpp/0
|
{
"file_path": "COCO-LM/fairseq/fused_ops/csrc/softmax_dropout/interface.cpp",
"repo_id": "COCO-LM",
"token_count": 1863
}
| 199 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
DATASET_PATH=$1
DICT_PATH=$2
mkdir -p $DATASET_PATH
cp $DICT_PATH/sp.model $DATASET_PATH
cp $DICT_PATH/dict.txt $DATASET_PATH
export TRAIN_FILE=$DATASET_PATH/train-v2.0.json
if [ ! -f $TRAIN_FILE ]
then
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json -O $TRAIN_FILE
fi
python squad_process.py --input $TRAIN_FILE --output $DATASET_PATH/train \
--sentencepiece-model $DICT_PATH/sp.model --vocab $DICT_PATH/dict.txt \
--is-training --version-2-with-negative
export DEV_FILE=$DATASET_PATH/dev-v2.0.json
if [ ! -f $DEV_FILE ]
then
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json -O $DEV_FILE
fi
python squad_process.py --input $DEV_FILE --output $DATASET_PATH/valid \
--sentencepiece-model $DICT_PATH/sp.model --vocab $DICT_PATH/dict.txt \
--version-2-with-negative
|
COCO-LM/fairseq/preprocess/squad/process.sh/0
|
{
"file_path": "COCO-LM/fairseq/preprocess/squad/process.sh",
"repo_id": "COCO-LM",
"token_count": 475
}
| 200 |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import re
import shutil
import sys
pt_regexp = re.compile(r"checkpoint(\d+|_\d+_\d+|_[a-z]+)\.pt")
pt_regexp_epoch_based = re.compile(r"checkpoint(\d+)\.pt")
pt_regexp_update_based = re.compile(r"checkpoint_\d+_(\d+)\.pt")
def parse_checkpoints(files):
entries = []
for f in files:
m = pt_regexp_epoch_based.fullmatch(f)
if m is not None:
entries.append((int(m.group(1)), m.group(0)))
else:
m = pt_regexp_update_based.fullmatch(f)
if m is not None:
entries.append((int(m.group(1)), m.group(0)))
return entries
def last_n_checkpoints(files, n):
entries = parse_checkpoints(files)
return [x[1] for x in sorted(entries, reverse=True)[:n]]
def every_n_checkpoints(files, n):
entries = parse_checkpoints(files)
return [x[1] for x in sorted(sorted(entries)[::-n])]
def main():
parser = argparse.ArgumentParser(
description=(
"Recursively delete checkpoint files from `root_dir`, "
"but preserve checkpoint_best.pt and checkpoint_last.pt"
)
)
parser.add_argument("root_dirs", nargs="*")
parser.add_argument(
"--save-last", type=int, default=0, help="number of last checkpoints to save"
)
parser.add_argument(
"--save-every", type=int, default=0, help="interval of checkpoints to save"
)
parser.add_argument(
"--preserve-test",
action="store_true",
help="preserve checkpoints in dirs that start with test_ prefix (default: delete them)",
)
parser.add_argument(
"--delete-best", action="store_true", help="delete checkpoint_best.pt"
)
parser.add_argument(
"--delete-last", action="store_true", help="delete checkpoint_last.pt"
)
parser.add_argument(
"--no-dereference", action="store_true", help="don't dereference symlinks"
)
args = parser.parse_args()
files_to_desymlink = []
files_to_preserve = []
files_to_delete = []
for root_dir in args.root_dirs:
for root, _subdirs, files in os.walk(root_dir):
if args.save_last > 0:
to_save = last_n_checkpoints(files, args.save_last)
else:
to_save = []
if args.save_every > 0:
to_save += every_n_checkpoints(files, args.save_every)
for file in files:
if not pt_regexp.fullmatch(file):
continue
full_path = os.path.join(root, file)
if (
not os.path.basename(root).startswith("test_") or args.preserve_test
) and (
(file == "checkpoint_last.pt" and not args.delete_last)
or (file == "checkpoint_best.pt" and not args.delete_best)
or file in to_save
):
if os.path.islink(full_path) and not args.no_dereference:
files_to_desymlink.append(full_path)
else:
files_to_preserve.append(full_path)
else:
files_to_delete.append(full_path)
if len(files_to_desymlink) == 0 and len(files_to_delete) == 0:
print("Nothing to do.")
sys.exit(0)
files_to_desymlink = sorted(files_to_desymlink)
files_to_preserve = sorted(files_to_preserve)
files_to_delete = sorted(files_to_delete)
print("Operations to perform (in order):")
if len(files_to_desymlink) > 0:
for file in files_to_desymlink:
print(" - preserve (and dereference symlink): " + file)
if len(files_to_preserve) > 0:
for file in files_to_preserve:
print(" - preserve: " + file)
if len(files_to_delete) > 0:
for file in files_to_delete:
print(" - delete: " + file)
while True:
resp = input("Continue? (Y/N): ")
if resp.strip().lower() == "y":
break
elif resp.strip().lower() == "n":
sys.exit(0)
print("Executing...")
if len(files_to_desymlink) > 0:
for file in files_to_desymlink:
realpath = os.path.realpath(file)
print("rm " + file)
os.remove(file)
print("cp {} {}".format(realpath, file))
shutil.copyfile(realpath, file)
if len(files_to_delete) > 0:
for file in files_to_delete:
print("rm " + file)
os.remove(file)
if __name__ == "__main__":
main()
|
COCO-LM/fairseq/scripts/rm_pt.py/0
|
{
"file_path": "COCO-LM/fairseq/scripts/rm_pt.py",
"repo_id": "COCO-LM",
"token_count": 2265
}
| 201 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
import unittest
import torch
from fairseq.token_generation_constraints import *
def tensorize(constraints: List[List[int]]) -> torch.Tensor:
return [torch.tensor(x) for x in constraints]
class TestHelperRoutines(unittest.TestCase):
def setUp(self):
self.examples = [
([[]], torch.tensor([[0]])),
([[], []], torch.tensor([[0], [0]])),
([[torch.tensor([1, 2])], []], torch.tensor([[1, 1, 2, 0], [0, 0, 0, 0]])),
(
[
[
torch.tensor([3, 1, 2]),
torch.tensor([3]),
torch.tensor([4, 5, 6, 7]),
],
[],
[torch.tensor([1, 8, 9, 10, 1, 4, 11, 12])],
],
torch.tensor(
[
[3, 3, 1, 2, 0, 3, 0, 4, 5, 6, 7, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 8, 9, 10, 1, 4, 11, 12, 0, 0, 0],
]
),
),
]
def test_packing(self):
"""Ensures the list of lists of tensors gets packed correctly."""
for batch_constraints, expected_tensor in self.examples:
packed = pack_constraints(batch_constraints)
assert torch.equal(packed, expected_tensor)
class TestUnorderedConstraintState(unittest.TestCase):
def setUp(self):
# Tuples of (contraint set, expected printed graph, token counts per node)
self.examples = [
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
"([None].False#6 ([1].True#4 ([2].False#1 [3].True#1) [3].True#1 [4].True#1) ([4].False#2 ([5].True#2 ([6].False#1 [7].True#1))))",
{1: 4, 2: 1, 3: 2, 4: 3, 5: 2, 6: 1, 7: 1},
),
([], "[None].False#0", {}),
(tensorize([[0]]), "([None].False#1 [0].True#1)", {0: 1}),
(
tensorize([[100000, 1, 2, 3, 4, 5]]),
"([None].False#1 ([100000].False#1 ([1].False#1 ([2].False#1 ([3].False#1 ([4].False#1 [5].True#1))))))",
{100000: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1},
),
(
tensorize([[1, 2], [1, 2]]),
"([None].False#2 ([1].False#2 [2].True#2))",
{1: 2, 2: 2},
),
(
tensorize([[1, 2], [3, 4]]),
"([None].False#2 ([1].False#1 [2].True#1) ([3].False#1 [4].True#1))",
{1: 1, 2: 1, 3: 1, 4: 1},
),
]
self.sequences = [
(
self.examples[0][0],
[],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
self.examples[0][0],
[1, 2],
{"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
),
(
self.examples[0][0],
[1, 2, 94],
{"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
),
(
self.examples[0][0],
[1, 3, 999, 1, 4],
{"bank": 4, "num_completed": 2, "finished": False, "is_root": False},
),
(
self.examples[0][0],
[1, 3, 999, 1, 4, 999],
{"bank": 4, "num_completed": 2, "finished": False, "is_root": True},
),
(
self.examples[0][0],
[4, 5, 6, 8],
{"bank": 2, "num_completed": 1, "finished": False, "is_root": True},
),
(
self.examples[0][0],
# Tricky, because in last three, goes down [1->4] branch, could miss [1] and [4->5]
# [[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]],
[1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
),
(
self.examples[0][0],
[1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": True},
),
(
tensorize([[1], [2, 3]]),
# Should not be able to get credit for entering 1 a second time
[1, 1],
{"bank": 1, "num_completed": 1, "finished": False, "is_root": True},
),
(
self.examples[4][0],
[1, 2, 1, 2],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
(
self.examples[4][0],
[1, 2, 1, 2, 1],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
),
(
self.examples[5][0],
[1, 2, 3, 4, 5],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": True},
),
]
def test_graphs(self):
"""
Test whether unordered graph systems are created correctly.
"""
for example in self.examples:
constraints, expected, gold_counts = example
c = ConstraintNode.create(constraints)
assert (
ConstraintNode.print_graph(c) == expected
), f"got {ConstraintNode.print_graph(c)}, expected {expected}"
assert (
c.token_counts() == gold_counts
), f"{c} got {c.token_counts()} wanted {gold_counts}"
def test_next_tokens(self):
"""
Tests that the set of next tokens is correct.
"""
for example in self.examples:
constraints, expected, gold_counts = example
root = ConstraintNode.create(constraints)
root_tokens = set(root.children.keys())
for sequence in constraints:
state = UnorderedConstraintState(root)
for token in sequence:
all_tokens = root_tokens.union(state.node.children.keys())
assert (
all_tokens == state.next_tokens()
), f"ALL {all_tokens} NEXT {state.next_tokens()}"
state = state.advance(token)
def test_sequences(self):
for constraints, tokens, expected in self.sequences:
state = UnorderedConstraintState.create(pack_constraints([constraints])[0])
for token in tokens:
state = state.advance(token)
result = {}
for attr in expected.keys():
result[attr] = getattr(state, attr)
assert (
result == expected
), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
class TestOrderedConstraintState(unittest.TestCase):
def setUp(self):
self.sequences = [
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2],
{"bank": 2, "num_completed": 0, "finished": False, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 94],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 3, 999, 1, 4],
{"bank": 0, "num_completed": 0, "finished": False, "is_root": True},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 3, 999, 999],
{"bank": 3, "num_completed": 1, "finished": False, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 3, 77, 1, 3, 1],
{"bank": 6, "num_completed": 2, "finished": False, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
),
(
tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]),
[1, 2, 999, 1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117],
{"bank": 14, "num_completed": 6, "finished": True, "is_root": False},
),
(
tensorize([[1], [2, 3]]),
[1, 1],
{"bank": 1, "num_completed": 1, "finished": False, "is_root": False},
),
(
tensorize([[1, 2], [1, 2]]),
[1, 2, 1, 2],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
(
tensorize([[1, 2], [1, 2]]),
[1, 2, 1, 2, 1],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
(
tensorize([[1, 2], [3, 4]]),
[1, 2, 3, 4, 5],
{"bank": 4, "num_completed": 2, "finished": True, "is_root": False},
),
]
def test_sequences(self):
for i, (constraints, tokens, expected) in enumerate(self.sequences):
state = OrderedConstraintState.create(pack_constraints([constraints])[0])
for token in tokens:
state = state.advance(token)
result = {}
for attr in expected.keys():
result[attr] = getattr(state, attr)
assert (
result == expected
), f"TEST({tokens}) GOT: {result} WANTED: {expected}"
if __name__ == "__main__":
unittest.main()
|
COCO-LM/fairseq/tests/test_constraints.py/0
|
{
"file_path": "COCO-LM/fairseq/tests/test_constraints.py",
"repo_id": "COCO-LM",
"token_count": 6160
}
| 202 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from collections import OrderedDict
import torch
from fairseq.data import LanguagePairDataset, TokenBlockDataset
from fairseq.data.multi_corpus_dataset import MultiCorpusDataset
from tests.test_train import mock_dict
class TestMultiCorpusDataset(unittest.TestCase):
def setUp(self):
d = mock_dict()
tokens_1 = torch.LongTensor([i for i in range(1, 5000, 2)]).view(1, -1)
tokens_ds1 = TokenBlockDataset(
tokens_1,
sizes=[tokens_1.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_1 = LanguagePairDataset(
tokens_ds1, tokens_ds1.sizes, d, shuffle=False
)
tokens_2 = torch.LongTensor([i for i in range(0, 5000, 2)]).view(1, -1)
tokens_ds2 = TokenBlockDataset(
tokens_2,
sizes=[tokens_2.size(-1)],
block_size=1,
pad=0,
eos=1,
include_targets=False,
)
self.dataset_2 = LanguagePairDataset(
tokens_ds2, tokens_ds2.sizes, d, shuffle=False
)
def _test_sample_helper(
self,
distribution,
):
m = MultiCorpusDataset(
OrderedDict({0: self.dataset_1, 1: self.dataset_2}),
distribution=distribution,
seed=0,
sort_indices=True,
)
m.set_epoch(1)
indices = m.ordered_indices()
count_sample_from_first_dataset = 0
items = set()
for i in indices:
item = m[i]["source"].item()
if item % 2 == 1:
count_sample_from_first_dataset += 1
items.add(item)
sample_from_first_ds_percentage = (
1.0 * count_sample_from_first_dataset / len(indices)
)
self.assertLess(
abs(sample_from_first_ds_percentage - distribution[0]),
0.01,
)
self.assertEqual(
len(items),
int(min(len(self.dataset_1), len(indices) * distribution[0])
+ min(len(self.dataset_1), len(indices) * distribution[1]))
)
print(distribution)
def test_multi_corpus_dataset(self):
for distribution in [[0.5, 0.5], [0.1, 0.9], [0.9, 0.1]]:
self._test_sample_helper(distribution=distribution)
|
COCO-LM/fairseq/tests/test_multi_corpus_dataset.py/0
|
{
"file_path": "COCO-LM/fairseq/tests/test_multi_corpus_dataset.py",
"repo_id": "COCO-LM",
"token_count": 1303
}
| 203 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# The script is largely adapted from the huggingface transformers library
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import sys
from io import open
from transformers.configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
COCOLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
'microsoft/cocolm-base': "https://huggingface.co/microsoft/cocolm-base/resolve/main/config.json",
'microsoft/cocolm-large': "https://huggingface.co/microsoft/cocolm-large/resolve/main/config.json",
}
class COCOLMConfig(PretrainedConfig):
model_type = "cocolm"
pretrained_config_archive_map = COCOLM_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(
self,
vocab_size=30522,
embedding_size=128,
hidden_size=256,
num_hidden_layers=12,
num_attention_heads=4,
intermediate_size=1024,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
summary_type="first",
summary_use_proj=True,
summary_activation="gelu",
summary_last_dropout=0.1,
pad_token_id=0,
rel_pos_bins=0,
max_rel_pos=0,
layer_norm_type='post',
**kwargs
):
super(COCOLMConfig, self).__init__(**kwargs)
if isinstance(vocab_size, str) or (sys.version_info[0] == 2
and isinstance(vocab_size, unicode)):
with open(vocab_size, "r", encoding='utf-8') as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif isinstance(vocab_size, int):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_last_dropout = summary_last_dropout
self.rel_pos_bins = rel_pos_bins
self.max_rel_pos = max_rel_pos
self.layer_norm_type = layer_norm_type
else:
raise ValueError("First argument must be either a vocabulary size (int)"
" or the path to a pretrained model config file (str)")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
return super().from_pretrained(pretrained_model_name_or_path, **kwargs)
|
COCO-LM/huggingface/cocolm/configuration_cocolm.py/0
|
{
"file_path": "COCO-LM/huggingface/cocolm/configuration_cocolm.py",
"repo_id": "COCO-LM",
"token_count": 1547
}
| 204 |
# ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
import argparse
import time
import yaml
import os
import logging
from collections import OrderedDict
from contextlib import suppress
from datetime import datetime
import models
import torch
import torch.nn as nn
import torchvision.utils
from torch.nn.parallel import DistributedDataParallel as NativeDDP
from timm.data import Dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
from timm.models import create_model, resume_checkpoint, convert_splitbn_model
from timm.utils import *
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
from timm.scheduler import create_scheduler
from timm.utils import ApexScaler, NativeScaler
from checkpoint_saver import CheckpointSaver
from labeled_memcached_dataset import McDataset
import torch.optim as optim
torch.backends.cudnn.benchmark = True
_logger = logging.getLogger('train')
# The first arg parser parses out only the --config argument, this argument is used to
# load a yaml file containing key-values that override the defaults for the main parser below
config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)
parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
help='YAML config file specifying default arguments')
parser = argparse.ArgumentParser(description='CSWin Training and Evaluating')
# Dataset / Model parameters
parser.add_argument('--data', default='/mnt/blob/testset/ImageNet', metavar='DIR',
help='path to dataset')
parser.add_argument('--model', default='', type=str, metavar='MODEL',
help='Name of model to train (default: "countception"')
parser.add_argument('--pretrained', action='store_true', default=False,
help='Start with pretrained version of specified network (if avail)')
parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
help='Initialize model from this checkpoint (default: none)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='Resume full model and optimizer state from checkpoint (default: none)')
parser.add_argument('--eval_checkpoint', default='', type=str, metavar='PATH',
help='path to eval checkpoint (default: none)')
parser.add_argument('--no-resume-opt', action='store_true', default=False,
help='prevent resume of optimizer state when resuming model')
parser.add_argument('--num-classes', type=int, default=1000, metavar='N',
help='number of label classes (default: 1000)')
parser.add_argument('--gp', default=None, type=str, metavar='POOL',
help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
parser.add_argument('--img-size', type=int, default=224, metavar='N',
help='Image patch size (default: None => model default)')
parser.add_argument('--crop-pct', default=None, type=float,
metavar='N', help='Input image center crop percent (for validation only)')
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
help='Override mean pixel value of dataset')
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
help='Override std deviation of of dataset')
parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
help='Image resize interpolation type (overrides model)')
parser.add_argument('-b', '--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N',
help='ratio of validation batch size to training batch size (default: 1)')
# Optimizer parameters
parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
help='Optimizer (default: "adamw"')
parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
help='Optimizer Epsilon (default: None, use opt default)')
parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='Optimizer momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=0.05,
help='weight decay (default: 0.005 for adamw)')
parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
# Learning rate schedule parameters
parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
help='LR scheduler (default: "cosine"')
parser.add_argument('--lr', type=float, default=5e-4, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--lr-scale', type=float, default=1e-2,
help='learning rate finetune scale(default: 0.01)')
parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
help='learning rate noise on/off epoch percentages')
parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
help='learning rate noise limit percent (default: 0.67)')
parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
help='learning rate noise std-dev (default: 1.0)')
parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
help='learning rate cycle len multiplier (default: 1.0)')
parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
help='learning rate cycle limit')
parser.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR',
help='warmup learning rate (default: 0.0001)')
parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--epochs', type=int, default=300, metavar='N',
help='number of epochs to train (default: 2)')
parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
help='epoch interval to decay LR')
parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
help='patience epochs for Plateau LR scheduler (default: 10')
parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
help='LR decay rate (default: 0.1)')
# Augmentation & regularization parameters
parser.add_argument('--no-aug', action='store_true', default=False,
help='Disable all training augmentation, override other train aug args')
parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
help='Random resize scale (default: 0.08 1.0)')
parser.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
help='Random resize aspect ratio (default: 0.75 1.33)')
parser.add_argument('--hflip', type=float, default=0.5,
help='Horizontal flip training aug probability')
parser.add_argument('--vflip', type=float, default=0.,
help='Vertical flip training aug probability')
parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
help='Color jitter factor (default: 0.4)')
parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
help='Use AutoAugment policy. "v0" or "original". (default: None)'),
parser.add_argument('--aug-splits', type=int, default=0,
help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
parser.add_argument('--jsd', action='store_true', default=False,
help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
help='Random erase prob (default: 0.25)')
parser.add_argument('--remode', type=str, default='pixel',
help='Random erase mode (default: "const")')
parser.add_argument('--recount', type=int, default=1,
help='Random erase count (default: 1)')
parser.add_argument('--resplit', action='store_true', default=False,
help='Do not random erase first (clean) augmentation split')
parser.add_argument('--mixup', type=float, default=0.8,
help='mixup alpha, mixup enabled if > 0. (default: 0.)')
parser.add_argument('--cutmix', type=float, default=1.0,
help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
parser.add_argument('--mixup-prob', type=float, default=1.0,
help='Probability of performing mixup or cutmix when either/both is enabled')
parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
help='Probability of switching to cutmix when both mixup and cutmix enabled')
parser.add_argument('--mixup-mode', type=str, default='batch',
help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
parser.add_argument('--smoothing', type=float, default=0.1,
help='Label smoothing (default: 0.1)')
parser.add_argument('--train-interpolation', type=str, default='random',
help='Training interpolation (random, bilinear, bicubic default: "random")')
parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
help='Dropout rate (default: 0.0)')
parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
parser.add_argument('--drop-path', type=float, default=0.1, metavar='PCT',
help='Drop path rate (default: None)')
parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
help='Drop block rate (default: None)')
# Batch norm parameters (only works with gen_efficientnet based models currently)
parser.add_argument('--bn-tf', action='store_true', default=False,
help='Use Tensorflow BatchNorm defaults for models that support it (default: False)')
parser.add_argument('--bn-momentum', type=float, default=None,
help='BatchNorm momentum override (if not None)')
parser.add_argument('--bn-eps', type=float, default=None,
help='BatchNorm epsilon override (if not None)')
parser.add_argument('--sync-bn', action='store_true',
help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
parser.add_argument('--dist-bn', type=str, default='',
help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
parser.add_argument('--split-bn', action='store_true',
help='Enable separate BN layers per augmentation split.')
# Model Exponential Moving Average
parser.add_argument('--model-ema', action='store_true', default=True,
help='Enable tracking moving average of model weights')
parser.add_argument('--model-ema-force-cpu', action='store_true', default=False,
help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
parser.add_argument('--model-ema-decay', type=float, default=0.99996,
help='decay factor for model weights moving average (default: 0.9998)')
# Misc
parser.add_argument('--seed', type=int, default=42, metavar='S',
help='random seed (default: 42)')
parser.add_argument('--log-interval', type=int, default=50, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
help='how many batches to wait before writing recovery checkpoint')
parser.add_argument('-j', '--workers', type=int, default=8, metavar='N',
help='how many training processes to use (default: 1)')
parser.add_argument('--num-gpu', type=int, default=1,
help='Number of GPUS to use')
parser.add_argument('--save-images', action='store_true', default=False,
help='save images of input bathes every log interval for debugging')
parser.add_argument('--amp', action='store_true', default=False,
help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
parser.add_argument('--apex-amp', action='store_true', default=False,
help='Use NVIDIA Apex AMP mixed precision')
parser.add_argument('--native-amp', action='store_true', default=False,
help='Use Native Torch AMP mixed precision')
parser.add_argument('--channels-last', action='store_true', default=False,
help='Use channels_last memory layout')
parser.add_argument('--pin-mem', action='store_true', default=False,
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no-prefetcher', action='store_true', default=False,
help='disable fast prefetcher')
parser.add_argument('--output', default='', type=str, metavar='PATH',
help='path to output folder (default: none, current dir)')
parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
help='Best metric (default: "top1"')
parser.add_argument('--tta', type=int, default=0, metavar='N',
help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
parser.add_argument("--local_rank", default=0, type=int)
parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
help='use the multi-epochs-loader to save time at the beginning of every epoch')
parser.add_argument('--finetune', default='', type=str, metavar='PATH',
help='finetune full model and optimizer state from checkpoint (default: none)')
parser.add_argument('--fine-22k', action='store_true', default=False,
help='finetune 22k model')
parser.add_argument('--use-chk', action='store_true', default=False,
help='Enable tracking moving average of model weights')
parser.add_argument('--ema-finetune', action='store_true', default=False,
help='Enable tracking moving average of model weights')
try:
from apex import amp
from apex.parallel import DistributedDataParallel as ApexDDP
from apex.parallel import convert_syncbn_model
has_apex = True
except ImportError:
has_apex = False
has_native_amp = False
try:
if getattr(torch.cuda.amp, 'autocast') is not None:
has_native_amp = True
except AttributeError:
pass
def _parse_args():
# Do we have a config file to parse?
args_config, remaining = config_parser.parse_known_args()
if args_config.config:
with open(args_config.config, 'r') as f:
cfg = yaml.safe_load(f)
parser.set_defaults(**cfg)
# The main arg parser parses the rest of the args, the usual
# defaults will have been overridden if config file specified.
args = parser.parse_args(remaining)
# Cache the args as a text string to save them in the output dir later
args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
return args, args_text
def load_checkpoint(args, model, checkpoint_path, use_ema=False):
if checkpoint_path and os.path.isfile(checkpoint_path):
checkpoint = torch.load(checkpoint_path, map_location='cpu')
state_dict_key = 'state_dict'
model_key = 'model'
if isinstance(checkpoint, dict):
if use_ema and 'state_dict_ema' in checkpoint:
state_dict_key = 'state_dict_ema'
if state_dict_key and state_dict_key in checkpoint:
new_state_dict = OrderedDict()
for k, v in checkpoint[state_dict_key].items():
# strip `module.` prefix
name = k[7:] if k.startswith('module') else k
if args.fine_22k and 'head' in k:
continue
new_state_dict[name] = v
state_dict = new_state_dict
elif model_key and model_key in checkpoint:
new_state_dict = OrderedDict()
for k, v in checkpoint[model_key].items():
# strip `module.` prefix
name = k[7:] if k.startswith('module') else k
if args.fine_22k and 'head' in k:
continue
new_state_dict[name] = v
state_dict = new_state_dict
else:
state_dict = checkpoint
if args.local_rank == 0:
_logger.info("Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path))
else:
if args.local_rank == 0:
_logger.error("No checkpoint found at '{}'".format(checkpoint_path))
raise FileNotFoundError()
model_dict = model.state_dict()
pretrained_dict = state_dict
loaded_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
load_dic = [k for k, v in pretrained_dict.items() if k in model_dict]
miss_dic = [k for k, v in pretrained_dict.items() if not (k in model_dict)]
unexpect_dic = [k for k, v in model_dict.items() if not (k in pretrained_dict)]
if args.local_rank == 0:
print ("Miss Keys:", miss_dic)
print ("Ubexpected Keys:", unexpect_dic)
model_dict.update(loaded_dict)
model.load_state_dict(model_dict, strict=True)
def create_optimizer(args, parameters, filter_bias_and_bn=True):
opt_lower = args.opt.lower()
weight_decay = args.weight_decay
if 'fused' in opt_lower:
assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
opt_args = dict(lr=args.lr, weight_decay=weight_decay)
if hasattr(args, 'opt_eps') and args.opt_eps is not None:
opt_args['eps'] = args.opt_eps
if hasattr(args, 'opt_betas') and args.opt_betas is not None:
opt_args['betas'] = args.opt_betas
if hasattr(args, 'opt_args') and args.opt_args is not None:
opt_args.update(args.opt_args)
opt_split = opt_lower.split('_')
opt_lower = opt_split[-1]
if opt_lower == 'sgd' or opt_lower == 'nesterov':
opt_args.pop('eps', None)
optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
elif opt_lower == 'momentum':
opt_args.pop('eps', None)
optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
elif opt_lower == 'adam':
optimizer = optim.Adam(parameters, **opt_args)
elif opt_lower == 'adamw':
optimizer = optim.AdamW(parameters, **opt_args)
elif opt_lower == 'nadam':
optimizer = Nadam(parameters, **opt_args)
elif opt_lower == 'radam':
optimizer = RAdam(parameters, **opt_args)
elif opt_lower == 'adamp':
optimizer = AdamP(parameters, wd_ratio=0.01, nesterov=True, **opt_args)
elif opt_lower == 'sgdp':
optimizer = SGDP(parameters, momentum=args.momentum, nesterov=True, **opt_args)
elif opt_lower == 'adadelta':
optimizer = optim.Adadelta(parameters, **opt_args)
elif opt_lower == 'adafactor':
if not args.lr:
opt_args['lr'] = None
optimizer = Adafactor(parameters, **opt_args)
elif opt_lower == 'adahessian':
optimizer = Adahessian(parameters, **opt_args)
elif opt_lower == 'rmsprop':
optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
elif opt_lower == 'rmsproptf':
optimizer = RMSpropTF(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
elif opt_lower == 'novograd':
optimizer = NovoGrad(parameters, **opt_args)
elif opt_lower == 'nvnovograd':
optimizer = NvNovoGrad(parameters, **opt_args)
elif opt_lower == 'fusedsgd':
opt_args.pop('eps', None)
optimizer = FusedSGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
elif opt_lower == 'fusedmomentum':
opt_args.pop('eps', None)
optimizer = FusedSGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
elif opt_lower == 'fusedadam':
optimizer = FusedAdam(parameters, adam_w_mode=False, **opt_args)
elif opt_lower == 'fusedadamw':
optimizer = FusedAdam(parameters, adam_w_mode=True, **opt_args)
elif opt_lower == 'fusedlamb':
optimizer = FusedLAMB(parameters, **opt_args)
elif opt_lower == 'fusednovograd':
opt_args.setdefault('betas', (0.95, 0.98))
optimizer = FusedNovoGrad(parameters, **opt_args)
else:
assert False and "Invalid optimizer"
raise ValueError
if len(opt_split) > 1:
if opt_split[0] == 'lookahead':
optimizer = Lookahead(optimizer)
return optimizer
def main():
setup_default_logging()
args, args_text = _parse_args()
args.prefetcher = not args.no_prefetcher
args.distributed = False
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
if args.distributed and args.num_gpu > 1:
_logger.warning(
'Using more than one GPU per process in distributed mode is not allowed.Setting num_gpu to 1.')
args.num_gpu = 1
args.device = 'cuda:0'
args.world_size = 1
args.rank = 0 # global rank
if args.distributed:
args.num_gpu = 1
args.device = 'cuda:%d' % args.local_rank
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
args.world_size = torch.distributed.get_world_size()
args.rank = torch.distributed.get_rank()
assert args.rank >= 0
if args.distributed:
_logger.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'
% (args.rank, args.world_size))
else:
_logger.info('Training with a single process on %d GPUs.' % args.num_gpu)
torch.manual_seed(args.seed + args.rank)
model = create_model(
args.model,
pretrained=args.pretrained,
num_classes=args.num_classes,
drop_rate=args.drop,
drop_connect_rate=args.drop_connect, # DEPRECATED, use drop_path
drop_path_rate=args.drop_path,
drop_block_rate=args.drop_block,
global_pool=args.gp,
bn_tf=args.bn_tf,
bn_momentum=args.bn_momentum,
bn_eps=args.bn_eps,
checkpoint_path=args.initial_checkpoint,
img_size=args.img_size,
use_chk=args.use_chk)
if args.local_rank == 0:
_logger.info('Model %s created, param count: %d' %
(args.model, sum([m.numel() for m in model.parameters()])))
data_config = resolve_data_config(vars(args), model=model, verbose=args.local_rank == 0)
num_aug_splits = 0
if args.aug_splits > 0:
assert args.aug_splits > 1, 'A split of 1 makes no sense'
num_aug_splits = args.aug_splits
if args.split_bn:
assert num_aug_splits > 1 or args.resplit
model = convert_splitbn_model(model, max(num_aug_splits, 2))
use_amp = None
if args.amp:
if has_apex:
args.apex_amp = True
elif has_native_amp:
args.native_amp = True
if args.apex_amp and has_apex:
use_amp = 'apex'
elif args.native_amp and has_native_amp:
use_amp = 'native'
elif args.apex_amp or args.native_amp:
_logger.warning("Neither APEX or native Torch AMP is available, using float32. "
"Install NVIDA apex or upgrade to PyTorch 1.6")
if args.num_gpu > 1:
if use_amp == 'apex':
_logger.warning(
'Apex AMP does not work well with nn.DataParallel, disabling. Use DDP or Torch AMP.')
use_amp = None
model = nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
assert not args.channels_last, "Channels last not supported with DP, use DDP."
else:
model.cuda()
if args.channels_last:
model = model.to(memory_format=torch.channels_last)
# optionally resume from a checkpoint
resume_epoch = None
if args.resume:
head_para = []
base_para = []
for name, para in model.named_parameters():
if 'head' in name:
head_para.append(para)
else:
base_para.append(para)
parameters = [{'params': base_para, 'lr': args.lr_scale * args.lr},
{'params': head_para}]
optimizer = create_optimizer(args, parameters)
resume_epoch = resume_checkpoint(
model, args.resume,
optimizer=None if args.no_resume_opt else optimizer,
log_info=args.local_rank == 0)
if args.finetune:
load_checkpoint(args, model, args.finetune, use_ema=args.ema_finetune)
model.cuda()
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
if args.local_rank == 0:
print ("Use EMA with decay:", args.model_ema_decay)
model_ema = ModelEma(
model,
decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '',
resume=args.resume)
head_para = []
base_para = []
if not args.resume:
for name, para in model.named_parameters():
if 'head' in name:
head_para.append(para)
else:
base_para.append(para)
parameters = [{'params': base_para, 'lr': args.lr_scale * args.lr},
{'params': head_para}]
optimizer = create_optimizer(args, parameters)
amp_autocast = suppress # do nothing
loss_scaler = None
if use_amp == 'apex':
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
loss_scaler = ApexScaler()
if args.local_rank == 0:
_logger.info('Using NVIDIA APEX AMP. Training in mixed precision.')
elif use_amp == 'native':
amp_autocast = torch.cuda.amp.autocast
loss_scaler = NativeScaler()
if args.local_rank == 0:
_logger.info('Using native Torch AMP. Training in mixed precision.')
else:
if args.local_rank == 0:
_logger.info('AMP not enabled. Training in float32.')
if args.distributed:
if args.sync_bn:
assert not args.split_bn
try:
if has_apex and use_amp != 'native':
# Apex SyncBN preferred unless native amp is activated
model = convert_syncbn_model(model)
else:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
if args.local_rank == 0:
_logger.info(
'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using '
'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.')
except Exception as e:
_logger.error('Failed to enable Synchronized BatchNorm. Install Apex or Torch >= 1.1')
if has_apex and use_amp != 'native':
# Apex DDP preferred unless native amp is activated
if args.local_rank == 0:
_logger.info("Using NVIDIA APEX DistributedDataParallel.")
model = ApexDDP(model, delay_allreduce=True)
else:
if args.local_rank == 0:
_logger.info("Using native Torch DistributedDataParallel.")
model = NativeDDP(model, device_ids=[args.local_rank], find_unused_parameters=False) # can use device str in Torch >= 1.1
# NOTE: EMA model does not need to be wrapped by DDP
lr_scheduler, num_epochs = create_scheduler(args, optimizer)
start_epoch = 0
if args.start_epoch is not None:
# a specified start_epoch will always override the resume epoch
start_epoch = args.start_epoch
elif resume_epoch is not None:
start_epoch = resume_epoch
if lr_scheduler is not None and start_epoch > 0:
lr_scheduler.step(start_epoch)
if args.local_rank == 0:
_logger.info('Scheduled epochs: {}'.format(num_epochs))
train_dir = os.path.join(args.data, 'train')
if not os.path.exists(train_dir):
_logger.error('Training folder does not exist at: {}'.format(train_dir))
exit(1)
dataset_train = McDataset(args.data, './dataset/ILSVRC2012_name_train.txt', 'train')
collate_fn = None
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
mixup_args = dict(
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
label_smoothing=args.smoothing, num_classes=args.num_classes)
if args.prefetcher:
assert not num_aug_splits # collate conflict (need to support deinterleaving in collate mixup)
collate_fn = FastCollateMixup(**mixup_args)
else:
mixup_fn = Mixup(**mixup_args)
if num_aug_splits > 1:
dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)
train_interpolation = args.train_interpolation
if args.no_aug or not train_interpolation:
train_interpolation = data_config['interpolation']
if args.aa == 'None':
args.aa = None
loader_train = create_loader(
dataset_train,
input_size=data_config['input_size'],
batch_size=args.batch_size,
is_training=True,
use_prefetcher=args.prefetcher,
no_aug=args.no_aug,
re_prob=args.reprob,
re_mode=args.remode,
re_count=args.recount,
re_split=args.resplit,
scale=args.scale,
ratio=args.ratio,
hflip=args.hflip,
vflip=args.vflip,
color_jitter=args.color_jitter,
auto_augment=args.aa,
num_aug_splits=num_aug_splits,
interpolation=train_interpolation,
mean=data_config['mean'],
std=data_config['std'],
num_workers=args.workers,
distributed=args.distributed,
collate_fn=collate_fn,
pin_memory=args.pin_mem,
use_multi_epochs_loader=args.use_multi_epochs_loader
)
eval_dir = os.path.join(args.data, 'val')
if not os.path.isdir(eval_dir):
eval_dir = os.path.join(args.data, 'validation')
if not os.path.isdir(eval_dir):
_logger.error('Validation folder does not exist at: {}'.format(eval_dir))
exit(1)
dataset_eval = McDataset(args.data, './dataset/ILSVRC2012_name_val.txt', 'val')
loader_eval = create_loader(
dataset_eval,
input_size=data_config['input_size'],
batch_size=args.validation_batch_size_multiplier * args.batch_size,
is_training=False,
use_prefetcher=args.prefetcher,
interpolation=data_config['interpolation'],
mean=data_config['mean'],
std=data_config['std'],
num_workers=args.workers,
distributed=args.distributed,
crop_pct=data_config['crop_pct'],
pin_memory=args.pin_mem,
)
if args.jsd:
assert num_aug_splits > 1 # JSD only valid with aug splits set
train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).cuda()
elif mixup_active:
# smoothing is handled with mixup target transform
train_loss_fn = SoftTargetCrossEntropy().cuda()
elif args.smoothing:
train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).cuda()
else:
train_loss_fn = nn.CrossEntropyLoss().cuda()
validate_loss_fn = nn.CrossEntropyLoss().cuda()
eval_metric = args.eval_metric
best_metric = None
best_epoch = None
if args.eval_checkpoint: # evaluate the model
load_checkpoint(model, args.eval_checkpoint, args.model_ema)
val_metrics = validate(model, loader_eval, validate_loss_fn, args)
print(f"Top-1 accuracy of the model is: {val_metrics['top1']:.1f}%")
return
saver = None
output_dir = ''
if args.local_rank == 0:
output_base = args.output if args.output else './output'
exp_name = '-'.join([
datetime.now().strftime("%Y%m%d-%H%M%S"),
args.model,
str(data_config['input_size'][-1])
])
output_dir = get_outdir(output_base, 'finetune', exp_name)
decreasing = True if eval_metric == 'loss' else False
saver = CheckpointSaver(
model=model, optimizer=optimizer, args=args, model_ema=model_ema, amp_scaler=loss_scaler,
checkpoint_dir=output_dir, recovery_dir=output_dir, decreasing=decreasing)
with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
f.write(args_text)
try: # train the model
for epoch in range(start_epoch, num_epochs):
if args.distributed:
loader_train.sampler.set_epoch(epoch)
train_metrics = train_epoch(
epoch, model, loader_train, optimizer, train_loss_fn, args,
lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
amp_autocast=amp_autocast, loss_scaler=loss_scaler, model_ema=model_ema, mixup_fn=mixup_fn)
if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
if args.local_rank == 0:
_logger.info("Distributing BatchNorm running means and vars")
distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
eval_metrics = validate(model, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast)
if model_ema is not None and not args.model_ema_force_cpu:
if args.distributed and args.dist_bn in ('broadcast', 'reduce'):
distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce')
ema_eval_metrics = validate(
model_ema.ema, loader_eval, validate_loss_fn, args, amp_autocast=amp_autocast, log_suffix=' (EMA)')
eval_metrics = ema_eval_metrics
if lr_scheduler is not None:
# step LR for next epoch
lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])
update_summary(
epoch, train_metrics, eval_metrics, os.path.join(output_dir, 'summary.csv'),
write_header=best_metric is None)
if saver is not None:
# save proper checkpoint with eval metric
save_metric = eval_metrics[eval_metric]
best_metric, best_epoch = saver.save_checkpoint(epoch, metric=save_metric)
except KeyboardInterrupt:
pass
if best_metric is not None:
_logger.info('*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch))
def train_epoch(
epoch, model, loader, optimizer, loss_fn, args, freeze=False,
lr_scheduler=None, saver=None, output_dir='', amp_autocast=suppress,
loss_scaler=None, model_ema=None, mixup_fn=None):
if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
if args.prefetcher and loader.mixup_enabled:
loader.mixup_enabled = False
elif mixup_fn is not None:
mixup_fn.mixup_enabled = False
second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
batch_time_m = AverageMeter()
data_time_m = AverageMeter()
losses_m = AverageMeter()
top1_m = AverageMeter()
top5_m = AverageMeter()
model.train()
end = time.time()
last_idx = len(loader) - 1
num_updates = epoch * len(loader)
for batch_idx, (input, target) in enumerate(loader):
last_batch = batch_idx == last_idx
data_time_m.update(time.time() - end)
if not args.prefetcher:
input, target = input.cuda(), target.cuda()
if mixup_fn is not None:
input, target = mixup_fn(input, target)
if args.channels_last:
input = input.contiguous(memory_format=torch.channels_last)
with amp_autocast():
output = model(input)
loss = loss_fn(output, target)
if not args.distributed:
losses_m.update(loss.item(), input.size(0))
#torch.cuda.synchronize()
#end = time.time()
optimizer.zero_grad()
if loss_scaler is not None:
loss_scaler(
loss, optimizer, clip_grad=args.clip_grad, parameters=model.parameters(), create_graph=second_order)
else:
loss.backward(create_graph=second_order)
if args.clip_grad is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad)
optimizer.step()
torch.cuda.synchronize()
if model_ema is not None:
model_ema.update(model)
num_updates += 1
batch_time_m.update(time.time() - end)
if last_batch or batch_idx % args.log_interval == 0:
lrl = [param_group['lr'] for param_group in optimizer.param_groups]
lr = sum(lrl) / len(lrl)
if args.distributed:
reduced_loss = reduce_tensor(loss.data, args.world_size)
losses_m.update(reduced_loss.item(), input.size(0))
if args.local_rank == 0:
_logger.info(
'Train:{} [{:>4d}/{} ({:>3.0f}%)] '
'Loss:{loss.val:>9.6f} ({loss.avg:>6.4f}) '
'Time:'
'({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s) '
'LR:{lr1:.3e} {lr2:.3e} '
'Data:({data_time.sum:.3f})'.format(
epoch,
batch_idx, len(loader),
100. * batch_idx / last_idx,
loss=losses_m,
batch_time=batch_time_m,
rate=input.size(0) * args.world_size / batch_time_m.val,
rate_avg=input.size(0) * args.world_size / batch_time_m.avg,
lr1=lrl[0], lr2=lrl[1],
data_time=data_time_m))
if args.save_images and output_dir:
torchvision.utils.save_image(
input,
os.path.join(output_dir, 'train-batch-%d.jpg' % batch_idx),
padding=0,
normalize=True)
if saver is not None and args.recovery_interval and (
last_batch or (batch_idx + 1) % args.recovery_interval == 0):
saver.save_recovery(epoch, batch_idx=batch_idx)
if lr_scheduler is not None:
lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg)
end = time.time()
# end for
if hasattr(optimizer, 'sync_lookahead'):
optimizer.sync_lookahead()
return OrderedDict([('loss', losses_m.avg)])
def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=''):
batch_time_m = AverageMeter()
losses_m = AverageMeter()
top1_m = AverageMeter()
top5_m = AverageMeter()
model.eval()
end = time.time()
last_idx = len(loader) - 1
with torch.no_grad():
for batch_idx, (input, target) in enumerate(loader):
last_batch = batch_idx == last_idx
if not args.prefetcher:
input = input.cuda()
target = target.cuda()
if args.channels_last:
input = input.contiguous(memory_format=torch.channels_last)
with amp_autocast():
output = model(input)
if isinstance(output, (tuple, list)):
output = output[0]
# augmentation reduction
reduce_factor = args.tta
if reduce_factor > 1:
output = output.unfold(0, reduce_factor, reduce_factor).mean(dim=2)
target = target[0:target.size(0):reduce_factor]
loss = loss_fn(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
if args.distributed:
reduced_loss = reduce_tensor(loss.data, args.world_size)
acc1 = reduce_tensor(acc1, args.world_size)
acc5 = reduce_tensor(acc5, args.world_size)
else:
reduced_loss = loss.data
torch.cuda.synchronize()
losses_m.update(reduced_loss.item(), input.size(0))
top1_m.update(acc1.item(), output.size(0))
top5_m.update(acc5.item(), output.size(0))
batch_time_m.update(time.time() - end)
end = time.time()
if args.local_rank == 0 and (last_batch or batch_idx % args.log_interval == 0):
log_name = 'Test' + log_suffix
_logger.info(
'{0}: [{1:>4d}/{2}] '
'Time: {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f}) '
'Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f}) '
'Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format(
log_name, batch_idx, last_idx, batch_time=batch_time_m,
loss=losses_m, top1=top1_m, top5=top5_m))
metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)])
return metrics
if __name__ == '__main__':
main()
|
CSWin-Transformer/finetune.py/0
|
{
"file_path": "CSWin-Transformer/finetune.py",
"repo_id": "CSWin-Transformer",
"token_count": 19498
}
| 205 |
# ------------------------------------------
# CSWin Transformer
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# written By Xiaoyi Dong
# ------------------------------------------
NUM_PROC=$1
shift
python -m torch.distributed.launch --nproc_per_node=$NUM_PROC main.py "$@"
|
CSWin-Transformer/train.sh/0
|
{
"file_path": "CSWin-Transformer/train.sh",
"repo_id": "CSWin-Transformer",
"token_count": 77
}
| 206 |
<p align="center">
<img src="https://user-images.githubusercontent.com/1785175/215624212-fc92ccb1-f14c-4cb6-982f-61f50b9f3c21.png" width="320px">
</p>
[](https://microsoft.github.io/ClimaX)
[](https://arxiv.org/abs/2301.10343)
This repository contains code accompanying the paper [**ClimaX: A foundation model for weather and climate**](https://arxiv.org/abs/2301.10343).
For details about usage please see [documentation](https://microsoft.github.io/ClimaX).
If you have any questions or suggestions please open a [discussion](https://github.com/microsoft/ClimaX/discussions). If you notice a bug, please open an [issue](https://github.com/microsoft/ClimaX/issues).
## Citation
If you find this repository useful in your research, please consider citing the following papers:
```bibtex
@article{nguyen2023climax,
title={ClimaX: A foundation model for weather and climate},
author={Nguyen, Tung and Brandstetter, Johannes and Kapoor, Ashish and Gupta, Jayesh K and Grover, Aditya},
journal={arXiv preprint arXiv:2301.10343},
year={2023}
}
```
## Acknowledgements
Thanks to [@rems75](https://github.com/rems75) and [@kdatta](https://github.com/kdatta) for noticing a performance bug with variable id transfer to GPUs.
## Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
|
ClimaX/README.md/0
|
{
"file_path": "ClimaX/README.md",
"repo_id": "ClimaX",
"token_count": 803
}
| 207 |
datadir: /data/CMIP6/AWI-ESM
name: specific_humidity
cmip_name: hus
era_name: q
run: r1i1p1f1
res:
- 1.40625
# - 5.625
|
ClimaX/snakemake_configs/AWI-ESM/config_specific_humidity.yml/0
|
{
"file_path": "ClimaX/snakemake_configs/AWI-ESM/config_specific_humidity.yml",
"repo_id": "ClimaX",
"token_count": 64
}
| 208 |
datadir: /data/CMIP6/HAMMOZ
name: u_component_of_wind
cmip_name: ua
era_name: u
run: r1i1p1f1
version: v20190628
res:
- 1.40625
# - 5.625
|
ClimaX/snakemake_configs/HAMMOZ/config_u_component_of_wind.yml/0
|
{
"file_path": "ClimaX/snakemake_configs/HAMMOZ/config_u_component_of_wind.yml",
"repo_id": "ClimaX",
"token_count": 74
}
| 209 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
from typing import Dict, Optional
import numpy as np
import torch
import torchdata.datapipes as dp
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
from climax.pretrain.dataset import (
Forecast,
IndividualForecastDataIter,
NpyReader,
ShuffleIterableDataset,
)
def collate_fn(batch):
inp = torch.stack([batch[i][0] for i in range(len(batch))])
out = torch.stack([batch[i][1] for i in range(len(batch))])
lead_times = torch.stack([batch[i][2] for i in range(len(batch))])
variables = batch[0][3]
out_variables = batch[0][4]
return (
inp,
out,
lead_times,
[v for v in variables],
[v for v in out_variables],
)
class MultiSourceDataModule(LightningDataModule):
"""DataModule for multi-source data.
Args:
dict_root_dirs (Dict): Dictionary of root directories for each source.
dict_start_idx (Dict): Dictionary of start indices ratio (between 0.0 and 1.0) for each source.
dict_end_idx (Dict): Dictionary of end indices ratio (between 0.0 and 1.0) for each source.
dict_buffer_sizes (Dict): Dictionary of buffer sizes for each source.
dict_in_variables (Dict): Dictionary of input variables for each source.
dict_out_variables (Dict): Dictionary of output variables for each source.
dict_max_predict_ranges (Dict, optional): Dictionary of maximum predict ranges for each source.
dict_random_lead_time (Dict, optional): Dictionary of whether to use random lead time for each source.
dict_hrs_each_step (Dict, optional): Dictionary of hours each step for each source.
batch_size (int, optional): Batch size.
num_workers (int, optional): Number of workers.
pin_memory (bool, optional): Whether to pin memory.
"""
def __init__(
self,
dict_root_dirs: Dict,
dict_start_idx: Dict,
dict_end_idx: Dict,
dict_buffer_sizes: Dict,
dict_in_variables: Dict,
dict_out_variables: Dict,
dict_max_predict_ranges: Dict = {"mpi-esm": 28},
dict_random_lead_time: Dict = {"mpi-esm": True},
dict_hrs_each_step: Dict = {"mpi-esm": 6},
batch_size: int = 64,
num_workers: int = 0,
pin_memory: bool = False,
):
super().__init__()
if num_workers > 1:
raise NotImplementedError(
"num_workers > 1 is not supported yet. Performance will likely degrage too with larger num_workers."
)
# this line allows to access init params with 'self.hparams' attribute
self.save_hyperparameters(logger=False)
out_variables = {}
for k, list_out in dict_out_variables.items():
if list_out is not None:
out_variables[k] = list_out
else:
out_variables[k] = dict_in_variables[k]
self.hparams.dict_out_variables = out_variables
self.dict_lister_trains = {
k: list(dp.iter.FileLister(os.path.join(root_dir, "train"))) for k, root_dir in dict_root_dirs.items()
}
self.train_dataset_args = {
k: {
"max_predict_range": dict_max_predict_ranges[k],
"random_lead_time": dict_random_lead_time[k],
"hrs_each_step": dict_hrs_each_step[k],
}
for k in dict_root_dirs.keys()
}
self.transforms = self.get_normalize()
self.output_transforms = self.get_normalize(self.hparams.dict_out_variables)
self.dict_data_train: Optional[Dict] = None
def get_normalize(self, dict_variables: Optional[Dict] = None):
if dict_variables is None:
dict_variables = self.hparams.dict_in_variables
dict_transforms = {}
for k in dict_variables.keys():
root_dir = self.hparams.dict_root_dirs[k]
variables = dict_variables[k]
normalize_mean = dict(np.load(os.path.join(root_dir, "normalize_mean.npz")))
mean = []
for var in variables:
if var != "total_precipitation":
mean.append(normalize_mean[var])
else:
mean.append(np.array([0.0]))
normalize_mean = np.concatenate(mean)
normalize_std = dict(np.load(os.path.join(root_dir, "normalize_std.npz")))
normalize_std = np.concatenate([normalize_std[var] for var in variables])
dict_transforms[k] = transforms.Normalize(normalize_mean, normalize_std)
return dict_transforms
def get_lat_lon(self):
# assume different data sources have the same lat and lon coverage
lat = np.load(os.path.join(list(self.hparams.dict_root_dirs.values())[0], "lat.npy"))
lon = np.load(os.path.join(list(self.hparams.dict_root_dirs.values())[0], "lon.npy"))
return lat, lon
def setup(self, stage: Optional[str] = None):
# load datasets only if they're not loaded already
if not self.dict_data_train:
dict_data_train = {}
for k in self.dict_lister_trains.keys():
lister_train = self.dict_lister_trains[k]
start_idx = self.hparams.dict_start_idx[k]
end_idx = self.hparams.dict_end_idx[k]
variables = self.hparams.dict_in_variables[k]
out_variables = self.hparams.dict_out_variables[k]
max_predict_range = self.hparams.dict_max_predict_ranges[k]
random_lead_time = self.hparams.dict_random_lead_time[k]
hrs_each_step = self.hparams.dict_hrs_each_step[k]
transforms = self.transforms[k]
output_transforms = self.output_transforms[k]
buffer_size = self.hparams.dict_buffer_sizes[k]
dict_data_train[k] = ShuffleIterableDataset(
IndividualForecastDataIter(
Forecast(
NpyReader(
lister_train,
start_idx=start_idx,
end_idx=end_idx,
variables=variables,
out_variables=out_variables,
shuffle=True,
multi_dataset_training=True,
),
max_predict_range=max_predict_range,
random_lead_time=random_lead_time,
hrs_each_step=hrs_each_step,
),
transforms,
output_transforms,
),
buffer_size,
)
self.dict_data_train = dict_data_train
def train_dataloader(self):
if not torch.distributed.is_initialized():
raise NotImplementedError("Only support distributed training")
else:
node_rank = int(os.environ["NODE_RANK"])
# assert that number of datasets is the same as number of nodes
num_nodes = os.environ.get("NODES", None)
if num_nodes is not None:
num_nodes = int(num_nodes)
assert num_nodes == len(self.dict_data_train.keys())
for idx, k in enumerate(self.dict_data_train.keys()):
if idx == node_rank:
data_train = self.dict_data_train[k]
break
# This assumes that the number of datapoints are going to be the same for all datasets
return DataLoader(
data_train,
batch_size=self.hparams.batch_size,
drop_last=True,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
collate_fn=collate_fn,
)
|
ClimaX/src/climax/pretrain/datamodule.py/0
|
{
"file_path": "ClimaX/src/climax/pretrain/datamodule.py",
"repo_id": "ClimaX",
"token_count": 3952
}
| 210 |
import os
from glob import glob
import click
import xarray as xr
import numpy as np
import xesmf as xe
def regrid(
ds_in,
ddeg_out,
method='bilinear',
reuse_weights=True,
cmip=False,
rename=None
):
"""
Regrid horizontally.
:param ds_in: Input xarray dataset
:param ddeg_out: Output resolution
:param method: Regridding method
:param reuse_weights: Reuse weights for regridding
:return: ds_out: Regridded dataset
"""
# import pdb; pdb.set_trace()
# Rename to ESMF compatible coordinates
if 'latitude' in ds_in.coords:
ds_in = ds_in.rename({'latitude': 'lat', 'longitude': 'lon'})
if cmip:
ds_in = ds_in.drop(('lat_bnds', 'lon_bnds'))
if hasattr(ds_in, 'plev_bnds'):
ds_in = ds_in.drop(('plev_bnds'))
if hasattr(ds_in, 'time_bnds'):
ds_in = ds_in.drop(('time_bnds'))
if rename is not None:
ds_in = ds_in.rename({rename[0]: rename[1]})
# Create output grid
grid_out = xr.Dataset(
{
'lat': (['lat'], np.arange(-90+ddeg_out/2, 90, ddeg_out)),
'lon': (['lon'], np.arange(0, 360, ddeg_out)),
}
)
# Create regridder
regridder = xe.Regridder(
ds_in, grid_out, method, periodic=True, reuse_weights=reuse_weights
)
# Hack to speed up regridding of large files
ds_out = regridder(ds_in, keep_attrs=True).astype('float32')
if rename is not None:
if rename[0] == 'zg':
ds_out['z'] *= 9.807
if rename[0] == 'rsdt':
ds_out['tisr'] *= 60*60
ds_out = ds_out.isel(time=slice(1, None, 12))
ds_out = ds_out.assign_coords({'time': ds_out.time + np.timedelta64(90, 'm')})
# # Regrid dataset
# ds_out = regridder(ds_in)
return ds_out
@click.command()
@click.argument("path", type=click.Path(exists=True))
@click.option("--save_path", type=str)
@click.option("--ddeg_out", type=float, default=5.625)
def main(
path,
save_path,
ddeg_out
):
if not os.path.exists(save_path):
os.makedirs(save_path, exist_ok=True)
list_simu = ['hist-GHG.nc', 'hist-aer.nc', 'historical.nc', 'ssp126.nc', 'ssp370.nc', 'ssp585.nc', 'ssp245.nc']
ps = glob(os.path.join(path, f"*.nc"))
ps_ = []
for p in ps:
for simu in list_simu:
if simu in p:
ps_.append(p)
ps = ps_
constant_vars = ['CO2', 'CH4']
for p in ps:
x = xr.open_dataset(p)
if 'input' in p:
for v in constant_vars:
x[v] = x[v].expand_dims(dim={'latitude': 96, 'longitude': 144}, axis=(1,2))
x_regridded = regrid(x, ddeg_out, reuse_weights=False)
x_regridded.to_netcdf(os.path.join(save_path, os.path.basename(p)))
if __name__ == "__main__":
main()
|
ClimaX/src/data_preprocessing/regrid_climatebench.py/0
|
{
"file_path": "ClimaX/src/data_preprocessing/regrid_climatebench.py",
"repo_id": "ClimaX",
"token_count": 1448
}
| 211 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
import torch.nn.functional as F
class FlowHead(nn.Module):
def __init__(self, input_dim=32, hidden_dim=64):
super(FlowHead, self).__init__()
candidate_num = 16
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, 2*candidate_num, 3, padding=1)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.conv2(x)
num = x.size()[1]
delta_offset_x, delta_offset_y = torch.split(x, [num//2, num//2], dim=1)
return delta_offset_x, delta_offset_y
class SepConvGRU(nn.Module):
def __init__(self, hidden_dim=32, input_dim=64):
super(SepConvGRU, self).__init__()
self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
def forward(self, h, x):
# horizontal
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz1(hx))
r = torch.sigmoid(self.convr1(hx))
q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1)))
h = (1-z) * h + z * q
# vertical
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz2(hx))
r = torch.sigmoid(self.convr2(hx))
q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1)))
h = (1-z) * h + z * q
return h
class BasicMotionEncoder(nn.Module):
def __init__(self):
super(BasicMotionEncoder, self).__init__()
candidate_num = 16
self.convc1 = nn.Conv2d(candidate_num, 64, 1, padding=0)
self.convc2 = nn.Conv2d(64, 64, 3, padding=1)
self.convf1 = nn.Conv2d(2*candidate_num, 64, 7, padding=3)
self.convf2 = nn.Conv2d(64, 64, 3, padding=1)
self.conv = nn.Conv2d(64+64, 64-2*candidate_num, 3, padding=1)
def forward(self, flow, corr):
cor = F.relu(self.convc1(corr))
cor = F.relu(self.convc2(cor))
flo = F.relu(self.convf1(flow))
flo = F.relu(self.convf2(flo))
cor_flo = torch.cat([cor, flo], dim=1)
out = F.relu(self.conv(cor_flo))
return torch.cat([out, flow], dim=1)
class BasicUpdateBlock(nn.Module):
def __init__(self):
super(BasicUpdateBlock, self).__init__()
self.encoder = BasicMotionEncoder()
self.gru = SepConvGRU()
self.flow_head = FlowHead()
def forward(self, net, corr, flow):
motion_features = self.encoder(flow, corr)
inp = motion_features
net = self.gru(net, inp)
delta_offset_x, delta_offset_y = self.flow_head(net)
return net, delta_offset_x, delta_offset_y
|
CoCosNet-v2/models/networks/convgru.py/0
|
{
"file_path": "CoCosNet-v2/models/networks/convgru.py",
"repo_id": "CoCosNet-v2",
"token_count": 1607
}
| 212 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from data.base_dataset import BaseDataset, get_params, get_transform
import torch
import torchvision.transforms as transforms
from PIL import Image
import util.util as util
import os
import random
#from scipy.ndimage.filters import gaussian_filter
class Pix2pixDataset(BaseDataset):
@staticmethod
def modify_commandline_options(parser, is_train):
parser.add_argument('--no_pairing_check', action='store_true',
help='If specified, skip sanity check of correct label-image file pairing')
return parser
def initialize(self, opt):
self.opt = opt
label_paths, image_paths = self.get_paths(opt)
if opt.dataset_mode != 'celebahq' and opt.dataset_mode != 'deepfashion':
util.natural_sort(label_paths)
util.natural_sort(image_paths)
label_paths = label_paths[:opt.max_dataset_size]
image_paths = image_paths[:opt.max_dataset_size]
if not opt.no_pairing_check:
for path1, path2 in zip(label_paths, image_paths):
assert self.paths_match(path1, path2), \
"The label-image pair (%s, %s) do not look like the right pair because the filenames are quite different. Are you sure about the pairing? Please see data/pix2pix_dataset.py to see what is going on, and use --no_pairing_check to bypass this." % (path1, path2)
self.label_paths = label_paths
self.image_paths = image_paths
size = len(self.label_paths)
self.dataset_size = size
self.real_reference_probability = 1 if opt.phase == 'test' else opt.real_reference_probability
self.hard_reference_probability = 0 if opt.phase == 'test' else opt.hard_reference_probability
self.ref_dict, self.train_test_folder = self.get_ref(opt)
def get_paths(self, opt):
label_paths = []
image_paths = []
assert False, "A subclass of Pix2pixDataset must override self.get_paths(self, opt)"
return label_paths, image_paths
def paths_match(self, path1, path2):
filename1_without_ext = os.path.splitext(os.path.basename(path1))[0]
filename2_without_ext = os.path.splitext(os.path.basename(path2))[0]
return filename1_without_ext == filename2_without_ext
def get_label_tensor(self, path):
label = Image.open(path)
params1 = get_params(self.opt, label.size)
transform_label = get_transform(self.opt, params1, method=Image.NEAREST, normalize=False)
label_tensor = transform_label(label) * 255.0
label_tensor[label_tensor == 255] = self.opt.label_nc # 'unknown' is opt.label_nc
return label_tensor, params1
def __getitem__(self, index):
# Label Image
label_path = self.label_paths[index]
label_tensor, params1 = self.get_label_tensor(label_path)
# input image (real images)
image_path = self.image_paths[index]
if not self.opt.no_pairing_check:
assert self.paths_match(label_path, image_path), \
"The label_path %s and image_path %s don't match." % \
(label_path, image_path)
image = Image.open(image_path)
image = image.convert('RGB')
transform_image = get_transform(self.opt, params1)
image_tensor = transform_image(image)
ref_tensor = 0
label_ref_tensor = 0
random_p = random.random()
if random_p < self.real_reference_probability or self.opt.phase == 'test':
key = image_path.replace('\\', '/').split('DeepFashion/')[-1] if self.opt.dataset_mode == 'deepfashion' else os.path.basename(image_path)
val = self.ref_dict[key]
if random_p < self.hard_reference_probability:
path_ref = val[1] #hard reference
else:
path_ref = val[0] #easy reference
if self.opt.dataset_mode == 'deepfashion':
path_ref = os.path.join(self.opt.dataroot, path_ref)
else:
path_ref = os.path.dirname(image_path).replace(self.train_test_folder[1], self.train_test_folder[0]) + '/' + path_ref
image_ref = Image.open(path_ref).convert('RGB')
if self.opt.dataset_mode != 'deepfashion':
path_ref_label = path_ref.replace('.jpg', '.png')
path_ref_label = self.imgpath_to_labelpath(path_ref_label)
else:
path_ref_label = self.imgpath_to_labelpath(path_ref)
label_ref_tensor, params = self.get_label_tensor(path_ref_label)
transform_image = get_transform(self.opt, params)
ref_tensor = transform_image(image_ref)
#ref_tensor = self.reference_transform(image_ref)
self_ref_flag = torch.zeros_like(ref_tensor)
else:
pair = False
if self.opt.dataset_mode == 'deepfashion' and self.opt.video_like:
# if self.opt.hdfs:
# key = image_path.split('DeepFashion.zip@/')[-1]
# else:
# key = image_path.split('DeepFashion/')[-1]
key = image_path.replace('\\', '/').split('DeepFashion/')[-1]
val = self.ref_dict[key]
ref_name = val[0]
key_name = key
if os.path.dirname(ref_name) == os.path.dirname(key_name) and os.path.basename(ref_name).split('_')[0] == os.path.basename(key_name).split('_')[0]:
path_ref = os.path.join(self.opt.dataroot, ref_name)
image_ref = Image.open(path_ref).convert('RGB')
label_ref_path = self.imgpath_to_labelpath(path_ref)
label_ref_tensor, params = self.get_label_tensor(label_ref_path)
transform_image = get_transform(self.opt, params)
ref_tensor = transform_image(image_ref)
pair = True
if not pair:
label_ref_tensor, params = self.get_label_tensor(label_path)
transform_image = get_transform(self.opt, params)
ref_tensor = transform_image(image)
#ref_tensor = self.reference_transform(image)
self_ref_flag = torch.ones_like(ref_tensor)
input_dict = {'label': label_tensor,
'image': image_tensor,
'path': image_path,
'self_ref': self_ref_flag,
'ref': ref_tensor,
'label_ref': label_ref_tensor
}
# Give subclasses a chance to modify the final output
self.postprocess(input_dict)
return input_dict
def postprocess(self, input_dict):
return input_dict
def __len__(self):
return self.dataset_size
def get_ref(self, opt):
pass
def imgpath_to_labelpath(self, path):
return path
|
CoCosNet/data/pix2pix_dataset.py/0
|
{
"file_path": "CoCosNet/data/pix2pix_dataset.py",
"repo_id": "CoCosNet",
"token_count": 3306
}
| 213 |
"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import re
import importlib
import torch
from argparse import Namespace
import numpy as np
from PIL import Image
import os
import sys
import argparse
import scipy.io as scio
#from numba import u1, u2, jit
# returns a configuration for creating a generator
# |default_opt| should be the opt of the current experiment
# |**kwargs|: if any configuration should be overriden, it can be specified here
colormap = scio.loadmat('./util/color150.mat')['colors']
def masktorgb(x):
mask = np.zeros((x.shape[0], 3, x.shape[2], x.shape[2]), dtype=np.uint8)
for k in range(x.shape[0]):
for i in range(x.shape[2]):
for j in range(x.shape[3]):
mask[k, :, i, j] = colormap[x[k, 0, i, j] - 1]
return mask
def feature_normalize(feature_in):
feature_in_norm = torch.norm(feature_in, 2, 1, keepdim=True) + sys.float_info.epsilon
feature_in_norm = torch.div(feature_in, feature_in_norm)
return feature_in_norm
def weighted_l1_loss(input, target, weights):
out = torch.abs(input - target)
out = out * weights.expand_as(out)
loss = out.mean()
return loss
def mse_loss(input, target=0):
return torch.mean((input - target)**2)
def vgg_preprocess(tensor, vgg_normal_correct=False):
if vgg_normal_correct:
tensor = (tensor + 1) / 2
# input is RGB tensor which ranges in [0,1]
# output is BGR tensor which ranges in [0,255]
tensor_bgr = torch.cat((tensor[:, 2:3, :, :], tensor[:, 1:2, :, :], tensor[:, 0:1, :, :]), dim=1)
# tensor_bgr = tensor[:, [2, 1, 0], ...]
tensor_bgr_ml = tensor_bgr - torch.Tensor([0.40760392, 0.45795686, 0.48501961]).type_as(tensor_bgr).view(1, 3, 1, 1)
tensor_rst = tensor_bgr_ml * 255
return tensor_rst
def copyconf(default_opt, **kwargs):
conf = argparse.Namespace(**vars(default_opt))
for key in kwargs:
print(key, kwargs[key])
setattr(conf, key, kwargs[key])
return conf
def tile_images(imgs, picturesPerRow=4):
""" Code borrowed from
https://stackoverflow.com/questions/26521365/cleanly-tile-numpy-array-of-images-stored-in-a-flattened-1d-format/26521997
"""
# Padding
if imgs.shape[0] % picturesPerRow == 0:
rowPadding = 0
else:
rowPadding = picturesPerRow - imgs.shape[0] % picturesPerRow
if rowPadding > 0:
imgs = np.concatenate([imgs, np.zeros((rowPadding, *imgs.shape[1:]), dtype=imgs.dtype)], axis=0)
# Tiling Loop (The conditionals are not necessary anymore)
tiled = []
for i in range(0, imgs.shape[0], picturesPerRow):
tiled.append(np.concatenate([imgs[j] for j in range(i, i + picturesPerRow)], axis=1))
tiled = np.concatenate(tiled, axis=0)
return tiled
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True, tile=False):
if isinstance(image_tensor, list):
image_numpy = []
for i in range(len(image_tensor)):
image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
return image_numpy
if image_tensor.dim() == 4:
# transform each image in the batch
images_np = []
for b in range(image_tensor.size(0)):
one_image = image_tensor[b]
one_image_np = tensor2im(one_image)
images_np.append(one_image_np.reshape(1, *one_image_np.shape))
images_np = np.concatenate(images_np, axis=0)
if tile:
images_tiled = tile_images(images_np)
return images_tiled
else:
return images_np
if image_tensor.dim() == 2:
image_tensor = image_tensor.unsqueeze(0)
image_numpy = image_tensor.detach().cpu().float().numpy()
if normalize:
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
else:
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
image_numpy = np.clip(image_numpy, 0, 255)
if image_numpy.shape[2] == 1:
image_numpy = image_numpy[:, :, 0]
return image_numpy.astype(imtype)
# Converts a one-hot tensor into a colorful label map
def tensor2label(label_tensor, n_label, imtype=np.uint8, tile=False):
if label_tensor.dim() == 4:
# transform each image in the batch
images_np = []
for b in range(label_tensor.size(0)):
one_image = label_tensor[b]
one_image_np = tensor2label(one_image, n_label, imtype)
images_np.append(one_image_np.reshape(1, *one_image_np.shape))
images_np = np.concatenate(images_np, axis=0)
if tile:
images_tiled = tile_images(images_np)
return images_tiled
else:
images_np = images_np[0]
return images_np
if label_tensor.dim() == 1:
return np.zeros((64, 64, 3), dtype=np.uint8)
if n_label == 0:
return tensor2im(label_tensor, imtype)
label_tensor = label_tensor.cpu().float()
if label_tensor.size()[0] > 1:
label_tensor = label_tensor.max(0, keepdim=True)[1]
label_tensor = Colorize(n_label)(label_tensor)
label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
result = label_numpy.astype(imtype)
return result
def save_image(image_numpy, image_path, create_dir=False):
if create_dir:
os.makedirs(os.path.dirname(image_path), exist_ok=True)
if len(image_numpy.shape) == 2:
image_numpy = np.expand_dims(image_numpy, axis=2)
if image_numpy.shape[2] == 1:
image_numpy = np.repeat(image_numpy, 3, 2)
image_pil = Image.fromarray(image_numpy)
# save to png
image_pil.save(image_path.replace('.jpg', '.png'))
def mkdirs(paths):
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def atoi(text):
return int(text) if text.isdigit() else text
def natural_keys(text):
'''
alist.sort(key=natural_keys) sorts in human order
http://nedbatchelder.com/blog/200712/human_sorting.html
(See Toothy's implementation in the comments)
'''
return [atoi(c) for c in re.split('(\d+)', text)]
def natural_sort(items):
items.sort(key=natural_keys)
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def print_network(model):
num_params = 0
for param in model.parameters():
num_params += param.numel()
print('Network [%s] was created. Total number of parameters: %.1f million. '
'To see the architecture, do print(network).'
% (type(model).__name__, num_params / 1000000))
def find_class_in_module(target_cls_name, module):
target_cls_name = target_cls_name.replace('_', '').lower()
clslib = importlib.import_module(module)
cls = None
for name, clsobj in clslib.__dict__.items():
if name.lower() == target_cls_name:
cls = clsobj
if cls is None:
print("In %s, there should be a class whose name matches %s in lowercase without underscore(_)" % (module, target_cls_name))
exit(0)
return cls
def save_network(net, label, epoch, opt):
save_filename = '%s_net_%s.pth' % (epoch, label)
save_path = os.path.join(opt.checkpoints_dir, opt.name, save_filename)
torch.save(net.cpu().state_dict(), save_path)
if len(opt.gpu_ids) and torch.cuda.is_available():
net.cuda()
def load_network(net, label, epoch, opt):
save_filename = '%s_net_%s.pth' % (epoch, label)
save_dir = os.path.join(opt.checkpoints_dir, opt.name)
save_path = os.path.join(save_dir, save_filename)
if not os.path.exists(save_path):
print('not find model :' + save_path + ', do not load model!')
return net
weights = torch.load(save_path)
try:
net.load_state_dict(weights)
except KeyError:
print('key error, not load!')
except RuntimeError as err:
print(err)
net.load_state_dict(weights, strict=False)
print('loaded with strict=False')
return net
###############################################################################
# Code from
# https://github.com/ycszen/pytorch-seg/blob/master/transform.py
# Modified so it complies with the Citscape label map colors
###############################################################################
def uint82bin(n, count=8):
"""returns the binary of integer n, count refers to amount of bits"""
return ''.join([str((n >> y) & 1) for y in range(count - 1, -1, -1)])
def labelcolormap(N):
if N == 35: # cityscape
cmap = np.array([(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (111, 74, 0), (81, 0, 81),
(128, 64, 128), (244, 35, 232), (250, 170, 160), (230, 150, 140), (70, 70, 70), (102, 102, 156), (190, 153, 153),
(180, 165, 180), (150, 100, 100), (150, 120, 90), (153, 153, 153), (153, 153, 153), (250, 170, 30), (220, 220, 0),
(107, 142, 35), (152, 251, 152), (70, 130, 180), (220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),
(0, 60, 100), (0, 0, 90), (0, 0, 110), (0, 80, 100), (0, 0, 230), (119, 11, 32), (0, 0, 142)],
dtype=np.uint8)
else:
cmap = np.zeros((N, 3), dtype=np.uint8)
for i in range(N):
r, g, b = 0, 0, 0
id = i + 1 # let's give 0 a color
for j in range(7):
str_id = uint82bin(id)
r = r ^ (np.uint8(str_id[-1]) << (7 - j))
g = g ^ (np.uint8(str_id[-2]) << (7 - j))
b = b ^ (np.uint8(str_id[-3]) << (7 - j))
id = id >> 3
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
if N == 182: # COCO
important_colors = {
'sea': (54, 62, 167),
'sky-other': (95, 219, 255),
'tree': (140, 104, 47),
'clouds': (170, 170, 170),
'grass': (29, 195, 49)
}
for i in range(N):
name = util.coco.id2label(i)
if name in important_colors:
color = important_colors[name]
cmap[i] = np.array(list(color))
return cmap
class Colorize(object):
def __init__(self, n=35):
self.cmap = labelcolormap(n)
self.cmap = torch.from_numpy(self.cmap[:n])
def __call__(self, gray_image):
size = gray_image.size()
color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
for label in range(0, len(self.cmap)):
mask = (label == gray_image[0]).cpu()
color_image[0][mask] = self.cmap[label][0]
color_image[1][mask] = self.cmap[label][1]
color_image[2][mask] = self.cmap[label][2]
return color_image
def print_current_errors(opt, epoch, i, errors, t):
message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t)
for k, v in errors.items():
#print(v)
#if v != 0:
v = v.mean().float()
message += '%s: %.3f ' % (k, v)
print(message)
log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
with open(log_name, "a") as log_file:
log_file.write('%s\n' % message)
# class Distortion_with_flow(object):
# """Elastic distortion
# """
# def __init__(self):
# return
# def __call__(self, inputs, dx, dy):
# inputs = np.array(inputs)
# shape = inputs.shape[0], inputs.shape[1]
# inputs = np.array(inputs)
# # x, y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij')
# # remap_image = cv2.remap(inputs, (dy + y).astype(np.float32), (dx + x).astype(np.float32), interpolation=cv2.INTER_LINEAR)
# # backward mapping
# remap_image = forward_mapping(inputs, dy, dx, maxIter=3, precision=1e-3)
# # remap_image = cv2.remap(inputs, (dy + y).astype(np.float32), (dx + x).astype(np.float32), interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
# # remap_image = inputs
# return Image.fromarray(remap_image)
# def forward_mapping(source_image, u, v, maxIter=5, precision=1e-2):
# '''
# warp the image according to the forward flow
# u: horizontal
# v: vertical
# '''
# H = source_image.shape[0]
# W = source_image.shape[1]
# distortImg = np.array(np.zeros((H + 1, W + 1, 3)), dtype=np.uint8)
# distortImg[0:H, 0:W] = source_image[0:H, 0:W]
# distortImg[H, 0:W] = source_image[H - 1, 0:W]
# distortImg[0:H, W] = source_image[0:H, W - 1]
# distortImg[H, W] = source_image[H - 1, W - 1]
# padu = np.array(np.zeros((H + 1, W + 1)), dtype=np.float32)
# padu[0:H, 0:W] = u[0:H, 0:W]
# padu[H, 0:W] = u[H - 1, 0:W]
# padu[0:H, W] = u[0:H, W - 1]
# padu[H, W] = u[H - 1, W - 1]
# padv = np.array(np.zeros((H + 1, W + 1)), dtype=np.float32)
# padv[0:H, 0:W] = v[0:H, 0:W]
# padv[H, 0:W] = v[H - 1, 0:W]
# padv[0:H, W] = v[0:H, W - 1]
# padv[H, W] = v[H - 1, W - 1]
# resultImg = np.array(np.zeros((H, W, 3)), dtype=np.uint8)
# iterSearch(distortImg, resultImg, padu, padv, W, H, maxIter, precision)
# return resultImg
# @jit(nopython=True)
# def iterSearchShader(padu, padv, xr, yr, W, H, maxIter, precision):
# # print('processing location', (xr, yr))
# #
# if abs(padu[yr, xr]) < precision and abs(padv[yr, xr]) < precision:
# return xr, yr
# else:
# # Our initialize method in this paper, can see the overleaf for detail
# if (xr + 1) <= (W - 1):
# dif = padu[yr, xr + 1] - padu[yr, xr]
# u_next = padu[yr, xr] / (1 + dif)
# else:
# dif = padu[yr, xr] - padu[yr, xr - 1]
# u_next = padu[yr, xr] / (1 + dif)
# if (yr + 1) <= (H - 1):
# dif = padv[yr + 1, xr] - padv[yr, xr]
# v_next = padv[yr, xr] / (1 + dif)
# else:
# dif = padv[yr, xr] - padv[yr - 1, xr]
# v_next = padv[yr, xr] / (1 + dif)
# i = xr - u_next
# j = yr - v_next
# i_int = int(i)
# j_int = int(j)
# # The same as traditional iterative search method
# for iter in range(maxIter):
# if 0 <= i <= (W - 1) and 0 <= j <= (H - 1):
# u11 = padu[j_int, i_int]
# v11 = padv[j_int, i_int]
# u12 = padu[j_int, i_int + 1]
# v12 = padv[j_int, i_int + 1]
# int1 = padu[j_int + 1, i_int]
# v21 = padv[j_int + 1, i_int]
# int2 = padu[j_int + 1, i_int + 1]
# v22 = padv[j_int + 1, i_int + 1]
# u = u11 * (i_int + 1 - i) * (j_int + 1 - j) + u12 * (i - i_int) * (j_int + 1 - j) + int1 * (i_int + 1 - i) * (j - j_int) + int2 * (
# i - i_int) * (j - j_int)
# v = v11 * (i_int + 1 - i) * (j_int + 1 - j) + v12 * (i - i_int) * (j_int + 1 - j) + v21 * (i_int + 1 - i) * (j - j_int) + v22 * (i - i_int) * (
# j - j_int)
# i_next = xr - u
# j_next = yr - v
# if abs(i - i_next) < precision and abs(j - j_next) < precision:
# return i, j
# i = i_next
# j = j_next
# else:
# return i, j
# # if the search doesn't converge within max iter, it will return the last iter result
# return i_next, j_next
# @jit(nopython=True)
# def biInterpolation(distorted, i, j):
# i = u2(i)
# j = u2(j)
# Q11 = distorted[j, i]
# Q12 = distorted[j, i + 1]
# Q21 = distorted[j + 1, i]
# Q22 = distorted[j + 1, i + 1]
# pixel = u1(Q11 * (i + 1 - i) * (j + 1 - j) + Q12 * (i - i) * (j + 1 - j) + Q21 * (i + 1 - i) * (j - j) + Q22 * (i - i) * (j - j))
# return pixel
# @jit(nopython=True)
# def iterSearch(distortImg, resultImg, padu, padv, W, H, maxIter=5, precision=1e-2):
# for xr in range(W):
# for yr in range(H):
# # (xr, yr) is the point in result image, (i, j) is the search result in distorted image
# i, j = iterSearchShader(padu, padv, xr, yr, W, H, maxIter, precision)
# # reflect the pixels outside the border
# if i > W - 1:
# i = 2 * W - 1 - i
# if i < 0:
# i = -i
# if j > H - 1:
# j = 2 * H - 1 - j
# if j < 0:
# j = -j
# # Bilinear interpolation to get the pixel at (i, j) in distorted image
# resultImg[yr, xr, 0] = biInterpolation(
# distortImg[:, :, 0],
# i,
# j,
# )
# resultImg[yr, xr, 1] = biInterpolation(
# distortImg[:, :, 1],
# i,
# j,
# )
# resultImg[yr, xr, 2] = biInterpolation(
# distortImg[:, :, 2],
# i,
# j,
# )
# return None
|
CoCosNet/util/util.py/0
|
{
"file_path": "CoCosNet/util/util.py",
"repo_id": "CoCosNet",
"token_count": 8833
}
| 214 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import re
from io import StringIO
import tokenize
def remove_comments_and_docstrings(source, lang):
if lang in ['python']:
"""
Returns 'source' minus comments and docstrings.
"""
io_obj = StringIO(source)
out = ""
prev_toktype = tokenize.INDENT
last_lineno = -1
last_col = 0
for tok in tokenize.generate_tokens(io_obj.readline):
token_type = tok[0]
token_string = tok[1]
start_line, start_col = tok[2]
end_line, end_col = tok[3]
ltext = tok[4]
if start_line > last_lineno:
last_col = 0
if start_col > last_col:
out += (" " * (start_col - last_col))
# Remove comments:
if token_type == tokenize.COMMENT:
pass
# This series of conditionals removes docstrings:
elif token_type == tokenize.STRING:
if prev_toktype != tokenize.INDENT:
# This is likely a docstring; double-check we're not inside an operator:
if prev_toktype != tokenize.NEWLINE:
if start_col > 0:
out += token_string
else:
out += token_string
prev_toktype = token_type
last_col = end_col
last_lineno = end_line
temp = []
for x in out.split('\n'):
if x.strip() != "":
temp.append(x)
return '\n'.join(temp)
elif lang in ['ruby']:
return source
else:
def replacer(match):
s = match.group(0)
if s.startswith('/'):
return " " # note: a space and not an empty string
else:
return s
pattern = re.compile(
r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\.|[^\\"])*"',
re.DOTALL | re.MULTILINE
)
temp = []
for x in re.sub(pattern, replacer, source).split('\n'):
if x.strip() != "":
temp.append(x)
return '\n'.join(temp)
def tree_to_token_index(root_node):
if (len(root_node.children) == 0 or root_node.type in ['string_literal', 'string',
'character_literal']) and root_node.type != 'comment':
return [(root_node.start_point, root_node.end_point)]
else:
code_tokens = []
for child in root_node.children:
code_tokens += tree_to_token_index(child)
return code_tokens
def tree_to_variable_index(root_node, index_to_code):
if (len(root_node.children) == 0 or root_node.type in ['string_literal', 'string',
'character_literal']) and root_node.type != 'comment':
index = (root_node.start_point, root_node.end_point)
_, code = index_to_code[index]
if root_node.type != code:
return [(root_node.start_point, root_node.end_point)]
else:
return []
else:
code_tokens = []
for child in root_node.children:
code_tokens += tree_to_variable_index(child, index_to_code)
return code_tokens
def index_to_code_token(index, code):
start_point = index[0]
end_point = index[1]
if start_point[0] == end_point[0]:
s = code[start_point[0]][start_point[1]:end_point[1]]
else:
s = ""
s += code[start_point[0]][start_point[1]:]
for i in range(start_point[0] + 1, end_point[0]):
s += code[i]
s += code[end_point[0]][:end_point[1]]
return s
|
CodeBERT/CodeReviewer/code/evaluator/CodeBLEU/parser/utils.py/0
|
{
"file_path": "CodeBERT/CodeReviewer/code/evaluator/CodeBLEU/parser/utils.py",
"repo_id": "CodeBERT",
"token_count": 1972
}
| 215 |
# batch size 12 for 16 GB GPU
mnt_dir="/home/codereview"
# You may change the following block for multiple gpu training
MASTER_HOST=localhost && echo MASTER_HOST: ${MASTER_HOST}
MASTER_PORT=23333 && echo MASTER_PORT: ${MASTER_PORT}
RANK=0 && echo RANK: ${RANK}
PER_NODE_GPU=1 && echo PER_NODE_GPU: ${PER_NODE_GPU}
WORLD_SIZE=1 && echo WORLD_SIZE: ${WORLD_SIZE}
NODES=1 && echo NODES: ${NODES}
NCCL_DEBUG=INFO
bash test_nltk.sh
# Change the arguments as required:
# model_name_or_path, load_model_path: the path of the model to be finetuned
# eval_file: the path of the evaluation data
# output_dir: the directory to save finetuned model (not used at infer/test time)
# out_file: the path of the output file
# train_file_name: can be a directory contraining files named with "train*.jsonl"
python -m torch.distributed.launch --nproc_per_node ${PER_NODE_GPU} --node_rank=${RANK} --nnodes=${NODES} --master_addr=${MASTER_HOST} --master_port=${MASTER_PORT} ../run_finetune_cls.py \
--train_epochs 30 \
--model_name_or_path microsoft/codereviewer \
--output_dir ../../save/cls \
--train_filename ../../dataset/Diff_Quality_Estimation \
--dev_filename ../../dataset/Diff_Quality_Estimation/cls-valid.jsonl \
--max_source_length 512 \
--max_target_length 128 \
--train_batch_size 12 \
--learning_rate 3e-4 \
--gradient_accumulation_steps 3 \
--mask_rate 0.15 \
--save_steps 3600 \
--log_steps 100 \
--train_steps 120000 \
--gpu_per_node=${PER_NODE_GPU} \
--node_index=${RANK} \
--seed 2233
|
CodeBERT/CodeReviewer/code/sh/finetune-cls.sh/0
|
{
"file_path": "CodeBERT/CodeReviewer/code/sh/finetune-cls.sh",
"repo_id": "CodeBERT",
"token_count": 586
}
| 216 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
using a masked language modeling (MLM) loss.
"""
import argparse
import logging
import os
import pickle
import random
import torch
import json
import numpy as np
from model import Model
from torch.nn import CrossEntropyLoss, MSELoss
from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler,TensorDataset
from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,
RobertaConfig, RobertaModel, RobertaTokenizer)
logger = logging.getLogger(__name__)
from tqdm import tqdm, trange
import multiprocessing
cpu_cont = 16
from parser import DFG_python,DFG_java,DFG_ruby,DFG_go,DFG_php,DFG_javascript
from parser import (remove_comments_and_docstrings,
tree_to_token_index,
index_to_code_token,
tree_to_variable_index)
from tree_sitter import Language, Parser
dfg_function={
'python':DFG_python,
'java':DFG_java,
'ruby':DFG_ruby,
'go':DFG_go,
'php':DFG_php,
'javascript':DFG_javascript
}
#load parsers
parsers={}
for lang in dfg_function:
LANGUAGE = Language('parser/my-languages.so', lang)
parser = Parser()
parser.set_language(LANGUAGE)
parser = [parser,dfg_function[lang]]
parsers[lang]= parser
#remove comments, tokenize code and extract dataflow
def extract_dataflow(code, parser,lang):
#remove comments
try:
code=remove_comments_and_docstrings(code,lang)
except:
pass
#obtain dataflow
if lang=="php":
code="<?php"+code+"?>"
try:
tree = parser[0].parse(bytes(code,'utf8'))
root_node = tree.root_node
tokens_index=tree_to_token_index(root_node)
code=code.split('\n')
code_tokens=[index_to_code_token(x,code) for x in tokens_index]
index_to_code={}
for idx,(index,code) in enumerate(zip(tokens_index,code_tokens)):
index_to_code[index]=(idx,code)
try:
DFG,_=parser[1](root_node,index_to_code,{})
except:
DFG=[]
DFG=sorted(DFG,key=lambda x:x[1])
indexs=set()
for d in DFG:
if len(d[-1])!=0:
indexs.add(d[1])
for x in d[-1]:
indexs.add(x)
new_DFG=[]
for d in DFG:
if d[1] in indexs:
new_DFG.append(d)
dfg=new_DFG
except:
dfg=[]
return code_tokens,dfg
class InputFeatures(object):
"""A single training/test features for a example."""
def __init__(self,
code_tokens,
code_ids,
position_idx,
dfg_to_code,
dfg_to_dfg,
nl_tokens,
nl_ids,
url,
):
self.code_tokens = code_tokens
self.code_ids = code_ids
self.position_idx=position_idx
self.dfg_to_code=dfg_to_code
self.dfg_to_dfg=dfg_to_dfg
self.nl_tokens = nl_tokens
self.nl_ids = nl_ids
self.url=url
def convert_examples_to_features(item):
js,tokenizer,args=item
#code
parser=parsers[args.lang]
#extract data flow
code_tokens,dfg=extract_dataflow(js['original_string'],parser,args.lang)
code_tokens=[tokenizer.tokenize('@ '+x)[1:] if idx!=0 else tokenizer.tokenize(x) for idx,x in enumerate(code_tokens)]
ori2cur_pos={}
ori2cur_pos[-1]=(0,0)
for i in range(len(code_tokens)):
ori2cur_pos[i]=(ori2cur_pos[i-1][1],ori2cur_pos[i-1][1]+len(code_tokens[i]))
code_tokens=[y for x in code_tokens for y in x]
#truncating
code_tokens=code_tokens[:args.code_length+args.data_flow_length-2-min(len(dfg),args.data_flow_length)]
code_tokens =[tokenizer.cls_token]+code_tokens+[tokenizer.sep_token]
code_ids = tokenizer.convert_tokens_to_ids(code_tokens)
position_idx = [i+tokenizer.pad_token_id + 1 for i in range(len(code_tokens))]
dfg=dfg[:args.code_length+args.data_flow_length-len(code_tokens)]
code_tokens+=[x[0] for x in dfg]
position_idx+=[0 for x in dfg]
code_ids+=[tokenizer.unk_token_id for x in dfg]
padding_length=args.code_length+args.data_flow_length-len(code_ids)
position_idx+=[tokenizer.pad_token_id]*padding_length
code_ids+=[tokenizer.pad_token_id]*padding_length
#reindex
reverse_index={}
for idx,x in enumerate(dfg):
reverse_index[x[1]]=idx
for idx,x in enumerate(dfg):
dfg[idx]=x[:-1]+([reverse_index[i] for i in x[-1] if i in reverse_index],)
dfg_to_dfg=[x[-1] for x in dfg]
dfg_to_code=[ori2cur_pos[x[1]] for x in dfg]
length=len([tokenizer.cls_token])
dfg_to_code=[(x[0]+length,x[1]+length) for x in dfg_to_code]
#nl
nl=' '.join(js['docstring_tokens'])
nl_tokens=tokenizer.tokenize(nl)[:args.nl_length-2]
nl_tokens =[tokenizer.cls_token]+nl_tokens+[tokenizer.sep_token]
nl_ids = tokenizer.convert_tokens_to_ids(nl_tokens)
padding_length = args.nl_length - len(nl_ids)
nl_ids+=[tokenizer.pad_token_id]*padding_length
return InputFeatures(code_tokens,code_ids,position_idx,dfg_to_code,dfg_to_dfg,nl_tokens,nl_ids,js['url'])
class TextDataset(Dataset):
def __init__(self, tokenizer, args, file_path=None,pool=None):
self.args=args
prefix=file_path.split('/')[-1][:-6]
cache_file=args.output_dir+'/'+prefix+'.pkl'
if os.path.exists(cache_file):
self.examples=pickle.load(open(cache_file,'rb'))
else:
self.examples = []
data=[]
with open(file_path) as f:
for line in f:
line=line.strip()
js=json.loads(line)
data.append((js,tokenizer,args))
self.examples=pool.map(convert_examples_to_features, tqdm(data,total=len(data)))
pickle.dump(self.examples,open(cache_file,'wb'))
if 'train' in file_path:
for idx, example in enumerate(self.examples[:3]):
logger.info("*** Example ***")
logger.info("idx: {}".format(idx))
logger.info("code_tokens: {}".format([x.replace('\u0120','_') for x in example.code_tokens]))
logger.info("code_ids: {}".format(' '.join(map(str, example.code_ids))))
logger.info("position_idx: {}".format(example.position_idx))
logger.info("dfg_to_code: {}".format(' '.join(map(str, example.dfg_to_code))))
logger.info("dfg_to_dfg: {}".format(' '.join(map(str, example.dfg_to_dfg))))
logger.info("nl_tokens: {}".format([x.replace('\u0120','_') for x in example.nl_tokens]))
logger.info("nl_ids: {}".format(' '.join(map(str, example.nl_ids))))
def __len__(self):
return len(self.examples)
def __getitem__(self, item):
#calculate graph-guided masked function
attn_mask=np.zeros((self.args.code_length+self.args.data_flow_length,
self.args.code_length+self.args.data_flow_length),dtype=np.bool)
#calculate begin index of node and max length of input
node_index=sum([i>1 for i in self.examples[item].position_idx])
max_length=sum([i!=1 for i in self.examples[item].position_idx])
#sequence can attend to sequence
attn_mask[:node_index,:node_index]=True
#special tokens attend to all tokens
for idx,i in enumerate(self.examples[item].code_ids):
if i in [0,2]:
attn_mask[idx,:max_length]=True
#nodes attend to code tokens that are identified from
for idx,(a,b) in enumerate(self.examples[item].dfg_to_code):
if a<node_index and b<node_index:
attn_mask[idx+node_index,a:b]=True
attn_mask[a:b,idx+node_index]=True
#nodes attend to adjacent nodes
for idx,nodes in enumerate(self.examples[item].dfg_to_dfg):
for a in nodes:
if a+node_index<len(self.examples[item].position_idx):
attn_mask[idx+node_index,a+node_index]=True
return (torch.tensor(self.examples[item].code_ids),
torch.tensor(attn_mask),
torch.tensor(self.examples[item].position_idx),
torch.tensor(self.examples[item].nl_ids))
def set_seed(seed=42):
random.seed(seed)
os.environ['PYHTONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
def train(args, model, tokenizer,pool):
""" Train the model """
#get training dataset
train_dataset=TextDataset(tokenizer, args, args.train_data_file, pool)
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size,num_workers=4)
#get optimizer and scheduler
optimizer = AdamW(model.parameters(), lr=args.learning_rate, eps=1e-8)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0,num_training_steps=len(train_dataloader)*args.num_train_epochs)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d", args.train_batch_size//args.n_gpu)
logger.info(" Total train batch size = %d", args.train_batch_size)
logger.info(" Total optimization steps = %d", len(train_dataloader)*args.num_train_epochs)
# model.resize_token_embeddings(len(tokenizer))
model.zero_grad()
model.train()
tr_num,tr_loss,best_mrr=0,0,0
for idx in range(args.num_train_epochs):
for step,batch in enumerate(train_dataloader):
#get inputs
code_inputs = batch[0].to(args.device)
attn_mask = batch[1].to(args.device)
position_idx = batch[2].to(args.device)
nl_inputs = batch[3].to(args.device)
#get code and nl vectors
code_vec = model(code_inputs=code_inputs,attn_mask=attn_mask,position_idx=position_idx)
nl_vec = model(nl_inputs=nl_inputs)
#calculate scores and loss
scores=torch.einsum("ab,cb->ac",nl_vec,code_vec)
loss_fct = CrossEntropyLoss()
loss = loss_fct(scores, torch.arange(code_inputs.size(0), device=scores.device))
#report loss
tr_loss += loss.item()
tr_num+=1
if (step+1)% 100==0:
logger.info("epoch {} step {} loss {}".format(idx,step+1,round(tr_loss/tr_num,5)))
tr_loss=0
tr_num=0
#backward
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
optimizer.zero_grad()
scheduler.step()
#evaluate
results = evaluate(args, model, tokenizer,args.eval_data_file, pool, eval_when_training=True)
for key, value in results.items():
logger.info(" %s = %s", key, round(value,4))
#save best model
if results['eval_mrr']>best_mrr:
best_mrr=results['eval_mrr']
logger.info(" "+"*"*20)
logger.info(" Best mrr:%s",round(best_mrr,4))
logger.info(" "+"*"*20)
checkpoint_prefix = 'checkpoint-best-mrr'
output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(model,'module') else model
output_dir = os.path.join(output_dir, '{}'.format('model.bin'))
torch.save(model_to_save.state_dict(), output_dir)
logger.info("Saving model checkpoint to %s", output_dir)
def evaluate(args, model, tokenizer,file_name,pool, eval_when_training=False):
query_dataset = TextDataset(tokenizer, args, file_name, pool)
query_sampler = SequentialSampler(query_dataset)
query_dataloader = DataLoader(query_dataset, sampler=query_sampler, batch_size=args.eval_batch_size,num_workers=4)
code_dataset = TextDataset(tokenizer, args, args.codebase_file, pool)
code_sampler = SequentialSampler(code_dataset)
code_dataloader = DataLoader(code_dataset, sampler=code_sampler, batch_size=args.eval_batch_size,num_workers=4)
# multi-gpu evaluate
if args.n_gpu > 1 and eval_when_training is False:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation *****")
logger.info(" Num queries = %d", len(query_dataset))
logger.info(" Num codes = %d", len(code_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
code_vecs=[]
nl_vecs=[]
for batch in query_dataloader:
nl_inputs = batch[3].to(args.device)
with torch.no_grad():
nl_vec = model(nl_inputs=nl_inputs)
nl_vecs.append(nl_vec.cpu().numpy())
for batch in code_dataloader:
code_inputs = batch[0].to(args.device)
attn_mask = batch[1].to(args.device)
position_idx =batch[2].to(args.device)
with torch.no_grad():
code_vec= model(code_inputs=code_inputs, attn_mask=attn_mask,position_idx=position_idx)
code_vecs.append(code_vec.cpu().numpy())
model.train()
code_vecs=np.concatenate(code_vecs,0)
nl_vecs=np.concatenate(nl_vecs,0)
scores=np.matmul(nl_vecs,code_vecs.T)
sort_ids=np.argsort(scores, axis=-1, kind='quicksort', order=None)[:,::-1]
nl_urls=[]
code_urls=[]
for example in query_dataset.examples:
nl_urls.append(example.url)
for example in code_dataset.examples:
code_urls.append(example.url)
ranks=[]
for url, sort_id in zip(nl_urls,sort_ids):
rank=0
find=False
for idx in sort_id[:1000]:
if find is False:
rank+=1
if code_urls[idx]==url:
find=True
if find:
ranks.append(1/rank)
else:
ranks.append(0)
result = {
"eval_mrr":float(np.mean(ranks))
}
return result
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--train_data_file", default=None, type=str, required=True,
help="The input training data file (a json file).")
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--eval_data_file", default=None, type=str,
help="An optional input evaluation data file to evaluate the MRR(a jsonl file).")
parser.add_argument("--test_data_file", default=None, type=str,
help="An optional input test data file to test the MRR(a josnl file).")
parser.add_argument("--codebase_file", default=None, type=str,
help="An optional input test data file to codebase (a jsonl file).")
parser.add_argument("--lang", default=None, type=str,
help="language.")
parser.add_argument("--model_name_or_path", default=None, type=str,
help="The model checkpoint for weights initialization.")
parser.add_argument("--config_name", default="", type=str,
help="Optional pretrained config name or path if not the same as model_name_or_path")
parser.add_argument("--tokenizer_name", default="", type=str,
help="Optional pretrained tokenizer name or path if not the same as model_name_or_path")
parser.add_argument("--nl_length", default=128, type=int,
help="Optional NL input sequence length after tokenization.")
parser.add_argument("--code_length", default=256, type=int,
help="Optional Code input sequence length after tokenization.")
parser.add_argument("--data_flow_length", default=64, type=int,
help="Optional Data Flow input sequence length after tokenization.")
parser.add_argument("--do_train", action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval", action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_test", action='store_true',
help="Whether to run eval on the test set.")
parser.add_argument("--train_batch_size", default=4, type=int,
help="Batch size for training.")
parser.add_argument("--eval_batch_size", default=4, type=int,
help="Batch size for evaluation.")
parser.add_argument("--learning_rate", default=5e-5, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=1, type=int,
help="Total number of training epochs to perform.")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
pool = multiprocessing.Pool(cpu_cont)
#print arguments
args = parser.parse_args()
#set log
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',level=logging.INFO )
#set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.n_gpu = torch.cuda.device_count()
args.device = device
logger.info("device: %s, n_gpu: %s",device, args.n_gpu)
# Set seed
set_seed(args.seed)
#build model
config = RobertaConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
tokenizer = RobertaTokenizer.from_pretrained(args.tokenizer_name)
model = RobertaModel.from_pretrained(args.model_name_or_path)
model=Model(model)
logger.info("Training/evaluation parameters %s", args)
model.to(args.device)
# Training
if args.do_train:
train(args, model, tokenizer, pool)
# Evaluation
results = {}
if args.do_eval:
checkpoint_prefix = 'checkpoint-best-mrr/model.bin'
output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))
model.load_state_dict(torch.load(output_dir),strict=False)
model.to(args.device)
result=evaluate(args, model, tokenizer,args.eval_data_file, pool)
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(round(result[key],4)))
if args.do_test:
checkpoint_prefix = 'checkpoint-best-mrr/model.bin'
output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))
model.load_state_dict(torch.load(output_dir),strict=False)
model.to(args.device)
result=evaluate(args, model, tokenizer,args.test_data_file, pool)
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(round(result[key],4)))
return results
if __name__ == "__main__":
main()
|
CodeBERT/GraphCodeBERT/codesearch/run.py/0
|
{
"file_path": "CodeBERT/GraphCodeBERT/codesearch/run.py",
"repo_id": "CodeBERT",
"token_count": 10060
}
| 217 |
{"index": "s262143287", "label": 3533, "func": ""}
{"index": "s760791944", "label": 3533, "func": ""}
{"index": "s450011718", "label": 3533, "func": ""}
{"index": "s528769751", "label": 3533, "func": ""}
{"index": "s791563124", "label": 3533, "func": ""}
{"index": "s433322217", "label": 3533, "func": ""}
{"index": "s969862567", "label": 3533, "func": ""}
{"index": "s084933849", "label": 3533, "func": ""}
{"index": "s279761332", "label": 3533, "func": ""}
{"index": "s768969263", "label": 3533, "func": ""}
{"index": "s123986371", "label": 797, "func": ""}
{"index": "s355482523", "label": 797, "func": ""}
{"index": "s399540061", "label": 797, "func": ""}
{"index": "s582552837", "label": 797, "func": ""}
{"index": "s662144314", "label": 797, "func": ""}
{"index": "s425577294", "label": 797, "func": ""}
{"index": "s229311855", "label": 797, "func": ""}
{"index": "s242676873", "label": 797, "func": ""}
{"index": "s593254691", "label": 492, "func": ""}
{"index": "s880115648", "label": 492, "func": ""}
{"index": "s899000323", "label": 492, "func": ""}
{"index": "s738260914", "label": 492, "func": ""}
{"index": "s311519908", "label": 492, "func": ""}
{"index": "s102991556", "label": 492, "func": ""}
{"index": "s676049828", "label": 492, "func": ""}
{"index": "s441627167", "label": 492, "func": ""}
{"index": "s088544612", "label": 492, "func": ""}
{"index": "s769329946", "label": 492, "func": ""}
{"index": "s638990829", "label": 2330, "func": ""}
{"index": "s660339623", "label": 2330, "func": ""}
{"index": "s742829153", "label": 2330, "func": ""}
{"index": "s328608205", "label": 2297, "func": ""}
{"index": "s857591154", "label": 2297, "func": ""}
{"index": "s781476487", "label": 2297, "func": ""}
{"index": "s139190181", "label": 2297, "func": ""}
{"index": "s458747891", "label": 2297, "func": ""}
{"index": "s092940336", "label": 2297, "func": ""}
{"index": "s746532460", "label": 2297, "func": ""}
{"index": "s692848814", "label": 2297, "func": ""}
{"index": "s551140625", "label": 2297, "func": ""}
{"index": "s664979334", "label": 2297, "func": ""}
{"index": "s400732698", "label": 2818, "func": ""}
{"index": "s308745277", "label": 2818, "func": ""}
{"index": "s641616340", "label": 2818, "func": ""}
{"index": "s905468901", "label": 2818, "func": ""}
{"index": "s231251001", "label": 2818, "func": ""}
{"index": "s503066939", "label": 2818, "func": ""}
{"index": "s443519988", "label": 2818, "func": ""}
{"index": "s538954689", "label": 2818, "func": ""}
{"index": "s384963542", "label": 2818, "func": ""}
{"index": "s570754467", "label": 2818, "func": ""}
{"index": "s112765867", "label": 125, "func": ""}
{"index": "s111047688", "label": 125, "func": ""}
{"index": "s473961026", "label": 125, "func": ""}
{"index": "s168009217", "label": 125, "func": ""}
{"index": "s161354288", "label": 125, "func": ""}
{"index": "s575984550", "label": 125, "func": ""}
{"index": "s588098846", "label": 125, "func": ""}
{"index": "s273900695", "label": 125, "func": ""}
{"index": "s783806731", "label": 125, "func": ""}
{"index": "s887944617", "label": 125, "func": ""}
{"index": "s833854143", "label": 2646, "func": ""}
{"index": "s503379197", "label": 2646, "func": ""}
{"index": "s968533184", "label": 2646, "func": ""}
{"index": "s111983785", "label": 2646, "func": ""}
{"index": "s615654148", "label": 2646, "func": ""}
{"index": "s217266184", "label": 2646, "func": ""}
{"index": "s570777734", "label": 2646, "func": ""}
{"index": "s791524135", "label": 2646, "func": ""}
{"index": "s640095008", "label": 2646, "func": ""}
{"index": "s957736605", "label": 2646, "func": ""}
{"index": "s311391346", "label": 1085, "func": ""}
{"index": "s181241505", "label": 1085, "func": ""}
{"index": "s667066312", "label": 1085, "func": ""}
{"index": "s729693606", "label": 1085, "func": ""}
{"index": "s628430897", "label": 1085, "func": ""}
{"index": "s594197545", "label": 1085, "func": ""}
{"index": "s730679444", "label": 1085, "func": ""}
{"index": "s592294584", "label": 1085, "func": ""}
{"index": "s645380446", "label": 1085, "func": ""}
{"index": "s675764111", "label": 1085, "func": ""}
{"index": "s693297132", "label": 3451, "func": ""}
{"index": "s739950549", "label": 3451, "func": ""}
{"index": "s201824531", "label": 3451, "func": ""}
{"index": "s342624572", "label": 3451, "func": ""}
{"index": "s083181566", "label": 3451, "func": ""}
{"index": "s747736046", "label": 3451, "func": ""}
{"index": "s595576280", "label": 3451, "func": ""}
{"index": "s803447085", "label": 3451, "func": ""}
{"index": "s882141128", "label": 3451, "func": ""}
{"index": "s763696324", "label": 3451, "func": ""}
{"index": "s831795885", "label": 3417, "func": ""}
{"index": "s238350676", "label": 3417, "func": ""}
{"index": "s680749667", "label": 3417, "func": ""}
{"index": "s834078606", "label": 3417, "func": ""}
{"index": "s886605273", "label": 3417, "func": ""}
{"index": "s927230772", "label": 3417, "func": ""}
{"index": "s516372282", "label": 3417, "func": ""}
{"index": "s820242227", "label": 3417, "func": ""}
{"index": "s280671672", "label": 3417, "func": ""}
{"index": "s028372485", "label": 3417, "func": ""}
{"index": "s182245394", "label": 2662, "func": ""}
{"index": "s607589553", "label": 2662, "func": ""}
{"index": "s828553712", "label": 2662, "func": ""}
{"index": "s660682678", "label": 2662, "func": ""}
{"index": "s682156858", "label": 2662, "func": ""}
{"index": "s162198263", "label": 2662, "func": ""}
{"index": "s114687493", "label": 2662, "func": ""}
{"index": "s579922264", "label": 2662, "func": ""}
{"index": "s525556457", "label": 2662, "func": ""}
{"index": "s796176487", "label": 2662, "func": ""}
{"index": "s750596100", "label": 1228, "func": ""}
{"index": "s282289029", "label": 1228, "func": ""}
{"index": "s350214656", "label": 1228, "func": ""}
{"index": "s879694279", "label": 1228, "func": ""}
{"index": "s322303193", "label": 2601, "func": ""}
{"index": "s485883201", "label": 2601, "func": ""}
{"index": "s989492704", "label": 2601, "func": ""}
{"index": "s094245880", "label": 2601, "func": ""}
{"index": "s863473884", "label": 2601, "func": ""}
{"index": "s134922230", "label": 2601, "func": ""}
{"index": "s234785632", "label": 2601, "func": ""}
{"index": "s847556774", "label": 2601, "func": ""}
{"index": "s080158994", "label": 2601, "func": ""}
{"index": "s640861111", "label": 2601, "func": ""}
{"index": "s544230493", "label": 3338, "func": ""}
{"index": "s702753651", "label": 3338, "func": ""}
{"index": "s745495483", "label": 3338, "func": ""}
{"index": "s991524000", "label": 3338, "func": ""}
{"index": "s525272101", "label": 3338, "func": ""}
{"index": "s728308341", "label": 3338, "func": ""}
{"index": "s772764543", "label": 3338, "func": ""}
{"index": "s562557797", "label": 3338, "func": ""}
{"index": "s541187898", "label": 3338, "func": ""}
{"index": "s184908267", "label": 3338, "func": ""}
{"index": "s150707362", "label": 330, "func": ""}
{"index": "s709496925", "label": 330, "func": ""}
{"index": "s623150087", "label": 330, "func": ""}
{"index": "s629272515", "label": 330, "func": ""}
{"index": "s313009765", "label": 330, "func": ""}
{"index": "s960940125", "label": 330, "func": ""}
{"index": "s672144581", "label": 330, "func": ""}
{"index": "s251891144", "label": 330, "func": ""}
{"index": "s672176721", "label": 330, "func": ""}
{"index": "s933072200", "label": 330, "func": ""}
{"index": "s473269997", "label": 185, "func": ""}
{"index": "s522162625", "label": 185, "func": ""}
{"index": "s056075048", "label": 185, "func": ""}
{"index": "s848760888", "label": 185, "func": ""}
{"index": "s700270129", "label": 185, "func": ""}
{"index": "s476298963", "label": 185, "func": ""}
{"index": "s079698951", "label": 185, "func": ""}
{"index": "s735863296", "label": 185, "func": ""}
{"index": "s288364490", "label": 185, "func": ""}
{"index": "s309984025", "label": 185, "func": ""}
{"index": "s930274286", "label": 274, "func": ""}
{"index": "s479405926", "label": 274, "func": ""}
{"index": "s021124879", "label": 274, "func": ""}
{"index": "s640535070", "label": 274, "func": ""}
{"index": "s234261610", "label": 274, "func": ""}
{"index": "s159053945", "label": 274, "func": ""}
{"index": "s305251614", "label": 274, "func": ""}
{"index": "s309705302", "label": 274, "func": ""}
{"index": "s744826590", "label": 274, "func": ""}
{"index": "s419789521", "label": 274, "func": ""}
{"index": "s431624641", "label": 2439, "func": ""}
{"index": "s458056783", "label": 2439, "func": ""}
{"index": "s332614218", "label": 2439, "func": ""}
{"index": "s594321194", "label": 2439, "func": ""}
{"index": "s099561009", "label": 2439, "func": ""}
{"index": "s140891099", "label": 2439, "func": ""}
{"index": "s773187572", "label": 2439, "func": ""}
{"index": "s715397016", "label": 2439, "func": ""}
{"index": "s909201372", "label": 2439, "func": ""}
{"index": "s553996290", "label": 2439, "func": ""}
{"index": "s390884301", "label": 910, "func": ""}
{"index": "s057465892", "label": 910, "func": ""}
{"index": "s776773257", "label": 127, "func": ""}
{"index": "s700668480", "label": 127, "func": ""}
{"index": "s201443426", "label": 127, "func": ""}
{"index": "s049439539", "label": 127, "func": ""}
{"index": "s771567433", "label": 127, "func": ""}
{"index": "s963118945", "label": 127, "func": ""}
{"index": "s076086942", "label": 127, "func": ""}
{"index": "s523936190", "label": 127, "func": ""}
{"index": "s751033890", "label": 127, "func": ""}
{"index": "s892060183", "label": 127, "func": ""}
{"index": "s290739216", "label": 932, "func": ""}
{"index": "s210104424", "label": 3561, "func": ""}
{"index": "s217690041", "label": 3561, "func": ""}
{"index": "s752873200", "label": 3561, "func": ""}
{"index": "s176580601", "label": 3561, "func": ""}
{"index": "s236644433", "label": 3561, "func": ""}
{"index": "s367072731", "label": 3561, "func": ""}
{"index": "s974468010", "label": 3561, "func": ""}
{"index": "s871661519", "label": 3561, "func": ""}
{"index": "s698261031", "label": 3561, "func": ""}
{"index": "s355627596", "label": 3561, "func": ""}
{"index": "s873139844", "label": 336, "func": ""}
{"index": "s285827300", "label": 212, "func": ""}
{"index": "s073031456", "label": 212, "func": ""}
{"index": "s605467677", "label": 212, "func": ""}
{"index": "s764874681", "label": 212, "func": ""}
{"index": "s278599552", "label": 212, "func": ""}
{"index": "s371263681", "label": 212, "func": ""}
{"index": "s522068313", "label": 212, "func": ""}
{"index": "s869919299", "label": 212, "func": ""}
{"index": "s123268431", "label": 212, "func": ""}
{"index": "s642591054", "label": 212, "func": ""}
{"index": "s728333630", "label": 2371, "func": ""}
{"index": "s414282375", "label": 2371, "func": ""}
{"index": "s823478623", "label": 2371, "func": ""}
{"index": "s804103028", "label": 2371, "func": ""}
{"index": "s248502471", "label": 2371, "func": ""}
{"index": "s559674868", "label": 2371, "func": ""}
{"index": "s913959265", "label": 2371, "func": ""}
{"index": "s180572318", "label": 2371, "func": ""}
{"index": "s842035953", "label": 2371, "func": ""}
{"index": "s186578892", "label": 2371, "func": ""}
{"index": "s767447431", "label": 3069, "func": ""}
{"index": "s529809831", "label": 3069, "func": ""}
{"index": "s022021449", "label": 3069, "func": ""}
{"index": "s054518541", "label": 3069, "func": ""}
{"index": "s008889825", "label": 3069, "func": ""}
{"index": "s847933755", "label": 3069, "func": ""}
{"index": "s491476646", "label": 3069, "func": ""}
{"index": "s729062904", "label": 3069, "func": ""}
{"index": "s990088821", "label": 3069, "func": ""}
{"index": "s137060374", "label": 3069, "func": ""}
{"index": "s177317651", "label": 3410, "func": ""}
{"index": "s620301114", "label": 3410, "func": ""}
{"index": "s127571242", "label": 3410, "func": ""}
{"index": "s152097263", "label": 3410, "func": ""}
{"index": "s735843425", "label": 3410, "func": ""}
{"index": "s093312075", "label": 3410, "func": ""}
{"index": "s204635878", "label": 3410, "func": ""}
{"index": "s033201664", "label": 3410, "func": ""}
{"index": "s629963480", "label": 3410, "func": ""}
{"index": "s782835107", "label": 3410, "func": ""}
{"index": "s369930080", "label": 7, "func": ""}
{"index": "s733691987", "label": 7, "func": ""}
{"index": "s815491425", "label": 7, "func": ""}
{"index": "s252565664", "label": 7, "func": ""}
{"index": "s939190913", "label": 7, "func": ""}
{"index": "s069720444", "label": 7, "func": ""}
{"index": "s039462714", "label": 7, "func": ""}
{"index": "s099549696", "label": 7, "func": ""}
{"index": "s265674703", "label": 7, "func": ""}
{"index": "s284133408", "label": 7, "func": ""}
{"index": "s607356550", "label": 2258, "func": ""}
{"index": "s958076856", "label": 2258, "func": ""}
{"index": "s589851000", "label": 2258, "func": ""}
{"index": "s608723079", "label": 2258, "func": ""}
{"index": "s256156688", "label": 2258, "func": ""}
{"index": "s088293186", "label": 2258, "func": ""}
{"index": "s795331209", "label": 2258, "func": ""}
{"index": "s050742899", "label": 2258, "func": ""}
{"index": "s327869104", "label": 2258, "func": ""}
{"index": "s257567879", "label": 2258, "func": ""}
{"index": "s812151017", "label": 740, "func": ""}
{"index": "s955959804", "label": 740, "func": ""}
{"index": "s221812614", "label": 740, "func": ""}
{"index": "s135228734", "label": 740, "func": ""}
{"index": "s075109116", "label": 740, "func": ""}
{"index": "s882544976", "label": 740, "func": ""}
{"index": "s599799863", "label": 740, "func": ""}
{"index": "s944682033", "label": 740, "func": ""}
{"index": "s776916164", "label": 740, "func": ""}
{"index": "s679865270", "label": 740, "func": ""}
{"index": "s017731659", "label": 3279, "func": ""}
{"index": "s201546081", "label": 3279, "func": ""}
{"index": "s967308859", "label": 3934, "func": ""}
{"index": "s368741853", "label": 3934, "func": ""}
{"index": "s339676851", "label": 3934, "func": ""}
{"index": "s119496902", "label": 2600, "func": ""}
{"index": "s267027453", "label": 2600, "func": ""}
{"index": "s149149206", "label": 2600, "func": ""}
{"index": "s075652822", "label": 2600, "func": ""}
{"index": "s067090443", "label": 2600, "func": ""}
{"index": "s053817670", "label": 2600, "func": ""}
{"index": "s383103831", "label": 2600, "func": ""}
{"index": "s562895445", "label": 2600, "func": ""}
{"index": "s333019481", "label": 2600, "func": ""}
{"index": "s367732647", "label": 2600, "func": ""}
{"index": "s671894681", "label": 3003, "func": ""}
{"index": "s936236396", "label": 3003, "func": ""}
{"index": "s527303658", "label": 3003, "func": ""}
{"index": "s964391806", "label": 3003, "func": ""}
{"index": "s908606337", "label": 3003, "func": ""}
{"index": "s674182148", "label": 3003, "func": ""}
{"index": "s991429316", "label": 3003, "func": ""}
{"index": "s616281544", "label": 3003, "func": ""}
{"index": "s603885121", "label": 3003, "func": ""}
{"index": "s647474496", "label": 3003, "func": ""}
{"index": "s882044503", "label": 2837, "func": ""}
{"index": "s830189329", "label": 2837, "func": ""}
{"index": "s512293666", "label": 2837, "func": ""}
{"index": "s544718426", "label": 2837, "func": ""}
{"index": "s551319211", "label": 2837, "func": ""}
{"index": "s458444115", "label": 2837, "func": ""}
{"index": "s721706116", "label": 2837, "func": ""}
{"index": "s898786455", "label": 2837, "func": ""}
{"index": "s812787104", "label": 2837, "func": ""}
{"index": "s974601972", "label": 2837, "func": ""}
{"index": "s639851837", "label": 1779, "func": ""}
{"index": "s367604119", "label": 3013, "func": ""}
{"index": "s649592758", "label": 3013, "func": ""}
{"index": "s751041011", "label": 3013, "func": ""}
{"index": "s656055206", "label": 3013, "func": ""}
{"index": "s221275250", "label": 3013, "func": ""}
{"index": "s319837164", "label": 3013, "func": ""}
{"index": "s285826574", "label": 3013, "func": ""}
{"index": "s900070236", "label": 3013, "func": ""}
{"index": "s943219080", "label": 3013, "func": ""}
{"index": "s943658987", "label": 3013, "func": ""}
{"index": "s771313476", "label": 3005, "func": ""}
{"index": "s268837963", "label": 3005, "func": ""}
{"index": "s004763845", "label": 3005, "func": ""}
{"index": "s119590680", "label": 3005, "func": ""}
{"index": "s265310475", "label": 3005, "func": ""}
{"index": "s216698915", "label": 3005, "func": ""}
{"index": "s326363388", "label": 3005, "func": ""}
{"index": "s622971572", "label": 3005, "func": ""}
{"index": "s955159713", "label": 3005, "func": ""}
{"index": "s067843846", "label": 3005, "func": ""}
{"index": "s259812256", "label": 202, "func": ""}
{"index": "s020433488", "label": 202, "func": ""}
{"index": "s226255689", "label": 202, "func": ""}
{"index": "s004899317", "label": 202, "func": ""}
{"index": "s754247363", "label": 202, "func": ""}
{"index": "s422016011", "label": 202, "func": ""}
{"index": "s251324218", "label": 202, "func": ""}
{"index": "s188215392", "label": 202, "func": ""}
{"index": "s790316248", "label": 202, "func": ""}
{"index": "s349676486", "label": 202, "func": ""}
{"index": "s193514455", "label": 2257, "func": ""}
{"index": "s124086965", "label": 2257, "func": ""}
{"index": "s546726749", "label": 2257, "func": ""}
{"index": "s152478234", "label": 2257, "func": ""}
{"index": "s779169452", "label": 2257, "func": ""}
{"index": "s546139420", "label": 2257, "func": ""}
{"index": "s665673030", "label": 2257, "func": ""}
{"index": "s650476358", "label": 2257, "func": ""}
{"index": "s498127285", "label": 2257, "func": ""}
{"index": "s295513815", "label": 2257, "func": ""}
{"index": "s590455643", "label": 3023, "func": ""}
{"index": "s329644048", "label": 3023, "func": ""}
{"index": "s335429979", "label": 3023, "func": ""}
{"index": "s897577947", "label": 3023, "func": ""}
{"index": "s751025433", "label": 3023, "func": ""}
{"index": "s106383888", "label": 3023, "func": ""}
{"index": "s481268277", "label": 3023, "func": ""}
{"index": "s250413147", "label": 3023, "func": ""}
{"index": "s457434300", "label": 3023, "func": ""}
{"index": "s431470029", "label": 3023, "func": ""}
{"index": "s381113001", "label": 1739, "func": ""}
{"index": "s207432581", "label": 1739, "func": ""}
{"index": "s709648079", "label": 2275, "func": ""}
{"index": "s290393422", "label": 2275, "func": ""}
{"index": "s578214807", "label": 2275, "func": ""}
{"index": "s398587357", "label": 2275, "func": ""}
{"index": "s785359620", "label": 2275, "func": ""}
{"index": "s549867456", "label": 2275, "func": ""}
{"index": "s955688108", "label": 2275, "func": ""}
{"index": "s746399267", "label": 2275, "func": ""}
{"index": "s403604323", "label": 2275, "func": ""}
{"index": "s695593903", "label": 2275, "func": ""}
{"index": "s128750257", "label": 2744, "func": ""}
{"index": "s132161042", "label": 2744, "func": ""}
{"index": "s115779289", "label": 2744, "func": ""}
{"index": "s097053808", "label": 2744, "func": ""}
{"index": "s752815940", "label": 2744, "func": ""}
{"index": "s594597216", "label": 2744, "func": ""}
{"index": "s011157504", "label": 2744, "func": ""}
{"index": "s125747627", "label": 2744, "func": ""}
{"index": "s317195966", "label": 2744, "func": ""}
{"index": "s486680589", "label": 2744, "func": ""}
{"index": "s748692813", "label": 4035, "func": ""}
{"index": "s419097929", "label": 4035, "func": ""}
{"index": "s072592282", "label": 4035, "func": ""}
{"index": "s831393515", "label": 4035, "func": ""}
{"index": "s205089228", "label": 4035, "func": ""}
{"index": "s757018813", "label": 4035, "func": ""}
{"index": "s258118298", "label": 4035, "func": ""}
{"index": "s795816217", "label": 4035, "func": ""}
{"index": "s045117335", "label": 4035, "func": ""}
{"index": "s066205670", "label": 4035, "func": ""}
{"index": "s247737661", "label": 2756, "func": ""}
{"index": "s134780191", "label": 2756, "func": ""}
{"index": "s014171193", "label": 2756, "func": ""}
{"index": "s367578648", "label": 2756, "func": ""}
{"index": "s336469433", "label": 2756, "func": ""}
{"index": "s053403257", "label": 2756, "func": ""}
{"index": "s927664326", "label": 2756, "func": ""}
{"index": "s107144504", "label": 2756, "func": ""}
{"index": "s313641349", "label": 2756, "func": ""}
{"index": "s859278834", "label": 2756, "func": ""}
{"index": "s495154282", "label": 89, "func": ""}
{"index": "s188449305", "label": 89, "func": ""}
{"index": "s574556484", "label": 89, "func": ""}
{"index": "s298722411", "label": 89, "func": ""}
{"index": "s625583527", "label": 89, "func": ""}
{"index": "s578153225", "label": 89, "func": ""}
{"index": "s400277501", "label": 89, "func": ""}
{"index": "s094965080", "label": 89, "func": ""}
{"index": "s265772759", "label": 89, "func": ""}
{"index": "s190777322", "label": 89, "func": ""}
{"index": "s365285382", "label": 16, "func": ""}
{"index": "s947260075", "label": 16, "func": ""}
{"index": "s582504071", "label": 16, "func": ""}
{"index": "s987499664", "label": 16, "func": ""}
{"index": "s731288056", "label": 16, "func": ""}
{"index": "s415885269", "label": 16, "func": ""}
{"index": "s860241115", "label": 16, "func": ""}
{"index": "s660420049", "label": 16, "func": ""}
{"index": "s331260521", "label": 16, "func": ""}
{"index": "s817590964", "label": 16, "func": ""}
{"index": "s938746404", "label": 1517, "func": ""}
{"index": "s244969156", "label": 1517, "func": ""}
{"index": "s183012968", "label": 1517, "func": ""}
{"index": "s637230454", "label": 1517, "func": ""}
{"index": "s241766593", "label": 1517, "func": ""}
{"index": "s323049727", "label": 1517, "func": ""}
{"index": "s516782606", "label": 1517, "func": ""}
{"index": "s919181845", "label": 1517, "func": ""}
{"index": "s083924538", "label": 1517, "func": ""}
{"index": "s685525348", "label": 1517, "func": ""}
{"index": "s028639271", "label": 3421, "func": ""}
{"index": "s905751181", "label": 3421, "func": ""}
{"index": "s517765214", "label": 3421, "func": ""}
{"index": "s610080984", "label": 3421, "func": ""}
{"index": "s203223603", "label": 3421, "func": ""}
{"index": "s478638917", "label": 3421, "func": ""}
{"index": "s144345392", "label": 3421, "func": ""}
{"index": "s007411983", "label": 3421, "func": ""}
{"index": "s815936497", "label": 3421, "func": ""}
{"index": "s455722944", "label": 3421, "func": ""}
{"index": "s357124940", "label": 1697, "func": ""}
{"index": "s770552430", "label": 1697, "func": ""}
{"index": "s948983562", "label": 1814, "func": ""}
{"index": "s733821102", "label": 1814, "func": ""}
{"index": "s029011186", "label": 3478, "func": ""}
{"index": "s382357795", "label": 3478, "func": ""}
{"index": "s646838988", "label": 3478, "func": ""}
{"index": "s684279096", "label": 3478, "func": ""}
{"index": "s248989803", "label": 3478, "func": ""}
{"index": "s963763373", "label": 3478, "func": ""}
{"index": "s877397703", "label": 3478, "func": ""}
{"index": "s022846496", "label": 3478, "func": ""}
{"index": "s620719459", "label": 3478, "func": ""}
{"index": "s310755417", "label": 3478, "func": ""}
{"index": "s815613975", "label": 465, "func": ""}
{"index": "s891146151", "label": 465, "func": ""}
{"index": "s000278911", "label": 465, "func": ""}
{"index": "s440011379", "label": 465, "func": ""}
{"index": "s236421637", "label": 465, "func": ""}
{"index": "s618291427", "label": 465, "func": ""}
{"index": "s405208884", "label": 465, "func": ""}
{"index": "s792608605", "label": 465, "func": ""}
{"index": "s881649082", "label": 465, "func": ""}
{"index": "s493668501", "label": 465, "func": ""}
{"index": "s828643880", "label": 3895, "func": ""}
{"index": "s035483186", "label": 3895, "func": ""}
{"index": "s948525656", "label": 701, "func": ""}
{"index": "s727571323", "label": 701, "func": ""}
{"index": "s687150188", "label": 701, "func": ""}
{"index": "s758343347", "label": 701, "func": ""}
{"index": "s830705535", "label": 701, "func": ""}
{"index": "s069358418", "label": 701, "func": ""}
{"index": "s360003607", "label": 701, "func": ""}
{"index": "s867478132", "label": 2394, "func": ""}
{"index": "s542963899", "label": 2394, "func": ""}
{"index": "s151671960", "label": 2394, "func": ""}
{"index": "s116110077", "label": 2394, "func": ""}
{"index": "s932339098", "label": 2394, "func": ""}
{"index": "s523828279", "label": 2394, "func": ""}
{"index": "s528469690", "label": 2394, "func": ""}
{"index": "s352630537", "label": 2394, "func": ""}
{"index": "s027270089", "label": 2394, "func": ""}
{"index": "s017325287", "label": 2394, "func": ""}
{"index": "s859632091", "label": 591, "func": ""}
{"index": "s576221423", "label": 591, "func": ""}
{"index": "s069063791", "label": 591, "func": ""}
{"index": "s708307175", "label": 591, "func": ""}
{"index": "s935960601", "label": 591, "func": ""}
{"index": "s613609463", "label": 591, "func": ""}
{"index": "s948028705", "label": 591, "func": ""}
{"index": "s013094428", "label": 591, "func": ""}
{"index": "s540896018", "label": 591, "func": ""}
{"index": "s129447756", "label": 591, "func": ""}
{"index": "s275265674", "label": 1754, "func": ""}
{"index": "s987675041", "label": 1754, "func": ""}
{"index": "s294814910", "label": 1754, "func": ""}
{"index": "s514354730", "label": 1754, "func": ""}
{"index": "s529997013", "label": 1754, "func": ""}
{"index": "s382202191", "label": 1754, "func": ""}
{"index": "s178841836", "label": 619, "func": ""}
{"index": "s684931921", "label": 619, "func": ""}
{"index": "s765575583", "label": 619, "func": ""}
{"index": "s538810526", "label": 619, "func": ""}
{"index": "s518922418", "label": 1722, "func": ""}
{"index": "s461160267", "label": 1722, "func": ""}
{"index": "s945301488", "label": 1722, "func": ""}
{"index": "s137392414", "label": 1722, "func": ""}
{"index": "s061080481", "label": 1722, "func": ""}
{"index": "s628140985", "label": 1722, "func": ""}
{"index": "s228101452", "label": 1722, "func": ""}
{"index": "s179470739", "label": 1722, "func": ""}
{"index": "s960951011", "label": 1722, "func": ""}
{"index": "s909337414", "label": 1722, "func": ""}
{"index": "s205728331", "label": 1511, "func": ""}
{"index": "s048158082", "label": 1511, "func": ""}
{"index": "s140197111", "label": 3800, "func": ""}
{"index": "s440771635", "label": 3800, "func": ""}
{"index": "s986114013", "label": 3800, "func": ""}
{"index": "s558075424", "label": 3800, "func": ""}
{"index": "s659795220", "label": 3800, "func": ""}
{"index": "s800992347", "label": 3800, "func": ""}
{"index": "s379066937", "label": 3800, "func": ""}
{"index": "s633800242", "label": 3800, "func": ""}
{"index": "s230293521", "label": 3800, "func": ""}
{"index": "s482319777", "label": 3800, "func": ""}
{"index": "s780944871", "label": 670, "func": ""}
{"index": "s310283600", "label": 670, "func": ""}
{"index": "s595038916", "label": 670, "func": ""}
{"index": "s139905326", "label": 670, "func": ""}
{"index": "s685566368", "label": 670, "func": ""}
{"index": "s358908561", "label": 670, "func": ""}
{"index": "s015219072", "label": 670, "func": ""}
{"index": "s617296364", "label": 670, "func": ""}
{"index": "s793363236", "label": 670, "func": ""}
{"index": "s814963233", "label": 670, "func": ""}
{"index": "s858244508", "label": 3311, "func": ""}
{"index": "s608907051", "label": 3311, "func": ""}
{"index": "s555920160", "label": 3311, "func": ""}
{"index": "s121061839", "label": 3311, "func": ""}
{"index": "s062322738", "label": 3311, "func": ""}
{"index": "s147391915", "label": 3311, "func": ""}
{"index": "s904980816", "label": 3311, "func": ""}
{"index": "s935043889", "label": 3311, "func": ""}
{"index": "s231138481", "label": 3311, "func": ""}
{"index": "s016886602", "label": 3311, "func": ""}
{"index": "s723004216", "label": 3174, "func": ""}
{"index": "s594434568", "label": 3174, "func": ""}
{"index": "s000482989", "label": 3174, "func": ""}
{"index": "s289874559", "label": 3174, "func": ""}
{"index": "s833724256", "label": 3174, "func": ""}
{"index": "s440337783", "label": 3174, "func": ""}
{"index": "s974935505", "label": 3174, "func": ""}
{"index": "s016808538", "label": 3174, "func": ""}
{"index": "s209070395", "label": 3174, "func": ""}
{"index": "s091943064", "label": 3174, "func": ""}
{"index": "s372674142", "label": 664, "func": ""}
{"index": "s011185162", "label": 664, "func": ""}
{"index": "s616130840", "label": 664, "func": ""}
{"index": "s624801153", "label": 664, "func": ""}
{"index": "s969793292", "label": 165, "func": ""}
{"index": "s547117898", "label": 165, "func": ""}
{"index": "s858532932", "label": 165, "func": ""}
{"index": "s266904186", "label": 165, "func": ""}
{"index": "s306687406", "label": 165, "func": ""}
{"index": "s196099491", "label": 165, "func": ""}
{"index": "s906700835", "label": 165, "func": ""}
{"index": "s076118980", "label": 165, "func": ""}
{"index": "s979174394", "label": 165, "func": ""}
{"index": "s902797102", "label": 165, "func": ""}
{"index": "s011978409", "label": 815, "func": ""}
{"index": "s044892678", "label": 815, "func": ""}
{"index": "s477601780", "label": 815, "func": ""}
{"index": "s279966717", "label": 815, "func": ""}
{"index": "s252096120", "label": 87, "func": ""}
{"index": "s556869270", "label": 87, "func": ""}
{"index": "s805752118", "label": 87, "func": ""}
{"index": "s145794844", "label": 87, "func": ""}
{"index": "s017262488", "label": 87, "func": ""}
{"index": "s540272606", "label": 87, "func": ""}
{"index": "s294562944", "label": 87, "func": ""}
{"index": "s843147130", "label": 87, "func": ""}
{"index": "s530410467", "label": 87, "func": ""}
{"index": "s588263469", "label": 87, "func": ""}
{"index": "s936797803", "label": 566, "func": ""}
{"index": "s116439126", "label": 566, "func": ""}
{"index": "s342721776", "label": 566, "func": ""}
{"index": "s267009571", "label": 566, "func": ""}
{"index": "s079379211", "label": 566, "func": ""}
{"index": "s027171893", "label": 566, "func": ""}
{"index": "s677216731", "label": 566, "func": ""}
{"index": "s048535582", "label": 566, "func": ""}
{"index": "s689240198", "label": 566, "func": ""}
{"index": "s150644766", "label": 2568, "func": ""}
{"index": "s070794808", "label": 2568, "func": ""}
{"index": "s057286270", "label": 2568, "func": ""}
{"index": "s460335712", "label": 2568, "func": ""}
{"index": "s668269523", "label": 2568, "func": ""}
{"index": "s835637657", "label": 2568, "func": ""}
{"index": "s171941465", "label": 2568, "func": ""}
{"index": "s544168155", "label": 2568, "func": ""}
{"index": "s979904416", "label": 2568, "func": ""}
{"index": "s629899473", "label": 2568, "func": ""}
{"index": "s358331848", "label": 1288, "func": ""}
{"index": "s224714616", "label": 1288, "func": ""}
{"index": "s181036136", "label": 1288, "func": ""}
{"index": "s951882746", "label": 1288, "func": ""}
{"index": "s860417909", "label": 1288, "func": ""}
{"index": "s293087919", "label": 1288, "func": ""}
{"index": "s919783082", "label": 1288, "func": ""}
{"index": "s322604856", "label": 1288, "func": ""}
{"index": "s774201528", "label": 1288, "func": ""}
{"index": "s600767866", "label": 1288, "func": ""}
{"index": "s740968896", "label": 904, "func": ""}
{"index": "s663573825", "label": 904, "func": ""}
{"index": "s585123523", "label": 904, "func": ""}
{"index": "s989817979", "label": 904, "func": ""}
{"index": "s945570117", "label": 904, "func": ""}
{"index": "s532915512", "label": 904, "func": ""}
{"index": "s767320547", "label": 904, "func": ""}
{"index": "s595097387", "label": 904, "func": ""}
{"index": "s167599444", "label": 904, "func": ""}
{"index": "s018164620", "label": 904, "func": ""}
{"index": "s865605884", "label": 137, "func": ""}
{"index": "s027030407", "label": 137, "func": ""}
{"index": "s338084298", "label": 137, "func": ""}
{"index": "s005849741", "label": 137, "func": ""}
{"index": "s838054204", "label": 137, "func": ""}
{"index": "s090969698", "label": 137, "func": ""}
{"index": "s833650516", "label": 137, "func": ""}
{"index": "s245027462", "label": 137, "func": ""}
{"index": "s826011433", "label": 137, "func": ""}
{"index": "s040431292", "label": 137, "func": ""}
{"index": "s890078833", "label": 3650, "func": ""}
{"index": "s282043794", "label": 3650, "func": ""}
{"index": "s801208585", "label": 3650, "func": ""}
{"index": "s091860368", "label": 3650, "func": ""}
{"index": "s493611522", "label": 1076, "func": ""}
{"index": "s665653308", "label": 1076, "func": ""}
{"index": "s981505565", "label": 363, "func": ""}
{"index": "s331279477", "label": 363, "func": ""}
{"index": "s828772702", "label": 363, "func": ""}
{"index": "s939823047", "label": 363, "func": ""}
{"index": "s927495846", "label": 363, "func": ""}
{"index": "s282085315", "label": 363, "func": ""}
{"index": "s701961213", "label": 363, "func": ""}
{"index": "s643298873", "label": 363, "func": ""}
{"index": "s010913415", "label": 363, "func": ""}
{"index": "s513275460", "label": 363, "func": ""}
{"index": "s838827042", "label": 1952, "func": ""}
{"index": "s194440444", "label": 1952, "func": ""}
{"index": "s564688495", "label": 1952, "func": ""}
{"index": "s175795214", "label": 2417, "func": ""}
{"index": "s475562737", "label": 2417, "func": ""}
{"index": "s150086697", "label": 2417, "func": ""}
{"index": "s374947266", "label": 2417, "func": ""}
{"index": "s123821158", "label": 2417, "func": ""}
{"index": "s693384434", "label": 2417, "func": ""}
{"index": "s907704769", "label": 2417, "func": ""}
{"index": "s821980479", "label": 2417, "func": ""}
{"index": "s291406370", "label": 2417, "func": ""}
{"index": "s240172000", "label": 2417, "func": ""}
{"index": "s152590024", "label": 3239, "func": ""}
{"index": "s792671452", "label": 3239, "func": ""}
{"index": "s976901954", "label": 3239, "func": ""}
{"index": "s351086154", "label": 3239, "func": ""}
{"index": "s806830129", "label": 3239, "func": ""}
{"index": "s230136591", "label": 3239, "func": ""}
{"index": "s293228290", "label": 3239, "func": ""}
{"index": "s854570386", "label": 3239, "func": ""}
{"index": "s842997444", "label": 3239, "func": ""}
{"index": "s144599050", "label": 3239, "func": ""}
{"index": "s244560031", "label": 1848, "func": ""}
{"index": "s465326620", "label": 3347, "func": ""}
{"index": "s784228808", "label": 3347, "func": ""}
{"index": "s567767580", "label": 3347, "func": ""}
{"index": "s208573201", "label": 3347, "func": ""}
{"index": "s360631176", "label": 3347, "func": ""}
{"index": "s287529109", "label": 3347, "func": ""}
{"index": "s774204293", "label": 3347, "func": ""}
{"index": "s325671810", "label": 3347, "func": ""}
{"index": "s573785412", "label": 3347, "func": ""}
{"index": "s521320589", "label": 3347, "func": ""}
{"index": "s969357126", "label": 276, "func": ""}
{"index": "s111606242", "label": 276, "func": ""}
{"index": "s065237572", "label": 276, "func": ""}
{"index": "s232441217", "label": 276, "func": ""}
{"index": "s973973542", "label": 276, "func": ""}
{"index": "s135238211", "label": 276, "func": ""}
{"index": "s693445953", "label": 276, "func": ""}
{"index": "s445029980", "label": 276, "func": ""}
{"index": "s257533585", "label": 276, "func": ""}
{"index": "s485344956", "label": 276, "func": ""}
{"index": "s116722517", "label": 27, "func": ""}
{"index": "s292563832", "label": 27, "func": ""}
{"index": "s543754900", "label": 27, "func": ""}
{"index": "s259223170", "label": 27, "func": ""}
{"index": "s637313057", "label": 27, "func": ""}
{"index": "s474963494", "label": 27, "func": ""}
{"index": "s377025637", "label": 27, "func": ""}
{"index": "s227935112", "label": 27, "func": ""}
{"index": "s555018596", "label": 27, "func": ""}
{"index": "s263298604", "label": 27, "func": ""}
{"index": "s290827179", "label": 3762, "func": ""}
{"index": "s645456024", "label": 3762, "func": ""}
{"index": "s696579605", "label": 3762, "func": ""}
{"index": "s466250396", "label": 3762, "func": ""}
{"index": "s322457686", "label": 3762, "func": ""}
{"index": "s217687900", "label": 3762, "func": ""}
{"index": "s213586520", "label": 3762, "func": ""}
{"index": "s801321761", "label": 3762, "func": ""}
{"index": "s402957295", "label": 3762, "func": ""}
{"index": "s455752457", "label": 3762, "func": ""}
{"index": "s906374275", "label": 1119, "func": ""}
{"index": "s223583464", "label": 2534, "func": ""}
{"index": "s313061017", "label": 2534, "func": ""}
{"index": "s444081474", "label": 2534, "func": ""}
{"index": "s097798926", "label": 2534, "func": ""}
{"index": "s691174419", "label": 2534, "func": ""}
{"index": "s373321519", "label": 2534, "func": ""}
{"index": "s678252440", "label": 2534, "func": ""}
{"index": "s849274913", "label": 2534, "func": ""}
{"index": "s048265144", "label": 2534, "func": ""}
{"index": "s831727756", "label": 2534, "func": ""}
{"index": "s217222531", "label": 4021, "func": ""}
{"index": "s162276688", "label": 4021, "func": ""}
{"index": "s913237848", "label": 4021, "func": ""}
{"index": "s439150859", "label": 4021, "func": ""}
{"index": "s397241243", "label": 4021, "func": ""}
{"index": "s506132970", "label": 4021, "func": ""}
{"index": "s901335370", "label": 4021, "func": ""}
{"index": "s542637861", "label": 4021, "func": ""}
{"index": "s730016107", "label": 4021, "func": ""}
{"index": "s327288861", "label": 4021, "func": ""}
{"index": "s273447266", "label": 3000, "func": ""}
{"index": "s772140629", "label": 3000, "func": ""}
{"index": "s114720632", "label": 3000, "func": ""}
{"index": "s629500890", "label": 3000, "func": ""}
{"index": "s273956480", "label": 3000, "func": ""}
{"index": "s870314984", "label": 3000, "func": ""}
{"index": "s372619245", "label": 3000, "func": ""}
{"index": "s562961015", "label": 3000, "func": ""}
{"index": "s558164972", "label": 3000, "func": ""}
{"index": "s646476180", "label": 3000, "func": ""}
{"index": "s796686695", "label": 3132, "func": ""}
{"index": "s034523636", "label": 3132, "func": ""}
{"index": "s668401468", "label": 3132, "func": ""}
{"index": "s418980822", "label": 3132, "func": ""}
{"index": "s163938259", "label": 3132, "func": ""}
{"index": "s439628203", "label": 3132, "func": ""}
{"index": "s469956071", "label": 3132, "func": ""}
{"index": "s475444445", "label": 3132, "func": ""}
{"index": "s386597250", "label": 3132, "func": ""}
{"index": "s224970405", "label": 3132, "func": ""}
{"index": "s237892915", "label": 452, "func": ""}
{"index": "s032752394", "label": 452, "func": ""}
{"index": "s309891004", "label": 452, "func": ""}
{"index": "s019406952", "label": 452, "func": ""}
{"index": "s019303384", "label": 452, "func": ""}
{"index": "s347138940", "label": 452, "func": ""}
{"index": "s596285013", "label": 452, "func": ""}
{"index": "s961843307", "label": 452, "func": ""}
{"index": "s241772701", "label": 452, "func": ""}
{"index": "s508093262", "label": 452, "func": ""}
{"index": "s833452305", "label": 2540, "func": ""}
{"index": "s303724939", "label": 2540, "func": ""}
{"index": "s522451607", "label": 2540, "func": ""}
{"index": "s154791904", "label": 2540, "func": ""}
{"index": "s606997340", "label": 2540, "func": ""}
{"index": "s434349970", "label": 2540, "func": ""}
{"index": "s644812064", "label": 2540, "func": ""}
{"index": "s933275641", "label": 2540, "func": ""}
{"index": "s216482236", "label": 2540, "func": ""}
{"index": "s970853282", "label": 2540, "func": ""}
{"index": "s932796013", "label": 756, "func": ""}
{"index": "s361282555", "label": 756, "func": ""}
{"index": "s844951879", "label": 756, "func": ""}
{"index": "s769161025", "label": 756, "func": ""}
{"index": "s896364373", "label": 756, "func": ""}
{"index": "s776663696", "label": 756, "func": ""}
{"index": "s592764590", "label": 756, "func": ""}
{"index": "s246560573", "label": 756, "func": ""}
{"index": "s672967965", "label": 756, "func": ""}
{"index": "s000829983", "label": 756, "func": ""}
{"index": "s477512102", "label": 2575, "func": ""}
{"index": "s723057484", "label": 2575, "func": ""}
{"index": "s630049437", "label": 2575, "func": ""}
{"index": "s423742848", "label": 2575, "func": ""}
{"index": "s469454272", "label": 2575, "func": ""}
{"index": "s958107776", "label": 2575, "func": ""}
{"index": "s200323747", "label": 2575, "func": ""}
{"index": "s635043393", "label": 2575, "func": ""}
{"index": "s022594428", "label": 2575, "func": ""}
{"index": "s980911156", "label": 2575, "func": ""}
{"index": "s683967964", "label": 3616, "func": ""}
{"index": "s242090296", "label": 3616, "func": ""}
{"index": "s199212138", "label": 3616, "func": ""}
{"index": "s843474763", "label": 3616, "func": ""}
{"index": "s823539168", "label": 3616, "func": ""}
{"index": "s798470654", "label": 3616, "func": ""}
{"index": "s500442493", "label": 3616, "func": ""}
{"index": "s681164488", "label": 3616, "func": ""}
{"index": "s034578309", "label": 3616, "func": ""}
{"index": "s151683833", "label": 3616, "func": ""}
{"index": "s019364088", "label": 3699, "func": ""}
{"index": "s307524068", "label": 3699, "func": ""}
{"index": "s490554313", "label": 3699, "func": ""}
{"index": "s076829593", "label": 3699, "func": ""}
{"index": "s508580746", "label": 3699, "func": ""}
{"index": "s050965204", "label": 3699, "func": ""}
{"index": "s498307810", "label": 3699, "func": ""}
{"index": "s743396945", "label": 3699, "func": ""}
{"index": "s417560034", "label": 3699, "func": ""}
{"index": "s707101789", "label": 3699, "func": ""}
{"index": "s063020117", "label": 941, "func": ""}
{"index": "s385520680", "label": 3870, "func": ""}
{"index": "s861660481", "label": 3870, "func": ""}
{"index": "s880298232", "label": 3566, "func": ""}
{"index": "s656533379", "label": 3566, "func": ""}
{"index": "s959122242", "label": 3566, "func": ""}
{"index": "s653411454", "label": 3566, "func": ""}
{"index": "s028173892", "label": 3566, "func": ""}
{"index": "s293874906", "label": 3566, "func": ""}
{"index": "s472443688", "label": 3566, "func": ""}
{"index": "s778735285", "label": 3566, "func": ""}
{"index": "s553453399", "label": 3566, "func": ""}
{"index": "s092721440", "label": 3566, "func": ""}
{"index": "s884952951", "label": 1394, "func": ""}
{"index": "s444544802", "label": 2494, "func": ""}
{"index": "s433833533", "label": 2494, "func": ""}
{"index": "s956506402", "label": 2494, "func": ""}
{"index": "s227768273", "label": 2494, "func": ""}
{"index": "s775571643", "label": 2494, "func": ""}
{"index": "s734259818", "label": 2494, "func": ""}
{"index": "s711192175", "label": 2494, "func": ""}
{"index": "s188458723", "label": 2494, "func": ""}
{"index": "s410227514", "label": 2494, "func": ""}
{"index": "s981138925", "label": 2494, "func": ""}
{"index": "s870626889", "label": 4030, "func": ""}
{"index": "s294620875", "label": 4030, "func": ""}
{"index": "s714153907", "label": 4030, "func": ""}
{"index": "s941222667", "label": 4030, "func": ""}
{"index": "s055885062", "label": 4030, "func": ""}
{"index": "s443674488", "label": 4030, "func": ""}
{"index": "s832907863", "label": 4030, "func": ""}
{"index": "s085270134", "label": 4030, "func": ""}
{"index": "s003926437", "label": 4030, "func": ""}
{"index": "s143493760", "label": 4030, "func": ""}
{"index": "s472924290", "label": 2974, "func": ""}
{"index": "s528466591", "label": 2974, "func": ""}
{"index": "s041896903", "label": 2974, "func": ""}
{"index": "s739796781", "label": 2974, "func": ""}
{"index": "s223934606", "label": 2974, "func": ""}
{"index": "s281750481", "label": 2974, "func": ""}
{"index": "s844071461", "label": 2974, "func": ""}
{"index": "s870766611", "label": 2974, "func": ""}
{"index": "s267027674", "label": 644, "func": ""}
{"index": "s818338834", "label": 644, "func": ""}
{"index": "s121229678", "label": 644, "func": ""}
{"index": "s324709181", "label": 644, "func": ""}
{"index": "s204569507", "label": 3187, "func": ""}
{"index": "s630524146", "label": 3187, "func": ""}
{"index": "s656526617", "label": 3187, "func": ""}
{"index": "s678724260", "label": 3187, "func": ""}
{"index": "s774845917", "label": 3187, "func": ""}
{"index": "s481965146", "label": 3187, "func": ""}
{"index": "s979564515", "label": 3187, "func": ""}
{"index": "s338646485", "label": 3187, "func": ""}
{"index": "s208296919", "label": 3187, "func": ""}
{"index": "s703153368", "label": 3801, "func": ""}
{"index": "s919532078", "label": 3801, "func": ""}
{"index": "s323792411", "label": 3801, "func": ""}
{"index": "s427551967", "label": 3801, "func": ""}
{"index": "s142947993", "label": 3801, "func": ""}
{"index": "s126893084", "label": 3801, "func": ""}
{"index": "s174205137", "label": 3801, "func": ""}
{"index": "s514211896", "label": 3801, "func": ""}
{"index": "s749122669", "label": 3801, "func": ""}
{"index": "s144959122", "label": 3801, "func": ""}
{"index": "s223771527", "label": 2658, "func": ""}
{"index": "s741503331", "label": 2658, "func": ""}
{"index": "s331294448", "label": 2658, "func": ""}
{"index": "s005760080", "label": 2658, "func": ""}
{"index": "s360598603", "label": 2658, "func": ""}
{"index": "s759019547", "label": 2658, "func": ""}
{"index": "s570566253", "label": 2658, "func": ""}
{"index": "s885225141", "label": 2658, "func": ""}
{"index": "s495058169", "label": 2658, "func": ""}
{"index": "s731387304", "label": 2658, "func": ""}
{"index": "s383001387", "label": 83, "func": ""}
{"index": "s478993776", "label": 83, "func": ""}
{"index": "s465321112", "label": 83, "func": ""}
{"index": "s839927950", "label": 83, "func": ""}
{"index": "s358849693", "label": 83, "func": ""}
{"index": "s509751981", "label": 83, "func": ""}
{"index": "s514538174", "label": 83, "func": ""}
{"index": "s019563989", "label": 83, "func": ""}
{"index": "s419757257", "label": 83, "func": ""}
{"index": "s897111396", "label": 83, "func": ""}
{"index": "s478029448", "label": 1327, "func": ""}
{"index": "s574247527", "label": 3835, "func": ""}
{"index": "s981227094", "label": 3835, "func": ""}
{"index": "s726570924", "label": 3835, "func": ""}
{"index": "s681109282", "label": 3835, "func": ""}
{"index": "s434641624", "label": 3835, "func": ""}
{"index": "s751804869", "label": 3835, "func": ""}
{"index": "s941438188", "label": 3835, "func": ""}
{"index": "s282616172", "label": 3835, "func": ""}
{"index": "s803922407", "label": 3835, "func": ""}
{"index": "s146390586", "label": 3835, "func": ""}
{"index": "s451966943", "label": 277, "func": ""}
{"index": "s083293937", "label": 277, "func": ""}
{"index": "s722946377", "label": 277, "func": ""}
{"index": "s424093630", "label": 277, "func": ""}
{"index": "s429882100", "label": 277, "func": ""}
{"index": "s327338551", "label": 277, "func": ""}
{"index": "s915319277", "label": 277, "func": ""}
{"index": "s251146440", "label": 277, "func": ""}
{"index": "s229798816", "label": 277, "func": ""}
{"index": "s858373726", "label": 277, "func": ""}
{"index": "s056373726", "label": 414, "func": ""}
{"index": "s030571286", "label": 414, "func": ""}
{"index": "s621959194", "label": 2482, "func": ""}
{"index": "s805608067", "label": 2482, "func": ""}
{"index": "s626905678", "label": 2482, "func": ""}
{"index": "s566695519", "label": 2482, "func": ""}
{"index": "s709136026", "label": 2482, "func": ""}
{"index": "s749255405", "label": 2482, "func": ""}
{"index": "s935458148", "label": 2482, "func": ""}
{"index": "s934293709", "label": 2482, "func": ""}
{"index": "s929971897", "label": 2482, "func": ""}
{"index": "s376305577", "label": 2482, "func": ""}
{"index": "s870989887", "label": 3713, "func": ""}
{"index": "s666239514", "label": 3713, "func": ""}
{"index": "s801466899", "label": 3713, "func": ""}
{"index": "s150937201", "label": 3713, "func": ""}
{"index": "s504371683", "label": 3713, "func": ""}
{"index": "s662952588", "label": 3713, "func": ""}
{"index": "s711911928", "label": 3713, "func": ""}
{"index": "s379305513", "label": 3713, "func": ""}
{"index": "s077286452", "label": 3713, "func": ""}
{"index": "s672907695", "label": 3713, "func": ""}
{"index": "s817482830", "label": 3525, "func": ""}
{"index": "s450998083", "label": 3525, "func": ""}
{"index": "s831730753", "label": 3525, "func": ""}
{"index": "s524505723", "label": 3525, "func": ""}
{"index": "s028719796", "label": 3525, "func": ""}
{"index": "s010097992", "label": 3525, "func": ""}
{"index": "s284163562", "label": 3525, "func": ""}
{"index": "s274471135", "label": 3525, "func": ""}
{"index": "s304574959", "label": 3525, "func": ""}
{"index": "s032642980", "label": 3525, "func": ""}
{"index": "s497308290", "label": 2539, "func": ""}
{"index": "s518739412", "label": 2539, "func": ""}
{"index": "s011600049", "label": 2539, "func": ""}
{"index": "s195681631", "label": 2539, "func": ""}
{"index": "s862514854", "label": 2539, "func": ""}
{"index": "s100879167", "label": 2539, "func": ""}
{"index": "s051573904", "label": 2539, "func": ""}
{"index": "s517024196", "label": 2539, "func": ""}
{"index": "s218944039", "label": 40, "func": ""}
{"index": "s280179286", "label": 40, "func": ""}
{"index": "s009397684", "label": 40, "func": ""}
{"index": "s631384674", "label": 40, "func": ""}
{"index": "s608371855", "label": 40, "func": ""}
{"index": "s966925916", "label": 40, "func": ""}
{"index": "s553670299", "label": 40, "func": ""}
{"index": "s714907586", "label": 40, "func": ""}
{"index": "s628439077", "label": 40, "func": ""}
{"index": "s961647768", "label": 40, "func": ""}
{"index": "s418119044", "label": 2190, "func": ""}
{"index": "s440859803", "label": 2190, "func": ""}
{"index": "s425460407", "label": 1639, "func": ""}
{"index": "s451232209", "label": 1639, "func": ""}
{"index": "s757762502", "label": 1977, "func": ""}
{"index": "s860039564", "label": 1977, "func": ""}
{"index": "s933957131", "label": 399, "func": ""}
{"index": "s827114056", "label": 399, "func": ""}
{"index": "s864345114", "label": 399, "func": ""}
{"index": "s089419094", "label": 399, "func": ""}
{"index": "s005590492", "label": 399, "func": ""}
{"index": "s333418876", "label": 399, "func": ""}
{"index": "s652256722", "label": 399, "func": ""}
{"index": "s687461218", "label": 399, "func": ""}
{"index": "s554407313", "label": 399, "func": ""}
{"index": "s098431807", "label": 1389, "func": ""}
{"index": "s603477733", "label": 1389, "func": ""}
{"index": "s706221365", "label": 1389, "func": ""}
{"index": "s731558939", "label": 1389, "func": ""}
{"index": "s202782225", "label": 1389, "func": ""}
{"index": "s906688177", "label": 1389, "func": ""}
{"index": "s985174767", "label": 1389, "func": ""}
{"index": "s043935572", "label": 1389, "func": ""}
{"index": "s893891818", "label": 1389, "func": ""}
{"index": "s088703887", "label": 1389, "func": ""}
{"index": "s208090492", "label": 2804, "func": ""}
{"index": "s869904293", "label": 2804, "func": ""}
{"index": "s683376629", "label": 2804, "func": ""}
{"index": "s013737700", "label": 2804, "func": ""}
{"index": "s026832289", "label": 2804, "func": ""}
{"index": "s912574745", "label": 2804, "func": ""}
{"index": "s510832352", "label": 2804, "func": ""}
{"index": "s200604774", "label": 2804, "func": ""}
{"index": "s057758982", "label": 2804, "func": ""}
{"index": "s581593939", "label": 2804, "func": ""}
{"index": "s150002000", "label": 3758, "func": ""}
{"index": "s475047763", "label": 3758, "func": ""}
{"index": "s899425305", "label": 3758, "func": ""}
{"index": "s967405090", "label": 3758, "func": ""}
{"index": "s396857007", "label": 3758, "func": ""}
{"index": "s483607179", "label": 3758, "func": ""}
{"index": "s283175710", "label": 3758, "func": ""}
{"index": "s384498437", "label": 3758, "func": ""}
{"index": "s163692495", "label": 3758, "func": ""}
{"index": "s665518484", "label": 3758, "func": ""}
{"index": "s376883591", "label": 1632, "func": ""}
{"index": "s991754608", "label": 1632, "func": ""}
{"index": "s749255075", "label": 1632, "func": ""}
{"index": "s142117236", "label": 3406, "func": ""}
{"index": "s667966288", "label": 2378, "func": ""}
{"index": "s067673353", "label": 2378, "func": ""}
{"index": "s159218762", "label": 2378, "func": ""}
{"index": "s266577788", "label": 2378, "func": ""}
{"index": "s003348081", "label": 2378, "func": ""}
{"index": "s632755958", "label": 2378, "func": ""}
{"index": "s559482632", "label": 2378, "func": ""}
{"index": "s515542431", "label": 2378, "func": ""}
{"index": "s447964193", "label": 2378, "func": ""}
{"index": "s688234942", "label": 2378, "func": ""}
{"index": "s853488667", "label": 2592, "func": ""}
{"index": "s377681404", "label": 2592, "func": ""}
{"index": "s715027819", "label": 2592, "func": ""}
{"index": "s099275402", "label": 2592, "func": ""}
{"index": "s145584239", "label": 2592, "func": ""}
{"index": "s600517166", "label": 1481, "func": ""}
{"index": "s391301184", "label": 1481, "func": ""}
{"index": "s711217907", "label": 1481, "func": ""}
{"index": "s556177091", "label": 1481, "func": ""}
{"index": "s798497003", "label": 4029, "func": ""}
{"index": "s655941684", "label": 4029, "func": ""}
{"index": "s431444950", "label": 4029, "func": ""}
{"index": "s436691786", "label": 4029, "func": ""}
{"index": "s927350771", "label": 4029, "func": ""}
{"index": "s588410035", "label": 4029, "func": ""}
{"index": "s612567169", "label": 4029, "func": ""}
{"index": "s810491616", "label": 4029, "func": ""}
{"index": "s266731827", "label": 4029, "func": ""}
{"index": "s063618464", "label": 4029, "func": ""}
{"index": "s780027629", "label": 3274, "func": ""}
{"index": "s781487141", "label": 3274, "func": ""}
{"index": "s251647439", "label": 3274, "func": ""}
{"index": "s937017467", "label": 3274, "func": ""}
{"index": "s962713100", "label": 3274, "func": ""}
{"index": "s762295758", "label": 3274, "func": ""}
{"index": "s609213341", "label": 3274, "func": ""}
{"index": "s554993912", "label": 3274, "func": ""}
{"index": "s501444848", "label": 3274, "func": ""}
{"index": "s761218936", "label": 3274, "func": ""}
{"index": "s193277461", "label": 2550, "func": ""}
{"index": "s973727088", "label": 2550, "func": ""}
{"index": "s717837125", "label": 2550, "func": ""}
{"index": "s621432644", "label": 2550, "func": ""}
{"index": "s768034942", "label": 2550, "func": ""}
{"index": "s266661508", "label": 2550, "func": ""}
{"index": "s483855477", "label": 2550, "func": ""}
{"index": "s283129418", "label": 2550, "func": ""}
{"index": "s382218738", "label": 2550, "func": ""}
{"index": "s128254389", "label": 2550, "func": ""}
{"index": "s802053596", "label": 2244, "func": ""}
{"index": "s421122235", "label": 2244, "func": ""}
{"index": "s371390844", "label": 2244, "func": ""}
{"index": "s466757016", "label": 2244, "func": ""}
{"index": "s141842207", "label": 2244, "func": ""}
{"index": "s355872340", "label": 2244, "func": ""}
{"index": "s845517535", "label": 2244, "func": ""}
{"index": "s532090911", "label": 2244, "func": ""}
{"index": "s030979401", "label": 2244, "func": ""}
{"index": "s154086864", "label": 2244, "func": ""}
{"index": "s689145523", "label": 82, "func": ""}
{"index": "s123366060", "label": 82, "func": ""}
{"index": "s270310590", "label": 82, "func": ""}
{"index": "s686167038", "label": 82, "func": ""}
{"index": "s932056727", "label": 82, "func": ""}
{"index": "s864941032", "label": 82, "func": ""}
{"index": "s683007199", "label": 82, "func": ""}
{"index": "s237806782", "label": 82, "func": ""}
{"index": "s709131450", "label": 82, "func": ""}
{"index": "s999236588", "label": 82, "func": ""}
{"index": "s680444334", "label": 2516, "func": ""}
{"index": "s869711877", "label": 2516, "func": ""}
{"index": "s914924127", "label": 2516, "func": ""}
{"index": "s718197619", "label": 2516, "func": ""}
{"index": "s229980818", "label": 2516, "func": ""}
{"index": "s519321717", "label": 2516, "func": ""}
{"index": "s162165142", "label": 2516, "func": ""}
{"index": "s076904798", "label": 2516, "func": ""}
{"index": "s176371049", "label": 2516, "func": ""}
{"index": "s571159149", "label": 2516, "func": ""}
{"index": "s910535938", "label": 2893, "func": ""}
{"index": "s738102373", "label": 2893, "func": ""}
{"index": "s389397173", "label": 2893, "func": ""}
{"index": "s800947445", "label": 2893, "func": ""}
{"index": "s084330574", "label": 765, "func": ""}
{"index": "s635782848", "label": 3452, "func": ""}
{"index": "s604004917", "label": 3452, "func": ""}
{"index": "s812681545", "label": 3452, "func": ""}
{"index": "s490403615", "label": 3452, "func": ""}
{"index": "s040095946", "label": 3452, "func": ""}
{"index": "s462690420", "label": 3452, "func": ""}
{"index": "s562759746", "label": 3452, "func": ""}
{"index": "s547925794", "label": 3452, "func": ""}
{"index": "s382800279", "label": 3452, "func": ""}
{"index": "s777902756", "label": 3452, "func": ""}
{"index": "s484395117", "label": 3915, "func": ""}
{"index": "s842324221", "label": 3915, "func": ""}
{"index": "s053105070", "label": 3915, "func": ""}
{"index": "s851927883", "label": 3348, "func": ""}
{"index": "s894162415", "label": 3348, "func": ""}
{"index": "s034727591", "label": 3348, "func": ""}
{"index": "s850528717", "label": 3348, "func": ""}
{"index": "s614948248", "label": 3348, "func": ""}
{"index": "s184732823", "label": 3348, "func": ""}
{"index": "s325249532", "label": 3348, "func": ""}
{"index": "s533536302", "label": 3348, "func": ""}
{"index": "s172956313", "label": 3493, "func": ""}
{"index": "s369968344", "label": 3493, "func": ""}
{"index": "s905982060", "label": 3493, "func": ""}
{"index": "s234754634", "label": 3493, "func": ""}
{"index": "s891919058", "label": 3493, "func": ""}
{"index": "s138636664", "label": 3493, "func": ""}
{"index": "s022401082", "label": 3493, "func": ""}
{"index": "s758468525", "label": 3493, "func": ""}
{"index": "s509189314", "label": 3493, "func": ""}
{"index": "s235128169", "label": 3493, "func": ""}
{"index": "s357525496", "label": 3997, "func": ""}
{"index": "s142565436", "label": 3997, "func": ""}
{"index": "s397207161", "label": 3997, "func": ""}
{"index": "s206701906", "label": 3997, "func": ""}
{"index": "s772990205", "label": 3997, "func": ""}
{"index": "s769241626", "label": 3997, "func": ""}
{"index": "s159307404", "label": 3997, "func": ""}
{"index": "s996836897", "label": 3997, "func": ""}
{"index": "s568801574", "label": 3997, "func": ""}
{"index": "s103213611", "label": 3997, "func": ""}
{"index": "s346123294", "label": 884, "func": ""}
{"index": "s001547590", "label": 884, "func": ""}
{"index": "s953967503", "label": 884, "func": ""}
{"index": "s229121463", "label": 884, "func": ""}
{"index": "s359877753", "label": 884, "func": ""}
{"index": "s049211636", "label": 884, "func": ""}
{"index": "s957575806", "label": 884, "func": ""}
{"index": "s177782423", "label": 884, "func": ""}
{"index": "s285533477", "label": 884, "func": ""}
{"index": "s051729386", "label": 884, "func": ""}
{"index": "s468682358", "label": 170, "func": ""}
{"index": "s651305253", "label": 170, "func": ""}
{"index": "s318955333", "label": 170, "func": ""}
{"index": "s454127569", "label": 170, "func": ""}
{"index": "s528435820", "label": 170, "func": ""}
{"index": "s531373659", "label": 170, "func": ""}
{"index": "s611751787", "label": 170, "func": ""}
{"index": "s275008010", "label": 170, "func": ""}
{"index": "s244487726", "label": 170, "func": ""}
{"index": "s454350405", "label": 170, "func": ""}
{"index": "s524538119", "label": 3104, "func": ""}
{"index": "s434637231", "label": 3104, "func": ""}
{"index": "s050936547", "label": 3104, "func": ""}
{"index": "s445132321", "label": 3104, "func": ""}
{"index": "s630625518", "label": 3104, "func": ""}
{"index": "s168011662", "label": 3104, "func": ""}
{"index": "s323069512", "label": 3104, "func": ""}
{"index": "s246970041", "label": 3104, "func": ""}
{"index": "s248777954", "label": 3104, "func": ""}
{"index": "s426850960", "label": 3104, "func": ""}
{"index": "s825056266", "label": 3231, "func": ""}
{"index": "s220647415", "label": 3231, "func": ""}
{"index": "s499097711", "label": 3231, "func": ""}
{"index": "s626201597", "label": 3231, "func": ""}
{"index": "s947254633", "label": 3231, "func": ""}
{"index": "s473059413", "label": 3231, "func": ""}
{"index": "s040427873", "label": 3231, "func": ""}
{"index": "s034364962", "label": 3231, "func": ""}
{"index": "s992486387", "label": 3231, "func": ""}
{"index": "s582828674", "label": 3231, "func": ""}
{"index": "s655499353", "label": 3286, "func": ""}
{"index": "s929430471", "label": 3286, "func": ""}
{"index": "s143775327", "label": 3286, "func": ""}
{"index": "s208998237", "label": 3286, "func": ""}
{"index": "s468119888", "label": 3286, "func": ""}
{"index": "s667720152", "label": 3286, "func": ""}
{"index": "s335399405", "label": 3286, "func": ""}
{"index": "s437537703", "label": 3286, "func": ""}
{"index": "s429022607", "label": 3286, "func": ""}
{"index": "s906707958", "label": 3286, "func": ""}
{"index": "s260375788", "label": 3847, "func": ""}
{"index": "s238346875", "label": 3847, "func": ""}
{"index": "s303702859", "label": 3847, "func": ""}
{"index": "s893165250", "label": 3847, "func": ""}
{"index": "s382795342", "label": 3847, "func": ""}
{"index": "s231488316", "label": 3847, "func": ""}
{"index": "s691856306", "label": 3847, "func": ""}
{"index": "s354465997", "label": 3847, "func": ""}
{"index": "s486604214", "label": 3847, "func": ""}
{"index": "s324767385", "label": 2021, "func": ""}
{"index": "s248312506", "label": 2021, "func": ""}
{"index": "s604092998", "label": 739, "func": ""}
{"index": "s847036729", "label": 739, "func": ""}
{"index": "s648554377", "label": 739, "func": ""}
{"index": "s344932936", "label": 653, "func": ""}
{"index": "s098856146", "label": 653, "func": ""}
{"index": "s812000596", "label": 653, "func": ""}
{"index": "s902916071", "label": 653, "func": ""}
{"index": "s113712645", "label": 653, "func": ""}
{"index": "s923808948", "label": 653, "func": ""}
{"index": "s374927319", "label": 653, "func": ""}
{"index": "s803154023", "label": 653, "func": ""}
{"index": "s922422222", "label": 653, "func": ""}
{"index": "s672540003", "label": 2777, "func": ""}
{"index": "s204756488", "label": 2777, "func": ""}
{"index": "s515765978", "label": 2777, "func": ""}
{"index": "s157948851", "label": 2777, "func": ""}
{"index": "s278312571", "label": 2777, "func": ""}
{"index": "s268145599", "label": 2777, "func": ""}
{"index": "s468190001", "label": 2777, "func": ""}
{"index": "s382475993", "label": 2777, "func": ""}
{"index": "s766101774", "label": 2777, "func": ""}
{"index": "s858362394", "label": 2777, "func": ""}
{"index": "s251350875", "label": 2937, "func": ""}
{"index": "s156199450", "label": 2937, "func": ""}
{"index": "s786447492", "label": 2937, "func": ""}
{"index": "s287449201", "label": 2937, "func": ""}
{"index": "s394097862", "label": 2937, "func": ""}
{"index": "s064808311", "label": 2937, "func": ""}
{"index": "s871122874", "label": 2937, "func": ""}
{"index": "s921962618", "label": 2937, "func": ""}
{"index": "s006715625", "label": 2937, "func": ""}
{"index": "s807632345", "label": 2937, "func": ""}
{"index": "s418993663", "label": 3248, "func": ""}
{"index": "s242931421", "label": 3248, "func": ""}
{"index": "s254651668", "label": 3248, "func": ""}
{"index": "s831381058", "label": 3248, "func": ""}
{"index": "s418916656", "label": 3248, "func": ""}
{"index": "s822529959", "label": 3248, "func": ""}
{"index": "s081593159", "label": 3248, "func": ""}
{"index": "s620786741", "label": 3248, "func": ""}
{"index": "s601459264", "label": 3248, "func": ""}
{"index": "s065667844", "label": 3248, "func": ""}
{"index": "s351392238", "label": 833, "func": ""}
{"index": "s910771283", "label": 833, "func": ""}
{"index": "s087040500", "label": 833, "func": ""}
{"index": "s777379902", "label": 206, "func": ""}
{"index": "s951940391", "label": 206, "func": ""}
{"index": "s993603342", "label": 206, "func": ""}
{"index": "s728673565", "label": 206, "func": ""}
{"index": "s233445021", "label": 206, "func": ""}
{"index": "s373673966", "label": 206, "func": ""}
{"index": "s438025235", "label": 206, "func": ""}
{"index": "s473458948", "label": 206, "func": ""}
{"index": "s930094214", "label": 206, "func": ""}
{"index": "s534579783", "label": 206, "func": ""}
{"index": "s864597845", "label": 2641, "func": ""}
{"index": "s375781445", "label": 2641, "func": ""}
{"index": "s549953800", "label": 2641, "func": ""}
{"index": "s655564021", "label": 2641, "func": ""}
{"index": "s174484432", "label": 2641, "func": ""}
{"index": "s630970029", "label": 2641, "func": ""}
{"index": "s083307420", "label": 2641, "func": ""}
{"index": "s740455779", "label": 2641, "func": ""}
{"index": "s058219048", "label": 2641, "func": ""}
{"index": "s234744734", "label": 2641, "func": ""}
{"index": "s902135741", "label": 1612, "func": ""}
{"index": "s074182770", "label": 3787, "func": ""}
{"index": "s704956697", "label": 3787, "func": ""}
{"index": "s553340874", "label": 3787, "func": ""}
{"index": "s136004274", "label": 2300, "func": ""}
{"index": "s309963920", "label": 2300, "func": ""}
{"index": "s143069295", "label": 2300, "func": ""}
{"index": "s361597729", "label": 2300, "func": ""}
{"index": "s253115657", "label": 2300, "func": ""}
{"index": "s332590741", "label": 2300, "func": ""}
{"index": "s389507963", "label": 2300, "func": ""}
{"index": "s307335151", "label": 2300, "func": ""}
{"index": "s897085787", "label": 2300, "func": ""}
{"index": "s265178265", "label": 993, "func": ""}
{"index": "s499130776", "label": 993, "func": ""}
{"index": "s468234768", "label": 993, "func": ""}
{"index": "s836655649", "label": 993, "func": ""}
{"index": "s932868922", "label": 993, "func": ""}
{"index": "s007872418", "label": 993, "func": ""}
{"index": "s069865928", "label": 993, "func": ""}
{"index": "s634203769", "label": 993, "func": ""}
{"index": "s566455859", "label": 993, "func": ""}
{"index": "s410159372", "label": 993, "func": ""}
{"index": "s688379384", "label": 2642, "func": ""}
{"index": "s404891938", "label": 2642, "func": ""}
{"index": "s789271467", "label": 2642, "func": ""}
{"index": "s754408150", "label": 2642, "func": ""}
{"index": "s598497190", "label": 2642, "func": ""}
{"index": "s609854357", "label": 2642, "func": ""}
{"index": "s781485768", "label": 2642, "func": ""}
{"index": "s431685205", "label": 2642, "func": ""}
{"index": "s394859912", "label": 2642, "func": ""}
{"index": "s878153544", "label": 2642, "func": ""}
{"index": "s906770349", "label": 1808, "func": ""}
{"index": "s175394648", "label": 3971, "func": ""}
{"index": "s544277044", "label": 3971, "func": ""}
{"index": "s864497214", "label": 3971, "func": ""}
{"index": "s286658634", "label": 3971, "func": ""}
{"index": "s425756479", "label": 3971, "func": ""}
{"index": "s981647106", "label": 3971, "func": ""}
{"index": "s439429583", "label": 3971, "func": ""}
{"index": "s559444354", "label": 3971, "func": ""}
{"index": "s047664689", "label": 3971, "func": ""}
{"index": "s501455156", "label": 3971, "func": ""}
{"index": "s234680639", "label": 454, "func": ""}
{"index": "s947951873", "label": 454, "func": ""}
{"index": "s945811620", "label": 454, "func": ""}
{"index": "s897194446", "label": 454, "func": ""}
{"index": "s189767508", "label": 454, "func": ""}
{"index": "s060421245", "label": 454, "func": ""}
{"index": "s776965803", "label": 454, "func": ""}
{"index": "s706379576", "label": 454, "func": ""}
{"index": "s858613445", "label": 454, "func": ""}
{"index": "s609468387", "label": 454, "func": ""}
{"index": "s380726447", "label": 3433, "func": ""}
{"index": "s022986189", "label": 3433, "func": ""}
{"index": "s083982584", "label": 3433, "func": ""}
{"index": "s601310604", "label": 3433, "func": ""}
{"index": "s397835687", "label": 3433, "func": ""}
{"index": "s817786545", "label": 3433, "func": ""}
{"index": "s735601321", "label": 3433, "func": ""}
{"index": "s365957127", "label": 3433, "func": ""}
{"index": "s356993863", "label": 3433, "func": ""}
{"index": "s636589540", "label": 3433, "func": ""}
{"index": "s534169305", "label": 3556, "func": ""}
{"index": "s239706224", "label": 3556, "func": ""}
{"index": "s963731612", "label": 3556, "func": ""}
{"index": "s860181378", "label": 3556, "func": ""}
{"index": "s660042750", "label": 3556, "func": ""}
{"index": "s206772951", "label": 3556, "func": ""}
{"index": "s146766372", "label": 3556, "func": ""}
{"index": "s153149426", "label": 3556, "func": ""}
{"index": "s070867466", "label": 3556, "func": ""}
{"index": "s566014364", "label": 3556, "func": ""}
{"index": "s611137925", "label": 1366, "func": ""}
{"index": "s708674639", "label": 1366, "func": ""}
{"index": "s970937926", "label": 1366, "func": ""}
{"index": "s923306699", "label": 1366, "func": ""}
{"index": "s594204344", "label": 1366, "func": ""}
{"index": "s323914390", "label": 1366, "func": ""}
{"index": "s192571646", "label": 1366, "func": ""}
{"index": "s554336261", "label": 1366, "func": ""}
{"index": "s972615137", "label": 1366, "func": ""}
{"index": "s284359579", "label": 1366, "func": ""}
{"index": "s668672683", "label": 3030, "func": ""}
{"index": "s548601673", "label": 3030, "func": ""}
{"index": "s430279758", "label": 3030, "func": ""}
{"index": "s464729075", "label": 3030, "func": ""}
{"index": "s634320584", "label": 3030, "func": ""}
{"index": "s780628960", "label": 3030, "func": ""}
{"index": "s500637019", "label": 3030, "func": ""}
{"index": "s959171294", "label": 3030, "func": ""}
{"index": "s723077849", "label": 3030, "func": ""}
{"index": "s277804291", "label": 3030, "func": ""}
{"index": "s098750251", "label": 1698, "func": ""}
{"index": "s037168121", "label": 1698, "func": ""}
{"index": "s231281689", "label": 1698, "func": ""}
{"index": "s904140964", "label": 1698, "func": ""}
{"index": "s311375307", "label": 3560, "func": ""}
{"index": "s539233279", "label": 3560, "func": ""}
{"index": "s920791192", "label": 3560, "func": ""}
{"index": "s887613049", "label": 3560, "func": ""}
{"index": "s325179239", "label": 3560, "func": ""}
{"index": "s847402388", "label": 3560, "func": ""}
{"index": "s173833397", "label": 3560, "func": ""}
{"index": "s555337227", "label": 3560, "func": ""}
{"index": "s378333388", "label": 3560, "func": ""}
{"index": "s621426794", "label": 3560, "func": ""}
{"index": "s284860606", "label": 3025, "func": ""}
{"index": "s483747348", "label": 3025, "func": ""}
{"index": "s411307181", "label": 3025, "func": ""}
{"index": "s334656714", "label": 3025, "func": ""}
{"index": "s890202653", "label": 3025, "func": ""}
{"index": "s370226912", "label": 3025, "func": ""}
{"index": "s563419927", "label": 3025, "func": ""}
{"index": "s883522587", "label": 3025, "func": ""}
{"index": "s121663013", "label": 3025, "func": ""}
{"index": "s261420183", "label": 3025, "func": ""}
{"index": "s024188530", "label": 3369, "func": ""}
{"index": "s788611255", "label": 3369, "func": ""}
{"index": "s637438021", "label": 3369, "func": ""}
{"index": "s840420203", "label": 3369, "func": ""}
{"index": "s618538187", "label": 3369, "func": ""}
{"index": "s193761632", "label": 3369, "func": ""}
{"index": "s790672840", "label": 3369, "func": ""}
{"index": "s775124945", "label": 3369, "func": ""}
{"index": "s385392005", "label": 3369, "func": ""}
{"index": "s076427165", "label": 3369, "func": ""}
{"index": "s741198773", "label": 578, "func": ""}
{"index": "s513707957", "label": 578, "func": ""}
{"index": "s074564861", "label": 578, "func": ""}
{"index": "s476634087", "label": 578, "func": ""}
{"index": "s285512101", "label": 578, "func": ""}
{"index": "s442406880", "label": 578, "func": ""}
{"index": "s028634275", "label": 578, "func": ""}
{"index": "s012709617", "label": 578, "func": ""}
{"index": "s128589202", "label": 3690, "func": ""}
{"index": "s987727891", "label": 3690, "func": ""}
{"index": "s692207559", "label": 3690, "func": ""}
{"index": "s168419160", "label": 3690, "func": ""}
{"index": "s299329672", "label": 3690, "func": ""}
{"index": "s200086087", "label": 3690, "func": ""}
{"index": "s308812699", "label": 2301, "func": ""}
{"index": "s020749936", "label": 2301, "func": ""}
{"index": "s151301888", "label": 2301, "func": ""}
{"index": "s074260060", "label": 2201, "func": ""}
{"index": "s513566535", "label": 2201, "func": ""}
{"index": "s266072585", "label": 2201, "func": ""}
{"index": "s813847866", "label": 3849, "func": ""}
{"index": "s419885682", "label": 3849, "func": ""}
{"index": "s748681877", "label": 3849, "func": ""}
{"index": "s365181821", "label": 3849, "func": ""}
{"index": "s417689877", "label": 3849, "func": ""}
{"index": "s972084305", "label": 3849, "func": ""}
{"index": "s598893529", "label": 3849, "func": ""}
{"index": "s101285519", "label": 3849, "func": ""}
{"index": "s374452330", "label": 3320, "func": ""}
{"index": "s012583519", "label": 3320, "func": ""}
{"index": "s217230889", "label": 3320, "func": ""}
{"index": "s693189945", "label": 3320, "func": ""}
{"index": "s498449690", "label": 3320, "func": ""}
{"index": "s813751401", "label": 3320, "func": ""}
{"index": "s595194863", "label": 3320, "func": ""}
{"index": "s319414977", "label": 3320, "func": ""}
{"index": "s686846899", "label": 3320, "func": ""}
{"index": "s538980458", "label": 3320, "func": ""}
{"index": "s890795310", "label": 2328, "func": ""}
{"index": "s175411219", "label": 2328, "func": ""}
{"index": "s147352510", "label": 2328, "func": ""}
{"index": "s341823286", "label": 2328, "func": ""}
{"index": "s179105392", "label": 2328, "func": ""}
{"index": "s635906899", "label": 2328, "func": ""}
{"index": "s289820357", "label": 3260, "func": ""}
{"index": "s159977469", "label": 3260, "func": ""}
{"index": "s265418916", "label": 3260, "func": ""}
{"index": "s507601451", "label": 3260, "func": ""}
{"index": "s289160104", "label": 3260, "func": ""}
{"index": "s478971703", "label": 3260, "func": ""}
{"index": "s127953573", "label": 3260, "func": ""}
{"index": "s047927104", "label": 3260, "func": ""}
{"index": "s536309608", "label": 3260, "func": ""}
{"index": "s957645566", "label": 3260, "func": ""}
{"index": "s493836719", "label": 3136, "func": ""}
{"index": "s201525496", "label": 3136, "func": ""}
{"index": "s718498598", "label": 3136, "func": ""}
{"index": "s530156428", "label": 3136, "func": ""}
{"index": "s890829769", "label": 3136, "func": ""}
{"index": "s206411521", "label": 3136, "func": ""}
{"index": "s802533862", "label": 3136, "func": ""}
{"index": "s895628199", "label": 3136, "func": ""}
{"index": "s486848556", "label": 3136, "func": ""}
{"index": "s955896192", "label": 3136, "func": ""}
{"index": "s704228731", "label": 291, "func": ""}
{"index": "s997162969", "label": 291, "func": ""}
{"index": "s908917301", "label": 291, "func": ""}
{"index": "s202155105", "label": 291, "func": ""}
{"index": "s270094746", "label": 291, "func": ""}
{"index": "s834676327", "label": 291, "func": ""}
{"index": "s624877263", "label": 291, "func": ""}
{"index": "s312291216", "label": 291, "func": ""}
{"index": "s079945584", "label": 291, "func": ""}
{"index": "s734689740", "label": 291, "func": ""}
{"index": "s204345968", "label": 3961, "func": ""}
{"index": "s963085389", "label": 3961, "func": ""}
{"index": "s390247681", "label": 4017, "func": ""}
{"index": "s383243549", "label": 4017, "func": ""}
{"index": "s590842051", "label": 4017, "func": ""}
{"index": "s731587104", "label": 4017, "func": ""}
{"index": "s568440909", "label": 4017, "func": ""}
{"index": "s091509888", "label": 4017, "func": ""}
{"index": "s684181284", "label": 4017, "func": ""}
{"index": "s248589071", "label": 4017, "func": ""}
{"index": "s652808974", "label": 4017, "func": ""}
{"index": "s503703432", "label": 4017, "func": ""}
{"index": "s251793914", "label": 1483, "func": ""}
{"index": "s983370799", "label": 3068, "func": ""}
{"index": "s442158465", "label": 3068, "func": ""}
{"index": "s225634035", "label": 3068, "func": ""}
{"index": "s278119947", "label": 3068, "func": ""}
{"index": "s070628276", "label": 3068, "func": ""}
{"index": "s798601208", "label": 3068, "func": ""}
{"index": "s507701385", "label": 3068, "func": ""}
{"index": "s703408851", "label": 3068, "func": ""}
{"index": "s676344108", "label": 3068, "func": ""}
{"index": "s604672581", "label": 3068, "func": ""}
{"index": "s300894579", "label": 2526, "func": ""}
{"index": "s094656976", "label": 2526, "func": ""}
{"index": "s336648602", "label": 2526, "func": ""}
{"index": "s426371231", "label": 2526, "func": ""}
{"index": "s978155433", "label": 2526, "func": ""}
{"index": "s671096841", "label": 2526, "func": ""}
{"index": "s335088870", "label": 2526, "func": ""}
{"index": "s652167299", "label": 2526, "func": ""}
{"index": "s904645003", "label": 2526, "func": ""}
{"index": "s633001447", "label": 2526, "func": ""}
{"index": "s152319050", "label": 2853, "func": ""}
{"index": "s288774279", "label": 2853, "func": ""}
{"index": "s974551816", "label": 2853, "func": ""}
{"index": "s894463931", "label": 2853, "func": ""}
{"index": "s134340715", "label": 2853, "func": ""}
{"index": "s550004167", "label": 2853, "func": ""}
{"index": "s520495050", "label": 2853, "func": ""}
{"index": "s162963857", "label": 2853, "func": ""}
{"index": "s464988131", "label": 2853, "func": ""}
{"index": "s037125857", "label": 2853, "func": ""}
{"index": "s138798516", "label": 392, "func": ""}
{"index": "s039439129", "label": 392, "func": ""}
{"index": "s036099247", "label": 1279, "func": ""}
{"index": "s524165095", "label": 1279, "func": ""}
{"index": "s314512711", "label": 1279, "func": ""}
{"index": "s983822337", "label": 1279, "func": ""}
{"index": "s887312785", "label": 3219, "func": ""}
{"index": "s841157171", "label": 3219, "func": ""}
{"index": "s092128302", "label": 3219, "func": ""}
{"index": "s042310282", "label": 3219, "func": ""}
{"index": "s018578637", "label": 3219, "func": ""}
{"index": "s059424336", "label": 3219, "func": ""}
{"index": "s071273782", "label": 3219, "func": ""}
{"index": "s451271802", "label": 3219, "func": ""}
{"index": "s152979887", "label": 3219, "func": ""}
{"index": "s831046394", "label": 3219, "func": ""}
{"index": "s103045398", "label": 3845, "func": ""}
{"index": "s701788373", "label": 3845, "func": ""}
{"index": "s732584132", "label": 3845, "func": ""}
{"index": "s596374649", "label": 3845, "func": ""}
{"index": "s870858260", "label": 3845, "func": ""}
{"index": "s722075203", "label": 3845, "func": ""}
{"index": "s903121935", "label": 3845, "func": ""}
{"index": "s385104113", "label": 3845, "func": ""}
{"index": "s503349463", "label": 3845, "func": ""}
{"index": "s177640140", "label": 3845, "func": ""}
{"index": "s601572097", "label": 3858, "func": ""}
{"index": "s597299480", "label": 3858, "func": ""}
{"index": "s387934101", "label": 3858, "func": ""}
{"index": "s549484518", "label": 3858, "func": ""}
{"index": "s089796587", "label": 3858, "func": ""}
{"index": "s047862515", "label": 3858, "func": ""}
{"index": "s876565148", "label": 3858, "func": ""}
{"index": "s539379087", "label": 3858, "func": ""}
{"index": "s774287623", "label": 3858, "func": ""}
{"index": "s565036137", "label": 3858, "func": ""}
{"index": "s916677810", "label": 998, "func": ""}
{"index": "s154029235", "label": 998, "func": ""}
{"index": "s791097735", "label": 998, "func": ""}
{"index": "s669259283", "label": 998, "func": ""}
{"index": "s077220583", "label": 998, "func": ""}
{"index": "s067580806", "label": 998, "func": ""}
{"index": "s100320620", "label": 3391, "func": ""}
{"index": "s497588069", "label": 3391, "func": ""}
{"index": "s181628426", "label": 3391, "func": ""}
{"index": "s032328830", "label": 3391, "func": ""}
{"index": "s543901906", "label": 3391, "func": ""}
{"index": "s787175397", "label": 3391, "func": ""}
{"index": "s027481112", "label": 3391, "func": ""}
{"index": "s982935896", "label": 3391, "func": ""}
{"index": "s690370226", "label": 3391, "func": ""}
{"index": "s497523475", "label": 3391, "func": ""}
{"index": "s025459787", "label": 3015, "func": ""}
{"index": "s375241863", "label": 3015, "func": ""}
{"index": "s605065661", "label": 3015, "func": ""}
{"index": "s103929277", "label": 3015, "func": ""}
{"index": "s923490426", "label": 3015, "func": ""}
{"index": "s938299562", "label": 3015, "func": ""}
{"index": "s453838911", "label": 3015, "func": ""}
{"index": "s139802498", "label": 3015, "func": ""}
{"index": "s953381376", "label": 3015, "func": ""}
{"index": "s531060011", "label": 3015, "func": ""}
{"index": "s896660090", "label": 2916, "func": ""}
{"index": "s104591769", "label": 2916, "func": ""}
{"index": "s341519855", "label": 2916, "func": ""}
{"index": "s027735711", "label": 2916, "func": ""}
{"index": "s290278107", "label": 2916, "func": ""}
{"index": "s954089639", "label": 2916, "func": ""}
{"index": "s002845856", "label": 2916, "func": ""}
{"index": "s632073363", "label": 2916, "func": ""}
{"index": "s246677526", "label": 2916, "func": ""}
{"index": "s792682770", "label": 2916, "func": ""}
{"index": "s508100938", "label": 107, "func": ""}
{"index": "s306423484", "label": 107, "func": ""}
{"index": "s203794184", "label": 107, "func": ""}
{"index": "s005200758", "label": 107, "func": ""}
{"index": "s137466796", "label": 107, "func": ""}
{"index": "s967056969", "label": 107, "func": ""}
{"index": "s927154915", "label": 107, "func": ""}
{"index": "s489291389", "label": 107, "func": ""}
{"index": "s965824893", "label": 107, "func": ""}
{"index": "s892361394", "label": 107, "func": ""}
{"index": "s229459088", "label": 3463, "func": ""}
{"index": "s079735432", "label": 3463, "func": ""}
{"index": "s376317855", "label": 3463, "func": ""}
{"index": "s713539949", "label": 3463, "func": ""}
{"index": "s280915824", "label": 3463, "func": ""}
{"index": "s689394489", "label": 3463, "func": ""}
{"index": "s173253331", "label": 3463, "func": ""}
{"index": "s000047486", "label": 3463, "func": ""}
{"index": "s421516041", "label": 3463, "func": ""}
{"index": "s554917594", "label": 3463, "func": ""}
{"index": "s627080697", "label": 102, "func": ""}
{"index": "s946592339", "label": 102, "func": ""}
{"index": "s927158457", "label": 102, "func": ""}
{"index": "s956390861", "label": 102, "func": ""}
{"index": "s053391253", "label": 102, "func": ""}
{"index": "s990178742", "label": 102, "func": ""}
{"index": "s252923333", "label": 102, "func": ""}
{"index": "s158536999", "label": 102, "func": ""}
{"index": "s238802498", "label": 102, "func": ""}
{"index": "s140502356", "label": 102, "func": ""}
{"index": "s343702010", "label": 3710, "func": ""}
{"index": "s629761972", "label": 3545, "func": ""}
{"index": "s782543989", "label": 3545, "func": ""}
{"index": "s657061220", "label": 3545, "func": ""}
{"index": "s664782335", "label": 3545, "func": ""}
{"index": "s299587187", "label": 3545, "func": ""}
{"index": "s666699732", "label": 3545, "func": ""}
{"index": "s736141203", "label": 3545, "func": ""}
{"index": "s174602209", "label": 3545, "func": ""}
{"index": "s818235023", "label": 3545, "func": ""}
{"index": "s705117564", "label": 3545, "func": ""}
{"index": "s951395505", "label": 2314, "func": ""}
{"index": "s311510554", "label": 2314, "func": ""}
{"index": "s612227192", "label": 2314, "func": ""}
{"index": "s075786008", "label": 2314, "func": ""}
{"index": "s700425402", "label": 2314, "func": ""}
{"index": "s239431847", "label": 2314, "func": ""}
{"index": "s822876338", "label": 2314, "func": ""}
{"index": "s133794994", "label": 2314, "func": ""}
{"index": "s337529847", "label": 2314, "func": ""}
{"index": "s370099020", "label": 2314, "func": ""}
{"index": "s892248429", "label": 2628, "func": ""}
{"index": "s168632840", "label": 2628, "func": ""}
{"index": "s295690269", "label": 2628, "func": ""}
{"index": "s477551807", "label": 2628, "func": ""}
{"index": "s206547499", "label": 2628, "func": ""}
{"index": "s919890117", "label": 2628, "func": ""}
{"index": "s909130297", "label": 2628, "func": ""}
{"index": "s914259137", "label": 2628, "func": ""}
{"index": "s984844122", "label": 2628, "func": ""}
{"index": "s988408294", "label": 2628, "func": ""}
{"index": "s668865066", "label": 2416, "func": ""}
{"index": "s940109470", "label": 2416, "func": ""}
{"index": "s342411304", "label": 2416, "func": ""}
{"index": "s452218775", "label": 2416, "func": ""}
{"index": "s535079977", "label": 2416, "func": ""}
{"index": "s692098671", "label": 2416, "func": ""}
{"index": "s893863399", "label": 2416, "func": ""}
{"index": "s475013969", "label": 2416, "func": ""}
{"index": "s047656700", "label": 2416, "func": ""}
{"index": "s608718407", "label": 2416, "func": ""}
{"index": "s507576728", "label": 630, "func": ""}
{"index": "s974783204", "label": 630, "func": ""}
{"index": "s261528578", "label": 630, "func": ""}
{"index": "s579111865", "label": 630, "func": ""}
{"index": "s867200548", "label": 630, "func": ""}
{"index": "s362362570", "label": 630, "func": ""}
{"index": "s035311701", "label": 630, "func": ""}
{"index": "s423363809", "label": 630, "func": ""}
{"index": "s451051269", "label": 630, "func": ""}
{"index": "s037970469", "label": 630, "func": ""}
{"index": "s140261472", "label": 3222, "func": ""}
{"index": "s588256860", "label": 3222, "func": ""}
{"index": "s984331197", "label": 3222, "func": ""}
{"index": "s880980268", "label": 3222, "func": ""}
{"index": "s498748393", "label": 3222, "func": ""}
{"index": "s241425393", "label": 3222, "func": ""}
{"index": "s948494088", "label": 3222, "func": ""}
{"index": "s567858206", "label": 3222, "func": ""}
{"index": "s958153677", "label": 3222, "func": ""}
{"index": "s232366261", "label": 3222, "func": ""}
{"index": "s595672075", "label": 596, "func": ""}
{"index": "s842321691", "label": 596, "func": ""}
{"index": "s562048750", "label": 596, "func": ""}
{"index": "s965818201", "label": 596, "func": ""}
{"index": "s560420117", "label": 596, "func": ""}
{"index": "s476571612", "label": 3472, "func": ""}
{"index": "s728828727", "label": 3472, "func": ""}
{"index": "s507337532", "label": 3472, "func": ""}
{"index": "s425291412", "label": 3472, "func": ""}
{"index": "s894976126", "label": 3472, "func": ""}
{"index": "s363035347", "label": 3472, "func": ""}
{"index": "s584056824", "label": 3472, "func": ""}
{"index": "s741836115", "label": 3472, "func": ""}
{"index": "s245055329", "label": 3472, "func": ""}
{"index": "s858448106", "label": 3472, "func": ""}
{"index": "s873637463", "label": 1298, "func": ""}
{"index": "s075478218", "label": 39, "func": ""}
{"index": "s022488967", "label": 39, "func": ""}
{"index": "s220975501", "label": 39, "func": ""}
{"index": "s482682270", "label": 39, "func": ""}
{"index": "s621240284", "label": 39, "func": ""}
{"index": "s569175888", "label": 39, "func": ""}
{"index": "s152047107", "label": 39, "func": ""}
{"index": "s665357557", "label": 39, "func": ""}
{"index": "s630115018", "label": 39, "func": ""}
{"index": "s183168487", "label": 39, "func": ""}
{"index": "s744319440", "label": 70, "func": ""}
{"index": "s868558704", "label": 70, "func": ""}
{"index": "s225179574", "label": 70, "func": ""}
{"index": "s905011836", "label": 70, "func": ""}
{"index": "s355263529", "label": 70, "func": ""}
{"index": "s788086810", "label": 70, "func": ""}
{"index": "s409826975", "label": 70, "func": ""}
{"index": "s762632859", "label": 70, "func": ""}
{"index": "s334201990", "label": 70, "func": ""}
{"index": "s632570778", "label": 70, "func": ""}
{"index": "s384738720", "label": 1325, "func": ""}
{"index": "s083989290", "label": 1325, "func": ""}
{"index": "s213245781", "label": 1325, "func": ""}
{"index": "s712475539", "label": 1325, "func": ""}
{"index": "s677633863", "label": 1325, "func": ""}
{"index": "s701105529", "label": 3940, "func": ""}
{"index": "s586881707", "label": 3940, "func": ""}
{"index": "s549378928", "label": 3940, "func": ""}
{"index": "s660038808", "label": 3189, "func": ""}
{"index": "s522931074", "label": 2285, "func": ""}
{"index": "s014775799", "label": 2285, "func": ""}
{"index": "s191427304", "label": 2285, "func": ""}
{"index": "s376338679", "label": 2285, "func": ""}
{"index": "s932049270", "label": 2285, "func": ""}
{"index": "s685764273", "label": 2285, "func": ""}
{"index": "s112326893", "label": 2285, "func": ""}
{"index": "s474160974", "label": 2285, "func": ""}
{"index": "s223139817", "label": 2285, "func": ""}
{"index": "s598378321", "label": 2285, "func": ""}
{"index": "s440138278", "label": 710, "func": ""}
{"index": "s377445750", "label": 710, "func": ""}
{"index": "s790108517", "label": 710, "func": ""}
{"index": "s075869639", "label": 710, "func": ""}
{"index": "s878627436", "label": 710, "func": ""}
{"index": "s338350686", "label": 710, "func": ""}
{"index": "s689317466", "label": 710, "func": ""}
{"index": "s728324425", "label": 710, "func": ""}
{"index": "s073040015", "label": 710, "func": ""}
{"index": "s201394850", "label": 710, "func": ""}
{"index": "s368943908", "label": 1774, "func": ""}
{"index": "s857201407", "label": 2735, "func": ""}
{"index": "s722838192", "label": 2735, "func": ""}
{"index": "s646125694", "label": 2735, "func": ""}
{"index": "s743901168", "label": 2735, "func": ""}
{"index": "s601843650", "label": 2735, "func": ""}
{"index": "s066348335", "label": 2735, "func": ""}
{"index": "s106587849", "label": 2735, "func": ""}
{"index": "s386770776", "label": 2735, "func": ""}
{"index": "s997622116", "label": 2735, "func": ""}
{"index": "s187365455", "label": 2735, "func": ""}
{"index": "s302652428", "label": 1081, "func": ""}
{"index": "s886403463", "label": 1081, "func": ""}
{"index": "s273425160", "label": 1750, "func": ""}
{"index": "s428073304", "label": 1907, "func": ""}
{"index": "s134369682", "label": 1907, "func": ""}
{"index": "s168376374", "label": 3282, "func": ""}
{"index": "s884777238", "label": 3282, "func": ""}
{"index": "s980489034", "label": 3282, "func": ""}
{"index": "s595082359", "label": 3282, "func": ""}
{"index": "s726944052", "label": 3282, "func": ""}
{"index": "s603459765", "label": 3282, "func": ""}
{"index": "s530318630", "label": 3282, "func": ""}
{"index": "s844997299", "label": 3282, "func": ""}
{"index": "s298363481", "label": 3282, "func": ""}
{"index": "s651102731", "label": 3282, "func": ""}
{"index": "s465964677", "label": 821, "func": ""}
{"index": "s647322475", "label": 1431, "func": ""}
{"index": "s737934898", "label": 3165, "func": ""}
{"index": "s073332747", "label": 3165, "func": ""}
{"index": "s469651085", "label": 3165, "func": ""}
{"index": "s892313677", "label": 3165, "func": ""}
{"index": "s743397637", "label": 3165, "func": ""}
{"index": "s530468606", "label": 3165, "func": ""}
{"index": "s890344516", "label": 3165, "func": ""}
{"index": "s854431351", "label": 3165, "func": ""}
{"index": "s458368785", "label": 3165, "func": ""}
{"index": "s863236424", "label": 3165, "func": ""}
{"index": "s167940972", "label": 2329, "func": ""}
{"index": "s991409893", "label": 2329, "func": ""}
{"index": "s634557708", "label": 2329, "func": ""}
{"index": "s924528247", "label": 2350, "func": ""}
{"index": "s307807737", "label": 2350, "func": ""}
{"index": "s954183752", "label": 2350, "func": ""}
{"index": "s182500531", "label": 2350, "func": ""}
{"index": "s187182384", "label": 2350, "func": ""}
{"index": "s232927131", "label": 2350, "func": ""}
{"index": "s628656476", "label": 2350, "func": ""}
{"index": "s651965970", "label": 2350, "func": ""}
{"index": "s218097886", "label": 2350, "func": ""}
{"index": "s644397933", "label": 2350, "func": ""}
{"index": "s351942257", "label": 3608, "func": ""}
{"index": "s550313890", "label": 3608, "func": ""}
{"index": "s694760766", "label": 3608, "func": ""}
{"index": "s780971616", "label": 3608, "func": ""}
{"index": "s355373819", "label": 3608, "func": ""}
{"index": "s304575620", "label": 3608, "func": ""}
{"index": "s630956316", "label": 3608, "func": ""}
{"index": "s682079346", "label": 3608, "func": ""}
{"index": "s156094234", "label": 3608, "func": ""}
{"index": "s839846878", "label": 3608, "func": ""}
{"index": "s115157214", "label": 3075, "func": ""}
{"index": "s856615368", "label": 3075, "func": ""}
{"index": "s107170110", "label": 3075, "func": ""}
{"index": "s621720777", "label": 3075, "func": ""}
{"index": "s440360947", "label": 3075, "func": ""}
{"index": "s157388379", "label": 3075, "func": ""}
{"index": "s752281078", "label": 3075, "func": ""}
{"index": "s789736603", "label": 3075, "func": ""}
{"index": "s977063597", "label": 3075, "func": ""}
{"index": "s351217262", "label": 3075, "func": ""}
{"index": "s465584609", "label": 3893, "func": ""}
{"index": "s717305588", "label": 3893, "func": ""}
{"index": "s754402170", "label": 3893, "func": ""}
{"index": "s831270747", "label": 3893, "func": ""}
{"index": "s079340567", "label": 3893, "func": ""}
{"index": "s103891722", "label": 2, "func": ""}
{"index": "s915992672", "label": 2, "func": ""}
{"index": "s339843148", "label": 2, "func": ""}
{"index": "s021798406", "label": 2, "func": ""}
{"index": "s717118667", "label": 2, "func": ""}
{"index": "s373943785", "label": 2, "func": ""}
{"index": "s744414270", "label": 2, "func": ""}
{"index": "s285417194", "label": 2, "func": ""}
{"index": "s110716960", "label": 2, "func": ""}
{"index": "s788036651", "label": 2, "func": ""}
{"index": "s034964659", "label": 499, "func": ""}
{"index": "s527483818", "label": 499, "func": ""}
{"index": "s511067776", "label": 499, "func": ""}
{"index": "s750860991", "label": 499, "func": ""}
{"index": "s611468519", "label": 499, "func": ""}
{"index": "s394334391", "label": 499, "func": ""}
{"index": "s935856904", "label": 499, "func": ""}
{"index": "s397595947", "label": 499, "func": ""}
{"index": "s125866701", "label": 499, "func": ""}
{"index": "s096765230", "label": 499, "func": ""}
{"index": "s556442897", "label": 728, "func": ""}
{"index": "s570420718", "label": 728, "func": ""}
{"index": "s966945110", "label": 728, "func": ""}
{"index": "s522672624", "label": 728, "func": ""}
{"index": "s033821397", "label": 728, "func": ""}
{"index": "s460794999", "label": 728, "func": ""}
{"index": "s856142938", "label": 728, "func": ""}
{"index": "s030932653", "label": 728, "func": ""}
{"index": "s703555617", "label": 728, "func": ""}
{"index": "s776506034", "label": 728, "func": ""}
{"index": "s622865996", "label": 28, "func": ""}
{"index": "s393339921", "label": 28, "func": ""}
{"index": "s312801727", "label": 28, "func": ""}
{"index": "s401697927", "label": 28, "func": ""}
{"index": "s835407093", "label": 28, "func": ""}
{"index": "s613980329", "label": 28, "func": ""}
{"index": "s719531835", "label": 28, "func": ""}
{"index": "s939101514", "label": 28, "func": ""}
{"index": "s453875299", "label": 28, "func": ""}
{"index": "s712838338", "label": 28, "func": ""}
{"index": "s036765214", "label": 1272, "func": ""}
{"index": "s340907428", "label": 3447, "func": ""}
{"index": "s110780843", "label": 3447, "func": ""}
{"index": "s390837419", "label": 3447, "func": ""}
{"index": "s382090735", "label": 3447, "func": ""}
{"index": "s675389907", "label": 3447, "func": ""}
{"index": "s340321318", "label": 3447, "func": ""}
{"index": "s827984533", "label": 3447, "func": ""}
{"index": "s159787772", "label": 3447, "func": ""}
{"index": "s528719609", "label": 3447, "func": ""}
{"index": "s259570680", "label": 3447, "func": ""}
{"index": "s331389862", "label": 589, "func": ""}
{"index": "s343375899", "label": 589, "func": ""}
{"index": "s069460543", "label": 589, "func": ""}
{"index": "s676740855", "label": 589, "func": ""}
{"index": "s368301987", "label": 589, "func": ""}
{"index": "s623477490", "label": 589, "func": ""}
{"index": "s045321883", "label": 589, "func": ""}
{"index": "s033904402", "label": 589, "func": ""}
{"index": "s841674555", "label": 589, "func": ""}
{"index": "s978830762", "label": 589, "func": ""}
{"index": "s039221364", "label": 2862, "func": ""}
{"index": "s409748292", "label": 2862, "func": ""}
{"index": "s798592090", "label": 2862, "func": ""}
{"index": "s571782431", "label": 2862, "func": ""}
{"index": "s883593437", "label": 2862, "func": ""}
{"index": "s453804272", "label": 2862, "func": ""}
{"index": "s507322027", "label": 2862, "func": ""}
{"index": "s862138967", "label": 2862, "func": ""}
{"index": "s020457032", "label": 2862, "func": ""}
{"index": "s671816544", "label": 2862, "func": ""}
{"index": "s714555679", "label": 1090, "func": ""}
{"index": "s896494160", "label": 906, "func": ""}
{"index": "s595514554", "label": 906, "func": ""}
{"index": "s134541883", "label": 906, "func": ""}
{"index": "s182530935", "label": 906, "func": ""}
{"index": "s993247388", "label": 906, "func": ""}
{"index": "s565599849", "label": 906, "func": ""}
{"index": "s719252159", "label": 906, "func": ""}
{"index": "s530786054", "label": 906, "func": ""}
{"index": "s497066901", "label": 906, "func": ""}
{"index": "s808288585", "label": 906, "func": ""}
{"index": "s857340821", "label": 3333, "func": ""}
{"index": "s404134315", "label": 3333, "func": ""}
{"index": "s888511346", "label": 3333, "func": ""}
{"index": "s730520187", "label": 3333, "func": ""}
{"index": "s376270763", "label": 3333, "func": ""}
{"index": "s748957555", "label": 3333, "func": ""}
{"index": "s267765763", "label": 3333, "func": ""}
{"index": "s814528096", "label": 3333, "func": ""}
{"index": "s099649899", "label": 3333, "func": ""}
{"index": "s896562828", "label": 3333, "func": ""}
{"index": "s975170554", "label": 271, "func": ""}
{"index": "s648311219", "label": 271, "func": ""}
{"index": "s475779714", "label": 271, "func": ""}
{"index": "s811180718", "label": 271, "func": ""}
{"index": "s908424886", "label": 271, "func": ""}
{"index": "s536837418", "label": 271, "func": ""}
{"index": "s850184641", "label": 271, "func": ""}
{"index": "s490005050", "label": 271, "func": ""}
{"index": "s446113399", "label": 271, "func": ""}
{"index": "s361518092", "label": 271, "func": ""}
{"index": "s038685514", "label": 534, "func": ""}
{"index": "s999956573", "label": 534, "func": ""}
{"index": "s758198860", "label": 534, "func": ""}
{"index": "s576744212", "label": 534, "func": ""}
{"index": "s645168534", "label": 534, "func": ""}
{"index": "s501850177", "label": 534, "func": ""}
{"index": "s679540799", "label": 534, "func": ""}
{"index": "s682981604", "label": 534, "func": ""}
{"index": "s046768378", "label": 534, "func": ""}
{"index": "s078098266", "label": 3461, "func": ""}
{"index": "s905612065", "label": 3461, "func": ""}
{"index": "s870330427", "label": 3461, "func": ""}
{"index": "s667539720", "label": 1680, "func": ""}
{"index": "s979638740", "label": 2631, "func": ""}
{"index": "s727142139", "label": 2631, "func": ""}
{"index": "s544195511", "label": 2631, "func": ""}
{"index": "s759383212", "label": 2631, "func": ""}
{"index": "s047609404", "label": 2631, "func": ""}
{"index": "s561251447", "label": 2631, "func": ""}
{"index": "s460029665", "label": 2631, "func": ""}
{"index": "s249149287", "label": 2631, "func": ""}
{"index": "s325866963", "label": 2631, "func": ""}
{"index": "s007290790", "label": 2631, "func": ""}
{"index": "s857659650", "label": 314, "func": ""}
{"index": "s339484202", "label": 314, "func": ""}
{"index": "s897262474", "label": 314, "func": ""}
{"index": "s386842122", "label": 314, "func": ""}
{"index": "s843914549", "label": 314, "func": ""}
{"index": "s079405928", "label": 314, "func": ""}
{"index": "s923345686", "label": 314, "func": ""}
{"index": "s941561933", "label": 314, "func": ""}
{"index": "s672630443", "label": 314, "func": ""}
{"index": "s725341408", "label": 314, "func": ""}
{"index": "s270817619", "label": 2627, "func": ""}
{"index": "s376845291", "label": 2627, "func": ""}
{"index": "s711762059", "label": 2627, "func": ""}
{"index": "s857836274", "label": 2627, "func": ""}
{"index": "s996405761", "label": 2627, "func": ""}
{"index": "s549318742", "label": 2627, "func": ""}
{"index": "s270515846", "label": 2627, "func": ""}
{"index": "s966288069", "label": 2627, "func": ""}
{"index": "s374597830", "label": 2627, "func": ""}
{"index": "s003553935", "label": 2627, "func": ""}
{"index": "s361535640", "label": 1934, "func": ""}
{"index": "s118743337", "label": 1934, "func": ""}
{"index": "s213196110", "label": 1934, "func": ""}
{"index": "s424556304", "label": 3377, "func": ""}
{"index": "s985415516", "label": 3377, "func": ""}
{"index": "s110560616", "label": 3377, "func": ""}
{"index": "s164484371", "label": 3377, "func": ""}
{"index": "s868706199", "label": 3377, "func": ""}
{"index": "s131788951", "label": 3377, "func": ""}
{"index": "s067866647", "label": 3377, "func": ""}
{"index": "s349456360", "label": 3377, "func": ""}
{"index": "s755033450", "label": 3377, "func": ""}
{"index": "s743026583", "label": 3377, "func": ""}
{"index": "s066154010", "label": 737, "func": ""}
{"index": "s912665163", "label": 737, "func": ""}
{"index": "s608135199", "label": 737, "func": ""}
{"index": "s920994140", "label": 737, "func": ""}
{"index": "s125401198", "label": 737, "func": ""}
{"index": "s236999539", "label": 737, "func": ""}
{"index": "s579495813", "label": 737, "func": ""}
{"index": "s389132852", "label": 737, "func": ""}
{"index": "s837451128", "label": 737, "func": ""}
{"index": "s224082293", "label": 737, "func": ""}
{"index": "s230688743", "label": 1695, "func": ""}
{"index": "s703967763", "label": 1695, "func": ""}
{"index": "s880533691", "label": 1695, "func": ""}
{"index": "s264399230", "label": 1695, "func": ""}
{"index": "s434195997", "label": 1695, "func": ""}
{"index": "s401283367", "label": 1695, "func": ""}
{"index": "s115037601", "label": 1695, "func": ""}
{"index": "s713849112", "label": 1695, "func": ""}
{"index": "s212277777", "label": 1695, "func": ""}
{"index": "s446547588", "label": 1695, "func": ""}
{"index": "s681792820", "label": 111, "func": ""}
{"index": "s760882476", "label": 111, "func": ""}
{"index": "s043441489", "label": 111, "func": ""}
{"index": "s279730417", "label": 111, "func": ""}
{"index": "s063298700", "label": 111, "func": ""}
{"index": "s422026244", "label": 111, "func": ""}
{"index": "s543636669", "label": 111, "func": ""}
{"index": "s878360359", "label": 111, "func": ""}
{"index": "s123369118", "label": 111, "func": ""}
{"index": "s826073590", "label": 111, "func": ""}
{"index": "s163827831", "label": 2037, "func": ""}
{"index": "s346742678", "label": 2037, "func": ""}
{"index": "s474871647", "label": 2037, "func": ""}
{"index": "s431411906", "label": 256, "func": ""}
{"index": "s237386683", "label": 256, "func": ""}
{"index": "s741814575", "label": 256, "func": ""}
{"index": "s854814708", "label": 256, "func": ""}
{"index": "s171018570", "label": 256, "func": ""}
{"index": "s270795899", "label": 256, "func": ""}
{"index": "s938886913", "label": 256, "func": ""}
{"index": "s079309924", "label": 256, "func": ""}
{"index": "s369006512", "label": 256, "func": ""}
{"index": "s180649254", "label": 3028, "func": ""}
{"index": "s782591929", "label": 3028, "func": ""}
{"index": "s184277372", "label": 1314, "func": ""}
{"index": "s106552622", "label": 1314, "func": ""}
{"index": "s656813908", "label": 1314, "func": ""}
{"index": "s086460490", "label": 1314, "func": ""}
{"index": "s484559167", "label": 1314, "func": ""}
{"index": "s266971241", "label": 1314, "func": ""}
{"index": "s924679609", "label": 1314, "func": ""}
{"index": "s396851605", "label": 1314, "func": ""}
{"index": "s514457449", "label": 1314, "func": ""}
{"index": "s562271594", "label": 1314, "func": ""}
{"index": "s280105833", "label": 224, "func": ""}
{"index": "s349022604", "label": 224, "func": ""}
{"index": "s009347433", "label": 224, "func": ""}
{"index": "s716186312", "label": 224, "func": ""}
{"index": "s696666632", "label": 224, "func": ""}
{"index": "s770192880", "label": 224, "func": ""}
{"index": "s130068947", "label": 224, "func": ""}
{"index": "s396936373", "label": 224, "func": ""}
{"index": "s392144906", "label": 224, "func": ""}
{"index": "s646962364", "label": 439, "func": ""}
{"index": "s243431712", "label": 439, "func": ""}
{"index": "s605321820", "label": 439, "func": ""}
{"index": "s552080605", "label": 439, "func": ""}
{"index": "s033993892", "label": 439, "func": ""}
{"index": "s958463839", "label": 439, "func": ""}
{"index": "s702079886", "label": 439, "func": ""}
{"index": "s561753473", "label": 439, "func": ""}
{"index": "s742846647", "label": 439, "func": ""}
{"index": "s077008806", "label": 439, "func": ""}
{"index": "s770165227", "label": 393, "func": ""}
{"index": "s490119320", "label": 3986, "func": ""}
{"index": "s816328743", "label": 3986, "func": ""}
{"index": "s870209174", "label": 3986, "func": ""}
{"index": "s762239321", "label": 3986, "func": ""}
{"index": "s074882043", "label": 3986, "func": ""}
{"index": "s013267950", "label": 3986, "func": ""}
{"index": "s834518401", "label": 3986, "func": ""}
{"index": "s742668385", "label": 3986, "func": ""}
{"index": "s798398433", "label": 3986, "func": ""}
{"index": "s367490980", "label": 3986, "func": ""}
{"index": "s970223494", "label": 3945, "func": ""}
{"index": "s056695297", "label": 3945, "func": ""}
{"index": "s665687686", "label": 3945, "func": ""}
{"index": "s613108947", "label": 3945, "func": ""}
{"index": "s443806557", "label": 3945, "func": ""}
{"index": "s615408597", "label": 3945, "func": ""}
{"index": "s131186332", "label": 3945, "func": ""}
{"index": "s343636479", "label": 3945, "func": ""}
{"index": "s575562523", "label": 3945, "func": ""}
{"index": "s775279575", "label": 3945, "func": ""}
{"index": "s036742007", "label": 3, "func": ""}
{"index": "s260700749", "label": 3, "func": ""}
{"index": "s182742191", "label": 3, "func": ""}
{"index": "s936316897", "label": 3, "func": ""}
{"index": "s993309997", "label": 3, "func": ""}
{"index": "s562700450", "label": 3, "func": ""}
{"index": "s604064110", "label": 3, "func": ""}
{"index": "s223485288", "label": 3, "func": ""}
{"index": "s691960035", "label": 3, "func": ""}
{"index": "s705085895", "label": 3, "func": ""}
{"index": "s699407172", "label": 2976, "func": ""}
{"index": "s472687052", "label": 2976, "func": ""}
{"index": "s700484579", "label": 2976, "func": ""}
{"index": "s985644561", "label": 2976, "func": ""}
{"index": "s217601194", "label": 2976, "func": ""}
{"index": "s498260630", "label": 2976, "func": ""}
{"index": "s313931176", "label": 2976, "func": ""}
{"index": "s133009041", "label": 2976, "func": ""}
{"index": "s862895259", "label": 2976, "func": ""}
{"index": "s594076216", "label": 2976, "func": ""}
{"index": "s265403064", "label": 1296, "func": ""}
{"index": "s892863305", "label": 1296, "func": ""}
{"index": "s961385616", "label": 1296, "func": ""}
{"index": "s912791031", "label": 460, "func": ""}
{"index": "s716856226", "label": 460, "func": ""}
{"index": "s539457167", "label": 460, "func": ""}
{"index": "s085514664", "label": 460, "func": ""}
{"index": "s110891531", "label": 293, "func": ""}
{"index": "s639300037", "label": 293, "func": ""}
{"index": "s845813263", "label": 293, "func": ""}
{"index": "s953865763", "label": 293, "func": ""}
{"index": "s469512048", "label": 293, "func": ""}
{"index": "s994582133", "label": 293, "func": ""}
{"index": "s421633073", "label": 293, "func": ""}
{"index": "s449239573", "label": 293, "func": ""}
{"index": "s982167900", "label": 293, "func": ""}
{"index": "s651246021", "label": 293, "func": ""}
{"index": "s765863982", "label": 3851, "func": ""}
{"index": "s260144922", "label": 3509, "func": ""}
{"index": "s818801202", "label": 1672, "func": ""}
{"index": "s849655179", "label": 1672, "func": ""}
{"index": "s390442084", "label": 1672, "func": ""}
{"index": "s611636448", "label": 1672, "func": ""}
{"index": "s504553126", "label": 3225, "func": ""}
{"index": "s011636712", "label": 3225, "func": ""}
{"index": "s027835332", "label": 3225, "func": ""}
{"index": "s324977477", "label": 3225, "func": ""}
{"index": "s034191650", "label": 1098, "func": ""}
{"index": "s496773666", "label": 1098, "func": ""}
{"index": "s193548317", "label": 1211, "func": ""}
{"index": "s664747024", "label": 1211, "func": ""}
{"index": "s457514695", "label": 3202, "func": ""}
{"index": "s423699023", "label": 3202, "func": ""}
{"index": "s583910894", "label": 3202, "func": ""}
{"index": "s186240019", "label": 3202, "func": ""}
{"index": "s225002102", "label": 3202, "func": ""}
{"index": "s077721965", "label": 3202, "func": ""}
{"index": "s204958796", "label": 3202, "func": ""}
{"index": "s925570902", "label": 3202, "func": ""}
{"index": "s723248622", "label": 2933, "func": ""}
{"index": "s580297107", "label": 2933, "func": ""}
{"index": "s909020678", "label": 2933, "func": ""}
{"index": "s274937433", "label": 2933, "func": ""}
{"index": "s809561685", "label": 2933, "func": ""}
{"index": "s164526656", "label": 2933, "func": ""}
{"index": "s934435295", "label": 2933, "func": ""}
{"index": "s897228269", "label": 2933, "func": ""}
{"index": "s597853092", "label": 2933, "func": ""}
{"index": "s447028621", "label": 2933, "func": ""}
{"index": "s333023423", "label": 3974, "func": ""}
{"index": "s701142387", "label": 3974, "func": ""}
{"index": "s754411059", "label": 3974, "func": ""}
{"index": "s088944297", "label": 3974, "func": ""}
{"index": "s053528650", "label": 3974, "func": ""}
{"index": "s767570774", "label": 375, "func": ""}
{"index": "s990705579", "label": 375, "func": ""}
{"index": "s927134714", "label": 375, "func": ""}
{"index": "s311689254", "label": 375, "func": ""}
{"index": "s664795248", "label": 375, "func": ""}
{"index": "s921273521", "label": 375, "func": ""}
{"index": "s408419883", "label": 375, "func": ""}
{"index": "s154237808", "label": 375, "func": ""}
{"index": "s833711949", "label": 375, "func": ""}
{"index": "s956776290", "label": 375, "func": ""}
{"index": "s707719481", "label": 2572, "func": ""}
{"index": "s978280615", "label": 2572, "func": ""}
{"index": "s608960110", "label": 2572, "func": ""}
{"index": "s170346832", "label": 2572, "func": ""}
{"index": "s460690630", "label": 2572, "func": ""}
{"index": "s290432934", "label": 2572, "func": ""}
{"index": "s238053297", "label": 2572, "func": ""}
{"index": "s569210364", "label": 2572, "func": ""}
{"index": "s653710996", "label": 2572, "func": ""}
{"index": "s688653099", "label": 2572, "func": ""}
{"index": "s610232904", "label": 1048, "func": ""}
{"index": "s650255126", "label": 1048, "func": ""}
{"index": "s989030178", "label": 1048, "func": ""}
{"index": "s401526163", "label": 3192, "func": ""}
{"index": "s532229900", "label": 3192, "func": ""}
{"index": "s942416113", "label": 3192, "func": ""}
{"index": "s065867444", "label": 3192, "func": ""}
{"index": "s513751715", "label": 3192, "func": ""}
{"index": "s262256845", "label": 3192, "func": ""}
{"index": "s255479798", "label": 3192, "func": ""}
{"index": "s905046174", "label": 3192, "func": ""}
{"index": "s676263227", "label": 3192, "func": ""}
{"index": "s879365352", "label": 3192, "func": ""}
{"index": "s269842924", "label": 3994, "func": ""}
{"index": "s619457578", "label": 3994, "func": ""}
{"index": "s780067986", "label": 3994, "func": ""}
{"index": "s639873950", "label": 3994, "func": ""}
{"index": "s272438159", "label": 3994, "func": ""}
{"index": "s247431684", "label": 3994, "func": ""}
{"index": "s253532323", "label": 3994, "func": ""}
{"index": "s653954614", "label": 3994, "func": ""}
{"index": "s165739380", "label": 3994, "func": ""}
{"index": "s008401709", "label": 3994, "func": ""}
{"index": "s246156981", "label": 3155, "func": ""}
{"index": "s435435079", "label": 3155, "func": ""}
{"index": "s290526381", "label": 3155, "func": ""}
{"index": "s175830799", "label": 3155, "func": ""}
{"index": "s115216739", "label": 3155, "func": ""}
{"index": "s862184551", "label": 3155, "func": ""}
{"index": "s669927404", "label": 3155, "func": ""}
{"index": "s934043643", "label": 3155, "func": ""}
{"index": "s668007389", "label": 3155, "func": ""}
{"index": "s361903655", "label": 3155, "func": ""}
{"index": "s698648977", "label": 247, "func": ""}
{"index": "s498643709", "label": 247, "func": ""}
{"index": "s919745639", "label": 247, "func": ""}
{"index": "s188885963", "label": 247, "func": ""}
{"index": "s660645411", "label": 247, "func": ""}
{"index": "s064590853", "label": 247, "func": ""}
{"index": "s867425894", "label": 247, "func": ""}
{"index": "s921121361", "label": 247, "func": ""}
{"index": "s428583909", "label": 247, "func": ""}
{"index": "s328297247", "label": 247, "func": ""}
{"index": "s092711472", "label": 3375, "func": ""}
{"index": "s386185855", "label": 3375, "func": ""}
{"index": "s947730455", "label": 2770, "func": ""}
{"index": "s787026032", "label": 2770, "func": ""}
{"index": "s416545876", "label": 2770, "func": ""}
{"index": "s439358966", "label": 2770, "func": ""}
{"index": "s711953499", "label": 2770, "func": ""}
{"index": "s755122912", "label": 2770, "func": ""}
{"index": "s986331132", "label": 2770, "func": ""}
{"index": "s125417528", "label": 2770, "func": ""}
{"index": "s407211073", "label": 2770, "func": ""}
{"index": "s697517870", "label": 2770, "func": ""}
{"index": "s231419577", "label": 3985, "func": ""}
{"index": "s437325882", "label": 645, "func": ""}
{"index": "s083452785", "label": 645, "func": ""}
{"index": "s434906567", "label": 645, "func": ""}
{"index": "s106142712", "label": 645, "func": ""}
{"index": "s408726289", "label": 645, "func": ""}
{"index": "s997574684", "label": 1458, "func": ""}
{"index": "s953120674", "label": 1458, "func": ""}
{"index": "s194138783", "label": 1458, "func": ""}
{"index": "s177482306", "label": 4016, "func": ""}
{"index": "s896357014", "label": 4016, "func": ""}
{"index": "s085982461", "label": 4016, "func": ""}
{"index": "s692470498", "label": 4016, "func": ""}
{"index": "s543974690", "label": 4016, "func": ""}
{"index": "s736773132", "label": 4016, "func": ""}
{"index": "s804095291", "label": 4016, "func": ""}
{"index": "s759376290", "label": 4016, "func": ""}
{"index": "s759246749", "label": 4016, "func": ""}
{"index": "s161049370", "label": 1321, "func": ""}
{"index": "s746855287", "label": 1321, "func": ""}
{"index": "s177193396", "label": 1321, "func": ""}
{"index": "s155985181", "label": 1321, "func": ""}
{"index": "s611925507", "label": 1321, "func": ""}
{"index": "s490651567", "label": 1321, "func": ""}
{"index": "s157361981", "label": 1321, "func": ""}
{"index": "s973582770", "label": 1321, "func": ""}
{"index": "s435824580", "label": 1321, "func": ""}
{"index": "s302092628", "label": 1321, "func": ""}
{"index": "s898811619", "label": 1586, "func": ""}
{"index": "s504196649", "label": 1586, "func": ""}
{"index": "s369220594", "label": 1132, "func": ""}
{"index": "s913573766", "label": 1132, "func": ""}
{"index": "s418492775", "label": 1132, "func": ""}
{"index": "s272622007", "label": 1132, "func": ""}
{"index": "s213742990", "label": 1132, "func": ""}
{"index": "s572553174", "label": 1132, "func": ""}
{"index": "s823172158", "label": 1132, "func": ""}
{"index": "s518875991", "label": 1132, "func": ""}
{"index": "s453078626", "label": 1132, "func": ""}
{"index": "s987995862", "label": 1132, "func": ""}
{"index": "s061984979", "label": 2909, "func": ""}
{"index": "s646002074", "label": 2909, "func": ""}
{"index": "s960810419", "label": 2909, "func": ""}
{"index": "s962167443", "label": 2909, "func": ""}
{"index": "s970675099", "label": 2909, "func": ""}
{"index": "s557162386", "label": 2909, "func": ""}
{"index": "s735744698", "label": 2909, "func": ""}
{"index": "s506874167", "label": 2909, "func": ""}
{"index": "s361293643", "label": 2909, "func": ""}
{"index": "s080223390", "label": 2909, "func": ""}
{"index": "s989785356", "label": 496, "func": ""}
{"index": "s674553974", "label": 496, "func": ""}
{"index": "s825758688", "label": 496, "func": ""}
{"index": "s598246221", "label": 496, "func": ""}
{"index": "s353149424", "label": 496, "func": ""}
{"index": "s610041094", "label": 496, "func": ""}
{"index": "s063431089", "label": 2410, "func": ""}
{"index": "s109343779", "label": 2410, "func": ""}
{"index": "s533273356", "label": 2410, "func": ""}
{"index": "s415006595", "label": 2410, "func": ""}
{"index": "s361539551", "label": 2410, "func": ""}
{"index": "s213029726", "label": 2410, "func": ""}
{"index": "s498953402", "label": 2410, "func": ""}
{"index": "s273807073", "label": 2410, "func": ""}
{"index": "s650327260", "label": 2410, "func": ""}
{"index": "s507052701", "label": 2410, "func": ""}
{"index": "s331692577", "label": 1560, "func": ""}
{"index": "s412227516", "label": 1560, "func": ""}
{"index": "s132040432", "label": 1560, "func": ""}
{"index": "s600825625", "label": 1560, "func": ""}
{"index": "s946242361", "label": 1560, "func": ""}
{"index": "s517655615", "label": 1560, "func": ""}
{"index": "s790866126", "label": 467, "func": ""}
{"index": "s190871954", "label": 467, "func": ""}
{"index": "s520164825", "label": 467, "func": ""}
{"index": "s424537366", "label": 467, "func": ""}
{"index": "s278322454", "label": 467, "func": ""}
{"index": "s623175234", "label": 467, "func": ""}
{"index": "s204226691", "label": 467, "func": ""}
{"index": "s954833382", "label": 467, "func": ""}
{"index": "s417696132", "label": 467, "func": ""}
{"index": "s751784295", "label": 467, "func": ""}
{"index": "s731829575", "label": 3066, "func": ""}
{"index": "s366623886", "label": 810, "func": ""}
{"index": "s761725062", "label": 810, "func": ""}
{"index": "s307710805", "label": 810, "func": ""}
{"index": "s626939601", "label": 810, "func": ""}
{"index": "s884268678", "label": 691, "func": ""}
{"index": "s021273235", "label": 691, "func": ""}
{"index": "s327919121", "label": 691, "func": ""}
{"index": "s286886698", "label": 691, "func": ""}
{"index": "s864297763", "label": 691, "func": ""}
{"index": "s712115188", "label": 691, "func": ""}
{"index": "s533874280", "label": 691, "func": ""}
{"index": "s331150748", "label": 691, "func": ""}
{"index": "s017354823", "label": 691, "func": ""}
{"index": "s583508128", "label": 691, "func": ""}
{"index": "s361294674", "label": 1425, "func": ""}
{"index": "s908069064", "label": 2245, "func": ""}
{"index": "s061250463", "label": 2245, "func": ""}
{"index": "s113739823", "label": 2245, "func": ""}
{"index": "s178308479", "label": 2245, "func": ""}
{"index": "s675928195", "label": 2245, "func": ""}
{"index": "s300601265", "label": 2245, "func": ""}
{"index": "s721181610", "label": 2245, "func": ""}
{"index": "s860361744", "label": 2245, "func": ""}
{"index": "s010033130", "label": 2245, "func": ""}
{"index": "s691363641", "label": 2245, "func": ""}
{"index": "s637403835", "label": 3150, "func": ""}
{"index": "s333861489", "label": 3150, "func": ""}
{"index": "s467374030", "label": 3150, "func": ""}
{"index": "s324750274", "label": 3150, "func": ""}
{"index": "s870942327", "label": 3150, "func": ""}
{"index": "s620302335", "label": 3150, "func": ""}
{"index": "s613844469", "label": 3150, "func": ""}
{"index": "s957781479", "label": 3150, "func": ""}
{"index": "s555458451", "label": 3150, "func": ""}
{"index": "s168892859", "label": 3150, "func": ""}
{"index": "s328895509", "label": 602, "func": ""}
{"index": "s471319123", "label": 602, "func": ""}
{"index": "s875137173", "label": 602, "func": ""}
{"index": "s241953082", "label": 602, "func": ""}
{"index": "s977095435", "label": 602, "func": ""}
{"index": "s460634903", "label": 602, "func": ""}
{"index": "s858305631", "label": 602, "func": ""}
{"index": "s285183433", "label": 602, "func": ""}
{"index": "s032530354", "label": 602, "func": ""}
{"index": "s406895177", "label": 602, "func": ""}
{"index": "s064289305", "label": 2954, "func": ""}
{"index": "s688700892", "label": 2954, "func": ""}
{"index": "s578743353", "label": 2954, "func": ""}
{"index": "s665330802", "label": 2954, "func": ""}
{"index": "s400171197", "label": 2954, "func": ""}
{"index": "s282716536", "label": 2954, "func": ""}
{"index": "s533316372", "label": 2954, "func": ""}
{"index": "s530198151", "label": 2954, "func": ""}
{"index": "s485853413", "label": 2954, "func": ""}
{"index": "s701063775", "label": 2954, "func": ""}
{"index": "s556814261", "label": 158, "func": ""}
{"index": "s843408374", "label": 158, "func": ""}
{"index": "s775377131", "label": 158, "func": ""}
{"index": "s823307359", "label": 158, "func": ""}
{"index": "s340527828", "label": 158, "func": ""}
{"index": "s961794029", "label": 158, "func": ""}
{"index": "s488713455", "label": 158, "func": ""}
{"index": "s696826099", "label": 158, "func": ""}
{"index": "s057019720", "label": 158, "func": ""}
{"index": "s329341453", "label": 158, "func": ""}
{"index": "s692499923", "label": 2737, "func": ""}
{"index": "s398364493", "label": 2737, "func": ""}
{"index": "s668155567", "label": 792, "func": ""}
{"index": "s627888406", "label": 792, "func": ""}
{"index": "s117825828", "label": 792, "func": ""}
{"index": "s779127712", "label": 792, "func": ""}
{"index": "s760497419", "label": 1049, "func": ""}
{"index": "s421043549", "label": 1049, "func": ""}
{"index": "s344971599", "label": 226, "func": ""}
{"index": "s199753027", "label": 226, "func": ""}
{"index": "s305466698", "label": 226, "func": ""}
{"index": "s979821015", "label": 226, "func": ""}
{"index": "s447359754", "label": 226, "func": ""}
{"index": "s594661131", "label": 226, "func": ""}
{"index": "s554026496", "label": 226, "func": ""}
{"index": "s025831791", "label": 226, "func": ""}
{"index": "s421653588", "label": 226, "func": ""}
{"index": "s699233778", "label": 226, "func": ""}
{"index": "s775957209", "label": 149, "func": ""}
{"index": "s880437702", "label": 149, "func": ""}
{"index": "s171736824", "label": 149, "func": ""}
{"index": "s953732011", "label": 149, "func": ""}
{"index": "s880120321", "label": 149, "func": ""}
{"index": "s236383815", "label": 149, "func": ""}
{"index": "s326343396", "label": 149, "func": ""}
{"index": "s292960859", "label": 149, "func": ""}
{"index": "s650127804", "label": 149, "func": ""}
{"index": "s110264300", "label": 149, "func": ""}
{"index": "s271055260", "label": 3143, "func": ""}
{"index": "s552692819", "label": 3143, "func": ""}
{"index": "s112936906", "label": 3143, "func": ""}
{"index": "s986580934", "label": 3143, "func": ""}
{"index": "s693408612", "label": 3143, "func": ""}
{"index": "s209297015", "label": 3143, "func": ""}
{"index": "s961097217", "label": 3143, "func": ""}
{"index": "s275639930", "label": 3143, "func": ""}
{"index": "s579212955", "label": 3143, "func": ""}
{"index": "s595788011", "label": 3143, "func": ""}
{"index": "s604316690", "label": 1540, "func": ""}
{"index": "s884818368", "label": 1540, "func": ""}
{"index": "s366026282", "label": 1540, "func": ""}
{"index": "s834100557", "label": 1540, "func": ""}
{"index": "s768109663", "label": 1540, "func": ""}
{"index": "s086086238", "label": 1540, "func": ""}
{"index": "s053384689", "label": 1540, "func": ""}
{"index": "s354182343", "label": 1540, "func": ""}
{"index": "s646493518", "label": 1540, "func": ""}
{"index": "s392684188", "label": 1540, "func": ""}
{"index": "s686007823", "label": 3606, "func": ""}
{"index": "s234571011", "label": 3606, "func": ""}
{"index": "s715276252", "label": 3606, "func": ""}
{"index": "s713877076", "label": 3606, "func": ""}
{"index": "s466897295", "label": 3606, "func": ""}
{"index": "s384467518", "label": 3606, "func": ""}
{"index": "s390330891", "label": 3606, "func": ""}
{"index": "s170424673", "label": 3606, "func": ""}
{"index": "s252501217", "label": 3606, "func": ""}
{"index": "s075465856", "label": 3606, "func": ""}
{"index": "s008256190", "label": 2243, "func": ""}
{"index": "s150933663", "label": 2243, "func": ""}
{"index": "s526321577", "label": 2243, "func": ""}
{"index": "s490778029", "label": 2243, "func": ""}
{"index": "s985338811", "label": 2243, "func": ""}
{"index": "s989061025", "label": 2243, "func": ""}
{"index": "s060890666", "label": 2243, "func": ""}
{"index": "s283454226", "label": 2243, "func": ""}
{"index": "s762666386", "label": 2243, "func": ""}
{"index": "s628253336", "label": 2243, "func": ""}
{"index": "s880545461", "label": 3077, "func": ""}
{"index": "s056940978", "label": 3077, "func": ""}
{"index": "s565998113", "label": 3077, "func": ""}
{"index": "s259308882", "label": 3077, "func": ""}
{"index": "s681005810", "label": 3077, "func": ""}
{"index": "s724495865", "label": 3077, "func": ""}
{"index": "s160599916", "label": 3077, "func": ""}
{"index": "s156587694", "label": 3077, "func": ""}
{"index": "s159002028", "label": 3077, "func": ""}
{"index": "s750804450", "label": 3077, "func": ""}
{"index": "s105279392", "label": 549, "func": ""}
{"index": "s080179642", "label": 3947, "func": ""}
{"index": "s122406874", "label": 3947, "func": ""}
{"index": "s952086017", "label": 3947, "func": ""}
{"index": "s925606829", "label": 3947, "func": ""}
{"index": "s571629838", "label": 3947, "func": ""}
{"index": "s622049226", "label": 3947, "func": ""}
{"index": "s266332955", "label": 3947, "func": ""}
{"index": "s473658472", "label": 3947, "func": ""}
{"index": "s204744922", "label": 3947, "func": ""}
{"index": "s070631875", "label": 3947, "func": ""}
{"index": "s897846239", "label": 1990, "func": ""}
{"index": "s640040478", "label": 1803, "func": ""}
{"index": "s738040061", "label": 1803, "func": ""}
{"index": "s163039527", "label": 1803, "func": ""}
{"index": "s713924392", "label": 1803, "func": ""}
{"index": "s049176441", "label": 1803, "func": ""}
{"index": "s217994383", "label": 1803, "func": ""}
{"index": "s190876760", "label": 1803, "func": ""}
{"index": "s828077730", "label": 1803, "func": ""}
{"index": "s105458459", "label": 1803, "func": ""}
{"index": "s861226215", "label": 1803, "func": ""}
{"index": "s248284083", "label": 1382, "func": ""}
{"index": "s118146476", "label": 1382, "func": ""}
{"index": "s201861085", "label": 1382, "func": ""}
{"index": "s796802900", "label": 1400, "func": ""}
{"index": "s973988455", "label": 1400, "func": ""}
{"index": "s392345292", "label": 1400, "func": ""}
{"index": "s161858836", "label": 3230, "func": ""}
{"index": "s799940458", "label": 3230, "func": ""}
{"index": "s915488090", "label": 3230, "func": ""}
{"index": "s516877719", "label": 3230, "func": ""}
{"index": "s303867816", "label": 3230, "func": ""}
{"index": "s868732426", "label": 3230, "func": ""}
{"index": "s468760211", "label": 3230, "func": ""}
{"index": "s290286971", "label": 3230, "func": ""}
{"index": "s538118418", "label": 3230, "func": ""}
{"index": "s531022734", "label": 3230, "func": ""}
{"index": "s175544904", "label": 3510, "func": ""}
{"index": "s072792585", "label": 3652, "func": ""}
{"index": "s728473896", "label": 3652, "func": ""}
{"index": "s114346813", "label": 3652, "func": ""}
{"index": "s681577557", "label": 3652, "func": ""}
{"index": "s228211336", "label": 3652, "func": ""}
{"index": "s178615199", "label": 3652, "func": ""}
{"index": "s331348423", "label": 3652, "func": ""}
{"index": "s894910571", "label": 3652, "func": ""}
{"index": "s865543447", "label": 3652, "func": ""}
{"index": "s435637391", "label": 3652, "func": ""}
{"index": "s735097241", "label": 1306, "func": ""}
{"index": "s948385739", "label": 1306, "func": ""}
{"index": "s164146543", "label": 1306, "func": ""}
{"index": "s216019595", "label": 1306, "func": ""}
{"index": "s443438959", "label": 1306, "func": ""}
{"index": "s380920977", "label": 180, "func": ""}
{"index": "s768492028", "label": 180, "func": ""}
{"index": "s170735118", "label": 180, "func": ""}
{"index": "s130262459", "label": 180, "func": ""}
{"index": "s737272772", "label": 180, "func": ""}
{"index": "s559400452", "label": 180, "func": ""}
{"index": "s916157070", "label": 180, "func": ""}
{"index": "s218929241", "label": 180, "func": ""}
{"index": "s674821360", "label": 180, "func": ""}
{"index": "s611926919", "label": 180, "func": ""}
{"index": "s464113774", "label": 1388, "func": ""}
{"index": "s078543948", "label": 1388, "func": ""}
{"index": "s100297627", "label": 1388, "func": ""}
{"index": "s833655488", "label": 1388, "func": ""}
{"index": "s476093807", "label": 1388, "func": ""}
{"index": "s098211726", "label": 1388, "func": ""}
{"index": "s725029473", "label": 1388, "func": ""}
{"index": "s604003867", "label": 1388, "func": ""}
{"index": "s233830720", "label": 1388, "func": ""}
{"index": "s903736536", "label": 1388, "func": ""}
{"index": "s534373382", "label": 1376, "func": ""}
{"index": "s928969919", "label": 1376, "func": ""}
{"index": "s489204354", "label": 1376, "func": ""}
{"index": "s478305901", "label": 1376, "func": ""}
{"index": "s506844068", "label": 1376, "func": ""}
{"index": "s619185737", "label": 1376, "func": ""}
{"index": "s634002641", "label": 1376, "func": ""}
{"index": "s162915697", "label": 1376, "func": ""}
{"index": "s342305775", "label": 1376, "func": ""}
{"index": "s115576786", "label": 1376, "func": ""}
{"index": "s879380909", "label": 3079, "func": ""}
{"index": "s962149330", "label": 3079, "func": ""}
{"index": "s800245248", "label": 3079, "func": ""}
{"index": "s042321847", "label": 3079, "func": ""}
{"index": "s291022122", "label": 3079, "func": ""}
{"index": "s449921969", "label": 3079, "func": ""}
{"index": "s952071069", "label": 3079, "func": ""}
{"index": "s464350610", "label": 3079, "func": ""}
{"index": "s112961140", "label": 3079, "func": ""}
{"index": "s290575001", "label": 3079, "func": ""}
{"index": "s118920467", "label": 482, "func": ""}
{"index": "s797110960", "label": 482, "func": ""}
{"index": "s679420848", "label": 3687, "func": ""}
{"index": "s487853884", "label": 3687, "func": ""}
{"index": "s584720620", "label": 3687, "func": ""}
{"index": "s000802142", "label": 3687, "func": ""}
{"index": "s482621792", "label": 3687, "func": ""}
{"index": "s114347717", "label": 3687, "func": ""}
{"index": "s311320555", "label": 3687, "func": ""}
{"index": "s867356016", "label": 3687, "func": ""}
{"index": "s252550140", "label": 3687, "func": ""}
{"index": "s185954535", "label": 3687, "func": ""}
{"index": "s583315143", "label": 3874, "func": ""}
{"index": "s648357581", "label": 2054, "func": ""}
{"index": "s621243151", "label": 918, "func": ""}
{"index": "s308530068", "label": 918, "func": ""}
{"index": "s532639402", "label": 918, "func": ""}
{"index": "s091954788", "label": 918, "func": ""}
{"index": "s592208427", "label": 918, "func": ""}
{"index": "s904655831", "label": 918, "func": ""}
{"index": "s276531215", "label": 3634, "func": ""}
{"index": "s051661355", "label": 3634, "func": ""}
{"index": "s831732394", "label": 3634, "func": ""}
{"index": "s002603424", "label": 3634, "func": ""}
{"index": "s122332010", "label": 3634, "func": ""}
{"index": "s430449405", "label": 3634, "func": ""}
{"index": "s061816129", "label": 3634, "func": ""}
{"index": "s458105152", "label": 3634, "func": ""}
{"index": "s065564765", "label": 3634, "func": ""}
{"index": "s486187562", "label": 3634, "func": ""}
{"index": "s477629123", "label": 2872, "func": ""}
{"index": "s762394004", "label": 2872, "func": ""}
{"index": "s350312289", "label": 2872, "func": ""}
{"index": "s542903843", "label": 2872, "func": ""}
{"index": "s696466928", "label": 2872, "func": ""}
{"index": "s612458578", "label": 2872, "func": ""}
{"index": "s610896714", "label": 2872, "func": ""}
{"index": "s692379867", "label": 2872, "func": ""}
{"index": "s838309648", "label": 2872, "func": ""}
{"index": "s815047141", "label": 2872, "func": ""}
{"index": "s778240605", "label": 881, "func": ""}
{"index": "s474963489", "label": 881, "func": ""}
{"index": "s152330982", "label": 881, "func": ""}
{"index": "s913835786", "label": 881, "func": ""}
{"index": "s991643682", "label": 881, "func": ""}
{"index": "s351278490", "label": 881, "func": ""}
{"index": "s552092286", "label": 881, "func": ""}
{"index": "s670553885", "label": 881, "func": ""}
{"index": "s620657213", "label": 881, "func": ""}
{"index": "s213086254", "label": 881, "func": ""}
{"index": "s215918445", "label": 2316, "func": ""}
{"index": "s275350503", "label": 2316, "func": ""}
{"index": "s567661414", "label": 2316, "func": ""}
{"index": "s502506018", "label": 2316, "func": ""}
{"index": "s569142968", "label": 2316, "func": ""}
{"index": "s163342142", "label": 2316, "func": ""}
{"index": "s119548550", "label": 2316, "func": ""}
{"index": "s891895840", "label": 2316, "func": ""}
{"index": "s757175536", "label": 2316, "func": ""}
{"index": "s620814599", "label": 2316, "func": ""}
{"index": "s096854740", "label": 2459, "func": ""}
{"index": "s803034229", "label": 2459, "func": ""}
{"index": "s460552937", "label": 2459, "func": ""}
{"index": "s950832107", "label": 2459, "func": ""}
{"index": "s596724664", "label": 2459, "func": ""}
{"index": "s665055136", "label": 2459, "func": ""}
{"index": "s909464020", "label": 2459, "func": ""}
{"index": "s790973234", "label": 2459, "func": ""}
{"index": "s261750212", "label": 2459, "func": ""}
{"index": "s948781421", "label": 2459, "func": ""}
{"index": "s529097580", "label": 1356, "func": ""}
{"index": "s564223815", "label": 262, "func": ""}
{"index": "s609819563", "label": 262, "func": ""}
{"index": "s104061873", "label": 262, "func": ""}
{"index": "s504401434", "label": 262, "func": ""}
{"index": "s246146882", "label": 262, "func": ""}
{"index": "s149432782", "label": 262, "func": ""}
{"index": "s802565281", "label": 262, "func": ""}
{"index": "s114841374", "label": 262, "func": ""}
{"index": "s972999401", "label": 262, "func": ""}
{"index": "s192597019", "label": 262, "func": ""}
{"index": "s807483676", "label": 24, "func": ""}
{"index": "s746242198", "label": 24, "func": ""}
{"index": "s628319515", "label": 24, "func": ""}
{"index": "s404824723", "label": 24, "func": ""}
{"index": "s667129475", "label": 24, "func": ""}
{"index": "s142521598", "label": 24, "func": ""}
{"index": "s480173190", "label": 24, "func": ""}
{"index": "s628679363", "label": 24, "func": ""}
{"index": "s722117200", "label": 24, "func": ""}
{"index": "s311195201", "label": 24, "func": ""}
{"index": "s708491477", "label": 75, "func": ""}
{"index": "s371383404", "label": 75, "func": ""}
{"index": "s606050100", "label": 75, "func": ""}
{"index": "s820625610", "label": 75, "func": ""}
{"index": "s787762047", "label": 75, "func": ""}
{"index": "s322164263", "label": 75, "func": ""}
{"index": "s351139246", "label": 75, "func": ""}
{"index": "s574200515", "label": 75, "func": ""}
{"index": "s672714991", "label": 75, "func": ""}
{"index": "s403744626", "label": 75, "func": ""}
{"index": "s305549483", "label": 84, "func": ""}
{"index": "s935345645", "label": 84, "func": ""}
{"index": "s665844478", "label": 84, "func": ""}
{"index": "s976280791", "label": 84, "func": ""}
{"index": "s958610649", "label": 84, "func": ""}
{"index": "s679414457", "label": 84, "func": ""}
{"index": "s724669506", "label": 84, "func": ""}
{"index": "s759501940", "label": 84, "func": ""}
{"index": "s453347037", "label": 84, "func": ""}
{"index": "s020230330", "label": 84, "func": ""}
{"index": "s390891734", "label": 2936, "func": ""}
{"index": "s970005659", "label": 2936, "func": ""}
{"index": "s535319154", "label": 2936, "func": ""}
{"index": "s561267638", "label": 2936, "func": ""}
{"index": "s390982284", "label": 2936, "func": ""}
{"index": "s163172520", "label": 2936, "func": ""}
{"index": "s872767270", "label": 2936, "func": ""}
{"index": "s939299993", "label": 2936, "func": ""}
{"index": "s659720889", "label": 2936, "func": ""}
{"index": "s481835433", "label": 2936, "func": ""}
{"index": "s785252567", "label": 1978, "func": ""}
{"index": "s441057645", "label": 1978, "func": ""}
{"index": "s384203635", "label": 3479, "func": ""}
{"index": "s097747893", "label": 3479, "func": ""}
{"index": "s594561004", "label": 3479, "func": ""}
{"index": "s800767935", "label": 3479, "func": ""}
{"index": "s031697748", "label": 3479, "func": ""}
{"index": "s351664651", "label": 3479, "func": ""}
{"index": "s961388831", "label": 3479, "func": ""}
{"index": "s551950721", "label": 3479, "func": ""}
{"index": "s808789250", "label": 3479, "func": ""}
{"index": "s325799341", "label": 3479, "func": ""}
{"index": "s807473297", "label": 3746, "func": ""}
{"index": "s474961951", "label": 3746, "func": ""}
{"index": "s024268728", "label": 3746, "func": ""}
{"index": "s233723799", "label": 3746, "func": ""}
{"index": "s188574840", "label": 3746, "func": ""}
{"index": "s647192066", "label": 3746, "func": ""}
{"index": "s264746636", "label": 3746, "func": ""}
{"index": "s785822588", "label": 3746, "func": ""}
{"index": "s442526286", "label": 3746, "func": ""}
{"index": "s874391772", "label": 3746, "func": ""}
{"index": "s642661806", "label": 1628, "func": ""}
{"index": "s828118339", "label": 1628, "func": ""}
{"index": "s312575731", "label": 1354, "func": ""}
{"index": "s810352349", "label": 1354, "func": ""}
{"index": "s946670315", "label": 1354, "func": ""}
{"index": "s276137799", "label": 1974, "func": ""}
{"index": "s679096993", "label": 1974, "func": ""}
{"index": "s289875298", "label": 1974, "func": ""}
{"index": "s039925788", "label": 1974, "func": ""}
{"index": "s882863330", "label": 1974, "func": ""}
{"index": "s672712177", "label": 2460, "func": ""}
{"index": "s624720021", "label": 2460, "func": ""}
{"index": "s478275542", "label": 2460, "func": ""}
{"index": "s998719519", "label": 2460, "func": ""}
{"index": "s844800865", "label": 2460, "func": ""}
{"index": "s442143835", "label": 2460, "func": ""}
{"index": "s749359934", "label": 2460, "func": ""}
{"index": "s689736693", "label": 2460, "func": ""}
{"index": "s731619693", "label": 2460, "func": ""}
{"index": "s173883644", "label": 2460, "func": ""}
{"index": "s362890740", "label": 3305, "func": ""}
{"index": "s695370064", "label": 3305, "func": ""}
{"index": "s094382377", "label": 3305, "func": ""}
{"index": "s717187858", "label": 3305, "func": ""}
{"index": "s395031562", "label": 3305, "func": ""}
{"index": "s126629343", "label": 3305, "func": ""}
{"index": "s498716717", "label": 3305, "func": ""}
{"index": "s786827111", "label": 3305, "func": ""}
{"index": "s904042530", "label": 3305, "func": ""}
{"index": "s144665401", "label": 3305, "func": ""}
{"index": "s242064096", "label": 2342, "func": ""}
{"index": "s123075501", "label": 2483, "func": ""}
{"index": "s236841573", "label": 2483, "func": ""}
{"index": "s418328940", "label": 2483, "func": ""}
{"index": "s352578505", "label": 2483, "func": ""}
{"index": "s943653263", "label": 2483, "func": ""}
{"index": "s610618270", "label": 2483, "func": ""}
{"index": "s497480081", "label": 2483, "func": ""}
{"index": "s122044991", "label": 2483, "func": ""}
{"index": "s528589695", "label": 2483, "func": ""}
{"index": "s394835123", "label": 2483, "func": ""}
{"index": "s619634623", "label": 3653, "func": ""}
{"index": "s833900580", "label": 3653, "func": ""}
{"index": "s936639448", "label": 3653, "func": ""}
{"index": "s992373226", "label": 3653, "func": ""}
{"index": "s457830751", "label": 3653, "func": ""}
{"index": "s616195759", "label": 3653, "func": ""}
{"index": "s229927750", "label": 1143, "func": ""}
{"index": "s863067920", "label": 1143, "func": ""}
{"index": "s325601072", "label": 1143, "func": ""}
{"index": "s908738459", "label": 1143, "func": ""}
{"index": "s595651896", "label": 1143, "func": ""}
{"index": "s884546726", "label": 1143, "func": ""}
{"index": "s428496914", "label": 1143, "func": ""}
{"index": "s350740630", "label": 1143, "func": ""}
{"index": "s901944331", "label": 1143, "func": ""}
{"index": "s185032695", "label": 1143, "func": ""}
{"index": "s685826393", "label": 817, "func": ""}
{"index": "s463763786", "label": 817, "func": ""}
{"index": "s764143912", "label": 817, "func": ""}
{"index": "s008041542", "label": 3009, "func": ""}
{"index": "s716410210", "label": 3783, "func": ""}
{"index": "s934342212", "label": 3783, "func": ""}
{"index": "s033065274", "label": 3397, "func": ""}
{"index": "s731622990", "label": 3397, "func": ""}
{"index": "s080371146", "label": 1898, "func": ""}
{"index": "s551103949", "label": 1898, "func": ""}
{"index": "s687465408", "label": 1898, "func": ""}
{"index": "s246836016", "label": 1898, "func": ""}
{"index": "s581660653", "label": 1898, "func": ""}
{"index": "s844462239", "label": 1898, "func": ""}
{"index": "s315324994", "label": 2595, "func": ""}
{"index": "s524776595", "label": 2595, "func": ""}
{"index": "s955351994", "label": 2595, "func": ""}
{"index": "s480726179", "label": 2595, "func": ""}
{"index": "s585639365", "label": 2595, "func": ""}
{"index": "s288215072", "label": 2595, "func": ""}
{"index": "s495893042", "label": 2595, "func": ""}
{"index": "s885606815", "label": 2595, "func": ""}
{"index": "s170136520", "label": 2595, "func": ""}
{"index": "s192229246", "label": 2595, "func": ""}
{"index": "s729533906", "label": 3719, "func": ""}
{"index": "s752194421", "label": 3719, "func": ""}
{"index": "s551133635", "label": 3719, "func": ""}
{"index": "s264528297", "label": 3719, "func": ""}
{"index": "s787064569", "label": 3719, "func": ""}
{"index": "s249372888", "label": 3719, "func": ""}
{"index": "s621969191", "label": 3719, "func": ""}
{"index": "s068094650", "label": 3719, "func": ""}
{"index": "s187129653", "label": 3719, "func": ""}
{"index": "s261835668", "label": 3719, "func": ""}
{"index": "s687392649", "label": 3816, "func": ""}
{"index": "s759566694", "label": 3816, "func": ""}
{"index": "s652278342", "label": 3816, "func": ""}
{"index": "s915000293", "label": 3816, "func": ""}
{"index": "s040345493", "label": 3816, "func": ""}
{"index": "s952497056", "label": 3816, "func": ""}
{"index": "s722133930", "label": 3816, "func": ""}
{"index": "s015622679", "label": 3816, "func": ""}
{"index": "s454058130", "label": 3816, "func": ""}
{"index": "s334904459", "label": 3816, "func": ""}
{"index": "s286151617", "label": 502, "func": ""}
{"index": "s813387926", "label": 502, "func": ""}
{"index": "s647468774", "label": 502, "func": ""}
{"index": "s055777239", "label": 502, "func": ""}
{"index": "s340901388", "label": 502, "func": ""}
{"index": "s615290649", "label": 502, "func": ""}
{"index": "s548077056", "label": 502, "func": ""}
{"index": "s994779758", "label": 502, "func": ""}
{"index": "s663093410", "label": 502, "func": ""}
{"index": "s405330812", "label": 502, "func": ""}
{"index": "s640454466", "label": 1559, "func": ""}
{"index": "s464310757", "label": 1559, "func": ""}
{"index": "s973901930", "label": 3093, "func": ""}
{"index": "s191959727", "label": 3093, "func": ""}
{"index": "s697293392", "label": 3288, "func": ""}
{"index": "s365973053", "label": 3288, "func": ""}
{"index": "s324006883", "label": 3288, "func": ""}
{"index": "s014753374", "label": 3288, "func": ""}
{"index": "s101368745", "label": 3288, "func": ""}
{"index": "s266023549", "label": 3288, "func": ""}
{"index": "s096803397", "label": 3288, "func": ""}
{"index": "s236264042", "label": 3288, "func": ""}
{"index": "s509701766", "label": 3288, "func": ""}
{"index": "s190215720", "label": 3288, "func": ""}
{"index": "s401922404", "label": 1667, "func": ""}
{"index": "s520346206", "label": 1587, "func": ""}
{"index": "s760739532", "label": 2856, "func": ""}
{"index": "s043437783", "label": 2856, "func": ""}
{"index": "s252085752", "label": 2856, "func": ""}
{"index": "s305397296", "label": 2856, "func": ""}
{"index": "s236055345", "label": 2856, "func": ""}
{"index": "s346919124", "label": 2856, "func": ""}
{"index": "s766785315", "label": 2856, "func": ""}
{"index": "s190870231", "label": 2856, "func": ""}
{"index": "s631778499", "label": 2856, "func": ""}
{"index": "s326388411", "label": 2856, "func": ""}
{"index": "s633935267", "label": 2629, "func": ""}
{"index": "s927190989", "label": 2629, "func": ""}
{"index": "s511555843", "label": 2629, "func": ""}
{"index": "s154618378", "label": 2629, "func": ""}
{"index": "s847333103", "label": 2629, "func": ""}
{"index": "s874661693", "label": 2629, "func": ""}
{"index": "s937952264", "label": 2629, "func": ""}
{"index": "s525009875", "label": 2629, "func": ""}
{"index": "s478885512", "label": 2629, "func": ""}
{"index": "s949714051", "label": 2629, "func": ""}
{"index": "s866614525", "label": 2892, "func": ""}
{"index": "s484987034", "label": 2892, "func": ""}
{"index": "s412048139", "label": 2892, "func": ""}
{"index": "s071948320", "label": 2892, "func": ""}
{"index": "s247036070", "label": 2892, "func": ""}
{"index": "s440747724", "label": 2892, "func": ""}
{"index": "s525953529", "label": 2892, "func": ""}
{"index": "s735105242", "label": 2892, "func": ""}
{"index": "s385105267", "label": 2892, "func": ""}
{"index": "s001194177", "label": 2892, "func": ""}
{"index": "s358637739", "label": 1615, "func": ""}
{"index": "s691795288", "label": 1615, "func": ""}
{"index": "s499062559", "label": 1615, "func": ""}
{"index": "s566484753", "label": 1615, "func": ""}
{"index": "s294225130", "label": 1615, "func": ""}
{"index": "s949387959", "label": 1615, "func": ""}
{"index": "s438103570", "label": 1615, "func": ""}
{"index": "s576880745", "label": 1615, "func": ""}
{"index": "s220321021", "label": 1615, "func": ""}
{"index": "s070288916", "label": 1415, "func": ""}
{"index": "s210765391", "label": 1415, "func": ""}
{"index": "s081907646", "label": 1415, "func": ""}
{"index": "s952337872", "label": 1415, "func": ""}
{"index": "s235681774", "label": 1415, "func": ""}
{"index": "s870864575", "label": 1415, "func": ""}
{"index": "s576317160", "label": 1415, "func": ""}
{"index": "s796663701", "label": 1415, "func": ""}
{"index": "s993942630", "label": 1415, "func": ""}
{"index": "s094984118", "label": 1415, "func": ""}
{"index": "s174333044", "label": 1064, "func": ""}
{"index": "s493274775", "label": 3852, "func": ""}
{"index": "s661360239", "label": 3852, "func": ""}
{"index": "s774808932", "label": 3852, "func": ""}
{"index": "s710625140", "label": 3852, "func": ""}
{"index": "s112730382", "label": 3852, "func": ""}
{"index": "s589836900", "label": 3852, "func": ""}
{"index": "s306193320", "label": 3852, "func": ""}
{"index": "s590914220", "label": 3852, "func": ""}
{"index": "s360162778", "label": 3852, "func": ""}
{"index": "s761878181", "label": 3852, "func": ""}
{"index": "s444918581", "label": 3695, "func": ""}
{"index": "s590539750", "label": 3695, "func": ""}
{"index": "s213982827", "label": 3695, "func": ""}
{"index": "s693326527", "label": 3695, "func": ""}
{"index": "s981889525", "label": 3695, "func": ""}
{"index": "s240155040", "label": 3695, "func": ""}
{"index": "s297848178", "label": 3695, "func": ""}
{"index": "s717989629", "label": 3695, "func": ""}
{"index": "s434011697", "label": 3695, "func": ""}
{"index": "s588707032", "label": 3695, "func": ""}
{"index": "s085220231", "label": 3761, "func": ""}
{"index": "s570496443", "label": 3761, "func": ""}
{"index": "s449280644", "label": 3761, "func": ""}
{"index": "s534136780", "label": 3761, "func": ""}
{"index": "s931806597", "label": 3761, "func": ""}
{"index": "s118350800", "label": 3761, "func": ""}
{"index": "s495405362", "label": 3761, "func": ""}
{"index": "s526276082", "label": 3761, "func": ""}
{"index": "s820058976", "label": 3761, "func": ""}
{"index": "s531280851", "label": 3761, "func": ""}
{"index": "s849257457", "label": 1574, "func": ""}
{"index": "s366244137", "label": 1720, "func": ""}
{"index": "s291785662", "label": 1720, "func": ""}
{"index": "s642107830", "label": 659, "func": ""}
{"index": "s141417791", "label": 659, "func": ""}
{"index": "s758864114", "label": 659, "func": ""}
{"index": "s160559830", "label": 659, "func": ""}
{"index": "s284653781", "label": 659, "func": ""}
{"index": "s163049872", "label": 659, "func": ""}
{"index": "s104502988", "label": 659, "func": ""}
{"index": "s633598945", "label": 659, "func": ""}
{"index": "s801281302", "label": 659, "func": ""}
{"index": "s436869803", "label": 659, "func": ""}
{"index": "s710944691", "label": 3393, "func": ""}
{"index": "s692444911", "label": 3393, "func": ""}
{"index": "s485276815", "label": 3393, "func": ""}
{"index": "s114461117", "label": 3393, "func": ""}
{"index": "s164978358", "label": 3393, "func": ""}
{"index": "s074842529", "label": 3393, "func": ""}
{"index": "s064104025", "label": 3393, "func": ""}
{"index": "s728283834", "label": 3393, "func": ""}
{"index": "s057512800", "label": 3393, "func": ""}
{"index": "s496993012", "label": 3393, "func": ""}
{"index": "s044266305", "label": 2928, "func": ""}
{"index": "s768727194", "label": 2928, "func": ""}
{"index": "s453127197", "label": 2928, "func": ""}
{"index": "s655151783", "label": 2928, "func": ""}
{"index": "s384177049", "label": 2928, "func": ""}
{"index": "s066695173", "label": 2928, "func": ""}
{"index": "s210890837", "label": 2928, "func": ""}
{"index": "s992351505", "label": 2928, "func": ""}
{"index": "s316441432", "label": 2928, "func": ""}
{"index": "s300498611", "label": 2928, "func": ""}
{"index": "s547565603", "label": 2217, "func": ""}
{"index": "s928133785", "label": 2711, "func": ""}
{"index": "s181346143", "label": 2711, "func": ""}
{"index": "s207341204", "label": 2711, "func": ""}
{"index": "s049300889", "label": 2711, "func": ""}
{"index": "s235003359", "label": 2711, "func": ""}
{"index": "s630471605", "label": 2711, "func": ""}
{"index": "s394458245", "label": 2711, "func": ""}
{"index": "s725331845", "label": 2711, "func": ""}
{"index": "s036364478", "label": 2711, "func": ""}
{"index": "s375732936", "label": 2711, "func": ""}
{"index": "s515854783", "label": 604, "func": ""}
{"index": "s888755414", "label": 604, "func": ""}
{"index": "s725804820", "label": 604, "func": ""}
{"index": "s868245891", "label": 604, "func": ""}
{"index": "s695927427", "label": 604, "func": ""}
{"index": "s884541954", "label": 604, "func": ""}
{"index": "s793735848", "label": 604, "func": ""}
{"index": "s303789718", "label": 604, "func": ""}
{"index": "s568612089", "label": 604, "func": ""}
{"index": "s233810359", "label": 604, "func": ""}
{"index": "s862119231", "label": 916, "func": ""}
{"index": "s628726305", "label": 916, "func": ""}
{"index": "s962442389", "label": 916, "func": ""}
{"index": "s776225931", "label": 916, "func": ""}
{"index": "s456628781", "label": 2902, "func": ""}
{"index": "s065029179", "label": 2902, "func": ""}
{"index": "s574062531", "label": 2902, "func": ""}
{"index": "s603345172", "label": 2902, "func": ""}
{"index": "s360674734", "label": 2902, "func": ""}
{"index": "s452332449", "label": 2902, "func": ""}
{"index": "s702624916", "label": 2902, "func": ""}
{"index": "s365771450", "label": 2902, "func": ""}
{"index": "s030390811", "label": 2902, "func": ""}
{"index": "s227022833", "label": 2902, "func": ""}
{"index": "s894310398", "label": 2602, "func": ""}
{"index": "s931380319", "label": 2602, "func": ""}
{"index": "s132650788", "label": 2602, "func": ""}
{"index": "s309953938", "label": 2602, "func": ""}
{"index": "s611169363", "label": 2602, "func": ""}
{"index": "s089082274", "label": 2602, "func": ""}
{"index": "s959132481", "label": 2602, "func": ""}
{"index": "s264425875", "label": 2602, "func": ""}
{"index": "s780947367", "label": 2602, "func": ""}
{"index": "s357353152", "label": 2602, "func": ""}
{"index": "s301665025", "label": 2370, "func": ""}
{"index": "s307287430", "label": 2370, "func": ""}
{"index": "s743654168", "label": 2370, "func": ""}
{"index": "s991570784", "label": 2370, "func": ""}
{"index": "s179262665", "label": 2370, "func": ""}
{"index": "s571820546", "label": 2370, "func": ""}
{"index": "s783944173", "label": 2370, "func": ""}
{"index": "s226072240", "label": 2370, "func": ""}
{"index": "s224138724", "label": 2370, "func": ""}
{"index": "s065465542", "label": 2370, "func": ""}
{"index": "s932520979", "label": 2199, "func": ""}
{"index": "s398166461", "label": 2199, "func": ""}
{"index": "s658464122", "label": 688, "func": ""}
{"index": "s588719499", "label": 688, "func": ""}
{"index": "s457873393", "label": 688, "func": ""}
{"index": "s248968235", "label": 688, "func": ""}
{"index": "s125454485", "label": 688, "func": ""}
{"index": "s471340286", "label": 1131, "func": ""}
{"index": "s758476321", "label": 1131, "func": ""}
{"index": "s034421677", "label": 1131, "func": ""}
{"index": "s940420005", "label": 1131, "func": ""}
{"index": "s631158098", "label": 1131, "func": ""}
{"index": "s323917723", "label": 1131, "func": ""}
{"index": "s211940189", "label": 1131, "func": ""}
{"index": "s076306285", "label": 1131, "func": ""}
{"index": "s117295715", "label": 1131, "func": ""}
{"index": "s255643327", "label": 1131, "func": ""}
{"index": "s456895682", "label": 865, "func": ""}
{"index": "s263398542", "label": 865, "func": ""}
{"index": "s981234635", "label": 865, "func": ""}
{"index": "s491805586", "label": 865, "func": ""}
{"index": "s189123310", "label": 865, "func": ""}
{"index": "s666449761", "label": 865, "func": ""}
{"index": "s661785665", "label": 865, "func": ""}
{"index": "s571834769", "label": 865, "func": ""}
{"index": "s625174016", "label": 865, "func": ""}
{"index": "s732220096", "label": 865, "func": ""}
{"index": "s500518548", "label": 915, "func": ""}
{"index": "s231272937", "label": 915, "func": ""}
{"index": "s939414201", "label": 915, "func": ""}
{"index": "s096398890", "label": 915, "func": ""}
{"index": "s542444379", "label": 915, "func": ""}
{"index": "s625934965", "label": 915, "func": ""}
{"index": "s715524819", "label": 915, "func": ""}
{"index": "s203095921", "label": 915, "func": ""}
{"index": "s075297495", "label": 915, "func": ""}
{"index": "s830534908", "label": 915, "func": ""}
{"index": "s181346843", "label": 3641, "func": ""}
{"index": "s432616917", "label": 3641, "func": ""}
{"index": "s108722823", "label": 3641, "func": ""}
{"index": "s926352654", "label": 3641, "func": ""}
{"index": "s678819667", "label": 3641, "func": ""}
{"index": "s592037753", "label": 3641, "func": ""}
{"index": "s833253793", "label": 3641, "func": ""}
{"index": "s531210866", "label": 3641, "func": ""}
{"index": "s863358775", "label": 3641, "func": ""}
{"index": "s793927345", "label": 3641, "func": ""}
{"index": "s559933720", "label": 3767, "func": ""}
{"index": "s824864089", "label": 3767, "func": ""}
{"index": "s659098727", "label": 3767, "func": ""}
{"index": "s514802023", "label": 3767, "func": ""}
{"index": "s312560759", "label": 3767, "func": ""}
{"index": "s324337348", "label": 3767, "func": ""}
{"index": "s436065778", "label": 3767, "func": ""}
{"index": "s414584684", "label": 3767, "func": ""}
{"index": "s067512992", "label": 3767, "func": ""}
{"index": "s925994985", "label": 3767, "func": ""}
{"index": "s209133542", "label": 2403, "func": ""}
{"index": "s376703273", "label": 2403, "func": ""}
{"index": "s190106229", "label": 2403, "func": ""}
{"index": "s901506967", "label": 2403, "func": ""}
{"index": "s987126550", "label": 2403, "func": ""}
{"index": "s179418482", "label": 2403, "func": ""}
{"index": "s302944916", "label": 2403, "func": ""}
{"index": "s620515636", "label": 2403, "func": ""}
{"index": "s176175215", "label": 2403, "func": ""}
{"index": "s381869148", "label": 2403, "func": ""}
{"index": "s578425069", "label": 2725, "func": ""}
{"index": "s307614327", "label": 2725, "func": ""}
{"index": "s745809392", "label": 2725, "func": ""}
{"index": "s691362604", "label": 2725, "func": ""}
{"index": "s275932859", "label": 2725, "func": ""}
{"index": "s845935946", "label": 2725, "func": ""}
{"index": "s947710144", "label": 2725, "func": ""}
{"index": "s267674020", "label": 2725, "func": ""}
{"index": "s579910761", "label": 2725, "func": ""}
{"index": "s585270374", "label": 2725, "func": ""}
{"index": "s272871720", "label": 3497, "func": ""}
{"index": "s757679954", "label": 3497, "func": ""}
{"index": "s619907660", "label": 3497, "func": ""}
{"index": "s611225090", "label": 3497, "func": ""}
{"index": "s337283263", "label": 3497, "func": ""}
{"index": "s157125509", "label": 3497, "func": ""}
{"index": "s230223715", "label": 3497, "func": ""}
{"index": "s180434988", "label": 3497, "func": ""}
{"index": "s220473110", "label": 3497, "func": ""}
{"index": "s212059046", "label": 3497, "func": ""}
{"index": "s354656254", "label": 2905, "func": ""}
{"index": "s675044699", "label": 2905, "func": ""}
{"index": "s599738660", "label": 2905, "func": ""}
{"index": "s197892842", "label": 2905, "func": ""}
{"index": "s353856160", "label": 2905, "func": ""}
{"index": "s819265391", "label": 2905, "func": ""}
{"index": "s344586236", "label": 2137, "func": ""}
{"index": "s033754641", "label": 2137, "func": ""}
{"index": "s756759967", "label": 689, "func": ""}
{"index": "s529738891", "label": 689, "func": ""}
{"index": "s108329088", "label": 689, "func": ""}
{"index": "s259321220", "label": 689, "func": ""}
{"index": "s197021778", "label": 3063, "func": ""}
{"index": "s056119988", "label": 3063, "func": ""}
{"index": "s334441048", "label": 3063, "func": ""}
{"index": "s191018047", "label": 3063, "func": ""}
{"index": "s985644405", "label": 3063, "func": ""}
{"index": "s573992707", "label": 3063, "func": ""}
{"index": "s835745519", "label": 3063, "func": ""}
{"index": "s746703520", "label": 3063, "func": ""}
{"index": "s972286801", "label": 3063, "func": ""}
{"index": "s482332943", "label": 3063, "func": ""}
{"index": "s767032773", "label": 3539, "func": ""}
{"index": "s605849016", "label": 2985, "func": ""}
{"index": "s409854091", "label": 2985, "func": ""}
{"index": "s830624593", "label": 2985, "func": ""}
{"index": "s500981246", "label": 2985, "func": ""}
{"index": "s943050655", "label": 2985, "func": ""}
{"index": "s365675017", "label": 2985, "func": ""}
{"index": "s057077680", "label": 2985, "func": ""}
{"index": "s535588873", "label": 2985, "func": ""}
{"index": "s644295304", "label": 2985, "func": ""}
{"index": "s935890654", "label": 2985, "func": ""}
{"index": "s268015958", "label": 1490, "func": ""}
{"index": "s475744330", "label": 2898, "func": ""}
{"index": "s382505874", "label": 2898, "func": ""}
{"index": "s393085765", "label": 2898, "func": ""}
{"index": "s338682015", "label": 2898, "func": ""}
{"index": "s986944132", "label": 2898, "func": ""}
{"index": "s218573876", "label": 2898, "func": ""}
{"index": "s703785384", "label": 2898, "func": ""}
{"index": "s395748163", "label": 2898, "func": ""}
{"index": "s175674115", "label": 2898, "func": ""}
{"index": "s237539787", "label": 2898, "func": ""}
{"index": "s067845418", "label": 553, "func": ""}
{"index": "s081314907", "label": 553, "func": ""}
{"index": "s740105927", "label": 553, "func": ""}
{"index": "s559693903", "label": 553, "func": ""}
{"index": "s360126656", "label": 553, "func": ""}
{"index": "s323945407", "label": 553, "func": ""}
{"index": "s481885907", "label": 553, "func": ""}
{"index": "s246791687", "label": 553, "func": ""}
{"index": "s419484187", "label": 553, "func": ""}
{"index": "s487879623", "label": 553, "func": ""}
{"index": "s705254489", "label": 154, "func": ""}
{"index": "s864936501", "label": 154, "func": ""}
{"index": "s243072228", "label": 154, "func": ""}
{"index": "s683254646", "label": 154, "func": ""}
{"index": "s059175409", "label": 154, "func": ""}
{"index": "s426017825", "label": 154, "func": ""}
{"index": "s096072518", "label": 154, "func": ""}
{"index": "s375782085", "label": 154, "func": ""}
{"index": "s207613195", "label": 154, "func": ""}
{"index": "s201520251", "label": 154, "func": ""}
{"index": "s807377294", "label": 3627, "func": ""}
{"index": "s176754340", "label": 3627, "func": ""}
{"index": "s234654167", "label": 3627, "func": ""}
{"index": "s130643326", "label": 3627, "func": ""}
{"index": "s927251217", "label": 3627, "func": ""}
{"index": "s474755641", "label": 3627, "func": ""}
{"index": "s581970584", "label": 3627, "func": ""}
{"index": "s048640568", "label": 3627, "func": ""}
{"index": "s928546710", "label": 3627, "func": ""}
{"index": "s417895733", "label": 3627, "func": ""}
{"index": "s688136263", "label": 447, "func": ""}
{"index": "s709063069", "label": 447, "func": ""}
{"index": "s914028808", "label": 447, "func": ""}
{"index": "s154038011", "label": 447, "func": ""}
{"index": "s385475583", "label": 447, "func": ""}
{"index": "s606577966", "label": 447, "func": ""}
{"index": "s969190135", "label": 447, "func": ""}
{"index": "s840904343", "label": 447, "func": ""}
{"index": "s771823427", "label": 447, "func": ""}
{"index": "s458120025", "label": 447, "func": ""}
{"index": "s077350248", "label": 3643, "func": ""}
{"index": "s169349059", "label": 3643, "func": ""}
{"index": "s714531607", "label": 3643, "func": ""}
{"index": "s659363245", "label": 3643, "func": ""}
{"index": "s946600355", "label": 3643, "func": ""}
{"index": "s733321814", "label": 3643, "func": ""}
{"index": "s778374033", "label": 3643, "func": ""}
{"index": "s539030054", "label": 3643, "func": ""}
{"index": "s437995112", "label": 3643, "func": ""}
{"index": "s119242561", "label": 3643, "func": ""}
{"index": "s954301015", "label": 1257, "func": ""}
{"index": "s816939086", "label": 3265, "func": ""}
{"index": "s424922603", "label": 3265, "func": ""}
{"index": "s960529208", "label": 3265, "func": ""}
{"index": "s405692690", "label": 3265, "func": ""}
{"index": "s696818168", "label": 3265, "func": ""}
{"index": "s416540910", "label": 3265, "func": ""}
{"index": "s718313585", "label": 3265, "func": ""}
{"index": "s155322254", "label": 3265, "func": ""}
{"index": "s754041275", "label": 3265, "func": ""}
{"index": "s208076631", "label": 3265, "func": ""}
{"index": "s988046908", "label": 1390, "func": ""}
{"index": "s521256703", "label": 1390, "func": ""}
{"index": "s020077737", "label": 1390, "func": ""}
{"index": "s606611155", "label": 1390, "func": ""}
{"index": "s098974421", "label": 1390, "func": ""}
{"index": "s979691752", "label": 1390, "func": ""}
{"index": "s008721775", "label": 1390, "func": ""}
{"index": "s200068346", "label": 1390, "func": ""}
{"index": "s633797058", "label": 1390, "func": ""}
{"index": "s302625059", "label": 1449, "func": ""}
{"index": "s690443989", "label": 1449, "func": ""}
{"index": "s088894717", "label": 1449, "func": ""}
{"index": "s501941684", "label": 1449, "func": ""}
{"index": "s153457728", "label": 1449, "func": ""}
{"index": "s535711072", "label": 1449, "func": ""}
{"index": "s468584476", "label": 1449, "func": ""}
{"index": "s141556512", "label": 1449, "func": ""}
{"index": "s193308594", "label": 1449, "func": ""}
{"index": "s875391325", "label": 1449, "func": ""}
{"index": "s725529249", "label": 783, "func": ""}
{"index": "s941325420", "label": 783, "func": ""}
{"index": "s194560335", "label": 783, "func": ""}
{"index": "s041055691", "label": 783, "func": ""}
{"index": "s902723112", "label": 783, "func": ""}
{"index": "s898612190", "label": 783, "func": ""}
{"index": "s552914812", "label": 783, "func": ""}
{"index": "s697050163", "label": 783, "func": ""}
{"index": "s731071369", "label": 783, "func": ""}
{"index": "s540396546", "label": 2475, "func": ""}
{"index": "s442627532", "label": 2475, "func": ""}
{"index": "s816008135", "label": 2475, "func": ""}
{"index": "s682167050", "label": 2475, "func": ""}
{"index": "s744044980", "label": 2475, "func": ""}
{"index": "s337033420", "label": 2475, "func": ""}
{"index": "s828725314", "label": 2475, "func": ""}
{"index": "s718629000", "label": 2475, "func": ""}
{"index": "s278255060", "label": 2475, "func": ""}
{"index": "s444944737", "label": 2475, "func": ""}
{"index": "s874003163", "label": 3495, "func": ""}
{"index": "s444848443", "label": 3495, "func": ""}
{"index": "s812260301", "label": 3495, "func": ""}
{"index": "s857880431", "label": 3495, "func": ""}
{"index": "s475516482", "label": 3495, "func": ""}
{"index": "s540120780", "label": 3495, "func": ""}
{"index": "s394022779", "label": 3495, "func": ""}
{"index": "s701443320", "label": 3495, "func": ""}
{"index": "s082849065", "label": 3495, "func": ""}
{"index": "s805666531", "label": 3495, "func": ""}
{"index": "s644238205", "label": 947, "func": ""}
{"index": "s094535355", "label": 947, "func": ""}
{"index": "s704541101", "label": 947, "func": ""}
{"index": "s821633169", "label": 947, "func": ""}
{"index": "s162341478", "label": 947, "func": ""}
{"index": "s328229370", "label": 947, "func": ""}
{"index": "s190558263", "label": 2769, "func": ""}
{"index": "s727953616", "label": 2769, "func": ""}
{"index": "s785908693", "label": 2769, "func": ""}
{"index": "s281208247", "label": 2769, "func": ""}
{"index": "s929620837", "label": 2769, "func": ""}
{"index": "s110697601", "label": 2769, "func": ""}
{"index": "s118950373", "label": 2769, "func": ""}
{"index": "s769061018", "label": 2769, "func": ""}
{"index": "s403002037", "label": 2769, "func": ""}
{"index": "s204282188", "label": 2769, "func": ""}
{"index": "s516024898", "label": 3611, "func": ""}
{"index": "s520747083", "label": 3611, "func": ""}
{"index": "s188788288", "label": 3611, "func": ""}
{"index": "s645292221", "label": 3611, "func": ""}
{"index": "s465413423", "label": 3611, "func": ""}
{"index": "s560753930", "label": 3611, "func": ""}
{"index": "s671265761", "label": 3611, "func": ""}
{"index": "s209814778", "label": 3611, "func": ""}
{"index": "s983266002", "label": 3611, "func": ""}
{"index": "s399519867", "label": 3611, "func": ""}
{"index": "s709340466", "label": 1627, "func": ""}
{"index": "s762921330", "label": 1627, "func": ""}
{"index": "s068212183", "label": 1627, "func": ""}
{"index": "s804990691", "label": 1627, "func": ""}
{"index": "s113365324", "label": 1627, "func": ""}
{"index": "s025484951", "label": 1627, "func": ""}
{"index": "s460328041", "label": 1627, "func": ""}
{"index": "s757041047", "label": 1627, "func": ""}
{"index": "s265623755", "label": 2578, "func": ""}
{"index": "s875086041", "label": 2578, "func": ""}
{"index": "s780364307", "label": 2578, "func": ""}
{"index": "s271204635", "label": 2578, "func": ""}
{"index": "s301967168", "label": 2578, "func": ""}
{"index": "s899404244", "label": 2578, "func": ""}
{"index": "s914735480", "label": 2578, "func": ""}
{"index": "s188413487", "label": 2578, "func": ""}
{"index": "s943781850", "label": 2578, "func": ""}
{"index": "s406332163", "label": 2578, "func": ""}
{"index": "s518165142", "label": 2390, "func": ""}
{"index": "s253150209", "label": 2390, "func": ""}
{"index": "s824775742", "label": 2390, "func": ""}
{"index": "s296679206", "label": 2390, "func": ""}
{"index": "s670031746", "label": 2390, "func": ""}
{"index": "s108296551", "label": 2390, "func": ""}
{"index": "s414684908", "label": 2390, "func": ""}
{"index": "s782077971", "label": 2390, "func": ""}
{"index": "s617159492", "label": 2390, "func": ""}
{"index": "s554078260", "label": 2390, "func": ""}
{"index": "s056049408", "label": 1341, "func": ""}
{"index": "s036526751", "label": 1341, "func": ""}
{"index": "s608411861", "label": 1341, "func": ""}
{"index": "s386968964", "label": 1341, "func": ""}
{"index": "s938748012", "label": 1341, "func": ""}
{"index": "s643937992", "label": 1341, "func": ""}
{"index": "s031226869", "label": 1341, "func": ""}
{"index": "s757132241", "label": 1341, "func": ""}
{"index": "s320139641", "label": 1341, "func": ""}
{"index": "s124862006", "label": 1341, "func": ""}
{"index": "s823990419", "label": 1538, "func": ""}
{"index": "s121648628", "label": 1538, "func": ""}
{"index": "s414180938", "label": 1538, "func": ""}
{"index": "s501366520", "label": 1538, "func": ""}
{"index": "s744908107", "label": 1538, "func": ""}
{"index": "s752921378", "label": 1538, "func": ""}
{"index": "s723025269", "label": 1538, "func": ""}
{"index": "s497218971", "label": 1538, "func": ""}
{"index": "s747823502", "label": 1538, "func": ""}
{"index": "s547620654", "label": 1538, "func": ""}
{"index": "s152870227", "label": 1457, "func": ""}
{"index": "s546246827", "label": 1457, "func": ""}
{"index": "s483593996", "label": 1457, "func": ""}
{"index": "s752674924", "label": 1457, "func": ""}
{"index": "s559907681", "label": 1457, "func": ""}
{"index": "s150746778", "label": 1457, "func": ""}
{"index": "s408732514", "label": 1457, "func": ""}
{"index": "s734004240", "label": 1457, "func": ""}
{"index": "s813847909", "label": 1457, "func": ""}
{"index": "s251188564", "label": 1457, "func": ""}
{"index": "s454893721", "label": 4014, "func": ""}
{"index": "s913362293", "label": 4014, "func": ""}
{"index": "s687431609", "label": 4014, "func": ""}
{"index": "s466563506", "label": 4014, "func": ""}
{"index": "s273636318", "label": 4014, "func": ""}
{"index": "s957658057", "label": 4014, "func": ""}
{"index": "s640141868", "label": 4014, "func": ""}
{"index": "s492512195", "label": 4014, "func": ""}
{"index": "s606329034", "label": 4014, "func": ""}
{"index": "s347265769", "label": 4014, "func": ""}
{"index": "s402230432", "label": 2339, "func": ""}
{"index": "s859408478", "label": 2339, "func": ""}
{"index": "s665589084", "label": 631, "func": ""}
{"index": "s278466365", "label": 631, "func": ""}
{"index": "s263979954", "label": 631, "func": ""}
{"index": "s437211425", "label": 631, "func": ""}
{"index": "s769597659", "label": 631, "func": ""}
{"index": "s682794823", "label": 631, "func": ""}
{"index": "s516205560", "label": 631, "func": ""}
{"index": "s781856368", "label": 631, "func": ""}
{"index": "s489050693", "label": 631, "func": ""}
{"index": "s661131255", "label": 631, "func": ""}
{"index": "s600987342", "label": 1810, "func": ""}
{"index": "s887803377", "label": 1810, "func": ""}
{"index": "s230880648", "label": 1810, "func": ""}
{"index": "s206319607", "label": 1810, "func": ""}
{"index": "s766762408", "label": 1810, "func": ""}
{"index": "s206456042", "label": 3721, "func": ""}
{"index": "s374193059", "label": 3721, "func": ""}
{"index": "s826735242", "label": 3721, "func": ""}
{"index": "s015421743", "label": 3721, "func": ""}
{"index": "s739870008", "label": 3721, "func": ""}
{"index": "s850206806", "label": 3721, "func": ""}
{"index": "s376283718", "label": 3721, "func": ""}
{"index": "s740804173", "label": 3721, "func": ""}
{"index": "s928388977", "label": 3721, "func": ""}
{"index": "s577695378", "label": 3721, "func": ""}
{"index": "s613427104", "label": 3426, "func": ""}
{"index": "s767199650", "label": 3426, "func": ""}
{"index": "s763381150", "label": 3426, "func": ""}
{"index": "s339160205", "label": 3426, "func": ""}
{"index": "s647705518", "label": 3426, "func": ""}
{"index": "s326805486", "label": 3426, "func": ""}
{"index": "s337701059", "label": 3426, "func": ""}
{"index": "s747476804", "label": 3426, "func": ""}
{"index": "s293609323", "label": 3426, "func": ""}
{"index": "s755612653", "label": 3426, "func": ""}
{"index": "s500282960", "label": 2574, "func": ""}
{"index": "s284236771", "label": 2574, "func": ""}
{"index": "s894984475", "label": 2574, "func": ""}
{"index": "s383195196", "label": 2574, "func": ""}
{"index": "s153031357", "label": 2574, "func": ""}
{"index": "s428961158", "label": 2574, "func": ""}
{"index": "s347036204", "label": 2574, "func": ""}
{"index": "s084702344", "label": 2574, "func": ""}
{"index": "s651468372", "label": 2574, "func": ""}
{"index": "s548507158", "label": 2574, "func": ""}
{"index": "s307358043", "label": 744, "func": ""}
{"index": "s700411376", "label": 744, "func": ""}
{"index": "s633688653", "label": 744, "func": ""}
{"index": "s243177039", "label": 744, "func": ""}
{"index": "s193911121", "label": 744, "func": ""}
{"index": "s954097638", "label": 744, "func": ""}
{"index": "s619739140", "label": 744, "func": ""}
{"index": "s192883304", "label": 744, "func": ""}
{"index": "s182219688", "label": 744, "func": ""}
{"index": "s676407469", "label": 744, "func": ""}
{"index": "s664528364", "label": 448, "func": ""}
{"index": "s219150443", "label": 448, "func": ""}
{"index": "s595543866", "label": 448, "func": ""}
{"index": "s647109273", "label": 448, "func": ""}
{"index": "s861592642", "label": 448, "func": ""}
{"index": "s671819721", "label": 448, "func": ""}
{"index": "s946190896", "label": 448, "func": ""}
{"index": "s198205581", "label": 448, "func": ""}
{"index": "s466106326", "label": 448, "func": ""}
{"index": "s396732664", "label": 448, "func": ""}
{"index": "s550384422", "label": 1265, "func": ""}
{"index": "s287990305", "label": 1265, "func": ""}
{"index": "s237863604", "label": 686, "func": ""}
{"index": "s073693336", "label": 686, "func": ""}
{"index": "s449206239", "label": 686, "func": ""}
{"index": "s036625344", "label": 686, "func": ""}
{"index": "s560205107", "label": 686, "func": ""}
{"index": "s391012197", "label": 686, "func": ""}
{"index": "s463455052", "label": 686, "func": ""}
{"index": "s980831486", "label": 686, "func": ""}
{"index": "s171320953", "label": 686, "func": ""}
{"index": "s933029493", "label": 686, "func": ""}
{"index": "s559902019", "label": 2276, "func": ""}
{"index": "s539609936", "label": 2276, "func": ""}
{"index": "s191686628", "label": 2276, "func": ""}
{"index": "s769724974", "label": 2276, "func": ""}
{"index": "s984711393", "label": 2276, "func": ""}
{"index": "s038006109", "label": 2276, "func": ""}
{"index": "s574475446", "label": 2276, "func": ""}
{"index": "s548990033", "label": 2276, "func": ""}
{"index": "s637009276", "label": 2276, "func": ""}
{"index": "s419691403", "label": 2276, "func": ""}
{"index": "s748247168", "label": 666, "func": ""}
{"index": "s899552913", "label": 666, "func": ""}
{"index": "s823914756", "label": 661, "func": ""}
{"index": "s386114987", "label": 661, "func": ""}
{"index": "s520235881", "label": 661, "func": ""}
{"index": "s945393789", "label": 1444, "func": ""}
{"index": "s697595022", "label": 2621, "func": ""}
{"index": "s178649496", "label": 2621, "func": ""}
{"index": "s616655312", "label": 2621, "func": ""}
{"index": "s584363035", "label": 2621, "func": ""}
{"index": "s434425073", "label": 2621, "func": ""}
{"index": "s698050758", "label": 2621, "func": ""}
{"index": "s576634481", "label": 2621, "func": ""}
{"index": "s698583272", "label": 2621, "func": ""}
{"index": "s515302672", "label": 2621, "func": ""}
{"index": "s488022052", "label": 2621, "func": ""}
{"index": "s121641216", "label": 2236, "func": ""}
{"index": "s132769801", "label": 2236, "func": ""}
{"index": "s486667941", "label": 2236, "func": ""}
{"index": "s684768996", "label": 2236, "func": ""}
{"index": "s490839321", "label": 2151, "func": ""}
{"index": "s588864306", "label": 3980, "func": ""}
{"index": "s777872431", "label": 3980, "func": ""}
{"index": "s152608533", "label": 3980, "func": ""}
{"index": "s394445168", "label": 3186, "func": ""}
{"index": "s634976260", "label": 3186, "func": ""}
{"index": "s102657387", "label": 3186, "func": ""}
{"index": "s050419209", "label": 3186, "func": ""}
{"index": "s693302530", "label": 3186, "func": ""}
{"index": "s863918816", "label": 3186, "func": ""}
{"index": "s710816560", "label": 3186, "func": ""}
{"index": "s835008500", "label": 3186, "func": ""}
{"index": "s514296533", "label": 3186, "func": ""}
{"index": "s468871279", "label": 3186, "func": ""}
{"index": "s204493909", "label": 3045, "func": ""}
{"index": "s130289883", "label": 3045, "func": ""}
{"index": "s744580283", "label": 3045, "func": ""}
{"index": "s728519907", "label": 3045, "func": ""}
{"index": "s557580726", "label": 3045, "func": ""}
{"index": "s663193453", "label": 3045, "func": ""}
{"index": "s316595220", "label": 3045, "func": ""}
{"index": "s079418136", "label": 3045, "func": ""}
{"index": "s865199232", "label": 3045, "func": ""}
{"index": "s718324387", "label": 3045, "func": ""}
{"index": "s739310754", "label": 2700, "func": ""}
{"index": "s297169195", "label": 2700, "func": ""}
{"index": "s295223812", "label": 2700, "func": ""}
{"index": "s472668325", "label": 2700, "func": ""}
{"index": "s744854463", "label": 2700, "func": ""}
{"index": "s627486394", "label": 2700, "func": ""}
{"index": "s133252340", "label": 2700, "func": ""}
{"index": "s275747041", "label": 2700, "func": ""}
{"index": "s654170695", "label": 2700, "func": ""}
{"index": "s394989493", "label": 2700, "func": ""}
{"index": "s842156028", "label": 760, "func": ""}
{"index": "s036489005", "label": 760, "func": ""}
{"index": "s493926941", "label": 760, "func": ""}
{"index": "s366790475", "label": 760, "func": ""}
{"index": "s389166000", "label": 760, "func": ""}
{"index": "s761889140", "label": 760, "func": ""}
{"index": "s843487057", "label": 760, "func": ""}
{"index": "s676338772", "label": 760, "func": ""}
{"index": "s584651149", "label": 760, "func": ""}
{"index": "s151768236", "label": 760, "func": ""}
{"index": "s551531220", "label": 2816, "func": ""}
{"index": "s599002063", "label": 2816, "func": ""}
{"index": "s135984927", "label": 2816, "func": ""}
{"index": "s083082032", "label": 2816, "func": ""}
{"index": "s747133914", "label": 2816, "func": ""}
{"index": "s930219821", "label": 2816, "func": ""}
{"index": "s821755280", "label": 2816, "func": ""}
{"index": "s578255148", "label": 2816, "func": ""}
{"index": "s631184135", "label": 2816, "func": ""}
{"index": "s497734960", "label": 1937, "func": ""}
{"index": "s906871599", "label": 1937, "func": ""}
{"index": "s553359244", "label": 3059, "func": ""}
{"index": "s345711175", "label": 3059, "func": ""}
{"index": "s255615864", "label": 3059, "func": ""}
{"index": "s841325351", "label": 3059, "func": ""}
{"index": "s889846605", "label": 3059, "func": ""}
{"index": "s049337443", "label": 3059, "func": ""}
{"index": "s855143444", "label": 3059, "func": ""}
{"index": "s786796063", "label": 3059, "func": ""}
{"index": "s500852562", "label": 3059, "func": ""}
{"index": "s758153933", "label": 3059, "func": ""}
{"index": "s212982166", "label": 172, "func": ""}
{"index": "s836087321", "label": 172, "func": ""}
{"index": "s022246574", "label": 172, "func": ""}
{"index": "s906919350", "label": 3552, "func": ""}
{"index": "s673553720", "label": 3552, "func": ""}
{"index": "s462508162", "label": 3552, "func": ""}
{"index": "s534053499", "label": 3552, "func": ""}
{"index": "s711899804", "label": 3552, "func": ""}
{"index": "s632032070", "label": 3552, "func": ""}
{"index": "s012062748", "label": 3552, "func": ""}
{"index": "s368073704", "label": 3552, "func": ""}
{"index": "s462089320", "label": 3552, "func": ""}
{"index": "s696590490", "label": 3552, "func": ""}
{"index": "s599763679", "label": 3264, "func": ""}
{"index": "s272126220", "label": 3264, "func": ""}
{"index": "s825051006", "label": 3264, "func": ""}
{"index": "s903473293", "label": 3264, "func": ""}
{"index": "s167114013", "label": 3264, "func": ""}
{"index": "s729639018", "label": 3264, "func": ""}
{"index": "s183796111", "label": 3264, "func": ""}
{"index": "s512794598", "label": 3264, "func": ""}
{"index": "s922219667", "label": 3264, "func": ""}
{"index": "s769541952", "label": 3264, "func": ""}
{"index": "s648410951", "label": 1809, "func": ""}
{"index": "s551043747", "label": 1809, "func": ""}
{"index": "s453801711", "label": 1809, "func": ""}
{"index": "s716141578", "label": 1809, "func": ""}
{"index": "s768552561", "label": 1809, "func": ""}
{"index": "s806816839", "label": 1809, "func": ""}
{"index": "s176258430", "label": 1809, "func": ""}
{"index": "s749533852", "label": 1809, "func": ""}
{"index": "s097595871", "label": 1809, "func": ""}
{"index": "s988566818", "label": 1809, "func": ""}
{"index": "s601479475", "label": 1137, "func": ""}
{"index": "s688737996", "label": 1137, "func": ""}
{"index": "s895737798", "label": 1137, "func": ""}
{"index": "s590014834", "label": 1137, "func": ""}
{"index": "s474225736", "label": 1137, "func": ""}
{"index": "s207054175", "label": 1137, "func": ""}
{"index": "s639593717", "label": 1137, "func": ""}
{"index": "s616966156", "label": 1137, "func": ""}
{"index": "s687246613", "label": 1137, "func": ""}
{"index": "s291697725", "label": 1137, "func": ""}
{"index": "s949599783", "label": 1677, "func": ""}
{"index": "s807965962", "label": 1677, "func": ""}
{"index": "s149804822", "label": 1677, "func": ""}
{"index": "s869174412", "label": 1677, "func": ""}
{"index": "s681198076", "label": 1677, "func": ""}
{"index": "s430859430", "label": 1677, "func": ""}
{"index": "s365064005", "label": 1677, "func": ""}
{"index": "s610240898", "label": 1677, "func": ""}
{"index": "s850002815", "label": 1677, "func": ""}
{"index": "s842809263", "label": 2634, "func": ""}
{"index": "s603790422", "label": 2634, "func": ""}
{"index": "s763340926", "label": 2634, "func": ""}
{"index": "s053707511", "label": 2634, "func": ""}
{"index": "s490279960", "label": 2634, "func": ""}
{"index": "s882283270", "label": 2634, "func": ""}
{"index": "s523291323", "label": 2634, "func": ""}
{"index": "s676139598", "label": 2634, "func": ""}
{"index": "s348612777", "label": 2634, "func": ""}
{"index": "s759809185", "label": 2634, "func": ""}
{"index": "s472307963", "label": 2941, "func": ""}
{"index": "s014284157", "label": 2941, "func": ""}
{"index": "s492794345", "label": 2941, "func": ""}
{"index": "s717134553", "label": 2941, "func": ""}
{"index": "s308217566", "label": 2941, "func": ""}
{"index": "s640700545", "label": 2941, "func": ""}
{"index": "s635513696", "label": 2941, "func": ""}
{"index": "s329632381", "label": 2941, "func": ""}
{"index": "s734763034", "label": 2941, "func": ""}
{"index": "s175357831", "label": 1135, "func": ""}
{"index": "s345442512", "label": 1135, "func": ""}
{"index": "s881257484", "label": 713, "func": ""}
{"index": "s614157762", "label": 713, "func": ""}
{"index": "s861116481", "label": 713, "func": ""}
{"index": "s407345873", "label": 713, "func": ""}
{"index": "s600745076", "label": 713, "func": ""}
{"index": "s036082810", "label": 713, "func": ""}
{"index": "s706940555", "label": 713, "func": ""}
{"index": "s640519302", "label": 713, "func": ""}
{"index": "s515119034", "label": 713, "func": ""}
{"index": "s973567708", "label": 713, "func": ""}
{"index": "s693053428", "label": 1976, "func": ""}
{"index": "s246861311", "label": 1976, "func": ""}
{"index": "s824094920", "label": 1976, "func": ""}
{"index": "s168104902", "label": 715, "func": ""}
{"index": "s817954669", "label": 715, "func": ""}
{"index": "s739940552", "label": 100, "func": ""}
{"index": "s655089122", "label": 100, "func": ""}
{"index": "s401184778", "label": 100, "func": ""}
{"index": "s274966382", "label": 100, "func": ""}
{"index": "s151179867", "label": 100, "func": ""}
{"index": "s731479207", "label": 100, "func": ""}
{"index": "s040662386", "label": 100, "func": ""}
{"index": "s507702177", "label": 100, "func": ""}
{"index": "s554272960", "label": 100, "func": ""}
{"index": "s408666557", "label": 100, "func": ""}
{"index": "s577596667", "label": 2273, "func": ""}
{"index": "s453465520", "label": 2273, "func": ""}
{"index": "s882893099", "label": 2273, "func": ""}
{"index": "s237946672", "label": 2273, "func": ""}
{"index": "s108233766", "label": 2273, "func": ""}
{"index": "s584797186", "label": 2273, "func": ""}
{"index": "s320798491", "label": 2273, "func": ""}
{"index": "s413844835", "label": 2273, "func": ""}
{"index": "s437941247", "label": 2273, "func": ""}
{"index": "s476860675", "label": 2273, "func": ""}
{"index": "s869627706", "label": 3458, "func": ""}
{"index": "s919870441", "label": 3458, "func": ""}
{"index": "s928474670", "label": 3458, "func": ""}
{"index": "s292482769", "label": 3458, "func": ""}
{"index": "s077256949", "label": 3458, "func": ""}
{"index": "s072697684", "label": 3458, "func": ""}
{"index": "s981205440", "label": 3458, "func": ""}
{"index": "s749420877", "label": 3458, "func": ""}
{"index": "s434540622", "label": 3458, "func": ""}
{"index": "s866664413", "label": 3458, "func": ""}
{"index": "s206730381", "label": 665, "func": ""}
{"index": "s681762167", "label": 665, "func": ""}
{"index": "s211282739", "label": 665, "func": ""}
{"index": "s716130285", "label": 1487, "func": ""}
{"index": "s395243126", "label": 1487, "func": ""}
{"index": "s478525519", "label": 1487, "func": ""}
{"index": "s294094609", "label": 2090, "func": ""}
{"index": "s485737550", "label": 1203, "func": ""}
{"index": "s340368780", "label": 2462, "func": ""}
{"index": "s308061519", "label": 2462, "func": ""}
{"index": "s035356146", "label": 2462, "func": ""}
{"index": "s674551193", "label": 2462, "func": ""}
{"index": "s061386913", "label": 2462, "func": ""}
{"index": "s633099169", "label": 2462, "func": ""}
{"index": "s967557225", "label": 2462, "func": ""}
{"index": "s395987743", "label": 2462, "func": ""}
{"index": "s260692971", "label": 1255, "func": ""}
{"index": "s341260390", "label": 1255, "func": ""}
{"index": "s408455298", "label": 3979, "func": ""}
{"index": "s860772163", "label": 2824, "func": ""}
{"index": "s594865260", "label": 2824, "func": ""}
{"index": "s707922293", "label": 2824, "func": ""}
{"index": "s165903951", "label": 2824, "func": ""}
{"index": "s424130057", "label": 2824, "func": ""}
{"index": "s635203428", "label": 2824, "func": ""}
{"index": "s683836852", "label": 2824, "func": ""}
{"index": "s122926335", "label": 2824, "func": ""}
{"index": "s359440916", "label": 2824, "func": ""}
{"index": "s712637307", "label": 2824, "func": ""}
{"index": "s039803839", "label": 3272, "func": ""}
{"index": "s814986994", "label": 3272, "func": ""}
{"index": "s541116096", "label": 3272, "func": ""}
{"index": "s121174500", "label": 3272, "func": ""}
{"index": "s332409454", "label": 3272, "func": ""}
{"index": "s012959185", "label": 3272, "func": ""}
{"index": "s037392005", "label": 3272, "func": ""}
{"index": "s932896842", "label": 3272, "func": ""}
{"index": "s968064103", "label": 3272, "func": ""}
{"index": "s392776036", "label": 3272, "func": ""}
{"index": "s026330035", "label": 2728, "func": ""}
{"index": "s636488319", "label": 2728, "func": ""}
{"index": "s801119097", "label": 2728, "func": ""}
{"index": "s188650553", "label": 2728, "func": ""}
{"index": "s135810116", "label": 2728, "func": ""}
{"index": "s894395282", "label": 2728, "func": ""}
{"index": "s640178450", "label": 2728, "func": ""}
{"index": "s496547910", "label": 2728, "func": ""}
{"index": "s297498717", "label": 2728, "func": ""}
{"index": "s234281782", "label": 2728, "func": ""}
{"index": "s251969630", "label": 2358, "func": ""}
{"index": "s972269893", "label": 2358, "func": ""}
{"index": "s094764021", "label": 2358, "func": ""}
{"index": "s422029441", "label": 2358, "func": ""}
{"index": "s112883394", "label": 2358, "func": ""}
{"index": "s368849948", "label": 498, "func": ""}
{"index": "s102664411", "label": 498, "func": ""}
{"index": "s884012537", "label": 1696, "func": ""}
{"index": "s078884080", "label": 1696, "func": ""}
{"index": "s329948724", "label": 1696, "func": ""}
{"index": "s911961520", "label": 1696, "func": ""}
{"index": "s537110432", "label": 1696, "func": ""}
{"index": "s861356741", "label": 1696, "func": ""}
{"index": "s092706228", "label": 1696, "func": ""}
{"index": "s212391344", "label": 1696, "func": ""}
{"index": "s802498843", "label": 1696, "func": ""}
{"index": "s987663660", "label": 1696, "func": ""}
{"index": "s512529095", "label": 2897, "func": ""}
{"index": "s275957260", "label": 2897, "func": ""}
{"index": "s937363424", "label": 2897, "func": ""}
{"index": "s265743294", "label": 2897, "func": ""}
{"index": "s097968696", "label": 2897, "func": ""}
{"index": "s105181816", "label": 2897, "func": ""}
{"index": "s682291829", "label": 2897, "func": ""}
{"index": "s486417562", "label": 2897, "func": ""}
{"index": "s871093895", "label": 2897, "func": ""}
{"index": "s891311403", "label": 2897, "func": ""}
{"index": "s401737871", "label": 3345, "func": ""}
{"index": "s131307302", "label": 3345, "func": ""}
{"index": "s870455229", "label": 3345, "func": ""}
{"index": "s000300458", "label": 3345, "func": ""}
{"index": "s773335366", "label": 3345, "func": ""}
{"index": "s498687877", "label": 3345, "func": ""}
{"index": "s995629441", "label": 3345, "func": ""}
{"index": "s655902237", "label": 3345, "func": ""}
{"index": "s372348179", "label": 3345, "func": ""}
{"index": "s882111848", "label": 3345, "func": ""}
{"index": "s751599959", "label": 484, "func": ""}
{"index": "s354064674", "label": 484, "func": ""}
{"index": "s885893044", "label": 484, "func": ""}
{"index": "s030551614", "label": 484, "func": ""}
{"index": "s856658021", "label": 484, "func": ""}
{"index": "s029929713", "label": 484, "func": ""}
{"index": "s910290891", "label": 484, "func": ""}
{"index": "s121681364", "label": 484, "func": ""}
{"index": "s102470804", "label": 484, "func": ""}
{"index": "s182957441", "label": 484, "func": ""}
{"index": "s337858220", "label": 3583, "func": ""}
{"index": "s703573524", "label": 3583, "func": ""}
{"index": "s956172496", "label": 3583, "func": ""}
{"index": "s845421587", "label": 3583, "func": ""}
{"index": "s758927407", "label": 3583, "func": ""}
{"index": "s208918498", "label": 3583, "func": ""}
{"index": "s519584221", "label": 3583, "func": ""}
{"index": "s133880490", "label": 3583, "func": ""}
{"index": "s700892028", "label": 3583, "func": ""}
{"index": "s566021344", "label": 3583, "func": ""}
{"index": "s871405206", "label": 2686, "func": ""}
{"index": "s383260866", "label": 2686, "func": ""}
{"index": "s758823013", "label": 2686, "func": ""}
{"index": "s754222226", "label": 2686, "func": ""}
{"index": "s879277055", "label": 2686, "func": ""}
{"index": "s936671282", "label": 2686, "func": ""}
{"index": "s572538232", "label": 2686, "func": ""}
{"index": "s915545555", "label": 2686, "func": ""}
{"index": "s263265235", "label": 2686, "func": ""}
{"index": "s073129926", "label": 2686, "func": ""}
{"index": "s483379590", "label": 1416, "func": ""}
{"index": "s419575406", "label": 1416, "func": ""}
{"index": "s421332917", "label": 1416, "func": ""}
{"index": "s598739988", "label": 1416, "func": ""}
{"index": "s079850679", "label": 1416, "func": ""}
{"index": "s674577404", "label": 1416, "func": ""}
{"index": "s592823415", "label": 1416, "func": ""}
{"index": "s171090780", "label": 1416, "func": ""}
{"index": "s116701356", "label": 1416, "func": ""}
{"index": "s016587262", "label": 1416, "func": ""}
{"index": "s112836476", "label": 3923, "func": ""}
{"index": "s535492250", "label": 3923, "func": ""}
{"index": "s857382250", "label": 3923, "func": ""}
{"index": "s237936420", "label": 3923, "func": ""}
{"index": "s267951853", "label": 152, "func": ""}
{"index": "s122735728", "label": 152, "func": ""}
{"index": "s592202546", "label": 152, "func": ""}
{"index": "s531580311", "label": 152, "func": ""}
{"index": "s477863392", "label": 152, "func": ""}
{"index": "s920221650", "label": 152, "func": ""}
{"index": "s665822513", "label": 152, "func": ""}
{"index": "s366179451", "label": 152, "func": ""}
{"index": "s083839754", "label": 152, "func": ""}
{"index": "s164076690", "label": 152, "func": ""}
{"index": "s762457710", "label": 1473, "func": ""}
{"index": "s001658591", "label": 1473, "func": ""}
{"index": "s726660001", "label": 1473, "func": ""}
{"index": "s058020777", "label": 1473, "func": ""}
{"index": "s152941506", "label": 1473, "func": ""}
{"index": "s022267542", "label": 1473, "func": ""}
{"index": "s853355612", "label": 1473, "func": ""}
{"index": "s973220719", "label": 1473, "func": ""}
{"index": "s457425035", "label": 1473, "func": ""}
{"index": "s958937791", "label": 1473, "func": ""}
{"index": "s593757848", "label": 23, "func": ""}
{"index": "s601484652", "label": 23, "func": ""}
{"index": "s354742095", "label": 23, "func": ""}
{"index": "s457146733", "label": 23, "func": ""}
{"index": "s555846414", "label": 23, "func": ""}
{"index": "s305468053", "label": 23, "func": ""}
{"index": "s283476227", "label": 23, "func": ""}
{"index": "s362046872", "label": 23, "func": ""}
{"index": "s296255066", "label": 23, "func": ""}
{"index": "s594588933", "label": 23, "func": ""}
{"index": "s717280371", "label": 2719, "func": ""}
{"index": "s098843300", "label": 2719, "func": ""}
{"index": "s897666599", "label": 2719, "func": ""}
{"index": "s274704290", "label": 2719, "func": ""}
{"index": "s232663864", "label": 2719, "func": ""}
{"index": "s383095576", "label": 2719, "func": ""}
{"index": "s613414929", "label": 2719, "func": ""}
{"index": "s899575976", "label": 2719, "func": ""}
{"index": "s163593893", "label": 2719, "func": ""}
{"index": "s712056569", "label": 2719, "func": ""}
{"index": "s334321692", "label": 2283, "func": ""}
{"index": "s683273767", "label": 2283, "func": ""}
{"index": "s802614889", "label": 2283, "func": ""}
{"index": "s172177502", "label": 2283, "func": ""}
{"index": "s990946771", "label": 2283, "func": ""}
{"index": "s806630724", "label": 2283, "func": ""}
{"index": "s895939212", "label": 2283, "func": ""}
{"index": "s579029254", "label": 2283, "func": ""}
{"index": "s985533283", "label": 2283, "func": ""}
{"index": "s052208872", "label": 2283, "func": ""}
{"index": "s185462976", "label": 3360, "func": ""}
{"index": "s205282365", "label": 3360, "func": ""}
{"index": "s004837205", "label": 3360, "func": ""}
{"index": "s704139653", "label": 3360, "func": ""}
{"index": "s663905416", "label": 3360, "func": ""}
{"index": "s270626463", "label": 3360, "func": ""}
{"index": "s484272258", "label": 3360, "func": ""}
{"index": "s408083164", "label": 3360, "func": ""}
{"index": "s959689396", "label": 3360, "func": ""}
{"index": "s625533506", "label": 3360, "func": ""}
{"index": "s052172207", "label": 2795, "func": ""}
{"index": "s797303369", "label": 2795, "func": ""}
{"index": "s306331282", "label": 2795, "func": ""}
{"index": "s031260738", "label": 2795, "func": ""}
{"index": "s729857874", "label": 2795, "func": ""}
{"index": "s914571776", "label": 2795, "func": ""}
{"index": "s911325789", "label": 2795, "func": ""}
{"index": "s963321762", "label": 2795, "func": ""}
{"index": "s029429121", "label": 2795, "func": ""}
{"index": "s934199111", "label": 2795, "func": ""}
{"index": "s520305192", "label": 683, "func": ""}
{"index": "s708548855", "label": 683, "func": ""}
{"index": "s784819035", "label": 683, "func": ""}
{"index": "s299266844", "label": 683, "func": ""}
{"index": "s020852941", "label": 683, "func": ""}
{"index": "s389608592", "label": 683, "func": ""}
{"index": "s726055533", "label": 683, "func": ""}
{"index": "s623292420", "label": 683, "func": ""}
{"index": "s482657922", "label": 592, "func": ""}
{"index": "s001397474", "label": 592, "func": ""}
{"index": "s565474856", "label": 592, "func": ""}
{"index": "s634972467", "label": 592, "func": ""}
{"index": "s329646171", "label": 592, "func": ""}
{"index": "s177063439", "label": 592, "func": ""}
{"index": "s390379715", "label": 592, "func": ""}
{"index": "s783119354", "label": 592, "func": ""}
{"index": "s820847747", "label": 592, "func": ""}
{"index": "s579964562", "label": 592, "func": ""}
{"index": "s977816596", "label": 3131, "func": ""}
{"index": "s824812283", "label": 3131, "func": ""}
{"index": "s093342031", "label": 3131, "func": ""}
{"index": "s263380358", "label": 3131, "func": ""}
{"index": "s384904464", "label": 3131, "func": ""}
{"index": "s701014657", "label": 3131, "func": ""}
{"index": "s768672945", "label": 3131, "func": ""}
{"index": "s472130855", "label": 3131, "func": ""}
{"index": "s677025750", "label": 3131, "func": ""}
{"index": "s891902233", "label": 3131, "func": ""}
{"index": "s510148520", "label": 3738, "func": ""}
{"index": "s462693389", "label": 3738, "func": ""}
{"index": "s628961070", "label": 3738, "func": ""}
{"index": "s530879751", "label": 3738, "func": ""}
{"index": "s375664699", "label": 3738, "func": ""}
{"index": "s020523987", "label": 3738, "func": ""}
{"index": "s234357192", "label": 3738, "func": ""}
{"index": "s296584277", "label": 3738, "func": ""}
{"index": "s740875503", "label": 3738, "func": ""}
{"index": "s332282581", "label": 3738, "func": ""}
{"index": "s298781688", "label": 3380, "func": ""}
{"index": "s281035464", "label": 3380, "func": ""}
{"index": "s117370450", "label": 3380, "func": ""}
{"index": "s529830478", "label": 3380, "func": ""}
{"index": "s209392160", "label": 3380, "func": ""}
{"index": "s955337944", "label": 3380, "func": ""}
{"index": "s000231851", "label": 3380, "func": ""}
{"index": "s775517848", "label": 3380, "func": ""}
{"index": "s729788606", "label": 3380, "func": ""}
{"index": "s375649165", "label": 3380, "func": ""}
{"index": "s983195771", "label": 1072, "func": ""}
{"index": "s579713500", "label": 1072, "func": ""}
{"index": "s306326373", "label": 1072, "func": ""}
{"index": "s179535523", "label": 3050, "func": ""}
{"index": "s027808528", "label": 3050, "func": ""}
{"index": "s096580770", "label": 3050, "func": ""}
{"index": "s036875748", "label": 3050, "func": ""}
{"index": "s174237532", "label": 3050, "func": ""}
{"index": "s490302920", "label": 3050, "func": ""}
{"index": "s734205201", "label": 3050, "func": ""}
{"index": "s758499917", "label": 3050, "func": ""}
{"index": "s015852901", "label": 3050, "func": ""}
{"index": "s751336411", "label": 3050, "func": ""}
{"index": "s885497342", "label": 1219, "func": ""}
{"index": "s841496748", "label": 1219, "func": ""}
{"index": "s374101992", "label": 1219, "func": ""}
{"index": "s825358110", "label": 3953, "func": ""}
{"index": "s592568039", "label": 3953, "func": ""}
{"index": "s361116302", "label": 2675, "func": ""}
{"index": "s736447234", "label": 2675, "func": ""}
{"index": "s476393343", "label": 2675, "func": ""}
{"index": "s177244737", "label": 2675, "func": ""}
{"index": "s455563130", "label": 2675, "func": ""}
{"index": "s389334647", "label": 2675, "func": ""}
{"index": "s679348158", "label": 2675, "func": ""}
{"index": "s431784219", "label": 2675, "func": ""}
{"index": "s071664151", "label": 2675, "func": ""}
{"index": "s779854413", "label": 2675, "func": ""}
{"index": "s029629483", "label": 2391, "func": ""}
{"index": "s406377515", "label": 2391, "func": ""}
{"index": "s905839840", "label": 2391, "func": ""}
{"index": "s309009640", "label": 2391, "func": ""}
{"index": "s218649251", "label": 2391, "func": ""}
{"index": "s182702942", "label": 2391, "func": ""}
{"index": "s962061938", "label": 2391, "func": ""}
{"index": "s185734456", "label": 2391, "func": ""}
{"index": "s778586821", "label": 2391, "func": ""}
{"index": "s089594655", "label": 2391, "func": ""}
{"index": "s555442677", "label": 65, "func": ""}
{"index": "s602124764", "label": 65, "func": ""}
{"index": "s251982751", "label": 65, "func": ""}
{"index": "s602269062", "label": 65, "func": ""}
{"index": "s979175579", "label": 65, "func": ""}
{"index": "s249071058", "label": 65, "func": ""}
{"index": "s347638461", "label": 65, "func": ""}
{"index": "s497867878", "label": 65, "func": ""}
{"index": "s558205322", "label": 65, "func": ""}
{"index": "s121428548", "label": 65, "func": ""}
{"index": "s400647747", "label": 1623, "func": ""}
{"index": "s074597321", "label": 1623, "func": ""}
{"index": "s414584729", "label": 1623, "func": ""}
{"index": "s989711302", "label": 1623, "func": ""}
{"index": "s277441268", "label": 1623, "func": ""}
{"index": "s490748566", "label": 1623, "func": ""}
{"index": "s925200119", "label": 1623, "func": ""}
{"index": "s110382784", "label": 1623, "func": ""}
{"index": "s654266907", "label": 1623, "func": ""}
{"index": "s586247611", "label": 3765, "func": ""}
{"index": "s388777066", "label": 3765, "func": ""}
{"index": "s671969777", "label": 3765, "func": ""}
{"index": "s126000979", "label": 3765, "func": ""}
{"index": "s267555730", "label": 3765, "func": ""}
{"index": "s484522649", "label": 3765, "func": ""}
{"index": "s342151650", "label": 3765, "func": ""}
{"index": "s821974398", "label": 3765, "func": ""}
{"index": "s810281083", "label": 3765, "func": ""}
{"index": "s868491026", "label": 3765, "func": ""}
{"index": "s647502248", "label": 799, "func": ""}
{"index": "s212297249", "label": 799, "func": ""}
{"index": "s849007186", "label": 799, "func": ""}
{"index": "s082282752", "label": 799, "func": ""}
{"index": "s837214301", "label": 1381, "func": ""}
{"index": "s797750301", "label": 2623, "func": ""}
{"index": "s808739763", "label": 2623, "func": ""}
{"index": "s740944352", "label": 2623, "func": ""}
{"index": "s667273347", "label": 2623, "func": ""}
{"index": "s747786667", "label": 2623, "func": ""}
{"index": "s338183553", "label": 2623, "func": ""}
{"index": "s434174758", "label": 2623, "func": ""}
{"index": "s571269312", "label": 2623, "func": ""}
{"index": "s742611582", "label": 2623, "func": ""}
{"index": "s278454407", "label": 2623, "func": ""}
{"index": "s636807106", "label": 3654, "func": ""}
{"index": "s332200850", "label": 3654, "func": ""}
{"index": "s210512762", "label": 3654, "func": ""}
{"index": "s861154187", "label": 1892, "func": ""}
{"index": "s522602380", "label": 1892, "func": ""}
{"index": "s729736490", "label": 3337, "func": ""}
{"index": "s859362336", "label": 3337, "func": ""}
{"index": "s071861988", "label": 3337, "func": ""}
{"index": "s990971725", "label": 3337, "func": ""}
{"index": "s501205490", "label": 3337, "func": ""}
{"index": "s682896220", "label": 3337, "func": ""}
{"index": "s202229095", "label": 3337, "func": ""}
{"index": "s498762330", "label": 3337, "func": ""}
{"index": "s207760155", "label": 3337, "func": ""}
{"index": "s551705612", "label": 3337, "func": ""}
{"index": "s609937492", "label": 3527, "func": ""}
{"index": "s583478360", "label": 3527, "func": ""}
{"index": "s042936977", "label": 3329, "func": ""}
{"index": "s271314724", "label": 3329, "func": ""}
{"index": "s307600902", "label": 3329, "func": ""}
{"index": "s928479979", "label": 3329, "func": ""}
{"index": "s182435980", "label": 3329, "func": ""}
{"index": "s881621464", "label": 3329, "func": ""}
{"index": "s187807361", "label": 3329, "func": ""}
{"index": "s527369198", "label": 3329, "func": ""}
{"index": "s885363636", "label": 3329, "func": ""}
{"index": "s310068957", "label": 3329, "func": ""}
{"index": "s744669093", "label": 1147, "func": ""}
{"index": "s708257735", "label": 1147, "func": ""}
{"index": "s433450151", "label": 1147, "func": ""}
{"index": "s895390807", "label": 1147, "func": ""}
{"index": "s679750686", "label": 1147, "func": ""}
{"index": "s534153601", "label": 1147, "func": ""}
{"index": "s552085941", "label": 1147, "func": ""}
{"index": "s773665179", "label": 1147, "func": ""}
{"index": "s965863993", "label": 1147, "func": ""}
{"index": "s464878070", "label": 1147, "func": ""}
{"index": "s965357105", "label": 2946, "func": ""}
{"index": "s421579008", "label": 2946, "func": ""}
{"index": "s203316797", "label": 2946, "func": ""}
{"index": "s138362627", "label": 2946, "func": ""}
{"index": "s186651881", "label": 2946, "func": ""}
{"index": "s636375515", "label": 2946, "func": ""}
{"index": "s510464080", "label": 2946, "func": ""}
{"index": "s422115462", "label": 2946, "func": ""}
{"index": "s674210347", "label": 2946, "func": ""}
{"index": "s852666922", "label": 2946, "func": ""}
{"index": "s132690709", "label": 2249, "func": ""}
{"index": "s872112181", "label": 2249, "func": ""}
{"index": "s894209299", "label": 2249, "func": ""}
{"index": "s752670406", "label": 2249, "func": ""}
{"index": "s142451203", "label": 2249, "func": ""}
{"index": "s701351430", "label": 1802, "func": ""}
{"index": "s449486339", "label": 1802, "func": ""}
{"index": "s536860521", "label": 1802, "func": ""}
{"index": "s496626729", "label": 1802, "func": ""}
{"index": "s326285738", "label": 1802, "func": ""}
{"index": "s306380718", "label": 1802, "func": ""}
{"index": "s097966305", "label": 1802, "func": ""}
{"index": "s266434541", "label": 1802, "func": ""}
{"index": "s807697382", "label": 1802, "func": ""}
{"index": "s966693311", "label": 1802, "func": ""}
{"index": "s996252263", "label": 2714, "func": ""}
{"index": "s595626459", "label": 2714, "func": ""}
{"index": "s906306262", "label": 2714, "func": ""}
{"index": "s716015807", "label": 2714, "func": ""}
{"index": "s484421049", "label": 2714, "func": ""}
{"index": "s118265882", "label": 2714, "func": ""}
{"index": "s211613141", "label": 2714, "func": ""}
{"index": "s489648502", "label": 2714, "func": ""}
{"index": "s908775969", "label": 2714, "func": ""}
{"index": "s880622412", "label": 2714, "func": ""}
{"index": "s561992950", "label": 2796, "func": ""}
{"index": "s276258086", "label": 2796, "func": ""}
{"index": "s867139971", "label": 2796, "func": ""}
{"index": "s028661039", "label": 2796, "func": ""}
{"index": "s159563054", "label": 2796, "func": ""}
{"index": "s482971040", "label": 2796, "func": ""}
{"index": "s644629553", "label": 2796, "func": ""}
{"index": "s719281998", "label": 2796, "func": ""}
{"index": "s834211439", "label": 2796, "func": ""}
{"index": "s890563311", "label": 2796, "func": ""}
{"index": "s533472736", "label": 1704, "func": ""}
{"index": "s216608854", "label": 3534, "func": ""}
{"index": "s746824031", "label": 3534, "func": ""}
{"index": "s039634214", "label": 3534, "func": ""}
{"index": "s414844755", "label": 3534, "func": ""}
{"index": "s712296112", "label": 3534, "func": ""}
{"index": "s807221304", "label": 3534, "func": ""}
{"index": "s428897545", "label": 3534, "func": ""}
{"index": "s931899938", "label": 3534, "func": ""}
{"index": "s229817952", "label": 3534, "func": ""}
{"index": "s925694848", "label": 3534, "func": ""}
{"index": "s616165963", "label": 2546, "func": ""}
{"index": "s283268205", "label": 2546, "func": ""}
{"index": "s605458199", "label": 2546, "func": ""}
{"index": "s542039623", "label": 2546, "func": ""}
{"index": "s436529405", "label": 2546, "func": ""}
{"index": "s635659136", "label": 2546, "func": ""}
{"index": "s057203610", "label": 2546, "func": ""}
{"index": "s681974240", "label": 2546, "func": ""}
{"index": "s223776325", "label": 2546, "func": ""}
{"index": "s497768641", "label": 2546, "func": ""}
{"index": "s344485730", "label": 3074, "func": ""}
{"index": "s165549145", "label": 3074, "func": ""}
{"index": "s571227140", "label": 3074, "func": ""}
{"index": "s826853160", "label": 3074, "func": ""}
{"index": "s969810127", "label": 3074, "func": ""}
{"index": "s702916557", "label": 3074, "func": ""}
{"index": "s512196494", "label": 3074, "func": ""}
{"index": "s487180679", "label": 3074, "func": ""}
{"index": "s704011642", "label": 3074, "func": ""}
{"index": "s571752323", "label": 3074, "func": ""}
{"index": "s814283127", "label": 777, "func": ""}
{"index": "s647114715", "label": 777, "func": ""}
{"index": "s507170144", "label": 777, "func": ""}
{"index": "s178931579", "label": 777, "func": ""}
{"index": "s860192989", "label": 777, "func": ""}
{"index": "s236065544", "label": 777, "func": ""}
{"index": "s328267829", "label": 777, "func": ""}
{"index": "s710702314", "label": 777, "func": ""}
{"index": "s804418359", "label": 777, "func": ""}
{"index": "s167635683", "label": 777, "func": ""}
{"index": "s832426714", "label": 2162, "func": ""}
{"index": "s902880512", "label": 2212, "func": ""}
{"index": "s077344445", "label": 2212, "func": ""}
{"index": "s700844513", "label": 2212, "func": ""}
{"index": "s939240508", "label": 2212, "func": ""}
{"index": "s545714040", "label": 2212, "func": ""}
{"index": "s011747452", "label": 2212, "func": ""}
{"index": "s167082752", "label": 1602, "func": ""}
{"index": "s666497558", "label": 1602, "func": ""}
{"index": "s199926327", "label": 1602, "func": ""}
{"index": "s641828360", "label": 1602, "func": ""}
{"index": "s383473639", "label": 1602, "func": ""}
{"index": "s757547674", "label": 1602, "func": ""}
{"index": "s644082281", "label": 1602, "func": ""}
{"index": "s938449315", "label": 1602, "func": ""}
{"index": "s032971618", "label": 1602, "func": ""}
{"index": "s413158256", "label": 1602, "func": ""}
{"index": "s131235938", "label": 2333, "func": ""}
{"index": "s360522970", "label": 2333, "func": ""}
{"index": "s383764748", "label": 2333, "func": ""}
{"index": "s650021564", "label": 3019, "func": ""}
{"index": "s401525768", "label": 3019, "func": ""}
{"index": "s177387731", "label": 3019, "func": ""}
{"index": "s835811965", "label": 3019, "func": ""}
{"index": "s334223104", "label": 3019, "func": ""}
{"index": "s262260592", "label": 2787, "func": ""}
{"index": "s403753205", "label": 2787, "func": ""}
{"index": "s505017250", "label": 2787, "func": ""}
{"index": "s146980197", "label": 2787, "func": ""}
{"index": "s992679491", "label": 2787, "func": ""}
{"index": "s976235787", "label": 2787, "func": ""}
{"index": "s103747763", "label": 2787, "func": ""}
{"index": "s139077238", "label": 2787, "func": ""}
{"index": "s035894707", "label": 2787, "func": ""}
{"index": "s707843673", "label": 2787, "func": ""}
{"index": "s473956637", "label": 1553, "func": ""}
{"index": "s292269462", "label": 1553, "func": ""}
{"index": "s893771832", "label": 1553, "func": ""}
{"index": "s456821352", "label": 1553, "func": ""}
{"index": "s434090132", "label": 1553, "func": ""}
{"index": "s312964378", "label": 1553, "func": ""}
{"index": "s395649828", "label": 69, "func": ""}
{"index": "s488437146", "label": 69, "func": ""}
{"index": "s324864954", "label": 69, "func": ""}
{"index": "s531126118", "label": 69, "func": ""}
{"index": "s509351000", "label": 69, "func": ""}
{"index": "s419708520", "label": 69, "func": ""}
{"index": "s543875380", "label": 69, "func": ""}
{"index": "s634308300", "label": 69, "func": ""}
{"index": "s394353389", "label": 69, "func": ""}
{"index": "s599293020", "label": 69, "func": ""}
{"index": "s487446022", "label": 4023, "func": ""}
{"index": "s938455084", "label": 3658, "func": ""}
{"index": "s945692507", "label": 3658, "func": ""}
{"index": "s752773847", "label": 3658, "func": ""}
{"index": "s934251510", "label": 3658, "func": ""}
{"index": "s421225246", "label": 3658, "func": ""}
{"index": "s313075170", "label": 3658, "func": ""}
{"index": "s765189903", "label": 3658, "func": ""}
{"index": "s847619862", "label": 3658, "func": ""}
{"index": "s816037744", "label": 3658, "func": ""}
{"index": "s711115042", "label": 3658, "func": ""}
{"index": "s504866335", "label": 3554, "func": ""}
{"index": "s372400007", "label": 3554, "func": ""}
{"index": "s081460807", "label": 3554, "func": ""}
{"index": "s014473003", "label": 3554, "func": ""}
{"index": "s083825736", "label": 3554, "func": ""}
{"index": "s452243556", "label": 3139, "func": ""}
{"index": "s190685911", "label": 3139, "func": ""}
{"index": "s455919128", "label": 3139, "func": ""}
{"index": "s496470381", "label": 3139, "func": ""}
{"index": "s358930601", "label": 3139, "func": ""}
{"index": "s275003013", "label": 3139, "func": ""}
{"index": "s764607498", "label": 3139, "func": ""}
{"index": "s820846675", "label": 3139, "func": ""}
{"index": "s761437615", "label": 3139, "func": ""}
{"index": "s582048394", "label": 3139, "func": ""}
{"index": "s165707760", "label": 1688, "func": ""}
{"index": "s731647538", "label": 1688, "func": ""}
{"index": "s001279751", "label": 1688, "func": ""}
{"index": "s153631968", "label": 1688, "func": ""}
{"index": "s237261282", "label": 1688, "func": ""}
{"index": "s661938913", "label": 1340, "func": ""}
{"index": "s429499488", "label": 1340, "func": ""}
{"index": "s151148102", "label": 1340, "func": ""}
{"index": "s236334120", "label": 1340, "func": ""}
{"index": "s038739645", "label": 1760, "func": ""}
{"index": "s987619974", "label": 3833, "func": ""}
{"index": "s513319435", "label": 3833, "func": ""}
{"index": "s085345199", "label": 3833, "func": ""}
{"index": "s688846030", "label": 1525, "func": ""}
{"index": "s925553525", "label": 2270, "func": ""}
{"index": "s063619575", "label": 2270, "func": ""}
{"index": "s738304444", "label": 2270, "func": ""}
{"index": "s022887755", "label": 2270, "func": ""}
{"index": "s157621579", "label": 2270, "func": ""}
{"index": "s263631486", "label": 2270, "func": ""}
{"index": "s607234505", "label": 2270, "func": ""}
{"index": "s691867980", "label": 2270, "func": ""}
{"index": "s869218821", "label": 2270, "func": ""}
{"index": "s880286171", "label": 2270, "func": ""}
{"index": "s558704143", "label": 1916, "func": ""}
{"index": "s975413870", "label": 1916, "func": ""}
{"index": "s166413314", "label": 1916, "func": ""}
{"index": "s950483083", "label": 1916, "func": ""}
{"index": "s511761518", "label": 639, "func": ""}
{"index": "s319639636", "label": 639, "func": ""}
{"index": "s176212559", "label": 2468, "func": ""}
{"index": "s148301906", "label": 2468, "func": ""}
{"index": "s456288256", "label": 2468, "func": ""}
{"index": "s717558920", "label": 2468, "func": ""}
{"index": "s819133588", "label": 2468, "func": ""}
{"index": "s898920771", "label": 2468, "func": ""}
{"index": "s750847235", "label": 2468, "func": ""}
{"index": "s749508036", "label": 2468, "func": ""}
{"index": "s941001939", "label": 2468, "func": ""}
{"index": "s482143678", "label": 2468, "func": ""}
{"index": "s952724386", "label": 819, "func": ""}
{"index": "s604782952", "label": 819, "func": ""}
{"index": "s425405425", "label": 819, "func": ""}
{"index": "s940111503", "label": 819, "func": ""}
{"index": "s067177752", "label": 819, "func": ""}
{"index": "s034295529", "label": 819, "func": ""}
{"index": "s747830715", "label": 819, "func": ""}
{"index": "s841631126", "label": 819, "func": ""}
{"index": "s350048555", "label": 819, "func": ""}
{"index": "s481254039", "label": 819, "func": ""}
{"index": "s537270521", "label": 2222, "func": ""}
{"index": "s312539726", "label": 31, "func": ""}
{"index": "s941245611", "label": 31, "func": ""}
{"index": "s096375527", "label": 31, "func": ""}
{"index": "s049816175", "label": 31, "func": ""}
{"index": "s266324429", "label": 31, "func": ""}
{"index": "s974778417", "label": 31, "func": ""}
{"index": "s773115198", "label": 31, "func": ""}
{"index": "s988201879", "label": 31, "func": ""}
{"index": "s614941524", "label": 31, "func": ""}
{"index": "s436180007", "label": 31, "func": ""}
{"index": "s435814735", "label": 856, "func": ""}
{"index": "s894668658", "label": 856, "func": ""}
{"index": "s155486451", "label": 856, "func": ""}
{"index": "s232534026", "label": 856, "func": ""}
{"index": "s350140137", "label": 856, "func": ""}
{"index": "s648995736", "label": 856, "func": ""}
{"index": "s622290640", "label": 856, "func": ""}
{"index": "s997542235", "label": 856, "func": ""}
{"index": "s804533072", "label": 856, "func": ""}
{"index": "s492139241", "label": 856, "func": ""}
{"index": "s357975649", "label": 280, "func": ""}
{"index": "s206677869", "label": 280, "func": ""}
{"index": "s006315477", "label": 280, "func": ""}
{"index": "s412036560", "label": 280, "func": ""}
{"index": "s008612868", "label": 280, "func": ""}
{"index": "s784416824", "label": 280, "func": ""}
{"index": "s945483547", "label": 280, "func": ""}
{"index": "s792509441", "label": 280, "func": ""}
{"index": "s521086270", "label": 3589, "func": ""}
{"index": "s584961994", "label": 3589, "func": ""}
{"index": "s122661084", "label": 3589, "func": ""}
{"index": "s201031494", "label": 3589, "func": ""}
{"index": "s244266222", "label": 3589, "func": ""}
{"index": "s264835941", "label": 3589, "func": ""}
{"index": "s721609554", "label": 3589, "func": ""}
{"index": "s543331963", "label": 3589, "func": ""}
{"index": "s115582584", "label": 3589, "func": ""}
{"index": "s741348432", "label": 3589, "func": ""}
{"index": "s310664689", "label": 3736, "func": ""}
{"index": "s705603739", "label": 3736, "func": ""}
{"index": "s308505235", "label": 3736, "func": ""}
{"index": "s306264442", "label": 3736, "func": ""}
{"index": "s579307860", "label": 3736, "func": ""}
{"index": "s428131876", "label": 3736, "func": ""}
{"index": "s407128011", "label": 3736, "func": ""}
{"index": "s406416315", "label": 1571, "func": ""}
{"index": "s450826494", "label": 2683, "func": ""}
{"index": "s208616175", "label": 2683, "func": ""}
{"index": "s069767335", "label": 2683, "func": ""}
{"index": "s596595092", "label": 2683, "func": ""}
{"index": "s927870476", "label": 2683, "func": ""}
{"index": "s893418433", "label": 2683, "func": ""}
{"index": "s460216050", "label": 2683, "func": ""}
{"index": "s775705452", "label": 2683, "func": ""}
{"index": "s492637834", "label": 2683, "func": ""}
{"index": "s221543788", "label": 2683, "func": ""}
{"index": "s646430071", "label": 2768, "func": ""}
{"index": "s615059076", "label": 2768, "func": ""}
{"index": "s227144416", "label": 2768, "func": ""}
{"index": "s389348606", "label": 2768, "func": ""}
{"index": "s556047376", "label": 2768, "func": ""}
{"index": "s257782516", "label": 2768, "func": ""}
{"index": "s529200008", "label": 2768, "func": ""}
{"index": "s332351916", "label": 2768, "func": ""}
{"index": "s048502965", "label": 2768, "func": ""}
{"index": "s126342018", "label": 2768, "func": ""}
{"index": "s620263468", "label": 2018, "func": ""}
{"index": "s803998350", "label": 2018, "func": ""}
{"index": "s989308104", "label": 876, "func": ""}
{"index": "s432803702", "label": 876, "func": ""}
{"index": "s422582874", "label": 876, "func": ""}
{"index": "s299960157", "label": 876, "func": ""}
{"index": "s501479436", "label": 876, "func": ""}
{"index": "s347104790", "label": 876, "func": ""}
{"index": "s086086910", "label": 876, "func": ""}
{"index": "s031080804", "label": 876, "func": ""}
{"index": "s263103445", "label": 3276, "func": ""}
{"index": "s857958990", "label": 3276, "func": ""}
{"index": "s258165843", "label": 3276, "func": ""}
{"index": "s051008617", "label": 3276, "func": ""}
{"index": "s843636694", "label": 3276, "func": ""}
{"index": "s403820665", "label": 3276, "func": ""}
{"index": "s226491253", "label": 3276, "func": ""}
{"index": "s963334228", "label": 3276, "func": ""}
{"index": "s351137030", "label": 3276, "func": ""}
{"index": "s530814625", "label": 3276, "func": ""}
{"index": "s131118640", "label": 3317, "func": ""}
{"index": "s830818473", "label": 3317, "func": ""}
{"index": "s571042331", "label": 3317, "func": ""}
{"index": "s320627001", "label": 3317, "func": ""}
{"index": "s123127240", "label": 3317, "func": ""}
{"index": "s191621337", "label": 3317, "func": ""}
{"index": "s990667496", "label": 3317, "func": ""}
{"index": "s772400078", "label": 3317, "func": ""}
{"index": "s773908453", "label": 3317, "func": ""}
{"index": "s249990089", "label": 3317, "func": ""}
{"index": "s293195990", "label": 1621, "func": ""}
{"index": "s086787371", "label": 1621, "func": ""}
{"index": "s719114100", "label": 1621, "func": ""}
{"index": "s635876149", "label": 1621, "func": ""}
{"index": "s777043998", "label": 1621, "func": ""}
{"index": "s969177943", "label": 1621, "func": ""}
{"index": "s087029687", "label": 1621, "func": ""}
{"index": "s225474120", "label": 1621, "func": ""}
{"index": "s942268056", "label": 1621, "func": ""}
{"index": "s312522753", "label": 1621, "func": ""}
{"index": "s239518444", "label": 2904, "func": ""}
{"index": "s140627947", "label": 2904, "func": ""}
{"index": "s207200287", "label": 2904, "func": ""}
{"index": "s999245823", "label": 2904, "func": ""}
{"index": "s745878956", "label": 2904, "func": ""}
{"index": "s570370257", "label": 2904, "func": ""}
{"index": "s656181610", "label": 2904, "func": ""}
{"index": "s379601068", "label": 2904, "func": ""}
{"index": "s344178220", "label": 2904, "func": ""}
{"index": "s256208100", "label": 2904, "func": ""}
{"index": "s780439791", "label": 3354, "func": ""}
{"index": "s492894846", "label": 3354, "func": ""}
{"index": "s607998064", "label": 3354, "func": ""}
{"index": "s598127821", "label": 3354, "func": ""}
{"index": "s760752020", "label": 3354, "func": ""}
{"index": "s342109352", "label": 3354, "func": ""}
{"index": "s393047097", "label": 3354, "func": ""}
{"index": "s677638964", "label": 3354, "func": ""}
{"index": "s226330616", "label": 3354, "func": ""}
{"index": "s500207912", "label": 3354, "func": ""}
{"index": "s112125749", "label": 2512, "func": ""}
{"index": "s936445123", "label": 2512, "func": ""}
{"index": "s518592508", "label": 2512, "func": ""}
{"index": "s730945730", "label": 2512, "func": ""}
{"index": "s667512068", "label": 2512, "func": ""}
{"index": "s409738171", "label": 2512, "func": ""}
{"index": "s920743599", "label": 2512, "func": ""}
{"index": "s915320893", "label": 2512, "func": ""}
{"index": "s695113890", "label": 2512, "func": ""}
{"index": "s201431193", "label": 2512, "func": ""}
{"index": "s015008954", "label": 1311, "func": ""}
{"index": "s290509959", "label": 117, "func": ""}
{"index": "s721390637", "label": 117, "func": ""}
{"index": "s236265023", "label": 117, "func": ""}
{"index": "s182387415", "label": 117, "func": ""}
{"index": "s424191996", "label": 117, "func": ""}
{"index": "s421976801", "label": 117, "func": ""}
{"index": "s864879081", "label": 117, "func": ""}
{"index": "s524634932", "label": 117, "func": ""}
{"index": "s413349826", "label": 117, "func": ""}
{"index": "s002428588", "label": 117, "func": ""}
{"index": "s880312513", "label": 2103, "func": ""}
{"index": "s298954158", "label": 3731, "func": ""}
{"index": "s868809278", "label": 3731, "func": ""}
{"index": "s779973515", "label": 3731, "func": ""}
{"index": "s328587152", "label": 3731, "func": ""}
{"index": "s009455823", "label": 3731, "func": ""}
{"index": "s872852167", "label": 3731, "func": ""}
{"index": "s106528581", "label": 3731, "func": ""}
{"index": "s579149394", "label": 3731, "func": ""}
{"index": "s472736694", "label": 3731, "func": ""}
{"index": "s710849908", "label": 3731, "func": ""}
{"index": "s320596974", "label": 2467, "func": ""}
{"index": "s861511783", "label": 2467, "func": ""}
{"index": "s791133991", "label": 2467, "func": ""}
{"index": "s716296871", "label": 2467, "func": ""}
{"index": "s890913661", "label": 2467, "func": ""}
{"index": "s072316920", "label": 2467, "func": ""}
{"index": "s936228258", "label": 2467, "func": ""}
{"index": "s827069520", "label": 2467, "func": ""}
{"index": "s760331190", "label": 2467, "func": ""}
{"index": "s418305756", "label": 2467, "func": ""}
{"index": "s784794472", "label": 2894, "func": ""}
{"index": "s522700071", "label": 2894, "func": ""}
{"index": "s581899782", "label": 2894, "func": ""}
{"index": "s281996721", "label": 2894, "func": ""}
{"index": "s348842804", "label": 469, "func": ""}
{"index": "s186879607", "label": 469, "func": ""}
{"index": "s604764271", "label": 469, "func": ""}
{"index": "s048011961", "label": 469, "func": ""}
{"index": "s007263248", "label": 469, "func": ""}
{"index": "s912135756", "label": 469, "func": ""}
{"index": "s763879969", "label": 469, "func": ""}
{"index": "s415939014", "label": 469, "func": ""}
{"index": "s730792095", "label": 469, "func": ""}
{"index": "s812528453", "label": 469, "func": ""}
{"index": "s280134216", "label": 42, "func": ""}
{"index": "s376363583", "label": 42, "func": ""}
{"index": "s768069949", "label": 42, "func": ""}
{"index": "s346260221", "label": 42, "func": ""}
{"index": "s217110883", "label": 42, "func": ""}
{"index": "s458968701", "label": 42, "func": ""}
{"index": "s960167235", "label": 42, "func": ""}
{"index": "s525564333", "label": 42, "func": ""}
{"index": "s925124231", "label": 42, "func": ""}
{"index": "s448575654", "label": 42, "func": ""}
{"index": "s906571528", "label": 2491, "func": ""}
{"index": "s345115772", "label": 2491, "func": ""}
{"index": "s542273348", "label": 2491, "func": ""}
{"index": "s369240669", "label": 2491, "func": ""}
{"index": "s320097584", "label": 2491, "func": ""}
{"index": "s125604208", "label": 2491, "func": ""}
{"index": "s979197243", "label": 2491, "func": ""}
{"index": "s807609893", "label": 2491, "func": ""}
{"index": "s447353143", "label": 2491, "func": ""}
{"index": "s287050914", "label": 2491, "func": ""}
{"index": "s899188824", "label": 2274, "func": ""}
{"index": "s988463740", "label": 2274, "func": ""}
{"index": "s805146857", "label": 2274, "func": ""}
{"index": "s768635658", "label": 2274, "func": ""}
{"index": "s036849514", "label": 2274, "func": ""}
{"index": "s731812348", "label": 2274, "func": ""}
{"index": "s773861745", "label": 2274, "func": ""}
{"index": "s135955659", "label": 2274, "func": ""}
{"index": "s678639196", "label": 2274, "func": ""}
{"index": "s028829553", "label": 2274, "func": ""}
{"index": "s367713738", "label": 3161, "func": ""}
{"index": "s754189946", "label": 3161, "func": ""}
{"index": "s146036116", "label": 3161, "func": ""}
{"index": "s743873196", "label": 3161, "func": ""}
{"index": "s924689886", "label": 3161, "func": ""}
{"index": "s423865892", "label": 3161, "func": ""}
{"index": "s664012534", "label": 3161, "func": ""}
{"index": "s540965795", "label": 3161, "func": ""}
{"index": "s715664696", "label": 3161, "func": ""}
{"index": "s118744095", "label": 3161, "func": ""}
{"index": "s469723161", "label": 2848, "func": ""}
{"index": "s383639468", "label": 2848, "func": ""}
{"index": "s234088304", "label": 2848, "func": ""}
{"index": "s754385359", "label": 2848, "func": ""}
{"index": "s541965944", "label": 2848, "func": ""}
{"index": "s622240104", "label": 2848, "func": ""}
{"index": "s924719395", "label": 2848, "func": ""}
{"index": "s365094939", "label": 2848, "func": ""}
{"index": "s215993438", "label": 2848, "func": ""}
{"index": "s663064698", "label": 2848, "func": ""}
{"index": "s833455914", "label": 2790, "func": ""}
{"index": "s645092286", "label": 2790, "func": ""}
{"index": "s481511895", "label": 2790, "func": ""}
{"index": "s079458275", "label": 2790, "func": ""}
{"index": "s890864188", "label": 2790, "func": ""}
{"index": "s160090750", "label": 2790, "func": ""}
{"index": "s327322041", "label": 2790, "func": ""}
{"index": "s719638163", "label": 2790, "func": ""}
{"index": "s861693732", "label": 2790, "func": ""}
{"index": "s138875726", "label": 2790, "func": ""}
{"index": "s949830246", "label": 461, "func": ""}
{"index": "s796567595", "label": 461, "func": ""}
{"index": "s246172547", "label": 461, "func": ""}
{"index": "s187606966", "label": 461, "func": ""}
{"index": "s150380720", "label": 461, "func": ""}
{"index": "s762691255", "label": 461, "func": ""}
{"index": "s962907897", "label": 461, "func": ""}
{"index": "s026042802", "label": 461, "func": ""}
{"index": "s680276495", "label": 461, "func": ""}
{"index": "s303394679", "label": 461, "func": ""}
{"index": "s343507657", "label": 980, "func": ""}
{"index": "s374855484", "label": 1877, "func": ""}
{"index": "s532439063", "label": 3884, "func": ""}
{"index": "s044210806", "label": 2397, "func": ""}
{"index": "s089359444", "label": 2397, "func": ""}
{"index": "s222391257", "label": 2397, "func": ""}
{"index": "s809454182", "label": 2397, "func": ""}
{"index": "s881304449", "label": 2397, "func": ""}
{"index": "s608432676", "label": 2397, "func": ""}
{"index": "s365578975", "label": 2397, "func": ""}
{"index": "s947751389", "label": 2397, "func": ""}
{"index": "s934250637", "label": 2397, "func": ""}
{"index": "s022351402", "label": 2397, "func": ""}
{"index": "s052235222", "label": 405, "func": ""}
{"index": "s932776228", "label": 405, "func": ""}
{"index": "s542841636", "label": 2945, "func": ""}
{"index": "s924509036", "label": 2945, "func": ""}
{"index": "s306204899", "label": 2945, "func": ""}
{"index": "s567778174", "label": 2945, "func": ""}
{"index": "s216895155", "label": 2945, "func": ""}
{"index": "s708107289", "label": 2945, "func": ""}
{"index": "s573880206", "label": 2945, "func": ""}
{"index": "s015159251", "label": 2945, "func": ""}
{"index": "s488862420", "label": 2945, "func": ""}
{"index": "s180668540", "label": 2945, "func": ""}
{"index": "s677200764", "label": 3137, "func": ""}
{"index": "s170555955", "label": 3137, "func": ""}
{"index": "s203997032", "label": 3137, "func": ""}
{"index": "s521128193", "label": 3137, "func": ""}
{"index": "s222032242", "label": 3137, "func": ""}
{"index": "s565855691", "label": 3137, "func": ""}
{"index": "s982996521", "label": 3137, "func": ""}
{"index": "s372688589", "label": 3137, "func": ""}
{"index": "s308497014", "label": 3137, "func": ""}
{"index": "s244860062", "label": 3137, "func": ""}
{"index": "s980269585", "label": 3035, "func": ""}
{"index": "s347625150", "label": 3035, "func": ""}
{"index": "s909438692", "label": 3035, "func": ""}
{"index": "s519782744", "label": 3035, "func": ""}
{"index": "s251221505", "label": 3035, "func": ""}
{"index": "s363470892", "label": 3035, "func": ""}
{"index": "s467879616", "label": 3035, "func": ""}
{"index": "s906691976", "label": 3035, "func": ""}
{"index": "s353554227", "label": 3035, "func": ""}
{"index": "s495990621", "label": 3035, "func": ""}
{"index": "s275371032", "label": 2569, "func": ""}
{"index": "s353566814", "label": 2569, "func": ""}
{"index": "s422270789", "label": 2569, "func": ""}
{"index": "s474758882", "label": 2569, "func": ""}
{"index": "s717660387", "label": 2569, "func": ""}
{"index": "s169892256", "label": 2569, "func": ""}
{"index": "s940860028", "label": 2569, "func": ""}
{"index": "s569320626", "label": 3363, "func": ""}
{"index": "s323548628", "label": 3363, "func": ""}
{"index": "s720537166", "label": 3363, "func": ""}
{"index": "s779189421", "label": 3363, "func": ""}
{"index": "s760633779", "label": 3363, "func": ""}
{"index": "s970533044", "label": 3363, "func": ""}
{"index": "s009980303", "label": 3363, "func": ""}
{"index": "s913378894", "label": 3363, "func": ""}
{"index": "s133825821", "label": 3363, "func": ""}
{"index": "s590972325", "label": 3363, "func": ""}
{"index": "s052833792", "label": 2924, "func": ""}
{"index": "s577276081", "label": 2924, "func": ""}
{"index": "s370965282", "label": 2924, "func": ""}
{"index": "s324577999", "label": 2924, "func": ""}
{"index": "s353936634", "label": 2924, "func": ""}
{"index": "s328176779", "label": 2924, "func": ""}
{"index": "s399275366", "label": 2924, "func": ""}
{"index": "s640106408", "label": 2924, "func": ""}
{"index": "s102618531", "label": 2924, "func": ""}
{"index": "s180470130", "label": 2924, "func": ""}
{"index": "s489539803", "label": 1939, "func": ""}
{"index": "s997353024", "label": 1939, "func": ""}
{"index": "s842770289", "label": 1858, "func": ""}
{"index": "s995114415", "label": 1858, "func": ""}
{"index": "s163495988", "label": 1858, "func": ""}
{"index": "s766401706", "label": 1858, "func": ""}
{"index": "s988542013", "label": 1858, "func": ""}
{"index": "s180693355", "label": 1858, "func": ""}
{"index": "s410548574", "label": 1858, "func": ""}
{"index": "s615278520", "label": 1858, "func": ""}
{"index": "s126800889", "label": 1858, "func": ""}
{"index": "s207893351", "label": 1858, "func": ""}
{"index": "s756895357", "label": 265, "func": ""}
{"index": "s798358461", "label": 265, "func": ""}
{"index": "s612333248", "label": 265, "func": ""}
{"index": "s340231738", "label": 265, "func": ""}
{"index": "s066022922", "label": 265, "func": ""}
{"index": "s195757362", "label": 265, "func": ""}
{"index": "s716014293", "label": 265, "func": ""}
{"index": "s020355065", "label": 265, "func": ""}
{"index": "s050764156", "label": 265, "func": ""}
{"index": "s823103548", "label": 265, "func": ""}
{"index": "s497715622", "label": 1635, "func": ""}
{"index": "s314633808", "label": 1635, "func": ""}
{"index": "s735159704", "label": 1635, "func": ""}
{"index": "s606919249", "label": 1635, "func": ""}
{"index": "s167901029", "label": 1635, "func": ""}
{"index": "s370041185", "label": 1635, "func": ""}
{"index": "s245691508", "label": 1635, "func": ""}
{"index": "s161252177", "label": 1635, "func": ""}
{"index": "s753463096", "label": 1635, "func": ""}
{"index": "s790068806", "label": 1982, "func": ""}
{"index": "s211559480", "label": 1982, "func": ""}
{"index": "s036819368", "label": 1982, "func": ""}
{"index": "s368471686", "label": 1982, "func": ""}
{"index": "s431546530", "label": 1982, "func": ""}
{"index": "s704111275", "label": 1982, "func": ""}
{"index": "s500036330", "label": 1982, "func": ""}
{"index": "s600578529", "label": 3453, "func": ""}
{"index": "s274167711", "label": 3453, "func": ""}
{"index": "s099241191", "label": 3453, "func": ""}
{"index": "s026303963", "label": 3453, "func": ""}
{"index": "s893602022", "label": 3453, "func": ""}
{"index": "s829079205", "label": 3453, "func": ""}
{"index": "s268431188", "label": 3453, "func": ""}
{"index": "s262913500", "label": 3453, "func": ""}
{"index": "s789067618", "label": 3453, "func": ""}
{"index": "s061714324", "label": 3453, "func": ""}
{"index": "s431095622", "label": 3106, "func": ""}
{"index": "s856558496", "label": 3106, "func": ""}
{"index": "s540663026", "label": 3106, "func": ""}
{"index": "s844641987", "label": 3106, "func": ""}
{"index": "s406954441", "label": 3106, "func": ""}
{"index": "s602671049", "label": 3106, "func": ""}
{"index": "s309158412", "label": 3106, "func": ""}
{"index": "s477216463", "label": 3106, "func": ""}
{"index": "s870855521", "label": 3106, "func": ""}
{"index": "s304111853", "label": 3106, "func": ""}
{"index": "s868387919", "label": 3111, "func": ""}
{"index": "s769996391", "label": 3111, "func": ""}
{"index": "s647283945", "label": 3111, "func": ""}
{"index": "s567107312", "label": 3111, "func": ""}
{"index": "s142389893", "label": 3111, "func": ""}
{"index": "s296966153", "label": 3111, "func": ""}
{"index": "s713828271", "label": 3111, "func": ""}
{"index": "s512519337", "label": 3111, "func": ""}
{"index": "s326787080", "label": 3111, "func": ""}
{"index": "s779952473", "label": 3111, "func": ""}
{"index": "s862739427", "label": 229, "func": ""}
{"index": "s696713706", "label": 229, "func": ""}
{"index": "s147021574", "label": 229, "func": ""}
{"index": "s615756368", "label": 229, "func": ""}
{"index": "s014271772", "label": 229, "func": ""}
{"index": "s773439743", "label": 229, "func": ""}
{"index": "s648448357", "label": 229, "func": ""}
{"index": "s931757772", "label": 229, "func": ""}
{"index": "s318623076", "label": 229, "func": ""}
{"index": "s860705304", "label": 229, "func": ""}
{"index": "s469161851", "label": 2846, "func": ""}
{"index": "s830281107", "label": 2846, "func": ""}
{"index": "s819478391", "label": 2846, "func": ""}
{"index": "s595808922", "label": 2846, "func": ""}
{"index": "s669239598", "label": 2846, "func": ""}
{"index": "s653286304", "label": 2846, "func": ""}
{"index": "s001001424", "label": 2846, "func": ""}
{"index": "s679606606", "label": 2846, "func": ""}
{"index": "s621306207", "label": 2846, "func": ""}
{"index": "s790058625", "label": 2846, "func": ""}
{"index": "s143796915", "label": 3975, "func": ""}
{"index": "s456003242", "label": 3975, "func": ""}
{"index": "s144925101", "label": 3975, "func": ""}
{"index": "s683599508", "label": 3975, "func": ""}
{"index": "s432889012", "label": 3975, "func": ""}
{"index": "s499024682", "label": 3975, "func": ""}
{"index": "s326013190", "label": 3975, "func": ""}
{"index": "s834403417", "label": 2457, "func": ""}
{"index": "s317416144", "label": 2457, "func": ""}
{"index": "s543964707", "label": 2457, "func": ""}
{"index": "s064824877", "label": 2457, "func": ""}
{"index": "s529560769", "label": 2457, "func": ""}
{"index": "s924022043", "label": 2457, "func": ""}
{"index": "s965306114", "label": 2457, "func": ""}
{"index": "s861149806", "label": 2457, "func": ""}
{"index": "s042712946", "label": 2457, "func": ""}
{"index": "s091647663", "label": 2457, "func": ""}
{"index": "s854254034", "label": 516, "func": ""}
{"index": "s928028831", "label": 516, "func": ""}
{"index": "s813363065", "label": 516, "func": ""}
{"index": "s415431283", "label": 516, "func": ""}
{"index": "s559178640", "label": 516, "func": ""}
{"index": "s187004539", "label": 516, "func": ""}
{"index": "s334312611", "label": 516, "func": ""}
{"index": "s649238774", "label": 516, "func": ""}
{"index": "s354315675", "label": 516, "func": ""}
{"index": "s804591252", "label": 516, "func": ""}
{"index": "s135017093", "label": 3147, "func": ""}
{"index": "s815086541", "label": 3147, "func": ""}
{"index": "s402612866", "label": 3147, "func": ""}
{"index": "s562701712", "label": 3147, "func": ""}
{"index": "s890279719", "label": 3147, "func": ""}
{"index": "s896865120", "label": 3147, "func": ""}
{"index": "s274376278", "label": 3147, "func": ""}
{"index": "s281036660", "label": 3147, "func": ""}
{"index": "s543581084", "label": 3147, "func": ""}
{"index": "s470959767", "label": 3147, "func": ""}
{"index": "s838888476", "label": 193, "func": ""}
{"index": "s010570296", "label": 193, "func": ""}
{"index": "s243254366", "label": 193, "func": ""}
{"index": "s594461255", "label": 193, "func": ""}
{"index": "s225929084", "label": 193, "func": ""}
{"index": "s259938647", "label": 193, "func": ""}
{"index": "s137470763", "label": 193, "func": ""}
{"index": "s886280100", "label": 193, "func": ""}
{"index": "s907162080", "label": 193, "func": ""}
{"index": "s763203650", "label": 1059, "func": ""}
{"index": "s939796390", "label": 1059, "func": ""}
{"index": "s045881366", "label": 1059, "func": ""}
{"index": "s757651723", "label": 3878, "func": ""}
{"index": "s076660991", "label": 3878, "func": ""}
{"index": "s882593701", "label": 3878, "func": ""}
{"index": "s366261216", "label": 3878, "func": ""}
{"index": "s403268188", "label": 3878, "func": ""}
{"index": "s776396072", "label": 3878, "func": ""}
{"index": "s889785198", "label": 3878, "func": ""}
{"index": "s177285361", "label": 3878, "func": ""}
{"index": "s027762480", "label": 3878, "func": ""}
{"index": "s291410142", "label": 3878, "func": ""}
{"index": "s233559688", "label": 3473, "func": ""}
{"index": "s368038693", "label": 3473, "func": ""}
{"index": "s836196442", "label": 3473, "func": ""}
{"index": "s182916204", "label": 3473, "func": ""}
{"index": "s800575141", "label": 3473, "func": ""}
{"index": "s300495141", "label": 3473, "func": ""}
{"index": "s295958371", "label": 3473, "func": ""}
{"index": "s484081027", "label": 3473, "func": ""}
{"index": "s297881697", "label": 3473, "func": ""}
{"index": "s826530768", "label": 3473, "func": ""}
{"index": "s556635729", "label": 712, "func": ""}
{"index": "s198168912", "label": 712, "func": ""}
{"index": "s821255495", "label": 712, "func": ""}
{"index": "s366998422", "label": 712, "func": ""}
{"index": "s362591019", "label": 712, "func": ""}
{"index": "s490752229", "label": 712, "func": ""}
{"index": "s989980898", "label": 712, "func": ""}
{"index": "s449031674", "label": 712, "func": ""}
{"index": "s673862635", "label": 712, "func": ""}
{"index": "s535093710", "label": 712, "func": ""}
{"index": "s384592864", "label": 3499, "func": ""}
{"index": "s967917062", "label": 3499, "func": ""}
{"index": "s134281249", "label": 115, "func": ""}
{"index": "s180922520", "label": 115, "func": ""}
{"index": "s223846208", "label": 115, "func": ""}
{"index": "s936522851", "label": 115, "func": ""}
{"index": "s681047479", "label": 3082, "func": ""}
{"index": "s836396000", "label": 3082, "func": ""}
{"index": "s247993606", "label": 3082, "func": ""}
{"index": "s115416497", "label": 3082, "func": ""}
{"index": "s855442640", "label": 3082, "func": ""}
{"index": "s805498430", "label": 3082, "func": ""}
{"index": "s109852179", "label": 3082, "func": ""}
{"index": "s164073392", "label": 3082, "func": ""}
{"index": "s121050622", "label": 3082, "func": ""}
{"index": "s139215382", "label": 3082, "func": ""}
{"index": "s156391213", "label": 15, "func": ""}
{"index": "s189800517", "label": 15, "func": ""}
{"index": "s590839495", "label": 15, "func": ""}
{"index": "s636065554", "label": 15, "func": ""}
{"index": "s205975462", "label": 15, "func": ""}
{"index": "s804021672", "label": 15, "func": ""}
{"index": "s767920813", "label": 15, "func": ""}
{"index": "s964963167", "label": 15, "func": ""}
{"index": "s088798258", "label": 15, "func": ""}
{"index": "s335394899", "label": 15, "func": ""}
{"index": "s755893978", "label": 2716, "func": ""}
{"index": "s559419535", "label": 2716, "func": ""}
{"index": "s990570809", "label": 2716, "func": ""}
{"index": "s831530893", "label": 2716, "func": ""}
{"index": "s151116683", "label": 2716, "func": ""}
{"index": "s079272267", "label": 2716, "func": ""}
{"index": "s617579757", "label": 2716, "func": ""}
{"index": "s319313900", "label": 2716, "func": ""}
{"index": "s480356412", "label": 2716, "func": ""}
{"index": "s510273765", "label": 2716, "func": ""}
{"index": "s871939586", "label": 1155, "func": ""}
{"index": "s378424305", "label": 1155, "func": ""}
{"index": "s959602696", "label": 1155, "func": ""}
{"index": "s851326336", "label": 1155, "func": ""}
{"index": "s337654788", "label": 1155, "func": ""}
{"index": "s340283188", "label": 1155, "func": ""}
{"index": "s171535391", "label": 1155, "func": ""}
{"index": "s031862868", "label": 1155, "func": ""}
{"index": "s799645352", "label": 1155, "func": ""}
{"index": "s406580880", "label": 586, "func": ""}
{"index": "s586989369", "label": 586, "func": ""}
{"index": "s150186213", "label": 586, "func": ""}
{"index": "s292909466", "label": 586, "func": ""}
{"index": "s817897988", "label": 586, "func": ""}
{"index": "s386301854", "label": 586, "func": ""}
{"index": "s929061565", "label": 586, "func": ""}
{"index": "s530378076", "label": 586, "func": ""}
{"index": "s490574449", "label": 586, "func": ""}
{"index": "s725341737", "label": 586, "func": ""}
{"index": "s960829418", "label": 1369, "func": ""}
{"index": "s984249133", "label": 1369, "func": ""}
{"index": "s689761099", "label": 1369, "func": ""}
{"index": "s229361553", "label": 1369, "func": ""}
{"index": "s229542781", "label": 1369, "func": ""}
{"index": "s952815431", "label": 1369, "func": ""}
{"index": "s311392523", "label": 1369, "func": ""}
{"index": "s822789696", "label": 1369, "func": ""}
{"index": "s329504965", "label": 1369, "func": ""}
{"index": "s266796370", "label": 1369, "func": ""}
{"index": "s009891628", "label": 64, "func": ""}
{"index": "s683042712", "label": 64, "func": ""}
{"index": "s972501906", "label": 64, "func": ""}
{"index": "s434178667", "label": 64, "func": ""}
{"index": "s483120177", "label": 64, "func": ""}
{"index": "s593867000", "label": 64, "func": ""}
{"index": "s975069046", "label": 64, "func": ""}
{"index": "s973256519", "label": 64, "func": ""}
{"index": "s807397023", "label": 64, "func": ""}
{"index": "s434901853", "label": 64, "func": ""}
{"index": "s971762983", "label": 1865, "func": ""}
{"index": "s426192787", "label": 1865, "func": ""}
{"index": "s548778085", "label": 1865, "func": ""}
{"index": "s227982806", "label": 1865, "func": ""}
{"index": "s059451439", "label": 1865, "func": ""}
{"index": "s500218657", "label": 1865, "func": ""}
{"index": "s797793884", "label": 3645, "func": ""}
{"index": "s595903180", "label": 3645, "func": ""}
{"index": "s166606439", "label": 3645, "func": ""}
{"index": "s165833336", "label": 3645, "func": ""}
{"index": "s309874790", "label": 3645, "func": ""}
{"index": "s631116023", "label": 3645, "func": ""}
{"index": "s392543981", "label": 3645, "func": ""}
{"index": "s455825245", "label": 3645, "func": ""}
{"index": "s094227208", "label": 3645, "func": ""}
{"index": "s318919394", "label": 3645, "func": ""}
{"index": "s832680283", "label": 3967, "func": ""}
{"index": "s618022851", "label": 3967, "func": ""}
{"index": "s195589111", "label": 3967, "func": ""}
{"index": "s464369758", "label": 3967, "func": ""}
{"index": "s333604256", "label": 3967, "func": ""}
{"index": "s148798042", "label": 3967, "func": ""}
{"index": "s044791150", "label": 3967, "func": ""}
{"index": "s065152571", "label": 3967, "func": ""}
{"index": "s442815324", "label": 3967, "func": ""}
{"index": "s236786793", "label": 3967, "func": ""}
{"index": "s673582330", "label": 781, "func": ""}
{"index": "s967605936", "label": 781, "func": ""}
{"index": "s217256230", "label": 781, "func": ""}
{"index": "s817911787", "label": 781, "func": ""}
{"index": "s655215690", "label": 781, "func": ""}
{"index": "s721326304", "label": 781, "func": ""}
{"index": "s581894006", "label": 781, "func": ""}
{"index": "s405178726", "label": 2664, "func": ""}
{"index": "s247677481", "label": 2664, "func": ""}
{"index": "s250634127", "label": 2664, "func": ""}
{"index": "s548663244", "label": 2664, "func": ""}
{"index": "s391315868", "label": 2664, "func": ""}
{"index": "s834899910", "label": 2664, "func": ""}
{"index": "s376082781", "label": 2664, "func": ""}
{"index": "s173883086", "label": 2664, "func": ""}
{"index": "s241588383", "label": 2664, "func": ""}
{"index": "s621605277", "label": 2664, "func": ""}
{"index": "s025429310", "label": 446, "func": ""}
{"index": "s172690395", "label": 446, "func": ""}
{"index": "s035249955", "label": 446, "func": ""}
{"index": "s896845062", "label": 446, "func": ""}
{"index": "s796215714", "label": 446, "func": ""}
{"index": "s098787775", "label": 446, "func": ""}
{"index": "s495479372", "label": 446, "func": ""}
{"index": "s364689903", "label": 446, "func": ""}
{"index": "s442498857", "label": 446, "func": ""}
{"index": "s976203747", "label": 446, "func": ""}
{"index": "s765058549", "label": 3869, "func": ""}
{"index": "s867663316", "label": 3869, "func": ""}
{"index": "s782748238", "label": 1905, "func": ""}
{"index": "s342561836", "label": 1905, "func": ""}
{"index": "s449419438", "label": 1905, "func": ""}
{"index": "s232695229", "label": 1905, "func": ""}
{"index": "s750132231", "label": 1905, "func": ""}
{"index": "s128634300", "label": 1905, "func": ""}
{"index": "s267776571", "label": 2938, "func": ""}
{"index": "s830026277", "label": 2938, "func": ""}
{"index": "s351775307", "label": 2938, "func": ""}
{"index": "s281290589", "label": 2938, "func": ""}
{"index": "s556853833", "label": 2938, "func": ""}
{"index": "s949410147", "label": 2938, "func": ""}
{"index": "s085693360", "label": 2938, "func": ""}
{"index": "s198205117", "label": 907, "func": ""}
{"index": "s107979807", "label": 907, "func": ""}
{"index": "s712447608", "label": 907, "func": ""}
{"index": "s517380873", "label": 3603, "func": ""}
{"index": "s683376965", "label": 3603, "func": ""}
{"index": "s919834476", "label": 3603, "func": ""}
{"index": "s900570631", "label": 3603, "func": ""}
{"index": "s798778116", "label": 3603, "func": ""}
{"index": "s368275251", "label": 3603, "func": ""}
{"index": "s422415564", "label": 3603, "func": ""}
{"index": "s836232524", "label": 3603, "func": ""}
{"index": "s541575415", "label": 3603, "func": ""}
{"index": "s897960226", "label": 3603, "func": ""}
{"index": "s026072314", "label": 2288, "func": ""}
{"index": "s031012316", "label": 2288, "func": ""}
{"index": "s339194021", "label": 2288, "func": ""}
{"index": "s405110681", "label": 2288, "func": ""}
{"index": "s017464199", "label": 2288, "func": ""}
{"index": "s040721575", "label": 2288, "func": ""}
{"index": "s248348148", "label": 2288, "func": ""}
{"index": "s853488850", "label": 2288, "func": ""}
{"index": "s742416809", "label": 2288, "func": ""}
{"index": "s189519398", "label": 2288, "func": ""}
{"index": "s809857019", "label": 3868, "func": ""}
{"index": "s378580500", "label": 3060, "func": ""}
{"index": "s275333652", "label": 3060, "func": ""}
{"index": "s537053026", "label": 3060, "func": ""}
{"index": "s244899188", "label": 3060, "func": ""}
{"index": "s170686638", "label": 3060, "func": ""}
{"index": "s690139696", "label": 3060, "func": ""}
{"index": "s163194582", "label": 3060, "func": ""}
{"index": "s711125510", "label": 3060, "func": ""}
{"index": "s483201798", "label": 3060, "func": ""}
{"index": "s377361405", "label": 3060, "func": ""}
{"index": "s348492809", "label": 463, "func": ""}
{"index": "s802433338", "label": 463, "func": ""}
{"index": "s081771465", "label": 463, "func": ""}
{"index": "s318614414", "label": 463, "func": ""}
{"index": "s681774259", "label": 463, "func": ""}
{"index": "s377945190", "label": 463, "func": ""}
{"index": "s176994058", "label": 3304, "func": ""}
{"index": "s231315044", "label": 3304, "func": ""}
{"index": "s720133643", "label": 3304, "func": ""}
{"index": "s598007160", "label": 3304, "func": ""}
{"index": "s203229117", "label": 3304, "func": ""}
{"index": "s937951764", "label": 3304, "func": ""}
{"index": "s351700494", "label": 3304, "func": ""}
{"index": "s047356461", "label": 3304, "func": ""}
{"index": "s972852792", "label": 3304, "func": ""}
{"index": "s963982968", "label": 3304, "func": ""}
{"index": "s120380759", "label": 1399, "func": ""}
{"index": "s983517127", "label": 1399, "func": ""}
{"index": "s233662733", "label": 1399, "func": ""}
{"index": "s198043454", "label": 1399, "func": ""}
{"index": "s963777993", "label": 1399, "func": ""}
{"index": "s923614753", "label": 1399, "func": ""}
{"index": "s529750657", "label": 1399, "func": ""}
{"index": "s106105503", "label": 1399, "func": ""}
{"index": "s660909275", "label": 1399, "func": ""}
{"index": "s778411490", "label": 304, "func": ""}
{"index": "s269936041", "label": 304, "func": ""}
{"index": "s069714654", "label": 304, "func": ""}
{"index": "s696977202", "label": 487, "func": ""}
{"index": "s912410433", "label": 487, "func": ""}
{"index": "s228441602", "label": 487, "func": ""}
{"index": "s328750916", "label": 487, "func": ""}
{"index": "s320564180", "label": 487, "func": ""}
{"index": "s777999332", "label": 487, "func": ""}
{"index": "s856227676", "label": 3232, "func": ""}
{"index": "s053222340", "label": 3232, "func": ""}
{"index": "s888410981", "label": 3232, "func": ""}
{"index": "s019042952", "label": 3232, "func": ""}
{"index": "s492996023", "label": 3232, "func": ""}
{"index": "s778798915", "label": 3232, "func": ""}
{"index": "s003754625", "label": 3232, "func": ""}
{"index": "s058502486", "label": 3232, "func": ""}
{"index": "s511307817", "label": 3232, "func": ""}
{"index": "s761522983", "label": 3232, "func": ""}
{"index": "s510773505", "label": 254, "func": ""}
{"index": "s749913092", "label": 254, "func": ""}
{"index": "s421340584", "label": 254, "func": ""}
{"index": "s287322118", "label": 254, "func": ""}
{"index": "s819030230", "label": 254, "func": ""}
{"index": "s250262121", "label": 254, "func": ""}
{"index": "s673638803", "label": 254, "func": ""}
{"index": "s902454062", "label": 254, "func": ""}
{"index": "s680242877", "label": 254, "func": ""}
{"index": "s802849017", "label": 254, "func": ""}
{"index": "s943697802", "label": 2022, "func": ""}
{"index": "s286595220", "label": 2022, "func": ""}
{"index": "s364342520", "label": 2806, "func": ""}
{"index": "s546276555", "label": 2806, "func": ""}
{"index": "s753506327", "label": 2806, "func": ""}
{"index": "s446657976", "label": 2806, "func": ""}
{"index": "s087218560", "label": 2806, "func": ""}
{"index": "s204456923", "label": 2806, "func": ""}
{"index": "s902922324", "label": 2806, "func": ""}
{"index": "s521388614", "label": 2806, "func": ""}
{"index": "s996480750", "label": 2806, "func": ""}
{"index": "s179697782", "label": 2806, "func": ""}
{"index": "s055105806", "label": 3558, "func": ""}
{"index": "s596222361", "label": 3558, "func": ""}
{"index": "s180868123", "label": 3558, "func": ""}
{"index": "s459965648", "label": 3558, "func": ""}
{"index": "s097217164", "label": 3558, "func": ""}
{"index": "s137581967", "label": 3558, "func": ""}
{"index": "s702415122", "label": 3558, "func": ""}
{"index": "s825571361", "label": 3558, "func": ""}
{"index": "s623129638", "label": 3558, "func": ""}
{"index": "s377092217", "label": 3558, "func": ""}
{"index": "s086471435", "label": 3575, "func": ""}
{"index": "s225093777", "label": 3575, "func": ""}
{"index": "s759912095", "label": 3575, "func": ""}
{"index": "s454577721", "label": 3575, "func": ""}
{"index": "s345738478", "label": 3575, "func": ""}
{"index": "s847186960", "label": 3575, "func": ""}
{"index": "s505243474", "label": 3575, "func": ""}
{"index": "s253730772", "label": 3575, "func": ""}
{"index": "s838632186", "label": 3575, "func": ""}
{"index": "s182215822", "label": 3575, "func": ""}
{"index": "s336675512", "label": 1208, "func": ""}
{"index": "s847399674", "label": 1208, "func": ""}
{"index": "s516119098", "label": 1208, "func": ""}
{"index": "s984934743", "label": 1208, "func": ""}
{"index": "s525718228", "label": 1208, "func": ""}
{"index": "s081105222", "label": 3648, "func": ""}
{"index": "s216573656", "label": 3648, "func": ""}
{"index": "s660667923", "label": 3648, "func": ""}
{"index": "s159490384", "label": 3648, "func": ""}
{"index": "s718471137", "label": 3648, "func": ""}
{"index": "s405932537", "label": 3648, "func": ""}
{"index": "s551477751", "label": 3648, "func": ""}
{"index": "s734949156", "label": 3648, "func": ""}
{"index": "s072986587", "label": 3648, "func": ""}
{"index": "s839638057", "label": 3648, "func": ""}
{"index": "s153266053", "label": 3839, "func": ""}
{"index": "s270220213", "label": 3839, "func": ""}
{"index": "s390459722", "label": 3839, "func": ""}
{"index": "s833001045", "label": 3839, "func": ""}
{"index": "s461290046", "label": 3839, "func": ""}
{"index": "s461658390", "label": 3839, "func": ""}
{"index": "s008184508", "label": 3839, "func": ""}
{"index": "s936288097", "label": 3839, "func": ""}
{"index": "s926258958", "label": 3839, "func": ""}
{"index": "s219679660", "label": 3839, "func": ""}
{"index": "s427747653", "label": 3751, "func": ""}
{"index": "s117415004", "label": 3217, "func": ""}
{"index": "s859615608", "label": 210, "func": ""}
{"index": "s648343267", "label": 210, "func": ""}
{"index": "s310076850", "label": 210, "func": ""}
{"index": "s039860433", "label": 210, "func": ""}
{"index": "s974369798", "label": 210, "func": ""}
{"index": "s018640737", "label": 210, "func": ""}
{"index": "s523676763", "label": 210, "func": ""}
{"index": "s861405301", "label": 210, "func": ""}
{"index": "s319667536", "label": 210, "func": ""}
{"index": "s418654938", "label": 210, "func": ""}
{"index": "s604442926", "label": 2514, "func": ""}
{"index": "s184630898", "label": 2514, "func": ""}
{"index": "s150182234", "label": 2514, "func": ""}
{"index": "s982834394", "label": 2514, "func": ""}
{"index": "s784401504", "label": 2514, "func": ""}
{"index": "s036711117", "label": 2514, "func": ""}
{"index": "s102906239", "label": 2514, "func": ""}
{"index": "s478604882", "label": 2514, "func": ""}
{"index": "s282539891", "label": 2514, "func": ""}
{"index": "s423580118", "label": 2514, "func": ""}
{"index": "s171783060", "label": 937, "func": ""}
{"index": "s180410381", "label": 937, "func": ""}
{"index": "s434026191", "label": 937, "func": ""}
{"index": "s063887105", "label": 937, "func": ""}
{"index": "s400531488", "label": 3863, "func": ""}
{"index": "s612870933", "label": 3863, "func": ""}
{"index": "s515027120", "label": 3863, "func": ""}
{"index": "s758594834", "label": 3863, "func": ""}
{"index": "s307435753", "label": 3863, "func": ""}
{"index": "s985570807", "label": 3863, "func": ""}
{"index": "s151146813", "label": 3863, "func": ""}
{"index": "s987167419", "label": 3863, "func": ""}
{"index": "s958555691", "label": 3863, "func": ""}
{"index": "s991372394", "label": 3863, "func": ""}
{"index": "s514229187", "label": 1513, "func": ""}
{"index": "s053076231", "label": 1513, "func": ""}
{"index": "s545040189", "label": 1513, "func": ""}
{"index": "s656619864", "label": 1513, "func": ""}
{"index": "s867720897", "label": 1513, "func": ""}
{"index": "s811175235", "label": 1513, "func": ""}
{"index": "s457996462", "label": 1513, "func": ""}
{"index": "s079774718", "label": 1513, "func": ""}
{"index": "s898065340", "label": 1513, "func": ""}
{"index": "s581760516", "label": 1513, "func": ""}
{"index": "s799832198", "label": 3623, "func": ""}
{"index": "s150752318", "label": 3623, "func": ""}
{"index": "s602558431", "label": 3623, "func": ""}
{"index": "s264198791", "label": 3623, "func": ""}
{"index": "s281592242", "label": 3623, "func": ""}
{"index": "s037867581", "label": 3623, "func": ""}
{"index": "s082548209", "label": 3623, "func": ""}
{"index": "s920840756", "label": 3623, "func": ""}
{"index": "s735509505", "label": 3623, "func": ""}
{"index": "s234463535", "label": 3623, "func": ""}
{"index": "s583950612", "label": 1640, "func": ""}
{"index": "s771630529", "label": 183, "func": ""}
{"index": "s904072525", "label": 183, "func": ""}
{"index": "s781236438", "label": 183, "func": ""}
{"index": "s829925945", "label": 183, "func": ""}
{"index": "s888701422", "label": 183, "func": ""}
{"index": "s100100924", "label": 183, "func": ""}
{"index": "s123874126", "label": 183, "func": ""}
{"index": "s154570534", "label": 183, "func": ""}
{"index": "s712022642", "label": 183, "func": ""}
{"index": "s929377458", "label": 183, "func": ""}
{"index": "s893174568", "label": 2404, "func": ""}
{"index": "s337612314", "label": 2404, "func": ""}
{"index": "s035139163", "label": 2404, "func": ""}
{"index": "s243890557", "label": 2404, "func": ""}
{"index": "s117236102", "label": 2404, "func": ""}
{"index": "s492969035", "label": 2404, "func": ""}
{"index": "s101281184", "label": 2404, "func": ""}
{"index": "s247863687", "label": 2404, "func": ""}
{"index": "s944787864", "label": 2404, "func": ""}
{"index": "s567087140", "label": 2404, "func": ""}
{"index": "s732886846", "label": 898, "func": ""}
{"index": "s499324947", "label": 2401, "func": ""}
{"index": "s820842717", "label": 2401, "func": ""}
{"index": "s378084219", "label": 2401, "func": ""}
{"index": "s634506331", "label": 2401, "func": ""}
{"index": "s609159460", "label": 2401, "func": ""}
{"index": "s787246887", "label": 2401, "func": ""}
{"index": "s305349240", "label": 2401, "func": ""}
{"index": "s961829996", "label": 2401, "func": ""}
{"index": "s197732917", "label": 2401, "func": ""}
{"index": "s731016681", "label": 2401, "func": ""}
{"index": "s872234734", "label": 3361, "func": ""}
{"index": "s066924474", "label": 3361, "func": ""}
{"index": "s556091369", "label": 3361, "func": ""}
{"index": "s336111983", "label": 3361, "func": ""}
{"index": "s595555004", "label": 3361, "func": ""}
{"index": "s608303683", "label": 3361, "func": ""}
{"index": "s702675602", "label": 3361, "func": ""}
{"index": "s335640099", "label": 3361, "func": ""}
{"index": "s032585502", "label": 3361, "func": ""}
{"index": "s171179690", "label": 3361, "func": ""}
{"index": "s494299548", "label": 2033, "func": ""}
{"index": "s779913515", "label": 3702, "func": ""}
{"index": "s947945110", "label": 3702, "func": ""}
{"index": "s774720441", "label": 3702, "func": ""}
{"index": "s723443590", "label": 3702, "func": ""}
{"index": "s533745163", "label": 3702, "func": ""}
{"index": "s568246440", "label": 3702, "func": ""}
{"index": "s790903715", "label": 3702, "func": ""}
{"index": "s600139059", "label": 3702, "func": ""}
{"index": "s838583319", "label": 3702, "func": ""}
{"index": "s363221152", "label": 3702, "func": ""}
{"index": "s172157503", "label": 628, "func": ""}
{"index": "s554230159", "label": 628, "func": ""}
{"index": "s099608787", "label": 628, "func": ""}
{"index": "s571343600", "label": 628, "func": ""}
{"index": "s889806762", "label": 628, "func": ""}
{"index": "s147904688", "label": 628, "func": ""}
{"index": "s107998069", "label": 628, "func": ""}
{"index": "s804816552", "label": 628, "func": ""}
{"index": "s126353548", "label": 628, "func": ""}
{"index": "s967064253", "label": 628, "func": ""}
{"index": "s847930918", "label": 58, "func": ""}
{"index": "s209590545", "label": 58, "func": ""}
{"index": "s783362128", "label": 58, "func": ""}
{"index": "s312403752", "label": 58, "func": ""}
{"index": "s253810941", "label": 58, "func": ""}
{"index": "s775076811", "label": 58, "func": ""}
{"index": "s241475755", "label": 58, "func": ""}
{"index": "s733770079", "label": 58, "func": ""}
{"index": "s614964362", "label": 58, "func": ""}
{"index": "s369856938", "label": 58, "func": ""}
{"index": "s084128449", "label": 3487, "func": ""}
{"index": "s655897060", "label": 3487, "func": ""}
{"index": "s922748815", "label": 3487, "func": ""}
{"index": "s886340802", "label": 3487, "func": ""}
{"index": "s960648552", "label": 3487, "func": ""}
{"index": "s993387121", "label": 3487, "func": ""}
{"index": "s506498326", "label": 3487, "func": ""}
{"index": "s768362368", "label": 3487, "func": ""}
{"index": "s376930616", "label": 3487, "func": ""}
{"index": "s655212459", "label": 3487, "func": ""}
{"index": "s730308911", "label": 1125, "func": ""}
{"index": "s223924945", "label": 1125, "func": ""}
{"index": "s390821637", "label": 1125, "func": ""}
{"index": "s942574723", "label": 1125, "func": ""}
{"index": "s151557612", "label": 1125, "func": ""}
{"index": "s701073351", "label": 1125, "func": ""}
{"index": "s280413434", "label": 1125, "func": ""}
{"index": "s675696414", "label": 1125, "func": ""}
{"index": "s155306471", "label": 1125, "func": ""}
{"index": "s637074560", "label": 1125, "func": ""}
{"index": "s493863641", "label": 2868, "func": ""}
{"index": "s679267062", "label": 2868, "func": ""}
{"index": "s299540616", "label": 2868, "func": ""}
{"index": "s545688466", "label": 2868, "func": ""}
{"index": "s078185715", "label": 2868, "func": ""}
{"index": "s459409595", "label": 2868, "func": ""}
{"index": "s048674055", "label": 2868, "func": ""}
{"index": "s831115579", "label": 2868, "func": ""}
{"index": "s301791316", "label": 2868, "func": ""}
{"index": "s168085831", "label": 2868, "func": ""}
{"index": "s534414436", "label": 1398, "func": ""}
{"index": "s054032363", "label": 1398, "func": ""}
{"index": "s726028788", "label": 1398, "func": ""}
{"index": "s513773906", "label": 1398, "func": ""}
{"index": "s071895056", "label": 1398, "func": ""}
{"index": "s113624393", "label": 1398, "func": ""}
{"index": "s885574267", "label": 1398, "func": ""}
{"index": "s109478898", "label": 1398, "func": ""}
{"index": "s985558731", "label": 1398, "func": ""}
{"index": "s542169066", "label": 1398, "func": ""}
{"index": "s823115578", "label": 366, "func": ""}
{"index": "s004566878", "label": 366, "func": ""}
{"index": "s467488626", "label": 366, "func": ""}
{"index": "s725256212", "label": 366, "func": ""}
{"index": "s962431156", "label": 3254, "func": ""}
{"index": "s538489238", "label": 3254, "func": ""}
{"index": "s274116906", "label": 3254, "func": ""}
{"index": "s010503166", "label": 3254, "func": ""}
{"index": "s488558042", "label": 3254, "func": ""}
{"index": "s761468457", "label": 3254, "func": ""}
{"index": "s415392776", "label": 3254, "func": ""}
{"index": "s470682651", "label": 3254, "func": ""}
{"index": "s671843225", "label": 3254, "func": ""}
{"index": "s573845669", "label": 3254, "func": ""}
{"index": "s610386762", "label": 1301, "func": ""}
{"index": "s838967367", "label": 1220, "func": ""}
{"index": "s465225120", "label": 428, "func": ""}
{"index": "s125382720", "label": 428, "func": ""}
{"index": "s997734477", "label": 428, "func": ""}
{"index": "s801793346", "label": 428, "func": ""}
{"index": "s261396854", "label": 428, "func": ""}
{"index": "s984859488", "label": 428, "func": ""}
{"index": "s492075206", "label": 428, "func": ""}
{"index": "s652077588", "label": 428, "func": ""}
{"index": "s378455968", "label": 428, "func": ""}
{"index": "s833405078", "label": 428, "func": ""}
{"index": "s621964328", "label": 1761, "func": ""}
{"index": "s084262743", "label": 1761, "func": ""}
{"index": "s354552215", "label": 1819, "func": ""}
{"index": "s635039610", "label": 3145, "func": ""}
{"index": "s614145769", "label": 3145, "func": ""}
{"index": "s549784253", "label": 3145, "func": ""}
{"index": "s712645391", "label": 3145, "func": ""}
{"index": "s090508773", "label": 3145, "func": ""}
{"index": "s204834167", "label": 3145, "func": ""}
{"index": "s883926368", "label": 3145, "func": ""}
{"index": "s958742417", "label": 3145, "func": ""}
{"index": "s809971774", "label": 3145, "func": ""}
{"index": "s691790994", "label": 3145, "func": ""}
{"index": "s006442547", "label": 2900, "func": ""}
{"index": "s535377276", "label": 2900, "func": ""}
{"index": "s459396928", "label": 2900, "func": ""}
{"index": "s003238705", "label": 2900, "func": ""}
{"index": "s603801062", "label": 2900, "func": ""}
{"index": "s636829485", "label": 2900, "func": ""}
{"index": "s615856328", "label": 2900, "func": ""}
{"index": "s327226486", "label": 2900, "func": ""}
{"index": "s156502511", "label": 2900, "func": ""}
{"index": "s071486631", "label": 2900, "func": ""}
{"index": "s936364486", "label": 1250, "func": ""}
{"index": "s757427533", "label": 1250, "func": ""}
{"index": "s331597500", "label": 1250, "func": ""}
{"index": "s954419339", "label": 1250, "func": ""}
{"index": "s940859378", "label": 1250, "func": ""}
{"index": "s291190608", "label": 1250, "func": ""}
{"index": "s067013454", "label": 3298, "func": ""}
{"index": "s141921058", "label": 3298, "func": ""}
{"index": "s125170038", "label": 3298, "func": ""}
{"index": "s434725317", "label": 3298, "func": ""}
{"index": "s961043644", "label": 3298, "func": ""}
{"index": "s070728360", "label": 3298, "func": ""}
{"index": "s481160105", "label": 3298, "func": ""}
{"index": "s381778105", "label": 3298, "func": ""}
{"index": "s315759851", "label": 3298, "func": ""}
{"index": "s156006454", "label": 3298, "func": ""}
{"index": "s248751387", "label": 2424, "func": ""}
{"index": "s435691720", "label": 2424, "func": ""}
{"index": "s495488183", "label": 2424, "func": ""}
{"index": "s615748464", "label": 2424, "func": ""}
{"index": "s151046372", "label": 2424, "func": ""}
{"index": "s175004680", "label": 2424, "func": ""}
{"index": "s513222223", "label": 2424, "func": ""}
{"index": "s288282306", "label": 2424, "func": ""}
{"index": "s883894729", "label": 2424, "func": ""}
{"index": "s322567320", "label": 2424, "func": ""}
{"index": "s444652752", "label": 234, "func": ""}
{"index": "s305808266", "label": 234, "func": ""}
{"index": "s902896771", "label": 234, "func": ""}
{"index": "s193529242", "label": 234, "func": ""}
{"index": "s941697764", "label": 234, "func": ""}
{"index": "s032540794", "label": 234, "func": ""}
{"index": "s316223913", "label": 234, "func": ""}
{"index": "s633740746", "label": 234, "func": ""}
{"index": "s055999705", "label": 234, "func": ""}
{"index": "s393852806", "label": 234, "func": ""}
{"index": "s768716627", "label": 3904, "func": ""}
{"index": "s701613702", "label": 2421, "func": ""}
{"index": "s583364274", "label": 2421, "func": ""}
{"index": "s777342411", "label": 2421, "func": ""}
{"index": "s055351195", "label": 2421, "func": ""}
{"index": "s176496481", "label": 2421, "func": ""}
{"index": "s471284587", "label": 2421, "func": ""}
{"index": "s349984167", "label": 2421, "func": ""}
{"index": "s569316700", "label": 2421, "func": ""}
{"index": "s325332914", "label": 2421, "func": ""}
{"index": "s499528054", "label": 2421, "func": ""}
{"index": "s613769761", "label": 1109, "func": ""}
{"index": "s947814278", "label": 1109, "func": ""}
{"index": "s234260619", "label": 1109, "func": ""}
{"index": "s762863627", "label": 1109, "func": ""}
{"index": "s257635461", "label": 1109, "func": ""}
{"index": "s567679811", "label": 1109, "func": ""}
{"index": "s983154521", "label": 1109, "func": ""}
{"index": "s726208999", "label": 1109, "func": ""}
{"index": "s404737214", "label": 1109, "func": ""}
{"index": "s735927365", "label": 1109, "func": ""}
{"index": "s670398211", "label": 1379, "func": ""}
{"index": "s513991917", "label": 1379, "func": ""}
{"index": "s496304251", "label": 1379, "func": ""}
{"index": "s278707544", "label": 1379, "func": ""}
{"index": "s442116605", "label": 1379, "func": ""}
{"index": "s990046856", "label": 1379, "func": ""}
{"index": "s042970090", "label": 1379, "func": ""}
{"index": "s151587640", "label": 3243, "func": ""}
{"index": "s408014260", "label": 3243, "func": ""}
{"index": "s456449755", "label": 3243, "func": ""}
{"index": "s792325903", "label": 3243, "func": ""}
{"index": "s732233521", "label": 3243, "func": ""}
{"index": "s308357724", "label": 3243, "func": ""}
{"index": "s500003390", "label": 3243, "func": ""}
{"index": "s435779504", "label": 3243, "func": ""}
{"index": "s900106975", "label": 3243, "func": ""}
{"index": "s458966504", "label": 3243, "func": ""}
{"index": "s855803090", "label": 3395, "func": ""}
{"index": "s621444911", "label": 3395, "func": ""}
{"index": "s611122186", "label": 3395, "func": ""}
{"index": "s822341209", "label": 3395, "func": ""}
{"index": "s802216039", "label": 3395, "func": ""}
{"index": "s968490678", "label": 3395, "func": ""}
{"index": "s654306601", "label": 3395, "func": ""}
{"index": "s543847865", "label": 2709, "func": ""}
{"index": "s019131689", "label": 2709, "func": ""}
{"index": "s391734115", "label": 2709, "func": ""}
{"index": "s576996341", "label": 2709, "func": ""}
{"index": "s385067263", "label": 2709, "func": ""}
{"index": "s206607784", "label": 2709, "func": ""}
{"index": "s877951617", "label": 2709, "func": ""}
{"index": "s260502452", "label": 2709, "func": ""}
{"index": "s952807223", "label": 2709, "func": ""}
{"index": "s002990685", "label": 2709, "func": ""}
{"index": "s147406018", "label": 979, "func": ""}
{"index": "s536162503", "label": 979, "func": ""}
{"index": "s160084902", "label": 979, "func": ""}
{"index": "s824063161", "label": 2766, "func": ""}
{"index": "s083331554", "label": 2766, "func": ""}
{"index": "s775596578", "label": 2766, "func": ""}
{"index": "s608452826", "label": 2766, "func": ""}
{"index": "s139975665", "label": 2766, "func": ""}
{"index": "s128646485", "label": 2766, "func": ""}
{"index": "s646243914", "label": 2766, "func": ""}
{"index": "s798921900", "label": 2766, "func": ""}
{"index": "s496756839", "label": 2766, "func": ""}
{"index": "s292497337", "label": 2766, "func": ""}
{"index": "s326149278", "label": 86, "func": ""}
{"index": "s998723981", "label": 86, "func": ""}
{"index": "s417126117", "label": 86, "func": ""}
{"index": "s267949342", "label": 86, "func": ""}
{"index": "s156731543", "label": 86, "func": ""}
{"index": "s468824025", "label": 86, "func": ""}
{"index": "s784374016", "label": 86, "func": ""}
{"index": "s208953220", "label": 86, "func": ""}
{"index": "s984863182", "label": 86, "func": ""}
{"index": "s691203779", "label": 86, "func": ""}
{"index": "s513751856", "label": 78, "func": ""}
{"index": "s893795090", "label": 78, "func": ""}
{"index": "s670500693", "label": 78, "func": ""}
{"index": "s382408038", "label": 78, "func": ""}
{"index": "s465933991", "label": 78, "func": ""}
{"index": "s176897643", "label": 78, "func": ""}
{"index": "s866341977", "label": 78, "func": ""}
{"index": "s322225004", "label": 78, "func": ""}
{"index": "s707760600", "label": 78, "func": ""}
{"index": "s228693985", "label": 78, "func": ""}
{"index": "s208922185", "label": 1168, "func": ""}
{"index": "s116468999", "label": 1168, "func": ""}
{"index": "s157574581", "label": 2532, "func": ""}
{"index": "s939179066", "label": 2532, "func": ""}
{"index": "s610580708", "label": 2532, "func": ""}
{"index": "s947463583", "label": 2532, "func": ""}
{"index": "s947891471", "label": 2532, "func": ""}
{"index": "s095069956", "label": 2532, "func": ""}
{"index": "s861450615", "label": 2532, "func": ""}
{"index": "s127279966", "label": 2532, "func": ""}
{"index": "s472708074", "label": 2532, "func": ""}
{"index": "s633087172", "label": 2532, "func": ""}
{"index": "s209182163", "label": 3962, "func": ""}
{"index": "s985639868", "label": 3962, "func": ""}
{"index": "s420590036", "label": 3962, "func": ""}
{"index": "s140588332", "label": 3962, "func": ""}
{"index": "s767977003", "label": 3962, "func": ""}
{"index": "s105476279", "label": 3962, "func": ""}
{"index": "s772179663", "label": 3962, "func": ""}
{"index": "s116470327", "label": 3962, "func": ""}
{"index": "s507236788", "label": 3962, "func": ""}
{"index": "s575579179", "label": 3962, "func": ""}
{"index": "s537182121", "label": 698, "func": ""}
{"index": "s306553132", "label": 698, "func": ""}
{"index": "s630302292", "label": 698, "func": ""}
{"index": "s193080588", "label": 698, "func": ""}
{"index": "s991179978", "label": 698, "func": ""}
{"index": "s608083697", "label": 698, "func": ""}
{"index": "s505183446", "label": 698, "func": ""}
{"index": "s891197160", "label": 698, "func": ""}
{"index": "s597748081", "label": 2987, "func": ""}
{"index": "s588056670", "label": 2987, "func": ""}
{"index": "s315966462", "label": 2987, "func": ""}
{"index": "s336985700", "label": 2987, "func": ""}
{"index": "s142998889", "label": 2987, "func": ""}
{"index": "s626512589", "label": 2987, "func": ""}
{"index": "s611199757", "label": 2987, "func": ""}
{"index": "s503647432", "label": 2987, "func": ""}
{"index": "s755940712", "label": 2987, "func": ""}
{"index": "s091252329", "label": 2987, "func": ""}
{"index": "s191174472", "label": 1593, "func": ""}
{"index": "s114136116", "label": 2747, "func": ""}
{"index": "s735668674", "label": 2747, "func": ""}
{"index": "s159521420", "label": 2747, "func": ""}
{"index": "s401544372", "label": 2747, "func": ""}
{"index": "s451682293", "label": 2747, "func": ""}
{"index": "s160176646", "label": 2747, "func": ""}
{"index": "s596099493", "label": 2747, "func": ""}
{"index": "s152926460", "label": 2747, "func": ""}
{"index": "s876115051", "label": 2747, "func": ""}
{"index": "s841365545", "label": 2747, "func": ""}
{"index": "s597591682", "label": 238, "func": ""}
{"index": "s603784062", "label": 238, "func": ""}
{"index": "s010246052", "label": 238, "func": ""}
{"index": "s022126659", "label": 238, "func": ""}
{"index": "s406433319", "label": 238, "func": ""}
{"index": "s788947121", "label": 238, "func": ""}
{"index": "s877095749", "label": 238, "func": ""}
{"index": "s726028014", "label": 238, "func": ""}
{"index": "s092583909", "label": 238, "func": ""}
{"index": "s924057155", "label": 238, "func": ""}
{"index": "s998945593", "label": 554, "func": ""}
{"index": "s612878490", "label": 554, "func": ""}
{"index": "s945893654", "label": 554, "func": ""}
{"index": "s532016240", "label": 554, "func": ""}
{"index": "s197715055", "label": 554, "func": ""}
{"index": "s149820634", "label": 554, "func": ""}
{"index": "s440100252", "label": 554, "func": ""}
{"index": "s264110001", "label": 554, "func": ""}
{"index": "s949320949", "label": 554, "func": ""}
{"index": "s994935784", "label": 554, "func": ""}
{"index": "s659641924", "label": 2523, "func": ""}
{"index": "s123214515", "label": 2523, "func": ""}
{"index": "s546040326", "label": 2523, "func": ""}
{"index": "s081216380", "label": 2523, "func": ""}
{"index": "s597975073", "label": 2523, "func": ""}
{"index": "s578193647", "label": 2523, "func": ""}
{"index": "s221382765", "label": 2523, "func": ""}
{"index": "s596405192", "label": 2523, "func": ""}
{"index": "s703975594", "label": 2523, "func": ""}
{"index": "s567648252", "label": 2523, "func": ""}
{"index": "s763131440", "label": 1515, "func": ""}
{"index": "s007084036", "label": 1515, "func": ""}
{"index": "s850454094", "label": 1515, "func": ""}
{"index": "s272947808", "label": 1515, "func": ""}
{"index": "s019223740", "label": 1515, "func": ""}
{"index": "s208097314", "label": 1515, "func": ""}
{"index": "s925564040", "label": 1515, "func": ""}
{"index": "s582071570", "label": 1515, "func": ""}
{"index": "s433815988", "label": 1515, "func": ""}
{"index": "s517529568", "label": 1515, "func": ""}
{"index": "s438786375", "label": 3810, "func": ""}
{"index": "s190644820", "label": 3810, "func": ""}
{"index": "s048233329", "label": 3810, "func": ""}
{"index": "s571061301", "label": 3418, "func": ""}
{"index": "s015676275", "label": 3418, "func": ""}
{"index": "s060008636", "label": 3418, "func": ""}
{"index": "s276738900", "label": 3418, "func": ""}
{"index": "s355559544", "label": 3418, "func": ""}
{"index": "s910300743", "label": 3418, "func": ""}
{"index": "s936672678", "label": 3418, "func": ""}
{"index": "s141429865", "label": 3418, "func": ""}
{"index": "s901473075", "label": 3418, "func": ""}
{"index": "s139808997", "label": 3418, "func": ""}
{"index": "s770523565", "label": 2605, "func": ""}
{"index": "s219164087", "label": 2605, "func": ""}
{"index": "s444926498", "label": 2605, "func": ""}
{"index": "s084480683", "label": 2605, "func": ""}
{"index": "s830457342", "label": 2605, "func": ""}
{"index": "s412664232", "label": 2605, "func": ""}
{"index": "s316125855", "label": 2605, "func": ""}
{"index": "s685820606", "label": 2605, "func": ""}
{"index": "s681874215", "label": 2605, "func": ""}
{"index": "s581660777", "label": 2605, "func": ""}
{"index": "s216653408", "label": 3281, "func": ""}
{"index": "s644392933", "label": 3281, "func": ""}
{"index": "s820017574", "label": 3281, "func": ""}
{"index": "s508257765", "label": 3281, "func": ""}
{"index": "s733816527", "label": 3281, "func": ""}
{"index": "s616105430", "label": 3281, "func": ""}
{"index": "s045137525", "label": 3281, "func": ""}
{"index": "s806057537", "label": 3281, "func": ""}
{"index": "s539778626", "label": 3281, "func": ""}
{"index": "s596344869", "label": 3281, "func": ""}
{"index": "s731026737", "label": 198, "func": ""}
{"index": "s432321555", "label": 198, "func": ""}
{"index": "s096279304", "label": 198, "func": ""}
{"index": "s791044165", "label": 198, "func": ""}
{"index": "s535602748", "label": 198, "func": ""}
{"index": "s270374611", "label": 198, "func": ""}
{"index": "s933572889", "label": 198, "func": ""}
{"index": "s852160675", "label": 198, "func": ""}
{"index": "s291037985", "label": 198, "func": ""}
{"index": "s954787092", "label": 198, "func": ""}
{"index": "s993147175", "label": 3321, "func": ""}
{"index": "s222990589", "label": 3321, "func": ""}
{"index": "s203601761", "label": 3321, "func": ""}
{"index": "s177904410", "label": 3321, "func": ""}
{"index": "s846760280", "label": 3321, "func": ""}
{"index": "s611187178", "label": 3321, "func": ""}
{"index": "s467373892", "label": 3321, "func": ""}
{"index": "s988437574", "label": 3321, "func": ""}
{"index": "s491263260", "label": 3321, "func": ""}
{"index": "s736109367", "label": 3321, "func": ""}
{"index": "s659497876", "label": 3152, "func": ""}
{"index": "s296217809", "label": 3152, "func": ""}
{"index": "s369234062", "label": 3152, "func": ""}
{"index": "s213973003", "label": 3152, "func": ""}
{"index": "s736481338", "label": 3152, "func": ""}
{"index": "s111409460", "label": 3152, "func": ""}
{"index": "s014926692", "label": 3152, "func": ""}
{"index": "s328981379", "label": 3152, "func": ""}
{"index": "s458336949", "label": 3152, "func": ""}
{"index": "s416958081", "label": 3152, "func": ""}
{"index": "s750346717", "label": 14, "func": ""}
{"index": "s319899325", "label": 14, "func": ""}
{"index": "s924696848", "label": 14, "func": ""}
{"index": "s588712853", "label": 14, "func": ""}
{"index": "s121328187", "label": 14, "func": ""}
{"index": "s677516259", "label": 14, "func": ""}
{"index": "s348196074", "label": 14, "func": ""}
{"index": "s250780637", "label": 14, "func": ""}
{"index": "s802616155", "label": 14, "func": ""}
{"index": "s554930251", "label": 14, "func": ""}
{"index": "s969624022", "label": 1682, "func": ""}
{"index": "s685375731", "label": 2817, "func": ""}
{"index": "s451193827", "label": 2817, "func": ""}
{"index": "s182408432", "label": 2817, "func": ""}
{"index": "s743982235", "label": 2817, "func": ""}
{"index": "s502526889", "label": 2817, "func": ""}
{"index": "s033294498", "label": 2817, "func": ""}
{"index": "s420433415", "label": 2817, "func": ""}
{"index": "s282902986", "label": 2817, "func": ""}
{"index": "s075408131", "label": 2817, "func": ""}
{"index": "s942139550", "label": 2817, "func": ""}
{"index": "s005548637", "label": 275, "func": ""}
{"index": "s111063890", "label": 275, "func": ""}
{"index": "s773296069", "label": 275, "func": ""}
{"index": "s925763724", "label": 275, "func": ""}
{"index": "s129736281", "label": 275, "func": ""}
{"index": "s564887869", "label": 275, "func": ""}
{"index": "s665326123", "label": 275, "func": ""}
{"index": "s226219253", "label": 275, "func": ""}
{"index": "s163218145", "label": 275, "func": ""}
{"index": "s393331616", "label": 275, "func": ""}
{"index": "s952165194", "label": 1226, "func": ""}
{"index": "s308509700", "label": 1226, "func": ""}
{"index": "s855395992", "label": 1226, "func": ""}
{"index": "s987546794", "label": 1226, "func": ""}
{"index": "s240449666", "label": 1226, "func": ""}
{"index": "s027948816", "label": 1226, "func": ""}
{"index": "s476218751", "label": 1226, "func": ""}
{"index": "s737245664", "label": 1226, "func": ""}
{"index": "s388073212", "label": 1226, "func": ""}
{"index": "s595576039", "label": 1226, "func": ""}
{"index": "s009691181", "label": 804, "func": ""}
{"index": "s965335838", "label": 804, "func": ""}
{"index": "s650319722", "label": 804, "func": ""}
{"index": "s647658597", "label": 218, "func": ""}
{"index": "s959609855", "label": 218, "func": ""}
{"index": "s105130133", "label": 218, "func": ""}
{"index": "s001999111", "label": 218, "func": ""}
{"index": "s641059958", "label": 218, "func": ""}
{"index": "s310870167", "label": 218, "func": ""}
{"index": "s067935308", "label": 218, "func": ""}
{"index": "s838746823", "label": 218, "func": ""}
{"index": "s797726577", "label": 218, "func": ""}
{"index": "s541878626", "label": 218, "func": ""}
{"index": "s074797657", "label": 803, "func": ""}
{"index": "s695913940", "label": 803, "func": ""}
{"index": "s251890162", "label": 803, "func": ""}
{"index": "s847929393", "label": 803, "func": ""}
{"index": "s269459976", "label": 803, "func": ""}
{"index": "s383731950", "label": 803, "func": ""}
{"index": "s721542171", "label": 803, "func": ""}
{"index": "s849901677", "label": 803, "func": ""}
{"index": "s135587686", "label": 803, "func": ""}
{"index": "s481316516", "label": 1991, "func": ""}
{"index": "s741211268", "label": 3101, "func": ""}
{"index": "s607431979", "label": 3101, "func": ""}
{"index": "s273132630", "label": 3101, "func": ""}
{"index": "s942772905", "label": 3101, "func": ""}
{"index": "s227565313", "label": 3101, "func": ""}
{"index": "s396865143", "label": 3101, "func": ""}
{"index": "s845399612", "label": 3101, "func": ""}
{"index": "s477925206", "label": 3101, "func": ""}
{"index": "s973551440", "label": 3101, "func": ""}
{"index": "s939457366", "label": 3101, "func": ""}
{"index": "s921735304", "label": 485, "func": ""}
{"index": "s239536873", "label": 485, "func": ""}
{"index": "s777908011", "label": 485, "func": ""}
{"index": "s132865912", "label": 485, "func": ""}
{"index": "s357854237", "label": 485, "func": ""}
{"index": "s659271760", "label": 485, "func": ""}
{"index": "s532059238", "label": 485, "func": ""}
{"index": "s581324698", "label": 485, "func": ""}
{"index": "s213485047", "label": 485, "func": ""}
{"index": "s840313047", "label": 485, "func": ""}
{"index": "s859564991", "label": 2389, "func": ""}
{"index": "s950842935", "label": 2389, "func": ""}
{"index": "s151597082", "label": 2389, "func": ""}
{"index": "s406005790", "label": 2389, "func": ""}
{"index": "s704715156", "label": 2389, "func": ""}
{"index": "s378020010", "label": 2389, "func": ""}
{"index": "s267971089", "label": 2389, "func": ""}
{"index": "s615553091", "label": 2389, "func": ""}
{"index": "s805045209", "label": 2389, "func": ""}
{"index": "s404233264", "label": 2389, "func": ""}
{"index": "s061382263", "label": 157, "func": ""}
{"index": "s944711522", "label": 157, "func": ""}
{"index": "s653303532", "label": 157, "func": ""}
{"index": "s637743641", "label": 157, "func": ""}
{"index": "s200108746", "label": 157, "func": ""}
{"index": "s735556695", "label": 157, "func": ""}
{"index": "s017579269", "label": 157, "func": ""}
{"index": "s624995676", "label": 157, "func": ""}
{"index": "s090584621", "label": 157, "func": ""}
{"index": "s039355136", "label": 157, "func": ""}
{"index": "s334830311", "label": 3029, "func": ""}
{"index": "s854509152", "label": 3029, "func": ""}
{"index": "s849844764", "label": 3029, "func": ""}
{"index": "s043483974", "label": 3029, "func": ""}
{"index": "s952836754", "label": 3029, "func": ""}
{"index": "s952478901", "label": 3029, "func": ""}
{"index": "s123421722", "label": 3029, "func": ""}
{"index": "s805455661", "label": 3029, "func": ""}
{"index": "s549071558", "label": 3029, "func": ""}
{"index": "s776602800", "label": 3029, "func": ""}
{"index": "s280908225", "label": 3792, "func": ""}
{"index": "s277475134", "label": 3792, "func": ""}
{"index": "s503907050", "label": 3792, "func": ""}
{"index": "s585989125", "label": 3792, "func": ""}
{"index": "s657131515", "label": 3792, "func": ""}
{"index": "s793713355", "label": 3792, "func": ""}
{"index": "s661609371", "label": 3430, "func": ""}
{"index": "s570316752", "label": 3430, "func": ""}
{"index": "s136772115", "label": 3430, "func": ""}
{"index": "s691068526", "label": 3430, "func": ""}
{"index": "s371609625", "label": 3430, "func": ""}
{"index": "s120047513", "label": 3430, "func": ""}
{"index": "s941548302", "label": 3430, "func": ""}
{"index": "s465378732", "label": 3430, "func": ""}
{"index": "s461671036", "label": 342, "func": ""}
{"index": "s842108242", "label": 342, "func": ""}
{"index": "s356079611", "label": 342, "func": ""}
{"index": "s955845914", "label": 342, "func": ""}
{"index": "s544526634", "label": 1912, "func": ""}
{"index": "s680678839", "label": 133, "func": ""}
{"index": "s022493102", "label": 133, "func": ""}
{"index": "s789712261", "label": 133, "func": ""}
{"index": "s091028206", "label": 133, "func": ""}
{"index": "s273700290", "label": 133, "func": ""}
{"index": "s412664054", "label": 133, "func": ""}
{"index": "s410667061", "label": 133, "func": ""}
{"index": "s266902518", "label": 133, "func": ""}
{"index": "s636814283", "label": 133, "func": ""}
{"index": "s484915280", "label": 133, "func": ""}
{"index": "s349607894", "label": 2842, "func": ""}
{"index": "s493288783", "label": 2842, "func": ""}
{"index": "s531933303", "label": 2842, "func": ""}
{"index": "s589423946", "label": 2842, "func": ""}
{"index": "s035495748", "label": 2842, "func": ""}
{"index": "s641758988", "label": 2842, "func": ""}
{"index": "s756152252", "label": 2842, "func": ""}
{"index": "s017010240", "label": 2842, "func": ""}
{"index": "s916786307", "label": 2842, "func": ""}
{"index": "s883608837", "label": 2842, "func": ""}
{"index": "s852682930", "label": 702, "func": ""}
{"index": "s332798712", "label": 702, "func": ""}
{"index": "s508157119", "label": 702, "func": ""}
{"index": "s424812474", "label": 702, "func": ""}
{"index": "s078782885", "label": 702, "func": ""}
{"index": "s296694439", "label": 702, "func": ""}
{"index": "s111804779", "label": 702, "func": ""}
{"index": "s125026868", "label": 702, "func": ""}
{"index": "s462655732", "label": 3158, "func": ""}
{"index": "s484450291", "label": 3158, "func": ""}
{"index": "s048446164", "label": 3158, "func": ""}
{"index": "s750668255", "label": 3158, "func": ""}
{"index": "s644039461", "label": 3158, "func": ""}
{"index": "s687091638", "label": 3158, "func": ""}
{"index": "s723652719", "label": 3158, "func": ""}
{"index": "s478060918", "label": 3158, "func": ""}
{"index": "s460628159", "label": 3158, "func": ""}
{"index": "s894535537", "label": 3158, "func": ""}
{"index": "s251726881", "label": 62, "func": ""}
{"index": "s469701975", "label": 62, "func": ""}
{"index": "s954859002", "label": 62, "func": ""}
{"index": "s047446990", "label": 62, "func": ""}
{"index": "s717448186", "label": 62, "func": ""}
{"index": "s578891391", "label": 62, "func": ""}
{"index": "s181261697", "label": 62, "func": ""}
{"index": "s977722289", "label": 62, "func": ""}
{"index": "s801107622", "label": 62, "func": ""}
{"index": "s677948798", "label": 62, "func": ""}
{"index": "s353588970", "label": 929, "func": ""}
{"index": "s195996065", "label": 929, "func": ""}
{"index": "s790208822", "label": 929, "func": ""}
{"index": "s482574686", "label": 3982, "func": ""}
{"index": "s164232155", "label": 41, "func": ""}
{"index": "s877632470", "label": 41, "func": ""}
{"index": "s471463015", "label": 41, "func": ""}
{"index": "s839995493", "label": 41, "func": ""}
{"index": "s632479707", "label": 41, "func": ""}
{"index": "s846721871", "label": 41, "func": ""}
{"index": "s089668015", "label": 41, "func": ""}
{"index": "s394419559", "label": 41, "func": ""}
{"index": "s058250842", "label": 41, "func": ""}
{"index": "s425267178", "label": 41, "func": ""}
{"index": "s231075266", "label": 3553, "func": ""}
{"index": "s001141980", "label": 3553, "func": ""}
{"index": "s217006504", "label": 3553, "func": ""}
{"index": "s766059321", "label": 3553, "func": ""}
{"index": "s312622353", "label": 3553, "func": ""}
{"index": "s259027429", "label": 3553, "func": ""}
{"index": "s303597642", "label": 3553, "func": ""}
{"index": "s042178599", "label": 3553, "func": ""}
{"index": "s910283726", "label": 3553, "func": ""}
{"index": "s391930390", "label": 3553, "func": ""}
{"index": "s713390397", "label": 177, "func": ""}
{"index": "s713926184", "label": 177, "func": ""}
{"index": "s072423052", "label": 177, "func": ""}
{"index": "s674166922", "label": 177, "func": ""}
{"index": "s373320419", "label": 177, "func": ""}
{"index": "s349532829", "label": 177, "func": ""}
{"index": "s308472487", "label": 177, "func": ""}
{"index": "s837277867", "label": 177, "func": ""}
{"index": "s181860282", "label": 177, "func": ""}
{"index": "s812081479", "label": 177, "func": ""}
{"index": "s606049245", "label": 660, "func": ""}
{"index": "s308568768", "label": 660, "func": ""}
{"index": "s039600634", "label": 2793, "func": ""}
{"index": "s977887094", "label": 2793, "func": ""}
{"index": "s634064014", "label": 2793, "func": ""}
{"index": "s010667725", "label": 2793, "func": ""}
{"index": "s646318815", "label": 2793, "func": ""}
{"index": "s543083878", "label": 2793, "func": ""}
{"index": "s337642908", "label": 2793, "func": ""}
{"index": "s545237646", "label": 2793, "func": ""}
{"index": "s793375159", "label": 2793, "func": ""}
{"index": "s902027651", "label": 2793, "func": ""}
{"index": "s926121769", "label": 2959, "func": ""}
{"index": "s359909515", "label": 2959, "func": ""}
{"index": "s128970752", "label": 2959, "func": ""}
{"index": "s230757054", "label": 2959, "func": ""}
{"index": "s005634308", "label": 2959, "func": ""}
{"index": "s195976248", "label": 2959, "func": ""}
{"index": "s575215045", "label": 2959, "func": ""}
{"index": "s736382930", "label": 2959, "func": ""}
{"index": "s752564385", "label": 2959, "func": ""}
{"index": "s836532463", "label": 2959, "func": ""}
{"index": "s337631217", "label": 3594, "func": ""}
{"index": "s303062607", "label": 3594, "func": ""}
{"index": "s468608020", "label": 3594, "func": ""}
{"index": "s085668175", "label": 3594, "func": ""}
{"index": "s281068503", "label": 3594, "func": ""}
{"index": "s285280024", "label": 3594, "func": ""}
{"index": "s064822107", "label": 3594, "func": ""}
{"index": "s187755639", "label": 3594, "func": ""}
{"index": "s867690861", "label": 3092, "func": ""}
{"index": "s711876303", "label": 3092, "func": ""}
{"index": "s162491509", "label": 3092, "func": ""}
{"index": "s078114579", "label": 3092, "func": ""}
{"index": "s917204873", "label": 2237, "func": ""}
{"index": "s251518579", "label": 2237, "func": ""}
{"index": "s037462932", "label": 2237, "func": ""}
{"index": "s559948095", "label": 2237, "func": ""}
{"index": "s939960155", "label": 2237, "func": ""}
{"index": "s680997391", "label": 2237, "func": ""}
{"index": "s127814687", "label": 2237, "func": ""}
{"index": "s394727462", "label": 2237, "func": ""}
{"index": "s365594211", "label": 2237, "func": ""}
{"index": "s456041767", "label": 2237, "func": ""}
{"index": "s182011729", "label": 564, "func": ""}
{"index": "s662524795", "label": 564, "func": ""}
{"index": "s916398522", "label": 564, "func": ""}
{"index": "s256439629", "label": 564, "func": ""}
{"index": "s146318835", "label": 564, "func": ""}
{"index": "s827559487", "label": 564, "func": ""}
{"index": "s830156844", "label": 564, "func": ""}
{"index": "s943838513", "label": 564, "func": ""}
{"index": "s231477341", "label": 564, "func": ""}
{"index": "s279885671", "label": 564, "func": ""}
{"index": "s132844045", "label": 436, "func": ""}
{"index": "s500192062", "label": 436, "func": ""}
{"index": "s078278821", "label": 436, "func": ""}
{"index": "s315927553", "label": 436, "func": ""}
{"index": "s573645430", "label": 436, "func": ""}
{"index": "s115488679", "label": 436, "func": ""}
{"index": "s600592201", "label": 436, "func": ""}
{"index": "s677038648", "label": 436, "func": ""}
{"index": "s971611826", "label": 436, "func": ""}
{"index": "s898649538", "label": 436, "func": ""}
{"index": "s444602477", "label": 1173, "func": ""}
{"index": "s917805071", "label": 2789, "func": ""}
{"index": "s200844715", "label": 2789, "func": ""}
{"index": "s544632868", "label": 2789, "func": ""}
{"index": "s645957153", "label": 2789, "func": ""}
{"index": "s269066618", "label": 2789, "func": ""}
{"index": "s842619418", "label": 2789, "func": ""}
{"index": "s338465473", "label": 2789, "func": ""}
{"index": "s534366130", "label": 2789, "func": ""}
{"index": "s304755518", "label": 2789, "func": ""}
{"index": "s058152548", "label": 2789, "func": ""}
{"index": "s398739180", "label": 877, "func": ""}
{"index": "s322602280", "label": 877, "func": ""}
{"index": "s126343593", "label": 877, "func": ""}
{"index": "s135436693", "label": 877, "func": ""}
{"index": "s613620485", "label": 877, "func": ""}
{"index": "s990086642", "label": 877, "func": ""}
{"index": "s430917696", "label": 877, "func": ""}
{"index": "s633804986", "label": 877, "func": ""}
{"index": "s312466161", "label": 877, "func": ""}
{"index": "s282274253", "label": 877, "func": ""}
{"index": "s020085244", "label": 869, "func": ""}
{"index": "s823584173", "label": 869, "func": ""}
{"index": "s477809381", "label": 869, "func": ""}
{"index": "s792339806", "label": 869, "func": ""}
{"index": "s950665606", "label": 869, "func": ""}
{"index": "s039432923", "label": 869, "func": ""}
{"index": "s374037746", "label": 2906, "func": ""}
{"index": "s873879725", "label": 2906, "func": ""}
{"index": "s068206732", "label": 1413, "func": ""}
{"index": "s087829199", "label": 1413, "func": ""}
{"index": "s359756644", "label": 1413, "func": ""}
{"index": "s279468723", "label": 512, "func": ""}
{"index": "s891926765", "label": 512, "func": ""}
{"index": "s196314153", "label": 512, "func": ""}
{"index": "s202632043", "label": 512, "func": ""}
{"index": "s380406476", "label": 512, "func": ""}
{"index": "s546325301", "label": 512, "func": ""}
{"index": "s513719714", "label": 512, "func": ""}
{"index": "s763649182", "label": 512, "func": ""}
{"index": "s060695360", "label": 512, "func": ""}
{"index": "s926945274", "label": 512, "func": ""}
{"index": "s272221106", "label": 2589, "func": ""}
{"index": "s218130617", "label": 2589, "func": ""}
{"index": "s739023801", "label": 2589, "func": ""}
{"index": "s569276438", "label": 2589, "func": ""}
{"index": "s512185272", "label": 2589, "func": ""}
{"index": "s508890942", "label": 2589, "func": ""}
{"index": "s170114642", "label": 2589, "func": ""}
{"index": "s868908331", "label": 2589, "func": ""}
{"index": "s791161204", "label": 2589, "func": ""}
{"index": "s878465657", "label": 2589, "func": ""}
{"index": "s246637646", "label": 3595, "func": ""}
{"index": "s754383707", "label": 2594, "func": ""}
{"index": "s490002036", "label": 2594, "func": ""}
{"index": "s936909936", "label": 2594, "func": ""}
{"index": "s179867566", "label": 2594, "func": ""}
{"index": "s678487445", "label": 2594, "func": ""}
{"index": "s188250098", "label": 2594, "func": ""}
{"index": "s202092778", "label": 2594, "func": ""}
{"index": "s859316532", "label": 2594, "func": ""}
{"index": "s832127554", "label": 2594, "func": ""}
{"index": "s632736002", "label": 2594, "func": ""}
{"index": "s112196230", "label": 2991, "func": ""}
{"index": "s472346127", "label": 2991, "func": ""}
{"index": "s625746551", "label": 2991, "func": ""}
{"index": "s619568656", "label": 2991, "func": ""}
{"index": "s161959161", "label": 2991, "func": ""}
{"index": "s084826877", "label": 2991, "func": ""}
{"index": "s442907723", "label": 2991, "func": ""}
{"index": "s925409869", "label": 2991, "func": ""}
{"index": "s250268192", "label": 2991, "func": ""}
{"index": "s880508463", "label": 2991, "func": ""}
{"index": "s613893568", "label": 2584, "func": ""}
{"index": "s592920623", "label": 2584, "func": ""}
{"index": "s941164852", "label": 2584, "func": ""}
{"index": "s686536148", "label": 2584, "func": ""}
{"index": "s989799339", "label": 2584, "func": ""}
{"index": "s452223711", "label": 2584, "func": ""}
{"index": "s991291178", "label": 2584, "func": ""}
{"index": "s564046105", "label": 2584, "func": ""}
{"index": "s453032275", "label": 2584, "func": ""}
{"index": "s515078772", "label": 2584, "func": ""}
{"index": "s529691093", "label": 5, "func": ""}
{"index": "s805659844", "label": 5, "func": ""}
{"index": "s556502150", "label": 5, "func": ""}
{"index": "s396575711", "label": 5, "func": ""}
{"index": "s927932544", "label": 5, "func": ""}
{"index": "s573007391", "label": 5, "func": ""}
{"index": "s771103429", "label": 5, "func": ""}
{"index": "s408033744", "label": 5, "func": ""}
{"index": "s818291157", "label": 5, "func": ""}
{"index": "s685993576", "label": 5, "func": ""}
{"index": "s344607448", "label": 2743, "func": ""}
{"index": "s645962562", "label": 2743, "func": ""}
{"index": "s120861340", "label": 2743, "func": ""}
{"index": "s338345766", "label": 2743, "func": ""}
{"index": "s047812368", "label": 2743, "func": ""}
{"index": "s389345537", "label": 2743, "func": ""}
{"index": "s940389615", "label": 2743, "func": ""}
{"index": "s562448118", "label": 2743, "func": ""}
{"index": "s002429874", "label": 2743, "func": ""}
{"index": "s182720542", "label": 2743, "func": ""}
{"index": "s913928473", "label": 3168, "func": ""}
{"index": "s608572809", "label": 3168, "func": ""}
{"index": "s553722184", "label": 3168, "func": ""}
{"index": "s501389735", "label": 3168, "func": ""}
{"index": "s384161525", "label": 3168, "func": ""}
{"index": "s326146405", "label": 3168, "func": ""}
{"index": "s689209240", "label": 3168, "func": ""}
{"index": "s084377278", "label": 3168, "func": ""}
{"index": "s794037330", "label": 3168, "func": ""}
{"index": "s990651404", "label": 3168, "func": ""}
{"index": "s828721162", "label": 2722, "func": ""}
{"index": "s884920806", "label": 2722, "func": ""}
{"index": "s583004362", "label": 2722, "func": ""}
{"index": "s154334891", "label": 2722, "func": ""}
{"index": "s179773806", "label": 2722, "func": ""}
{"index": "s989207553", "label": 2722, "func": ""}
{"index": "s904208805", "label": 2722, "func": ""}
{"index": "s436458482", "label": 2722, "func": ""}
{"index": "s009505155", "label": 2722, "func": ""}
{"index": "s356777891", "label": 2722, "func": ""}
{"index": "s305823501", "label": 1040, "func": ""}
{"index": "s294568232", "label": 590, "func": ""}
{"index": "s024578361", "label": 590, "func": ""}
{"index": "s703332279", "label": 590, "func": ""}
{"index": "s380504753", "label": 590, "func": ""}
{"index": "s142868999", "label": 590, "func": ""}
{"index": "s977661684", "label": 590, "func": ""}
{"index": "s632627701", "label": 590, "func": ""}
{"index": "s163994202", "label": 590, "func": ""}
{"index": "s420923993", "label": 590, "func": ""}
{"index": "s041176829", "label": 590, "func": ""}
{"index": "s782110866", "label": 3176, "func": ""}
{"index": "s607980303", "label": 3176, "func": ""}
{"index": "s338461657", "label": 3176, "func": ""}
{"index": "s388533495", "label": 3176, "func": ""}
{"index": "s620291757", "label": 3176, "func": ""}
{"index": "s500318238", "label": 3176, "func": ""}
{"index": "s051177273", "label": 3176, "func": ""}
{"index": "s448620897", "label": 3176, "func": ""}
{"index": "s143599381", "label": 3176, "func": ""}
{"index": "s064701768", "label": 3176, "func": ""}
{"index": "s951982962", "label": 3428, "func": ""}
{"index": "s428749243", "label": 3428, "func": ""}
{"index": "s114808368", "label": 3428, "func": ""}
{"index": "s659348314", "label": 3428, "func": ""}
{"index": "s437686373", "label": 3428, "func": ""}
{"index": "s514043036", "label": 3428, "func": ""}
{"index": "s648400788", "label": 3428, "func": ""}
{"index": "s125004628", "label": 3428, "func": ""}
{"index": "s624851250", "label": 3428, "func": ""}
{"index": "s287083091", "label": 3428, "func": ""}
{"index": "s178262233", "label": 2667, "func": ""}
{"index": "s760841641", "label": 2667, "func": ""}
{"index": "s288915273", "label": 1404, "func": ""}
{"index": "s101693948", "label": 2670, "func": ""}
{"index": "s816144842", "label": 2670, "func": ""}
{"index": "s534393672", "label": 2670, "func": ""}
{"index": "s061419389", "label": 2670, "func": ""}
{"index": "s768298196", "label": 2670, "func": ""}
{"index": "s269949126", "label": 2670, "func": ""}
{"index": "s735418905", "label": 2670, "func": ""}
{"index": "s383530459", "label": 2670, "func": ""}
{"index": "s992608672", "label": 2670, "func": ""}
{"index": "s432507006", "label": 2670, "func": ""}
{"index": "s114721058", "label": 3467, "func": ""}
{"index": "s368615501", "label": 3467, "func": ""}
{"index": "s084205664", "label": 3467, "func": ""}
{"index": "s151797250", "label": 2990, "func": ""}
{"index": "s911864364", "label": 2990, "func": ""}
{"index": "s036302615", "label": 2990, "func": ""}
{"index": "s941702956", "label": 2990, "func": ""}
{"index": "s903253734", "label": 2990, "func": ""}
{"index": "s354159412", "label": 2990, "func": ""}
{"index": "s340559967", "label": 2990, "func": ""}
{"index": "s003879978", "label": 2990, "func": ""}
{"index": "s494597964", "label": 2990, "func": ""}
{"index": "s974867663", "label": 2990, "func": ""}
{"index": "s266428586", "label": 848, "func": ""}
{"index": "s272712841", "label": 848, "func": ""}
{"index": "s649698404", "label": 848, "func": ""}
{"index": "s591833629", "label": 848, "func": ""}
{"index": "s513052683", "label": 848, "func": ""}
{"index": "s797174403", "label": 848, "func": ""}
{"index": "s820556347", "label": 848, "func": ""}
{"index": "s975084649", "label": 848, "func": ""}
{"index": "s405757610", "label": 848, "func": ""}
{"index": "s162770659", "label": 848, "func": ""}
{"index": "s449984557", "label": 2960, "func": ""}
{"index": "s730035019", "label": 2960, "func": ""}
{"index": "s311382140", "label": 2960, "func": ""}
{"index": "s325165882", "label": 2960, "func": ""}
{"index": "s918949370", "label": 2960, "func": ""}
{"index": "s759937782", "label": 2960, "func": ""}
{"index": "s884895448", "label": 2960, "func": ""}
{"index": "s429372018", "label": 2960, "func": ""}
{"index": "s138918389", "label": 2960, "func": ""}
{"index": "s950076299", "label": 2960, "func": ""}
{"index": "s297508412", "label": 1158, "func": ""}
{"index": "s947643296", "label": 1158, "func": ""}
{"index": "s591748935", "label": 2088, "func": ""}
{"index": "s313102004", "label": 1944, "func": ""}
{"index": "s913147988", "label": 77, "func": ""}
{"index": "s268352147", "label": 77, "func": ""}
{"index": "s079219000", "label": 77, "func": ""}
{"index": "s004063591", "label": 77, "func": ""}
{"index": "s156462326", "label": 77, "func": ""}
{"index": "s992322210", "label": 77, "func": ""}
{"index": "s823568427", "label": 77, "func": ""}
{"index": "s867965282", "label": 77, "func": ""}
{"index": "s252993573", "label": 77, "func": ""}
{"index": "s442909894", "label": 77, "func": ""}
{"index": "s842699215", "label": 3999, "func": ""}
{"index": "s851061318", "label": 3999, "func": ""}
{"index": "s582649447", "label": 3999, "func": ""}
{"index": "s271489456", "label": 3999, "func": ""}
{"index": "s677125172", "label": 3999, "func": ""}
{"index": "s940440675", "label": 3999, "func": ""}
{"index": "s304075948", "label": 3999, "func": ""}
{"index": "s000297564", "label": 3999, "func": ""}
{"index": "s002871215", "label": 3999, "func": ""}
{"index": "s186821890", "label": 3999, "func": ""}
{"index": "s155614001", "label": 2969, "func": ""}
{"index": "s801810481", "label": 2969, "func": ""}
{"index": "s784411578", "label": 2969, "func": ""}
{"index": "s497227589", "label": 2969, "func": ""}
{"index": "s176695315", "label": 2969, "func": ""}
{"index": "s056538477", "label": 2969, "func": ""}
{"index": "s033096449", "label": 2969, "func": ""}
{"index": "s942100553", "label": 2969, "func": ""}
{"index": "s301144011", "label": 2969, "func": ""}
{"index": "s993840969", "label": 2969, "func": ""}
{"index": "s406584644", "label": 3110, "func": ""}
{"index": "s011024071", "label": 3110, "func": ""}
{"index": "s338704812", "label": 3110, "func": ""}
{"index": "s359189298", "label": 3110, "func": ""}
{"index": "s453402611", "label": 3110, "func": ""}
{"index": "s771977692", "label": 3110, "func": ""}
{"index": "s063726791", "label": 3110, "func": ""}
{"index": "s047682345", "label": 3110, "func": ""}
{"index": "s483545834", "label": 3110, "func": ""}
{"index": "s349690842", "label": 3110, "func": ""}
{"index": "s707471304", "label": 648, "func": ""}
{"index": "s135640462", "label": 648, "func": ""}
{"index": "s314741232", "label": 648, "func": ""}
{"index": "s795120913", "label": 3241, "func": ""}
{"index": "s708429530", "label": 3241, "func": ""}
{"index": "s009298818", "label": 3241, "func": ""}
{"index": "s960486109", "label": 3241, "func": ""}
{"index": "s333625358", "label": 3241, "func": ""}
{"index": "s009935972", "label": 3241, "func": ""}
{"index": "s520966621", "label": 3241, "func": ""}
{"index": "s371545021", "label": 3241, "func": ""}
{"index": "s964374273", "label": 3241, "func": ""}
{"index": "s302225666", "label": 3241, "func": ""}
{"index": "s374477284", "label": 3807, "func": ""}
{"index": "s342490929", "label": 3807, "func": ""}
{"index": "s719052016", "label": 3807, "func": ""}
{"index": "s463160604", "label": 3807, "func": ""}
{"index": "s833197354", "label": 3807, "func": ""}
{"index": "s337051328", "label": 3807, "func": ""}
{"index": "s497101267", "label": 3807, "func": ""}
{"index": "s081904365", "label": 3807, "func": ""}
{"index": "s134039209", "label": 3807, "func": ""}
{"index": "s689251435", "label": 3807, "func": ""}
{"index": "s630757276", "label": 3146, "func": ""}
{"index": "s264669466", "label": 3146, "func": ""}
{"index": "s485954795", "label": 3146, "func": ""}
{"index": "s581214420", "label": 3146, "func": ""}
{"index": "s823386500", "label": 3146, "func": ""}
{"index": "s818749640", "label": 3146, "func": ""}
{"index": "s810606714", "label": 3146, "func": ""}
{"index": "s970788132", "label": 3146, "func": ""}
{"index": "s567702446", "label": 3146, "func": ""}
{"index": "s669667712", "label": 3146, "func": ""}
{"index": "s471249540", "label": 3409, "func": ""}
{"index": "s377974352", "label": 3409, "func": ""}
{"index": "s271977512", "label": 3409, "func": ""}
{"index": "s997338775", "label": 3409, "func": ""}
{"index": "s092621442", "label": 3409, "func": ""}
{"index": "s211892117", "label": 3409, "func": ""}
{"index": "s525590900", "label": 3409, "func": ""}
{"index": "s509519136", "label": 3409, "func": ""}
{"index": "s738047036", "label": 3409, "func": ""}
{"index": "s877199324", "label": 3409, "func": ""}
{"index": "s753889746", "label": 3140, "func": ""}
{"index": "s604482679", "label": 3140, "func": ""}
{"index": "s825424232", "label": 3140, "func": ""}
{"index": "s311078843", "label": 3140, "func": ""}
{"index": "s544980375", "label": 3140, "func": ""}
{"index": "s076706177", "label": 3140, "func": ""}
{"index": "s806042928", "label": 3140, "func": ""}
{"index": "s236405603", "label": 3140, "func": ""}
{"index": "s267788463", "label": 3140, "func": ""}
{"index": "s040715755", "label": 3140, "func": ""}
{"index": "s583738607", "label": 875, "func": ""}
{"index": "s042538472", "label": 875, "func": ""}
{"index": "s498762382", "label": 875, "func": ""}
{"index": "s168372695", "label": 875, "func": ""}
{"index": "s471463837", "label": 875, "func": ""}
{"index": "s276705891", "label": 875, "func": ""}
{"index": "s336125368", "label": 875, "func": ""}
{"index": "s222794186", "label": 875, "func": ""}
{"index": "s769104919", "label": 875, "func": ""}
{"index": "s961029568", "label": 875, "func": ""}
{"index": "s125634754", "label": 3332, "func": ""}
{"index": "s904689643", "label": 3332, "func": ""}
{"index": "s797439128", "label": 3332, "func": ""}
{"index": "s585868898", "label": 3332, "func": ""}
{"index": "s855475503", "label": 3332, "func": ""}
{"index": "s716793065", "label": 3332, "func": ""}
{"index": "s940333169", "label": 3332, "func": ""}
{"index": "s735226883", "label": 3332, "func": ""}
{"index": "s827749478", "label": 3332, "func": ""}
{"index": "s142565539", "label": 3332, "func": ""}
{"index": "s308869164", "label": 4015, "func": ""}
{"index": "s529161731", "label": 4015, "func": ""}
{"index": "s630888732", "label": 4015, "func": ""}
{"index": "s832025587", "label": 4015, "func": ""}
{"index": "s212413346", "label": 4015, "func": ""}
{"index": "s918346662", "label": 4015, "func": ""}
{"index": "s413840969", "label": 4015, "func": ""}
{"index": "s866114040", "label": 4015, "func": ""}
{"index": "s300262789", "label": 4015, "func": ""}
{"index": "s244227740", "label": 4015, "func": ""}
{"index": "s956132874", "label": 2519, "func": ""}
{"index": "s766094331", "label": 2519, "func": ""}
{"index": "s127124603", "label": 2519, "func": ""}
{"index": "s997156084", "label": 2519, "func": ""}
{"index": "s953653451", "label": 2519, "func": ""}
{"index": "s619139200", "label": 2519, "func": ""}
{"index": "s219842273", "label": 3201, "func": ""}
{"index": "s386313347", "label": 3201, "func": ""}
{"index": "s241597959", "label": 3201, "func": ""}
{"index": "s142745919", "label": 3201, "func": ""}
{"index": "s968016256", "label": 3201, "func": ""}
{"index": "s400544371", "label": 3201, "func": ""}
{"index": "s392716007", "label": 3201, "func": ""}
{"index": "s362788461", "label": 3201, "func": ""}
{"index": "s406474678", "label": 3201, "func": ""}
{"index": "s358587348", "label": 3201, "func": ""}
{"index": "s400739946", "label": 1468, "func": ""}
{"index": "s067935525", "label": 1468, "func": ""}
{"index": "s271516068", "label": 464, "func": ""}
{"index": "s004385321", "label": 464, "func": ""}
{"index": "s295585822", "label": 464, "func": ""}
{"index": "s391784718", "label": 464, "func": ""}
{"index": "s904612387", "label": 464, "func": ""}
{"index": "s558944976", "label": 464, "func": ""}
{"index": "s838490972", "label": 464, "func": ""}
{"index": "s115759740", "label": 464, "func": ""}
{"index": "s005940704", "label": 464, "func": ""}
{"index": "s010610133", "label": 464, "func": ""}
{"index": "s712204597", "label": 4000, "func": ""}
{"index": "s508936619", "label": 4000, "func": ""}
{"index": "s903960157", "label": 4000, "func": ""}
{"index": "s132739681", "label": 4000, "func": ""}
{"index": "s052479165", "label": 4000, "func": ""}
{"index": "s444318909", "label": 4000, "func": ""}
{"index": "s555163654", "label": 4000, "func": ""}
{"index": "s581611468", "label": 4000, "func": ""}
{"index": "s558202572", "label": 4000, "func": ""}
{"index": "s963702934", "label": 4000, "func": ""}
{"index": "s222701564", "label": 3674, "func": ""}
{"index": "s582125248", "label": 3674, "func": ""}
{"index": "s704906181", "label": 3674, "func": ""}
{"index": "s564295576", "label": 3674, "func": ""}
{"index": "s228101926", "label": 3674, "func": ""}
{"index": "s967244795", "label": 3674, "func": ""}
{"index": "s320844786", "label": 3674, "func": ""}
{"index": "s478090639", "label": 3674, "func": ""}
{"index": "s091759331", "label": 3674, "func": ""}
{"index": "s241830839", "label": 3674, "func": ""}
{"index": "s568715090", "label": 3742, "func": ""}
{"index": "s116464238", "label": 3742, "func": ""}
{"index": "s589795213", "label": 3742, "func": ""}
{"index": "s642613240", "label": 3742, "func": ""}
{"index": "s095984588", "label": 3742, "func": ""}
{"index": "s278921638", "label": 3742, "func": ""}
{"index": "s786465015", "label": 3742, "func": ""}
{"index": "s796331470", "label": 3742, "func": ""}
{"index": "s740915416", "label": 3742, "func": ""}
{"index": "s055128196", "label": 3742, "func": ""}
{"index": "s494634878", "label": 2723, "func": ""}
{"index": "s424540761", "label": 2723, "func": ""}
{"index": "s997670970", "label": 2723, "func": ""}
{"index": "s024034619", "label": 2723, "func": ""}
{"index": "s599802504", "label": 2723, "func": ""}
{"index": "s518678788", "label": 2723, "func": ""}
{"index": "s722266417", "label": 2723, "func": ""}
{"index": "s867223531", "label": 2723, "func": ""}
{"index": "s178416212", "label": 2723, "func": ""}
{"index": "s563423061", "label": 2723, "func": ""}
{"index": "s586214199", "label": 3842, "func": ""}
{"index": "s983620130", "label": 3667, "func": ""}
{"index": "s481870335", "label": 3667, "func": ""}
{"index": "s309201366", "label": 3667, "func": ""}
{"index": "s860842044", "label": 3436, "func": ""}
{"index": "s149429785", "label": 3436, "func": ""}
{"index": "s260897684", "label": 3436, "func": ""}
{"index": "s791585639", "label": 3436, "func": ""}
{"index": "s151893372", "label": 3436, "func": ""}
{"index": "s099666669", "label": 3436, "func": ""}
{"index": "s544563237", "label": 3436, "func": ""}
{"index": "s272753773", "label": 3436, "func": ""}
{"index": "s606632003", "label": 3436, "func": ""}
{"index": "s038110293", "label": 3436, "func": ""}
{"index": "s451592944", "label": 864, "func": ""}
{"index": "s197347234", "label": 864, "func": ""}
{"index": "s521302677", "label": 864, "func": ""}
{"index": "s714330544", "label": 864, "func": ""}
{"index": "s137732049", "label": 864, "func": ""}
{"index": "s858175110", "label": 864, "func": ""}
{"index": "s649551173", "label": 864, "func": ""}
{"index": "s894703636", "label": 864, "func": ""}
{"index": "s300330915", "label": 864, "func": ""}
{"index": "s154470010", "label": 864, "func": ""}
{"index": "s423061445", "label": 2701, "func": ""}
{"index": "s518931329", "label": 2701, "func": ""}
{"index": "s867249452", "label": 2701, "func": ""}
{"index": "s816606583", "label": 2701, "func": ""}
{"index": "s554957280", "label": 2701, "func": ""}
{"index": "s683386650", "label": 2701, "func": ""}
{"index": "s671168932", "label": 2701, "func": ""}
{"index": "s158719069", "label": 2701, "func": ""}
{"index": "s824382853", "label": 2701, "func": ""}
{"index": "s120726738", "label": 2701, "func": ""}
{"index": "s525580695", "label": 3977, "func": ""}
{"index": "s729818245", "label": 3977, "func": ""}
{"index": "s400668795", "label": 3977, "func": ""}
{"index": "s380470704", "label": 4004, "func": ""}
{"index": "s429490178", "label": 4004, "func": ""}
{"index": "s849812079", "label": 3076, "func": ""}
{"index": "s036753330", "label": 3076, "func": ""}
{"index": "s725153533", "label": 3076, "func": ""}
{"index": "s057628188", "label": 3076, "func": ""}
{"index": "s700965216", "label": 3076, "func": ""}
{"index": "s044630609", "label": 3076, "func": ""}
{"index": "s952314872", "label": 3076, "func": ""}
{"index": "s594646992", "label": 3076, "func": ""}
{"index": "s654620927", "label": 3076, "func": ""}
{"index": "s304006110", "label": 3076, "func": ""}
{"index": "s027315658", "label": 3269, "func": ""}
{"index": "s591023245", "label": 3269, "func": ""}
{"index": "s404482305", "label": 3269, "func": ""}
{"index": "s293766501", "label": 3269, "func": ""}
{"index": "s887120422", "label": 3269, "func": ""}
{"index": "s047388634", "label": 3269, "func": ""}
{"index": "s576425611", "label": 3269, "func": ""}
{"index": "s931228199", "label": 3269, "func": ""}
{"index": "s499459409", "label": 3269, "func": ""}
{"index": "s790111644", "label": 3269, "func": ""}
{"index": "s828475352", "label": 1412, "func": ""}
{"index": "s541395626", "label": 441, "func": ""}
{"index": "s144607634", "label": 441, "func": ""}
{"index": "s160477614", "label": 441, "func": ""}
{"index": "s013430947", "label": 441, "func": ""}
{"index": "s646271076", "label": 441, "func": ""}
{"index": "s161952309", "label": 441, "func": ""}
{"index": "s699363195", "label": 441, "func": ""}
{"index": "s117076531", "label": 441, "func": ""}
{"index": "s712916231", "label": 441, "func": ""}
{"index": "s588506849", "label": 441, "func": ""}
{"index": "s274100373", "label": 3134, "func": ""}
{"index": "s076939814", "label": 3134, "func": ""}
{"index": "s249836681", "label": 3134, "func": ""}
{"index": "s543661333", "label": 3134, "func": ""}
{"index": "s863408352", "label": 3134, "func": ""}
{"index": "s125682014", "label": 3134, "func": ""}
{"index": "s540746192", "label": 3134, "func": ""}
{"index": "s096071839", "label": 3134, "func": ""}
{"index": "s207362583", "label": 3134, "func": ""}
{"index": "s291512123", "label": 3134, "func": ""}
{"index": "s120007453", "label": 3376, "func": ""}
{"index": "s825666605", "label": 3632, "func": ""}
{"index": "s365368852", "label": 3632, "func": ""}
{"index": "s364005628", "label": 3632, "func": ""}
{"index": "s639681879", "label": 3632, "func": ""}
{"index": "s248679452", "label": 3632, "func": ""}
{"index": "s869774147", "label": 3632, "func": ""}
{"index": "s909114527", "label": 3632, "func": ""}
{"index": "s623430925", "label": 3632, "func": ""}
{"index": "s843986864", "label": 3632, "func": ""}
{"index": "s035404988", "label": 3632, "func": ""}
{"index": "s951042419", "label": 2486, "func": ""}
{"index": "s534756315", "label": 2486, "func": ""}
{"index": "s441428951", "label": 2486, "func": ""}
{"index": "s654203883", "label": 2486, "func": ""}
{"index": "s638078978", "label": 2486, "func": ""}
{"index": "s953771920", "label": 2486, "func": ""}
{"index": "s921123081", "label": 2486, "func": ""}
{"index": "s369635107", "label": 2486, "func": ""}
{"index": "s877886272", "label": 2486, "func": ""}
{"index": "s221983408", "label": 2486, "func": ""}
{"index": "s096252782", "label": 3200, "func": ""}
{"index": "s120646667", "label": 3200, "func": ""}
{"index": "s893770906", "label": 3200, "func": ""}
{"index": "s368233761", "label": 3200, "func": ""}
{"index": "s279763118", "label": 3200, "func": ""}
{"index": "s219652196", "label": 3200, "func": ""}
{"index": "s265310485", "label": 3200, "func": ""}
{"index": "s732941560", "label": 3200, "func": ""}
{"index": "s352972912", "label": 3200, "func": ""}
{"index": "s365116303", "label": 3200, "func": ""}
{"index": "s283513817", "label": 3693, "func": ""}
{"index": "s381649564", "label": 3693, "func": ""}
{"index": "s503589110", "label": 3693, "func": ""}
{"index": "s984065458", "label": 3693, "func": ""}
{"index": "s257436385", "label": 3693, "func": ""}
{"index": "s199209293", "label": 3693, "func": ""}
{"index": "s228950699", "label": 3693, "func": ""}
{"index": "s121521944", "label": 3693, "func": ""}
{"index": "s867887877", "label": 3693, "func": ""}
{"index": "s421770833", "label": 3693, "func": ""}
{"index": "s155284996", "label": 3007, "func": ""}
{"index": "s075511675", "label": 3007, "func": ""}
{"index": "s029293741", "label": 3007, "func": ""}
{"index": "s569825532", "label": 3007, "func": ""}
{"index": "s796361647", "label": 3007, "func": ""}
{"index": "s306540963", "label": 3007, "func": ""}
{"index": "s952022237", "label": 3007, "func": ""}
{"index": "s259782991", "label": 3007, "func": ""}
{"index": "s341010448", "label": 3007, "func": ""}
{"index": "s809211640", "label": 3007, "func": ""}
{"index": "s627427442", "label": 1052, "func": ""}
{"index": "s804983541", "label": 3593, "func": ""}
{"index": "s860883261", "label": 3593, "func": ""}
{"index": "s763039162", "label": 3593, "func": ""}
{"index": "s671987714", "label": 3593, "func": ""}
{"index": "s449084319", "label": 3593, "func": ""}
{"index": "s519956898", "label": 3593, "func": ""}
{"index": "s041465817", "label": 3593, "func": ""}
{"index": "s106483296", "label": 3593, "func": ""}
{"index": "s391021187", "label": 3593, "func": ""}
{"index": "s172547570", "label": 3593, "func": ""}
{"index": "s970384725", "label": 598, "func": ""}
{"index": "s383102667", "label": 598, "func": ""}
{"index": "s448061142", "label": 598, "func": ""}
{"index": "s937515646", "label": 598, "func": ""}
{"index": "s690353649", "label": 598, "func": ""}
{"index": "s563848397", "label": 1656, "func": ""}
{"index": "s416114206", "label": 1656, "func": ""}
{"index": "s956697944", "label": 1656, "func": ""}
{"index": "s074360689", "label": 1656, "func": ""}
{"index": "s981888833", "label": 1656, "func": ""}
{"index": "s793570632", "label": 1656, "func": ""}
{"index": "s154922220", "label": 1656, "func": ""}
{"index": "s844218381", "label": 1656, "func": ""}
{"index": "s208444104", "label": 3018, "func": ""}
{"index": "s370397166", "label": 3018, "func": ""}
{"index": "s995878823", "label": 3018, "func": ""}
{"index": "s611753601", "label": 3018, "func": ""}
{"index": "s463956772", "label": 3018, "func": ""}
{"index": "s352924339", "label": 3018, "func": ""}
{"index": "s055718453", "label": 3018, "func": ""}
{"index": "s585294654", "label": 3018, "func": ""}
{"index": "s196692107", "label": 3018, "func": ""}
{"index": "s402134588", "label": 3018, "func": ""}
{"index": "s742445739", "label": 673, "func": ""}
{"index": "s575592408", "label": 673, "func": ""}
{"index": "s049190151", "label": 673, "func": ""}
{"index": "s366463272", "label": 3277, "func": ""}
{"index": "s978370901", "label": 3277, "func": ""}
{"index": "s327383185", "label": 3277, "func": ""}
{"index": "s105818841", "label": 3277, "func": ""}
{"index": "s129938286", "label": 3277, "func": ""}
{"index": "s810318235", "label": 3277, "func": ""}
{"index": "s782501769", "label": 3277, "func": ""}
{"index": "s488492930", "label": 3277, "func": ""}
{"index": "s910020685", "label": 3277, "func": ""}
{"index": "s018515159", "label": 3277, "func": ""}
{"index": "s950223284", "label": 968, "func": ""}
{"index": "s906265728", "label": 968, "func": ""}
{"index": "s086706836", "label": 3465, "func": ""}
{"index": "s133637420", "label": 3465, "func": ""}
{"index": "s925800373", "label": 3465, "func": ""}
{"index": "s284834294", "label": 3465, "func": ""}
{"index": "s057643625", "label": 3465, "func": ""}
{"index": "s149446973", "label": 3465, "func": ""}
{"index": "s460415200", "label": 3465, "func": ""}
{"index": "s951293376", "label": 3465, "func": ""}
{"index": "s843838113", "label": 3465, "func": ""}
{"index": "s347239155", "label": 3465, "func": ""}
{"index": "s109616139", "label": 2786, "func": ""}
{"index": "s503088778", "label": 2786, "func": ""}
{"index": "s265463658", "label": 2786, "func": ""}
{"index": "s693171251", "label": 2786, "func": ""}
{"index": "s405795098", "label": 2786, "func": ""}
{"index": "s405767448", "label": 2786, "func": ""}
{"index": "s549750833", "label": 2786, "func": ""}
{"index": "s134911574", "label": 2786, "func": ""}
{"index": "s003003304", "label": 2786, "func": ""}
{"index": "s664946291", "label": 2786, "func": ""}
{"index": "s326549293", "label": 427, "func": ""}
{"index": "s357879504", "label": 427, "func": ""}
{"index": "s947186564", "label": 427, "func": ""}
{"index": "s575901931", "label": 427, "func": ""}
{"index": "s793818357", "label": 427, "func": ""}
{"index": "s982773759", "label": 427, "func": ""}
{"index": "s760136589", "label": 427, "func": ""}
{"index": "s129800906", "label": 427, "func": ""}
{"index": "s849360904", "label": 2324, "func": ""}
{"index": "s178370830", "label": 2324, "func": ""}
{"index": "s867925968", "label": 381, "func": ""}
{"index": "s053358883", "label": 381, "func": ""}
{"index": "s357141530", "label": 2150, "func": ""}
{"index": "s699364342", "label": 2150, "func": ""}
{"index": "s580655582", "label": 1975, "func": ""}
{"index": "s580626107", "label": 1102, "func": ""}
{"index": "s781144516", "label": 1102, "func": ""}
{"index": "s340126581", "label": 1102, "func": ""}
{"index": "s080700786", "label": 1102, "func": ""}
{"index": "s319022514", "label": 1102, "func": ""}
{"index": "s701732175", "label": 1102, "func": ""}
{"index": "s938422134", "label": 1102, "func": ""}
{"index": "s991731610", "label": 1102, "func": ""}
{"index": "s021810978", "label": 1102, "func": ""}
{"index": "s902337296", "label": 1102, "func": ""}
{"index": "s906312773", "label": 3802, "func": ""}
{"index": "s084675573", "label": 3334, "func": ""}
{"index": "s280365767", "label": 3334, "func": ""}
{"index": "s702427758", "label": 3334, "func": ""}
{"index": "s297805032", "label": 21, "func": ""}
{"index": "s849248236", "label": 21, "func": ""}
{"index": "s519785211", "label": 21, "func": ""}
{"index": "s817719734", "label": 21, "func": ""}
{"index": "s165667187", "label": 21, "func": ""}
{"index": "s284246366", "label": 21, "func": ""}
{"index": "s839880737", "label": 21, "func": ""}
{"index": "s065383704", "label": 21, "func": ""}
{"index": "s657284340", "label": 21, "func": ""}
{"index": "s503947572", "label": 21, "func": ""}
{"index": "s265122507", "label": 581, "func": ""}
{"index": "s874972008", "label": 1420, "func": ""}
{"index": "s643296721", "label": 1420, "func": ""}
{"index": "s050089278", "label": 1420, "func": ""}
{"index": "s455559185", "label": 1420, "func": ""}
{"index": "s973689975", "label": 1420, "func": ""}
{"index": "s280424871", "label": 1420, "func": ""}
{"index": "s577095999", "label": 1420, "func": ""}
{"index": "s213773039", "label": 1420, "func": ""}
{"index": "s718002661", "label": 3399, "func": ""}
{"index": "s269357254", "label": 3399, "func": ""}
{"index": "s868109530", "label": 3399, "func": ""}
{"index": "s608862381", "label": 3399, "func": ""}
{"index": "s652533497", "label": 3399, "func": ""}
{"index": "s341401763", "label": 3399, "func": ""}
{"index": "s557046549", "label": 3399, "func": ""}
{"index": "s967328336", "label": 3399, "func": ""}
{"index": "s332405690", "label": 3399, "func": ""}
{"index": "s587381089", "label": 3399, "func": ""}
{"index": "s363238448", "label": 3141, "func": ""}
{"index": "s652404611", "label": 3141, "func": ""}
{"index": "s165390830", "label": 3141, "func": ""}
{"index": "s727041011", "label": 3141, "func": ""}
{"index": "s943773163", "label": 3141, "func": ""}
{"index": "s664321430", "label": 3141, "func": ""}
{"index": "s218047303", "label": 3141, "func": ""}
{"index": "s259012748", "label": 3141, "func": ""}
{"index": "s850538552", "label": 3141, "func": ""}
{"index": "s116296958", "label": 3141, "func": ""}
{"index": "s971806787", "label": 2286, "func": ""}
{"index": "s686326685", "label": 2286, "func": ""}
{"index": "s121471839", "label": 2286, "func": ""}
{"index": "s970260617", "label": 2286, "func": ""}
{"index": "s534845469", "label": 2286, "func": ""}
{"index": "s014556284", "label": 2286, "func": ""}
{"index": "s673247413", "label": 2286, "func": ""}
{"index": "s516713414", "label": 2286, "func": ""}
{"index": "s819135679", "label": 2286, "func": ""}
{"index": "s188802252", "label": 2286, "func": ""}
{"index": "s077786245", "label": 521, "func": ""}
{"index": "s270963164", "label": 521, "func": ""}
{"index": "s408969225", "label": 521, "func": ""}
{"index": "s834985212", "label": 521, "func": ""}
{"index": "s229870679", "label": 521, "func": ""}
{"index": "s211887296", "label": 521, "func": ""}
{"index": "s042263747", "label": 2850, "func": ""}
{"index": "s340810556", "label": 2850, "func": ""}
{"index": "s194523240", "label": 2850, "func": ""}
{"index": "s963569167", "label": 2850, "func": ""}
{"index": "s315586544", "label": 2850, "func": ""}
{"index": "s495501890", "label": 2850, "func": ""}
{"index": "s033825576", "label": 2850, "func": ""}
{"index": "s678128313", "label": 2850, "func": ""}
{"index": "s082966075", "label": 2850, "func": ""}
{"index": "s930528472", "label": 2850, "func": ""}
{"index": "s360337737", "label": 334, "func": ""}
{"index": "s824585949", "label": 334, "func": ""}
{"index": "s951709474", "label": 334, "func": ""}
{"index": "s197232099", "label": 334, "func": ""}
{"index": "s956082000", "label": 3366, "func": ""}
{"index": "s215626082", "label": 3505, "func": ""}
{"index": "s151542086", "label": 3505, "func": ""}
{"index": "s554147978", "label": 468, "func": ""}
{"index": "s909134779", "label": 468, "func": ""}
{"index": "s618842207", "label": 468, "func": ""}
{"index": "s241227031", "label": 468, "func": ""}
{"index": "s317113130", "label": 468, "func": ""}
{"index": "s027428427", "label": 468, "func": ""}
{"index": "s187453804", "label": 468, "func": ""}
{"index": "s587566511", "label": 468, "func": ""}
{"index": "s434332614", "label": 468, "func": ""}
{"index": "s554337226", "label": 468, "func": ""}
{"index": "s567480086", "label": 1841, "func": ""}
{"index": "s290754176", "label": 1841, "func": ""}
{"index": "s961475470", "label": 1841, "func": ""}
{"index": "s256854193", "label": 1841, "func": ""}
{"index": "s000661164", "label": 830, "func": ""}
{"index": "s388941590", "label": 830, "func": ""}
{"index": "s726705672", "label": 830, "func": ""}
{"index": "s875241275", "label": 830, "func": ""}
{"index": "s327619318", "label": 830, "func": ""}
{"index": "s784168513", "label": 830, "func": ""}
{"index": "s295637762", "label": 10, "func": ""}
{"index": "s126780469", "label": 10, "func": ""}
{"index": "s369706580", "label": 10, "func": ""}
{"index": "s014186974", "label": 10, "func": ""}
{"index": "s566800591", "label": 10, "func": ""}
{"index": "s578809113", "label": 10, "func": ""}
{"index": "s876859655", "label": 10, "func": ""}
{"index": "s613256995", "label": 10, "func": ""}
{"index": "s259202344", "label": 10, "func": ""}
{"index": "s510874481", "label": 10, "func": ""}
{"index": "s598852202", "label": 597, "func": ""}
{"index": "s738803824", "label": 597, "func": ""}
{"index": "s354384021", "label": 597, "func": ""}
{"index": "s155786592", "label": 597, "func": ""}
{"index": "s819935656", "label": 597, "func": ""}
{"index": "s836892572", "label": 597, "func": ""}
{"index": "s274456240", "label": 2035, "func": ""}
{"index": "s545264042", "label": 59, "func": ""}
{"index": "s654253001", "label": 59, "func": ""}
{"index": "s125778941", "label": 59, "func": ""}
{"index": "s408451665", "label": 59, "func": ""}
{"index": "s958116706", "label": 59, "func": ""}
{"index": "s539931165", "label": 59, "func": ""}
{"index": "s164168352", "label": 59, "func": ""}
{"index": "s792672576", "label": 59, "func": ""}
{"index": "s933879368", "label": 59, "func": ""}
{"index": "s761356116", "label": 59, "func": ""}
{"index": "s722720502", "label": 140, "func": ""}
{"index": "s327725543", "label": 140, "func": ""}
{"index": "s450191861", "label": 140, "func": ""}
{"index": "s890213947", "label": 140, "func": ""}
{"index": "s344807145", "label": 140, "func": ""}
{"index": "s321861969", "label": 140, "func": ""}
{"index": "s886998377", "label": 140, "func": ""}
{"index": "s571078505", "label": 140, "func": ""}
{"index": "s898444117", "label": 140, "func": ""}
{"index": "s896220564", "label": 140, "func": ""}
{"index": "s128945654", "label": 66, "func": ""}
{"index": "s294460406", "label": 66, "func": ""}
{"index": "s065141633", "label": 66, "func": ""}
{"index": "s590990659", "label": 66, "func": ""}
{"index": "s592153047", "label": 66, "func": ""}
{"index": "s167071903", "label": 66, "func": ""}
{"index": "s690009999", "label": 66, "func": ""}
{"index": "s276286882", "label": 66, "func": ""}
{"index": "s658048046", "label": 66, "func": ""}
{"index": "s712033414", "label": 66, "func": ""}
{"index": "s925984741", "label": 3614, "func": ""}
{"index": "s201884684", "label": 3614, "func": ""}
{"index": "s111825186", "label": 3614, "func": ""}
{"index": "s721074335", "label": 3614, "func": ""}
{"index": "s043243492", "label": 3614, "func": ""}
{"index": "s542875206", "label": 3614, "func": ""}
{"index": "s860516522", "label": 3614, "func": ""}
{"index": "s270205609", "label": 3614, "func": ""}
{"index": "s215547013", "label": 3614, "func": ""}
{"index": "s296165473", "label": 3614, "func": ""}
{"index": "s813613417", "label": 3664, "func": ""}
{"index": "s341308461", "label": 3664, "func": ""}
{"index": "s173700425", "label": 3664, "func": ""}
{"index": "s242686273", "label": 191, "func": ""}
{"index": "s406996693", "label": 191, "func": ""}
{"index": "s611311410", "label": 191, "func": ""}
{"index": "s754764534", "label": 191, "func": ""}
{"index": "s962926809", "label": 191, "func": ""}
{"index": "s998221261", "label": 191, "func": ""}
{"index": "s922321767", "label": 191, "func": ""}
{"index": "s558620400", "label": 191, "func": ""}
{"index": "s426435292", "label": 191, "func": ""}
{"index": "s917924243", "label": 191, "func": ""}
{"index": "s506875074", "label": 757, "func": ""}
{"index": "s227531353", "label": 757, "func": ""}
{"index": "s548920931", "label": 757, "func": ""}
{"index": "s962144125", "label": 757, "func": ""}
{"index": "s888621240", "label": 757, "func": ""}
{"index": "s183536818", "label": 757, "func": ""}
{"index": "s940646922", "label": 757, "func": ""}
{"index": "s008081683", "label": 757, "func": ""}
{"index": "s170897489", "label": 757, "func": ""}
{"index": "s131984379", "label": 757, "func": ""}
{"index": "s997624356", "label": 593, "func": ""}
{"index": "s002652000", "label": 593, "func": ""}
{"index": "s847747156", "label": 593, "func": ""}
{"index": "s477028606", "label": 593, "func": ""}
{"index": "s473129199", "label": 593, "func": ""}
{"index": "s044536506", "label": 593, "func": ""}
{"index": "s822097107", "label": 593, "func": ""}
{"index": "s050824419", "label": 593, "func": ""}
{"index": "s168735572", "label": 593, "func": ""}
{"index": "s517761229", "label": 593, "func": ""}
{"index": "s449495353", "label": 18, "func": ""}
{"index": "s938261055", "label": 18, "func": ""}
{"index": "s577780800", "label": 18, "func": ""}
{"index": "s132950151", "label": 18, "func": ""}
{"index": "s724498813", "label": 18, "func": ""}
{"index": "s489886002", "label": 18, "func": ""}
{"index": "s647333670", "label": 18, "func": ""}
{"index": "s724695576", "label": 18, "func": ""}
{"index": "s021273004", "label": 18, "func": ""}
{"index": "s757479567", "label": 18, "func": ""}
{"index": "s893448626", "label": 635, "func": ""}
{"index": "s878738616", "label": 635, "func": ""}
{"index": "s816786837", "label": 635, "func": ""}
{"index": "s615029696", "label": 635, "func": ""}
{"index": "s849856526", "label": 635, "func": ""}
{"index": "s403168194", "label": 635, "func": ""}
{"index": "s293558947", "label": 3278, "func": ""}
{"index": "s060657120", "label": 2581, "func": ""}
{"index": "s231757074", "label": 2581, "func": ""}
{"index": "s517478693", "label": 2581, "func": ""}
{"index": "s866155120", "label": 1212, "func": ""}
{"index": "s034685523", "label": 181, "func": ""}
{"index": "s023239815", "label": 181, "func": ""}
{"index": "s712500294", "label": 181, "func": ""}
{"index": "s842995516", "label": 181, "func": ""}
{"index": "s283922043", "label": 181, "func": ""}
{"index": "s154166749", "label": 181, "func": ""}
{"index": "s744183787", "label": 181, "func": ""}
{"index": "s398844552", "label": 181, "func": ""}
{"index": "s899778314", "label": 181, "func": ""}
{"index": "s047147532", "label": 181, "func": ""}
{"index": "s971342178", "label": 3290, "func": ""}
{"index": "s100771357", "label": 3290, "func": ""}
{"index": "s762158399", "label": 3290, "func": ""}
{"index": "s491356964", "label": 3290, "func": ""}
{"index": "s805438870", "label": 3290, "func": ""}
{"index": "s305075090", "label": 3290, "func": ""}
{"index": "s465491976", "label": 3290, "func": ""}
{"index": "s937074521", "label": 3290, "func": ""}
{"index": "s248046090", "label": 3290, "func": ""}
{"index": "s493885284", "label": 3290, "func": ""}
{"index": "s013270600", "label": 3992, "func": ""}
{"index": "s700412519", "label": 3992, "func": ""}
{"index": "s923432778", "label": 3992, "func": ""}
{"index": "s267597417", "label": 3992, "func": ""}
{"index": "s203731124", "label": 3992, "func": ""}
{"index": "s142287284", "label": 3992, "func": ""}
{"index": "s789217695", "label": 3992, "func": ""}
{"index": "s441261026", "label": 3992, "func": ""}
{"index": "s041849854", "label": 3992, "func": ""}
{"index": "s957987177", "label": 3992, "func": ""}
{"index": "s123458733", "label": 2890, "func": ""}
{"index": "s027395813", "label": 2890, "func": ""}
{"index": "s432154205", "label": 2890, "func": ""}
{"index": "s620689098", "label": 2890, "func": ""}
{"index": "s525579610", "label": 2890, "func": ""}
{"index": "s157997598", "label": 2890, "func": ""}
{"index": "s538506750", "label": 2890, "func": ""}
{"index": "s714198694", "label": 2890, "func": ""}
{"index": "s293848751", "label": 2890, "func": ""}
{"index": "s820871118", "label": 2890, "func": ""}
{"index": "s957638270", "label": 2413, "func": ""}
{"index": "s629754634", "label": 2413, "func": ""}
{"index": "s536008026", "label": 2413, "func": ""}
{"index": "s354510957", "label": 2413, "func": ""}
{"index": "s667738238", "label": 2413, "func": ""}
{"index": "s294093613", "label": 2413, "func": ""}
{"index": "s720522589", "label": 2413, "func": ""}
{"index": "s392619056", "label": 2413, "func": ""}
{"index": "s237226119", "label": 2413, "func": ""}
{"index": "s930795976", "label": 2413, "func": ""}
{"index": "s853853527", "label": 3942, "func": ""}
{"index": "s370985044", "label": 3336, "func": ""}
{"index": "s249386750", "label": 2607, "func": ""}
{"index": "s184730543", "label": 2607, "func": ""}
{"index": "s130441569", "label": 2607, "func": ""}
{"index": "s122575633", "label": 2607, "func": ""}
{"index": "s876248460", "label": 2607, "func": ""}
{"index": "s748508111", "label": 2607, "func": ""}
{"index": "s361107998", "label": 2607, "func": ""}
{"index": "s419174195", "label": 2607, "func": ""}
{"index": "s321342262", "label": 2607, "func": ""}
{"index": "s354273769", "label": 2607, "func": ""}
{"index": "s350675503", "label": 1392, "func": ""}
{"index": "s338055674", "label": 2442, "func": ""}
{"index": "s200403556", "label": 2442, "func": ""}
{"index": "s642903559", "label": 2442, "func": ""}
{"index": "s407083591", "label": 2442, "func": ""}
{"index": "s923869292", "label": 2442, "func": ""}
{"index": "s270262496", "label": 2442, "func": ""}
{"index": "s361620256", "label": 2442, "func": ""}
{"index": "s913793697", "label": 2442, "func": ""}
{"index": "s924299080", "label": 2442, "func": ""}
{"index": "s594903891", "label": 2442, "func": ""}
{"index": "s401916303", "label": 2625, "func": ""}
{"index": "s742580172", "label": 2625, "func": ""}
{"index": "s022616280", "label": 2625, "func": ""}
{"index": "s247796408", "label": 2625, "func": ""}
{"index": "s312158318", "label": 2625, "func": ""}
{"index": "s114648938", "label": 2625, "func": ""}
{"index": "s632550140", "label": 2625, "func": ""}
{"index": "s144957024", "label": 2625, "func": ""}
{"index": "s102316070", "label": 2625, "func": ""}
{"index": "s294781407", "label": 2625, "func": ""}
{"index": "s069570454", "label": 1524, "func": ""}
{"index": "s683844262", "label": 1524, "func": ""}
{"index": "s758378797", "label": 1524, "func": ""}
{"index": "s101266122", "label": 1524, "func": ""}
{"index": "s161930246", "label": 1261, "func": ""}
{"index": "s185256888", "label": 1261, "func": ""}
{"index": "s929132015", "label": 1261, "func": ""}
{"index": "s004991595", "label": 2543, "func": ""}
{"index": "s693467064", "label": 855, "func": ""}
{"index": "s536632018", "label": 855, "func": ""}
{"index": "s549319482", "label": 855, "func": ""}
{"index": "s395775309", "label": 855, "func": ""}
{"index": "s625791311", "label": 855, "func": ""}
{"index": "s952463681", "label": 855, "func": ""}
{"index": "s973685373", "label": 855, "func": ""}
{"index": "s205296906", "label": 855, "func": ""}
{"index": "s163957377", "label": 855, "func": ""}
{"index": "s108352207", "label": 855, "func": ""}
{"index": "s574556266", "label": 1037, "func": ""}
{"index": "s005693698", "label": 1037, "func": ""}
{"index": "s458205149", "label": 1037, "func": ""}
{"index": "s476066663", "label": 1037, "func": ""}
{"index": "s360650596", "label": 2582, "func": ""}
{"index": "s539113704", "label": 2582, "func": ""}
{"index": "s295867409", "label": 2582, "func": ""}
{"index": "s922263813", "label": 2582, "func": ""}
{"index": "s678090315", "label": 2582, "func": ""}
{"index": "s807863900", "label": 2582, "func": ""}
{"index": "s613975608", "label": 2582, "func": ""}
{"index": "s019790335", "label": 2582, "func": ""}
{"index": "s077299132", "label": 2582, "func": ""}
{"index": "s990525640", "label": 2582, "func": ""}
{"index": "s373499302", "label": 3024, "func": ""}
{"index": "s774743307", "label": 3024, "func": ""}
{"index": "s265268844", "label": 3024, "func": ""}
{"index": "s049981328", "label": 3024, "func": ""}
{"index": "s220342600", "label": 3024, "func": ""}
{"index": "s342863001", "label": 3024, "func": ""}
{"index": "s860217787", "label": 3024, "func": ""}
{"index": "s347594657", "label": 3024, "func": ""}
{"index": "s421867006", "label": 3024, "func": ""}
{"index": "s123950799", "label": 3024, "func": ""}
{"index": "s309297791", "label": 1762, "func": ""}
{"index": "s015201017", "label": 1762, "func": ""}
{"index": "s045337899", "label": 2811, "func": ""}
{"index": "s186028142", "label": 2811, "func": ""}
{"index": "s463435639", "label": 2811, "func": ""}
{"index": "s594336712", "label": 2811, "func": ""}
{"index": "s628160930", "label": 2811, "func": ""}
{"index": "s434847306", "label": 2811, "func": ""}
{"index": "s214015768", "label": 2811, "func": ""}
{"index": "s777044894", "label": 2811, "func": ""}
{"index": "s257115910", "label": 2811, "func": ""}
{"index": "s836962934", "label": 2811, "func": ""}
{"index": "s170809535", "label": 4013, "func": ""}
{"index": "s844296231", "label": 4013, "func": ""}
{"index": "s421005966", "label": 4013, "func": ""}
{"index": "s584222098", "label": 4013, "func": ""}
{"index": "s634430403", "label": 4013, "func": ""}
{"index": "s341949513", "label": 4013, "func": ""}
{"index": "s473757037", "label": 4013, "func": ""}
{"index": "s527677480", "label": 4013, "func": ""}
{"index": "s814344716", "label": 4013, "func": ""}
{"index": "s154895840", "label": 4013, "func": ""}
{"index": "s711746454", "label": 3748, "func": ""}
{"index": "s416776586", "label": 1702, "func": ""}
{"index": "s582034403", "label": 1702, "func": ""}
{"index": "s282376522", "label": 1702, "func": ""}
{"index": "s836834289", "label": 1702, "func": ""}
{"index": "s362784543", "label": 1702, "func": ""}
{"index": "s288983956", "label": 1702, "func": ""}
{"index": "s478494573", "label": 2139, "func": ""}
{"index": "s924347077", "label": 3859, "func": ""}
{"index": "s416813990", "label": 3859, "func": ""}
{"index": "s944897105", "label": 3859, "func": ""}
{"index": "s116055804", "label": 3859, "func": ""}
{"index": "s099046840", "label": 3859, "func": ""}
{"index": "s397684201", "label": 3859, "func": ""}
{"index": "s400555126", "label": 1229, "func": ""}
{"index": "s348287037", "label": 861, "func": ""}
{"index": "s489612902", "label": 861, "func": ""}
{"index": "s929875734", "label": 861, "func": ""}
{"index": "s355000134", "label": 861, "func": ""}
{"index": "s129021246", "label": 861, "func": ""}
{"index": "s370118894", "label": 861, "func": ""}
{"index": "s562948880", "label": 861, "func": ""}
{"index": "s995823279", "label": 3649, "func": ""}
{"index": "s698724914", "label": 3649, "func": ""}
{"index": "s295460665", "label": 3649, "func": ""}
{"index": "s570222522", "label": 3649, "func": ""}
{"index": "s922498611", "label": 3649, "func": ""}
{"index": "s178660612", "label": 3649, "func": ""}
{"index": "s144273036", "label": 3649, "func": ""}
{"index": "s311631571", "label": 3649, "func": ""}
{"index": "s210320775", "label": 3649, "func": ""}
{"index": "s002355085", "label": 3649, "func": ""}
{"index": "s801546876", "label": 825, "func": ""}
{"index": "s038855936", "label": 825, "func": ""}
{"index": "s770779763", "label": 825, "func": ""}
{"index": "s720853044", "label": 825, "func": ""}
{"index": "s480395681", "label": 825, "func": ""}
{"index": "s151559391", "label": 813, "func": ""}
{"index": "s849036068", "label": 813, "func": ""}
{"index": "s453328120", "label": 3782, "func": ""}
{"index": "s734290365", "label": 3782, "func": ""}
{"index": "s549219504", "label": 3782, "func": ""}
{"index": "s614347733", "label": 3782, "func": ""}
{"index": "s912719590", "label": 3782, "func": ""}
{"index": "s744712987", "label": 3782, "func": ""}
{"index": "s230308884", "label": 3782, "func": ""}
{"index": "s951707374", "label": 3782, "func": ""}
{"index": "s890590572", "label": 3782, "func": ""}
{"index": "s208515589", "label": 3782, "func": ""}
{"index": "s026368611", "label": 3735, "func": ""}
{"index": "s839104196", "label": 3735, "func": ""}
{"index": "s258560325", "label": 3735, "func": ""}
{"index": "s588841859", "label": 3735, "func": ""}
{"index": "s055180314", "label": 3735, "func": ""}
{"index": "s142402427", "label": 3837, "func": ""}
{"index": "s851840768", "label": 3837, "func": ""}
{"index": "s935204208", "label": 3837, "func": ""}
{"index": "s808042688", "label": 3837, "func": ""}
{"index": "s742264806", "label": 3837, "func": ""}
{"index": "s298604745", "label": 3837, "func": ""}
{"index": "s683631585", "label": 3837, "func": ""}
{"index": "s369124544", "label": 3837, "func": ""}
{"index": "s533399652", "label": 3837, "func": ""}
{"index": "s567712824", "label": 3837, "func": ""}
{"index": "s446794690", "label": 2506, "func": ""}
{"index": "s063104132", "label": 2506, "func": ""}
{"index": "s095554620", "label": 2506, "func": ""}
{"index": "s769048014", "label": 2506, "func": ""}
{"index": "s694199301", "label": 2506, "func": ""}
{"index": "s242940732", "label": 2506, "func": ""}
{"index": "s707419811", "label": 2506, "func": ""}
{"index": "s228874237", "label": 2506, "func": ""}
{"index": "s201047450", "label": 2506, "func": ""}
{"index": "s292858230", "label": 2506, "func": ""}
{"index": "s637530690", "label": 841, "func": ""}
{"index": "s100293162", "label": 841, "func": ""}
{"index": "s676381309", "label": 841, "func": ""}
{"index": "s378873266", "label": 841, "func": ""}
{"index": "s786344369", "label": 1855, "func": ""}
{"index": "s798650562", "label": 1368, "func": ""}
{"index": "s534756880", "label": 1368, "func": ""}
{"index": "s576641131", "label": 1368, "func": ""}
{"index": "s025947359", "label": 1368, "func": ""}
{"index": "s875282901", "label": 1368, "func": ""}
{"index": "s860523681", "label": 2149, "func": ""}
{"index": "s285194684", "label": 2149, "func": ""}
{"index": "s718665129", "label": 2149, "func": ""}
{"index": "s144490275", "label": 2149, "func": ""}
{"index": "s744665428", "label": 2149, "func": ""}
{"index": "s958717782", "label": 1117, "func": ""}
{"index": "s656288484", "label": 1117, "func": ""}
{"index": "s400016307", "label": 1117, "func": ""}
{"index": "s069006619", "label": 1117, "func": ""}
{"index": "s285201053", "label": 227, "func": ""}
{"index": "s627047898", "label": 227, "func": ""}
{"index": "s994384308", "label": 227, "func": ""}
{"index": "s775686626", "label": 227, "func": ""}
{"index": "s680992190", "label": 227, "func": ""}
{"index": "s471442923", "label": 227, "func": ""}
{"index": "s348358588", "label": 227, "func": ""}
{"index": "s004375461", "label": 227, "func": ""}
{"index": "s995250094", "label": 227, "func": ""}
{"index": "s513703349", "label": 227, "func": ""}
{"index": "s256003672", "label": 426, "func": ""}
{"index": "s127985809", "label": 426, "func": ""}
{"index": "s335969431", "label": 426, "func": ""}
{"index": "s957698621", "label": 426, "func": ""}
{"index": "s914177749", "label": 426, "func": ""}
{"index": "s142676438", "label": 426, "func": ""}
{"index": "s569009539", "label": 426, "func": ""}
{"index": "s084741471", "label": 426, "func": ""}
{"index": "s204783855", "label": 426, "func": ""}
{"index": "s810485383", "label": 426, "func": ""}
{"index": "s839300673", "label": 2815, "func": ""}
{"index": "s542694514", "label": 2815, "func": ""}
{"index": "s530734499", "label": 2815, "func": ""}
{"index": "s024696862", "label": 2815, "func": ""}
{"index": "s745185745", "label": 2815, "func": ""}
{"index": "s569706692", "label": 2815, "func": ""}
{"index": "s944474184", "label": 2815, "func": ""}
{"index": "s209115347", "label": 2815, "func": ""}
{"index": "s313589920", "label": 2815, "func": ""}
{"index": "s556193004", "label": 2815, "func": ""}
{"index": "s698810470", "label": 1580, "func": ""}
{"index": "s258011476", "label": 1580, "func": ""}
{"index": "s950310013", "label": 3362, "func": ""}
{"index": "s650176375", "label": 3362, "func": ""}
{"index": "s558810940", "label": 3362, "func": ""}
{"index": "s246243845", "label": 3362, "func": ""}
{"index": "s712357412", "label": 3362, "func": ""}
{"index": "s227744166", "label": 3362, "func": ""}
{"index": "s744156804", "label": 3362, "func": ""}
{"index": "s607437181", "label": 3362, "func": ""}
{"index": "s412648406", "label": 3362, "func": ""}
{"index": "s491651435", "label": 3362, "func": ""}
{"index": "s704855942", "label": 3299, "func": ""}
{"index": "s877129338", "label": 3299, "func": ""}
{"index": "s221839273", "label": 3299, "func": ""}
{"index": "s523769927", "label": 147, "func": ""}
{"index": "s972761822", "label": 147, "func": ""}
{"index": "s998850303", "label": 147, "func": ""}
{"index": "s309503804", "label": 147, "func": ""}
{"index": "s941912476", "label": 147, "func": ""}
{"index": "s461220645", "label": 147, "func": ""}
{"index": "s664497596", "label": 147, "func": ""}
{"index": "s374769690", "label": 147, "func": ""}
{"index": "s451290671", "label": 147, "func": ""}
{"index": "s876638792", "label": 147, "func": ""}
{"index": "s791100519", "label": 235, "func": ""}
{"index": "s963656154", "label": 235, "func": ""}
{"index": "s455966586", "label": 235, "func": ""}
{"index": "s586964816", "label": 235, "func": ""}
{"index": "s920325886", "label": 235, "func": ""}
{"index": "s606672648", "label": 235, "func": ""}
{"index": "s365240561", "label": 235, "func": ""}
{"index": "s515600762", "label": 235, "func": ""}
{"index": "s826981551", "label": 235, "func": ""}
{"index": "s666972730", "label": 2393, "func": ""}
{"index": "s472850150", "label": 2393, "func": ""}
{"index": "s680084112", "label": 2393, "func": ""}
{"index": "s775153247", "label": 2393, "func": ""}
{"index": "s361668406", "label": 2393, "func": ""}
{"index": "s979658341", "label": 2393, "func": ""}
{"index": "s927261810", "label": 2393, "func": ""}
{"index": "s650552861", "label": 2393, "func": ""}
{"index": "s653607270", "label": 2393, "func": ""}
{"index": "s233813041", "label": 2393, "func": ""}
{"index": "s262352725", "label": 1997, "func": ""}
{"index": "s837781077", "label": 1997, "func": ""}
{"index": "s767978562", "label": 1997, "func": ""}
{"index": "s201726932", "label": 155, "func": ""}
{"index": "s329472732", "label": 155, "func": ""}
{"index": "s652678450", "label": 155, "func": ""}
{"index": "s631365479", "label": 155, "func": ""}
{"index": "s855567522", "label": 155, "func": ""}
{"index": "s594637049", "label": 155, "func": ""}
{"index": "s865602266", "label": 155, "func": ""}
{"index": "s487658065", "label": 155, "func": ""}
{"index": "s231847145", "label": 155, "func": ""}
{"index": "s153959181", "label": 155, "func": ""}
{"index": "s986948639", "label": 1655, "func": ""}
{"index": "s295596014", "label": 1655, "func": ""}
{"index": "s751321946", "label": 1655, "func": ""}
{"index": "s515742440", "label": 3836, "func": ""}
{"index": "s865176008", "label": 3836, "func": ""}
{"index": "s321025266", "label": 3836, "func": ""}
{"index": "s926380071", "label": 3836, "func": ""}
{"index": "s311791729", "label": 3836, "func": ""}
{"index": "s720415802", "label": 3836, "func": ""}
{"index": "s065451454", "label": 3836, "func": ""}
{"index": "s030870200", "label": 3836, "func": ""}
{"index": "s461889713", "label": 3836, "func": ""}
{"index": "s340499156", "label": 3836, "func": ""}
{"index": "s223544702", "label": 3537, "func": ""}
{"index": "s466504535", "label": 3537, "func": ""}
{"index": "s468385193", "label": 1726, "func": ""}
{"index": "s440074305", "label": 1726, "func": ""}
{"index": "s637500401", "label": 2422, "func": ""}
{"index": "s237268795", "label": 2422, "func": ""}
{"index": "s357791885", "label": 2422, "func": ""}
{"index": "s844403851", "label": 2422, "func": ""}
{"index": "s303594981", "label": 2422, "func": ""}
{"index": "s957005257", "label": 2422, "func": ""}
{"index": "s892983828", "label": 2422, "func": ""}
{"index": "s823443241", "label": 2422, "func": ""}
{"index": "s591602711", "label": 2422, "func": ""}
{"index": "s470157291", "label": 2422, "func": ""}
{"index": "s415324747", "label": 2996, "func": ""}
{"index": "s459591590", "label": 2996, "func": ""}
{"index": "s832875310", "label": 2996, "func": ""}
{"index": "s719945761", "label": 2996, "func": ""}
{"index": "s008780273", "label": 2996, "func": ""}
{"index": "s634125583", "label": 2996, "func": ""}
{"index": "s322908869", "label": 2996, "func": ""}
{"index": "s772697479", "label": 2996, "func": ""}
{"index": "s786944218", "label": 2996, "func": ""}
{"index": "s728578754", "label": 2996, "func": ""}
{"index": "s693690082", "label": 3048, "func": ""}
{"index": "s380888639", "label": 3048, "func": ""}
{"index": "s808437924", "label": 3048, "func": ""}
{"index": "s707911574", "label": 3048, "func": ""}
{"index": "s082417206", "label": 3048, "func": ""}
{"index": "s293603547", "label": 3048, "func": ""}
{"index": "s832758696", "label": 3048, "func": ""}
{"index": "s755014114", "label": 3048, "func": ""}
{"index": "s447266878", "label": 3048, "func": ""}
{"index": "s985667863", "label": 3048, "func": ""}
{"index": "s665750301", "label": 3485, "func": ""}
{"index": "s694375252", "label": 3485, "func": ""}
{"index": "s312339809", "label": 3485, "func": ""}
{"index": "s777006843", "label": 3485, "func": ""}
{"index": "s899146813", "label": 3485, "func": ""}
{"index": "s209939322", "label": 3485, "func": ""}
{"index": "s105958806", "label": 3485, "func": ""}
{"index": "s945063078", "label": 3485, "func": ""}
{"index": "s847450514", "label": 3485, "func": ""}
{"index": "s895043993", "label": 3485, "func": ""}
{"index": "s822576468", "label": 471, "func": ""}
{"index": "s274802697", "label": 471, "func": ""}
{"index": "s369396018", "label": 471, "func": ""}
{"index": "s619458387", "label": 471, "func": ""}
{"index": "s680157163", "label": 471, "func": ""}
{"index": "s333395809", "label": 471, "func": ""}
{"index": "s975667981", "label": 471, "func": ""}
{"index": "s444018524", "label": 471, "func": ""}
{"index": "s065157345", "label": 471, "func": ""}
{"index": "s039176964", "label": 471, "func": ""}
{"index": "s087395142", "label": 3779, "func": ""}
{"index": "s781915128", "label": 3779, "func": ""}
{"index": "s034723236", "label": 3779, "func": ""}
{"index": "s276151690", "label": 3779, "func": ""}
{"index": "s409893235", "label": 3779, "func": ""}
{"index": "s065663698", "label": 3779, "func": ""}
{"index": "s134751631", "label": 3779, "func": ""}
{"index": "s778158927", "label": 3779, "func": ""}
{"index": "s394697149", "label": 3779, "func": ""}
{"index": "s646636302", "label": 3779, "func": ""}
{"index": "s938979887", "label": 3639, "func": ""}
{"index": "s770613599", "label": 3639, "func": ""}
{"index": "s317720187", "label": 3639, "func": ""}
{"index": "s607087320", "label": 3639, "func": ""}
{"index": "s230439627", "label": 3639, "func": ""}
{"index": "s084237051", "label": 3639, "func": ""}
{"index": "s152481599", "label": 3639, "func": ""}
{"index": "s604777217", "label": 3639, "func": ""}
{"index": "s360445949", "label": 3639, "func": ""}
{"index": "s462417956", "label": 3639, "func": ""}
{"index": "s849308705", "label": 2551, "func": ""}
{"index": "s314010654", "label": 2551, "func": ""}
{"index": "s545457451", "label": 2551, "func": ""}
{"index": "s232147537", "label": 2551, "func": ""}
{"index": "s508887714", "label": 2551, "func": ""}
{"index": "s744390264", "label": 2551, "func": ""}
{"index": "s378687368", "label": 2551, "func": ""}
{"index": "s039341515", "label": 2551, "func": ""}
{"index": "s513228660", "label": 2551, "func": ""}
{"index": "s733961823", "label": 2551, "func": ""}
{"index": "s950729465", "label": 3862, "func": ""}
{"index": "s897321434", "label": 3862, "func": ""}
{"index": "s739799334", "label": 3862, "func": ""}
{"index": "s080877921", "label": 3862, "func": ""}
{"index": "s141209202", "label": 3862, "func": ""}
{"index": "s854613409", "label": 3862, "func": ""}
{"index": "s925835532", "label": 3862, "func": ""}
{"index": "s671049151", "label": 3862, "func": ""}
{"index": "s078672254", "label": 3862, "func": ""}
{"index": "s953259638", "label": 3862, "func": ""}
{"index": "s833735863", "label": 1225, "func": ""}
{"index": "s769677231", "label": 1225, "func": ""}
{"index": "s263462306", "label": 1225, "func": ""}
{"index": "s455456617", "label": 1225, "func": ""}
{"index": "s833188395", "label": 1225, "func": ""}
{"index": "s894187996", "label": 1225, "func": ""}
{"index": "s057450516", "label": 1225, "func": ""}
{"index": "s765767544", "label": 1225, "func": ""}
{"index": "s499065285", "label": 1225, "func": ""}
{"index": "s662963558", "label": 1225, "func": ""}
{"index": "s604033369", "label": 3112, "func": ""}
{"index": "s032612818", "label": 3112, "func": ""}
{"index": "s115379940", "label": 3112, "func": ""}
{"index": "s268917005", "label": 3112, "func": ""}
{"index": "s723984170", "label": 3112, "func": ""}
{"index": "s980405410", "label": 3112, "func": ""}
{"index": "s057634107", "label": 3112, "func": ""}
{"index": "s117065982", "label": 3112, "func": ""}
{"index": "s697364885", "label": 3112, "func": ""}
{"index": "s517422376", "label": 3112, "func": ""}
{"index": "s676650263", "label": 515, "func": ""}
{"index": "s592632084", "label": 515, "func": ""}
{"index": "s999340594", "label": 515, "func": ""}
{"index": "s859708355", "label": 515, "func": ""}
{"index": "s284790029", "label": 515, "func": ""}
{"index": "s494247113", "label": 515, "func": ""}
{"index": "s654005513", "label": 515, "func": ""}
{"index": "s932331061", "label": 515, "func": ""}
{"index": "s864670396", "label": 515, "func": ""}
{"index": "s697108891", "label": 515, "func": ""}
{"index": "s070897625", "label": 1447, "func": ""}
{"index": "s291104651", "label": 1447, "func": ""}
{"index": "s028056393", "label": 1447, "func": ""}
{"index": "s641749033", "label": 1447, "func": ""}
{"index": "s034990597", "label": 1447, "func": ""}
{"index": "s440842646", "label": 1447, "func": ""}
{"index": "s929435672", "label": 1447, "func": ""}
{"index": "s209641459", "label": 1447, "func": ""}
{"index": "s680847861", "label": 1447, "func": ""}
{"index": "s361007187", "label": 1447, "func": ""}
{"index": "s674471543", "label": 1199, "func": ""}
{"index": "s544088708", "label": 1199, "func": ""}
{"index": "s089807687", "label": 1199, "func": ""}
{"index": "s915006626", "label": 1643, "func": ""}
{"index": "s402729293", "label": 355, "func": ""}
{"index": "s439288280", "label": 355, "func": ""}
{"index": "s045759236", "label": 355, "func": ""}
{"index": "s497700044", "label": 355, "func": ""}
{"index": "s693895201", "label": 355, "func": ""}
{"index": "s129308193", "label": 355, "func": ""}
{"index": "s536130505", "label": 355, "func": ""}
{"index": "s850488414", "label": 355, "func": ""}
{"index": "s034413461", "label": 355, "func": ""}
{"index": "s692245464", "label": 355, "func": ""}
{"index": "s192183076", "label": 891, "func": ""}
{"index": "s763399427", "label": 891, "func": ""}
{"index": "s505558771", "label": 3033, "func": ""}
{"index": "s790267943", "label": 3033, "func": ""}
{"index": "s858382785", "label": 3033, "func": ""}
{"index": "s031571551", "label": 3033, "func": ""}
{"index": "s835943573", "label": 3033, "func": ""}
{"index": "s529109034", "label": 3033, "func": ""}
{"index": "s096734714", "label": 3033, "func": ""}
{"index": "s830779503", "label": 3033, "func": ""}
{"index": "s275734198", "label": 3033, "func": ""}
{"index": "s173841359", "label": 3033, "func": ""}
{"index": "s853377892", "label": 1038, "func": ""}
{"index": "s147117394", "label": 1038, "func": ""}
{"index": "s538098418", "label": 1038, "func": ""}
{"index": "s834731524", "label": 1869, "func": ""}
{"index": "s986855833", "label": 1869, "func": ""}
{"index": "s444722108", "label": 2606, "func": ""}
{"index": "s413473487", "label": 2606, "func": ""}
{"index": "s769074051", "label": 2606, "func": ""}
{"index": "s456209861", "label": 2606, "func": ""}
{"index": "s527033322", "label": 2606, "func": ""}
{"index": "s912913911", "label": 2606, "func": ""}
{"index": "s581778851", "label": 2606, "func": ""}
{"index": "s847997334", "label": 2606, "func": ""}
{"index": "s654326126", "label": 2606, "func": ""}
{"index": "s421710455", "label": 2606, "func": ""}
{"index": "s067798538", "label": 3204, "func": ""}
{"index": "s301486064", "label": 3204, "func": ""}
{"index": "s077583792", "label": 3204, "func": ""}
{"index": "s643881337", "label": 3209, "func": ""}
{"index": "s688278389", "label": 3209, "func": ""}
{"index": "s015700728", "label": 3209, "func": ""}
{"index": "s353308238", "label": 3209, "func": ""}
{"index": "s102068983", "label": 3209, "func": ""}
{"index": "s863302117", "label": 3209, "func": ""}
{"index": "s349884059", "label": 3209, "func": ""}
{"index": "s229449059", "label": 3209, "func": ""}
{"index": "s647837030", "label": 3209, "func": ""}
{"index": "s631113182", "label": 3209, "func": ""}
{"index": "s425657739", "label": 445, "func": ""}
{"index": "s294153252", "label": 445, "func": ""}
{"index": "s482783034", "label": 445, "func": ""}
{"index": "s888885406", "label": 445, "func": ""}
{"index": "s486551437", "label": 445, "func": ""}
{"index": "s519366891", "label": 445, "func": ""}
{"index": "s963746656", "label": 445, "func": ""}
{"index": "s532027291", "label": 445, "func": ""}
{"index": "s265762619", "label": 445, "func": ""}
{"index": "s016892633", "label": 445, "func": ""}
{"index": "s307063695", "label": 814, "func": ""}
{"index": "s841456016", "label": 814, "func": ""}
{"index": "s098524312", "label": 814, "func": ""}
{"index": "s399241052", "label": 814, "func": ""}
{"index": "s748168339", "label": 814, "func": ""}
{"index": "s225805543", "label": 814, "func": ""}
{"index": "s766851249", "label": 814, "func": ""}
{"index": "s085006864", "label": 2272, "func": ""}
{"index": "s507850040", "label": 2272, "func": ""}
{"index": "s763478671", "label": 2272, "func": ""}
{"index": "s693495507", "label": 2272, "func": ""}
{"index": "s518027444", "label": 2272, "func": ""}
{"index": "s509242210", "label": 2272, "func": ""}
{"index": "s081862193", "label": 2272, "func": ""}
{"index": "s345789005", "label": 2272, "func": ""}
{"index": "s415862777", "label": 2272, "func": ""}
{"index": "s939763401", "label": 2272, "func": ""}
{"index": "s047607532", "label": 215, "func": ""}
{"index": "s826691698", "label": 215, "func": ""}
{"index": "s256943361", "label": 215, "func": ""}
{"index": "s228015192", "label": 215, "func": ""}
{"index": "s926197025", "label": 215, "func": ""}
{"index": "s050336607", "label": 215, "func": ""}
{"index": "s241131842", "label": 215, "func": ""}
{"index": "s959150324", "label": 215, "func": ""}
{"index": "s117545003", "label": 215, "func": ""}
{"index": "s473396681", "label": 215, "func": ""}
{"index": "s712272887", "label": 2973, "func": ""}
{"index": "s987450935", "label": 2973, "func": ""}
{"index": "s131220770", "label": 2973, "func": ""}
{"index": "s286980290", "label": 2973, "func": ""}
{"index": "s682336972", "label": 2973, "func": ""}
{"index": "s764803714", "label": 2973, "func": ""}
{"index": "s142765933", "label": 2973, "func": ""}
{"index": "s496476327", "label": 2973, "func": ""}
{"index": "s771670610", "label": 2973, "func": ""}
{"index": "s139101228", "label": 2973, "func": ""}
{"index": "s655443754", "label": 1289, "func": ""}
{"index": "s121788305", "label": 1289, "func": ""}
{"index": "s936473966", "label": 1289, "func": ""}
{"index": "s700783621", "label": 2163, "func": ""}
{"index": "s309653769", "label": 2779, "func": ""}
{"index": "s104003693", "label": 2779, "func": ""}
{"index": "s487041062", "label": 2779, "func": ""}
{"index": "s179187262", "label": 2779, "func": ""}
{"index": "s322178763", "label": 2779, "func": ""}
{"index": "s261597436", "label": 2779, "func": ""}
{"index": "s406483097", "label": 2779, "func": ""}
{"index": "s302215138", "label": 2779, "func": ""}
{"index": "s883085085", "label": 2779, "func": ""}
{"index": "s983333742", "label": 2779, "func": ""}
{"index": "s918356072", "label": 1906, "func": ""}
{"index": "s812703777", "label": 1906, "func": ""}
{"index": "s032023513", "label": 1906, "func": ""}
{"index": "s003553188", "label": 1906, "func": ""}
{"index": "s249752768", "label": 1906, "func": ""}
{"index": "s195284231", "label": 1906, "func": ""}
{"index": "s124972734", "label": 1906, "func": ""}
{"index": "s306494002", "label": 1712, "func": ""}
{"index": "s141937006", "label": 1712, "func": ""}
{"index": "s937429820", "label": 1712, "func": ""}
{"index": "s713651924", "label": 1712, "func": ""}
{"index": "s984653008", "label": 1712, "func": ""}
{"index": "s551996893", "label": 1712, "func": ""}
{"index": "s272650350", "label": 1712, "func": ""}
{"index": "s591864129", "label": 1712, "func": ""}
{"index": "s980668622", "label": 1712, "func": ""}
{"index": "s054026669", "label": 1712, "func": ""}
{"index": "s980165753", "label": 3815, "func": ""}
{"index": "s668731323", "label": 3815, "func": ""}
{"index": "s764725081", "label": 3815, "func": ""}
{"index": "s111581652", "label": 3815, "func": ""}
{"index": "s702697001", "label": 3815, "func": ""}
{"index": "s165134939", "label": 3815, "func": ""}
{"index": "s154228993", "label": 3815, "func": ""}
{"index": "s652816613", "label": 3815, "func": ""}
{"index": "s948035701", "label": 3815, "func": ""}
{"index": "s360502263", "label": 3815, "func": ""}
{"index": "s873554686", "label": 3912, "func": ""}
{"index": "s943563004", "label": 3912, "func": ""}
{"index": "s450381789", "label": 3912, "func": ""}
{"index": "s453120695", "label": 3912, "func": ""}
{"index": "s973254367", "label": 3912, "func": ""}
{"index": "s589956498", "label": 3912, "func": ""}
{"index": "s989642656", "label": 3912, "func": ""}
{"index": "s140961983", "label": 3912, "func": ""}
{"index": "s749748875", "label": 3912, "func": ""}
{"index": "s655468668", "label": 3912, "func": ""}
{"index": "s001151437", "label": 121, "func": ""}
{"index": "s703497815", "label": 121, "func": ""}
{"index": "s553877297", "label": 121, "func": ""}
{"index": "s117821435", "label": 121, "func": ""}
{"index": "s372442200", "label": 121, "func": ""}
{"index": "s785607135", "label": 121, "func": ""}
{"index": "s785435170", "label": 121, "func": ""}
{"index": "s261188286", "label": 121, "func": ""}
{"index": "s177696592", "label": 121, "func": ""}
{"index": "s568428238", "label": 121, "func": ""}
{"index": "s293750139", "label": 1775, "func": ""}
{"index": "s184692986", "label": 1775, "func": ""}
{"index": "s245426566", "label": 2427, "func": ""}
{"index": "s143014244", "label": 2427, "func": ""}
{"index": "s730397255", "label": 2427, "func": ""}
{"index": "s983428366", "label": 2427, "func": ""}
{"index": "s644206829", "label": 2427, "func": ""}
{"index": "s653870397", "label": 2427, "func": ""}
{"index": "s071764607", "label": 2427, "func": ""}
{"index": "s991346917", "label": 2427, "func": ""}
{"index": "s796142644", "label": 2427, "func": ""}
{"index": "s024978349", "label": 2427, "func": ""}
{"index": "s384698389", "label": 497, "func": ""}
{"index": "s471790034", "label": 497, "func": ""}
{"index": "s890287793", "label": 497, "func": ""}
{"index": "s721440917", "label": 497, "func": ""}
{"index": "s721638969", "label": 497, "func": ""}
{"index": "s137991740", "label": 497, "func": ""}
{"index": "s474927831", "label": 497, "func": ""}
{"index": "s102888416", "label": 497, "func": ""}
{"index": "s479506459", "label": 3563, "func": ""}
{"index": "s167348012", "label": 3563, "func": ""}
{"index": "s287737570", "label": 3563, "func": ""}
{"index": "s534043821", "label": 3563, "func": ""}
{"index": "s878158663", "label": 3563, "func": ""}
{"index": "s167035049", "label": 3563, "func": ""}
{"index": "s384989612", "label": 3563, "func": ""}
{"index": "s652152307", "label": 3563, "func": ""}
{"index": "s291901476", "label": 3563, "func": ""}
{"index": "s619294250", "label": 3563, "func": ""}
{"index": "s680328784", "label": 1401, "func": ""}
{"index": "s056919898", "label": 1401, "func": ""}
{"index": "s068117311", "label": 1401, "func": ""}
{"index": "s631608950", "label": 1401, "func": ""}
{"index": "s865880067", "label": 1401, "func": ""}
{"index": "s544233016", "label": 4046, "func": ""}
{"index": "s254354389", "label": 4046, "func": ""}
{"index": "s618428214", "label": 4046, "func": ""}
{"index": "s403412405", "label": 4046, "func": ""}
{"index": "s906484237", "label": 4046, "func": ""}
{"index": "s879724883", "label": 4046, "func": ""}
{"index": "s768911458", "label": 4046, "func": ""}
{"index": "s041388351", "label": 4046, "func": ""}
{"index": "s385895691", "label": 4046, "func": ""}
{"index": "s620166135", "label": 4046, "func": ""}
{"index": "s388020689", "label": 264, "func": ""}
{"index": "s489558939", "label": 264, "func": ""}
{"index": "s634803233", "label": 264, "func": ""}
{"index": "s001861462", "label": 1441, "func": ""}
{"index": "s350614643", "label": 1060, "func": ""}
{"index": "s326301444", "label": 1060, "func": ""}
{"index": "s591220189", "label": 1060, "func": ""}
{"index": "s573044429", "label": 1060, "func": ""}
{"index": "s306991999", "label": 3249, "func": ""}
{"index": "s001957454", "label": 3249, "func": ""}
{"index": "s309718439", "label": 3249, "func": ""}
{"index": "s313140341", "label": 3249, "func": ""}
{"index": "s796976624", "label": 3249, "func": ""}
{"index": "s745168728", "label": 3078, "func": ""}
{"index": "s128807393", "label": 3078, "func": ""}
{"index": "s374126665", "label": 3078, "func": ""}
{"index": "s514271939", "label": 3078, "func": ""}
{"index": "s523867944", "label": 3078, "func": ""}
{"index": "s734110795", "label": 3078, "func": ""}
{"index": "s217416256", "label": 3078, "func": ""}
{"index": "s441026607", "label": 3078, "func": ""}
{"index": "s690343574", "label": 3078, "func": ""}
{"index": "s157515645", "label": 3078, "func": ""}
{"index": "s078834825", "label": 1945, "func": ""}
{"index": "s118025783", "label": 1945, "func": ""}
{"index": "s688525233", "label": 2100, "func": ""}
{"index": "s589934836", "label": 2100, "func": ""}
{"index": "s347305953", "label": 20, "func": ""}
{"index": "s852125813", "label": 20, "func": ""}
{"index": "s190603961", "label": 20, "func": ""}
{"index": "s047781660", "label": 20, "func": ""}
{"index": "s256616518", "label": 20, "func": ""}
{"index": "s050746263", "label": 20, "func": ""}
{"index": "s032062109", "label": 20, "func": ""}
{"index": "s891625063", "label": 20, "func": ""}
{"index": "s003210769", "label": 20, "func": ""}
{"index": "s186076308", "label": 20, "func": ""}
{"index": "s431680885", "label": 1608, "func": ""}
{"index": "s232610014", "label": 2702, "func": ""}
{"index": "s031195592", "label": 2702, "func": ""}
{"index": "s115730354", "label": 2702, "func": ""}
{"index": "s263369172", "label": 2702, "func": ""}
{"index": "s915207357", "label": 2702, "func": ""}
{"index": "s952481709", "label": 2702, "func": ""}
{"index": "s616282631", "label": 2702, "func": ""}
{"index": "s582941873", "label": 2702, "func": ""}
{"index": "s122612360", "label": 2702, "func": ""}
{"index": "s156809228", "label": 2702, "func": ""}
{"index": "s975711582", "label": 3394, "func": ""}
{"index": "s108794380", "label": 3394, "func": ""}
{"index": "s454322632", "label": 3394, "func": ""}
{"index": "s710898587", "label": 3394, "func": ""}
{"index": "s718874988", "label": 3394, "func": ""}
{"index": "s685687011", "label": 3394, "func": ""}
{"index": "s267063853", "label": 3394, "func": ""}
{"index": "s216994263", "label": 3394, "func": ""}
{"index": "s301148696", "label": 3394, "func": ""}
{"index": "s979275866", "label": 3394, "func": ""}
{"index": "s971313347", "label": 2517, "func": ""}
{"index": "s939142868", "label": 2517, "func": ""}
{"index": "s888597183", "label": 2517, "func": ""}
{"index": "s791745456", "label": 2517, "func": ""}
{"index": "s096016656", "label": 2517, "func": ""}
{"index": "s934444098", "label": 2517, "func": ""}
{"index": "s378740247", "label": 3256, "func": ""}
{"index": "s470597907", "label": 3256, "func": ""}
{"index": "s614341098", "label": 3256, "func": ""}
{"index": "s649919890", "label": 3256, "func": ""}
{"index": "s940668716", "label": 3256, "func": ""}
{"index": "s579097536", "label": 3256, "func": ""}
{"index": "s259004733", "label": 3256, "func": ""}
{"index": "s133345570", "label": 3256, "func": ""}
{"index": "s392065668", "label": 3256, "func": ""}
{"index": "s745717487", "label": 3256, "func": ""}
{"index": "s040872122", "label": 3359, "func": ""}
{"index": "s144386285", "label": 3359, "func": ""}
{"index": "s062517323", "label": 3359, "func": ""}
{"index": "s568062586", "label": 3359, "func": ""}
{"index": "s199061502", "label": 3359, "func": ""}
{"index": "s224709145", "label": 3359, "func": ""}
{"index": "s364985380", "label": 3359, "func": ""}
{"index": "s990335093", "label": 3359, "func": ""}
{"index": "s696163686", "label": 3359, "func": ""}
{"index": "s316023491", "label": 3359, "func": ""}
{"index": "s360250734", "label": 672, "func": ""}
{"index": "s735953944", "label": 672, "func": ""}
{"index": "s764844007", "label": 2382, "func": ""}
{"index": "s910789486", "label": 2382, "func": ""}
{"index": "s362539205", "label": 2382, "func": ""}
{"index": "s351043222", "label": 2382, "func": ""}
{"index": "s898369903", "label": 2382, "func": ""}
{"index": "s160830801", "label": 2382, "func": ""}
{"index": "s089629477", "label": 2382, "func": ""}
{"index": "s658154408", "label": 2382, "func": ""}
{"index": "s737052611", "label": 2382, "func": ""}
{"index": "s228262589", "label": 2382, "func": ""}
{"index": "s616067446", "label": 2354, "func": ""}
{"index": "s390122679", "label": 2354, "func": ""}
{"index": "s294934280", "label": 2354, "func": ""}
{"index": "s759931123", "label": 2354, "func": ""}
{"index": "s014591728", "label": 2354, "func": ""}
{"index": "s447255377", "label": 2354, "func": ""}
{"index": "s047245859", "label": 2354, "func": ""}
{"index": "s690574273", "label": 1271, "func": ""}
{"index": "s961820452", "label": 1271, "func": ""}
{"index": "s070892343", "label": 1271, "func": ""}
{"index": "s312933008", "label": 1271, "func": ""}
{"index": "s821092372", "label": 1271, "func": ""}
{"index": "s294755381", "label": 1271, "func": ""}
{"index": "s597675351", "label": 1271, "func": ""}
{"index": "s394815367", "label": 1271, "func": ""}
{"index": "s039455979", "label": 1271, "func": ""}
{"index": "s032126225", "label": 1271, "func": ""}
{"index": "s909961272", "label": 1305, "func": ""}
{"index": "s919773524", "label": 1305, "func": ""}
{"index": "s539895926", "label": 1305, "func": ""}
{"index": "s149807289", "label": 1305, "func": ""}
{"index": "s185683824", "label": 1305, "func": ""}
{"index": "s391913402", "label": 1305, "func": ""}
{"index": "s835757915", "label": 1305, "func": ""}
{"index": "s107866318", "label": 1305, "func": ""}
{"index": "s138277529", "label": 1305, "func": ""}
{"index": "s820588449", "label": 1305, "func": ""}
{"index": "s046473829", "label": 1541, "func": ""}
{"index": "s275438988", "label": 434, "func": ""}
{"index": "s911540540", "label": 434, "func": ""}
{"index": "s428943989", "label": 434, "func": ""}
{"index": "s186154540", "label": 434, "func": ""}
{"index": "s308985216", "label": 434, "func": ""}
{"index": "s334184501", "label": 434, "func": ""}
{"index": "s341106014", "label": 434, "func": ""}
{"index": "s178355233", "label": 434, "func": ""}
{"index": "s013688252", "label": 434, "func": ""}
{"index": "s078036154", "label": 434, "func": ""}
{"index": "s466651192", "label": 820, "func": ""}
{"index": "s711625251", "label": 820, "func": ""}
{"index": "s576995240", "label": 820, "func": ""}
{"index": "s981503304", "label": 820, "func": ""}
{"index": "s866169566", "label": 820, "func": ""}
{"index": "s683122704", "label": 820, "func": ""}
{"index": "s854778459", "label": 820, "func": ""}
{"index": "s474294531", "label": 820, "func": ""}
{"index": "s194561218", "label": 820, "func": ""}
{"index": "s238958471", "label": 820, "func": ""}
{"index": "s142503618", "label": 481, "func": ""}
{"index": "s994520317", "label": 481, "func": ""}
{"index": "s832560587", "label": 481, "func": ""}
{"index": "s843537492", "label": 481, "func": ""}
{"index": "s949255945", "label": 481, "func": ""}
{"index": "s082320236", "label": 481, "func": ""}
{"index": "s749145584", "label": 481, "func": ""}
{"index": "s789471949", "label": 481, "func": ""}
{"index": "s256835109", "label": 481, "func": ""}
{"index": "s294152830", "label": 481, "func": ""}
{"index": "s519951105", "label": 3853, "func": ""}
{"index": "s515507995", "label": 3853, "func": ""}
{"index": "s067148643", "label": 3853, "func": ""}
{"index": "s229799581", "label": 3853, "func": ""}
{"index": "s459034625", "label": 3853, "func": ""}
{"index": "s538731890", "label": 3853, "func": ""}
{"index": "s991485545", "label": 3853, "func": ""}
{"index": "s490843480", "label": 3853, "func": ""}
{"index": "s198636491", "label": 3853, "func": ""}
{"index": "s392591421", "label": 3853, "func": ""}
{"index": "s466302294", "label": 3159, "func": ""}
{"index": "s153426129", "label": 3159, "func": ""}
{"index": "s694161242", "label": 3159, "func": ""}
{"index": "s492915633", "label": 2978, "func": ""}
{"index": "s742120602", "label": 2978, "func": ""}
{"index": "s280158108", "label": 2978, "func": ""}
{"index": "s729657787", "label": 2799, "func": ""}
{"index": "s600873651", "label": 2799, "func": ""}
{"index": "s544878004", "label": 2799, "func": ""}
{"index": "s005031048", "label": 2799, "func": ""}
{"index": "s857830915", "label": 2799, "func": ""}
{"index": "s441426030", "label": 462, "func": ""}
{"index": "s314648363", "label": 462, "func": ""}
{"index": "s948751567", "label": 462, "func": ""}
{"index": "s911259680", "label": 462, "func": ""}
{"index": "s182148017", "label": 462, "func": ""}
{"index": "s947884392", "label": 462, "func": ""}
{"index": "s252821580", "label": 462, "func": ""}
{"index": "s264901741", "label": 462, "func": ""}
{"index": "s731428162", "label": 462, "func": ""}
{"index": "s851445591", "label": 462, "func": ""}
{"index": "s275445931", "label": 76, "func": ""}
{"index": "s138053314", "label": 76, "func": ""}
{"index": "s443645139", "label": 76, "func": ""}
{"index": "s239215199", "label": 76, "func": ""}
{"index": "s167759717", "label": 76, "func": ""}
{"index": "s727870025", "label": 76, "func": ""}
{"index": "s938770687", "label": 76, "func": ""}
{"index": "s174923445", "label": 76, "func": ""}
{"index": "s604292720", "label": 76, "func": ""}
{"index": "s533856577", "label": 76, "func": ""}
{"index": "s058276432", "label": 2429, "func": ""}
{"index": "s421339533", "label": 2429, "func": ""}
{"index": "s633393955", "label": 2429, "func": ""}
{"index": "s516324305", "label": 2429, "func": ""}
{"index": "s815820696", "label": 2429, "func": ""}
{"index": "s368716318", "label": 2429, "func": ""}
{"index": "s548500414", "label": 2429, "func": ""}
{"index": "s624567384", "label": 2429, "func": ""}
{"index": "s357065752", "label": 576, "func": ""}
{"index": "s091293763", "label": 576, "func": ""}
{"index": "s936857695", "label": 576, "func": ""}
{"index": "s167866411", "label": 576, "func": ""}
{"index": "s049714654", "label": 576, "func": ""}
{"index": "s249642334", "label": 576, "func": ""}
{"index": "s341733214", "label": 576, "func": ""}
{"index": "s456986318", "label": 576, "func": ""}
{"index": "s472235101", "label": 576, "func": ""}
{"index": "s233099510", "label": 576, "func": ""}
{"index": "s615422011", "label": 1061, "func": ""}
{"index": "s101681410", "label": 1061, "func": ""}
{"index": "s347071365", "label": 2891, "func": ""}
{"index": "s705373139", "label": 2891, "func": ""}
{"index": "s532363986", "label": 2891, "func": ""}
{"index": "s938987060", "label": 2891, "func": ""}
{"index": "s138680929", "label": 2891, "func": ""}
{"index": "s465317242", "label": 2891, "func": ""}
{"index": "s259126515", "label": 2891, "func": ""}
{"index": "s228324672", "label": 2891, "func": ""}
{"index": "s396281570", "label": 2891, "func": ""}
{"index": "s825527551", "label": 2891, "func": ""}
{"index": "s659258691", "label": 2554, "func": ""}
{"index": "s968857033", "label": 2554, "func": ""}
{"index": "s629177486", "label": 2554, "func": ""}
{"index": "s922544503", "label": 2554, "func": ""}
{"index": "s076256096", "label": 2554, "func": ""}
{"index": "s688310108", "label": 2554, "func": ""}
{"index": "s754498502", "label": 2554, "func": ""}
{"index": "s918553121", "label": 2554, "func": ""}
{"index": "s967222875", "label": 2554, "func": ""}
{"index": "s810047084", "label": 2554, "func": ""}
{"index": "s068848896", "label": 3324, "func": ""}
{"index": "s052996668", "label": 3324, "func": ""}
{"index": "s856167596", "label": 3324, "func": ""}
{"index": "s332260089", "label": 3324, "func": ""}
{"index": "s598173128", "label": 3324, "func": ""}
{"index": "s351953022", "label": 3324, "func": ""}
{"index": "s959839564", "label": 3324, "func": ""}
{"index": "s306386448", "label": 3324, "func": ""}
{"index": "s018188824", "label": 3324, "func": ""}
{"index": "s462910972", "label": 3324, "func": ""}
{"index": "s224804158", "label": 3365, "func": ""}
{"index": "s829264582", "label": 3365, "func": ""}
{"index": "s195205493", "label": 3365, "func": ""}
{"index": "s149362257", "label": 3365, "func": ""}
{"index": "s511287324", "label": 3365, "func": ""}
{"index": "s587302733", "label": 3365, "func": ""}
{"index": "s881591663", "label": 3365, "func": ""}
{"index": "s155030527", "label": 3365, "func": ""}
{"index": "s265964329", "label": 3365, "func": ""}
{"index": "s725809212", "label": 3365, "func": ""}
{"index": "s020330014", "label": 3294, "func": ""}
{"index": "s981950371", "label": 3294, "func": ""}
{"index": "s401572959", "label": 3294, "func": ""}
{"index": "s176140485", "label": 3294, "func": ""}
{"index": "s745998597", "label": 3294, "func": ""}
{"index": "s306200440", "label": 3294, "func": ""}
{"index": "s262536314", "label": 3294, "func": ""}
{"index": "s834063795", "label": 3294, "func": ""}
{"index": "s135491757", "label": 3294, "func": ""}
{"index": "s425345591", "label": 3294, "func": ""}
{"index": "s977390775", "label": 1594, "func": ""}
{"index": "s627521171", "label": 1501, "func": ""}
{"index": "s797641603", "label": 2742, "func": ""}
{"index": "s848568010", "label": 2742, "func": ""}
{"index": "s504936448", "label": 2742, "func": ""}
{"index": "s674267261", "label": 2742, "func": ""}
{"index": "s200449719", "label": 2742, "func": ""}
{"index": "s500490531", "label": 2742, "func": ""}
{"index": "s807334015", "label": 2742, "func": ""}
{"index": "s417343462", "label": 2742, "func": ""}
{"index": "s731260979", "label": 2742, "func": ""}
{"index": "s156003678", "label": 2742, "func": ""}
{"index": "s060109413", "label": 2566, "func": ""}
{"index": "s304998352", "label": 2566, "func": ""}
{"index": "s372563153", "label": 2566, "func": ""}
{"index": "s117427945", "label": 2566, "func": ""}
{"index": "s260130485", "label": 2566, "func": ""}
{"index": "s068133977", "label": 2566, "func": ""}
{"index": "s229788710", "label": 3303, "func": ""}
{"index": "s116462015", "label": 3303, "func": ""}
{"index": "s688791132", "label": 3303, "func": ""}
{"index": "s143995203", "label": 3303, "func": ""}
{"index": "s688433255", "label": 3303, "func": ""}
{"index": "s499336508", "label": 3303, "func": ""}
{"index": "s032266764", "label": 3303, "func": ""}
{"index": "s525981867", "label": 3303, "func": ""}
{"index": "s413837371", "label": 3303, "func": ""}
{"index": "s454501542", "label": 3303, "func": ""}
{"index": "s147882465", "label": 2213, "func": ""}
{"index": "s624894669", "label": 2213, "func": ""}
{"index": "s357253448", "label": 240, "func": ""}
{"index": "s512759960", "label": 240, "func": ""}
{"index": "s170205658", "label": 240, "func": ""}
{"index": "s691118532", "label": 240, "func": ""}
{"index": "s249772758", "label": 240, "func": ""}
{"index": "s560098169", "label": 240, "func": ""}
{"index": "s751437114", "label": 240, "func": ""}
{"index": "s049330579", "label": 240, "func": ""}
{"index": "s798590778", "label": 240, "func": ""}
{"index": "s049826725", "label": 240, "func": ""}
{"index": "s442001292", "label": 2919, "func": ""}
{"index": "s984982892", "label": 2919, "func": ""}
{"index": "s474154570", "label": 2919, "func": ""}
{"index": "s945291129", "label": 2919, "func": ""}
{"index": "s968179527", "label": 2919, "func": ""}
{"index": "s832425079", "label": 2919, "func": ""}
{"index": "s143405206", "label": 2919, "func": ""}
{"index": "s247785192", "label": 2919, "func": ""}
{"index": "s268063889", "label": 2919, "func": ""}
{"index": "s847026151", "label": 2919, "func": ""}
{"index": "s636719878", "label": 3113, "func": ""}
{"index": "s351223574", "label": 3113, "func": ""}
{"index": "s261824224", "label": 1077, "func": ""}
{"index": "s799167806", "label": 1077, "func": ""}
{"index": "s557699848", "label": 1077, "func": ""}
{"index": "s871238498", "label": 3764, "func": ""}
{"index": "s924307517", "label": 3764, "func": ""}
{"index": "s839505992", "label": 3764, "func": ""}
{"index": "s936853885", "label": 3764, "func": ""}
{"index": "s618152198", "label": 3764, "func": ""}
{"index": "s621068252", "label": 3764, "func": ""}
{"index": "s198721968", "label": 3764, "func": ""}
{"index": "s445754715", "label": 3764, "func": ""}
{"index": "s820202241", "label": 3764, "func": ""}
{"index": "s213325652", "label": 3764, "func": ""}
{"index": "s629218207", "label": 2445, "func": ""}
{"index": "s482747057", "label": 2445, "func": ""}
{"index": "s958063979", "label": 2445, "func": ""}
{"index": "s276482652", "label": 2445, "func": ""}
{"index": "s736835691", "label": 2445, "func": ""}
{"index": "s585639429", "label": 2445, "func": ""}
{"index": "s949783947", "label": 2445, "func": ""}
{"index": "s725335321", "label": 2445, "func": ""}
{"index": "s360879320", "label": 2445, "func": ""}
{"index": "s636196981", "label": 2445, "func": ""}
{"index": "s192159076", "label": 387, "func": ""}
{"index": "s961414402", "label": 387, "func": ""}
{"index": "s149321471", "label": 387, "func": ""}
{"index": "s435423199", "label": 387, "func": ""}
{"index": "s079133491", "label": 387, "func": ""}
{"index": "s316440573", "label": 387, "func": ""}
{"index": "s506263508", "label": 387, "func": ""}
{"index": "s316861715", "label": 387, "func": ""}
{"index": "s175870938", "label": 1317, "func": ""}
{"index": "s373571445", "label": 1317, "func": ""}
{"index": "s585874332", "label": 1317, "func": ""}
{"index": "s893381288", "label": 1317, "func": ""}
{"index": "s215189242", "label": 1317, "func": ""}
{"index": "s863877078", "label": 1317, "func": ""}
{"index": "s263577235", "label": 1317, "func": ""}
{"index": "s791380389", "label": 1317, "func": ""}
{"index": "s565818074", "label": 1317, "func": ""}
{"index": "s331745830", "label": 1317, "func": ""}
{"index": "s634100127", "label": 2822, "func": ""}
{"index": "s413838336", "label": 2822, "func": ""}
{"index": "s624498579", "label": 2822, "func": ""}
{"index": "s733386554", "label": 2822, "func": ""}
{"index": "s402133135", "label": 2822, "func": ""}
{"index": "s392557267", "label": 2822, "func": ""}
{"index": "s431753683", "label": 2822, "func": ""}
{"index": "s808536651", "label": 2056, "func": ""}
{"index": "s761712486", "label": 2056, "func": ""}
{"index": "s969984848", "label": 245, "func": ""}
{"index": "s589768285", "label": 245, "func": ""}
{"index": "s203279730", "label": 245, "func": ""}
{"index": "s960264138", "label": 245, "func": ""}
{"index": "s414716853", "label": 245, "func": ""}
{"index": "s299934560", "label": 245, "func": ""}
{"index": "s944750415", "label": 245, "func": ""}
{"index": "s309935751", "label": 245, "func": ""}
{"index": "s160856846", "label": 245, "func": ""}
{"index": "s704491309", "label": 245, "func": ""}
{"index": "s111539395", "label": 130, "func": ""}
{"index": "s122337275", "label": 130, "func": ""}
{"index": "s924764320", "label": 130, "func": ""}
{"index": "s256075389", "label": 130, "func": ""}
{"index": "s928364198", "label": 130, "func": ""}
{"index": "s268195306", "label": 130, "func": ""}
{"index": "s977432294", "label": 130, "func": ""}
{"index": "s499997241", "label": 130, "func": ""}
{"index": "s224431892", "label": 130, "func": ""}
{"index": "s624908672", "label": 130, "func": ""}
{"index": "s659020987", "label": 2207, "func": ""}
{"index": "s733830757", "label": 1111, "func": ""}
{"index": "s921474421", "label": 1111, "func": ""}
{"index": "s396468174", "label": 1111, "func": ""}
{"index": "s551781110", "label": 1111, "func": ""}
{"index": "s347567352", "label": 1111, "func": ""}
{"index": "s723212808", "label": 1111, "func": ""}
{"index": "s216975126", "label": 1111, "func": ""}
{"index": "s096560670", "label": 1111, "func": ""}
{"index": "s167002168", "label": 1111, "func": ""}
{"index": "s624190061", "label": 1111, "func": ""}
{"index": "s914398388", "label": 524, "func": ""}
{"index": "s382295309", "label": 524, "func": ""}
{"index": "s388573009", "label": 524, "func": ""}
{"index": "s713278524", "label": 640, "func": ""}
{"index": "s782408822", "label": 640, "func": ""}
{"index": "s577419590", "label": 640, "func": ""}
{"index": "s211193232", "label": 640, "func": ""}
{"index": "s728232182", "label": 640, "func": ""}
{"index": "s908373199", "label": 640, "func": ""}
{"index": "s448553373", "label": 640, "func": ""}
{"index": "s984080103", "label": 640, "func": ""}
{"index": "s230065302", "label": 640, "func": ""}
{"index": "s586621361", "label": 640, "func": ""}
{"index": "s879211163", "label": 2481, "func": ""}
{"index": "s200013886", "label": 2481, "func": ""}
{"index": "s077334714", "label": 2481, "func": ""}
{"index": "s507634337", "label": 2481, "func": ""}
{"index": "s418143616", "label": 2481, "func": ""}
{"index": "s285272761", "label": 2481, "func": ""}
{"index": "s312605627", "label": 2481, "func": ""}
{"index": "s119509989", "label": 2481, "func": ""}
{"index": "s027557079", "label": 2481, "func": ""}
{"index": "s562443668", "label": 2481, "func": ""}
{"index": "s378747157", "label": 3488, "func": ""}
{"index": "s072005797", "label": 3488, "func": ""}
{"index": "s511911146", "label": 3488, "func": ""}
{"index": "s319601936", "label": 3488, "func": ""}
{"index": "s816333985", "label": 3488, "func": ""}
{"index": "s693422399", "label": 3488, "func": ""}
{"index": "s949591407", "label": 3488, "func": ""}
{"index": "s648647419", "label": 3488, "func": ""}
{"index": "s537899905", "label": 3488, "func": ""}
{"index": "s335989666", "label": 3488, "func": ""}
{"index": "s477770408", "label": 1300, "func": ""}
{"index": "s428318817", "label": 1300, "func": ""}
{"index": "s419317322", "label": 1300, "func": ""}
{"index": "s971305922", "label": 1300, "func": ""}
{"index": "s747781968", "label": 1300, "func": ""}
{"index": "s504904130", "label": 1300, "func": ""}
{"index": "s823721916", "label": 1300, "func": ""}
{"index": "s864263291", "label": 1300, "func": ""}
{"index": "s882749766", "label": 1300, "func": ""}
{"index": "s122231404", "label": 1300, "func": ""}
{"index": "s360800072", "label": 827, "func": ""}
{"index": "s227382446", "label": 827, "func": ""}
{"index": "s561623516", "label": 827, "func": ""}
{"index": "s830989591", "label": 827, "func": ""}
{"index": "s817350981", "label": 827, "func": ""}
{"index": "s320644034", "label": 827, "func": ""}
{"index": "s646052771", "label": 827, "func": ""}
{"index": "s945463029", "label": 827, "func": ""}
{"index": "s117900084", "label": 827, "func": ""}
{"index": "s387048893", "label": 827, "func": ""}
{"index": "s145760364", "label": 139, "func": ""}
{"index": "s497898429", "label": 139, "func": ""}
{"index": "s732294904", "label": 139, "func": ""}
{"index": "s391893416", "label": 139, "func": ""}
{"index": "s590557682", "label": 139, "func": ""}
{"index": "s011420063", "label": 139, "func": ""}
{"index": "s917564111", "label": 139, "func": ""}
{"index": "s578517780", "label": 139, "func": ""}
{"index": "s375492781", "label": 139, "func": ""}
{"index": "s584543790", "label": 139, "func": ""}
{"index": "s738019123", "label": 3838, "func": ""}
{"index": "s484425155", "label": 3838, "func": ""}
{"index": "s808943178", "label": 3838, "func": ""}
{"index": "s812483696", "label": 3838, "func": ""}
{"index": "s455624286", "label": 3838, "func": ""}
{"index": "s719936082", "label": 3838, "func": ""}
{"index": "s461344797", "label": 3838, "func": ""}
{"index": "s596002665", "label": 3838, "func": ""}
{"index": "s871419115", "label": 3838, "func": ""}
{"index": "s129474126", "label": 3838, "func": ""}
{"index": "s415148316", "label": 3544, "func": ""}
{"index": "s391883019", "label": 3544, "func": ""}
{"index": "s270329048", "label": 3544, "func": ""}
{"index": "s931420862", "label": 3544, "func": ""}
{"index": "s292057671", "label": 3544, "func": ""}
{"index": "s031464660", "label": 3544, "func": ""}
{"index": "s744673942", "label": 3544, "func": ""}
{"index": "s967862861", "label": 3544, "func": ""}
{"index": "s482683101", "label": 3544, "func": ""}
{"index": "s183642968", "label": 3544, "func": ""}
{"index": "s298235948", "label": 2292, "func": ""}
{"index": "s124538384", "label": 2292, "func": ""}
{"index": "s388915079", "label": 2292, "func": ""}
{"index": "s199414080", "label": 2292, "func": ""}
{"index": "s043872881", "label": 2292, "func": ""}
{"index": "s145928974", "label": 2292, "func": ""}
{"index": "s199319317", "label": 2292, "func": ""}
{"index": "s016010007", "label": 2292, "func": ""}
{"index": "s827259902", "label": 2292, "func": ""}
{"index": "s142833014", "label": 2292, "func": ""}
{"index": "s825708793", "label": 2495, "func": ""}
{"index": "s148497943", "label": 2495, "func": ""}
{"index": "s646091814", "label": 2495, "func": ""}
{"index": "s758995305", "label": 2495, "func": ""}
{"index": "s117849622", "label": 2495, "func": ""}
{"index": "s517204019", "label": 2495, "func": ""}
{"index": "s840031279", "label": 2495, "func": ""}
{"index": "s821312765", "label": 2495, "func": ""}
{"index": "s941311296", "label": 2495, "func": ""}
{"index": "s832073999", "label": 2495, "func": ""}
{"index": "s737550565", "label": 3195, "func": ""}
{"index": "s125434874", "label": 3195, "func": ""}
{"index": "s876850025", "label": 3195, "func": ""}
{"index": "s194148164", "label": 3195, "func": ""}
{"index": "s877103052", "label": 3195, "func": ""}
{"index": "s454774616", "label": 3195, "func": ""}
{"index": "s383661252", "label": 3195, "func": ""}
{"index": "s573551345", "label": 3195, "func": ""}
{"index": "s418504306", "label": 3195, "func": ""}
{"index": "s541967680", "label": 3195, "func": ""}
{"index": "s369156674", "label": 678, "func": ""}
{"index": "s167652346", "label": 678, "func": ""}
{"index": "s779943255", "label": 2999, "func": ""}
{"index": "s341348249", "label": 2999, "func": ""}
{"index": "s972502562", "label": 2999, "func": ""}
{"index": "s827194534", "label": 2999, "func": ""}
{"index": "s998434574", "label": 2999, "func": ""}
{"index": "s297634171", "label": 2999, "func": ""}
{"index": "s877608481", "label": 2999, "func": ""}
{"index": "s321235522", "label": 2999, "func": ""}
{"index": "s322057836", "label": 2999, "func": ""}
{"index": "s631896553", "label": 2999, "func": ""}
{"index": "s043443214", "label": 2983, "func": ""}
{"index": "s132920152", "label": 2983, "func": ""}
{"index": "s476235148", "label": 2983, "func": ""}
{"index": "s318325953", "label": 2983, "func": ""}
{"index": "s974411677", "label": 2983, "func": ""}
{"index": "s030123827", "label": 2983, "func": ""}
{"index": "s541580813", "label": 2983, "func": ""}
{"index": "s017265762", "label": 2983, "func": ""}
{"index": "s457386444", "label": 2983, "func": ""}
{"index": "s877458931", "label": 2983, "func": ""}
{"index": "s247856350", "label": 55, "func": ""}
{"index": "s355717052", "label": 55, "func": ""}
{"index": "s526806452", "label": 55, "func": ""}
{"index": "s303633383", "label": 55, "func": ""}
{"index": "s899667109", "label": 55, "func": ""}
{"index": "s675422311", "label": 55, "func": ""}
{"index": "s307883737", "label": 55, "func": ""}
{"index": "s804761106", "label": 55, "func": ""}
{"index": "s509365032", "label": 55, "func": ""}
{"index": "s084177604", "label": 55, "func": ""}
{"index": "s580746795", "label": 3156, "func": ""}
{"index": "s917643285", "label": 3156, "func": ""}
{"index": "s315286757", "label": 3156, "func": ""}
{"index": "s165841513", "label": 3156, "func": ""}
{"index": "s570214583", "label": 3156, "func": ""}
{"index": "s387003677", "label": 3156, "func": ""}
{"index": "s532173176", "label": 3156, "func": ""}
{"index": "s961994129", "label": 3156, "func": ""}
{"index": "s324218813", "label": 3156, "func": ""}
{"index": "s569837007", "label": 3156, "func": ""}
{"index": "s970432760", "label": 1658, "func": ""}
{"index": "s566076964", "label": 1658, "func": ""}
{"index": "s015392586", "label": 1658, "func": ""}
{"index": "s953397926", "label": 1658, "func": ""}
{"index": "s004063304", "label": 3424, "func": ""}
{"index": "s039711654", "label": 3424, "func": ""}
{"index": "s313345839", "label": 3424, "func": ""}
{"index": "s349003066", "label": 3424, "func": ""}
{"index": "s795649445", "label": 3424, "func": ""}
{"index": "s190842981", "label": 3424, "func": ""}
{"index": "s011481234", "label": 3424, "func": ""}
{"index": "s364302285", "label": 3424, "func": ""}
{"index": "s149231933", "label": 3424, "func": ""}
{"index": "s313964954", "label": 3424, "func": ""}
{"index": "s679399346", "label": 353, "func": ""}
{"index": "s902133010", "label": 353, "func": ""}
{"index": "s153206395", "label": 353, "func": ""}
{"index": "s844379000", "label": 353, "func": ""}
{"index": "s319171121", "label": 353, "func": ""}
{"index": "s388197915", "label": 353, "func": ""}
{"index": "s024669708", "label": 353, "func": ""}
{"index": "s577276064", "label": 353, "func": ""}
{"index": "s446028556", "label": 353, "func": ""}
{"index": "s965096967", "label": 353, "func": ""}
{"index": "s667498283", "label": 109, "func": ""}
{"index": "s041960379", "label": 109, "func": ""}
{"index": "s516616044", "label": 109, "func": ""}
{"index": "s080617301", "label": 109, "func": ""}
{"index": "s631489177", "label": 109, "func": ""}
{"index": "s899713647", "label": 109, "func": ""}
{"index": "s267132328", "label": 109, "func": ""}
{"index": "s048982866", "label": 109, "func": ""}
{"index": "s990035639", "label": 109, "func": ""}
{"index": "s966889027", "label": 109, "func": ""}
{"index": "s795317762", "label": 54, "func": ""}
{"index": "s472343804", "label": 54, "func": ""}
{"index": "s944238862", "label": 54, "func": ""}
{"index": "s162587306", "label": 54, "func": ""}
{"index": "s924914194", "label": 54, "func": ""}
{"index": "s050212295", "label": 54, "func": ""}
{"index": "s902326357", "label": 54, "func": ""}
{"index": "s161816298", "label": 54, "func": ""}
{"index": "s052797346", "label": 54, "func": ""}
{"index": "s390037906", "label": 54, "func": ""}
{"index": "s307084779", "label": 2438, "func": ""}
{"index": "s276974582", "label": 2438, "func": ""}
{"index": "s261657039", "label": 2438, "func": ""}
{"index": "s250189475", "label": 2438, "func": ""}
{"index": "s744833648", "label": 2438, "func": ""}
{"index": "s409432926", "label": 2438, "func": ""}
{"index": "s318909078", "label": 2438, "func": ""}
{"index": "s970260067", "label": 2438, "func": ""}
{"index": "s144133904", "label": 2438, "func": ""}
{"index": "s051111106", "label": 2343, "func": ""}
{"index": "s052592195", "label": 2343, "func": ""}
{"index": "s113822991", "label": 2343, "func": ""}
{"index": "s724254985", "label": 2343, "func": ""}
{"index": "s148822539", "label": 2343, "func": ""}
{"index": "s465516937", "label": 2343, "func": ""}
{"index": "s895492122", "label": 2343, "func": ""}
{"index": "s112145661", "label": 2343, "func": ""}
{"index": "s505350870", "label": 2343, "func": ""}
{"index": "s536110266", "label": 2343, "func": ""}
{"index": "s383356097", "label": 885, "func": ""}
{"index": "s485091851", "label": 885, "func": ""}
{"index": "s959469231", "label": 885, "func": ""}
{"index": "s855795015", "label": 885, "func": ""}
{"index": "s094483045", "label": 885, "func": ""}
{"index": "s692886980", "label": 885, "func": ""}
{"index": "s470843215", "label": 885, "func": ""}
{"index": "s859065733", "label": 885, "func": ""}
{"index": "s334273684", "label": 885, "func": ""}
{"index": "s103803348", "label": 885, "func": ""}
{"index": "s146642470", "label": 2338, "func": ""}
{"index": "s689761453", "label": 3860, "func": ""}
{"index": "s461303017", "label": 3860, "func": ""}
{"index": "s362475778", "label": 3860, "func": ""}
{"index": "s736805024", "label": 3860, "func": ""}
{"index": "s691233712", "label": 3860, "func": ""}
{"index": "s189415269", "label": 3860, "func": ""}
{"index": "s683834650", "label": 3860, "func": ""}
{"index": "s457896335", "label": 3860, "func": ""}
{"index": "s217031838", "label": 3860, "func": ""}
{"index": "s382636586", "label": 3860, "func": ""}
{"index": "s043731904", "label": 3675, "func": ""}
{"index": "s938405597", "label": 3675, "func": ""}
{"index": "s509488448", "label": 3675, "func": ""}
{"index": "s781402516", "label": 3675, "func": ""}
{"index": "s738603982", "label": 3675, "func": ""}
{"index": "s401169308", "label": 3675, "func": ""}
{"index": "s357729301", "label": 3675, "func": ""}
{"index": "s684539365", "label": 3675, "func": ""}
{"index": "s425801157", "label": 3675, "func": ""}
{"index": "s266129181", "label": 3675, "func": ""}
{"index": "s386339276", "label": 1180, "func": ""}
{"index": "s504834163", "label": 1180, "func": ""}
{"index": "s340804934", "label": 1180, "func": ""}
{"index": "s418660101", "label": 1180, "func": ""}
{"index": "s064921778", "label": 1180, "func": ""}
{"index": "s913062161", "label": 1180, "func": ""}
{"index": "s272604114", "label": 1180, "func": ""}
{"index": "s391831285", "label": 2755, "func": ""}
{"index": "s830796871", "label": 2755, "func": ""}
{"index": "s387987396", "label": 2755, "func": ""}
{"index": "s301199034", "label": 2755, "func": ""}
{"index": "s903048588", "label": 2755, "func": ""}
{"index": "s524967067", "label": 2755, "func": ""}
{"index": "s262485988", "label": 2755, "func": ""}
{"index": "s327990468", "label": 2755, "func": ""}
{"index": "s501603236", "label": 2755, "func": ""}
{"index": "s345842499", "label": 2755, "func": ""}
{"index": "s091364865", "label": 478, "func": ""}
{"index": "s783865444", "label": 478, "func": ""}
{"index": "s911167882", "label": 478, "func": ""}
{"index": "s082579729", "label": 478, "func": ""}
{"index": "s933088555", "label": 478, "func": ""}
{"index": "s940011354", "label": 478, "func": ""}
{"index": "s432898488", "label": 478, "func": ""}
{"index": "s854213140", "label": 478, "func": ""}
{"index": "s628927607", "label": 478, "func": ""}
{"index": "s631196568", "label": 478, "func": ""}
{"index": "s357259713", "label": 3562, "func": ""}
{"index": "s526794556", "label": 2864, "func": ""}
{"index": "s701622624", "label": 2864, "func": ""}
{"index": "s527150605", "label": 2864, "func": ""}
{"index": "s978242151", "label": 2864, "func": ""}
{"index": "s424567464", "label": 2864, "func": ""}
{"index": "s910199706", "label": 2864, "func": ""}
{"index": "s438269481", "label": 2864, "func": ""}
{"index": "s492501437", "label": 2864, "func": ""}
{"index": "s466103968", "label": 2864, "func": ""}
{"index": "s168119791", "label": 2864, "func": ""}
{"index": "s387124648", "label": 2323, "func": ""}
{"index": "s646812846", "label": 2323, "func": ""}
{"index": "s228857483", "label": 2323, "func": ""}
{"index": "s895615925", "label": 2323, "func": ""}
{"index": "s148802497", "label": 2323, "func": ""}
{"index": "s486435042", "label": 2323, "func": ""}
{"index": "s571660270", "label": 2323, "func": ""}
{"index": "s184188665", "label": 2323, "func": ""}
{"index": "s692138065", "label": 2323, "func": ""}
{"index": "s179859000", "label": 2323, "func": ""}
{"index": "s094108371", "label": 1003, "func": ""}
{"index": "s862152008", "label": 1003, "func": ""}
{"index": "s221374068", "label": 3685, "func": ""}
{"index": "s873440118", "label": 3685, "func": ""}
{"index": "s055479957", "label": 3685, "func": ""}
{"index": "s906741916", "label": 3685, "func": ""}
{"index": "s298099055", "label": 3685, "func": ""}
{"index": "s770657796", "label": 3685, "func": ""}
{"index": "s684689354", "label": 3685, "func": ""}
{"index": "s128811243", "label": 3685, "func": ""}
{"index": "s329096150", "label": 3685, "func": ""}
{"index": "s537288165", "label": 3685, "func": ""}
{"index": "s014853901", "label": 2488, "func": ""}
{"index": "s574475641", "label": 2488, "func": ""}
{"index": "s987836059", "label": 2488, "func": ""}
{"index": "s741199723", "label": 2488, "func": ""}
{"index": "s038430711", "label": 2488, "func": ""}
{"index": "s088977510", "label": 2488, "func": ""}
{"index": "s852373589", "label": 2488, "func": ""}
{"index": "s274409000", "label": 2488, "func": ""}
{"index": "s975514858", "label": 2488, "func": ""}
{"index": "s072560845", "label": 2488, "func": ""}
{"index": "s083582932", "label": 2487, "func": ""}
{"index": "s092274398", "label": 2487, "func": ""}
{"index": "s439690517", "label": 2487, "func": ""}
{"index": "s136262161", "label": 2487, "func": ""}
{"index": "s414886990", "label": 2487, "func": ""}
{"index": "s636111313", "label": 2487, "func": ""}
{"index": "s231747111", "label": 2487, "func": ""}
{"index": "s272012801", "label": 2487, "func": ""}
{"index": "s239478022", "label": 2487, "func": ""}
{"index": "s280213315", "label": 2487, "func": ""}
{"index": "s498182726", "label": 2765, "func": ""}
{"index": "s142518050", "label": 2765, "func": ""}
{"index": "s616275422", "label": 2765, "func": ""}
{"index": "s684882727", "label": 2765, "func": ""}
{"index": "s451453351", "label": 2765, "func": ""}
{"index": "s205311222", "label": 2765, "func": ""}
{"index": "s172360329", "label": 2765, "func": ""}
{"index": "s197501754", "label": 2765, "func": ""}
{"index": "s621592634", "label": 2765, "func": ""}
{"index": "s021452607", "label": 2765, "func": ""}
{"index": "s644444343", "label": 343, "func": ""}
{"index": "s443991538", "label": 343, "func": ""}
{"index": "s156541206", "label": 343, "func": ""}
{"index": "s938072138", "label": 119, "func": ""}
{"index": "s290074165", "label": 119, "func": ""}
{"index": "s771290950", "label": 119, "func": ""}
{"index": "s174284406", "label": 119, "func": ""}
{"index": "s427104219", "label": 119, "func": ""}
{"index": "s327587233", "label": 119, "func": ""}
{"index": "s816908216", "label": 119, "func": ""}
{"index": "s341412395", "label": 119, "func": ""}
{"index": "s332298713", "label": 119, "func": ""}
{"index": "s475970771", "label": 119, "func": ""}
{"index": "s630118523", "label": 1550, "func": ""}
{"index": "s989525806", "label": 1550, "func": ""}
{"index": "s036263472", "label": 1550, "func": ""}
{"index": "s035351934", "label": 1550, "func": ""}
{"index": "s434750676", "label": 1550, "func": ""}
{"index": "s753001504", "label": 798, "func": ""}
{"index": "s654163648", "label": 798, "func": ""}
{"index": "s229920928", "label": 798, "func": ""}
{"index": "s048848927", "label": 798, "func": ""}
{"index": "s717261254", "label": 798, "func": ""}
{"index": "s910469813", "label": 798, "func": ""}
{"index": "s983386443", "label": 798, "func": ""}
{"index": "s024162538", "label": 798, "func": ""}
{"index": "s374211976", "label": 798, "func": ""}
{"index": "s862861153", "label": 762, "func": ""}
{"index": "s700247886", "label": 762, "func": ""}
{"index": "s438509175", "label": 762, "func": ""}
{"index": "s551932395", "label": 762, "func": ""}
{"index": "s028725509", "label": 762, "func": ""}
{"index": "s230463286", "label": 762, "func": ""}
{"index": "s627524234", "label": 762, "func": ""}
{"index": "s908651972", "label": 762, "func": ""}
{"index": "s407367529", "label": 762, "func": ""}
{"index": "s770051781", "label": 762, "func": ""}
{"index": "s305005082", "label": 1850, "func": ""}
{"index": "s044517309", "label": 1850, "func": ""}
{"index": "s036055643", "label": 879, "func": ""}
{"index": "s162933384", "label": 3198, "func": ""}
{"index": "s516482333", "label": 3198, "func": ""}
{"index": "s022548756", "label": 3198, "func": ""}
{"index": "s989391536", "label": 3198, "func": ""}
{"index": "s038295407", "label": 3198, "func": ""}
{"index": "s289398665", "label": 3198, "func": ""}
{"index": "s361996890", "label": 3198, "func": ""}
{"index": "s565224553", "label": 854, "func": ""}
{"index": "s222996424", "label": 854, "func": ""}
{"index": "s756588904", "label": 854, "func": ""}
{"index": "s280328857", "label": 854, "func": ""}
{"index": "s361559186", "label": 854, "func": ""}
{"index": "s147273271", "label": 854, "func": ""}
{"index": "s471452607", "label": 854, "func": ""}
{"index": "s193415586", "label": 854, "func": ""}
{"index": "s113056857", "label": 854, "func": ""}
{"index": "s022441480", "label": 854, "func": ""}
{"index": "s059146672", "label": 3806, "func": ""}
{"index": "s640434977", "label": 3806, "func": ""}
{"index": "s692924666", "label": 3806, "func": ""}
{"index": "s556343340", "label": 3806, "func": ""}
{"index": "s376091113", "label": 3806, "func": ""}
{"index": "s847721785", "label": 3806, "func": ""}
{"index": "s415742153", "label": 3806, "func": ""}
{"index": "s133262903", "label": 3806, "func": ""}
{"index": "s040512613", "label": 3806, "func": ""}
{"index": "s229487048", "label": 3806, "func": ""}
{"index": "s013473576", "label": 823, "func": ""}
{"index": "s221483900", "label": 823, "func": ""}
{"index": "s156703025", "label": 823, "func": ""}
{"index": "s644506744", "label": 823, "func": ""}
{"index": "s999549663", "label": 823, "func": ""}
{"index": "s203309850", "label": 823, "func": ""}
{"index": "s264924433", "label": 823, "func": ""}
{"index": "s119219578", "label": 823, "func": ""}
{"index": "s807183730", "label": 823, "func": ""}
{"index": "s237074233", "label": 2024, "func": ""}
{"index": "s303200258", "label": 2384, "func": ""}
{"index": "s761287129", "label": 2384, "func": ""}
{"index": "s059322184", "label": 2384, "func": ""}
{"index": "s556396221", "label": 2384, "func": ""}
{"index": "s242721559", "label": 2384, "func": ""}
{"index": "s485455345", "label": 2384, "func": ""}
{"index": "s767556658", "label": 2384, "func": ""}
{"index": "s644112112", "label": 2384, "func": ""}
{"index": "s116720697", "label": 2384, "func": ""}
{"index": "s734525822", "label": 2384, "func": ""}
{"index": "s914602633", "label": 1996, "func": ""}
{"index": "s789210841", "label": 1996, "func": ""}
{"index": "s079925292", "label": 3341, "func": ""}
{"index": "s203735294", "label": 3341, "func": ""}
{"index": "s233725116", "label": 3341, "func": ""}
{"index": "s964045581", "label": 3341, "func": ""}
{"index": "s702569553", "label": 3341, "func": ""}
{"index": "s954743650", "label": 3341, "func": ""}
{"index": "s325980935", "label": 3341, "func": ""}
{"index": "s021193870", "label": 3341, "func": ""}
{"index": "s896616152", "label": 3341, "func": ""}
{"index": "s683425576", "label": 3341, "func": ""}
{"index": "s301799611", "label": 1227, "func": ""}
{"index": "s667917128", "label": 1227, "func": ""}
{"index": "s177937650", "label": 1227, "func": ""}
{"index": "s249863512", "label": 1227, "func": ""}
{"index": "s180254144", "label": 1227, "func": ""}
{"index": "s003644812", "label": 1227, "func": ""}
{"index": "s121619678", "label": 1227, "func": ""}
{"index": "s656287738", "label": 1227, "func": ""}
{"index": "s268733053", "label": 1227, "func": ""}
{"index": "s258008125", "label": 1227, "func": ""}
{"index": "s500559537", "label": 1144, "func": ""}
{"index": "s096082405", "label": 1144, "func": ""}
{"index": "s979741763", "label": 1144, "func": ""}
{"index": "s716613191", "label": 1144, "func": ""}
{"index": "s617730536", "label": 1144, "func": ""}
{"index": "s832584053", "label": 1144, "func": ""}
{"index": "s123028298", "label": 1144, "func": ""}
{"index": "s087641625", "label": 1144, "func": ""}
{"index": "s570971306", "label": 1144, "func": ""}
{"index": "s099031172", "label": 1144, "func": ""}
{"index": "s077095788", "label": 1206, "func": ""}
{"index": "s904226289", "label": 2016, "func": ""}
{"index": "s244175133", "label": 2016, "func": ""}
{"index": "s124830261", "label": 920, "func": ""}
{"index": "s217617072", "label": 920, "func": ""}
{"index": "s480359239", "label": 2773, "func": ""}
{"index": "s682900923", "label": 2773, "func": ""}
{"index": "s439393054", "label": 2773, "func": ""}
{"index": "s655019594", "label": 2773, "func": ""}
{"index": "s044963966", "label": 2773, "func": ""}
{"index": "s522451391", "label": 2773, "func": ""}
{"index": "s800527578", "label": 2773, "func": ""}
{"index": "s718686635", "label": 2773, "func": ""}
{"index": "s733613568", "label": 2773, "func": ""}
{"index": "s623237674", "label": 2773, "func": ""}
{"index": "s326964286", "label": 2927, "func": ""}
{"index": "s289092017", "label": 2927, "func": ""}
{"index": "s853454977", "label": 2927, "func": ""}
{"index": "s168696907", "label": 2927, "func": ""}
{"index": "s175629356", "label": 2927, "func": ""}
{"index": "s260713681", "label": 2927, "func": ""}
{"index": "s525233157", "label": 2927, "func": ""}
{"index": "s407950384", "label": 2927, "func": ""}
{"index": "s316736604", "label": 2927, "func": ""}
{"index": "s741382710", "label": 2927, "func": ""}
{"index": "s403557654", "label": 44, "func": ""}
{"index": "s651296450", "label": 44, "func": ""}
{"index": "s963156519", "label": 44, "func": ""}
{"index": "s110667777", "label": 44, "func": ""}
{"index": "s942297210", "label": 44, "func": ""}
{"index": "s958176858", "label": 44, "func": ""}
{"index": "s046412313", "label": 44, "func": ""}
{"index": "s736253671", "label": 44, "func": ""}
{"index": "s294699757", "label": 44, "func": ""}
{"index": "s883834966", "label": 44, "func": ""}
{"index": "s747109466", "label": 3481, "func": ""}
{"index": "s726274852", "label": 3481, "func": ""}
{"index": "s106797829", "label": 3481, "func": ""}
{"index": "s699656328", "label": 3481, "func": ""}
{"index": "s449758511", "label": 3481, "func": ""}
{"index": "s433180974", "label": 3481, "func": ""}
{"index": "s110792925", "label": 3481, "func": ""}
{"index": "s882426411", "label": 3481, "func": ""}
{"index": "s541129644", "label": 3481, "func": ""}
{"index": "s603806965", "label": 3481, "func": ""}
{"index": "s870616719", "label": 2309, "func": ""}
{"index": "s340900298", "label": 2309, "func": ""}
{"index": "s299091043", "label": 2309, "func": ""}
{"index": "s245668003", "label": 2309, "func": ""}
{"index": "s140903166", "label": 122, "func": ""}
{"index": "s821583550", "label": 122, "func": ""}
{"index": "s673469661", "label": 122, "func": ""}
{"index": "s407486601", "label": 122, "func": ""}
{"index": "s123047016", "label": 122, "func": ""}
{"index": "s268875761", "label": 122, "func": ""}
{"index": "s919493391", "label": 122, "func": ""}
{"index": "s065923680", "label": 122, "func": ""}
{"index": "s626398860", "label": 122, "func": ""}
{"index": "s014594867", "label": 122, "func": ""}
{"index": "s477760977", "label": 2841, "func": ""}
{"index": "s722862149", "label": 2841, "func": ""}
{"index": "s187588727", "label": 2841, "func": ""}
{"index": "s568907071", "label": 2841, "func": ""}
{"index": "s468713944", "label": 2841, "func": ""}
{"index": "s241849090", "label": 2841, "func": ""}
{"index": "s623040719", "label": 2841, "func": ""}
{"index": "s616255594", "label": 2841, "func": ""}
{"index": "s738344800", "label": 2841, "func": ""}
{"index": "s272785706", "label": 2841, "func": ""}
{"index": "s164649306", "label": 1970, "func": ""}
{"index": "s090282358", "label": 2277, "func": ""}
{"index": "s694734036", "label": 2277, "func": ""}
{"index": "s377554223", "label": 2277, "func": ""}
{"index": "s608273618", "label": 2277, "func": ""}
{"index": "s800123076", "label": 2277, "func": ""}
{"index": "s280763038", "label": 2277, "func": ""}
{"index": "s074600121", "label": 2277, "func": ""}
{"index": "s199295318", "label": 2277, "func": ""}
{"index": "s477198973", "label": 2277, "func": ""}
{"index": "s256739216", "label": 2277, "func": ""}
{"index": "s115741091", "label": 2363, "func": ""}
{"index": "s300553796", "label": 2363, "func": ""}
{"index": "s231417446", "label": 2363, "func": ""}
{"index": "s588433133", "label": 2363, "func": ""}
{"index": "s853581218", "label": 2363, "func": ""}
{"index": "s017143896", "label": 2363, "func": ""}
{"index": "s865446312", "label": 2363, "func": ""}
{"index": "s951647027", "label": 2363, "func": ""}
{"index": "s391039060", "label": 2363, "func": ""}
{"index": "s490165295", "label": 2363, "func": ""}
{"index": "s673787552", "label": 2840, "func": ""}
{"index": "s047801620", "label": 2840, "func": ""}
{"index": "s612295167", "label": 2840, "func": ""}
{"index": "s150296421", "label": 2840, "func": ""}
{"index": "s628331701", "label": 2840, "func": ""}
{"index": "s689047098", "label": 2840, "func": ""}
{"index": "s563328768", "label": 3720, "func": ""}
{"index": "s240412272", "label": 3720, "func": ""}
{"index": "s947445771", "label": 3720, "func": ""}
{"index": "s513414020", "label": 3720, "func": ""}
{"index": "s143055169", "label": 3720, "func": ""}
{"index": "s208215037", "label": 3720, "func": ""}
{"index": "s898161495", "label": 3720, "func": ""}
{"index": "s996852962", "label": 3720, "func": ""}
{"index": "s987624961", "label": 3720, "func": ""}
{"index": "s700478568", "label": 3720, "func": ""}
{"index": "s885420733", "label": 413, "func": ""}
{"index": "s302365683", "label": 3489, "func": ""}
{"index": "s469893650", "label": 3489, "func": ""}
{"index": "s443428378", "label": 3489, "func": ""}
{"index": "s958891827", "label": 3489, "func": ""}
{"index": "s710515213", "label": 3489, "func": ""}
{"index": "s570615993", "label": 3489, "func": ""}
{"index": "s094204144", "label": 3489, "func": ""}
{"index": "s306758795", "label": 3489, "func": ""}
{"index": "s652529777", "label": 3489, "func": ""}
{"index": "s543126776", "label": 3489, "func": ""}
{"index": "s510642806", "label": 1968, "func": ""}
{"index": "s914985156", "label": 1039, "func": ""}
{"index": "s670583540", "label": 37, "func": ""}
{"index": "s585829853", "label": 37, "func": ""}
{"index": "s253163737", "label": 37, "func": ""}
{"index": "s549759039", "label": 37, "func": ""}
{"index": "s870121002", "label": 37, "func": ""}
{"index": "s109769088", "label": 37, "func": ""}
{"index": "s998990938", "label": 37, "func": ""}
{"index": "s892893645", "label": 37, "func": ""}
{"index": "s142060214", "label": 37, "func": ""}
{"index": "s740741973", "label": 37, "func": ""}
{"index": "s854996673", "label": 3644, "func": ""}
{"index": "s637582510", "label": 3644, "func": ""}
{"index": "s938133809", "label": 3644, "func": ""}
{"index": "s386450144", "label": 3644, "func": ""}
{"index": "s618908569", "label": 3644, "func": ""}
{"index": "s058188997", "label": 3644, "func": ""}
{"index": "s218099674", "label": 3644, "func": ""}
{"index": "s676256084", "label": 3644, "func": ""}
{"index": "s735721720", "label": 3644, "func": ""}
{"index": "s665531037", "label": 3644, "func": ""}
{"index": "s436974383", "label": 2182, "func": ""}
{"index": "s103859681", "label": 2182, "func": ""}
{"index": "s846841523", "label": 507, "func": ""}
{"index": "s236864507", "label": 507, "func": ""}
{"index": "s153232354", "label": 507, "func": ""}
{"index": "s955491959", "label": 507, "func": ""}
{"index": "s214470264", "label": 507, "func": ""}
{"index": "s381712054", "label": 507, "func": ""}
{"index": "s266772565", "label": 507, "func": ""}
{"index": "s304483612", "label": 507, "func": ""}
{"index": "s658020797", "label": 507, "func": ""}
{"index": "s596094747", "label": 507, "func": ""}
{"index": "s189445065", "label": 3126, "func": ""}
{"index": "s456603938", "label": 3126, "func": ""}
{"index": "s895334131", "label": 3126, "func": ""}
{"index": "s362833862", "label": 3126, "func": ""}
{"index": "s403510566", "label": 3126, "func": ""}
{"index": "s560949068", "label": 3126, "func": ""}
{"index": "s772145082", "label": 3126, "func": ""}
{"index": "s497796799", "label": 3126, "func": ""}
{"index": "s585396433", "label": 3126, "func": ""}
{"index": "s309340199", "label": 3126, "func": ""}
{"index": "s031103200", "label": 310, "func": ""}
{"index": "s347097212", "label": 310, "func": ""}
{"index": "s401289590", "label": 310, "func": ""}
{"index": "s375558306", "label": 310, "func": ""}
{"index": "s272808797", "label": 310, "func": ""}
{"index": "s327150544", "label": 310, "func": ""}
{"index": "s818193415", "label": 310, "func": ""}
{"index": "s827765943", "label": 310, "func": ""}
{"index": "s087756048", "label": 310, "func": ""}
{"index": "s005016839", "label": 310, "func": ""}
{"index": "s448296534", "label": 442, "func": ""}
{"index": "s332696608", "label": 442, "func": ""}
{"index": "s207366576", "label": 442, "func": ""}
{"index": "s180793211", "label": 442, "func": ""}
{"index": "s404074681", "label": 442, "func": ""}
{"index": "s263680551", "label": 442, "func": ""}
{"index": "s410551682", "label": 442, "func": ""}
{"index": "s224631414", "label": 442, "func": ""}
{"index": "s753852578", "label": 442, "func": ""}
{"index": "s482186588", "label": 442, "func": ""}
{"index": "s047739433", "label": 3663, "func": ""}
{"index": "s306404533", "label": 3663, "func": ""}
{"index": "s254741564", "label": 3663, "func": ""}
{"index": "s825759649", "label": 3663, "func": ""}
{"index": "s885520852", "label": 3663, "func": ""}
{"index": "s990556813", "label": 128, "func": ""}
{"index": "s086109833", "label": 128, "func": ""}
{"index": "s500075970", "label": 128, "func": ""}
{"index": "s860769642", "label": 128, "func": ""}
{"index": "s593249105", "label": 128, "func": ""}
{"index": "s426042241", "label": 128, "func": ""}
{"index": "s279176035", "label": 128, "func": ""}
{"index": "s284666541", "label": 128, "func": ""}
{"index": "s839618301", "label": 128, "func": ""}
{"index": "s560774877", "label": 128, "func": ""}
{"index": "s791467942", "label": 1685, "func": ""}
{"index": "s075836027", "label": 1685, "func": ""}
{"index": "s810226568", "label": 449, "func": ""}
{"index": "s484530828", "label": 449, "func": ""}
{"index": "s940365883", "label": 449, "func": ""}
{"index": "s571450330", "label": 449, "func": ""}
{"index": "s684679043", "label": 449, "func": ""}
{"index": "s804701861", "label": 449, "func": ""}
{"index": "s502912557", "label": 449, "func": ""}
{"index": "s097698307", "label": 449, "func": ""}
{"index": "s048782147", "label": 449, "func": ""}
{"index": "s680700253", "label": 449, "func": ""}
{"index": "s895313581", "label": 2984, "func": ""}
{"index": "s352449045", "label": 2984, "func": ""}
{"index": "s408281775", "label": 2984, "func": ""}
{"index": "s966406707", "label": 2984, "func": ""}
{"index": "s189734372", "label": 2984, "func": ""}
{"index": "s929984452", "label": 2984, "func": ""}
{"index": "s983043957", "label": 2984, "func": ""}
{"index": "s027227505", "label": 2984, "func": ""}
{"index": "s343001960", "label": 2984, "func": ""}
{"index": "s589825467", "label": 2984, "func": ""}
{"index": "s225862816", "label": 2672, "func": ""}
{"index": "s201969076", "label": 2672, "func": ""}
{"index": "s337330413", "label": 3813, "func": ""}
{"index": "s958084374", "label": 3813, "func": ""}
{"index": "s291319025", "label": 3813, "func": ""}
{"index": "s706490305", "label": 3813, "func": ""}
{"index": "s975908703", "label": 3813, "func": ""}
{"index": "s957185907", "label": 3813, "func": ""}
{"index": "s443894913", "label": 3813, "func": ""}
{"index": "s452589130", "label": 3813, "func": ""}
{"index": "s039294045", "label": 3813, "func": ""}
{"index": "s510541638", "label": 3813, "func": ""}
{"index": "s328446772", "label": 123, "func": ""}
{"index": "s809185808", "label": 123, "func": ""}
{"index": "s670992261", "label": 123, "func": ""}
{"index": "s369782414", "label": 123, "func": ""}
{"index": "s666083294", "label": 123, "func": ""}
{"index": "s753863416", "label": 123, "func": ""}
{"index": "s505163634", "label": 123, "func": ""}
{"index": "s990108920", "label": 123, "func": ""}
{"index": "s900813808", "label": 123, "func": ""}
{"index": "s957477379", "label": 123, "func": ""}
{"index": "s058269593", "label": 2626, "func": ""}
{"index": "s983788329", "label": 2626, "func": ""}
{"index": "s374767642", "label": 2626, "func": ""}
{"index": "s516487976", "label": 2626, "func": ""}
{"index": "s641208361", "label": 2626, "func": ""}
{"index": "s471159320", "label": 2626, "func": ""}
{"index": "s811539541", "label": 2626, "func": ""}
{"index": "s726905662", "label": 2626, "func": ""}
{"index": "s860893098", "label": 2626, "func": ""}
{"index": "s088037601", "label": 2626, "func": ""}
{"index": "s971860776", "label": 2326, "func": ""}
{"index": "s099290864", "label": 2326, "func": ""}
{"index": "s118889138", "label": 2326, "func": ""}
{"index": "s353944561", "label": 2326, "func": ""}
{"index": "s515879573", "label": 2326, "func": ""}
{"index": "s924551570", "label": 2326, "func": ""}
{"index": "s359375187", "label": 2326, "func": ""}
{"index": "s140041193", "label": 2326, "func": ""}
{"index": "s793055827", "label": 2326, "func": ""}
{"index": "s770316539", "label": 2326, "func": ""}
{"index": "s923136208", "label": 3647, "func": ""}
{"index": "s698901433", "label": 3647, "func": ""}
{"index": "s959187930", "label": 3647, "func": ""}
{"index": "s535388981", "label": 3647, "func": ""}
{"index": "s170785160", "label": 3647, "func": ""}
{"index": "s947711953", "label": 3647, "func": ""}
{"index": "s424562165", "label": 3647, "func": ""}
{"index": "s449193144", "label": 3647, "func": ""}
{"index": "s448831160", "label": 3647, "func": ""}
{"index": "s296855389", "label": 3647, "func": ""}
{"index": "s857465560", "label": 49, "func": ""}
{"index": "s740197755", "label": 49, "func": ""}
{"index": "s524711318", "label": 49, "func": ""}
{"index": "s845900087", "label": 49, "func": ""}
{"index": "s264277901", "label": 49, "func": ""}
{"index": "s579672636", "label": 49, "func": ""}
{"index": "s825534903", "label": 49, "func": ""}
{"index": "s906013352", "label": 49, "func": ""}
{"index": "s360424345", "label": 49, "func": ""}
{"index": "s073363093", "label": 49, "func": ""}
{"index": "s655462506", "label": 738, "func": ""}
{"index": "s900878683", "label": 738, "func": ""}
{"index": "s725323245", "label": 738, "func": ""}
{"index": "s286363401", "label": 738, "func": ""}
{"index": "s744226910", "label": 738, "func": ""}
{"index": "s954346999", "label": 738, "func": ""}
{"index": "s433503858", "label": 738, "func": ""}
{"index": "s787260982", "label": 738, "func": ""}
{"index": "s396135521", "label": 738, "func": ""}
{"index": "s275706168", "label": 738, "func": ""}
{"index": "s570695392", "label": 722, "func": ""}
{"index": "s901973485", "label": 722, "func": ""}
{"index": "s714784383", "label": 722, "func": ""}
{"index": "s204631291", "label": 722, "func": ""}
{"index": "s179841014", "label": 722, "func": ""}
{"index": "s929296706", "label": 722, "func": ""}
{"index": "s262710364", "label": 722, "func": ""}
{"index": "s548534377", "label": 722, "func": ""}
{"index": "s049274592", "label": 722, "func": ""}
{"index": "s340079688", "label": 722, "func": ""}
{"index": "s847686768", "label": 3946, "func": ""}
{"index": "s242890936", "label": 3946, "func": ""}
{"index": "s798815835", "label": 3946, "func": ""}
{"index": "s918513934", "label": 3946, "func": ""}
{"index": "s187118457", "label": 3946, "func": ""}
{"index": "s098626108", "label": 3946, "func": ""}
{"index": "s090138669", "label": 3946, "func": ""}
{"index": "s101040191", "label": 3946, "func": ""}
{"index": "s411290967", "label": 3946, "func": ""}
{"index": "s868773303", "label": 3946, "func": ""}
{"index": "s772561979", "label": 2886, "func": ""}
{"index": "s751297888", "label": 2886, "func": ""}
{"index": "s666338609", "label": 2886, "func": ""}
{"index": "s030794825", "label": 2886, "func": ""}
{"index": "s772726177", "label": 2886, "func": ""}
{"index": "s451709214", "label": 2886, "func": ""}
{"index": "s050614315", "label": 2886, "func": ""}
{"index": "s114476054", "label": 2886, "func": ""}
{"index": "s398373592", "label": 2886, "func": ""}
{"index": "s557342232", "label": 2886, "func": ""}
{"index": "s752496991", "label": 741, "func": ""}
{"index": "s550121863", "label": 741, "func": ""}
{"index": "s413967364", "label": 741, "func": ""}
{"index": "s150820013", "label": 741, "func": ""}
{"index": "s339932434", "label": 741, "func": ""}
{"index": "s483355324", "label": 741, "func": ""}
{"index": "s386520365", "label": 741, "func": ""}
{"index": "s256167082", "label": 741, "func": ""}
{"index": "s442839569", "label": 741, "func": ""}
{"index": "s667960220", "label": 741, "func": ""}
{"index": "s784666117", "label": 3889, "func": ""}
{"index": "s590955666", "label": 3889, "func": ""}
{"index": "s356483212", "label": 3889, "func": ""}
{"index": "s014933037", "label": 3889, "func": ""}
{"index": "s864179330", "label": 3889, "func": ""}
{"index": "s428480962", "label": 3889, "func": ""}
{"index": "s307368855", "label": 3814, "func": ""}
{"index": "s958277274", "label": 3814, "func": ""}
{"index": "s356319560", "label": 3814, "func": ""}
{"index": "s860655489", "label": 3814, "func": ""}
{"index": "s319238401", "label": 3814, "func": ""}
{"index": "s073007252", "label": 3814, "func": ""}
{"index": "s915650605", "label": 3814, "func": ""}
{"index": "s730040973", "label": 3814, "func": ""}
{"index": "s975302960", "label": 3814, "func": ""}
{"index": "s590990383", "label": 3814, "func": ""}
{"index": "s121051810", "label": 3257, "func": ""}
{"index": "s076575387", "label": 3257, "func": ""}
{"index": "s978264281", "label": 3257, "func": ""}
{"index": "s869028867", "label": 466, "func": ""}
{"index": "s455102998", "label": 466, "func": ""}
{"index": "s574618781", "label": 466, "func": ""}
{"index": "s754700488", "label": 466, "func": ""}
{"index": "s287529476", "label": 466, "func": ""}
{"index": "s674511968", "label": 466, "func": ""}
{"index": "s128891211", "label": 466, "func": ""}
{"index": "s754165874", "label": 466, "func": ""}
{"index": "s911543350", "label": 466, "func": ""}
{"index": "s035272877", "label": 466, "func": ""}
{"index": "s493197608", "label": 3383, "func": ""}
{"index": "s148089336", "label": 3383, "func": ""}
{"index": "s199872120", "label": 3383, "func": ""}
{"index": "s783597469", "label": 3383, "func": ""}
{"index": "s675797487", "label": 3383, "func": ""}
{"index": "s051168479", "label": 3383, "func": ""}
{"index": "s040064757", "label": 3383, "func": ""}
{"index": "s558547859", "label": 3383, "func": ""}
{"index": "s592578665", "label": 3383, "func": ""}
{"index": "s976388427", "label": 3383, "func": ""}
{"index": "s149274623", "label": 1183, "func": ""}
{"index": "s440745582", "label": 1183, "func": ""}
{"index": "s669660095", "label": 1183, "func": ""}
{"index": "s283462765", "label": 2266, "func": ""}
{"index": "s588556780", "label": 2266, "func": ""}
{"index": "s691711610", "label": 2266, "func": ""}
{"index": "s710722177", "label": 2266, "func": ""}
{"index": "s570204786", "label": 2266, "func": ""}
{"index": "s795730292", "label": 2266, "func": ""}
{"index": "s633247819", "label": 2266, "func": ""}
{"index": "s730941307", "label": 2266, "func": ""}
{"index": "s471682328", "label": 2266, "func": ""}
{"index": "s078990720", "label": 2266, "func": ""}
{"index": "s240736574", "label": 2065, "func": ""}
{"index": "s583800904", "label": 782, "func": ""}
{"index": "s671233300", "label": 782, "func": ""}
{"index": "s346576608", "label": 782, "func": ""}
{"index": "s519217584", "label": 782, "func": ""}
{"index": "s227577689", "label": 782, "func": ""}
{"index": "s377824400", "label": 782, "func": ""}
{"index": "s974091965", "label": 782, "func": ""}
{"index": "s947695903", "label": 782, "func": ""}
{"index": "s211891905", "label": 2004, "func": ""}
{"index": "s824699212", "label": 3102, "func": ""}
{"index": "s362950619", "label": 3102, "func": ""}
{"index": "s430907889", "label": 3102, "func": ""}
{"index": "s571525335", "label": 3102, "func": ""}
{"index": "s035079054", "label": 3102, "func": ""}
{"index": "s361632817", "label": 3102, "func": ""}
{"index": "s592147715", "label": 3102, "func": ""}
{"index": "s818690935", "label": 3102, "func": ""}
{"index": "s064329969", "label": 3102, "func": ""}
{"index": "s314078712", "label": 3102, "func": ""}
{"index": "s633005765", "label": 3739, "func": ""}
{"index": "s709454686", "label": 3739, "func": ""}
{"index": "s303515866", "label": 3739, "func": ""}
{"index": "s533448872", "label": 3739, "func": ""}
{"index": "s004462336", "label": 3739, "func": ""}
{"index": "s094453858", "label": 3739, "func": ""}
{"index": "s969952150", "label": 3739, "func": ""}
{"index": "s972612904", "label": 3739, "func": ""}
{"index": "s629777758", "label": 3739, "func": ""}
{"index": "s243276665", "label": 3739, "func": ""}
{"index": "s028124281", "label": 2901, "func": ""}
{"index": "s969722497", "label": 2901, "func": ""}
{"index": "s730199890", "label": 2901, "func": ""}
{"index": "s055453885", "label": 2901, "func": ""}
{"index": "s668938180", "label": 2901, "func": ""}
{"index": "s789547931", "label": 2901, "func": ""}
{"index": "s323843689", "label": 2901, "func": ""}
{"index": "s556112628", "label": 2901, "func": ""}
{"index": "s813447692", "label": 2901, "func": ""}
{"index": "s854160944", "label": 2901, "func": ""}
{"index": "s121348500", "label": 4040, "func": ""}
{"index": "s900569537", "label": 4040, "func": ""}
{"index": "s982986125", "label": 4040, "func": ""}
{"index": "s589451294", "label": 4040, "func": ""}
{"index": "s109431763", "label": 4040, "func": ""}
{"index": "s698623585", "label": 4040, "func": ""}
{"index": "s376985248", "label": 4040, "func": ""}
{"index": "s053064210", "label": 4040, "func": ""}
{"index": "s204073576", "label": 4040, "func": ""}
{"index": "s243466196", "label": 4040, "func": ""}
{"index": "s746584917", "label": 508, "func": ""}
{"index": "s921234278", "label": 508, "func": ""}
{"index": "s741734339", "label": 508, "func": ""}
{"index": "s848208796", "label": 508, "func": ""}
{"index": "s613841751", "label": 508, "func": ""}
{"index": "s498014911", "label": 508, "func": ""}
{"index": "s481722914", "label": 508, "func": ""}
{"index": "s109843897", "label": 508, "func": ""}
{"index": "s240882090", "label": 508, "func": ""}
{"index": "s293896635", "label": 508, "func": ""}
{"index": "s739365196", "label": 459, "func": ""}
{"index": "s695338560", "label": 459, "func": ""}
{"index": "s087953928", "label": 459, "func": ""}
{"index": "s396116438", "label": 459, "func": ""}
{"index": "s385946603", "label": 459, "func": ""}
{"index": "s751830822", "label": 459, "func": ""}
{"index": "s735653359", "label": 459, "func": ""}
{"index": "s014864153", "label": 459, "func": ""}
{"index": "s312691232", "label": 459, "func": ""}
{"index": "s184476282", "label": 459, "func": ""}
{"index": "s417726535", "label": 3677, "func": ""}
{"index": "s922777611", "label": 3677, "func": ""}
{"index": "s020705841", "label": 3677, "func": ""}
{"index": "s129662537", "label": 3677, "func": ""}
{"index": "s092652297", "label": 3677, "func": ""}
{"index": "s996587370", "label": 3677, "func": ""}
{"index": "s408880694", "label": 3677, "func": ""}
{"index": "s037301318", "label": 3677, "func": ""}
{"index": "s753168455", "label": 3677, "func": ""}
{"index": "s878343123", "label": 3677, "func": ""}
{"index": "s348536837", "label": 662, "func": ""}
{"index": "s484856015", "label": 662, "func": ""}
{"index": "s672526819", "label": 662, "func": ""}
{"index": "s795197767", "label": 662, "func": ""}
{"index": "s572437002", "label": 662, "func": ""}
{"index": "s582607334", "label": 662, "func": ""}
{"index": "s277462820", "label": 662, "func": ""}
{"index": "s194474332", "label": 662, "func": ""}
{"index": "s369638133", "label": 662, "func": ""}
{"index": "s936238186", "label": 662, "func": ""}
{"index": "s746865890", "label": 3760, "func": ""}
{"index": "s193924798", "label": 3760, "func": ""}
{"index": "s214955122", "label": 3760, "func": ""}
{"index": "s475525440", "label": 3760, "func": ""}
{"index": "s049975149", "label": 3760, "func": ""}
{"index": "s617748452", "label": 3760, "func": ""}
{"index": "s980632081", "label": 3760, "func": ""}
{"index": "s820233624", "label": 3760, "func": ""}
{"index": "s332512112", "label": 3760, "func": ""}
{"index": "s884006096", "label": 3760, "func": ""}
{"index": "s825371826", "label": 1724, "func": ""}
{"index": "s586655933", "label": 2440, "func": ""}
{"index": "s187784404", "label": 2440, "func": ""}
{"index": "s752696621", "label": 2440, "func": ""}
{"index": "s827452459", "label": 2440, "func": ""}
{"index": "s642517049", "label": 2440, "func": ""}
{"index": "s717758342", "label": 2440, "func": ""}
{"index": "s254342725", "label": 2440, "func": ""}
{"index": "s846528476", "label": 2440, "func": ""}
{"index": "s236579383", "label": 2440, "func": ""}
{"index": "s306255195", "label": 2440, "func": ""}
{"index": "s023955686", "label": 1434, "func": ""}
{"index": "s347976008", "label": 1434, "func": ""}
{"index": "s358918097", "label": 1434, "func": ""}
{"index": "s644297928", "label": 1434, "func": ""}
{"index": "s782957276", "label": 1434, "func": ""}
{"index": "s245145339", "label": 1653, "func": ""}
{"index": "s720662411", "label": 3273, "func": ""}
{"index": "s854879084", "label": 3273, "func": ""}
{"index": "s235382866", "label": 3273, "func": ""}
{"index": "s476957067", "label": 3273, "func": ""}
{"index": "s564017874", "label": 3273, "func": ""}
{"index": "s661103631", "label": 3273, "func": ""}
{"index": "s534870963", "label": 3273, "func": ""}
{"index": "s414203236", "label": 3273, "func": ""}
{"index": "s219686599", "label": 3273, "func": ""}
{"index": "s301582631", "label": 3273, "func": ""}
{"index": "s383219681", "label": 1555, "func": ""}
{"index": "s932788857", "label": 1555, "func": ""}
{"index": "s037432383", "label": 1555, "func": ""}
{"index": "s586084980", "label": 1555, "func": ""}
{"index": "s960898942", "label": 1555, "func": ""}
{"index": "s529573691", "label": 1555, "func": ""}
{"index": "s546728673", "label": 1555, "func": ""}
{"index": "s276969294", "label": 1555, "func": ""}
{"index": "s509286256", "label": 1555, "func": ""}
{"index": "s144216758", "label": 1555, "func": ""}
{"index": "s826636929", "label": 725, "func": ""}
{"index": "s470455181", "label": 725, "func": ""}
{"index": "s450355584", "label": 725, "func": ""}
{"index": "s054023835", "label": 725, "func": ""}
{"index": "s975594924", "label": 725, "func": ""}
{"index": "s106200072", "label": 725, "func": ""}
{"index": "s319320541", "label": 725, "func": ""}
{"index": "s336891802", "label": 725, "func": ""}
{"index": "s110167403", "label": 725, "func": ""}
{"index": "s294456231", "label": 725, "func": ""}
{"index": "s437221070", "label": 233, "func": ""}
{"index": "s328763644", "label": 233, "func": ""}
{"index": "s941411379", "label": 233, "func": ""}
{"index": "s285027229", "label": 233, "func": ""}
{"index": "s646276650", "label": 233, "func": ""}
{"index": "s500530138", "label": 233, "func": ""}
{"index": "s688013191", "label": 671, "func": ""}
{"index": "s283118837", "label": 671, "func": ""}
{"index": "s419128730", "label": 671, "func": ""}
{"index": "s659974598", "label": 2764, "func": ""}
{"index": "s935018738", "label": 2764, "func": ""}
{"index": "s303678950", "label": 2764, "func": ""}
{"index": "s393489004", "label": 2764, "func": ""}
{"index": "s471368772", "label": 2764, "func": ""}
{"index": "s115533958", "label": 2764, "func": ""}
{"index": "s543875255", "label": 629, "func": ""}
{"index": "s036096650", "label": 629, "func": ""}
{"index": "s530928823", "label": 629, "func": ""}
{"index": "s458590435", "label": 629, "func": ""}
{"index": "s287094527", "label": 629, "func": ""}
{"index": "s828120438", "label": 629, "func": ""}
{"index": "s991797449", "label": 629, "func": ""}
{"index": "s412985856", "label": 629, "func": ""}
{"index": "s086452120", "label": 629, "func": ""}
{"index": "s650763307", "label": 629, "func": ""}
{"index": "s618243028", "label": 1086, "func": ""}
{"index": "s301427550", "label": 1086, "func": ""}
{"index": "s595257011", "label": 1086, "func": ""}
{"index": "s178077295", "label": 1086, "func": ""}
{"index": "s374106015", "label": 1086, "func": ""}
{"index": "s937806510", "label": 1086, "func": ""}
{"index": "s299039715", "label": 1086, "func": ""}
{"index": "s544073449", "label": 1086, "func": ""}
{"index": "s796093440", "label": 1086, "func": ""}
{"index": "s517777111", "label": 1086, "func": ""}
{"index": "s839992419", "label": 1156, "func": ""}
{"index": "s715545531", "label": 1156, "func": ""}
{"index": "s742503920", "label": 1156, "func": ""}
{"index": "s463236626", "label": 1156, "func": ""}
{"index": "s762941462", "label": 1156, "func": ""}
{"index": "s117633476", "label": 1156, "func": ""}
{"index": "s689982557", "label": 1156, "func": ""}
{"index": "s891617246", "label": 1156, "func": ""}
{"index": "s003462031", "label": 178, "func": ""}
{"index": "s067865237", "label": 178, "func": ""}
{"index": "s499421860", "label": 178, "func": ""}
{"index": "s678728501", "label": 178, "func": ""}
{"index": "s406858294", "label": 178, "func": ""}
{"index": "s726023832", "label": 1062, "func": ""}
{"index": "s639236663", "label": 1256, "func": ""}
{"index": "s159993676", "label": 1256, "func": ""}
{"index": "s531180408", "label": 1256, "func": ""}
{"index": "s738077005", "label": 1256, "func": ""}
{"index": "s062380094", "label": 3306, "func": ""}
{"index": "s161037203", "label": 3306, "func": ""}
{"index": "s072503467", "label": 3306, "func": ""}
{"index": "s986823714", "label": 3306, "func": ""}
{"index": "s303925205", "label": 3306, "func": ""}
{"index": "s221126695", "label": 3306, "func": ""}
{"index": "s434644826", "label": 2685, "func": ""}
{"index": "s538034395", "label": 2685, "func": ""}
{"index": "s505052071", "label": 2685, "func": ""}
{"index": "s915716526", "label": 2685, "func": ""}
{"index": "s664070019", "label": 2685, "func": ""}
{"index": "s173722235", "label": 2685, "func": ""}
{"index": "s566678285", "label": 2685, "func": ""}
{"index": "s552426598", "label": 2685, "func": ""}
{"index": "s314333037", "label": 2685, "func": ""}
{"index": "s001022666", "label": 2685, "func": ""}
{"index": "s350795086", "label": 3089, "func": ""}
{"index": "s142985578", "label": 3089, "func": ""}
{"index": "s277996182", "label": 3089, "func": ""}
{"index": "s035097038", "label": 3089, "func": ""}
{"index": "s200537971", "label": 3089, "func": ""}
{"index": "s754764035", "label": 3089, "func": ""}
{"index": "s302019406", "label": 3089, "func": ""}
{"index": "s782325366", "label": 3089, "func": ""}
{"index": "s728279774", "label": 3089, "func": ""}
{"index": "s184302847", "label": 3089, "func": ""}
{"index": "s272564048", "label": 106, "func": ""}
{"index": "s388297865", "label": 106, "func": ""}
{"index": "s143117449", "label": 106, "func": ""}
{"index": "s288843469", "label": 106, "func": ""}
{"index": "s887813004", "label": 106, "func": ""}
{"index": "s017230931", "label": 106, "func": ""}
{"index": "s887571110", "label": 106, "func": ""}
{"index": "s632062606", "label": 106, "func": ""}
{"index": "s722476174", "label": 106, "func": ""}
{"index": "s503023214", "label": 106, "func": ""}
{"index": "s022282099", "label": 2914, "func": ""}
{"index": "s886209145", "label": 2914, "func": ""}
{"index": "s242514659", "label": 2914, "func": ""}
{"index": "s426594774", "label": 2914, "func": ""}
{"index": "s109709925", "label": 2914, "func": ""}
{"index": "s718687731", "label": 2914, "func": ""}
{"index": "s217351571", "label": 2914, "func": ""}
{"index": "s558628253", "label": 2914, "func": ""}
{"index": "s459404824", "label": 2914, "func": ""}
{"index": "s264887357", "label": 2914, "func": ""}
{"index": "s559077844", "label": 3169, "func": ""}
{"index": "s846358512", "label": 3169, "func": ""}
{"index": "s460580736", "label": 3169, "func": ""}
{"index": "s888836999", "label": 3169, "func": ""}
{"index": "s196456622", "label": 3169, "func": ""}
{"index": "s632316311", "label": 3169, "func": ""}
{"index": "s560612755", "label": 3169, "func": ""}
{"index": "s866368579", "label": 3169, "func": ""}
{"index": "s999992472", "label": 3169, "func": ""}
{"index": "s385493264", "label": 3169, "func": ""}
{"index": "s947667788", "label": 533, "func": ""}
{"index": "s540240131", "label": 533, "func": ""}
{"index": "s683071415", "label": 533, "func": ""}
{"index": "s335996113", "label": 533, "func": ""}
{"index": "s671937197", "label": 533, "func": ""}
{"index": "s861819414", "label": 533, "func": ""}
{"index": "s871083990", "label": 533, "func": ""}
{"index": "s486718545", "label": 533, "func": ""}
{"index": "s891385162", "label": 533, "func": ""}
{"index": "s431746900", "label": 533, "func": ""}
{"index": "s290347104", "label": 1770, "func": ""}
{"index": "s047305865", "label": 1770, "func": ""}
{"index": "s275616082", "label": 1770, "func": ""}
{"index": "s538704356", "label": 870, "func": ""}
{"index": "s557243141", "label": 870, "func": ""}
{"index": "s077942552", "label": 870, "func": ""}
{"index": "s368444772", "label": 2433, "func": ""}
{"index": "s752157491", "label": 2433, "func": ""}
{"index": "s455247267", "label": 2433, "func": ""}
{"index": "s351618006", "label": 2433, "func": ""}
{"index": "s218860127", "label": 2433, "func": ""}
{"index": "s544558849", "label": 2433, "func": ""}
{"index": "s261522519", "label": 2433, "func": ""}
{"index": "s706989300", "label": 2433, "func": ""}
{"index": "s953974948", "label": 2433, "func": ""}
{"index": "s250743701", "label": 2433, "func": ""}
{"index": "s481923509", "label": 526, "func": ""}
{"index": "s697477706", "label": 526, "func": ""}
{"index": "s864178553", "label": 153, "func": ""}
{"index": "s372653092", "label": 153, "func": ""}
{"index": "s896753329", "label": 153, "func": ""}
{"index": "s813896570", "label": 153, "func": ""}
{"index": "s797700897", "label": 153, "func": ""}
{"index": "s793559568", "label": 153, "func": ""}
{"index": "s588459634", "label": 153, "func": ""}
{"index": "s861373149", "label": 153, "func": ""}
{"index": "s609129187", "label": 153, "func": ""}
{"index": "s533472712", "label": 153, "func": ""}
{"index": "s536864283", "label": 3572, "func": ""}
{"index": "s362120348", "label": 3572, "func": ""}
{"index": "s150942861", "label": 3745, "func": ""}
{"index": "s236543621", "label": 3745, "func": ""}
{"index": "s684942713", "label": 3745, "func": ""}
{"index": "s654592510", "label": 3745, "func": ""}
{"index": "s976501757", "label": 3745, "func": ""}
{"index": "s221396021", "label": 3745, "func": ""}
{"index": "s332432772", "label": 3745, "func": ""}
{"index": "s779291773", "label": 3745, "func": ""}
{"index": "s973366324", "label": 3745, "func": ""}
{"index": "s659404573", "label": 3745, "func": ""}
{"index": "s097967485", "label": 3577, "func": ""}
{"index": "s966791998", "label": 3577, "func": ""}
{"index": "s410294886", "label": 3577, "func": ""}
{"index": "s566008320", "label": 3577, "func": ""}
{"index": "s272994859", "label": 3577, "func": ""}
{"index": "s137099074", "label": 3577, "func": ""}
{"index": "s461351197", "label": 3577, "func": ""}
{"index": "s718910806", "label": 3577, "func": ""}
{"index": "s216340356", "label": 3577, "func": ""}
{"index": "s367940248", "label": 3577, "func": ""}
{"index": "s635970360", "label": 1583, "func": ""}
{"index": "s148953960", "label": 151, "func": ""}
{"index": "s984736761", "label": 151, "func": ""}
{"index": "s931412696", "label": 151, "func": ""}
{"index": "s434696983", "label": 151, "func": ""}
{"index": "s937406402", "label": 151, "func": ""}
{"index": "s158440812", "label": 151, "func": ""}
{"index": "s732527566", "label": 151, "func": ""}
{"index": "s297123679", "label": 151, "func": ""}
{"index": "s227395638", "label": 151, "func": ""}
{"index": "s650444547", "label": 151, "func": ""}
{"index": "s120746582", "label": 685, "func": ""}
{"index": "s807984773", "label": 685, "func": ""}
{"index": "s085320992", "label": 685, "func": ""}
{"index": "s328329525", "label": 685, "func": ""}
{"index": "s097258872", "label": 685, "func": ""}
{"index": "s606244135", "label": 685, "func": ""}
{"index": "s199763789", "label": 685, "func": ""}
{"index": "s101635405", "label": 685, "func": ""}
{"index": "s966982360", "label": 685, "func": ""}
{"index": "s296055687", "label": 685, "func": ""}
{"index": "s493521496", "label": 1287, "func": ""}
{"index": "s899170558", "label": 1287, "func": ""}
{"index": "s680541581", "label": 1287, "func": ""}
{"index": "s173816225", "label": 494, "func": ""}
{"index": "s776827398", "label": 494, "func": ""}
{"index": "s325873623", "label": 494, "func": ""}
{"index": "s917066347", "label": 494, "func": ""}
{"index": "s423891989", "label": 494, "func": ""}
{"index": "s864935561", "label": 494, "func": ""}
{"index": "s852462285", "label": 494, "func": ""}
{"index": "s244074172", "label": 494, "func": ""}
{"index": "s977657834", "label": 494, "func": ""}
{"index": "s975733243", "label": 494, "func": ""}
{"index": "s856913790", "label": 2515, "func": ""}
{"index": "s672587091", "label": 2515, "func": ""}
{"index": "s592235976", "label": 2515, "func": ""}
{"index": "s828958938", "label": 2515, "func": ""}
{"index": "s628860699", "label": 2515, "func": ""}
{"index": "s762827540", "label": 2515, "func": ""}
{"index": "s291694163", "label": 1467, "func": ""}
{"index": "s729304856", "label": 1467, "func": ""}
{"index": "s540808494", "label": 1467, "func": ""}
{"index": "s367173530", "label": 1467, "func": ""}
{"index": "s221130244", "label": 1467, "func": ""}
{"index": "s683011725", "label": 1467, "func": ""}
{"index": "s789566705", "label": 1467, "func": ""}
{"index": "s978266990", "label": 1467, "func": ""}
{"index": "s704560294", "label": 1579, "func": ""}
{"index": "s632230392", "label": 3072, "func": ""}
{"index": "s155834171", "label": 3072, "func": ""}
{"index": "s158675632", "label": 3072, "func": ""}
{"index": "s008234538", "label": 3072, "func": ""}
{"index": "s824230885", "label": 3072, "func": ""}
{"index": "s708323551", "label": 3072, "func": ""}
{"index": "s232879908", "label": 3072, "func": ""}
{"index": "s194382024", "label": 3072, "func": ""}
{"index": "s281862105", "label": 3072, "func": ""}
{"index": "s971234158", "label": 3072, "func": ""}
{"index": "s380605449", "label": 1757, "func": ""}
{"index": "s880560165", "label": 2782, "func": ""}
{"index": "s673595714", "label": 2782, "func": ""}
{"index": "s785511626", "label": 2782, "func": ""}
{"index": "s045997746", "label": 2782, "func": ""}
{"index": "s081323694", "label": 2782, "func": ""}
{"index": "s493231353", "label": 2782, "func": ""}
{"index": "s946840643", "label": 2782, "func": ""}
{"index": "s646738699", "label": 2782, "func": ""}
{"index": "s203115372", "label": 2782, "func": ""}
{"index": "s052984149", "label": 2782, "func": ""}
{"index": "s986512556", "label": 340, "func": ""}
{"index": "s391110061", "label": 340, "func": ""}
{"index": "s270485301", "label": 340, "func": ""}
{"index": "s513612208", "label": 340, "func": ""}
{"index": "s174825248", "label": 340, "func": ""}
{"index": "s307124745", "label": 340, "func": ""}
{"index": "s546043768", "label": 340, "func": ""}
{"index": "s974276424", "label": 340, "func": ""}
{"index": "s406237860", "label": 340, "func": ""}
{"index": "s956828397", "label": 340, "func": ""}
{"index": "s239712890", "label": 900, "func": ""}
{"index": "s521410749", "label": 2031, "func": ""}
{"index": "s844102233", "label": 2031, "func": ""}
{"index": "s276526279", "label": 3998, "func": ""}
{"index": "s634000499", "label": 3998, "func": ""}
{"index": "s346265769", "label": 3998, "func": ""}
{"index": "s265302183", "label": 3998, "func": ""}
{"index": "s613841530", "label": 3998, "func": ""}
{"index": "s523188173", "label": 3998, "func": ""}
{"index": "s871898773", "label": 3998, "func": ""}
{"index": "s447519928", "label": 3998, "func": ""}
{"index": "s754174640", "label": 3998, "func": ""}
{"index": "s511896810", "label": 3998, "func": ""}
{"index": "s745238109", "label": 3021, "func": ""}
{"index": "s605203585", "label": 2923, "func": ""}
{"index": "s608897427", "label": 2923, "func": ""}
{"index": "s671485590", "label": 2923, "func": ""}
{"index": "s170846461", "label": 2923, "func": ""}
{"index": "s936165024", "label": 2923, "func": ""}
{"index": "s636428232", "label": 2923, "func": ""}
{"index": "s105629383", "label": 2923, "func": ""}
{"index": "s021046998", "label": 2923, "func": ""}
{"index": "s344927981", "label": 2923, "func": ""}
{"index": "s374581147", "label": 2923, "func": ""}
{"index": "s556201804", "label": 3789, "func": ""}
{"index": "s932952650", "label": 2189, "func": ""}
{"index": "s507424678", "label": 2189, "func": ""}
{"index": "s861999367", "label": 3405, "func": ""}
{"index": "s242537213", "label": 3405, "func": ""}
{"index": "s066781256", "label": 3405, "func": ""}
{"index": "s476949595", "label": 3405, "func": ""}
{"index": "s625455623", "label": 1923, "func": ""}
{"index": "s845762173", "label": 1923, "func": ""}
{"index": "s899566444", "label": 1923, "func": ""}
{"index": "s189767055", "label": 1923, "func": ""}
{"index": "s151251899", "label": 1923, "func": ""}
{"index": "s096373047", "label": 1923, "func": ""}
{"index": "s299531660", "label": 1923, "func": ""}
{"index": "s685331596", "label": 1923, "func": ""}
{"index": "s596661472", "label": 1923, "func": ""}
{"index": "s403178005", "label": 1923, "func": ""}
{"index": "s792283529", "label": 1448, "func": ""}
{"index": "s003623491", "label": 1448, "func": ""}
{"index": "s464238199", "label": 1448, "func": ""}
{"index": "s960366195", "label": 1448, "func": ""}
{"index": "s331317927", "label": 1448, "func": ""}
{"index": "s564207162", "label": 1448, "func": ""}
{"index": "s684464223", "label": 1448, "func": ""}
{"index": "s517691782", "label": 1448, "func": ""}
{"index": "s227293087", "label": 1448, "func": ""}
{"index": "s839449065", "label": 1448, "func": ""}
{"index": "s557577234", "label": 156, "func": ""}
{"index": "s880213929", "label": 156, "func": ""}
{"index": "s220473099", "label": 156, "func": ""}
{"index": "s727438962", "label": 156, "func": ""}
{"index": "s291739164", "label": 156, "func": ""}
{"index": "s143650868", "label": 156, "func": ""}
{"index": "s808425771", "label": 156, "func": ""}
{"index": "s448260695", "label": 156, "func": ""}
{"index": "s111199890", "label": 156, "func": ""}
{"index": "s899868172", "label": 156, "func": ""}
{"index": "s285603538", "label": 3322, "func": ""}
{"index": "s585402355", "label": 2415, "func": ""}
{"index": "s982118728", "label": 2415, "func": ""}
{"index": "s830933954", "label": 2415, "func": ""}
{"index": "s867293902", "label": 2415, "func": ""}
{"index": "s652453004", "label": 2415, "func": ""}
{"index": "s472002711", "label": 2415, "func": ""}
{"index": "s007900812", "label": 2415, "func": ""}
{"index": "s413447371", "label": 2415, "func": ""}
{"index": "s445095276", "label": 2415, "func": ""}
{"index": "s852183340", "label": 2415, "func": ""}
{"index": "s599714845", "label": 912, "func": ""}
{"index": "s564438475", "label": 912, "func": ""}
{"index": "s698412905", "label": 912, "func": ""}
{"index": "s701989757", "label": 25, "func": ""}
{"index": "s736113206", "label": 25, "func": ""}
{"index": "s117834716", "label": 25, "func": ""}
{"index": "s929736366", "label": 25, "func": ""}
{"index": "s289301886", "label": 25, "func": ""}
{"index": "s282964571", "label": 25, "func": ""}
{"index": "s357492455", "label": 25, "func": ""}
{"index": "s806174190", "label": 25, "func": ""}
{"index": "s567295865", "label": 25, "func": ""}
{"index": "s054432538", "label": 25, "func": ""}
{"index": "s172517440", "label": 3579, "func": ""}
{"index": "s011217273", "label": 3579, "func": ""}
{"index": "s147767379", "label": 3579, "func": ""}
{"index": "s290647214", "label": 3579, "func": ""}
{"index": "s705175406", "label": 3579, "func": ""}
{"index": "s260011366", "label": 3579, "func": ""}
{"index": "s334077357", "label": 3579, "func": ""}
{"index": "s332105716", "label": 3579, "func": ""}
{"index": "s837993487", "label": 3579, "func": ""}
{"index": "s073001964", "label": 3579, "func": ""}
{"index": "s533414682", "label": 3503, "func": ""}
{"index": "s049109220", "label": 3503, "func": ""}
{"index": "s697682393", "label": 3503, "func": ""}
{"index": "s651916324", "label": 3503, "func": ""}
{"index": "s044744828", "label": 3503, "func": ""}
{"index": "s139990792", "label": 3503, "func": ""}
{"index": "s917240496", "label": 3503, "func": ""}
{"index": "s453900815", "label": 3503, "func": ""}
{"index": "s865078709", "label": 3503, "func": ""}
{"index": "s525708091", "label": 3503, "func": ""}
{"index": "s465737250", "label": 2615, "func": ""}
{"index": "s475803870", "label": 2615, "func": ""}
{"index": "s683495849", "label": 2615, "func": ""}
{"index": "s552201390", "label": 2615, "func": ""}
{"index": "s498529069", "label": 2615, "func": ""}
{"index": "s933779990", "label": 2615, "func": ""}
{"index": "s657273562", "label": 2615, "func": ""}
{"index": "s438895026", "label": 2615, "func": ""}
{"index": "s160961899", "label": 2615, "func": ""}
{"index": "s393589688", "label": 2615, "func": ""}
{"index": "s639796780", "label": 4032, "func": ""}
{"index": "s536651315", "label": 4032, "func": ""}
{"index": "s325767447", "label": 4032, "func": ""}
{"index": "s743829823", "label": 4032, "func": ""}
{"index": "s866421314", "label": 4032, "func": ""}
{"index": "s378228987", "label": 4032, "func": ""}
{"index": "s188309957", "label": 4032, "func": ""}
{"index": "s065059206", "label": 4032, "func": ""}
{"index": "s937112004", "label": 4032, "func": ""}
{"index": "s717533465", "label": 4032, "func": ""}
{"index": "s244641639", "label": 116, "func": ""}
{"index": "s853181009", "label": 116, "func": ""}
{"index": "s252947192", "label": 116, "func": ""}
{"index": "s742875524", "label": 116, "func": ""}
{"index": "s117745714", "label": 116, "func": ""}
{"index": "s260667322", "label": 116, "func": ""}
{"index": "s206003098", "label": 116, "func": ""}
{"index": "s659225050", "label": 116, "func": ""}
{"index": "s931583951", "label": 116, "func": ""}
{"index": "s514552790", "label": 116, "func": ""}
{"index": "s169555192", "label": 162, "func": ""}
{"index": "s850807331", "label": 162, "func": ""}
{"index": "s325819378", "label": 162, "func": ""}
{"index": "s758384952", "label": 162, "func": ""}
{"index": "s800636125", "label": 162, "func": ""}
{"index": "s284629554", "label": 162, "func": ""}
{"index": "s380251007", "label": 162, "func": ""}
{"index": "s101935057", "label": 162, "func": ""}
{"index": "s546010406", "label": 162, "func": ""}
{"index": "s674809781", "label": 162, "func": ""}
{"index": "s661401580", "label": 2644, "func": ""}
{"index": "s250046120", "label": 2644, "func": ""}
{"index": "s521784363", "label": 2644, "func": ""}
{"index": "s902304857", "label": 2644, "func": ""}
{"index": "s331741484", "label": 2644, "func": ""}
{"index": "s619316469", "label": 2644, "func": ""}
{"index": "s886036378", "label": 2644, "func": ""}
{"index": "s578602849", "label": 2644, "func": ""}
{"index": "s676830358", "label": 2644, "func": ""}
{"index": "s522843354", "label": 2644, "func": ""}
{"index": "s823160211", "label": 2934, "func": ""}
{"index": "s580229460", "label": 2934, "func": ""}
{"index": "s190909384", "label": 2934, "func": ""}
{"index": "s914576556", "label": 2934, "func": ""}
{"index": "s483653141", "label": 2934, "func": ""}
{"index": "s300714738", "label": 2934, "func": ""}
{"index": "s877435037", "label": 2934, "func": ""}
{"index": "s405808890", "label": 2934, "func": ""}
{"index": "s354012397", "label": 2934, "func": ""}
{"index": "s516386828", "label": 2934, "func": ""}
{"index": "s646380681", "label": 1589, "func": ""}
{"index": "s116944339", "label": 1589, "func": ""}
{"index": "s869074527", "label": 677, "func": ""}
{"index": "s458238598", "label": 677, "func": ""}
{"index": "s180691610", "label": 1486, "func": ""}
{"index": "s426485797", "label": 1486, "func": ""}
{"index": "s276333714", "label": 1486, "func": ""}
{"index": "s265820398", "label": 1486, "func": ""}
{"index": "s370896311", "label": 1486, "func": ""}
{"index": "s175125491", "label": 1486, "func": ""}
{"index": "s038139178", "label": 1486, "func": ""}
{"index": "s382003118", "label": 1486, "func": ""}
{"index": "s275837281", "label": 1486, "func": ""}
{"index": "s103104346", "label": 1486, "func": ""}
{"index": "s881389712", "label": 3302, "func": ""}
{"index": "s366923085", "label": 3302, "func": ""}
{"index": "s644633320", "label": 3302, "func": ""}
{"index": "s280500779", "label": 3302, "func": ""}
{"index": "s047377689", "label": 3302, "func": ""}
{"index": "s977025754", "label": 3302, "func": ""}
{"index": "s599599718", "label": 3302, "func": ""}
{"index": "s858086539", "label": 3302, "func": ""}
{"index": "s474556020", "label": 3302, "func": ""}
{"index": "s765943610", "label": 3302, "func": ""}
{"index": "s393146903", "label": 1648, "func": ""}
{"index": "s747015183", "label": 1648, "func": ""}
{"index": "s444527700", "label": 1648, "func": ""}
{"index": "s066685609", "label": 1648, "func": ""}
{"index": "s003014812", "label": 1648, "func": ""}
{"index": "s216403482", "label": 2196, "func": ""}
{"index": "s956945602", "label": 1136, "func": ""}
{"index": "s424912871", "label": 1136, "func": ""}
{"index": "s081485060", "label": 1136, "func": ""}
{"index": "s635817157", "label": 1136, "func": ""}
{"index": "s700744029", "label": 1136, "func": ""}
{"index": "s968771753", "label": 1136, "func": ""}
{"index": "s069306410", "label": 1136, "func": ""}
{"index": "s277164195", "label": 1136, "func": ""}
{"index": "s831579785", "label": 1136, "func": ""}
{"index": "s430756602", "label": 1136, "func": ""}
{"index": "s293191741", "label": 1359, "func": ""}
{"index": "s853170570", "label": 1359, "func": ""}
{"index": "s173944358", "label": 1359, "func": ""}
{"index": "s558404171", "label": 1359, "func": ""}
{"index": "s964958464", "label": 1359, "func": ""}
{"index": "s002886262", "label": 1359, "func": ""}
{"index": "s054155170", "label": 1359, "func": ""}
{"index": "s033819318", "label": 1359, "func": ""}
{"index": "s303723632", "label": 1359, "func": ""}
{"index": "s486902307", "label": 1359, "func": ""}
{"index": "s750147481", "label": 3442, "func": ""}
{"index": "s385731699", "label": 3442, "func": ""}
{"index": "s326575259", "label": 3442, "func": ""}
{"index": "s719226996", "label": 1088, "func": ""}
{"index": "s605388382", "label": 1088, "func": ""}
{"index": "s266239311", "label": 1088, "func": ""}
{"index": "s568239928", "label": 1088, "func": ""}
{"index": "s018643207", "label": 253, "func": ""}
{"index": "s515678760", "label": 253, "func": ""}
{"index": "s959716567", "label": 253, "func": ""}
{"index": "s854699528", "label": 253, "func": ""}
{"index": "s726556573", "label": 253, "func": ""}
{"index": "s663462733", "label": 253, "func": ""}
{"index": "s000313569", "label": 253, "func": ""}
{"index": "s594196128", "label": 253, "func": ""}
{"index": "s328475443", "label": 253, "func": ""}
{"index": "s257060905", "label": 253, "func": ""}
{"index": "s834813078", "label": 3759, "func": ""}
{"index": "s742105054", "label": 3759, "func": ""}
{"index": "s169753699", "label": 3759, "func": ""}
{"index": "s858592842", "label": 3759, "func": ""}
{"index": "s731861930", "label": 3759, "func": ""}
{"index": "s497125685", "label": 3759, "func": ""}
{"index": "s693800011", "label": 3759, "func": ""}
{"index": "s202150503", "label": 3759, "func": ""}
{"index": "s480790413", "label": 3759, "func": ""}
{"index": "s783276825", "label": 3759, "func": ""}
{"index": "s780184650", "label": 2203, "func": ""}
{"index": "s474481488", "label": 2203, "func": ""}
{"index": "s349039946", "label": 1230, "func": ""}
{"index": "s997263885", "label": 1230, "func": ""}
{"index": "s205170808", "label": 1230, "func": ""}
{"index": "s085470745", "label": 1230, "func": ""}
{"index": "s767896308", "label": 1644, "func": ""}
{"index": "s946405113", "label": 2771, "func": ""}
{"index": "s843692991", "label": 2771, "func": ""}
{"index": "s997409728", "label": 2771, "func": ""}
{"index": "s986375508", "label": 2771, "func": ""}
{"index": "s458607881", "label": 2771, "func": ""}
{"index": "s768853735", "label": 2771, "func": ""}
{"index": "s210074549", "label": 2771, "func": ""}
{"index": "s108727111", "label": 2771, "func": ""}
{"index": "s675930896", "label": 2771, "func": ""}
{"index": "s251866475", "label": 2771, "func": ""}
{"index": "s058725546", "label": 1636, "func": ""}
{"index": "s512861867", "label": 1636, "func": ""}
{"index": "s302072273", "label": 1636, "func": ""}
{"index": "s170084170", "label": 1636, "func": ""}
{"index": "s544419072", "label": 1636, "func": ""}
{"index": "s670472803", "label": 1636, "func": ""}
{"index": "s728091519", "label": 1636, "func": ""}
{"index": "s932961028", "label": 1636, "func": ""}
{"index": "s215088938", "label": 1636, "func": ""}
{"index": "s466594604", "label": 1636, "func": ""}
{"index": "s508631521", "label": 3524, "func": ""}
{"index": "s073794551", "label": 3524, "func": ""}
{"index": "s174592315", "label": 3524, "func": ""}
{"index": "s851571436", "label": 3524, "func": ""}
{"index": "s425353820", "label": 3524, "func": ""}
{"index": "s972959530", "label": 3524, "func": ""}
{"index": "s106922738", "label": 3524, "func": ""}
{"index": "s808545276", "label": 3524, "func": ""}
{"index": "s493852931", "label": 3524, "func": ""}
{"index": "s040066217", "label": 3524, "func": ""}
{"index": "s227980847", "label": 853, "func": ""}
{"index": "s370412919", "label": 860, "func": ""}
{"index": "s293942580", "label": 860, "func": ""}
{"index": "s056933128", "label": 860, "func": ""}
{"index": "s640456671", "label": 3981, "func": ""}
{"index": "s739624452", "label": 2813, "func": ""}
{"index": "s506955802", "label": 2813, "func": ""}
{"index": "s503438039", "label": 2813, "func": ""}
{"index": "s862335459", "label": 2813, "func": ""}
{"index": "s751834380", "label": 2813, "func": ""}
{"index": "s215156111", "label": 2813, "func": ""}
{"index": "s236975525", "label": 2813, "func": ""}
{"index": "s282358905", "label": 2813, "func": ""}
{"index": "s280154728", "label": 2813, "func": ""}
{"index": "s803163964", "label": 2813, "func": ""}
{"index": "s490101920", "label": 3900, "func": ""}
{"index": "s537700010", "label": 3900, "func": ""}
{"index": "s372024584", "label": 874, "func": ""}
{"index": "s041637322", "label": 874, "func": ""}
{"index": "s648016624", "label": 874, "func": ""}
{"index": "s096252883", "label": 874, "func": ""}
{"index": "s657699054", "label": 874, "func": ""}
{"index": "s643618618", "label": 874, "func": ""}
{"index": "s737120971", "label": 874, "func": ""}
{"index": "s921476305", "label": 874, "func": ""}
{"index": "s066542148", "label": 874, "func": ""}
{"index": "s796545845", "label": 874, "func": ""}
{"index": "s826864932", "label": 1050, "func": ""}
{"index": "s316186854", "label": 1050, "func": ""}
{"index": "s532988726", "label": 1956, "func": ""}
{"index": "s237720947", "label": 558, "func": ""}
{"index": "s612547814", "label": 3584, "func": ""}
{"index": "s834840349", "label": 3584, "func": ""}
{"index": "s926230268", "label": 3584, "func": ""}
{"index": "s302379088", "label": 3584, "func": ""}
{"index": "s607819577", "label": 3584, "func": ""}
{"index": "s386549882", "label": 3584, "func": ""}
{"index": "s160096103", "label": 3584, "func": ""}
{"index": "s306792792", "label": 3584, "func": ""}
{"index": "s103676613", "label": 3584, "func": ""}
{"index": "s537048969", "label": 3584, "func": ""}
{"index": "s448800794", "label": 3475, "func": ""}
{"index": "s453311886", "label": 3475, "func": ""}
{"index": "s459105558", "label": 3475, "func": ""}
{"index": "s776819543", "label": 3475, "func": ""}
{"index": "s092108277", "label": 3475, "func": ""}
{"index": "s919666229", "label": 3475, "func": ""}
{"index": "s206637361", "label": 3475, "func": ""}
{"index": "s262307912", "label": 3475, "func": ""}
{"index": "s009306061", "label": 3475, "func": ""}
{"index": "s789781857", "label": 3475, "func": ""}
{"index": "s803859418", "label": 3178, "func": ""}
{"index": "s318247769", "label": 3178, "func": ""}
{"index": "s796132589", "label": 3178, "func": ""}
{"index": "s215620358", "label": 3178, "func": ""}
{"index": "s677220813", "label": 3178, "func": ""}
{"index": "s792192435", "label": 3178, "func": ""}
{"index": "s631932803", "label": 3178, "func": ""}
{"index": "s575373624", "label": 3178, "func": ""}
{"index": "s408404000", "label": 3178, "func": ""}
{"index": "s236827906", "label": 3178, "func": ""}
{"index": "s378396552", "label": 3732, "func": ""}
{"index": "s357243832", "label": 3732, "func": ""}
{"index": "s602221092", "label": 3732, "func": ""}
{"index": "s046429546", "label": 3732, "func": ""}
{"index": "s157337744", "label": 3732, "func": ""}
{"index": "s336560190", "label": 3732, "func": ""}
{"index": "s662806461", "label": 3732, "func": ""}
{"index": "s867630429", "label": 3732, "func": ""}
{"index": "s453726110", "label": 3732, "func": ""}
{"index": "s338436346", "label": 3732, "func": ""}
{"index": "s880798812", "label": 1642, "func": ""}
{"index": "s635891924", "label": 2063, "func": ""}
{"index": "s807495679", "label": 3313, "func": ""}
{"index": "s889603104", "label": 3313, "func": ""}
{"index": "s223625209", "label": 3313, "func": ""}
{"index": "s920530760", "label": 3313, "func": ""}
{"index": "s931044495", "label": 3313, "func": ""}
{"index": "s257122953", "label": 3313, "func": ""}
{"index": "s017257115", "label": 3313, "func": ""}
{"index": "s943965029", "label": 3313, "func": ""}
{"index": "s101450420", "label": 3313, "func": ""}
{"index": "s375129445", "label": 3313, "func": ""}
{"index": "s867773164", "label": 1600, "func": ""}
{"index": "s654577017", "label": 1600, "func": ""}
{"index": "s857398812", "label": 164, "func": ""}
{"index": "s367666905", "label": 164, "func": ""}
{"index": "s575845366", "label": 164, "func": ""}
{"index": "s800567468", "label": 164, "func": ""}
{"index": "s186400794", "label": 164, "func": ""}
{"index": "s001306973", "label": 164, "func": ""}
{"index": "s924792918", "label": 164, "func": ""}
{"index": "s000364771", "label": 164, "func": ""}
{"index": "s955070343", "label": 164, "func": ""}
{"index": "s999633388", "label": 164, "func": ""}
{"index": "s736759015", "label": 517, "func": ""}
{"index": "s242529471", "label": 517, "func": ""}
{"index": "s579280690", "label": 517, "func": ""}
{"index": "s922697099", "label": 517, "func": ""}
{"index": "s520951009", "label": 517, "func": ""}
{"index": "s696784911", "label": 517, "func": ""}
{"index": "s845235899", "label": 517, "func": ""}
{"index": "s199211854", "label": 517, "func": ""}
{"index": "s586857023", "label": 517, "func": ""}
{"index": "s709721272", "label": 517, "func": ""}
{"index": "s494823708", "label": 542, "func": ""}
{"index": "s673906258", "label": 542, "func": ""}
{"index": "s654815617", "label": 542, "func": ""}
{"index": "s262838683", "label": 542, "func": ""}
{"index": "s799178254", "label": 542, "func": ""}
{"index": "s340997946", "label": 542, "func": ""}
{"index": "s492354710", "label": 542, "func": ""}
{"index": "s032870921", "label": 542, "func": ""}
{"index": "s698349853", "label": 542, "func": ""}
{"index": "s044975986", "label": 542, "func": ""}
{"index": "s840033410", "label": 2537, "func": ""}
{"index": "s859206830", "label": 2537, "func": ""}
{"index": "s847382605", "label": 2537, "func": ""}
{"index": "s424444730", "label": 2537, "func": ""}
{"index": "s362199414", "label": 2537, "func": ""}
{"index": "s107706018", "label": 2537, "func": ""}
{"index": "s474954035", "label": 2537, "func": ""}
{"index": "s783822756", "label": 2537, "func": ""}
{"index": "s654237886", "label": 2537, "func": ""}
{"index": "s766919139", "label": 2537, "func": ""}
{"index": "s884215501", "label": 1318, "func": ""}
{"index": "s137071699", "label": 1318, "func": ""}
{"index": "s003139396", "label": 1318, "func": ""}
{"index": "s530714724", "label": 1318, "func": ""}
{"index": "s553725552", "label": 1318, "func": ""}
{"index": "s354617287", "label": 1318, "func": ""}
{"index": "s469425205", "label": 443, "func": ""}
{"index": "s436315144", "label": 443, "func": ""}
{"index": "s334263424", "label": 443, "func": ""}
{"index": "s044820100", "label": 443, "func": ""}
{"index": "s874547863", "label": 443, "func": ""}
{"index": "s936404213", "label": 443, "func": ""}
{"index": "s125004950", "label": 443, "func": ""}
{"index": "s228526210", "label": 443, "func": ""}
{"index": "s170691820", "label": 443, "func": ""}
{"index": "s700913428", "label": 443, "func": ""}
{"index": "s925197851", "label": 1403, "func": ""}
{"index": "s065463933", "label": 1403, "func": ""}
{"index": "s813933448", "label": 1403, "func": ""}
{"index": "s316150506", "label": 1403, "func": ""}
{"index": "s711175969", "label": 1275, "func": ""}
{"index": "s456930506", "label": 1275, "func": ""}
{"index": "s925967522", "label": 1275, "func": ""}
{"index": "s628857753", "label": 1275, "func": ""}
{"index": "s999378039", "label": 1275, "func": ""}
{"index": "s196406078", "label": 3747, "func": ""}
{"index": "s149875457", "label": 3747, "func": ""}
{"index": "s600077155", "label": 3747, "func": ""}
{"index": "s247022065", "label": 3747, "func": ""}
{"index": "s151503074", "label": 3747, "func": ""}
{"index": "s821444757", "label": 3747, "func": ""}
{"index": "s855706954", "label": 2349, "func": ""}
{"index": "s877661034", "label": 2349, "func": ""}
{"index": "s189655893", "label": 2349, "func": ""}
{"index": "s016241523", "label": 2349, "func": ""}
{"index": "s131483707", "label": 2349, "func": ""}
{"index": "s894590971", "label": 2349, "func": ""}
{"index": "s563365617", "label": 2349, "func": ""}
{"index": "s323917638", "label": 2349, "func": ""}
{"index": "s330649963", "label": 2349, "func": ""}
{"index": "s261285595", "label": 2349, "func": ""}
{"index": "s352107450", "label": 2616, "func": ""}
{"index": "s207392162", "label": 2616, "func": ""}
{"index": "s633719799", "label": 2616, "func": ""}
{"index": "s343007530", "label": 2616, "func": ""}
{"index": "s014955582", "label": 2616, "func": ""}
{"index": "s557924051", "label": 2616, "func": ""}
{"index": "s777676098", "label": 2616, "func": ""}
{"index": "s778424148", "label": 2616, "func": ""}
{"index": "s514721282", "label": 2616, "func": ""}
{"index": "s956183604", "label": 2616, "func": ""}
{"index": "s426587957", "label": 2281, "func": ""}
{"index": "s595949910", "label": 2281, "func": ""}
{"index": "s251880502", "label": 2281, "func": ""}
{"index": "s100559957", "label": 2281, "func": ""}
{"index": "s383840538", "label": 2281, "func": ""}
{"index": "s031442583", "label": 2281, "func": ""}
{"index": "s785706963", "label": 2281, "func": ""}
{"index": "s703259380", "label": 2281, "func": ""}
{"index": "s139415234", "label": 2281, "func": ""}
{"index": "s955746826", "label": 2281, "func": ""}
{"index": "s516438671", "label": 3470, "func": ""}
{"index": "s573544088", "label": 3470, "func": ""}
{"index": "s121012983", "label": 3470, "func": ""}
{"index": "s921301260", "label": 3470, "func": ""}
{"index": "s037779040", "label": 3470, "func": ""}
{"index": "s216831732", "label": 3470, "func": ""}
{"index": "s887959179", "label": 3470, "func": ""}
{"index": "s172444994", "label": 3470, "func": ""}
{"index": "s929310543", "label": 3470, "func": ""}
{"index": "s018882852", "label": 3470, "func": ""}
{"index": "s153170836", "label": 136, "func": ""}
{"index": "s416249134", "label": 136, "func": ""}
{"index": "s996833641", "label": 136, "func": ""}
{"index": "s869852150", "label": 136, "func": ""}
{"index": "s914871166", "label": 136, "func": ""}
{"index": "s177468821", "label": 136, "func": ""}
{"index": "s190340552", "label": 136, "func": ""}
{"index": "s747209247", "label": 136, "func": ""}
{"index": "s188908252", "label": 136, "func": ""}
{"index": "s063947072", "label": 136, "func": ""}
{"index": "s359574435", "label": 1899, "func": ""}
{"index": "s303846707", "label": 1899, "func": ""}
{"index": "s057221064", "label": 1899, "func": ""}
{"index": "s604753632", "label": 1899, "func": ""}
{"index": "s710216976", "label": 1899, "func": ""}
{"index": "s453071431", "label": 1899, "func": ""}
{"index": "s363526695", "label": 129, "func": ""}
{"index": "s847443748", "label": 129, "func": ""}
{"index": "s039392548", "label": 129, "func": ""}
{"index": "s088762013", "label": 129, "func": ""}
{"index": "s704566619", "label": 129, "func": ""}
{"index": "s602525741", "label": 129, "func": ""}
{"index": "s766373698", "label": 129, "func": ""}
{"index": "s999933129", "label": 129, "func": ""}
{"index": "s789345164", "label": 129, "func": ""}
{"index": "s068350342", "label": 129, "func": ""}
{"index": "s807105080", "label": 3520, "func": ""}
{"index": "s027379334", "label": 1149, "func": ""}
{"index": "s066309447", "label": 1149, "func": ""}
{"index": "s534286209", "label": 1149, "func": ""}
{"index": "s974137896", "label": 1149, "func": ""}
{"index": "s354989900", "label": 1149, "func": ""}
{"index": "s426557841", "label": 1149, "func": ""}
{"index": "s063104064", "label": 1149, "func": ""}
{"index": "s946312588", "label": 2961, "func": ""}
{"index": "s062098295", "label": 2961, "func": ""}
{"index": "s457347757", "label": 2961, "func": ""}
{"index": "s340545275", "label": 2961, "func": ""}
{"index": "s253423014", "label": 2961, "func": ""}
{"index": "s297977912", "label": 2961, "func": ""}
{"index": "s298943602", "label": 2961, "func": ""}
{"index": "s012382210", "label": 2961, "func": ""}
{"index": "s792623855", "label": 1174, "func": ""}
{"index": "s046159811", "label": 1842, "func": ""}
{"index": "s967965780", "label": 1842, "func": ""}
{"index": "s972032804", "label": 2198, "func": ""}
{"index": "s293065542", "label": 2198, "func": ""}
{"index": "s407479906", "label": 726, "func": ""}
{"index": "s789476916", "label": 726, "func": ""}
{"index": "s724747587", "label": 726, "func": ""}
{"index": "s033530266", "label": 726, "func": ""}
{"index": "s853228622", "label": 726, "func": ""}
{"index": "s583070530", "label": 726, "func": ""}
{"index": "s821414203", "label": 726, "func": ""}
{"index": "s588553683", "label": 726, "func": ""}
{"index": "s057896956", "label": 726, "func": ""}
{"index": "s417343890", "label": 726, "func": ""}
{"index": "s696619200", "label": 1878, "func": ""}
{"index": "s839534831", "label": 1878, "func": ""}
{"index": "s254898493", "label": 1637, "func": ""}
{"index": "s342686632", "label": 1637, "func": ""}
{"index": "s560678580", "label": 1637, "func": ""}
{"index": "s306307092", "label": 1637, "func": ""}
{"index": "s744954669", "label": 1453, "func": ""}
{"index": "s528290318", "label": 1453, "func": ""}
{"index": "s132663723", "label": 3548, "func": ""}
{"index": "s972042677", "label": 3548, "func": ""}
{"index": "s022761918", "label": 3548, "func": ""}
{"index": "s146187566", "label": 3548, "func": ""}
{"index": "s859089423", "label": 3548, "func": ""}
{"index": "s434499440", "label": 3548, "func": ""}
{"index": "s514804589", "label": 3548, "func": ""}
{"index": "s644978767", "label": 3548, "func": ""}
{"index": "s442651220", "label": 3548, "func": ""}
{"index": "s362228680", "label": 3548, "func": ""}
{"index": "s975043690", "label": 3580, "func": ""}
{"index": "s937022542", "label": 3580, "func": ""}
{"index": "s407978131", "label": 3580, "func": ""}
{"index": "s005521386", "label": 3580, "func": ""}
{"index": "s707111633", "label": 3580, "func": ""}
{"index": "s304356968", "label": 3580, "func": ""}
{"index": "s060713066", "label": 3580, "func": ""}
{"index": "s636479462", "label": 2364, "func": ""}
{"index": "s659308994", "label": 2364, "func": ""}
{"index": "s008488819", "label": 2364, "func": ""}
{"index": "s144855087", "label": 2364, "func": ""}
{"index": "s630235761", "label": 2364, "func": ""}
{"index": "s343562668", "label": 2364, "func": ""}
{"index": "s114161034", "label": 2364, "func": ""}
{"index": "s179259914", "label": 2364, "func": ""}
{"index": "s797286411", "label": 2364, "func": ""}
{"index": "s657036938", "label": 2364, "func": ""}
{"index": "s861992430", "label": 3011, "func": ""}
{"index": "s668967960", "label": 3011, "func": ""}
{"index": "s796488202", "label": 3011, "func": ""}
{"index": "s181160099", "label": 3011, "func": ""}
{"index": "s440103965", "label": 3011, "func": ""}
{"index": "s852656479", "label": 3011, "func": ""}
{"index": "s819762636", "label": 3011, "func": ""}
{"index": "s281621815", "label": 3011, "func": ""}
{"index": "s744035669", "label": 3011, "func": ""}
{"index": "s228221533", "label": 3011, "func": ""}
{"index": "s648364151", "label": 3177, "func": ""}
{"index": "s441510697", "label": 3177, "func": ""}
{"index": "s552908156", "label": 3177, "func": ""}
{"index": "s520319671", "label": 3177, "func": ""}
{"index": "s425124054", "label": 3177, "func": ""}
{"index": "s415945548", "label": 3177, "func": ""}
{"index": "s017113335", "label": 3177, "func": ""}
{"index": "s570742149", "label": 3177, "func": ""}
{"index": "s564919165", "label": 3177, "func": ""}
{"index": "s535324519", "label": 3177, "func": ""}
{"index": "s469865124", "label": 1163, "func": ""}
{"index": "s271672376", "label": 1163, "func": ""}
{"index": "s358470458", "label": 1163, "func": ""}
{"index": "s027895894", "label": 1163, "func": ""}
{"index": "s544411143", "label": 1163, "func": ""}
{"index": "s990263845", "label": 1163, "func": ""}
{"index": "s176223796", "label": 1163, "func": ""}
{"index": "s001204904", "label": 1163, "func": ""}
{"index": "s469557268", "label": 1163, "func": ""}
{"index": "s008173002", "label": 1163, "func": ""}
{"index": "s137224078", "label": 793, "func": ""}
{"index": "s930272630", "label": 793, "func": ""}
{"index": "s442450805", "label": 793, "func": ""}
{"index": "s543405100", "label": 3199, "func": ""}
{"index": "s330699976", "label": 3199, "func": ""}
{"index": "s956349708", "label": 3199, "func": ""}
{"index": "s759305970", "label": 3352, "func": ""}
{"index": "s948182239", "label": 3352, "func": ""}
{"index": "s855886902", "label": 3352, "func": ""}
{"index": "s386357372", "label": 3352, "func": ""}
{"index": "s367176157", "label": 3352, "func": ""}
{"index": "s585428808", "label": 3352, "func": ""}
{"index": "s678032298", "label": 3352, "func": ""}
{"index": "s515205582", "label": 3352, "func": ""}
{"index": "s324113322", "label": 3352, "func": ""}
{"index": "s004474576", "label": 3352, "func": ""}
{"index": "s176564512", "label": 2577, "func": ""}
{"index": "s939687060", "label": 2577, "func": ""}
{"index": "s411038805", "label": 2577, "func": ""}
{"index": "s171763017", "label": 2577, "func": ""}
{"index": "s787833876", "label": 2577, "func": ""}
{"index": "s153697053", "label": 2577, "func": ""}
{"index": "s714103779", "label": 2577, "func": ""}
{"index": "s774205869", "label": 2577, "func": ""}
{"index": "s835469347", "label": 2577, "func": ""}
{"index": "s314758771", "label": 2577, "func": ""}
{"index": "s449484920", "label": 595, "func": ""}
{"index": "s020080208", "label": 595, "func": ""}
{"index": "s478451495", "label": 595, "func": ""}
{"index": "s086731024", "label": 595, "func": ""}
{"index": "s603090028", "label": 595, "func": ""}
{"index": "s925093578", "label": 595, "func": ""}
{"index": "s612039690", "label": 595, "func": ""}
{"index": "s022709935", "label": 595, "func": ""}
{"index": "s339179373", "label": 595, "func": ""}
{"index": "s484041017", "label": 595, "func": ""}
{"index": "s782152424", "label": 641, "func": ""}
{"index": "s955741174", "label": 641, "func": ""}
{"index": "s037758769", "label": 641, "func": ""}
{"index": "s694665039", "label": 641, "func": ""}
{"index": "s494895747", "label": 1153, "func": ""}
{"index": "s500002420", "label": 201, "func": ""}
{"index": "s349195521", "label": 201, "func": ""}
{"index": "s745702516", "label": 201, "func": ""}
{"index": "s225400136", "label": 201, "func": ""}
{"index": "s374155861", "label": 201, "func": ""}
{"index": "s719486192", "label": 201, "func": ""}
{"index": "s934297997", "label": 201, "func": ""}
{"index": "s685479798", "label": 201, "func": ""}
{"index": "s534390260", "label": 201, "func": ""}
{"index": "s023119902", "label": 201, "func": ""}
{"index": "s899997939", "label": 3504, "func": ""}
{"index": "s661517877", "label": 3504, "func": ""}
{"index": "s649909374", "label": 3504, "func": ""}
{"index": "s346528018", "label": 3504, "func": ""}
{"index": "s722701562", "label": 3504, "func": ""}
{"index": "s914312293", "label": 3504, "func": ""}
{"index": "s601247674", "label": 3504, "func": ""}
{"index": "s426088237", "label": 3504, "func": ""}
{"index": "s271414377", "label": 3504, "func": ""}
{"index": "s990954556", "label": 3504, "func": ""}
{"index": "s698243306", "label": 124, "func": ""}
{"index": "s842681883", "label": 124, "func": ""}
{"index": "s863726903", "label": 124, "func": ""}
{"index": "s717506236", "label": 124, "func": ""}
{"index": "s365984676", "label": 124, "func": ""}
{"index": "s403905867", "label": 124, "func": ""}
{"index": "s826592426", "label": 124, "func": ""}
{"index": "s073351293", "label": 124, "func": ""}
{"index": "s666786582", "label": 124, "func": ""}
{"index": "s048767309", "label": 124, "func": ""}
{"index": "s391050410", "label": 175, "func": ""}
{"index": "s301428334", "label": 175, "func": ""}
{"index": "s626323678", "label": 175, "func": ""}
{"index": "s581576257", "label": 175, "func": ""}
{"index": "s857267333", "label": 175, "func": ""}
{"index": "s632086074", "label": 175, "func": ""}
{"index": "s239362552", "label": 175, "func": ""}
{"index": "s401135726", "label": 175, "func": ""}
{"index": "s579823004", "label": 175, "func": ""}
{"index": "s992957030", "label": 175, "func": ""}
{"index": "s881260302", "label": 1430, "func": ""}
{"index": "s878130197", "label": 1430, "func": ""}
{"index": "s077687991", "label": 1323, "func": ""}
{"index": "s301403521", "label": 1323, "func": ""}
{"index": "s412024071", "label": 1323, "func": ""}
{"index": "s838836655", "label": 1323, "func": ""}
{"index": "s777237088", "label": 1323, "func": ""}
{"index": "s166624501", "label": 1323, "func": ""}
{"index": "s690728467", "label": 1323, "func": ""}
{"index": "s540550262", "label": 1323, "func": ""}
{"index": "s527107554", "label": 1323, "func": ""}
{"index": "s659446069", "label": 1323, "func": ""}
{"index": "s614463654", "label": 613, "func": ""}
{"index": "s333283048", "label": 613, "func": ""}
{"index": "s499146229", "label": 613, "func": ""}
{"index": "s960335726", "label": 613, "func": ""}
{"index": "s098530830", "label": 613, "func": ""}
{"index": "s932628527", "label": 613, "func": ""}
{"index": "s436849764", "label": 613, "func": ""}
{"index": "s735921849", "label": 613, "func": ""}
{"index": "s379247522", "label": 613, "func": ""}
{"index": "s128447174", "label": 613, "func": ""}
{"index": "s854377611", "label": 1207, "func": ""}
{"index": "s528350219", "label": 488, "func": ""}
{"index": "s728000018", "label": 488, "func": ""}
{"index": "s035576555", "label": 488, "func": ""}
{"index": "s690103489", "label": 488, "func": ""}
{"index": "s432620303", "label": 488, "func": ""}
{"index": "s420947295", "label": 488, "func": ""}
{"index": "s143043575", "label": 488, "func": ""}
{"index": "s684774016", "label": 488, "func": ""}
{"index": "s546416138", "label": 488, "func": ""}
{"index": "s084277580", "label": 488, "func": ""}
{"index": "s643572115", "label": 166, "func": ""}
{"index": "s204411807", "label": 166, "func": ""}
{"index": "s843924446", "label": 166, "func": ""}
{"index": "s887771552", "label": 166, "func": ""}
{"index": "s994922215", "label": 166, "func": ""}
{"index": "s686786056", "label": 166, "func": ""}
{"index": "s624151201", "label": 166, "func": ""}
{"index": "s673147067", "label": 166, "func": ""}
{"index": "s787848447", "label": 166, "func": ""}
{"index": "s733794730", "label": 166, "func": ""}
{"index": "s509295475", "label": 1646, "func": ""}
{"index": "s164017271", "label": 1646, "func": ""}
{"index": "s481730514", "label": 1646, "func": ""}
{"index": "s888421339", "label": 2265, "func": ""}
{"index": "s173558946", "label": 2265, "func": ""}
{"index": "s405154502", "label": 2265, "func": ""}
{"index": "s593012941", "label": 2265, "func": ""}
{"index": "s422824962", "label": 2265, "func": ""}
{"index": "s943511326", "label": 2265, "func": ""}
{"index": "s994826361", "label": 2265, "func": ""}
{"index": "s876060365", "label": 2265, "func": ""}
{"index": "s897582494", "label": 2265, "func": ""}
{"index": "s030606968", "label": 2265, "func": ""}
{"index": "s784602011", "label": 1002, "func": ""}
{"index": "s827400639", "label": 1002, "func": ""}
{"index": "s393710136", "label": 2507, "func": ""}
{"index": "s736990458", "label": 568, "func": ""}
{"index": "s960804755", "label": 568, "func": ""}
{"index": "s609615172", "label": 1101, "func": ""}
{"index": "s013838527", "label": 1101, "func": ""}
{"index": "s552970496", "label": 1101, "func": ""}
{"index": "s704633811", "label": 1101, "func": ""}
{"index": "s888133860", "label": 1101, "func": ""}
{"index": "s038883120", "label": 1101, "func": ""}
{"index": "s511247251", "label": 1101, "func": ""}
{"index": "s274936620", "label": 1101, "func": ""}
{"index": "s911247360", "label": 1101, "func": ""}
{"index": "s031264685", "label": 1101, "func": ""}
{"index": "s585787174", "label": 2611, "func": ""}
{"index": "s197589999", "label": 2611, "func": ""}
{"index": "s420558407", "label": 294, "func": ""}
{"index": "s664205414", "label": 294, "func": ""}
{"index": "s147137380", "label": 294, "func": ""}
{"index": "s350919332", "label": 294, "func": ""}
{"index": "s202748694", "label": 294, "func": ""}
{"index": "s522048229", "label": 294, "func": ""}
{"index": "s049531806", "label": 294, "func": ""}
{"index": "s435008927", "label": 294, "func": ""}
{"index": "s803481516", "label": 294, "func": ""}
{"index": "s288667999", "label": 1437, "func": ""}
{"index": "s924362837", "label": 1437, "func": ""}
{"index": "s694498335", "label": 1437, "func": ""}
{"index": "s270426968", "label": 1437, "func": ""}
{"index": "s504004318", "label": 1437, "func": ""}
{"index": "s890557712", "label": 1437, "func": ""}
{"index": "s240982800", "label": 1437, "func": ""}
{"index": "s063092636", "label": 1437, "func": ""}
{"index": "s181868726", "label": 1437, "func": ""}
{"index": "s957181470", "label": 1437, "func": ""}
{"index": "s197328841", "label": 2241, "func": ""}
{"index": "s170315976", "label": 2241, "func": ""}
{"index": "s092669848", "label": 2241, "func": ""}
{"index": "s085577029", "label": 2241, "func": ""}
{"index": "s888642438", "label": 2241, "func": ""}
{"index": "s733399777", "label": 2241, "func": ""}
{"index": "s816585151", "label": 2241, "func": ""}
{"index": "s622343578", "label": 2241, "func": ""}
{"index": "s825394456", "label": 2241, "func": ""}
{"index": "s653641681", "label": 2241, "func": ""}
{"index": "s666486879", "label": 3978, "func": ""}
{"index": "s443400688", "label": 3978, "func": ""}
{"index": "s831389818", "label": 3978, "func": ""}
{"index": "s568901780", "label": 3978, "func": ""}
{"index": "s433563570", "label": 3978, "func": ""}
{"index": "s010357376", "label": 3978, "func": ""}
{"index": "s437017766", "label": 3978, "func": ""}
{"index": "s789444821", "label": 3978, "func": ""}
{"index": "s099411485", "label": 3978, "func": ""}
{"index": "s162606153", "label": 1372, "func": ""}
{"index": "s411046012", "label": 1372, "func": ""}
{"index": "s922784276", "label": 1372, "func": ""}
{"index": "s834157516", "label": 1372, "func": ""}
{"index": "s042960603", "label": 1372, "func": ""}
{"index": "s206885343", "label": 1372, "func": ""}
{"index": "s174217961", "label": 1372, "func": ""}
{"index": "s791152865", "label": 1372, "func": ""}
{"index": "s260444172", "label": 1372, "func": ""}
{"index": "s604095344", "label": 1372, "func": ""}
{"index": "s069430373", "label": 617, "func": ""}
{"index": "s907579518", "label": 617, "func": ""}
{"index": "s938828753", "label": 617, "func": ""}
{"index": "s919652154", "label": 617, "func": ""}
{"index": "s215720788", "label": 617, "func": ""}
{"index": "s946974134", "label": 617, "func": ""}
{"index": "s331196118", "label": 617, "func": ""}
{"index": "s249961772", "label": 617, "func": ""}
{"index": "s087541424", "label": 3896, "func": ""}
{"index": "s812901593", "label": 3896, "func": ""}
{"index": "s723597989", "label": 43, "func": ""}
{"index": "s689530551", "label": 43, "func": ""}
{"index": "s902295878", "label": 43, "func": ""}
{"index": "s640950960", "label": 43, "func": ""}
{"index": "s088739954", "label": 43, "func": ""}
{"index": "s723264712", "label": 43, "func": ""}
{"index": "s705549510", "label": 43, "func": ""}
{"index": "s749602919", "label": 43, "func": ""}
{"index": "s678805904", "label": 43, "func": ""}
{"index": "s765292469", "label": 43, "func": ""}
{"index": "s878837104", "label": 3233, "func": ""}
{"index": "s805374879", "label": 3233, "func": ""}
{"index": "s962813739", "label": 3233, "func": ""}
{"index": "s423808926", "label": 246, "func": ""}
{"index": "s933697384", "label": 246, "func": ""}
{"index": "s089824446", "label": 246, "func": ""}
{"index": "s256439055", "label": 246, "func": ""}
{"index": "s018752506", "label": 246, "func": ""}
{"index": "s688948031", "label": 246, "func": ""}
{"index": "s255505236", "label": 246, "func": ""}
{"index": "s148670108", "label": 246, "func": ""}
{"index": "s218998630", "label": 246, "func": ""}
{"index": "s440030899", "label": 246, "func": ""}
{"index": "s341403042", "label": 190, "func": ""}
{"index": "s669725449", "label": 190, "func": ""}
{"index": "s620394696", "label": 190, "func": ""}
{"index": "s555835101", "label": 190, "func": ""}
{"index": "s820800811", "label": 190, "func": ""}
{"index": "s713724118", "label": 190, "func": ""}
{"index": "s176782688", "label": 2865, "func": ""}
{"index": "s533104156", "label": 2865, "func": ""}
{"index": "s194586583", "label": 2865, "func": ""}
{"index": "s545432181", "label": 2865, "func": ""}
{"index": "s836698616", "label": 2865, "func": ""}
{"index": "s556016958", "label": 2865, "func": ""}
{"index": "s190533995", "label": 2865, "func": ""}
{"index": "s190869605", "label": 2865, "func": ""}
{"index": "s084990396", "label": 2865, "func": ""}
{"index": "s985271395", "label": 2865, "func": ""}
{"index": "s330572242", "label": 700, "func": ""}
{"index": "s326807002", "label": 700, "func": ""}
{"index": "s278848696", "label": 700, "func": ""}
{"index": "s576701002", "label": 700, "func": ""}
{"index": "s468297399", "label": 700, "func": ""}
{"index": "s943478314", "label": 700, "func": ""}
{"index": "s685929314", "label": 700, "func": ""}
{"index": "s607333260", "label": 700, "func": ""}
{"index": "s199605753", "label": 700, "func": ""}
{"index": "s340240533", "label": 700, "func": ""}
{"index": "s573825770", "label": 2019, "func": ""}
{"index": "s225850629", "label": 2019, "func": ""}
{"index": "s804151845", "label": 2019, "func": ""}
{"index": "s752130704", "label": 2019, "func": ""}
{"index": "s874136055", "label": 3459, "func": ""}
{"index": "s059600053", "label": 3459, "func": ""}
{"index": "s602657033", "label": 3459, "func": ""}
{"index": "s334479851", "label": 3459, "func": ""}
{"index": "s881255824", "label": 3459, "func": ""}
{"index": "s534505204", "label": 3459, "func": ""}
{"index": "s962442179", "label": 3459, "func": ""}
{"index": "s228819969", "label": 3459, "func": ""}
{"index": "s158905806", "label": 3459, "func": ""}
{"index": "s364297114", "label": 3459, "func": ""}
{"index": "s997873704", "label": 3351, "func": ""}
{"index": "s392887468", "label": 3351, "func": ""}
{"index": "s315813375", "label": 3351, "func": ""}
{"index": "s976817650", "label": 3351, "func": ""}
{"index": "s516099896", "label": 3351, "func": ""}
{"index": "s610215907", "label": 3351, "func": ""}
{"index": "s555632533", "label": 3351, "func": ""}
{"index": "s049736372", "label": 3351, "func": ""}
{"index": "s680057367", "label": 3351, "func": ""}
{"index": "s709769552", "label": 3351, "func": ""}
{"index": "s008276878", "label": 736, "func": ""}
{"index": "s399171336", "label": 736, "func": ""}
{"index": "s591696702", "label": 736, "func": ""}
{"index": "s834270988", "label": 736, "func": ""}
{"index": "s109177058", "label": 736, "func": ""}
{"index": "s658053400", "label": 736, "func": ""}
{"index": "s961844524", "label": 736, "func": ""}
{"index": "s314587555", "label": 736, "func": ""}
{"index": "s452513535", "label": 736, "func": ""}
{"index": "s442221160", "label": 736, "func": ""}
{"index": "s120869479", "label": 3753, "func": ""}
{"index": "s637072897", "label": 3053, "func": ""}
{"index": "s566791191", "label": 3053, "func": ""}
{"index": "s452544557", "label": 3053, "func": ""}
{"index": "s115636655", "label": 3053, "func": ""}
{"index": "s939513461", "label": 3053, "func": ""}
{"index": "s214368499", "label": 3053, "func": ""}
{"index": "s706055048", "label": 3053, "func": ""}
{"index": "s087650193", "label": 3053, "func": ""}
{"index": "s973361195", "label": 3053, "func": ""}
{"index": "s245507507", "label": 3053, "func": ""}
{"index": "s913919756", "label": 1942, "func": ""}
{"index": "s888378047", "label": 724, "func": ""}
{"index": "s224753671", "label": 724, "func": ""}
{"index": "s536293552", "label": 2951, "func": ""}
{"index": "s707732127", "label": 2951, "func": ""}
{"index": "s216086244", "label": 2951, "func": ""}
{"index": "s503006860", "label": 2951, "func": ""}
{"index": "s581770355", "label": 2951, "func": ""}
{"index": "s192897098", "label": 2951, "func": ""}
{"index": "s439564645", "label": 2951, "func": ""}
{"index": "s926464998", "label": 2951, "func": ""}
{"index": "s862990709", "label": 2951, "func": ""}
{"index": "s863221984", "label": 2951, "func": ""}
{"index": "s608752975", "label": 3557, "func": ""}
{"index": "s452775173", "label": 3557, "func": ""}
{"index": "s395814371", "label": 3557, "func": ""}
{"index": "s813886305", "label": 3557, "func": ""}
{"index": "s761784363", "label": 3557, "func": ""}
{"index": "s913312050", "label": 3557, "func": ""}
{"index": "s943394263", "label": 3557, "func": ""}
{"index": "s138851620", "label": 3557, "func": ""}
{"index": "s770271985", "label": 3557, "func": ""}
{"index": "s206021101", "label": 3557, "func": ""}
{"index": "s874352846", "label": 846, "func": ""}
{"index": "s745660446", "label": 846, "func": ""}
{"index": "s998836368", "label": 846, "func": ""}
{"index": "s385078770", "label": 846, "func": ""}
{"index": "s435737464", "label": 365, "func": ""}
{"index": "s156424503", "label": 365, "func": ""}
{"index": "s919637992", "label": 2409, "func": ""}
{"index": "s305498132", "label": 2409, "func": ""}
{"index": "s959185366", "label": 2409, "func": ""}
{"index": "s441950524", "label": 2409, "func": ""}
{"index": "s437865787", "label": 2409, "func": ""}
{"index": "s566362426", "label": 2409, "func": ""}
{"index": "s539740076", "label": 2409, "func": ""}
{"index": "s130317386", "label": 2409, "func": ""}
{"index": "s641961955", "label": 2409, "func": ""}
{"index": "s007045264", "label": 2409, "func": ""}
{"index": "s562316752", "label": 1338, "func": ""}
{"index": "s218595101", "label": 1338, "func": ""}
{"index": "s234604601", "label": 2510, "func": ""}
{"index": "s350824451", "label": 2510, "func": ""}
{"index": "s130832895", "label": 2510, "func": ""}
{"index": "s143152019", "label": 2510, "func": ""}
{"index": "s855049092", "label": 2510, "func": ""}
{"index": "s381606142", "label": 2510, "func": ""}
{"index": "s931017967", "label": 2510, "func": ""}
{"index": "s808538529", "label": 2510, "func": ""}
{"index": "s840228348", "label": 2510, "func": ""}
{"index": "s675932011", "label": 2510, "func": ""}
{"index": "s612276700", "label": 3635, "func": ""}
{"index": "s567968638", "label": 3635, "func": ""}
{"index": "s205636800", "label": 3635, "func": ""}
{"index": "s312857139", "label": 3635, "func": ""}
{"index": "s914160845", "label": 3635, "func": ""}
{"index": "s986049369", "label": 3635, "func": ""}
{"index": "s470160031", "label": 3635, "func": ""}
{"index": "s196958533", "label": 3635, "func": ""}
{"index": "s291214244", "label": 3635, "func": ""}
{"index": "s961305544", "label": 3635, "func": ""}
{"index": "s050132467", "label": 2335, "func": ""}
{"index": "s001741018", "label": 667, "func": ""}
{"index": "s160560073", "label": 667, "func": ""}
{"index": "s096938632", "label": 57, "func": ""}
{"index": "s146927703", "label": 57, "func": ""}
{"index": "s513974086", "label": 57, "func": ""}
{"index": "s684729245", "label": 57, "func": ""}
{"index": "s536888787", "label": 57, "func": ""}
{"index": "s477424892", "label": 57, "func": ""}
{"index": "s179516704", "label": 57, "func": ""}
{"index": "s235789227", "label": 57, "func": ""}
{"index": "s277194519", "label": 57, "func": ""}
{"index": "s249739106", "label": 57, "func": ""}
{"index": "s538983113", "label": 755, "func": ""}
{"index": "s557487295", "label": 755, "func": ""}
{"index": "s942039716", "label": 755, "func": ""}
{"index": "s870473113", "label": 755, "func": ""}
{"index": "s147265893", "label": 755, "func": ""}
{"index": "s055895739", "label": 755, "func": ""}
{"index": "s185235609", "label": 755, "func": ""}
{"index": "s348320981", "label": 755, "func": ""}
{"index": "s760010328", "label": 755, "func": ""}
{"index": "s830509553", "label": 755, "func": ""}
{"index": "s681307390", "label": 3605, "func": ""}
{"index": "s841962734", "label": 3605, "func": ""}
{"index": "s223859111", "label": 3605, "func": ""}
{"index": "s035623950", "label": 3605, "func": ""}
{"index": "s066949021", "label": 3605, "func": ""}
{"index": "s575708828", "label": 3605, "func": ""}
{"index": "s947128292", "label": 3605, "func": ""}
{"index": "s316249524", "label": 3605, "func": ""}
{"index": "s622466942", "label": 3605, "func": ""}
{"index": "s483482889", "label": 3605, "func": ""}
{"index": "s920569478", "label": 3207, "func": ""}
{"index": "s863342434", "label": 3207, "func": ""}
{"index": "s698404410", "label": 3207, "func": ""}
{"index": "s893087636", "label": 3207, "func": ""}
{"index": "s427750094", "label": 3207, "func": ""}
{"index": "s485618105", "label": 3207, "func": ""}
{"index": "s612122543", "label": 3207, "func": ""}
{"index": "s677657915", "label": 3207, "func": ""}
{"index": "s795718307", "label": 3207, "func": ""}
{"index": "s088917542", "label": 3207, "func": ""}
{"index": "s681240076", "label": 696, "func": ""}
{"index": "s562036812", "label": 696, "func": ""}
{"index": "s717446189", "label": 696, "func": ""}
{"index": "s824289907", "label": 1828, "func": ""}
{"index": "s457597228", "label": 1828, "func": ""}
{"index": "s701837474", "label": 1828, "func": ""}
{"index": "s074750124", "label": 1828, "func": ""}
{"index": "s463580449", "label": 1828, "func": ""}
{"index": "s399291890", "label": 1828, "func": ""}
{"index": "s143897558", "label": 1828, "func": ""}
{"index": "s813252056", "label": 4024, "func": ""}
{"index": "s985482111", "label": 2496, "func": ""}
{"index": "s720090630", "label": 2496, "func": ""}
{"index": "s262172201", "label": 2496, "func": ""}
{"index": "s242095450", "label": 2496, "func": ""}
{"index": "s085082386", "label": 2496, "func": ""}
{"index": "s417315870", "label": 2496, "func": ""}
{"index": "s288552334", "label": 2496, "func": ""}
{"index": "s032731195", "label": 2496, "func": ""}
{"index": "s997716315", "label": 2496, "func": ""}
{"index": "s452628779", "label": 2496, "func": ""}
{"index": "s899566074", "label": 682, "func": ""}
{"index": "s970974370", "label": 682, "func": ""}
{"index": "s723614559", "label": 682, "func": ""}
{"index": "s963283120", "label": 682, "func": ""}
{"index": "s597697630", "label": 682, "func": ""}
{"index": "s106299043", "label": 682, "func": ""}
{"index": "s896009208", "label": 682, "func": ""}
{"index": "s119122162", "label": 682, "func": ""}
{"index": "s109413106", "label": 682, "func": ""}
{"index": "s698349684", "label": 682, "func": ""}
{"index": "s641230166", "label": 2917, "func": ""}
{"index": "s254759036", "label": 2917, "func": ""}
{"index": "s945262136", "label": 2917, "func": ""}
{"index": "s267969707", "label": 2917, "func": ""}
{"index": "s542308969", "label": 2917, "func": ""}
{"index": "s912707943", "label": 2917, "func": ""}
{"index": "s991886466", "label": 2917, "func": ""}
{"index": "s340431213", "label": 2917, "func": ""}
{"index": "s220279909", "label": 2917, "func": ""}
{"index": "s037201583", "label": 2917, "func": ""}
{"index": "s967429661", "label": 638, "func": ""}
{"index": "s518889216", "label": 638, "func": ""}
{"index": "s097541571", "label": 638, "func": ""}
{"index": "s504566231", "label": 638, "func": ""}
{"index": "s103564796", "label": 638, "func": ""}
{"index": "s108954041", "label": 638, "func": ""}
{"index": "s413839612", "label": 638, "func": ""}
{"index": "s335051701", "label": 638, "func": ""}
{"index": "s428382908", "label": 638, "func": ""}
{"index": "s751521433", "label": 638, "func": ""}
{"index": "s101236670", "label": 2948, "func": ""}
{"index": "s148061399", "label": 2948, "func": ""}
{"index": "s696596648", "label": 2948, "func": ""}
{"index": "s792771041", "label": 2948, "func": ""}
{"index": "s631548393", "label": 2948, "func": ""}
{"index": "s500948637", "label": 2948, "func": ""}
{"index": "s959230030", "label": 2948, "func": ""}
{"index": "s686626097", "label": 2948, "func": ""}
{"index": "s994231956", "label": 2948, "func": ""}
{"index": "s252840331", "label": 2948, "func": ""}
{"index": "s403533976", "label": 4007, "func": ""}
{"index": "s544648681", "label": 4007, "func": ""}
{"index": "s449223402", "label": 4007, "func": ""}
{"index": "s655922242", "label": 4007, "func": ""}
{"index": "s884168504", "label": 4007, "func": ""}
{"index": "s702326785", "label": 4007, "func": ""}
{"index": "s969837269", "label": 4007, "func": ""}
{"index": "s215196038", "label": 4007, "func": ""}
{"index": "s794425951", "label": 4007, "func": ""}
{"index": "s787412707", "label": 4007, "func": ""}
{"index": "s605881909", "label": 2558, "func": ""}
{"index": "s769383806", "label": 2558, "func": ""}
{"index": "s609487061", "label": 2558, "func": ""}
{"index": "s182460829", "label": 2558, "func": ""}
{"index": "s661990710", "label": 2558, "func": ""}
{"index": "s289138852", "label": 2558, "func": ""}
{"index": "s469471562", "label": 2558, "func": ""}
{"index": "s118593231", "label": 2558, "func": ""}
{"index": "s361420135", "label": 2558, "func": ""}
{"index": "s938159906", "label": 2558, "func": ""}
{"index": "s016052580", "label": 2649, "func": ""}
{"index": "s428341827", "label": 2649, "func": ""}
{"index": "s482030621", "label": 2649, "func": ""}
{"index": "s850217408", "label": 2649, "func": ""}
{"index": "s066711501", "label": 2649, "func": ""}
{"index": "s631868709", "label": 2649, "func": ""}
{"index": "s660863993", "label": 2649, "func": ""}
{"index": "s722052385", "label": 3415, "func": ""}
{"index": "s008565167", "label": 3415, "func": ""}
{"index": "s080767611", "label": 3415, "func": ""}
{"index": "s853156155", "label": 3415, "func": ""}
{"index": "s976321527", "label": 3415, "func": ""}
{"index": "s871086572", "label": 3415, "func": ""}
{"index": "s508741525", "label": 3415, "func": ""}
{"index": "s892012178", "label": 3415, "func": ""}
{"index": "s934723924", "label": 3415, "func": ""}
{"index": "s245113592", "label": 3415, "func": ""}
{"index": "s151377286", "label": 3973, "func": ""}
{"index": "s464706041", "label": 3973, "func": ""}
{"index": "s116588549", "label": 3973, "func": ""}
{"index": "s142998772", "label": 3973, "func": ""}
{"index": "s797571700", "label": 3973, "func": ""}
{"index": "s746700615", "label": 3973, "func": ""}
{"index": "s613701489", "label": 3973, "func": ""}
{"index": "s443365364", "label": 3973, "func": ""}
{"index": "s849487373", "label": 3973, "func": ""}
{"index": "s508260590", "label": 3973, "func": ""}
{"index": "s295883512", "label": 719, "func": ""}
{"index": "s641563795", "label": 719, "func": ""}
{"index": "s445992730", "label": 719, "func": ""}
{"index": "s110629530", "label": 719, "func": ""}
{"index": "s255343180", "label": 719, "func": ""}
{"index": "s771445575", "label": 719, "func": ""}
{"index": "s905031009", "label": 719, "func": ""}
{"index": "s815555868", "label": 719, "func": ""}
{"index": "s372640879", "label": 719, "func": ""}
{"index": "s341666688", "label": 719, "func": ""}
{"index": "s032359056", "label": 2400, "func": ""}
{"index": "s338213132", "label": 2400, "func": ""}
{"index": "s628599629", "label": 2400, "func": ""}
{"index": "s311535255", "label": 2400, "func": ""}
{"index": "s459477791", "label": 2400, "func": ""}
{"index": "s199355156", "label": 2400, "func": ""}
{"index": "s878083431", "label": 2400, "func": ""}
{"index": "s780082024", "label": 2400, "func": ""}
{"index": "s653496735", "label": 2400, "func": ""}
{"index": "s663607716", "label": 2400, "func": ""}
{"index": "s224134571", "label": 3943, "func": ""}
{"index": "s923353399", "label": 3943, "func": ""}
{"index": "s893565394", "label": 3943, "func": ""}
{"index": "s718539765", "label": 3943, "func": ""}
{"index": "s308214460", "label": 3943, "func": ""}
{"index": "s906098664", "label": 3943, "func": ""}
{"index": "s937414330", "label": 3943, "func": ""}
{"index": "s462516051", "label": 3943, "func": ""}
{"index": "s351443869", "label": 3943, "func": ""}
{"index": "s868404386", "label": 3943, "func": ""}
{"index": "s228881390", "label": 2915, "func": ""}
{"index": "s166807039", "label": 2915, "func": ""}
{"index": "s324810370", "label": 2915, "func": ""}
{"index": "s057923449", "label": 2915, "func": ""}
{"index": "s685312890", "label": 2915, "func": ""}
{"index": "s906386356", "label": 2915, "func": ""}
{"index": "s102944924", "label": 2915, "func": ""}
{"index": "s056289110", "label": 2915, "func": ""}
{"index": "s353869200", "label": 2915, "func": ""}
{"index": "s927869022", "label": 2915, "func": ""}
{"index": "s390221468", "label": 3714, "func": ""}
{"index": "s350125633", "label": 3714, "func": ""}
{"index": "s158993836", "label": 3714, "func": ""}
{"index": "s671890836", "label": 3714, "func": ""}
{"index": "s318070028", "label": 3714, "func": ""}
{"index": "s373396321", "label": 3714, "func": ""}
{"index": "s007430437", "label": 3714, "func": ""}
{"index": "s232588423", "label": 3714, "func": ""}
{"index": "s826103481", "label": 3714, "func": ""}
{"index": "s354754728", "label": 3714, "func": ""}
{"index": "s900653743", "label": 3038, "func": ""}
{"index": "s414618462", "label": 3038, "func": ""}
{"index": "s901859292", "label": 3038, "func": ""}
{"index": "s546501767", "label": 3038, "func": ""}
{"index": "s495684306", "label": 3038, "func": ""}
{"index": "s606263031", "label": 3038, "func": ""}
{"index": "s250726099", "label": 3038, "func": ""}
{"index": "s885076037", "label": 3038, "func": ""}
{"index": "s387453073", "label": 3038, "func": ""}
{"index": "s925075176", "label": 3038, "func": ""}
{"index": "s384850029", "label": 1614, "func": ""}
{"index": "s550758315", "label": 1614, "func": ""}
{"index": "s906800445", "label": 1614, "func": ""}
{"index": "s334246269", "label": 1614, "func": ""}
{"index": "s749806018", "label": 1614, "func": ""}
{"index": "s068565267", "label": 1614, "func": ""}
{"index": "s728401084", "label": 1614, "func": ""}
{"index": "s072534934", "label": 1614, "func": ""}
{"index": "s923671798", "label": 1614, "func": ""}
{"index": "s335916096", "label": 1614, "func": ""}
{"index": "s131114123", "label": 735, "func": ""}
{"index": "s407570242", "label": 735, "func": ""}
{"index": "s836578951", "label": 735, "func": ""}
{"index": "s615428645", "label": 735, "func": ""}
{"index": "s677781228", "label": 735, "func": ""}
{"index": "s039157383", "label": 735, "func": ""}
{"index": "s597776033", "label": 735, "func": ""}
{"index": "s892334033", "label": 735, "func": ""}
{"index": "s510145155", "label": 735, "func": ""}
{"index": "s021288525", "label": 735, "func": ""}
{"index": "s587167156", "label": 1278, "func": ""}
{"index": "s916513787", "label": 243, "func": ""}
{"index": "s515019472", "label": 243, "func": ""}
{"index": "s703273708", "label": 243, "func": ""}
{"index": "s043851268", "label": 243, "func": ""}
{"index": "s437052458", "label": 243, "func": ""}
{"index": "s709260419", "label": 243, "func": ""}
{"index": "s228579983", "label": 243, "func": ""}
{"index": "s440138697", "label": 2205, "func": ""}
{"index": "s946500205", "label": 2205, "func": ""}
{"index": "s929467037", "label": 2205, "func": ""}
{"index": "s760866962", "label": 3173, "func": ""}
{"index": "s084023624", "label": 3173, "func": ""}
{"index": "s610791863", "label": 3173, "func": ""}
{"index": "s194841364", "label": 3173, "func": ""}
{"index": "s877481957", "label": 3173, "func": ""}
{"index": "s976750854", "label": 3173, "func": ""}
{"index": "s460758485", "label": 3173, "func": ""}
{"index": "s262297627", "label": 3173, "func": ""}
{"index": "s535500752", "label": 3173, "func": ""}
{"index": "s029449093", "label": 3173, "func": ""}
{"index": "s698401967", "label": 1093, "func": ""}
{"index": "s179136160", "label": 1093, "func": ""}
{"index": "s307026526", "label": 1093, "func": ""}
{"index": "s610151763", "label": 1093, "func": ""}
{"index": "s422543860", "label": 1093, "func": ""}
{"index": "s701037177", "label": 1093, "func": ""}
{"index": "s916176277", "label": 1093, "func": ""}
{"index": "s208642017", "label": 1093, "func": ""}
{"index": "s645530583", "label": 1093, "func": ""}
{"index": "s091657965", "label": 1093, "func": ""}
{"index": "s829483136", "label": 837, "func": ""}
{"index": "s646522962", "label": 837, "func": ""}
{"index": "s426941835", "label": 837, "func": ""}
{"index": "s638205549", "label": 207, "func": ""}
{"index": "s453968962", "label": 207, "func": ""}
{"index": "s601504246", "label": 207, "func": ""}
{"index": "s723428975", "label": 207, "func": ""}
{"index": "s648343506", "label": 207, "func": ""}
{"index": "s509552818", "label": 207, "func": ""}
{"index": "s583548512", "label": 207, "func": ""}
{"index": "s569763554", "label": 207, "func": ""}
{"index": "s162389051", "label": 207, "func": ""}
{"index": "s180292735", "label": 207, "func": ""}
{"index": "s724911857", "label": 2930, "func": ""}
{"index": "s891874790", "label": 2930, "func": ""}
{"index": "s823331755", "label": 2930, "func": ""}
{"index": "s892860847", "label": 2930, "func": ""}
{"index": "s348189050", "label": 2930, "func": ""}
{"index": "s913590081", "label": 2930, "func": ""}
{"index": "s311086850", "label": 2930, "func": ""}
{"index": "s613201262", "label": 2930, "func": ""}
{"index": "s994803591", "label": 2930, "func": ""}
{"index": "s353866689", "label": 2930, "func": ""}
{"index": "s047846219", "label": 2844, "func": ""}
{"index": "s401114631", "label": 2844, "func": ""}
{"index": "s438395174", "label": 2844, "func": ""}
{"index": "s054989245", "label": 2844, "func": ""}
{"index": "s414085321", "label": 2844, "func": ""}
{"index": "s849824734", "label": 2844, "func": ""}
{"index": "s183073017", "label": 2844, "func": ""}
{"index": "s446860437", "label": 2844, "func": ""}
{"index": "s495013031", "label": 2844, "func": ""}
{"index": "s282851074", "label": 2844, "func": ""}
{"index": "s153430091", "label": 1171, "func": ""}
{"index": "s682192205", "label": 1171, "func": ""}
{"index": "s858488098", "label": 1171, "func": ""}
{"index": "s095774503", "label": 1171, "func": ""}
{"index": "s665616757", "label": 1171, "func": ""}
{"index": "s312829827", "label": 1171, "func": ""}
{"index": "s552599125", "label": 1171, "func": ""}
{"index": "s046771475", "label": 3316, "func": ""}
{"index": "s727353759", "label": 3316, "func": ""}
{"index": "s054624846", "label": 3316, "func": ""}
{"index": "s337829054", "label": 3316, "func": ""}
{"index": "s871286498", "label": 3316, "func": ""}
{"index": "s161616930", "label": 3316, "func": ""}
{"index": "s192061011", "label": 3316, "func": ""}
{"index": "s160932152", "label": 3316, "func": ""}
{"index": "s933427800", "label": 3316, "func": ""}
{"index": "s103349313", "label": 3316, "func": ""}
{"index": "s758680064", "label": 1927, "func": ""}
{"index": "s088224197", "label": 3180, "func": ""}
{"index": "s493106909", "label": 3180, "func": ""}
{"index": "s083079706", "label": 3180, "func": ""}
{"index": "s684218547", "label": 3180, "func": ""}
{"index": "s298244924", "label": 3180, "func": ""}
{"index": "s458590282", "label": 3180, "func": ""}
{"index": "s403755158", "label": 3180, "func": ""}
{"index": "s074358852", "label": 3180, "func": ""}
{"index": "s426766121", "label": 3180, "func": ""}
{"index": "s379788432", "label": 3180, "func": ""}
{"index": "s329836986", "label": 367, "func": ""}
{"index": "s596913755", "label": 367, "func": ""}
{"index": "s798228919", "label": 367, "func": ""}
{"index": "s688239949", "label": 1104, "func": ""}
{"index": "s036807190", "label": 1104, "func": ""}
{"index": "s359248630", "label": 3788, "func": ""}
{"index": "s318523508", "label": 3788, "func": ""}
{"index": "s782057752", "label": 3788, "func": ""}
{"index": "s583067617", "label": 3788, "func": ""}
{"index": "s829249271", "label": 3788, "func": ""}
{"index": "s919938100", "label": 3788, "func": ""}
{"index": "s259523619", "label": 3008, "func": ""}
{"index": "s390288921", "label": 3008, "func": ""}
{"index": "s020127651", "label": 3008, "func": ""}
{"index": "s567735263", "label": 3008, "func": ""}
{"index": "s703187342", "label": 3008, "func": ""}
{"index": "s796496155", "label": 3008, "func": ""}
{"index": "s764050926", "label": 3008, "func": ""}
{"index": "s273206614", "label": 3008, "func": ""}
{"index": "s735512966", "label": 3008, "func": ""}
{"index": "s660684790", "label": 3008, "func": ""}
{"index": "s515563410", "label": 2315, "func": ""}
{"index": "s333270604", "label": 2315, "func": ""}
{"index": "s941539430", "label": 2315, "func": ""}
{"index": "s386832603", "label": 2315, "func": ""}
{"index": "s386559228", "label": 2315, "func": ""}
{"index": "s830022209", "label": 2315, "func": ""}
{"index": "s877604567", "label": 2315, "func": ""}
{"index": "s126844145", "label": 2315, "func": ""}
{"index": "s235273805", "label": 2315, "func": ""}
{"index": "s604417569", "label": 2315, "func": ""}
{"index": "s191790890", "label": 3619, "func": ""}
{"index": "s196089839", "label": 3619, "func": ""}
{"index": "s037370096", "label": 3619, "func": ""}
{"index": "s812368747", "label": 3619, "func": ""}
{"index": "s521758930", "label": 3619, "func": ""}
{"index": "s559077297", "label": 3619, "func": ""}
{"index": "s700623770", "label": 3619, "func": ""}
{"index": "s674117543", "label": 3619, "func": ""}
{"index": "s001256536", "label": 3619, "func": ""}
{"index": "s514966337", "label": 3619, "func": ""}
{"index": "s528558189", "label": 489, "func": ""}
{"index": "s618416751", "label": 489, "func": ""}
{"index": "s731059320", "label": 489, "func": ""}
{"index": "s639642081", "label": 489, "func": ""}
{"index": "s761834452", "label": 489, "func": ""}
{"index": "s889152367", "label": 489, "func": ""}
{"index": "s766568488", "label": 489, "func": ""}
{"index": "s989940604", "label": 489, "func": ""}
{"index": "s257859242", "label": 489, "func": ""}
{"index": "s041064394", "label": 489, "func": ""}
{"index": "s512964609", "label": 45, "func": ""}
{"index": "s410820620", "label": 45, "func": ""}
{"index": "s076102949", "label": 45, "func": ""}
{"index": "s268734790", "label": 45, "func": ""}
{"index": "s394642355", "label": 45, "func": ""}
{"index": "s152625684", "label": 45, "func": ""}
{"index": "s471989794", "label": 45, "func": ""}
{"index": "s004759327", "label": 45, "func": ""}
{"index": "s655006365", "label": 45, "func": ""}
{"index": "s150848988", "label": 45, "func": ""}
{"index": "s528904416", "label": 2690, "func": ""}
{"index": "s745585671", "label": 2690, "func": ""}
{"index": "s612770189", "label": 2690, "func": ""}
{"index": "s758481830", "label": 2690, "func": ""}
{"index": "s252410307", "label": 2690, "func": ""}
{"index": "s785517512", "label": 2690, "func": ""}
{"index": "s241886721", "label": 2690, "func": ""}
{"index": "s834682220", "label": 2690, "func": ""}
{"index": "s025311459", "label": 2690, "func": ""}
{"index": "s753525185", "label": 2690, "func": ""}
{"index": "s183798197", "label": 1113, "func": ""}
{"index": "s234121571", "label": 3879, "func": ""}
{"index": "s265672967", "label": 3879, "func": ""}
{"index": "s444482922", "label": 3879, "func": ""}
{"index": "s249448632", "label": 3879, "func": ""}
{"index": "s108942199", "label": 3879, "func": ""}
{"index": "s315421292", "label": 3250, "func": ""}
{"index": "s151300742", "label": 3250, "func": ""}
{"index": "s675815105", "label": 3250, "func": ""}
{"index": "s802645851", "label": 3250, "func": ""}
{"index": "s616685796", "label": 3250, "func": ""}
{"index": "s455479047", "label": 3250, "func": ""}
{"index": "s933103466", "label": 3250, "func": ""}
{"index": "s768110901", "label": 3250, "func": ""}
{"index": "s613326578", "label": 3250, "func": ""}
{"index": "s703892540", "label": 3250, "func": ""}
{"index": "s400487449", "label": 112, "func": ""}
{"index": "s457070344", "label": 112, "func": ""}
{"index": "s951133234", "label": 112, "func": ""}
{"index": "s633094032", "label": 112, "func": ""}
{"index": "s716218960", "label": 112, "func": ""}
{"index": "s100608957", "label": 112, "func": ""}
{"index": "s033579915", "label": 112, "func": ""}
{"index": "s994409778", "label": 112, "func": ""}
{"index": "s064076102", "label": 112, "func": ""}
{"index": "s252096427", "label": 112, "func": ""}
{"index": "s960199192", "label": 3523, "func": ""}
{"index": "s548141058", "label": 3523, "func": ""}
{"index": "s781340639", "label": 3523, "func": ""}
{"index": "s883319333", "label": 3523, "func": ""}
{"index": "s265423929", "label": 3523, "func": ""}
{"index": "s740635503", "label": 3523, "func": ""}
{"index": "s018362050", "label": 3523, "func": ""}
{"index": "s540299396", "label": 3523, "func": ""}
{"index": "s259205284", "label": 3523, "func": ""}
{"index": "s413264064", "label": 3523, "func": ""}
{"index": "s680815713", "label": 3741, "func": ""}
{"index": "s705185921", "label": 3741, "func": ""}
{"index": "s003037052", "label": 3741, "func": ""}
{"index": "s743727024", "label": 3741, "func": ""}
{"index": "s797278383", "label": 3741, "func": ""}
{"index": "s552982853", "label": 3741, "func": ""}
{"index": "s439274962", "label": 3741, "func": ""}
{"index": "s978206709", "label": 3741, "func": ""}
{"index": "s357705851", "label": 3741, "func": ""}
{"index": "s660718807", "label": 3741, "func": ""}
{"index": "s570403810", "label": 4012, "func": ""}
{"index": "s767456037", "label": 4012, "func": ""}
{"index": "s351531613", "label": 4012, "func": ""}
{"index": "s279246791", "label": 4012, "func": ""}
{"index": "s904585329", "label": 4012, "func": ""}
{"index": "s852190623", "label": 4012, "func": ""}
{"index": "s264277767", "label": 4012, "func": ""}
{"index": "s537011579", "label": 4012, "func": ""}
{"index": "s836219826", "label": 4012, "func": ""}
{"index": "s428153761", "label": 4012, "func": ""}
{"index": "s010519303", "label": 1989, "func": ""}
{"index": "s791637049", "label": 1989, "func": ""}
{"index": "s798541903", "label": 255, "func": ""}
{"index": "s715465315", "label": 255, "func": ""}
{"index": "s315158212", "label": 255, "func": ""}
{"index": "s942318867", "label": 255, "func": ""}
{"index": "s284000086", "label": 255, "func": ""}
{"index": "s166275168", "label": 255, "func": ""}
{"index": "s625405142", "label": 255, "func": ""}
{"index": "s017805430", "label": 255, "func": ""}
{"index": "s565497742", "label": 255, "func": ""}
{"index": "s392385153", "label": 255, "func": ""}
{"index": "s352696059", "label": 259, "func": ""}
{"index": "s737989417", "label": 259, "func": ""}
{"index": "s838176721", "label": 259, "func": ""}
{"index": "s382393005", "label": 259, "func": ""}
{"index": "s078734107", "label": 259, "func": ""}
{"index": "s262114728", "label": 3300, "func": ""}
{"index": "s667531638", "label": 1631, "func": ""}
{"index": "s250867935", "label": 1631, "func": ""}
{"index": "s195025659", "label": 1205, "func": ""}
{"index": "s406789541", "label": 3205, "func": ""}
{"index": "s261484708", "label": 1755, "func": ""}
{"index": "s543883693", "label": 1755, "func": ""}
{"index": "s899569946", "label": 721, "func": ""}
{"index": "s330118917", "label": 721, "func": ""}
{"index": "s494965094", "label": 721, "func": ""}
{"index": "s241865103", "label": 721, "func": ""}
{"index": "s224934842", "label": 721, "func": ""}
{"index": "s123329763", "label": 721, "func": ""}
{"index": "s080678984", "label": 721, "func": ""}
{"index": "s029734916", "label": 721, "func": ""}
{"index": "s376745505", "label": 721, "func": ""}
{"index": "s207978740", "label": 721, "func": ""}
{"index": "s460411835", "label": 2246, "func": ""}
{"index": "s755605970", "label": 2246, "func": ""}
{"index": "s250879105", "label": 2246, "func": ""}
{"index": "s234525574", "label": 2246, "func": ""}
{"index": "s712084011", "label": 2246, "func": ""}
{"index": "s670738822", "label": 2246, "func": ""}
{"index": "s691695608", "label": 2246, "func": ""}
{"index": "s637628654", "label": 2246, "func": ""}
{"index": "s052880128", "label": 2246, "func": ""}
{"index": "s573631456", "label": 2246, "func": ""}
{"index": "s622518889", "label": 3941, "func": ""}
{"index": "s190334899", "label": 1725, "func": ""}
{"index": "s937976570", "label": 1725, "func": ""}
{"index": "s245000448", "label": 3582, "func": ""}
{"index": "s304775316", "label": 3582, "func": ""}
{"index": "s246452107", "label": 2720, "func": ""}
{"index": "s171801809", "label": 2720, "func": ""}
{"index": "s147294006", "label": 2720, "func": ""}
{"index": "s167609207", "label": 2720, "func": ""}
{"index": "s902901720", "label": 2720, "func": ""}
{"index": "s193523369", "label": 2720, "func": ""}
{"index": "s568974179", "label": 2720, "func": ""}
{"index": "s547032002", "label": 2720, "func": ""}
{"index": "s232969063", "label": 2720, "func": ""}
{"index": "s377472734", "label": 2720, "func": ""}
{"index": "s336301229", "label": 3183, "func": ""}
{"index": "s264401006", "label": 3183, "func": ""}
{"index": "s200982343", "label": 3183, "func": ""}
{"index": "s391724982", "label": 3183, "func": ""}
{"index": "s133269991", "label": 3183, "func": ""}
{"index": "s216675066", "label": 3183, "func": ""}
{"index": "s771691101", "label": 3183, "func": ""}
{"index": "s294097269", "label": 3183, "func": ""}
{"index": "s881080480", "label": 3183, "func": ""}
{"index": "s724047719", "label": 3183, "func": ""}
{"index": "s507702184", "label": 3466, "func": ""}
{"index": "s218289152", "label": 3466, "func": ""}
{"index": "s008169158", "label": 3466, "func": ""}
{"index": "s828784507", "label": 2052, "func": ""}
{"index": "s030489886", "label": 2052, "func": ""}
{"index": "s259025137", "label": 2052, "func": ""}
{"index": "s459333943", "label": 440, "func": ""}
{"index": "s671187280", "label": 440, "func": ""}
{"index": "s474248261", "label": 440, "func": ""}
{"index": "s108420142", "label": 440, "func": ""}
{"index": "s027928562", "label": 440, "func": ""}
{"index": "s277993648", "label": 440, "func": ""}
{"index": "s764339271", "label": 440, "func": ""}
{"index": "s042093223", "label": 440, "func": ""}
{"index": "s766096250", "label": 440, "func": ""}
{"index": "s158152025", "label": 440, "func": ""}
{"index": "s557636689", "label": 1303, "func": ""}
{"index": "s882297203", "label": 1303, "func": ""}
{"index": "s954558596", "label": 1303, "func": ""}
{"index": "s730723888", "label": 1303, "func": ""}
{"index": "s370617924", "label": 1303, "func": ""}
{"index": "s523930317", "label": 1303, "func": ""}
{"index": "s369455843", "label": 1303, "func": ""}
{"index": "s836456150", "label": 1303, "func": ""}
{"index": "s181300887", "label": 1303, "func": ""}
{"index": "s867288721", "label": 1303, "func": ""}
{"index": "s100231274", "label": 96, "func": ""}
{"index": "s915045950", "label": 96, "func": ""}
{"index": "s498856051", "label": 96, "func": ""}
{"index": "s089985765", "label": 96, "func": ""}
{"index": "s970121644", "label": 96, "func": ""}
{"index": "s736770688", "label": 96, "func": ""}
{"index": "s802130948", "label": 96, "func": ""}
{"index": "s128363906", "label": 96, "func": ""}
{"index": "s502866278", "label": 96, "func": ""}
{"index": "s018169178", "label": 96, "func": ""}
{"index": "s018783674", "label": 3717, "func": ""}
{"index": "s369349347", "label": 3717, "func": ""}
{"index": "s591190350", "label": 3717, "func": ""}
{"index": "s520055794", "label": 3717, "func": ""}
{"index": "s006112065", "label": 3717, "func": ""}
{"index": "s374425497", "label": 3717, "func": ""}
{"index": "s604312698", "label": 3717, "func": ""}
{"index": "s349397465", "label": 490, "func": ""}
{"index": "s597246103", "label": 490, "func": ""}
{"index": "s530676865", "label": 490, "func": ""}
{"index": "s575344551", "label": 490, "func": ""}
{"index": "s640356075", "label": 490, "func": ""}
{"index": "s413561969", "label": 490, "func": ""}
{"index": "s687716279", "label": 490, "func": ""}
{"index": "s397489991", "label": 490, "func": ""}
{"index": "s450800324", "label": 490, "func": ""}
{"index": "s183865713", "label": 490, "func": ""}
{"index": "s939877705", "label": 2564, "func": ""}
{"index": "s702441537", "label": 2564, "func": ""}
{"index": "s290356129", "label": 2564, "func": ""}
{"index": "s674296823", "label": 2564, "func": ""}
{"index": "s634851182", "label": 2564, "func": ""}
{"index": "s815585161", "label": 2564, "func": ""}
{"index": "s979760981", "label": 2564, "func": ""}
{"index": "s885969072", "label": 2564, "func": ""}
{"index": "s015482396", "label": 2564, "func": ""}
{"index": "s359720409", "label": 2564, "func": ""}
{"index": "s900196267", "label": 2305, "func": ""}
{"index": "s934545228", "label": 2305, "func": ""}
{"index": "s720458173", "label": 2305, "func": ""}
{"index": "s981459477", "label": 2305, "func": ""}
{"index": "s783446971", "label": 2305, "func": ""}
{"index": "s441157187", "label": 2305, "func": ""}
{"index": "s915924306", "label": 2305, "func": ""}
{"index": "s937880612", "label": 2305, "func": ""}
{"index": "s889873926", "label": 2305, "func": ""}
{"index": "s840480484", "label": 2305, "func": ""}
{"index": "s694832516", "label": 2451, "func": ""}
{"index": "s111447817", "label": 2451, "func": ""}
{"index": "s654165648", "label": 2451, "func": ""}
{"index": "s056649897", "label": 2451, "func": ""}
{"index": "s520914840", "label": 2451, "func": ""}
{"index": "s517829184", "label": 2451, "func": ""}
{"index": "s165775290", "label": 2451, "func": ""}
{"index": "s719728316", "label": 2451, "func": ""}
{"index": "s090889632", "label": 2451, "func": ""}
{"index": "s656237168", "label": 2451, "func": ""}
{"index": "s027398014", "label": 3937, "func": ""}
{"index": "s071140591", "label": 3937, "func": ""}
{"index": "s790737551", "label": 3937, "func": ""}
{"index": "s923733819", "label": 3937, "func": ""}
{"index": "s931350808", "label": 3937, "func": ""}
{"index": "s400830597", "label": 3937, "func": ""}
{"index": "s364470943", "label": 3937, "func": ""}
{"index": "s183179944", "label": 3937, "func": ""}
{"index": "s541805279", "label": 3937, "func": ""}
{"index": "s126228188", "label": 3937, "func": ""}
{"index": "s996990690", "label": 2383, "func": ""}
{"index": "s729528810", "label": 2383, "func": ""}
{"index": "s468173492", "label": 2383, "func": ""}
{"index": "s310926261", "label": 2383, "func": ""}
{"index": "s849391665", "label": 2383, "func": ""}
{"index": "s206516850", "label": 2383, "func": ""}
{"index": "s993845056", "label": 2383, "func": ""}
{"index": "s091413834", "label": 2383, "func": ""}
{"index": "s255881130", "label": 2383, "func": ""}
{"index": "s435044653", "label": 2383, "func": ""}
{"index": "s587223675", "label": 2369, "func": ""}
{"index": "s331846510", "label": 2369, "func": ""}
{"index": "s574581513", "label": 2369, "func": ""}
{"index": "s882929985", "label": 2369, "func": ""}
{"index": "s891392605", "label": 2369, "func": ""}
{"index": "s763044107", "label": 2369, "func": ""}
{"index": "s693493302", "label": 2369, "func": ""}
{"index": "s630697007", "label": 2369, "func": ""}
{"index": "s164523550", "label": 2369, "func": ""}
{"index": "s989596628", "label": 2369, "func": ""}
{"index": "s562550683", "label": 2428, "func": ""}
{"index": "s238809384", "label": 2428, "func": ""}
{"index": "s490952058", "label": 2428, "func": ""}
{"index": "s011004973", "label": 2428, "func": ""}
{"index": "s559571907", "label": 2428, "func": ""}
{"index": "s084390592", "label": 2428, "func": ""}
{"index": "s248684082", "label": 2428, "func": ""}
{"index": "s351681099", "label": 2428, "func": ""}
{"index": "s217347251", "label": 2428, "func": ""}
{"index": "s371470308", "label": 60, "func": ""}
{"index": "s178598857", "label": 60, "func": ""}
{"index": "s522664718", "label": 60, "func": ""}
{"index": "s039112774", "label": 60, "func": ""}
{"index": "s951263407", "label": 60, "func": ""}
{"index": "s251660633", "label": 60, "func": ""}
{"index": "s485535026", "label": 60, "func": ""}
{"index": "s923047800", "label": 60, "func": ""}
{"index": "s537966411", "label": 60, "func": ""}
{"index": "s566268331", "label": 60, "func": ""}
{"index": "s992206984", "label": 2294, "func": ""}
{"index": "s854526228", "label": 2294, "func": ""}
{"index": "s549006639", "label": 2294, "func": ""}
{"index": "s058915520", "label": 2294, "func": ""}
{"index": "s819515982", "label": 2294, "func": ""}
{"index": "s821745459", "label": 2294, "func": ""}
{"index": "s674382821", "label": 2294, "func": ""}
{"index": "s049789612", "label": 2294, "func": ""}
{"index": "s865930559", "label": 2294, "func": ""}
{"index": "s563422240", "label": 2294, "func": ""}
{"index": "s925147095", "label": 1526, "func": ""}
{"index": "s264767862", "label": 1526, "func": ""}
{"index": "s338804938", "label": 1526, "func": ""}
{"index": "s683785294", "label": 1526, "func": ""}
{"index": "s243113950", "label": 1526, "func": ""}
{"index": "s253788266", "label": 1526, "func": ""}
{"index": "s925477545", "label": 1526, "func": ""}
{"index": "s617410908", "label": 2913, "func": ""}
{"index": "s374480838", "label": 2913, "func": ""}
{"index": "s280232006", "label": 2913, "func": ""}
{"index": "s851057405", "label": 2913, "func": ""}
{"index": "s080782335", "label": 2913, "func": ""}
{"index": "s126624192", "label": 2913, "func": ""}
{"index": "s225696092", "label": 2913, "func": ""}
{"index": "s232153087", "label": 2913, "func": ""}
{"index": "s323742421", "label": 2913, "func": ""}
{"index": "s383733936", "label": 2913, "func": ""}
{"index": "s643640935", "label": 2855, "func": ""}
{"index": "s848522525", "label": 2855, "func": ""}
{"index": "s200132717", "label": 2855, "func": ""}
{"index": "s798564918", "label": 2855, "func": ""}
{"index": "s096463006", "label": 2855, "func": ""}
{"index": "s544352623", "label": 2855, "func": ""}
{"index": "s205899189", "label": 2855, "func": ""}
{"index": "s315952528", "label": 2855, "func": ""}
{"index": "s665516955", "label": 2855, "func": ""}
{"index": "s455459004", "label": 2855, "func": ""}
{"index": "s120853301", "label": 2911, "func": ""}
{"index": "s028994536", "label": 2911, "func": ""}
{"index": "s930388496", "label": 2911, "func": ""}
{"index": "s781956060", "label": 2911, "func": ""}
{"index": "s762146054", "label": 2911, "func": ""}
{"index": "s868527738", "label": 2911, "func": ""}
{"index": "s093959878", "label": 2911, "func": ""}
{"index": "s314406369", "label": 2911, "func": ""}
{"index": "s643201487", "label": 2911, "func": ""}
{"index": "s012524652", "label": 2911, "func": ""}
{"index": "s996963410", "label": 3698, "func": ""}
{"index": "s664556739", "label": 3698, "func": ""}
{"index": "s140467696", "label": 3698, "func": ""}
{"index": "s745425668", "label": 3698, "func": ""}
{"index": "s957263315", "label": 3698, "func": ""}
{"index": "s999257206", "label": 3698, "func": ""}
{"index": "s856348333", "label": 3698, "func": ""}
{"index": "s463437543", "label": 3698, "func": ""}
{"index": "s565971239", "label": 3698, "func": ""}
{"index": "s966647998", "label": 3698, "func": ""}
{"index": "s865517121", "label": 1414, "func": ""}
{"index": "s561814202", "label": 1414, "func": ""}
{"index": "s614974715", "label": 1414, "func": ""}
{"index": "s834464173", "label": 1461, "func": ""}
{"index": "s114324862", "label": 2470, "func": ""}
{"index": "s892353646", "label": 2470, "func": ""}
{"index": "s693904669", "label": 2470, "func": ""}
{"index": "s024168998", "label": 2470, "func": ""}
{"index": "s430630457", "label": 2470, "func": ""}
{"index": "s768797690", "label": 2470, "func": ""}
{"index": "s621281605", "label": 2470, "func": ""}
{"index": "s606976335", "label": 2470, "func": ""}
{"index": "s748084281", "label": 2470, "func": ""}
{"index": "s951159044", "label": 2470, "func": ""}
{"index": "s981491756", "label": 2122, "func": ""}
{"index": "s034107742", "label": 2640, "func": ""}
{"index": "s986107150", "label": 2640, "func": ""}
{"index": "s900982940", "label": 2640, "func": ""}
{"index": "s804478057", "label": 2640, "func": ""}
{"index": "s564068679", "label": 2640, "func": ""}
{"index": "s755656242", "label": 2640, "func": ""}
{"index": "s577962733", "label": 2640, "func": ""}
{"index": "s997230351", "label": 2640, "func": ""}
{"index": "s782300462", "label": 2640, "func": ""}
{"index": "s309215219", "label": 2640, "func": ""}
{"index": "s775684630", "label": 1402, "func": ""}
{"index": "s138000191", "label": 1402, "func": ""}
{"index": "s351100530", "label": 3932, "func": ""}
{"index": "s876920834", "label": 3932, "func": ""}
{"index": "s004280554", "label": 3270, "func": ""}
{"index": "s056197864", "label": 3270, "func": ""}
{"index": "s175620072", "label": 3270, "func": ""}
{"index": "s231777276", "label": 3270, "func": ""}
{"index": "s723205220", "label": 3270, "func": ""}
{"index": "s236869268", "label": 3270, "func": ""}
{"index": "s447675678", "label": 3270, "func": ""}
{"index": "s018695302", "label": 3270, "func": ""}
{"index": "s185127645", "label": 3270, "func": ""}
{"index": "s115560068", "label": 3270, "func": ""}
{"index": "s302147798", "label": 3197, "func": ""}
{"index": "s351513291", "label": 3197, "func": ""}
{"index": "s481699206", "label": 3197, "func": ""}
{"index": "s314666281", "label": 3197, "func": ""}
{"index": "s921300306", "label": 3197, "func": ""}
{"index": "s637551871", "label": 3197, "func": ""}
{"index": "s411358121", "label": 3197, "func": ""}
{"index": "s020788559", "label": 3197, "func": ""}
{"index": "s479567121", "label": 3197, "func": ""}
{"index": "s184850098", "label": 3197, "func": ""}
{"index": "s662107703", "label": 4006, "func": ""}
{"index": "s729822821", "label": 4006, "func": ""}
{"index": "s277484383", "label": 4006, "func": ""}
{"index": "s517446727", "label": 4006, "func": ""}
{"index": "s976996639", "label": 4006, "func": ""}
{"index": "s080434706", "label": 4006, "func": ""}
{"index": "s501723977", "label": 4006, "func": ""}
{"index": "s325289884", "label": 4006, "func": ""}
{"index": "s575554375", "label": 4006, "func": ""}
{"index": "s177324922", "label": 4006, "func": ""}
{"index": "s013268915", "label": 435, "func": ""}
{"index": "s770686554", "label": 435, "func": ""}
{"index": "s179001711", "label": 435, "func": ""}
{"index": "s909647927", "label": 435, "func": ""}
{"index": "s683203680", "label": 435, "func": ""}
{"index": "s135629402", "label": 435, "func": ""}
{"index": "s239138377", "label": 435, "func": ""}
{"index": "s073500549", "label": 435, "func": ""}
{"index": "s601536892", "label": 435, "func": ""}
{"index": "s479230101", "label": 435, "func": ""}
{"index": "s315195984", "label": 71, "func": ""}
{"index": "s101553924", "label": 71, "func": ""}
{"index": "s665033827", "label": 71, "func": ""}
{"index": "s977259481", "label": 71, "func": ""}
{"index": "s323228320", "label": 71, "func": ""}
{"index": "s646582833", "label": 71, "func": ""}
{"index": "s740445559", "label": 71, "func": ""}
{"index": "s256207406", "label": 71, "func": ""}
{"index": "s849787501", "label": 71, "func": ""}
{"index": "s547760791", "label": 71, "func": ""}
{"index": "s221319306", "label": 2224, "func": ""}
{"index": "s531472852", "label": 2224, "func": ""}
{"index": "s408525226", "label": 2975, "func": ""}
{"index": "s383053913", "label": 2975, "func": ""}
{"index": "s384475359", "label": 2975, "func": ""}
{"index": "s890136371", "label": 2975, "func": ""}
{"index": "s054489555", "label": 2975, "func": ""}
{"index": "s874108019", "label": 2975, "func": ""}
{"index": "s746239974", "label": 2975, "func": ""}
{"index": "s973846537", "label": 2975, "func": ""}
{"index": "s870573525", "label": 2975, "func": ""}
{"index": "s030809309", "label": 2975, "func": ""}
{"index": "s832558699", "label": 3408, "func": ""}
{"index": "s899055921", "label": 3408, "func": ""}
{"index": "s810826162", "label": 3408, "func": ""}
{"index": "s645139990", "label": 3408, "func": ""}
{"index": "s921084612", "label": 3408, "func": ""}
{"index": "s135389872", "label": 3408, "func": ""}
{"index": "s057106349", "label": 3408, "func": ""}
{"index": "s719561152", "label": 3408, "func": ""}
{"index": "s343890233", "label": 3408, "func": ""}
{"index": "s756098848", "label": 3408, "func": ""}
{"index": "s624929293", "label": 1606, "func": ""}
{"index": "s588406294", "label": 1606, "func": ""}
{"index": "s899625334", "label": 1606, "func": ""}
{"index": "s944766574", "label": 1606, "func": ""}
{"index": "s193852681", "label": 1606, "func": ""}
{"index": "s313304837", "label": 1992, "func": ""}
{"index": "s113287479", "label": 2710, "func": ""}
{"index": "s742075430", "label": 2710, "func": ""}
{"index": "s853252171", "label": 2710, "func": ""}
{"index": "s478230954", "label": 2710, "func": ""}
{"index": "s719381769", "label": 2710, "func": ""}
{"index": "s938484816", "label": 2710, "func": ""}
{"index": "s639915948", "label": 2710, "func": ""}
{"index": "s661599395", "label": 2710, "func": ""}
{"index": "s684447216", "label": 2710, "func": ""}
{"index": "s366102882", "label": 2710, "func": ""}
{"index": "s592185944", "label": 795, "func": ""}
{"index": "s639927209", "label": 795, "func": ""}
{"index": "s761778757", "label": 795, "func": ""}
{"index": "s591068632", "label": 795, "func": ""}
{"index": "s866401975", "label": 646, "func": ""}
{"index": "s931617228", "label": 646, "func": ""}
{"index": "s874746593", "label": 646, "func": ""}
{"index": "s672315856", "label": 646, "func": ""}
{"index": "s739822882", "label": 1281, "func": ""}
{"index": "s512103020", "label": 890, "func": ""}
{"index": "s581155531", "label": 890, "func": ""}
{"index": "s920465408", "label": 890, "func": ""}
{"index": "s067315658", "label": 890, "func": ""}
{"index": "s922152071", "label": 890, "func": ""}
{"index": "s988321719", "label": 890, "func": ""}
{"index": "s087435663", "label": 2939, "func": ""}
{"index": "s102615964", "label": 2939, "func": ""}
{"index": "s839921776", "label": 2939, "func": ""}
{"index": "s625540237", "label": 2939, "func": ""}
{"index": "s811420461", "label": 2939, "func": ""}
{"index": "s632714982", "label": 2939, "func": ""}
{"index": "s441922468", "label": 2939, "func": ""}
{"index": "s975172909", "label": 2939, "func": ""}
{"index": "s013497859", "label": 2939, "func": ""}
{"index": "s727846380", "label": 2939, "func": ""}
{"index": "s256016480", "label": 113, "func": ""}
{"index": "s340562830", "label": 113, "func": ""}
{"index": "s417942270", "label": 113, "func": ""}
{"index": "s473931726", "label": 113, "func": ""}
{"index": "s797262620", "label": 113, "func": ""}
{"index": "s387288806", "label": 113, "func": ""}
{"index": "s472453957", "label": 113, "func": ""}
{"index": "s757324073", "label": 113, "func": ""}
{"index": "s651097829", "label": 113, "func": ""}
{"index": "s307206403", "label": 113, "func": ""}
{"index": "s809001327", "label": 3133, "func": ""}
{"index": "s053047999", "label": 3133, "func": ""}
{"index": "s936973060", "label": 3133, "func": ""}
{"index": "s585466646", "label": 3133, "func": ""}
{"index": "s485868854", "label": 3133, "func": ""}
{"index": "s906539827", "label": 3133, "func": ""}
{"index": "s491728956", "label": 3133, "func": ""}
{"index": "s489131726", "label": 3133, "func": ""}
{"index": "s154342442", "label": 3133, "func": ""}
{"index": "s148881909", "label": 3133, "func": ""}
{"index": "s973828716", "label": 3880, "func": ""}
{"index": "s659129370", "label": 3880, "func": ""}
{"index": "s712531178", "label": 3880, "func": ""}
{"index": "s142367943", "label": 3880, "func": ""}
{"index": "s092106551", "label": 3880, "func": ""}
{"index": "s414709486", "label": 3880, "func": ""}
{"index": "s433012066", "label": 743, "func": ""}
{"index": "s392679166", "label": 743, "func": ""}
{"index": "s883862650", "label": 743, "func": ""}
{"index": "s233106393", "label": 743, "func": ""}
{"index": "s988484045", "label": 743, "func": ""}
{"index": "s279258660", "label": 743, "func": ""}
{"index": "s106539967", "label": 743, "func": ""}
{"index": "s033358456", "label": 743, "func": ""}
{"index": "s598183861", "label": 743, "func": ""}
{"index": "s399999523", "label": 743, "func": ""}
{"index": "s482961443", "label": 3046, "func": ""}
{"index": "s670883517", "label": 3046, "func": ""}
{"index": "s919794910", "label": 3046, "func": ""}
{"index": "s316999118", "label": 3046, "func": ""}
{"index": "s379489982", "label": 3046, "func": ""}
{"index": "s071027288", "label": 3046, "func": ""}
{"index": "s216212247", "label": 3046, "func": ""}
{"index": "s340483078", "label": 3046, "func": ""}
{"index": "s224950093", "label": 3046, "func": ""}
{"index": "s484212361", "label": 3046, "func": ""}
{"index": "s171529537", "label": 425, "func": ""}
{"index": "s709454180", "label": 425, "func": ""}
{"index": "s429695362", "label": 425, "func": ""}
{"index": "s036783881", "label": 425, "func": ""}
{"index": "s996490168", "label": 425, "func": ""}
{"index": "s330911426", "label": 425, "func": ""}
{"index": "s563492573", "label": 425, "func": ""}
{"index": "s459742871", "label": 425, "func": ""}
{"index": "s442483521", "label": 425, "func": ""}
{"index": "s755090304", "label": 425, "func": ""}
{"index": "s146701325", "label": 3318, "func": ""}
{"index": "s062784783", "label": 3318, "func": ""}
{"index": "s258897881", "label": 3318, "func": ""}
{"index": "s539285290", "label": 3318, "func": ""}
{"index": "s398225225", "label": 3318, "func": ""}
{"index": "s978838614", "label": 3318, "func": ""}
{"index": "s584497875", "label": 3318, "func": ""}
{"index": "s952654562", "label": 3318, "func": ""}
{"index": "s613305742", "label": 3318, "func": ""}
{"index": "s724272927", "label": 3318, "func": ""}
{"index": "s737603246", "label": 2989, "func": ""}
{"index": "s745482371", "label": 2989, "func": ""}
{"index": "s141225777", "label": 2989, "func": ""}
{"index": "s507152597", "label": 2989, "func": ""}
{"index": "s844332045", "label": 2989, "func": ""}
{"index": "s177369595", "label": 2989, "func": ""}
{"index": "s142729812", "label": 2989, "func": ""}
{"index": "s162318348", "label": 2989, "func": ""}
{"index": "s021467693", "label": 2989, "func": ""}
{"index": "s594739136", "label": 2989, "func": ""}
{"index": "s687161137", "label": 2955, "func": ""}
{"index": "s142619646", "label": 2955, "func": ""}
{"index": "s650950908", "label": 2955, "func": ""}
{"index": "s101856213", "label": 2955, "func": ""}
{"index": "s168669184", "label": 2955, "func": ""}
{"index": "s784962737", "label": 2955, "func": ""}
{"index": "s785765852", "label": 2955, "func": ""}
{"index": "s503201593", "label": 2955, "func": ""}
{"index": "s490584731", "label": 2955, "func": ""}
{"index": "s518498054", "label": 2955, "func": ""}
{"index": "s930902002", "label": 2359, "func": ""}
{"index": "s823174424", "label": 2359, "func": ""}
{"index": "s946526650", "label": 2359, "func": ""}
{"index": "s973505874", "label": 2359, "func": ""}
{"index": "s094331005", "label": 2359, "func": ""}
{"index": "s243355109", "label": 2359, "func": ""}
{"index": "s576710795", "label": 2359, "func": ""}
{"index": "s134069249", "label": 2359, "func": ""}
{"index": "s641067219", "label": 2359, "func": ""}
{"index": "s533090211", "label": 2359, "func": ""}
{"index": "s573225720", "label": 2910, "func": ""}
{"index": "s277248861", "label": 2910, "func": ""}
{"index": "s270031214", "label": 2910, "func": ""}
{"index": "s192488778", "label": 2910, "func": ""}
{"index": "s229903714", "label": 2910, "func": ""}
{"index": "s377135837", "label": 2910, "func": ""}
{"index": "s943802641", "label": 2910, "func": ""}
{"index": "s226120512", "label": 2910, "func": ""}
{"index": "s400867330", "label": 2910, "func": ""}
{"index": "s514604196", "label": 2910, "func": ""}
{"index": "s580915572", "label": 3613, "func": ""}
{"index": "s131525469", "label": 3613, "func": ""}
{"index": "s537883091", "label": 3613, "func": ""}
{"index": "s562242705", "label": 3613, "func": ""}
{"index": "s598512539", "label": 3613, "func": ""}
{"index": "s122631825", "label": 3613, "func": ""}
{"index": "s861885323", "label": 3613, "func": ""}
{"index": "s018915433", "label": 3613, "func": ""}
{"index": "s798288529", "label": 3613, "func": ""}
{"index": "s386160615", "label": 3613, "func": ""}
{"index": "s439175768", "label": 360, "func": ""}
{"index": "s318267861", "label": 647, "func": ""}
{"index": "s218280707", "label": 647, "func": ""}
{"index": "s193546010", "label": 647, "func": ""}
{"index": "s233360196", "label": 647, "func": ""}
{"index": "s640212575", "label": 647, "func": ""}
{"index": "s214711892", "label": 647, "func": ""}
{"index": "s605110302", "label": 647, "func": ""}
{"index": "s268472432", "label": 647, "func": ""}
{"index": "s380243115", "label": 3208, "func": ""}
{"index": "s114069557", "label": 3208, "func": ""}
{"index": "s326766229", "label": 3208, "func": ""}
{"index": "s544975657", "label": 3208, "func": ""}
{"index": "s475686132", "label": 3208, "func": ""}
{"index": "s740812981", "label": 3208, "func": ""}
{"index": "s751270714", "label": 3208, "func": ""}
{"index": "s944022499", "label": 3208, "func": ""}
{"index": "s403213815", "label": 3208, "func": ""}
{"index": "s688507911", "label": 3208, "func": ""}
{"index": "s090220202", "label": 3726, "func": ""}
{"index": "s434034379", "label": 3726, "func": ""}
{"index": "s781367822", "label": 3726, "func": ""}
{"index": "s668892800", "label": 3726, "func": ""}
{"index": "s560270582", "label": 3726, "func": ""}
{"index": "s861535981", "label": 3726, "func": ""}
{"index": "s884336678", "label": 3726, "func": ""}
{"index": "s522358596", "label": 3726, "func": ""}
{"index": "s775106780", "label": 3726, "func": ""}
{"index": "s168672686", "label": 3726, "func": ""}
{"index": "s655629946", "label": 110, "func": ""}
{"index": "s169631338", "label": 110, "func": ""}
{"index": "s467579731", "label": 110, "func": ""}
{"index": "s097306672", "label": 110, "func": ""}
{"index": "s536706518", "label": 110, "func": ""}
{"index": "s688618970", "label": 110, "func": ""}
{"index": "s289455533", "label": 110, "func": ""}
{"index": "s452404837", "label": 110, "func": ""}
{"index": "s372680522", "label": 110, "func": ""}
{"index": "s665473711", "label": 110, "func": ""}
{"index": "s419674037", "label": 364, "func": ""}
{"index": "s246900377", "label": 364, "func": ""}
{"index": "s182136359", "label": 364, "func": ""}
{"index": "s987180358", "label": 364, "func": ""}
{"index": "s961108122", "label": 364, "func": ""}
{"index": "s333990071", "label": 1866, "func": ""}
{"index": "s041796526", "label": 1866, "func": ""}
{"index": "s635826335", "label": 1866, "func": ""}
{"index": "s174171084", "label": 1866, "func": ""}
{"index": "s835223909", "label": 1866, "func": ""}
{"index": "s408955071", "label": 1866, "func": ""}
{"index": "s025777599", "label": 1866, "func": ""}
{"index": "s939944217", "label": 2449, "func": ""}
{"index": "s367760044", "label": 2449, "func": ""}
{"index": "s637751720", "label": 2449, "func": ""}
{"index": "s707932100", "label": 2449, "func": ""}
{"index": "s150660271", "label": 2449, "func": ""}
{"index": "s992034443", "label": 2449, "func": ""}
{"index": "s226208352", "label": 2449, "func": ""}
{"index": "s562189319", "label": 2449, "func": ""}
{"index": "s131330760", "label": 2449, "func": ""}
{"index": "s381800673", "label": 2449, "func": ""}
{"index": "s104305669", "label": 3244, "func": ""}
{"index": "s867312796", "label": 3244, "func": ""}
{"index": "s726776529", "label": 3244, "func": ""}
{"index": "s476873355", "label": 3244, "func": ""}
{"index": "s742052960", "label": 3244, "func": ""}
{"index": "s271391011", "label": 3244, "func": ""}
{"index": "s454716707", "label": 3244, "func": ""}
{"index": "s939708134", "label": 3244, "func": ""}
{"index": "s985016560", "label": 3244, "func": ""}
{"index": "s486437401", "label": 3244, "func": ""}
{"index": "s927665639", "label": 1711, "func": ""}
{"index": "s372936144", "label": 1711, "func": ""}
{"index": "s408593554", "label": 161, "func": ""}
{"index": "s385730233", "label": 161, "func": ""}
{"index": "s796444077", "label": 161, "func": ""}
{"index": "s169364435", "label": 161, "func": ""}
{"index": "s752195775", "label": 161, "func": ""}
{"index": "s959766925", "label": 161, "func": ""}
{"index": "s450645148", "label": 161, "func": ""}
{"index": "s176208987", "label": 161, "func": ""}
{"index": "s787728971", "label": 161, "func": ""}
{"index": "s880935816", "label": 161, "func": ""}
{"index": "s480260093", "label": 936, "func": ""}
{"index": "s128945402", "label": 936, "func": ""}
{"index": "s165545459", "label": 936, "func": ""}
{"index": "s771464294", "label": 936, "func": ""}
{"index": "s443236584", "label": 936, "func": ""}
{"index": "s307305321", "label": 936, "func": ""}
{"index": "s722594357", "label": 4005, "func": ""}
{"index": "s332282582", "label": 4005, "func": ""}
{"index": "s328663091", "label": 4005, "func": ""}
{"index": "s356270473", "label": 4005, "func": ""}
{"index": "s125591876", "label": 4005, "func": ""}
{"index": "s081616546", "label": 4005, "func": ""}
{"index": "s589766415", "label": 4005, "func": ""}
{"index": "s809847687", "label": 4005, "func": ""}
{"index": "s254886226", "label": 4005, "func": ""}
{"index": "s134842004", "label": 4005, "func": ""}
{"index": "s212838633", "label": 1353, "func": ""}
{"index": "s830971148", "label": 1353, "func": ""}
{"index": "s798923889", "label": 1353, "func": ""}
{"index": "s654988884", "label": 1353, "func": ""}
{"index": "s099461995", "label": 1353, "func": ""}
{"index": "s152175506", "label": 1352, "func": ""}
{"index": "s470341880", "label": 1352, "func": ""}
{"index": "s687975981", "label": 3581, "func": ""}
{"index": "s314299287", "label": 2645, "func": ""}
{"index": "s277397002", "label": 2645, "func": ""}
{"index": "s162143504", "label": 2645, "func": ""}
{"index": "s291735853", "label": 2645, "func": ""}
{"index": "s749402102", "label": 2645, "func": ""}
{"index": "s436776551", "label": 2645, "func": ""}
{"index": "s964944612", "label": 2645, "func": ""}
{"index": "s689600087", "label": 2645, "func": ""}
{"index": "s014912776", "label": 2645, "func": ""}
{"index": "s947128179", "label": 2645, "func": ""}
{"index": "s998287799", "label": 2206, "func": ""}
{"index": "s289152949", "label": 2977, "func": ""}
{"index": "s313057601", "label": 2977, "func": ""}
{"index": "s314862748", "label": 2977, "func": ""}
{"index": "s094646507", "label": 2977, "func": ""}
{"index": "s535426869", "label": 2977, "func": ""}
{"index": "s219245131", "label": 2977, "func": ""}
{"index": "s019839091", "label": 2977, "func": ""}
{"index": "s620336126", "label": 2977, "func": ""}
{"index": "s468761208", "label": 2977, "func": ""}
{"index": "s765824470", "label": 3212, "func": ""}
{"index": "s540274597", "label": 3212, "func": ""}
{"index": "s691489326", "label": 3212, "func": ""}
{"index": "s499443427", "label": 3212, "func": ""}
{"index": "s335490151", "label": 3212, "func": ""}
{"index": "s253200480", "label": 3212, "func": ""}
{"index": "s254389720", "label": 3212, "func": ""}
{"index": "s707383711", "label": 3212, "func": ""}
{"index": "s516154706", "label": 3212, "func": ""}
{"index": "s972330904", "label": 3212, "func": ""}
{"index": "s008658774", "label": 2698, "func": ""}
{"index": "s213794789", "label": 2698, "func": ""}
{"index": "s877825744", "label": 2698, "func": ""}
{"index": "s039562983", "label": 2698, "func": ""}
{"index": "s449090567", "label": 2698, "func": ""}
{"index": "s866024896", "label": 2698, "func": ""}
{"index": "s804081641", "label": 2698, "func": ""}
{"index": "s168537637", "label": 2698, "func": ""}
{"index": "s836199805", "label": 2698, "func": ""}
{"index": "s757706676", "label": 2698, "func": ""}
{"index": "s190025668", "label": 22, "func": ""}
{"index": "s688794330", "label": 22, "func": ""}
{"index": "s209960239", "label": 22, "func": ""}
{"index": "s415175031", "label": 22, "func": ""}
{"index": "s074825969", "label": 22, "func": ""}
{"index": "s313665663", "label": 22, "func": ""}
{"index": "s453391872", "label": 22, "func": ""}
{"index": "s313780681", "label": 22, "func": ""}
{"index": "s162835920", "label": 22, "func": ""}
{"index": "s525757723", "label": 22, "func": ""}
{"index": "s890042009", "label": 2988, "func": ""}
{"index": "s269008764", "label": 2988, "func": ""}
{"index": "s911241094", "label": 2988, "func": ""}
{"index": "s661413923", "label": 2988, "func": ""}
{"index": "s203663409", "label": 2988, "func": ""}
{"index": "s960107561", "label": 2988, "func": ""}
{"index": "s524395888", "label": 2988, "func": ""}
{"index": "s059111511", "label": 2988, "func": ""}
{"index": "s180062973", "label": 2988, "func": ""}
{"index": "s622396748", "label": 2988, "func": ""}
{"index": "s111808367", "label": 3797, "func": ""}
{"index": "s910701004", "label": 3797, "func": ""}
{"index": "s202406224", "label": 3797, "func": ""}
{"index": "s971134497", "label": 3797, "func": ""}
{"index": "s185305064", "label": 3797, "func": ""}
{"index": "s844602074", "label": 3797, "func": ""}
{"index": "s220595204", "label": 3797, "func": ""}
{"index": "s571868424", "label": 3797, "func": ""}
{"index": "s910413374", "label": 3797, "func": ""}
{"index": "s729131737", "label": 3797, "func": ""}
{"index": "s922916497", "label": 3328, "func": ""}
{"index": "s011154431", "label": 3328, "func": ""}
{"index": "s114043168", "label": 3328, "func": ""}
{"index": "s302126893", "label": 3328, "func": ""}
{"index": "s359560616", "label": 3328, "func": ""}
{"index": "s543770082", "label": 3328, "func": ""}
{"index": "s565286884", "label": 3328, "func": ""}
{"index": "s607207752", "label": 3328, "func": ""}
{"index": "s281638149", "label": 3328, "func": ""}
{"index": "s453647848", "label": 3328, "func": ""}
{"index": "s981704175", "label": 952, "func": ""}
{"index": "s557938112", "label": 2636, "func": ""}
{"index": "s621915382", "label": 2636, "func": ""}
{"index": "s585164599", "label": 2636, "func": ""}
{"index": "s983470964", "label": 1328, "func": ""}
{"index": "s664158064", "label": 2477, "func": ""}
{"index": "s293757896", "label": 2477, "func": ""}
{"index": "s523118060", "label": 2477, "func": ""}
{"index": "s501389691", "label": 2477, "func": ""}
{"index": "s619173871", "label": 2477, "func": ""}
{"index": "s824888862", "label": 2477, "func": ""}
{"index": "s998248777", "label": 2477, "func": ""}
{"index": "s093993826", "label": 2477, "func": ""}
{"index": "s879815736", "label": 2477, "func": ""}
{"index": "s796378914", "label": 2477, "func": ""}
{"index": "s375804912", "label": 577, "func": ""}
{"index": "s641059148", "label": 577, "func": ""}
{"index": "s161085885", "label": 577, "func": ""}
{"index": "s965873208", "label": 577, "func": ""}
{"index": "s009619414", "label": 577, "func": ""}
{"index": "s761112251", "label": 577, "func": ""}
{"index": "s973934256", "label": 577, "func": ""}
{"index": "s475240880", "label": 577, "func": ""}
{"index": "s661538497", "label": 577, "func": ""}
{"index": "s032858891", "label": 577, "func": ""}
{"index": "s501129247", "label": 356, "func": ""}
{"index": "s167825323", "label": 356, "func": ""}
{"index": "s200884664", "label": 356, "func": ""}
{"index": "s235291953", "label": 356, "func": ""}
{"index": "s539855003", "label": 356, "func": ""}
{"index": "s203680747", "label": 356, "func": ""}
{"index": "s864477865", "label": 356, "func": ""}
{"index": "s983046121", "label": 356, "func": ""}
{"index": "s266153070", "label": 356, "func": ""}
{"index": "s074560449", "label": 356, "func": ""}
{"index": "s797624040", "label": 2235, "func": ""}
{"index": "s642187618", "label": 2235, "func": ""}
{"index": "s651028151", "label": 2235, "func": ""}
{"index": "s257205358", "label": 2235, "func": ""}
{"index": "s205515258", "label": 2235, "func": ""}
{"index": "s146009467", "label": 2235, "func": ""}
{"index": "s634466494", "label": 2235, "func": ""}
{"index": "s357583865", "label": 2235, "func": ""}
{"index": "s078974710", "label": 2235, "func": ""}
{"index": "s422561660", "label": 2235, "func": ""}
{"index": "s093479344", "label": 1840, "func": ""}
{"index": "s948722311", "label": 1840, "func": ""}
{"index": "s771498297", "label": 1840, "func": ""}
{"index": "s511831049", "label": 1840, "func": ""}
{"index": "s700273708", "label": 1840, "func": ""}
{"index": "s857437320", "label": 1840, "func": ""}
{"index": "s685719994", "label": 1840, "func": ""}
{"index": "s881102102", "label": 1840, "func": ""}
{"index": "s503622833", "label": 1840, "func": ""}
{"index": "s273254457", "label": 1840, "func": ""}
{"index": "s822957591", "label": 3625, "func": ""}
{"index": "s527842048", "label": 3625, "func": ""}
{"index": "s923715305", "label": 3625, "func": ""}
{"index": "s647603107", "label": 3625, "func": ""}
{"index": "s470991783", "label": 3625, "func": ""}
{"index": "s485759984", "label": 3625, "func": ""}
{"index": "s912019842", "label": 3625, "func": ""}
{"index": "s611274272", "label": 3625, "func": ""}
{"index": "s326916083", "label": 3625, "func": ""}
{"index": "s408892885", "label": 3625, "func": ""}
{"index": "s755667517", "label": 1630, "func": ""}
{"index": "s946046906", "label": 1630, "func": ""}
{"index": "s971677144", "label": 1232, "func": ""}
{"index": "s141136150", "label": 4050, "func": ""}
{"index": "s727736534", "label": 4050, "func": ""}
{"index": "s762099514", "label": 4050, "func": ""}
{"index": "s354572378", "label": 4050, "func": ""}
{"index": "s165104174", "label": 4050, "func": ""}
{"index": "s564542882", "label": 4050, "func": ""}
{"index": "s284480125", "label": 4050, "func": ""}
{"index": "s966354106", "label": 543, "func": ""}
{"index": "s864912366", "label": 543, "func": ""}
{"index": "s768486106", "label": 543, "func": ""}
{"index": "s432461365", "label": 543, "func": ""}
{"index": "s428425006", "label": 543, "func": ""}
{"index": "s554231966", "label": 543, "func": ""}
{"index": "s924486961", "label": 543, "func": ""}
{"index": "s488291899", "label": 543, "func": ""}
{"index": "s880879942", "label": 543, "func": ""}
{"index": "s781309784", "label": 95, "func": ""}
{"index": "s530796477", "label": 95, "func": ""}
{"index": "s237499006", "label": 95, "func": ""}
{"index": "s192258758", "label": 95, "func": ""}
{"index": "s964124441", "label": 95, "func": ""}
{"index": "s630805270", "label": 95, "func": ""}
{"index": "s869961586", "label": 95, "func": ""}
{"index": "s674983386", "label": 95, "func": ""}
{"index": "s889135289", "label": 95, "func": ""}
{"index": "s245194561", "label": 95, "func": ""}
{"index": "s010168665", "label": 594, "func": ""}
{"index": "s733210070", "label": 594, "func": ""}
{"index": "s855929021", "label": 594, "func": ""}
{"index": "s223191563", "label": 594, "func": ""}
{"index": "s141678091", "label": 594, "func": ""}
{"index": "s997444456", "label": 594, "func": ""}
{"index": "s104964763", "label": 594, "func": ""}
{"index": "s806477790", "label": 594, "func": ""}
{"index": "s007772676", "label": 594, "func": ""}
{"index": "s440410416", "label": 594, "func": ""}
{"index": "s288795499", "label": 3707, "func": ""}
{"index": "s940481462", "label": 3707, "func": ""}
{"index": "s801015815", "label": 3707, "func": ""}
{"index": "s834904733", "label": 3707, "func": ""}
{"index": "s613302312", "label": 3707, "func": ""}
{"index": "s740861851", "label": 3707, "func": ""}
{"index": "s293548429", "label": 3707, "func": ""}
{"index": "s497884242", "label": 189, "func": ""}
{"index": "s093569195", "label": 189, "func": ""}
{"index": "s558317132", "label": 189, "func": ""}
{"index": "s440442196", "label": 189, "func": ""}
{"index": "s203654494", "label": 189, "func": ""}
{"index": "s486203408", "label": 189, "func": ""}
{"index": "s450394104", "label": 189, "func": ""}
{"index": "s412459779", "label": 189, "func": ""}
{"index": "s479938164", "label": 189, "func": ""}
{"index": "s973684729", "label": 189, "func": ""}
{"index": "s128056089", "label": 3921, "func": ""}
{"index": "s457145972", "label": 3921, "func": ""}
{"index": "s869547494", "label": 3921, "func": ""}
{"index": "s308499088", "label": 3921, "func": ""}
{"index": "s957905678", "label": 3921, "func": ""}
{"index": "s396366937", "label": 3921, "func": ""}
{"index": "s215802920", "label": 3921, "func": ""}
{"index": "s881064821", "label": 3921, "func": ""}
{"index": "s155663582", "label": 3921, "func": ""}
{"index": "s843367208", "label": 3921, "func": ""}
{"index": "s651904345", "label": 1322, "func": ""}
{"index": "s807123414", "label": 1322, "func": ""}
{"index": "s052408091", "label": 1322, "func": ""}
{"index": "s237999256", "label": 1322, "func": ""}
{"index": "s658827731", "label": 1322, "func": ""}
{"index": "s057520009", "label": 1322, "func": ""}
{"index": "s430211830", "label": 1322, "func": ""}
{"index": "s183425113", "label": 1322, "func": ""}
{"index": "s187929890", "label": 1322, "func": ""}
{"index": "s624864338", "label": 1322, "func": ""}
{"index": "s522536689", "label": 939, "func": ""}
{"index": "s894389995", "label": 168, "func": ""}
{"index": "s886568582", "label": 168, "func": ""}
{"index": "s918716876", "label": 168, "func": ""}
{"index": "s430368585", "label": 168, "func": ""}
{"index": "s337602634", "label": 168, "func": ""}
{"index": "s887770169", "label": 168, "func": ""}
{"index": "s709388564", "label": 168, "func": ""}
{"index": "s969534813", "label": 168, "func": ""}
{"index": "s835338918", "label": 168, "func": ""}
{"index": "s424666011", "label": 168, "func": ""}
{"index": "s911696320", "label": 2879, "func": ""}
{"index": "s325382861", "label": 2879, "func": ""}
{"index": "s277003530", "label": 2879, "func": ""}
{"index": "s188695433", "label": 2879, "func": ""}
{"index": "s445147432", "label": 2879, "func": ""}
{"index": "s558716909", "label": 2879, "func": ""}
{"index": "s881321316", "label": 2879, "func": ""}
{"index": "s326790964", "label": 2879, "func": ""}
{"index": "s775196058", "label": 2879, "func": ""}
{"index": "s191135187", "label": 2879, "func": ""}
{"index": "s506729594", "label": 2060, "func": ""}
{"index": "s483186929", "label": 2060, "func": ""}
{"index": "s312333431", "label": 1732, "func": ""}
{"index": "s499862740", "label": 1732, "func": ""}
{"index": "s452191291", "label": 1732, "func": ""}
{"index": "s933067478", "label": 1732, "func": ""}
{"index": "s551480586", "label": 1732, "func": ""}
{"index": "s751228665", "label": 1732, "func": ""}
{"index": "s211963742", "label": 3211, "func": ""}
{"index": "s505239415", "label": 3211, "func": ""}
{"index": "s704698099", "label": 3211, "func": ""}
{"index": "s033247599", "label": 3211, "func": ""}
{"index": "s560695270", "label": 3211, "func": ""}
{"index": "s438650098", "label": 3211, "func": ""}
{"index": "s882169151", "label": 3211, "func": ""}
{"index": "s342757133", "label": 3211, "func": ""}
{"index": "s166845366", "label": 3211, "func": ""}
{"index": "s515674533", "label": 3211, "func": ""}
{"index": "s014334357", "label": 174, "func": ""}
{"index": "s551783329", "label": 174, "func": ""}
{"index": "s837590233", "label": 174, "func": ""}
{"index": "s952583413", "label": 174, "func": ""}
{"index": "s812191138", "label": 174, "func": ""}
{"index": "s687794735", "label": 174, "func": ""}
{"index": "s857137710", "label": 174, "func": ""}
{"index": "s643016294", "label": 174, "func": ""}
{"index": "s267936728", "label": 174, "func": ""}
{"index": "s770301069", "label": 174, "func": ""}
{"index": "s445437472", "label": 160, "func": ""}
{"index": "s175845865", "label": 160, "func": ""}
{"index": "s275801372", "label": 160, "func": ""}
{"index": "s162329379", "label": 160, "func": ""}
{"index": "s360102892", "label": 160, "func": ""}
{"index": "s834822160", "label": 160, "func": ""}
{"index": "s458595381", "label": 160, "func": ""}
{"index": "s330442435", "label": 160, "func": ""}
{"index": "s139599922", "label": 160, "func": ""}
{"index": "s391604852", "label": 160, "func": ""}
{"index": "s182625899", "label": 1687, "func": ""}
{"index": "s608745327", "label": 1687, "func": ""}
{"index": "s220632312", "label": 1687, "func": ""}
{"index": "s909466508", "label": 1687, "func": ""}
{"index": "s062878281", "label": 1687, "func": ""}
{"index": "s164994571", "label": 1687, "func": ""}
{"index": "s403785723", "label": 1687, "func": ""}
{"index": "s382191105", "label": 1687, "func": ""}
{"index": "s062379335", "label": 1687, "func": ""}
{"index": "s507092824", "label": 1687, "func": ""}
{"index": "s724853975", "label": 3291, "func": ""}
{"index": "s891141376", "label": 3291, "func": ""}
{"index": "s123683345", "label": 3291, "func": ""}
{"index": "s839297496", "label": 3291, "func": ""}
{"index": "s110351511", "label": 3291, "func": ""}
{"index": "s429467580", "label": 3291, "func": ""}
{"index": "s403979599", "label": 3291, "func": ""}
{"index": "s130882386", "label": 3291, "func": ""}
{"index": "s664028059", "label": 3291, "func": ""}
{"index": "s519995256", "label": 3291, "func": ""}
{"index": "s298935200", "label": 3841, "func": ""}
{"index": "s267709473", "label": 3841, "func": ""}
{"index": "s839092164", "label": 3841, "func": ""}
{"index": "s318009735", "label": 3841, "func": ""}
{"index": "s530788506", "label": 3841, "func": ""}
{"index": "s807695884", "label": 3841, "func": ""}
{"index": "s587508096", "label": 3841, "func": ""}
{"index": "s833296217", "label": 3841, "func": ""}
{"index": "s944966079", "label": 3841, "func": ""}
{"index": "s307740951", "label": 3841, "func": ""}
{"index": "s245809954", "label": 3108, "func": ""}
{"index": "s483017167", "label": 3108, "func": ""}
{"index": "s129934012", "label": 3108, "func": ""}
{"index": "s096702670", "label": 3108, "func": ""}
{"index": "s893270963", "label": 3108, "func": ""}
{"index": "s720538855", "label": 3108, "func": ""}
{"index": "s876189967", "label": 3108, "func": ""}
{"index": "s259658505", "label": 3108, "func": ""}
{"index": "s561283836", "label": 3108, "func": ""}
{"index": "s438992885", "label": 3108, "func": ""}
{"index": "s939149219", "label": 3550, "func": ""}
{"index": "s569941633", "label": 3550, "func": ""}
{"index": "s449759840", "label": 3550, "func": ""}
{"index": "s406761958", "label": 3550, "func": ""}
{"index": "s042131145", "label": 3550, "func": ""}
{"index": "s497483343", "label": 3550, "func": ""}
{"index": "s915502398", "label": 3550, "func": ""}
{"index": "s114007594", "label": 3550, "func": ""}
{"index": "s175737931", "label": 3550, "func": ""}
{"index": "s111249211", "label": 3550, "func": ""}
{"index": "s766576373", "label": 2887, "func": ""}
{"index": "s698336454", "label": 2887, "func": ""}
{"index": "s563133329", "label": 2887, "func": ""}
{"index": "s138111203", "label": 2887, "func": ""}
{"index": "s119760992", "label": 2887, "func": ""}
{"index": "s369976774", "label": 2887, "func": ""}
{"index": "s391903484", "label": 2887, "func": ""}
{"index": "s105687460", "label": 2887, "func": ""}
{"index": "s111214489", "label": 2887, "func": ""}
{"index": "s521441295", "label": 2887, "func": ""}
{"index": "s002297574", "label": 1241, "func": ""}
{"index": "s987757039", "label": 1241, "func": ""}
{"index": "s228443730", "label": 1241, "func": ""}
{"index": "s912927703", "label": 2820, "func": ""}
{"index": "s750549235", "label": 2820, "func": ""}
{"index": "s622853861", "label": 2820, "func": ""}
{"index": "s168062814", "label": 2820, "func": ""}
{"index": "s604404447", "label": 2820, "func": ""}
{"index": "s249246854", "label": 2820, "func": ""}
{"index": "s553096482", "label": 2820, "func": ""}
{"index": "s418806257", "label": 2820, "func": ""}
{"index": "s657955586", "label": 2820, "func": ""}
{"index": "s054303037", "label": 2820, "func": ""}
{"index": "s943915425", "label": 1620, "func": ""}
{"index": "s404737501", "label": 1620, "func": ""}
{"index": "s255775625", "label": 1620, "func": ""}
{"index": "s713275690", "label": 1620, "func": ""}
{"index": "s628730973", "label": 1620, "func": ""}
{"index": "s752973839", "label": 1620, "func": ""}
{"index": "s744571820", "label": 1620, "func": ""}
{"index": "s991424689", "label": 1620, "func": ""}
{"index": "s331229375", "label": 1620, "func": ""}
{"index": "s728252363", "label": 1620, "func": ""}
{"index": "s881225979", "label": 1578, "func": ""}
{"index": "s458920228", "label": 1000, "func": ""}
{"index": "s429846728", "label": 1000, "func": ""}
{"index": "s682776794", "label": 1000, "func": ""}
{"index": "s663988430", "label": 1000, "func": ""}
{"index": "s271841257", "label": 1000, "func": ""}
{"index": "s077709822", "label": 1000, "func": ""}
{"index": "s122476333", "label": 1000, "func": ""}
{"index": "s359660971", "label": 1000, "func": ""}
{"index": "s662207044", "label": 1000, "func": ""}
{"index": "s909729101", "label": 3723, "func": ""}
{"index": "s228138717", "label": 3723, "func": ""}
{"index": "s473089697", "label": 3723, "func": ""}
{"index": "s631513042", "label": 3723, "func": ""}
{"index": "s490606441", "label": 3723, "func": ""}
{"index": "s089555787", "label": 3723, "func": ""}
{"index": "s322842221", "label": 3723, "func": ""}
{"index": "s430527723", "label": 3723, "func": ""}
{"index": "s297169618", "label": 3723, "func": ""}
{"index": "s079145996", "label": 3723, "func": ""}
{"index": "s450726996", "label": 2377, "func": ""}
{"index": "s136410083", "label": 2377, "func": ""}
{"index": "s293597480", "label": 2377, "func": ""}
{"index": "s062302753", "label": 2377, "func": ""}
{"index": "s946966177", "label": 2377, "func": ""}
{"index": "s669761654", "label": 2377, "func": ""}
{"index": "s759824670", "label": 2377, "func": ""}
{"index": "s574624391", "label": 2377, "func": ""}
{"index": "s555273639", "label": 2377, "func": ""}
{"index": "s818648118", "label": 2377, "func": ""}
{"index": "s211513708", "label": 145, "func": ""}
{"index": "s733473750", "label": 145, "func": ""}
{"index": "s689759532", "label": 145, "func": ""}
{"index": "s961606202", "label": 145, "func": ""}
{"index": "s007736201", "label": 145, "func": ""}
{"index": "s537182760", "label": 145, "func": ""}
{"index": "s084887313", "label": 145, "func": ""}
{"index": "s278007666", "label": 145, "func": ""}
{"index": "s094262689", "label": 145, "func": ""}
{"index": "s441995348", "label": 145, "func": ""}
{"index": "s870084403", "label": 3253, "func": ""}
{"index": "s989408666", "label": 3253, "func": ""}
{"index": "s349065325", "label": 3253, "func": ""}
{"index": "s334925591", "label": 3253, "func": ""}
{"index": "s055452414", "label": 3253, "func": ""}
{"index": "s009545519", "label": 3253, "func": ""}
{"index": "s580287120", "label": 3253, "func": ""}
{"index": "s934861681", "label": 3253, "func": ""}
{"index": "s285777256", "label": 3253, "func": ""}
{"index": "s725597300", "label": 3253, "func": ""}
{"index": "s157821480", "label": 2351, "func": ""}
{"index": "s167355682", "label": 2351, "func": ""}
{"index": "s072738947", "label": 2351, "func": ""}
{"index": "s142186901", "label": 2351, "func": ""}
{"index": "s563314391", "label": 2351, "func": ""}
{"index": "s737982869", "label": 2351, "func": ""}
{"index": "s400957809", "label": 2351, "func": ""}
{"index": "s121699790", "label": 2351, "func": ""}
{"index": "s523488155", "label": 2351, "func": ""}
{"index": "s580248263", "label": 2351, "func": ""}
{"index": "s130059421", "label": 1253, "func": ""}
{"index": "s043576987", "label": 1377, "func": ""}
{"index": "s104457628", "label": 1377, "func": ""}
{"index": "s971495067", "label": 1377, "func": ""}
{"index": "s619582359", "label": 1377, "func": ""}
{"index": "s868795801", "label": 1377, "func": ""}
{"index": "s511092234", "label": 1377, "func": ""}
{"index": "s576788312", "label": 1377, "func": ""}
{"index": "s586055357", "label": 1377, "func": ""}
{"index": "s285109819", "label": 1377, "func": ""}
{"index": "s703716497", "label": 1377, "func": ""}
{"index": "s949684431", "label": 1753, "func": ""}
{"index": "s452872602", "label": 1753, "func": ""}
{"index": "s439651954", "label": 1753, "func": ""}
{"index": "s335670111", "label": 1753, "func": ""}
{"index": "s267711603", "label": 1454, "func": ""}
{"index": "s038407988", "label": 1454, "func": ""}
{"index": "s205112232", "label": 1454, "func": ""}
{"index": "s469711641", "label": 2407, "func": ""}
{"index": "s650567265", "label": 2407, "func": ""}
{"index": "s379333729", "label": 2407, "func": ""}
{"index": "s224157506", "label": 2407, "func": ""}
{"index": "s621720121", "label": 2407, "func": ""}
{"index": "s051939861", "label": 2407, "func": ""}
{"index": "s543148180", "label": 2407, "func": ""}
{"index": "s562587539", "label": 2407, "func": ""}
{"index": "s692705170", "label": 2407, "func": ""}
{"index": "s093711691", "label": 2407, "func": ""}
{"index": "s674282964", "label": 3164, "func": ""}
{"index": "s004929226", "label": 3164, "func": ""}
{"index": "s161008539", "label": 3164, "func": ""}
{"index": "s662307665", "label": 3164, "func": ""}
{"index": "s319492659", "label": 3164, "func": ""}
{"index": "s049883820", "label": 3164, "func": ""}
{"index": "s878594507", "label": 3164, "func": ""}
{"index": "s525294226", "label": 3164, "func": ""}
{"index": "s197868983", "label": 3164, "func": ""}
{"index": "s643958922", "label": 3164, "func": ""}
{"index": "s641080725", "label": 2663, "func": ""}
{"index": "s216641210", "label": 2663, "func": ""}
{"index": "s127604210", "label": 2663, "func": ""}
{"index": "s817826555", "label": 2663, "func": ""}
{"index": "s487301967", "label": 2663, "func": ""}
{"index": "s000508534", "label": 2663, "func": ""}
{"index": "s284887548", "label": 2663, "func": ""}
{"index": "s698898613", "label": 2663, "func": ""}
{"index": "s480320517", "label": 2663, "func": ""}
{"index": "s894228952", "label": 2663, "func": ""}
{"index": "s223179173", "label": 1450, "func": ""}
{"index": "s923631367", "label": 1450, "func": ""}
{"index": "s256007916", "label": 1450, "func": ""}
{"index": "s639319044", "label": 1450, "func": ""}
{"index": "s246250900", "label": 1450, "func": ""}
{"index": "s978254638", "label": 1450, "func": ""}
{"index": "s309951004", "label": 1450, "func": ""}
{"index": "s632781490", "label": 1450, "func": ""}
{"index": "s349143857", "label": 1450, "func": ""}
{"index": "s898973870", "label": 971, "func": ""}
{"index": "s003166287", "label": 971, "func": ""}
{"index": "s835703783", "label": 2450, "func": ""}
{"index": "s998842245", "label": 2450, "func": ""}
{"index": "s115084620", "label": 2450, "func": ""}
{"index": "s986105160", "label": 2450, "func": ""}
{"index": "s597510228", "label": 2450, "func": ""}
{"index": "s738212880", "label": 2450, "func": ""}
{"index": "s006673153", "label": 2450, "func": ""}
{"index": "s666305755", "label": 2450, "func": ""}
{"index": "s698563386", "label": 2450, "func": ""}
{"index": "s315651915", "label": 2450, "func": ""}
{"index": "s537205678", "label": 1531, "func": ""}
{"index": "s436891280", "label": 1531, "func": ""}
{"index": "s050575215", "label": 1531, "func": ""}
{"index": "s877051466", "label": 1531, "func": ""}
{"index": "s470969936", "label": 1531, "func": ""}
{"index": "s359898524", "label": 1531, "func": ""}
{"index": "s957207914", "label": 1531, "func": ""}
{"index": "s535703020", "label": 1531, "func": ""}
{"index": "s597299402", "label": 1531, "func": ""}
{"index": "s226159489", "label": 1531, "func": ""}
{"index": "s120306106", "label": 3679, "func": ""}
{"index": "s768481125", "label": 3679, "func": ""}
{"index": "s442831980", "label": 3679, "func": ""}
{"index": "s576315970", "label": 3679, "func": ""}
{"index": "s096333480", "label": 3679, "func": ""}
{"index": "s205226730", "label": 3679, "func": ""}
{"index": "s212915275", "label": 3679, "func": ""}
{"index": "s146500934", "label": 3679, "func": ""}
{"index": "s878796254", "label": 3679, "func": ""}
{"index": "s882413811", "label": 3679, "func": ""}
{"index": "s285367564", "label": 840, "func": ""}
{"index": "s717850053", "label": 840, "func": ""}
{"index": "s716019893", "label": 840, "func": ""}
{"index": "s528225863", "label": 4019, "func": ""}
{"index": "s837808250", "label": 4019, "func": ""}
{"index": "s131134661", "label": 4019, "func": ""}
{"index": "s756589939", "label": 4019, "func": ""}
{"index": "s730390436", "label": 4019, "func": ""}
{"index": "s850522117", "label": 4019, "func": ""}
{"index": "s753441568", "label": 4019, "func": ""}
{"index": "s211283419", "label": 4019, "func": ""}
{"index": "s762448001", "label": 4019, "func": ""}
{"index": "s324664397", "label": 4019, "func": ""}
{"index": "s241670136", "label": 3389, "func": ""}
{"index": "s250697387", "label": 3389, "func": ""}
{"index": "s699479501", "label": 3389, "func": ""}
{"index": "s448226650", "label": 3389, "func": ""}
{"index": "s752695864", "label": 3389, "func": ""}
{"index": "s151906467", "label": 3389, "func": ""}
{"index": "s414977052", "label": 3389, "func": ""}
{"index": "s153624775", "label": 3389, "func": ""}
{"index": "s337334355", "label": 3389, "func": ""}
{"index": "s432977096", "label": 3389, "func": ""}
{"index": "s195571394", "label": 3857, "func": ""}
{"index": "s676726766", "label": 3857, "func": ""}
{"index": "s400721901", "label": 3857, "func": ""}
{"index": "s201223834", "label": 3857, "func": ""}
{"index": "s183129044", "label": 3857, "func": ""}
{"index": "s970571905", "label": 3857, "func": ""}
{"index": "s934877257", "label": 3857, "func": ""}
{"index": "s914298717", "label": 3857, "func": ""}
{"index": "s032628155", "label": 3857, "func": ""}
{"index": "s527540078", "label": 3857, "func": ""}
{"index": "s441963943", "label": 1741, "func": ""}
{"index": "s127666681", "label": 1741, "func": ""}
{"index": "s382576912", "label": 1741, "func": ""}
{"index": "s501737115", "label": 1741, "func": ""}
{"index": "s774477989", "label": 1741, "func": ""}
{"index": "s083298225", "label": 3786, "func": ""}
{"index": "s552266630", "label": 3786, "func": ""}
{"index": "s434116233", "label": 3786, "func": ""}
{"index": "s213717806", "label": 3786, "func": ""}
{"index": "s749882600", "label": 3786, "func": ""}
{"index": "s599478245", "label": 3786, "func": ""}
{"index": "s918812170", "label": 3786, "func": ""}
{"index": "s479136730", "label": 3786, "func": ""}
{"index": "s383718619", "label": 3786, "func": ""}
{"index": "s271866875", "label": 3786, "func": ""}
{"index": "s841051062", "label": 437, "func": ""}
{"index": "s552859775", "label": 437, "func": ""}
{"index": "s709664851", "label": 437, "func": ""}
{"index": "s375907940", "label": 437, "func": ""}
{"index": "s754525334", "label": 437, "func": ""}
{"index": "s943166121", "label": 437, "func": ""}
{"index": "s977674963", "label": 437, "func": ""}
{"index": "s638887287", "label": 437, "func": ""}
{"index": "s478006157", "label": 437, "func": ""}
{"index": "s852575278", "label": 437, "func": ""}
{"index": "s008820777", "label": 501, "func": ""}
{"index": "s716123587", "label": 501, "func": ""}
{"index": "s178379644", "label": 501, "func": ""}
{"index": "s280112870", "label": 501, "func": ""}
{"index": "s122411596", "label": 501, "func": ""}
{"index": "s048970725", "label": 501, "func": ""}
{"index": "s261112036", "label": 501, "func": ""}
{"index": "s435632250", "label": 501, "func": ""}
{"index": "s261523468", "label": 501, "func": ""}
{"index": "s953716902", "label": 501, "func": ""}
{"index": "s264051291", "label": 2754, "func": ""}
{"index": "s593610143", "label": 2754, "func": ""}
{"index": "s806925389", "label": 2754, "func": ""}
{"index": "s881101775", "label": 2754, "func": ""}
{"index": "s837119662", "label": 2754, "func": ""}
{"index": "s381365148", "label": 2754, "func": ""}
{"index": "s048912335", "label": 2754, "func": ""}
{"index": "s450281613", "label": 2754, "func": ""}
{"index": "s492601963", "label": 2754, "func": ""}
{"index": "s526548777", "label": 2754, "func": ""}
{"index": "s375296115", "label": 3283, "func": ""}
{"index": "s357568308", "label": 3283, "func": ""}
{"index": "s205575114", "label": 3283, "func": ""}
{"index": "s424803997", "label": 3283, "func": ""}
{"index": "s452993821", "label": 3283, "func": ""}
{"index": "s471096316", "label": 3283, "func": ""}
{"index": "s596215749", "label": 3283, "func": ""}
{"index": "s411025858", "label": 3283, "func": ""}
{"index": "s078245308", "label": 3283, "func": ""}
{"index": "s674938170", "label": 3283, "func": ""}
{"index": "s664133446", "label": 1421, "func": ""}
{"index": "s471073731", "label": 1421, "func": ""}
{"index": "s876513246", "label": 1421, "func": ""}
{"index": "s328813188", "label": 1421, "func": ""}
{"index": "s992414631", "label": 1421, "func": ""}
{"index": "s418266303", "label": 1891, "func": ""}
{"index": "s756918890", "label": 3538, "func": ""}
{"index": "s558833662", "label": 3450, "func": ""}
{"index": "s696234859", "label": 3450, "func": ""}
{"index": "s000162937", "label": 3450, "func": ""}
{"index": "s919426248", "label": 3450, "func": ""}
{"index": "s029088897", "label": 3450, "func": ""}
{"index": "s497596731", "label": 3450, "func": ""}
{"index": "s304201749", "label": 3450, "func": ""}
{"index": "s332251239", "label": 3450, "func": ""}
{"index": "s834913076", "label": 3450, "func": ""}
{"index": "s305956703", "label": 3450, "func": ""}
{"index": "s321673347", "label": 1133, "func": ""}
{"index": "s985658464", "label": 1133, "func": ""}
{"index": "s467945003", "label": 1133, "func": ""}
{"index": "s756327485", "label": 1133, "func": ""}
{"index": "s381290958", "label": 1133, "func": ""}
{"index": "s321310456", "label": 1133, "func": ""}
{"index": "s020525560", "label": 1133, "func": ""}
{"index": "s813803594", "label": 1133, "func": ""}
{"index": "s530141470", "label": 1133, "func": ""}
{"index": "s830777812", "label": 1133, "func": ""}
{"index": "s894583282", "label": 479, "func": ""}
{"index": "s467378437", "label": 479, "func": ""}
{"index": "s062475785", "label": 479, "func": ""}
{"index": "s081153300", "label": 479, "func": ""}
{"index": "s485907393", "label": 479, "func": ""}
{"index": "s904998743", "label": 479, "func": ""}
{"index": "s285980604", "label": 479, "func": ""}
{"index": "s744337542", "label": 479, "func": ""}
{"index": "s765740190", "label": 479, "func": ""}
{"index": "s297743607", "label": 479, "func": ""}
{"index": "s127132134", "label": 3596, "func": ""}
{"index": "s850398608", "label": 3596, "func": ""}
{"index": "s127689505", "label": 3596, "func": ""}
{"index": "s484953170", "label": 142, "func": ""}
{"index": "s166317568", "label": 142, "func": ""}
{"index": "s063061920", "label": 142, "func": ""}
{"index": "s820169253", "label": 142, "func": ""}
{"index": "s392995697", "label": 142, "func": ""}
{"index": "s243277560", "label": 142, "func": ""}
{"index": "s098812006", "label": 142, "func": ""}
{"index": "s547358705", "label": 142, "func": ""}
{"index": "s967383428", "label": 142, "func": ""}
{"index": "s597178543", "label": 142, "func": ""}
{"index": "s002839958", "label": 3901, "func": ""}
{"index": "s402887544", "label": 3901, "func": ""}
{"index": "s011548010", "label": 3901, "func": ""}
{"index": "s501067852", "label": 836, "func": ""}
{"index": "s904291606", "label": 836, "func": ""}
{"index": "s740392068", "label": 836, "func": ""}
{"index": "s493289249", "label": 836, "func": ""}
{"index": "s586575169", "label": 836, "func": ""}
{"index": "s797887446", "label": 836, "func": ""}
{"index": "s155002931", "label": 836, "func": ""}
{"index": "s635942622", "label": 836, "func": ""}
{"index": "s276733936", "label": 836, "func": ""}
{"index": "s970948983", "label": 836, "func": ""}
{"index": "s344890923", "label": 504, "func": ""}
{"index": "s617689247", "label": 504, "func": ""}
{"index": "s783147801", "label": 791, "func": ""}
{"index": "s306636760", "label": 791, "func": ""}
{"index": "s727196874", "label": 791, "func": ""}
{"index": "s493930152", "label": 791, "func": ""}
{"index": "s962840412", "label": 791, "func": ""}
{"index": "s229178535", "label": 791, "func": ""}
{"index": "s918540748", "label": 1874, "func": ""}
{"index": "s055013045", "label": 1874, "func": ""}
{"index": "s090456171", "label": 615, "func": ""}
{"index": "s434372321", "label": 615, "func": ""}
{"index": "s953084061", "label": 615, "func": ""}
{"index": "s189539597", "label": 615, "func": ""}
{"index": "s199104805", "label": 615, "func": ""}
{"index": "s070037352", "label": 615, "func": ""}
{"index": "s394501661", "label": 615, "func": ""}
{"index": "s509165787", "label": 615, "func": ""}
{"index": "s169159701", "label": 615, "func": ""}
{"index": "s080063528", "label": 615, "func": ""}
{"index": "s730222871", "label": 2318, "func": ""}
{"index": "s287950852", "label": 2318, "func": ""}
{"index": "s365149989", "label": 2318, "func": ""}
{"index": "s434692311", "label": 2318, "func": ""}
{"index": "s912082405", "label": 2318, "func": ""}
{"index": "s224086592", "label": 2318, "func": ""}
{"index": "s478158739", "label": 2318, "func": ""}
{"index": "s849966693", "label": 2318, "func": ""}
{"index": "s361082241", "label": 2318, "func": ""}
{"index": "s473121306", "label": 2318, "func": ""}
{"index": "s854440574", "label": 1701, "func": ""}
{"index": "s252519502", "label": 1701, "func": ""}
{"index": "s458049244", "label": 1701, "func": ""}
{"index": "s296755038", "label": 1701, "func": ""}
{"index": "s196688512", "label": 1701, "func": ""}
{"index": "s406345490", "label": 1701, "func": ""}
{"index": "s753562096", "label": 1701, "func": ""}
{"index": "s594567063", "label": 1701, "func": ""}
{"index": "s234229803", "label": 1701, "func": ""}
{"index": "s101367041", "label": 1701, "func": ""}
{"index": "s886650282", "label": 2745, "func": ""}
{"index": "s987637976", "label": 2745, "func": ""}
{"index": "s431815562", "label": 2745, "func": ""}
{"index": "s650530295", "label": 2745, "func": ""}
{"index": "s100223279", "label": 2745, "func": ""}
{"index": "s205045693", "label": 2745, "func": ""}
{"index": "s665717600", "label": 2745, "func": ""}
{"index": "s728257303", "label": 2745, "func": ""}
{"index": "s704007173", "label": 2745, "func": ""}
{"index": "s709168979", "label": 2745, "func": ""}
{"index": "s028060874", "label": 1546, "func": ""}
{"index": "s826102055", "label": 1546, "func": ""}
{"index": "s185822270", "label": 1546, "func": ""}
{"index": "s540772972", "label": 3144, "func": ""}
{"index": "s969519580", "label": 2682, "func": ""}
{"index": "s652136658", "label": 2682, "func": ""}
{"index": "s631055291", "label": 2682, "func": ""}
{"index": "s863579985", "label": 2682, "func": ""}
{"index": "s481262356", "label": 2682, "func": ""}
{"index": "s921516898", "label": 2682, "func": ""}
{"index": "s010468187", "label": 2682, "func": ""}
{"index": "s856215884", "label": 2682, "func": ""}
{"index": "s050229952", "label": 2682, "func": ""}
{"index": "s068957669", "label": 2682, "func": ""}
{"index": "s215569666", "label": 108, "func": ""}
{"index": "s392971625", "label": 108, "func": ""}
{"index": "s533257981", "label": 108, "func": ""}
{"index": "s984130745", "label": 108, "func": ""}
{"index": "s247930933", "label": 108, "func": ""}
{"index": "s713584528", "label": 108, "func": ""}
{"index": "s864529447", "label": 108, "func": ""}
{"index": "s062795271", "label": 108, "func": ""}
{"index": "s672503152", "label": 108, "func": ""}
{"index": "s178523778", "label": 108, "func": ""}
{"index": "s183563333", "label": 3535, "func": ""}
{"index": "s400808377", "label": 3535, "func": ""}
{"index": "s510176704", "label": 3535, "func": ""}
{"index": "s679050597", "label": 3535, "func": ""}
{"index": "s140946383", "label": 3535, "func": ""}
{"index": "s840399954", "label": 3535, "func": ""}
{"index": "s876162967", "label": 283, "func": ""}
{"index": "s600228691", "label": 283, "func": ""}
{"index": "s244207669", "label": 283, "func": ""}
{"index": "s087715024", "label": 283, "func": ""}
{"index": "s679992246", "label": 283, "func": ""}
{"index": "s332190365", "label": 283, "func": ""}
{"index": "s676240996", "label": 283, "func": ""}
{"index": "s620029543", "label": 283, "func": ""}
{"index": "s977673693", "label": 2579, "func": ""}
{"index": "s150186022", "label": 2579, "func": ""}
{"index": "s279983167", "label": 2579, "func": ""}
{"index": "s038548458", "label": 2579, "func": ""}
{"index": "s364859070", "label": 2579, "func": ""}
{"index": "s835720430", "label": 2579, "func": ""}
{"index": "s570967377", "label": 2579, "func": ""}
{"index": "s188664624", "label": 2579, "func": ""}
{"index": "s368277107", "label": 2579, "func": ""}
{"index": "s687143668", "label": 2579, "func": ""}
{"index": "s531524827", "label": 2557, "func": ""}
{"index": "s865247982", "label": 2557, "func": ""}
{"index": "s387526365", "label": 2557, "func": ""}
{"index": "s565914873", "label": 2557, "func": ""}
{"index": "s082439619", "label": 2557, "func": ""}
{"index": "s708323638", "label": 2557, "func": ""}
{"index": "s623271499", "label": 2557, "func": ""}
{"index": "s120482002", "label": 2557, "func": ""}
{"index": "s201730722", "label": 2557, "func": ""}
{"index": "s663803014", "label": 2557, "func": ""}
{"index": "s727142471", "label": 2639, "func": ""}
{"index": "s902552459", "label": 2639, "func": ""}
{"index": "s072204538", "label": 2639, "func": ""}
{"index": "s775253969", "label": 2639, "func": ""}
{"index": "s677886557", "label": 2639, "func": ""}
{"index": "s990406530", "label": 2639, "func": ""}
{"index": "s669327600", "label": 2639, "func": ""}
{"index": "s876210477", "label": 2639, "func": ""}
{"index": "s737598904", "label": 2639, "func": ""}
{"index": "s846821004", "label": 2639, "func": ""}
{"index": "s520236942", "label": 623, "func": ""}
{"index": "s598821624", "label": 623, "func": ""}
{"index": "s452154374", "label": 623, "func": ""}
{"index": "s199024894", "label": 623, "func": ""}
{"index": "s054895502", "label": 623, "func": ""}
{"index": "s852316978", "label": 623, "func": ""}
{"index": "s591197387", "label": 623, "func": ""}
{"index": "s492919134", "label": 623, "func": ""}
{"index": "s104208393", "label": 2465, "func": ""}
{"index": "s450600698", "label": 2465, "func": ""}
{"index": "s257711229", "label": 2465, "func": ""}
{"index": "s293899442", "label": 2465, "func": ""}
{"index": "s041010843", "label": 2465, "func": ""}
{"index": "s733674864", "label": 2465, "func": ""}
{"index": "s406677312", "label": 2465, "func": ""}
{"index": "s893011250", "label": 2465, "func": ""}
{"index": "s685220522", "label": 2465, "func": ""}
{"index": "s674698000", "label": 2465, "func": ""}
{"index": "s813856206", "label": 2576, "func": ""}
{"index": "s626214054", "label": 2576, "func": ""}
{"index": "s321380757", "label": 2576, "func": ""}
{"index": "s081029417", "label": 2576, "func": ""}
{"index": "s411036506", "label": 2576, "func": ""}
{"index": "s346401553", "label": 2576, "func": ""}
{"index": "s985440125", "label": 2576, "func": ""}
{"index": "s807151795", "label": 2576, "func": ""}
{"index": "s042109047", "label": 2576, "func": ""}
{"index": "s449604975", "label": 2576, "func": ""}
{"index": "s173142949", "label": 114, "func": ""}
{"index": "s887567739", "label": 114, "func": ""}
{"index": "s518169742", "label": 114, "func": ""}
{"index": "s031977305", "label": 114, "func": ""}
{"index": "s060445326", "label": 114, "func": ""}
{"index": "s877967834", "label": 114, "func": ""}
{"index": "s848047380", "label": 114, "func": ""}
{"index": "s528055320", "label": 114, "func": ""}
{"index": "s411960410", "label": 114, "func": ""}
{"index": "s401781103", "label": 114, "func": ""}
{"index": "s607602437", "label": 1900, "func": ""}
{"index": "s440623054", "label": 1900, "func": ""}
{"index": "s351731048", "label": 1900, "func": ""}
{"index": "s196246472", "label": 1900, "func": ""}
{"index": "s085694588", "label": 1286, "func": ""}
{"index": "s319552817", "label": 1286, "func": ""}
{"index": "s704515118", "label": 1286, "func": ""}
{"index": "s295717274", "label": 3261, "func": ""}
{"index": "s200936710", "label": 3261, "func": ""}
{"index": "s196793901", "label": 3261, "func": ""}
{"index": "s838963250", "label": 3261, "func": ""}
{"index": "s081604778", "label": 3261, "func": ""}
{"index": "s561175761", "label": 3261, "func": ""}
{"index": "s316834369", "label": 3261, "func": ""}
{"index": "s821647309", "label": 3261, "func": ""}
{"index": "s121141729", "label": 3261, "func": ""}
{"index": "s916010697", "label": 3261, "func": ""}
{"index": "s996281474", "label": 922, "func": ""}
{"index": "s415320872", "label": 922, "func": ""}
{"index": "s331720389", "label": 3049, "func": ""}
{"index": "s405812398", "label": 3049, "func": ""}
{"index": "s707535975", "label": 3049, "func": ""}
{"index": "s791573826", "label": 3049, "func": ""}
{"index": "s514076902", "label": 3049, "func": ""}
{"index": "s134748363", "label": 3049, "func": ""}
{"index": "s574398071", "label": 3049, "func": ""}
{"index": "s061185052", "label": 3049, "func": ""}
{"index": "s072647130", "label": 3049, "func": ""}
{"index": "s352274537", "label": 3049, "func": ""}
{"index": "s127920958", "label": 3766, "func": ""}
{"index": "s278959837", "label": 3766, "func": ""}
{"index": "s997998477", "label": 3766, "func": ""}
{"index": "s170566408", "label": 3766, "func": ""}
{"index": "s152553293", "label": 872, "func": ""}
{"index": "s741088751", "label": 3559, "func": ""}
{"index": "s439826070", "label": 3559, "func": ""}
{"index": "s922714874", "label": 3559, "func": ""}
{"index": "s571001858", "label": 3559, "func": ""}
{"index": "s621282357", "label": 3559, "func": ""}
{"index": "s515590853", "label": 3559, "func": ""}
{"index": "s434101560", "label": 3559, "func": ""}
{"index": "s513936483", "label": 3559, "func": ""}
{"index": "s971323500", "label": 3559, "func": ""}
{"index": "s080104957", "label": 3559, "func": ""}
{"index": "s972509984", "label": 1520, "func": ""}
{"index": "s522841168", "label": 1520, "func": ""}
{"index": "s331369096", "label": 1520, "func": ""}
{"index": "s158313609", "label": 1520, "func": ""}
{"index": "s972018761", "label": 1520, "func": ""}
{"index": "s592287476", "label": 1520, "func": ""}
{"index": "s980331332", "label": 1520, "func": ""}
{"index": "s717798799", "label": 1520, "func": ""}
{"index": "s374069832", "label": 1520, "func": ""}
{"index": "s341968864", "label": 1520, "func": ""}
{"index": "s713732151", "label": 600, "func": ""}
{"index": "s513167214", "label": 600, "func": ""}
{"index": "s760490225", "label": 600, "func": ""}
{"index": "s321651445", "label": 600, "func": ""}
{"index": "s882591508", "label": 600, "func": ""}
{"index": "s558648166", "label": 600, "func": ""}
{"index": "s456619240", "label": 2489, "func": ""}
{"index": "s928349335", "label": 2489, "func": ""}
{"index": "s664176089", "label": 2489, "func": ""}
{"index": "s096327701", "label": 2489, "func": ""}
{"index": "s931777022", "label": 2489, "func": ""}
{"index": "s241054450", "label": 2489, "func": ""}
{"index": "s772115837", "label": 2489, "func": ""}
{"index": "s771982828", "label": 2489, "func": ""}
{"index": "s041704108", "label": 2489, "func": ""}
{"index": "s326081924", "label": 2489, "func": ""}
{"index": "s500037533", "label": 2814, "func": ""}
{"index": "s252075917", "label": 2814, "func": ""}
{"index": "s811106292", "label": 2814, "func": ""}
{"index": "s093073178", "label": 2814, "func": ""}
{"index": "s048255052", "label": 2814, "func": ""}
{"index": "s739249076", "label": 2814, "func": ""}
{"index": "s172172334", "label": 2814, "func": ""}
{"index": "s066144504", "label": 2814, "func": ""}
{"index": "s469162886", "label": 2814, "func": ""}
{"index": "s001213003", "label": 2814, "func": ""}
{"index": "s450365451", "label": 3866, "func": ""}
{"index": "s419661514", "label": 3866, "func": ""}
{"index": "s679284198", "label": 3866, "func": ""}
{"index": "s908778061", "label": 3866, "func": ""}
{"index": "s558069119", "label": 3866, "func": ""}
{"index": "s678619534", "label": 3866, "func": ""}
{"index": "s722842146", "label": 3866, "func": ""}
{"index": "s560636047", "label": 3866, "func": ""}
{"index": "s910958796", "label": 3866, "func": ""}
{"index": "s547072102", "label": 3866, "func": ""}
{"index": "s119899196", "label": 1262, "func": ""}
{"index": "s818604602", "label": 1262, "func": ""}
{"index": "s508284406", "label": 946, "func": ""}
{"index": "s175662158", "label": 946, "func": ""}
{"index": "s191219894", "label": 946, "func": ""}
{"index": "s804821769", "label": 946, "func": ""}
{"index": "s902606212", "label": 946, "func": ""}
{"index": "s508471059", "label": 946, "func": ""}
{"index": "s626872564", "label": 946, "func": ""}
{"index": "s851425452", "label": 946, "func": ""}
{"index": "s066634242", "label": 946, "func": ""}
{"index": "s042690296", "label": 946, "func": ""}
{"index": "s364848953", "label": 2759, "func": ""}
{"index": "s499527936", "label": 2759, "func": ""}
{"index": "s070266819", "label": 2759, "func": ""}
{"index": "s011216842", "label": 2759, "func": ""}
{"index": "s188172476", "label": 2759, "func": ""}
{"index": "s251898163", "label": 2759, "func": ""}
{"index": "s844261758", "label": 2759, "func": ""}
{"index": "s655569890", "label": 2759, "func": ""}
{"index": "s833636536", "label": 2759, "func": ""}
{"index": "s786347593", "label": 2759, "func": ""}
{"index": "s188662347", "label": 2881, "func": ""}
{"index": "s029466024", "label": 2881, "func": ""}
{"index": "s099950977", "label": 2881, "func": ""}
{"index": "s218438164", "label": 2881, "func": ""}
{"index": "s323133268", "label": 2881, "func": ""}
{"index": "s257103427", "label": 2881, "func": ""}
{"index": "s759097168", "label": 2881, "func": ""}
{"index": "s003990588", "label": 2881, "func": ""}
{"index": "s872815239", "label": 2881, "func": ""}
{"index": "s132098408", "label": 2881, "func": ""}
{"index": "s379196535", "label": 1075, "func": ""}
{"index": "s255631607", "label": 1075, "func": ""}
{"index": "s824470557", "label": 1075, "func": ""}
{"index": "s275522524", "label": 1075, "func": ""}
{"index": "s838059707", "label": 1075, "func": ""}
{"index": "s630862552", "label": 705, "func": ""}
{"index": "s117697456", "label": 705, "func": ""}
{"index": "s896444566", "label": 705, "func": ""}
{"index": "s474850431", "label": 705, "func": ""}
{"index": "s809545115", "label": 705, "func": ""}
{"index": "s377578079", "label": 705, "func": ""}
{"index": "s444874475", "label": 705, "func": ""}
{"index": "s537153251", "label": 705, "func": ""}
{"index": "s093845560", "label": 705, "func": ""}
{"index": "s304909140", "label": 705, "func": ""}
{"index": "s137133772", "label": 812, "func": ""}
{"index": "s437432163", "label": 812, "func": ""}
{"index": "s097381574", "label": 812, "func": ""}
{"index": "s325084171", "label": 812, "func": ""}
{"index": "s268980127", "label": 1477, "func": ""}
{"index": "s919179023", "label": 3330, "func": ""}
{"index": "s761768473", "label": 3330, "func": ""}
{"index": "s441544485", "label": 3330, "func": ""}
{"index": "s736689615", "label": 3330, "func": ""}
{"index": "s472971481", "label": 3330, "func": ""}
{"index": "s947898549", "label": 3330, "func": ""}
{"index": "s232848604", "label": 3330, "func": ""}
{"index": "s155746273", "label": 3330, "func": ""}
{"index": "s210255416", "label": 3330, "func": ""}
{"index": "s022801968", "label": 3330, "func": ""}
{"index": "s052227355", "label": 3036, "func": ""}
{"index": "s879036036", "label": 3036, "func": ""}
{"index": "s535925016", "label": 3036, "func": ""}
{"index": "s624413960", "label": 3036, "func": ""}
{"index": "s855710971", "label": 3036, "func": ""}
{"index": "s143294500", "label": 3036, "func": ""}
{"index": "s634529895", "label": 3036, "func": ""}
{"index": "s829073917", "label": 3036, "func": ""}
{"index": "s184282438", "label": 3036, "func": ""}
{"index": "s998959201", "label": 3036, "func": ""}
{"index": "s794031750", "label": 3482, "func": ""}
{"index": "s138252273", "label": 3482, "func": ""}
{"index": "s830571645", "label": 3482, "func": ""}
{"index": "s742357221", "label": 3482, "func": ""}
{"index": "s021925306", "label": 3482, "func": ""}
{"index": "s475541396", "label": 3482, "func": ""}
{"index": "s822207892", "label": 3482, "func": ""}
{"index": "s187393554", "label": 3482, "func": ""}
{"index": "s959923999", "label": 3482, "func": ""}
{"index": "s798209152", "label": 3482, "func": ""}
{"index": "s967982661", "label": 1690, "func": ""}
{"index": "s486971953", "label": 1690, "func": ""}
{"index": "s507476488", "label": 2836, "func": ""}
{"index": "s711807121", "label": 2836, "func": ""}
{"index": "s322253892", "label": 2836, "func": ""}
{"index": "s129514847", "label": 2836, "func": ""}
{"index": "s981350210", "label": 2836, "func": ""}
{"index": "s428347630", "label": 2836, "func": ""}
{"index": "s122927307", "label": 2836, "func": ""}
{"index": "s802745882", "label": 2836, "func": ""}
{"index": "s968096206", "label": 2836, "func": ""}
{"index": "s019338392", "label": 2836, "func": ""}
{"index": "s667767238", "label": 3331, "func": ""}
{"index": "s476902865", "label": 3331, "func": ""}
{"index": "s499683575", "label": 3331, "func": ""}
{"index": "s701912133", "label": 3331, "func": ""}
{"index": "s643165171", "label": 3331, "func": ""}
{"index": "s499332416", "label": 3331, "func": ""}
{"index": "s209136476", "label": 3331, "func": ""}
{"index": "s446074583", "label": 3331, "func": ""}
{"index": "s244462622", "label": 3331, "func": ""}
{"index": "s070396268", "label": 3331, "func": ""}
{"index": "s097067670", "label": 2308, "func": ""}
{"index": "s173455588", "label": 2308, "func": ""}
{"index": "s667181274", "label": 2308, "func": ""}
{"index": "s265307332", "label": 2308, "func": ""}
{"index": "s470273252", "label": 3175, "func": ""}
{"index": "s754870685", "label": 3175, "func": ""}
{"index": "s805054689", "label": 3175, "func": ""}
{"index": "s092679628", "label": 3175, "func": ""}
{"index": "s276416152", "label": 3175, "func": ""}
{"index": "s967046810", "label": 3175, "func": ""}
{"index": "s864180106", "label": 3175, "func": ""}
{"index": "s777972031", "label": 3175, "func": ""}
{"index": "s466032998", "label": 3175, "func": ""}
{"index": "s165914206", "label": 3175, "func": ""}
{"index": "s765492132", "label": 3170, "func": ""}
{"index": "s595113129", "label": 3170, "func": ""}
{"index": "s751041665", "label": 3170, "func": ""}
{"index": "s599135866", "label": 3170, "func": ""}
{"index": "s054739684", "label": 3170, "func": ""}
{"index": "s778177183", "label": 3170, "func": ""}
{"index": "s407554454", "label": 3170, "func": ""}
{"index": "s282247369", "label": 3170, "func": ""}
{"index": "s265573322", "label": 3170, "func": ""}
{"index": "s460753644", "label": 3170, "func": ""}
{"index": "s410836479", "label": 2028, "func": ""}
{"index": "s932053941", "label": 2028, "func": ""}
{"index": "s698177189", "label": 179, "func": ""}
{"index": "s383415065", "label": 179, "func": ""}
{"index": "s944821373", "label": 179, "func": ""}
{"index": "s287221919", "label": 179, "func": ""}
{"index": "s087301224", "label": 179, "func": ""}
{"index": "s037881162", "label": 179, "func": ""}
{"index": "s337055935", "label": 179, "func": ""}
{"index": "s459917263", "label": 179, "func": ""}
{"index": "s055884864", "label": 179, "func": ""}
{"index": "s525342753", "label": 179, "func": ""}
{"index": "s804786063", "label": 1443, "func": ""}
{"index": "s693771924", "label": 2785, "func": ""}
{"index": "s869841329", "label": 2785, "func": ""}
{"index": "s463342627", "label": 2785, "func": ""}
{"index": "s418505147", "label": 2785, "func": ""}
{"index": "s106319693", "label": 2785, "func": ""}
{"index": "s745495970", "label": 2785, "func": ""}
{"index": "s217369867", "label": 2785, "func": ""}
{"index": "s383393933", "label": 2785, "func": ""}
{"index": "s993846199", "label": 2785, "func": ""}
{"index": "s853072383", "label": 2785, "func": ""}
{"index": "s155691908", "label": 2528, "func": ""}
{"index": "s537948392", "label": 2528, "func": ""}
{"index": "s944573731", "label": 2528, "func": ""}
{"index": "s550481690", "label": 2528, "func": ""}
{"index": "s828781374", "label": 2528, "func": ""}
{"index": "s273109984", "label": 2528, "func": ""}
{"index": "s475164149", "label": 2528, "func": ""}
{"index": "s944525747", "label": 2528, "func": ""}
{"index": "s661053048", "label": 2528, "func": ""}
{"index": "s139613529", "label": 2528, "func": ""}
{"index": "s387722584", "label": 26, "func": ""}
{"index": "s021304076", "label": 26, "func": ""}
{"index": "s108701942", "label": 26, "func": ""}
{"index": "s283289495", "label": 26, "func": ""}
{"index": "s648047147", "label": 26, "func": ""}
{"index": "s445297166", "label": 26, "func": ""}
{"index": "s198426632", "label": 26, "func": ""}
{"index": "s438305360", "label": 26, "func": ""}
{"index": "s556802543", "label": 26, "func": ""}
{"index": "s081336894", "label": 26, "func": ""}
{"index": "s503182689", "label": 2590, "func": ""}
{"index": "s879931728", "label": 2126, "func": ""}
{"index": "s522197747", "label": 2126, "func": ""}
{"index": "s136490782", "label": 432, "func": ""}
{"index": "s166448379", "label": 432, "func": ""}
{"index": "s938431141", "label": 432, "func": ""}
{"index": "s319780649", "label": 432, "func": ""}
{"index": "s827771709", "label": 432, "func": ""}
{"index": "s361270927", "label": 432, "func": ""}
{"index": "s821426965", "label": 432, "func": ""}
{"index": "s377753432", "label": 432, "func": ""}
{"index": "s472695934", "label": 432, "func": ""}
{"index": "s513841798", "label": 320, "func": ""}
{"index": "s713388686", "label": 320, "func": ""}
{"index": "s366493636", "label": 2520, "func": ""}
{"index": "s253818737", "label": 2520, "func": ""}
{"index": "s273449573", "label": 2520, "func": ""}
{"index": "s749379826", "label": 2520, "func": ""}
{"index": "s859709631", "label": 2520, "func": ""}
{"index": "s093054630", "label": 1462, "func": ""}
{"index": "s823250602", "label": 2993, "func": ""}
{"index": "s726312767", "label": 2993, "func": ""}
{"index": "s462741818", "label": 2993, "func": ""}
{"index": "s228706213", "label": 2993, "func": ""}
{"index": "s498728475", "label": 2993, "func": ""}
{"index": "s923132708", "label": 2993, "func": ""}
{"index": "s950123924", "label": 2993, "func": ""}
{"index": "s154587164", "label": 2993, "func": ""}
{"index": "s181006917", "label": 2993, "func": ""}
{"index": "s918375690", "label": 2993, "func": ""}
{"index": "s964171801", "label": 3454, "func": ""}
{"index": "s112372109", "label": 3454, "func": ""}
{"index": "s473932140", "label": 475, "func": ""}
{"index": "s695370863", "label": 475, "func": ""}
{"index": "s530916308", "label": 3911, "func": ""}
{"index": "s588428434", "label": 3911, "func": ""}
{"index": "s658913056", "label": 3911, "func": ""}
{"index": "s040330373", "label": 3911, "func": ""}
{"index": "s190506311", "label": 3911, "func": ""}
{"index": "s753785880", "label": 3911, "func": ""}
{"index": "s010585262", "label": 3911, "func": ""}
{"index": "s274449702", "label": 3911, "func": ""}
{"index": "s476569442", "label": 3911, "func": ""}
{"index": "s953117170", "label": 3911, "func": ""}
{"index": "s977587743", "label": 2361, "func": ""}
{"index": "s025109445", "label": 2361, "func": ""}
{"index": "s838442158", "label": 2361, "func": ""}
{"index": "s664080433", "label": 2361, "func": ""}
{"index": "s426121374", "label": 2361, "func": ""}
{"index": "s363541936", "label": 2361, "func": ""}
{"index": "s951857099", "label": 2361, "func": ""}
{"index": "s765871548", "label": 2361, "func": ""}
{"index": "s150327581", "label": 2361, "func": ""}
{"index": "s834048421", "label": 2361, "func": ""}
{"index": "s525681701", "label": 2874, "func": ""}
{"index": "s029934233", "label": 2874, "func": ""}
{"index": "s906172692", "label": 2874, "func": ""}
{"index": "s392028596", "label": 2874, "func": ""}
{"index": "s844591151", "label": 2874, "func": ""}
{"index": "s937355007", "label": 2874, "func": ""}
{"index": "s382835906", "label": 2874, "func": ""}
{"index": "s125105755", "label": 2874, "func": ""}
{"index": "s527568269", "label": 2874, "func": ""}
{"index": "s748629545", "label": 2874, "func": ""}
{"index": "s505271619", "label": 2691, "func": ""}
{"index": "s990213348", "label": 2691, "func": ""}
{"index": "s529616651", "label": 2691, "func": ""}
{"index": "s838686592", "label": 2691, "func": ""}
{"index": "s599892529", "label": 2691, "func": ""}
{"index": "s367652324", "label": 2691, "func": ""}
{"index": "s939354754", "label": 2691, "func": ""}
{"index": "s533633637", "label": 2691, "func": ""}
{"index": "s143884826", "label": 2691, "func": ""}
{"index": "s092323961", "label": 2691, "func": ""}
{"index": "s602775350", "label": 1118, "func": ""}
{"index": "s245291130", "label": 2233, "func": ""}
{"index": "s701153237", "label": 2233, "func": ""}
{"index": "s153107768", "label": 2233, "func": ""}
{"index": "s904379383", "label": 2233, "func": ""}
{"index": "s987238387", "label": 2233, "func": ""}
{"index": "s395130948", "label": 2233, "func": ""}
{"index": "s526627059", "label": 2233, "func": ""}
{"index": "s616768341", "label": 2233, "func": ""}
{"index": "s501424624", "label": 2233, "func": ""}
{"index": "s001111587", "label": 2233, "func": ""}
{"index": "s231077545", "label": 2492, "func": ""}
{"index": "s402441257", "label": 2492, "func": ""}
{"index": "s521748412", "label": 2492, "func": ""}
{"index": "s953703848", "label": 2492, "func": ""}
{"index": "s705786623", "label": 2492, "func": ""}
{"index": "s569978135", "label": 2492, "func": ""}
{"index": "s969598300", "label": 2492, "func": ""}
{"index": "s349005330", "label": 2492, "func": ""}
{"index": "s798670629", "label": 2492, "func": ""}
{"index": "s316961294", "label": 2492, "func": ""}
{"index": "s099274179", "label": 999, "func": ""}
{"index": "s805286118", "label": 999, "func": ""}
{"index": "s464683854", "label": 999, "func": ""}
{"index": "s628532219", "label": 999, "func": ""}
{"index": "s041865469", "label": 1694, "func": ""}
{"index": "s855657199", "label": 1694, "func": ""}
{"index": "s160864798", "label": 1694, "func": ""}
{"index": "s549420993", "label": 1694, "func": ""}
{"index": "s803174451", "label": 1694, "func": ""}
{"index": "s880557105", "label": 1694, "func": ""}
{"index": "s721053420", "label": 1694, "func": ""}
{"index": "s386269232", "label": 1694, "func": ""}
{"index": "s232835894", "label": 1694, "func": ""}
{"index": "s892413650", "label": 1694, "func": ""}
{"index": "s516108197", "label": 888, "func": ""}
{"index": "s695289712", "label": 888, "func": ""}
{"index": "s348992606", "label": 888, "func": ""}
{"index": "s358681944", "label": 888, "func": ""}
{"index": "s741521046", "label": 888, "func": ""}
{"index": "s897573687", "label": 888, "func": ""}
{"index": "s861131453", "label": 888, "func": ""}
{"index": "s135189898", "label": 888, "func": ""}
{"index": "s643933664", "label": 444, "func": ""}
{"index": "s803070128", "label": 444, "func": ""}
{"index": "s020452501", "label": 444, "func": ""}
{"index": "s315499208", "label": 444, "func": ""}
{"index": "s222079137", "label": 444, "func": ""}
{"index": "s352753479", "label": 444, "func": ""}
{"index": "s742791799", "label": 444, "func": ""}
{"index": "s810636922", "label": 444, "func": ""}
{"index": "s878803453", "label": 444, "func": ""}
{"index": "s004277774", "label": 444, "func": ""}
{"index": "s852002880", "label": 472, "func": ""}
{"index": "s917635742", "label": 472, "func": ""}
{"index": "s585672291", "label": 472, "func": ""}
{"index": "s216454048", "label": 472, "func": ""}
{"index": "s930370269", "label": 472, "func": ""}
{"index": "s801287255", "label": 472, "func": ""}
{"index": "s524881038", "label": 472, "func": ""}
{"index": "s300327873", "label": 472, "func": ""}
{"index": "s028656455", "label": 472, "func": ""}
{"index": "s062802616", "label": 472, "func": ""}
{"index": "s523109256", "label": 2803, "func": ""}
{"index": "s524060194", "label": 2803, "func": ""}
{"index": "s155707406", "label": 2803, "func": ""}
{"index": "s327543207", "label": 2803, "func": ""}
{"index": "s592740319", "label": 2803, "func": ""}
{"index": "s191312850", "label": 2803, "func": ""}
{"index": "s041675387", "label": 2803, "func": ""}
{"index": "s139014612", "label": 2803, "func": ""}
{"index": "s912955167", "label": 2803, "func": ""}
{"index": "s657996252", "label": 2803, "func": ""}
{"index": "s412218070", "label": 94, "func": ""}
{"index": "s749687490", "label": 94, "func": ""}
{"index": "s711838205", "label": 94, "func": ""}
{"index": "s534376242", "label": 94, "func": ""}
{"index": "s598140418", "label": 94, "func": ""}
{"index": "s225246705", "label": 94, "func": ""}
{"index": "s583781045", "label": 94, "func": ""}
{"index": "s163952180", "label": 94, "func": ""}
{"index": "s807752919", "label": 94, "func": ""}
{"index": "s046343708", "label": 94, "func": ""}
{"index": "s197915370", "label": 1017, "func": ""}
{"index": "s925068453", "label": 2345, "func": ""}
{"index": "s565859419", "label": 2345, "func": ""}
{"index": "s029692058", "label": 2345, "func": ""}
{"index": "s444408765", "label": 2345, "func": ""}
{"index": "s596017677", "label": 2345, "func": ""}
{"index": "s044948596", "label": 2345, "func": ""}
{"index": "s909156752", "label": 2345, "func": ""}
{"index": "s519399763", "label": 2345, "func": ""}
{"index": "s134243690", "label": 2345, "func": ""}
{"index": "s220894709", "label": 2345, "func": ""}
{"index": "s336134832", "label": 3854, "func": ""}
{"index": "s749469370", "label": 3854, "func": ""}
{"index": "s165678393", "label": 3854, "func": ""}
{"index": "s160746523", "label": 3854, "func": ""}
{"index": "s374100878", "label": 3854, "func": ""}
{"index": "s385860901", "label": 3854, "func": ""}
{"index": "s349904642", "label": 3854, "func": ""}
{"index": "s493869380", "label": 3854, "func": ""}
{"index": "s484144539", "label": 3854, "func": ""}
{"index": "s902093334", "label": 3854, "func": ""}
{"index": "s769825540", "label": 3773, "func": ""}
{"index": "s373550399", "label": 3773, "func": ""}
{"index": "s980116619", "label": 3773, "func": ""}
{"index": "s887912451", "label": 3773, "func": ""}
{"index": "s898137054", "label": 3773, "func": ""}
{"index": "s972688203", "label": 3773, "func": ""}
{"index": "s551231747", "label": 3773, "func": ""}
{"index": "s854833794", "label": 3773, "func": ""}
{"index": "s887940741", "label": 3773, "func": ""}
{"index": "s675921850", "label": 3773, "func": ""}
{"index": "s293941938", "label": 322, "func": ""}
{"index": "s728128887", "label": 322, "func": ""}
{"index": "s522742070", "label": 322, "func": ""}
{"index": "s825529032", "label": 2299, "func": ""}
{"index": "s071340334", "label": 2299, "func": ""}
{"index": "s192652903", "label": 2299, "func": ""}
{"index": "s759475413", "label": 2299, "func": ""}
{"index": "s389435292", "label": 2299, "func": ""}
{"index": "s500681623", "label": 2299, "func": ""}
{"index": "s469093871", "label": 2299, "func": ""}
{"index": "s748482408", "label": 2299, "func": ""}
{"index": "s159728548", "label": 2353, "func": ""}
{"index": "s002057480", "label": 2353, "func": ""}
{"index": "s743076973", "label": 2353, "func": ""}
{"index": "s320309738", "label": 2353, "func": ""}
{"index": "s658195625", "label": 2353, "func": ""}
{"index": "s059628493", "label": 2353, "func": ""}
{"index": "s866716788", "label": 2353, "func": ""}
{"index": "s758043148", "label": 2353, "func": ""}
{"index": "s850175003", "label": 2353, "func": ""}
{"index": "s331820059", "label": 2353, "func": ""}
{"index": "s098489166", "label": 3518, "func": ""}
{"index": "s654718502", "label": 3498, "func": ""}
{"index": "s205266572", "label": 3498, "func": ""}
{"index": "s465241409", "label": 3498, "func": ""}
{"index": "s927538204", "label": 3498, "func": ""}
{"index": "s658751070", "label": 3498, "func": ""}
{"index": "s985957245", "label": 3498, "func": ""}
{"index": "s473524770", "label": 3498, "func": ""}
{"index": "s840171011", "label": 3498, "func": ""}
{"index": "s502652715", "label": 3498, "func": ""}
{"index": "s912093970", "label": 3498, "func": ""}
{"index": "s959878077", "label": 1417, "func": ""}
{"index": "s339626607", "label": 1417, "func": ""}
{"index": "s883279534", "label": 1417, "func": ""}
{"index": "s392423686", "label": 1417, "func": ""}
{"index": "s046664147", "label": 1417, "func": ""}
{"index": "s596264763", "label": 1417, "func": ""}
{"index": "s733405909", "label": 1417, "func": ""}
{"index": "s928488480", "label": 1417, "func": ""}
{"index": "s121951770", "label": 1417, "func": ""}
{"index": "s503581014", "label": 1417, "func": ""}
{"index": "s514353221", "label": 523, "func": ""}
{"index": "s642372534", "label": 523, "func": ""}
{"index": "s080977234", "label": 523, "func": ""}
{"index": "s250961532", "label": 2784, "func": ""}
{"index": "s497820400", "label": 2784, "func": ""}
{"index": "s954125406", "label": 2784, "func": ""}
{"index": "s195781662", "label": 2784, "func": ""}
{"index": "s382070215", "label": 2784, "func": ""}
{"index": "s743782212", "label": 2784, "func": ""}
{"index": "s900711704", "label": 2784, "func": ""}
{"index": "s497937315", "label": 2784, "func": ""}
{"index": "s395363983", "label": 2784, "func": ""}
{"index": "s374398699", "label": 2784, "func": ""}
{"index": "s008882017", "label": 575, "func": ""}
{"index": "s146434956", "label": 575, "func": ""}
{"index": "s949967271", "label": 575, "func": ""}
{"index": "s551285842", "label": 575, "func": ""}
{"index": "s485987910", "label": 575, "func": ""}
{"index": "s338579235", "label": 575, "func": ""}
{"index": "s971993650", "label": 575, "func": ""}
{"index": "s137266061", "label": 575, "func": ""}
{"index": "s806721730", "label": 575, "func": ""}
{"index": "s344957953", "label": 575, "func": ""}
{"index": "s450809181", "label": 1142, "func": ""}
{"index": "s949077970", "label": 1142, "func": ""}
{"index": "s021420908", "label": 1142, "func": ""}
{"index": "s580734402", "label": 103, "func": ""}
{"index": "s638611622", "label": 103, "func": ""}
{"index": "s824053305", "label": 103, "func": ""}
{"index": "s282901050", "label": 103, "func": ""}
{"index": "s427832062", "label": 103, "func": ""}
{"index": "s004212601", "label": 103, "func": ""}
{"index": "s090150524", "label": 103, "func": ""}
{"index": "s623365301", "label": 103, "func": ""}
{"index": "s442101746", "label": 103, "func": ""}
{"index": "s349420330", "label": 103, "func": ""}
{"index": "s694770882", "label": 1309, "func": ""}
{"index": "s431985394", "label": 1309, "func": ""}
{"index": "s045390075", "label": 1309, "func": ""}
{"index": "s574337555", "label": 1309, "func": ""}
{"index": "s050494214", "label": 1309, "func": ""}
{"index": "s441226400", "label": 141, "func": ""}
{"index": "s086256630", "label": 141, "func": ""}
{"index": "s658738624", "label": 141, "func": ""}
{"index": "s999180173", "label": 141, "func": ""}
{"index": "s828209418", "label": 141, "func": ""}
{"index": "s393449345", "label": 141, "func": ""}
{"index": "s477189048", "label": 141, "func": ""}
{"index": "s723538794", "label": 141, "func": ""}
{"index": "s736508776", "label": 141, "func": ""}
{"index": "s242684898", "label": 141, "func": ""}
{"index": "s710014975", "label": 3501, "func": ""}
{"index": "s750577523", "label": 3501, "func": ""}
{"index": "s739202004", "label": 3501, "func": ""}
{"index": "s903043030", "label": 3501, "func": ""}
{"index": "s324097804", "label": 3501, "func": ""}
{"index": "s904059195", "label": 3501, "func": ""}
{"index": "s895599466", "label": 3501, "func": ""}
{"index": "s304717224", "label": 3501, "func": ""}
{"index": "s778799999", "label": 3501, "func": ""}
{"index": "s612490919", "label": 3501, "func": ""}
{"index": "s086477574", "label": 2284, "func": ""}
{"index": "s484821362", "label": 2284, "func": ""}
{"index": "s481285765", "label": 2284, "func": ""}
{"index": "s934864482", "label": 2284, "func": ""}
{"index": "s988903329", "label": 2284, "func": ""}
{"index": "s973656821", "label": 2284, "func": ""}
{"index": "s884345189", "label": 2284, "func": ""}
{"index": "s478502450", "label": 2284, "func": ""}
{"index": "s638023799", "label": 2284, "func": ""}
{"index": "s629782457", "label": 2284, "func": ""}
{"index": "s392031721", "label": 2738, "func": ""}
{"index": "s263210588", "label": 2738, "func": ""}
{"index": "s037929398", "label": 2738, "func": ""}
{"index": "s876090607", "label": 2279, "func": ""}
{"index": "s908438084", "label": 2279, "func": ""}
{"index": "s863379893", "label": 2279, "func": ""}
{"index": "s034209745", "label": 2279, "func": ""}
{"index": "s121249754", "label": 2279, "func": ""}
{"index": "s009164653", "label": 2279, "func": ""}
{"index": "s058918945", "label": 2279, "func": ""}
{"index": "s270453679", "label": 2279, "func": ""}
{"index": "s033724353", "label": 2279, "func": ""}
{"index": "s224264738", "label": 2279, "func": ""}
{"index": "s092786227", "label": 3220, "func": ""}
{"index": "s257647339", "label": 3220, "func": ""}
{"index": "s867247672", "label": 3220, "func": ""}
{"index": "s352094365", "label": 3220, "func": ""}
{"index": "s987491989", "label": 3220, "func": ""}
{"index": "s029707288", "label": 3220, "func": ""}
{"index": "s627591259", "label": 3220, "func": ""}
{"index": "s541905823", "label": 3220, "func": ""}
{"index": "s980581400", "label": 3220, "func": ""}
{"index": "s476781080", "label": 3220, "func": ""}
{"index": "s625451680", "label": 3339, "func": ""}
{"index": "s550793710", "label": 3339, "func": ""}
{"index": "s953023823", "label": 3339, "func": ""}
{"index": "s459309267", "label": 3339, "func": ""}
{"index": "s507677892", "label": 3339, "func": ""}
{"index": "s142255660", "label": 3339, "func": ""}
{"index": "s929236769", "label": 3339, "func": ""}
{"index": "s933376072", "label": 3339, "func": ""}
{"index": "s891775960", "label": 3339, "func": ""}
{"index": "s429901871", "label": 3339, "func": ""}
{"index": "s517143800", "label": 669, "func": ""}
{"index": "s762723052", "label": 669, "func": ""}
{"index": "s976649257", "label": 669, "func": ""}
{"index": "s348897632", "label": 669, "func": ""}
{"index": "s127720512", "label": 669, "func": ""}
{"index": "s452234281", "label": 669, "func": ""}
{"index": "s063638450", "label": 669, "func": ""}
{"index": "s207835423", "label": 669, "func": ""}
{"index": "s740731653", "label": 669, "func": ""}
{"index": "s560760479", "label": 893, "func": ""}
{"index": "s089093170", "label": 893, "func": ""}
{"index": "s513521225", "label": 893, "func": ""}
{"index": "s488331901", "label": 893, "func": ""}
{"index": "s809568311", "label": 2405, "func": ""}
{"index": "s451784750", "label": 2405, "func": ""}
{"index": "s990174902", "label": 2405, "func": ""}
{"index": "s175867103", "label": 2405, "func": ""}
{"index": "s643524835", "label": 2405, "func": ""}
{"index": "s805593507", "label": 2405, "func": ""}
{"index": "s721666098", "label": 2405, "func": ""}
{"index": "s695435579", "label": 2405, "func": ""}
{"index": "s265547485", "label": 2405, "func": ""}
{"index": "s380845019", "label": 2405, "func": ""}
{"index": "s263701053", "label": 2423, "func": ""}
{"index": "s635649093", "label": 2423, "func": ""}
{"index": "s906786511", "label": 2423, "func": ""}
{"index": "s621422513", "label": 2423, "func": ""}
{"index": "s849540252", "label": 2423, "func": ""}
{"index": "s567393718", "label": 2423, "func": ""}
{"index": "s298605845", "label": 2423, "func": ""}
{"index": "s977794161", "label": 2423, "func": ""}
{"index": "s741055086", "label": 2423, "func": ""}
{"index": "s341845605", "label": 2423, "func": ""}
{"index": "s953238620", "label": 3574, "func": ""}
{"index": "s926904884", "label": 3574, "func": ""}
{"index": "s175730616", "label": 3574, "func": ""}
{"index": "s990797599", "label": 3574, "func": ""}
{"index": "s198871434", "label": 3574, "func": ""}
{"index": "s811747931", "label": 3574, "func": ""}
{"index": "s109849119", "label": 3574, "func": ""}
{"index": "s969095161", "label": 3574, "func": ""}
{"index": "s259533499", "label": 3574, "func": ""}
{"index": "s098929539", "label": 3574, "func": ""}
{"index": "s568010034", "label": 902, "func": ""}
{"index": "s486862900", "label": 902, "func": ""}
{"index": "s239974100", "label": 104, "func": ""}
{"index": "s984240099", "label": 104, "func": ""}
{"index": "s323436300", "label": 104, "func": ""}
{"index": "s941822779", "label": 104, "func": ""}
{"index": "s542165380", "label": 104, "func": ""}
{"index": "s024970078", "label": 104, "func": ""}
{"index": "s862977952", "label": 104, "func": ""}
{"index": "s125197570", "label": 104, "func": ""}
{"index": "s896717135", "label": 104, "func": ""}
{"index": "s921772182", "label": 104, "func": ""}
{"index": "s658069668", "label": 2295, "func": ""}
{"index": "s894283233", "label": 2295, "func": ""}
{"index": "s229486814", "label": 2295, "func": ""}
{"index": "s427112655", "label": 2295, "func": ""}
{"index": "s430149187", "label": 2295, "func": ""}
{"index": "s533685321", "label": 2295, "func": ""}
{"index": "s098957958", "label": 2295, "func": ""}
{"index": "s631586469", "label": 2295, "func": ""}
{"index": "s451606975", "label": 2295, "func": ""}
{"index": "s020688832", "label": 2295, "func": ""}
{"index": "s549727607", "label": 1617, "func": ""}
{"index": "s159693192", "label": 1617, "func": ""}
{"index": "s577454978", "label": 3296, "func": ""}
{"index": "s818219665", "label": 3296, "func": ""}
{"index": "s304645017", "label": 3296, "func": ""}
{"index": "s685619624", "label": 3296, "func": ""}
{"index": "s177852325", "label": 3296, "func": ""}
{"index": "s798686461", "label": 3296, "func": ""}
{"index": "s922885495", "label": 3296, "func": ""}
{"index": "s730742143", "label": 3296, "func": ""}
{"index": "s556190717", "label": 3296, "func": ""}
{"index": "s181216661", "label": 3296, "func": ""}
{"index": "s687407906", "label": 4026, "func": ""}
{"index": "s325594461", "label": 4026, "func": ""}
{"index": "s599760895", "label": 4026, "func": ""}
{"index": "s802096091", "label": 4026, "func": ""}
{"index": "s686116154", "label": 4026, "func": ""}
{"index": "s481718759", "label": 4026, "func": ""}
{"index": "s073331949", "label": 4026, "func": ""}
{"index": "s789934609", "label": 4026, "func": ""}
{"index": "s577832339", "label": 4026, "func": ""}
{"index": "s316958591", "label": 4026, "func": ""}
{"index": "s755120372", "label": 3095, "func": ""}
{"index": "s204563664", "label": 3095, "func": ""}
{"index": "s629475108", "label": 3095, "func": ""}
{"index": "s009934937", "label": 3095, "func": ""}
{"index": "s418647491", "label": 3095, "func": ""}
{"index": "s763125710", "label": 3095, "func": ""}
{"index": "s625768865", "label": 3095, "func": ""}
{"index": "s065751807", "label": 3095, "func": ""}
{"index": "s572397035", "label": 3095, "func": ""}
{"index": "s601605976", "label": 3095, "func": ""}
{"index": "s816893801", "label": 1073, "func": ""}
{"index": "s305821647", "label": 1073, "func": ""}
{"index": "s724558574", "label": 1073, "func": ""}
{"index": "s642549704", "label": 1073, "func": ""}
{"index": "s875916404", "label": 424, "func": ""}
{"index": "s553777467", "label": 424, "func": ""}
{"index": "s603366535", "label": 424, "func": ""}
{"index": "s681164922", "label": 424, "func": ""}
{"index": "s338361898", "label": 424, "func": ""}
{"index": "s300221969", "label": 424, "func": ""}
{"index": "s006695316", "label": 424, "func": ""}
{"index": "s443308862", "label": 424, "func": ""}
{"index": "s843872308", "label": 424, "func": ""}
{"index": "s312872060", "label": 424, "func": ""}
{"index": "s349925114", "label": 1260, "func": ""}
{"index": "s084697595", "label": 1260, "func": ""}
{"index": "s336986221", "label": 1260, "func": ""}
{"index": "s801775323", "label": 1087, "func": ""}
{"index": "s342594883", "label": 1087, "func": ""}
{"index": "s786567457", "label": 1087, "func": ""}
{"index": "s396519580", "label": 1087, "func": ""}
{"index": "s811128190", "label": 1087, "func": ""}
{"index": "s462418229", "label": 1087, "func": ""}
{"index": "s004543595", "label": 1087, "func": ""}
{"index": "s601253401", "label": 1087, "func": ""}
{"index": "s361721000", "label": 1087, "func": ""}
{"index": "s095376636", "label": 1087, "func": ""}
{"index": "s068302322", "label": 3954, "func": ""}
{"index": "s134955630", "label": 3954, "func": ""}
{"index": "s403216550", "label": 2411, "func": ""}
{"index": "s235243050", "label": 2411, "func": ""}
{"index": "s685153401", "label": 2411, "func": ""}
{"index": "s705828281", "label": 2411, "func": ""}
{"index": "s994159621", "label": 2411, "func": ""}
{"index": "s644528955", "label": 2411, "func": ""}
{"index": "s384931752", "label": 2411, "func": ""}
{"index": "s402647977", "label": 2411, "func": ""}
{"index": "s688117748", "label": 2411, "func": ""}
{"index": "s659534691", "label": 2411, "func": ""}
{"index": "s893812839", "label": 3565, "func": ""}
{"index": "s040205092", "label": 3565, "func": ""}
{"index": "s339946419", "label": 3565, "func": ""}
{"index": "s932978305", "label": 3565, "func": ""}
{"index": "s126418457", "label": 3565, "func": ""}
{"index": "s071412226", "label": 3565, "func": ""}
{"index": "s171712292", "label": 3565, "func": ""}
{"index": "s389037247", "label": 3565, "func": ""}
{"index": "s769127925", "label": 3565, "func": ""}
{"index": "s684841046", "label": 3565, "func": ""}
{"index": "s096247669", "label": 3374, "func": ""}
{"index": "s986080624", "label": 3374, "func": ""}
{"index": "s968951078", "label": 3374, "func": ""}
{"index": "s932558830", "label": 3374, "func": ""}
{"index": "s943234488", "label": 3374, "func": ""}
{"index": "s072418094", "label": 3374, "func": ""}
{"index": "s077483883", "label": 3374, "func": ""}
{"index": "s708933301", "label": 3374, "func": ""}
{"index": "s133818192", "label": 3374, "func": ""}
{"index": "s001321677", "label": 3374, "func": ""}
{"index": "s657704365", "label": 4039, "func": ""}
{"index": "s559252050", "label": 4039, "func": ""}
{"index": "s158581458", "label": 4039, "func": ""}
{"index": "s684217547", "label": 4039, "func": ""}
{"index": "s723707566", "label": 4039, "func": ""}
{"index": "s258164670", "label": 4039, "func": ""}
{"index": "s633339693", "label": 4039, "func": ""}
{"index": "s449189355", "label": 4039, "func": ""}
{"index": "s016794726", "label": 4039, "func": ""}
{"index": "s432703202", "label": 4039, "func": ""}
{"index": "s771483802", "label": 3323, "func": ""}
{"index": "s020627362", "label": 3323, "func": ""}
{"index": "s910513096", "label": 3323, "func": ""}
{"index": "s564433496", "label": 3323, "func": ""}
{"index": "s001987716", "label": 3323, "func": ""}
{"index": "s868764948", "label": 3323, "func": ""}
{"index": "s195950166", "label": 3323, "func": ""}
{"index": "s617195440", "label": 3323, "func": ""}
{"index": "s476244578", "label": 3323, "func": ""}
{"index": "s577973286", "label": 3323, "func": ""}
{"index": "s563983536", "label": 3609, "func": ""}
{"index": "s422817638", "label": 3609, "func": ""}
{"index": "s586194245", "label": 3609, "func": ""}
{"index": "s322975607", "label": 3609, "func": ""}
{"index": "s194952002", "label": 3609, "func": ""}
{"index": "s921659405", "label": 3609, "func": ""}
{"index": "s153989325", "label": 3609, "func": ""}
{"index": "s790663748", "label": 3609, "func": ""}
{"index": "s412933739", "label": 3609, "func": ""}
{"index": "s309325471", "label": 3609, "func": ""}
{"index": "s367600486", "label": 4001, "func": ""}
{"index": "s437993814", "label": 4001, "func": ""}
{"index": "s687249177", "label": 4001, "func": ""}
{"index": "s820177022", "label": 4001, "func": ""}
{"index": "s723970554", "label": 4001, "func": ""}
{"index": "s617320203", "label": 4001, "func": ""}
{"index": "s229465423", "label": 4001, "func": ""}
{"index": "s586612878", "label": 4001, "func": ""}
{"index": "s843843463", "label": 4001, "func": ""}
{"index": "s654560024", "label": 4001, "func": ""}
{"index": "s222456453", "label": 4002, "func": ""}
{"index": "s023304658", "label": 4002, "func": ""}
{"index": "s506326239", "label": 4002, "func": ""}
{"index": "s471857164", "label": 4002, "func": ""}
{"index": "s588102947", "label": 4002, "func": ""}
{"index": "s901512504", "label": 4002, "func": ""}
{"index": "s123941824", "label": 4002, "func": ""}
{"index": "s353844481", "label": 4002, "func": ""}
{"index": "s692182429", "label": 4002, "func": ""}
{"index": "s899660190", "label": 4002, "func": ""}
{"index": "s441715290", "label": 2348, "func": ""}
{"index": "s262608537", "label": 2348, "func": ""}
{"index": "s593010480", "label": 2348, "func": ""}
{"index": "s583110481", "label": 2348, "func": ""}
{"index": "s552987453", "label": 2348, "func": ""}
{"index": "s952029089", "label": 2348, "func": ""}
{"index": "s914196156", "label": 2348, "func": ""}
{"index": "s740113968", "label": 2348, "func": ""}
{"index": "s837499596", "label": 2348, "func": ""}
{"index": "s423669845", "label": 2348, "func": ""}
{"index": "s318572707", "label": 2061, "func": ""}
{"index": "s134874858", "label": 2061, "func": ""}
{"index": "s593213322", "label": 2061, "func": ""}
{"index": "s591578891", "label": 1820, "func": ""}
{"index": "s399768829", "label": 868, "func": ""}
{"index": "s033415561", "label": 868, "func": ""}
{"index": "s058581579", "label": 868, "func": ""}
{"index": "s415808697", "label": 868, "func": ""}
{"index": "s167643678", "label": 868, "func": ""}
{"index": "s711632253", "label": 868, "func": ""}
{"index": "s994534673", "label": 3638, "func": ""}
{"index": "s024353379", "label": 3638, "func": ""}
{"index": "s206174774", "label": 3638, "func": ""}
{"index": "s151509696", "label": 3638, "func": ""}
{"index": "s808411062", "label": 3638, "func": ""}
{"index": "s581778201", "label": 3638, "func": ""}
{"index": "s215499641", "label": 3638, "func": ""}
{"index": "s225055667", "label": 3638, "func": ""}
{"index": "s359214845", "label": 3638, "func": ""}
{"index": "s623652022", "label": 3638, "func": ""}
{"index": "s128208370", "label": 544, "func": ""}
{"index": "s716016285", "label": 544, "func": ""}
{"index": "s460461709", "label": 544, "func": ""}
{"index": "s284280719", "label": 544, "func": ""}
{"index": "s856473232", "label": 544, "func": ""}
{"index": "s395787280", "label": 544, "func": ""}
{"index": "s455394002", "label": 544, "func": ""}
{"index": "s556874614", "label": 544, "func": ""}
{"index": "s986737839", "label": 544, "func": ""}
{"index": "s241195532", "label": 544, "func": ""}
{"index": "s834614528", "label": 742, "func": ""}
{"index": "s360625844", "label": 742, "func": ""}
{"index": "s706288932", "label": 742, "func": ""}
{"index": "s548489337", "label": 742, "func": ""}
{"index": "s062228816", "label": 742, "func": ""}
{"index": "s829270724", "label": 742, "func": ""}
{"index": "s723260825", "label": 742, "func": ""}
{"index": "s489817163", "label": 742, "func": ""}
{"index": "s659268231", "label": 742, "func": ""}
{"index": "s221690064", "label": 742, "func": ""}
{"index": "s749280197", "label": 1364, "func": ""}
{"index": "s191648185", "label": 1364, "func": ""}
{"index": "s171809973", "label": 1364, "func": ""}
{"index": "s481013285", "label": 3085, "func": ""}
{"index": "s173943725", "label": 3085, "func": ""}
{"index": "s357261969", "label": 3085, "func": ""}
{"index": "s634755773", "label": 3085, "func": ""}
{"index": "s085791917", "label": 3085, "func": ""}
{"index": "s555093291", "label": 3085, "func": ""}
{"index": "s038790012", "label": 3085, "func": ""}
{"index": "s817518950", "label": 3085, "func": ""}
{"index": "s103397393", "label": 3085, "func": ""}
{"index": "s143997560", "label": 3085, "func": ""}
{"index": "s485821181", "label": 2726, "func": ""}
{"index": "s855514725", "label": 2726, "func": ""}
{"index": "s487084950", "label": 2726, "func": ""}
{"index": "s725545812", "label": 2726, "func": ""}
{"index": "s421930379", "label": 2726, "func": ""}
{"index": "s472329397", "label": 2726, "func": ""}
{"index": "s363285429", "label": 2726, "func": ""}
{"index": "s362089682", "label": 2726, "func": ""}
{"index": "s003099908", "label": 2726, "func": ""}
{"index": "s477202008", "label": 2726, "func": ""}
{"index": "s125423867", "label": 3061, "func": ""}
{"index": "s440252142", "label": 3061, "func": ""}
{"index": "s027475070", "label": 3061, "func": ""}
{"index": "s871543452", "label": 3061, "func": ""}
{"index": "s023083073", "label": 3061, "func": ""}
{"index": "s196680688", "label": 3061, "func": ""}
{"index": "s719207879", "label": 3061, "func": ""}
{"index": "s946075278", "label": 3061, "func": ""}
{"index": "s205930830", "label": 3061, "func": ""}
{"index": "s347103997", "label": 3061, "func": ""}
{"index": "s154959690", "label": 3358, "func": ""}
{"index": "s850414282", "label": 3358, "func": ""}
{"index": "s403357970", "label": 2918, "func": ""}
{"index": "s013673830", "label": 2918, "func": ""}
{"index": "s088036761", "label": 2918, "func": ""}
{"index": "s225555515", "label": 2918, "func": ""}
{"index": "s939928344", "label": 2918, "func": ""}
{"index": "s665114391", "label": 2918, "func": ""}
{"index": "s459435177", "label": 2918, "func": ""}
{"index": "s289595852", "label": 2918, "func": ""}
{"index": "s207613266", "label": 2918, "func": ""}
{"index": "s877611171", "label": 2918, "func": ""}
{"index": "s012342557", "label": 2437, "func": ""}
{"index": "s516497085", "label": 2437, "func": ""}
{"index": "s551001486", "label": 2437, "func": ""}
{"index": "s226968743", "label": 2437, "func": ""}
{"index": "s240367464", "label": 2437, "func": ""}
{"index": "s636300395", "label": 2437, "func": ""}
{"index": "s417491339", "label": 2437, "func": ""}
{"index": "s807427617", "label": 2437, "func": ""}
{"index": "s965824388", "label": 2437, "func": ""}
{"index": "s021767949", "label": 2437, "func": ""}
{"index": "s680878068", "label": 3229, "func": ""}
{"index": "s289847055", "label": 3229, "func": ""}
{"index": "s978566445", "label": 3229, "func": ""}
{"index": "s789298764", "label": 3229, "func": ""}
{"index": "s360853255", "label": 3229, "func": ""}
{"index": "s578184715", "label": 3229, "func": ""}
{"index": "s223585976", "label": 3229, "func": ""}
{"index": "s199166089", "label": 3229, "func": ""}
{"index": "s132857579", "label": 3229, "func": ""}
{"index": "s964365433", "label": 3229, "func": ""}
{"index": "s413184540", "label": 85, "func": ""}
{"index": "s310875254", "label": 85, "func": ""}
{"index": "s739904469", "label": 85, "func": ""}
{"index": "s068122248", "label": 85, "func": ""}
{"index": "s677783180", "label": 85, "func": ""}
{"index": "s339862954", "label": 85, "func": ""}
{"index": "s498277543", "label": 85, "func": ""}
{"index": "s503933323", "label": 85, "func": ""}
{"index": "s434667658", "label": 85, "func": ""}
{"index": "s392467310", "label": 85, "func": ""}
{"index": "s367939081", "label": 948, "func": ""}
{"index": "s630127485", "label": 948, "func": ""}
{"index": "s086537328", "label": 948, "func": ""}
{"index": "s682048542", "label": 1881, "func": ""}
{"index": "s913595786", "label": 1881, "func": ""}
{"index": "s996814203", "label": 388, "func": ""}
{"index": "s904855295", "label": 388, "func": ""}
{"index": "s183918823", "label": 388, "func": ""}
{"index": "s055492187", "label": 388, "func": ""}
{"index": "s765856228", "label": 388, "func": ""}
{"index": "s159623414", "label": 2630, "func": ""}
{"index": "s073284236", "label": 2630, "func": ""}
{"index": "s216172480", "label": 2630, "func": ""}
{"index": "s951877688", "label": 2630, "func": ""}
{"index": "s841469390", "label": 2630, "func": ""}
{"index": "s393272328", "label": 2630, "func": ""}
{"index": "s952444303", "label": 2630, "func": ""}
{"index": "s507339057", "label": 2630, "func": ""}
{"index": "s351391491", "label": 2630, "func": ""}
{"index": "s070563602", "label": 2630, "func": ""}
{"index": "s022150856", "label": 411, "func": ""}
{"index": "s836827469", "label": 2099, "func": ""}
{"index": "s271433609", "label": 2099, "func": ""}
{"index": "s292983428", "label": 2099, "func": ""}
{"index": "s931113412", "label": 2099, "func": ""}
{"index": "s524210383", "label": 1845, "func": ""}
{"index": "s087634143", "label": 1845, "func": ""}
{"index": "s830904333", "label": 1845, "func": ""}
{"index": "s625775302", "label": 1845, "func": ""}
{"index": "s420106458", "label": 1845, "func": ""}
{"index": "s452792784", "label": 1845, "func": ""}
{"index": "s759008625", "label": 1845, "func": ""}
{"index": "s866629159", "label": 1845, "func": ""}
{"index": "s700005514", "label": 1845, "func": ""}
{"index": "s154934135", "label": 1845, "func": ""}
{"index": "s719537173", "label": 3412, "func": ""}
{"index": "s397023920", "label": 3412, "func": ""}
{"index": "s973001377", "label": 3412, "func": ""}
{"index": "s890281019", "label": 3412, "func": ""}
{"index": "s143013799", "label": 3412, "func": ""}
{"index": "s305637786", "label": 3412, "func": ""}
{"index": "s851792369", "label": 3412, "func": ""}
{"index": "s687713980", "label": 3412, "func": ""}
{"index": "s481424361", "label": 3412, "func": ""}
{"index": "s685221863", "label": 3412, "func": ""}
{"index": "s113698588", "label": 1284, "func": ""}
{"index": "s851719933", "label": 1284, "func": ""}
{"index": "s117894649", "label": 1284, "func": ""}
{"index": "s786802115", "label": 1284, "func": ""}
{"index": "s311965423", "label": 1284, "func": ""}
{"index": "s854701815", "label": 1284, "func": ""}
{"index": "s809185256", "label": 1284, "func": ""}
{"index": "s044160070", "label": 1284, "func": ""}
{"index": "s493850562", "label": 1284, "func": ""}
{"index": "s531041015", "label": 1284, "func": ""}
{"index": "s925986917", "label": 2821, "func": ""}
{"index": "s465653766", "label": 2821, "func": ""}
{"index": "s864577352", "label": 2821, "func": ""}
{"index": "s662582654", "label": 2821, "func": ""}
{"index": "s661102749", "label": 2821, "func": ""}
{"index": "s359357678", "label": 2821, "func": ""}
{"index": "s793752538", "label": 2821, "func": ""}
{"index": "s082610049", "label": 2821, "func": ""}
{"index": "s484841030", "label": 2821, "func": ""}
{"index": "s032317900", "label": 2821, "func": ""}
{"index": "s522811734", "label": 1980, "func": ""}
{"index": "s149157380", "label": 3071, "func": ""}
{"index": "s789758961", "label": 3071, "func": ""}
{"index": "s233415360", "label": 3071, "func": ""}
{"index": "s649705885", "label": 3071, "func": ""}
{"index": "s981922668", "label": 3071, "func": ""}
{"index": "s063622417", "label": 3071, "func": ""}
{"index": "s421610309", "label": 3071, "func": ""}
{"index": "s156520712", "label": 3071, "func": ""}
{"index": "s971254228", "label": 3071, "func": ""}
{"index": "s718329184", "label": 3071, "func": ""}
{"index": "s097390159", "label": 3636, "func": ""}
{"index": "s691577565", "label": 3636, "func": ""}
{"index": "s689754131", "label": 3636, "func": ""}
{"index": "s320149654", "label": 3636, "func": ""}
{"index": "s588812470", "label": 3636, "func": ""}
{"index": "s915194313", "label": 3636, "func": ""}
{"index": "s415874889", "label": 3636, "func": ""}
{"index": "s832212914", "label": 3636, "func": ""}
{"index": "s348505765", "label": 3636, "func": ""}
{"index": "s782417396", "label": 3636, "func": ""}
{"index": "s922389145", "label": 991, "func": ""}
{"index": "s005697949", "label": 991, "func": ""}
{"index": "s961339052", "label": 991, "func": ""}
{"index": "s200955329", "label": 991, "func": ""}
{"index": "s491995260", "label": 991, "func": ""}
{"index": "s083548270", "label": 991, "func": ""}
{"index": "s457853800", "label": 991, "func": ""}
{"index": "s077138773", "label": 991, "func": ""}
{"index": "s075049627", "label": 991, "func": ""}
{"index": "s970947381", "label": 991, "func": ""}
{"index": "s436667996", "label": 2044, "func": ""}
{"index": "s256269291", "label": 2731, "func": ""}
{"index": "s115132326", "label": 2731, "func": ""}
{"index": "s642900324", "label": 2731, "func": ""}
{"index": "s071282938", "label": 2731, "func": ""}
{"index": "s813694122", "label": 2731, "func": ""}
{"index": "s966422051", "label": 2731, "func": ""}
{"index": "s906711851", "label": 2731, "func": ""}
{"index": "s853348324", "label": 2731, "func": ""}
{"index": "s833377568", "label": 2731, "func": ""}
{"index": "s376538371", "label": 2731, "func": ""}
{"index": "s856772530", "label": 3213, "func": ""}
{"index": "s073821642", "label": 3213, "func": ""}
{"index": "s558669285", "label": 3213, "func": ""}
{"index": "s455305713", "label": 3213, "func": ""}
{"index": "s450883769", "label": 3213, "func": ""}
{"index": "s086622498", "label": 3213, "func": ""}
{"index": "s528880449", "label": 3213, "func": ""}
{"index": "s721604504", "label": 3213, "func": ""}
{"index": "s478377861", "label": 3213, "func": ""}
{"index": "s094935107", "label": 3213, "func": ""}
{"index": "s352649156", "label": 1919, "func": ""}
{"index": "s583053303", "label": 2994, "func": ""}
{"index": "s172486013", "label": 2994, "func": ""}
{"index": "s851105340", "label": 2994, "func": ""}
{"index": "s706418662", "label": 2994, "func": ""}
{"index": "s897277313", "label": 2994, "func": ""}
{"index": "s564307945", "label": 2994, "func": ""}
{"index": "s925371287", "label": 2994, "func": ""}
{"index": "s599414604", "label": 2994, "func": ""}
{"index": "s317809241", "label": 2994, "func": ""}
{"index": "s476355312", "label": 2994, "func": ""}
{"index": "s396898892", "label": 3808, "func": ""}
{"index": "s262663237", "label": 3808, "func": ""}
{"index": "s248052350", "label": 3808, "func": ""}
{"index": "s859911954", "label": 3808, "func": ""}
{"index": "s625198208", "label": 3808, "func": ""}
{"index": "s707981487", "label": 3808, "func": ""}
{"index": "s443868635", "label": 3808, "func": ""}
{"index": "s235831654", "label": 3808, "func": ""}
{"index": "s806878553", "label": 3808, "func": ""}
{"index": "s933323344", "label": 3808, "func": ""}
{"index": "s068234098", "label": 789, "func": ""}
{"index": "s601021999", "label": 789, "func": ""}
{"index": "s569783274", "label": 789, "func": ""}
{"index": "s278578072", "label": 789, "func": ""}
{"index": "s539299174", "label": 789, "func": ""}
{"index": "s449376856", "label": 789, "func": ""}
{"index": "s974693243", "label": 789, "func": ""}
{"index": "s695071433", "label": 789, "func": ""}
{"index": "s390921064", "label": 789, "func": ""}
{"index": "s403308111", "label": 789, "func": ""}
{"index": "s176727947", "label": 1591, "func": ""}
{"index": "s252494456", "label": 2699, "func": ""}
{"index": "s063394039", "label": 2699, "func": ""}
{"index": "s202789660", "label": 2699, "func": ""}
{"index": "s697252950", "label": 2699, "func": ""}
{"index": "s749242763", "label": 2699, "func": ""}
{"index": "s547172807", "label": 2699, "func": ""}
{"index": "s044874353", "label": 2699, "func": ""}
{"index": "s618956621", "label": 2699, "func": ""}
{"index": "s577772401", "label": 2699, "func": ""}
{"index": "s717005618", "label": 2699, "func": ""}
{"index": "s790237554", "label": 3422, "func": ""}
{"index": "s486339986", "label": 3422, "func": ""}
{"index": "s281946222", "label": 3422, "func": ""}
{"index": "s189716651", "label": 3422, "func": ""}
{"index": "s055058648", "label": 3422, "func": ""}
{"index": "s467257189", "label": 3733, "func": ""}
{"index": "s464086397", "label": 3733, "func": ""}
{"index": "s628489093", "label": 3733, "func": ""}
{"index": "s610354701", "label": 3733, "func": ""}
{"index": "s865022731", "label": 3733, "func": ""}
{"index": "s728700887", "label": 3733, "func": ""}
{"index": "s520523430", "label": 3733, "func": ""}
{"index": "s019272902", "label": 3733, "func": ""}
{"index": "s974262888", "label": 3733, "func": ""}
{"index": "s624408315", "label": 3733, "func": ""}
{"index": "s115065010", "label": 1439, "func": ""}
{"index": "s110078311", "label": 1733, "func": ""}
{"index": "s995432663", "label": 1733, "func": ""}
{"index": "s172738638", "label": 894, "func": ""}
{"index": "s046018669", "label": 894, "func": ""}
{"index": "s194460186", "label": 894, "func": ""}
{"index": "s150647450", "label": 894, "func": ""}
{"index": "s740461867", "label": 894, "func": ""}
{"index": "s973306294", "label": 894, "func": ""}
{"index": "s325941766", "label": 894, "func": ""}
{"index": "s147957657", "label": 894, "func": ""}
{"index": "s746108214", "label": 894, "func": ""}
{"index": "s943787153", "label": 894, "func": ""}
{"index": "s596038581", "label": 1181, "func": ""}
{"index": "s827941739", "label": 1181, "func": ""}
{"index": "s353961195", "label": 1181, "func": ""}
{"index": "s307895913", "label": 1181, "func": ""}
{"index": "s251016095", "label": 1181, "func": ""}
{"index": "s839819183", "label": 2355, "func": ""}
{"index": "s807858164", "label": 2355, "func": ""}
{"index": "s322452368", "label": 2355, "func": ""}
{"index": "s023249310", "label": 159, "func": ""}
{"index": "s251888778", "label": 159, "func": ""}
{"index": "s218747387", "label": 159, "func": ""}
{"index": "s658991474", "label": 159, "func": ""}
{"index": "s502630127", "label": 159, "func": ""}
{"index": "s690781649", "label": 159, "func": ""}
{"index": "s063796399", "label": 159, "func": ""}
{"index": "s382695580", "label": 159, "func": ""}
{"index": "s495789916", "label": 159, "func": ""}
{"index": "s752267976", "label": 159, "func": ""}
{"index": "s821310738", "label": 519, "func": ""}
{"index": "s920882948", "label": 519, "func": ""}
{"index": "s031360405", "label": 519, "func": ""}
{"index": "s546906781", "label": 519, "func": ""}
{"index": "s922365926", "label": 519, "func": ""}
{"index": "s188936272", "label": 519, "func": ""}
{"index": "s728664009", "label": 519, "func": ""}
{"index": "s240633720", "label": 519, "func": ""}
{"index": "s711685050", "label": 519, "func": ""}
{"index": "s443844634", "label": 519, "func": ""}
{"index": "s392755764", "label": 2368, "func": ""}
{"index": "s232384520", "label": 2368, "func": ""}
{"index": "s615506505", "label": 2368, "func": ""}
{"index": "s739792263", "label": 2368, "func": ""}
{"index": "s218967901", "label": 2368, "func": ""}
{"index": "s365509257", "label": 2368, "func": ""}
{"index": "s025146020", "label": 2368, "func": ""}
{"index": "s726190373", "label": 2368, "func": ""}
{"index": "s807246912", "label": 2368, "func": ""}
{"index": "s171056692", "label": 2368, "func": ""}
{"index": "s290262405", "label": 2688, "func": ""}
{"index": "s212873070", "label": 2688, "func": ""}
{"index": "s200846258", "label": 2688, "func": ""}
{"index": "s197785323", "label": 2688, "func": ""}
{"index": "s545123342", "label": 2688, "func": ""}
{"index": "s630776180", "label": 2688, "func": ""}
{"index": "s113732837", "label": 2688, "func": ""}
{"index": "s412582006", "label": 2688, "func": ""}
{"index": "s159862226", "label": 2688, "func": ""}
{"index": "s241375799", "label": 2688, "func": ""}
{"index": "s314170416", "label": 731, "func": ""}
{"index": "s446472759", "label": 731, "func": ""}
{"index": "s065618725", "label": 731, "func": ""}
{"index": "s996720141", "label": 731, "func": ""}
{"index": "s215826665", "label": 731, "func": ""}
{"index": "s257776644", "label": 731, "func": ""}
{"index": "s854365637", "label": 731, "func": ""}
{"index": "s829839650", "label": 731, "func": ""}
{"index": "s023705886", "label": 731, "func": ""}
{"index": "s678095194", "label": 731, "func": ""}
{"index": "s743250002", "label": 3449, "func": ""}
{"index": "s421879342", "label": 3449, "func": ""}
{"index": "s830289371", "label": 3449, "func": ""}
{"index": "s984990507", "label": 3449, "func": ""}
{"index": "s429083430", "label": 3449, "func": ""}
{"index": "s641418535", "label": 3449, "func": ""}
{"index": "s407204481", "label": 3449, "func": ""}
{"index": "s506258046", "label": 3449, "func": ""}
{"index": "s121222182", "label": 3449, "func": ""}
{"index": "s410157356", "label": 3449, "func": ""}
{"index": "s501708439", "label": 438, "func": ""}
{"index": "s400806639", "label": 438, "func": ""}
{"index": "s239619169", "label": 438, "func": ""}
{"index": "s878347146", "label": 438, "func": ""}
{"index": "s721309589", "label": 438, "func": ""}
{"index": "s743739224", "label": 438, "func": ""}
{"index": "s077012342", "label": 438, "func": ""}
{"index": "s129902753", "label": 438, "func": ""}
{"index": "s057424896", "label": 438, "func": ""}
{"index": "s292223936", "label": 438, "func": ""}
{"index": "s223193046", "label": 3867, "func": ""}
{"index": "s150989274", "label": 3867, "func": ""}
{"index": "s535413540", "label": 3867, "func": ""}
{"index": "s452395751", "label": 3867, "func": ""}
{"index": "s520803839", "label": 557, "func": ""}
{"index": "s048401784", "label": 557, "func": ""}
{"index": "s110170414", "label": 557, "func": ""}
{"index": "s344124503", "label": 35, "func": ""}
{"index": "s952452551", "label": 35, "func": ""}
{"index": "s276334236", "label": 35, "func": ""}
{"index": "s954598609", "label": 35, "func": ""}
{"index": "s350180750", "label": 35, "func": ""}
{"index": "s913460026", "label": 35, "func": ""}
{"index": "s402787595", "label": 35, "func": ""}
{"index": "s792616287", "label": 35, "func": ""}
{"index": "s642143343", "label": 35, "func": ""}
{"index": "s795804307", "label": 35, "func": ""}
{"index": "s800348720", "label": 2215, "func": ""}
{"index": "s190050779", "label": 3754, "func": ""}
{"index": "s889939903", "label": 3754, "func": ""}
{"index": "s325974504", "label": 3754, "func": ""}
{"index": "s600966891", "label": 1371, "func": ""}
{"index": "s810943176", "label": 1371, "func": ""}
{"index": "s076643396", "label": 1371, "func": ""}
{"index": "s782911605", "label": 1371, "func": ""}
{"index": "s740315258", "label": 1371, "func": ""}
{"index": "s743877057", "label": 1371, "func": ""}
{"index": "s873723309", "label": 1371, "func": ""}
{"index": "s647352291", "label": 1371, "func": ""}
{"index": "s606055793", "label": 1371, "func": ""}
{"index": "s941493373", "label": 1371, "func": ""}
{"index": "s439785825", "label": 2525, "func": ""}
{"index": "s982840448", "label": 2525, "func": ""}
{"index": "s880676931", "label": 2525, "func": ""}
{"index": "s614071081", "label": 2525, "func": ""}
{"index": "s316202561", "label": 2525, "func": ""}
{"index": "s696012062", "label": 2525, "func": ""}
{"index": "s983949479", "label": 2525, "func": ""}
{"index": "s310230402", "label": 2525, "func": ""}
{"index": "s500113634", "label": 2525, "func": ""}
{"index": "s748530499", "label": 2525, "func": ""}
{"index": "s556298891", "label": 1094, "func": ""}
{"index": "s345464736", "label": 1094, "func": ""}
{"index": "s094898593", "label": 1094, "func": ""}
{"index": "s118654848", "label": 1094, "func": ""}
{"index": "s642486920", "label": 1094, "func": ""}
{"index": "s189598751", "label": 1094, "func": ""}
{"index": "s957587108", "label": 1094, "func": ""}
{"index": "s138165952", "label": 1094, "func": ""}
{"index": "s658138951", "label": 1094, "func": ""}
{"index": "s623441054", "label": 1094, "func": ""}
{"index": "s667867211", "label": 2571, "func": ""}
{"index": "s505193285", "label": 2571, "func": ""}
{"index": "s920359562", "label": 2571, "func": ""}
{"index": "s330934260", "label": 2571, "func": ""}
{"index": "s706290736", "label": 2571, "func": ""}
{"index": "s904844588", "label": 2571, "func": ""}
{"index": "s074238644", "label": 2571, "func": ""}
{"index": "s998149750", "label": 2571, "func": ""}
{"index": "s572490085", "label": 2571, "func": ""}
{"index": "s852171834", "label": 2571, "func": ""}
{"index": "s131698621", "label": 1491, "func": ""}
{"index": "s893818313", "label": 1491, "func": ""}
{"index": "s296619583", "label": 1151, "func": ""}
{"index": "s829768929", "label": 312, "func": ""}
{"index": "s193449725", "label": 312, "func": ""}
{"index": "s712951219", "label": 312, "func": ""}
{"index": "s800254728", "label": 312, "func": ""}
{"index": "s207187498", "label": 312, "func": ""}
{"index": "s689093575", "label": 312, "func": ""}
{"index": "s144998739", "label": 312, "func": ""}
{"index": "s208315936", "label": 312, "func": ""}
{"index": "s755560358", "label": 312, "func": ""}
{"index": "s551922449", "label": 312, "func": ""}
{"index": "s987971235", "label": 1766, "func": ""}
{"index": "s007834880", "label": 1766, "func": ""}
{"index": "s499161971", "label": 1766, "func": ""}
{"index": "s018433133", "label": 1766, "func": ""}
{"index": "s015106177", "label": 1766, "func": ""}
{"index": "s331961321", "label": 1766, "func": ""}
{"index": "s460965171", "label": 1766, "func": ""}
{"index": "s602477329", "label": 1766, "func": ""}
{"index": "s670015645", "label": 3551, "func": ""}
{"index": "s339805528", "label": 3551, "func": ""}
{"index": "s409515819", "label": 3551, "func": ""}
{"index": "s977697959", "label": 3551, "func": ""}
{"index": "s095518626", "label": 3551, "func": ""}
{"index": "s238136180", "label": 3551, "func": ""}
{"index": "s489994869", "label": 3551, "func": ""}
{"index": "s937118060", "label": 3551, "func": ""}
{"index": "s172287432", "label": 3551, "func": ""}
{"index": "s733617301", "label": 3551, "func": ""}
{"index": "s590443862", "label": 3817, "func": ""}
{"index": "s048815631", "label": 3817, "func": ""}
{"index": "s205676631", "label": 3817, "func": ""}
{"index": "s289184030", "label": 3817, "func": ""}
{"index": "s574375390", "label": 3817, "func": ""}
{"index": "s728865876", "label": 3817, "func": ""}
{"index": "s001032208", "label": 3817, "func": ""}
{"index": "s943054608", "label": 3817, "func": ""}
{"index": "s792921780", "label": 3817, "func": ""}
{"index": "s614342028", "label": 3817, "func": ""}
{"index": "s486437290", "label": 531, "func": ""}
{"index": "s322782548", "label": 531, "func": ""}
{"index": "s425747367", "label": 531, "func": ""}
{"index": "s482644841", "label": 531, "func": ""}
{"index": "s490023171", "label": 531, "func": ""}
{"index": "s325833474", "label": 531, "func": ""}
{"index": "s076497293", "label": 531, "func": ""}
{"index": "s910214755", "label": 531, "func": ""}
{"index": "s211851685", "label": 531, "func": ""}
{"index": "s788416905", "label": 531, "func": ""}
{"index": "s965706274", "label": 2025, "func": ""}
{"index": "s397072915", "label": 806, "func": ""}
{"index": "s832424357", "label": 806, "func": ""}
{"index": "s985744870", "label": 806, "func": ""}
{"index": "s219003529", "label": 806, "func": ""}
{"index": "s787108659", "label": 806, "func": ""}
{"index": "s984651571", "label": 806, "func": ""}
{"index": "s264393274", "label": 513, "func": ""}
{"index": "s505028097", "label": 513, "func": ""}
{"index": "s162946542", "label": 513, "func": ""}
{"index": "s646091186", "label": 513, "func": ""}
{"index": "s843764465", "label": 513, "func": ""}
{"index": "s383748073", "label": 513, "func": ""}
{"index": "s073643371", "label": 513, "func": ""}
{"index": "s923582920", "label": 513, "func": ""}
{"index": "s211540779", "label": 513, "func": ""}
{"index": "s617662398", "label": 3951, "func": ""}
{"index": "s030690436", "label": 3951, "func": ""}
{"index": "s296286444", "label": 3951, "func": ""}
{"index": "s884569292", "label": 3951, "func": ""}
{"index": "s194783406", "label": 3951, "func": ""}
{"index": "s794863870", "label": 3951, "func": ""}
{"index": "s091121009", "label": 3951, "func": ""}
{"index": "s100044276", "label": 3951, "func": ""}
{"index": "s447486678", "label": 3951, "func": ""}
{"index": "s738721901", "label": 3951, "func": ""}
{"index": "s859507951", "label": 3919, "func": ""}
{"index": "s306766643", "label": 3919, "func": ""}
{"index": "s489264709", "label": 3919, "func": ""}
{"index": "s515942796", "label": 3919, "func": ""}
{"index": "s979437876", "label": 3919, "func": ""}
{"index": "s952004793", "label": 3919, "func": ""}
{"index": "s991705375", "label": 3919, "func": ""}
{"index": "s270397756", "label": 3919, "func": ""}
{"index": "s010333884", "label": 3919, "func": ""}
{"index": "s734190248", "label": 3919, "func": ""}
{"index": "s419398485", "label": 3379, "func": ""}
{"index": "s587348148", "label": 3379, "func": ""}
{"index": "s803040532", "label": 3379, "func": ""}
{"index": "s312731162", "label": 3379, "func": ""}
{"index": "s345064385", "label": 3379, "func": ""}
{"index": "s356478895", "label": 3379, "func": ""}
{"index": "s999182301", "label": 3379, "func": ""}
{"index": "s766263793", "label": 3379, "func": ""}
{"index": "s392928026", "label": 3379, "func": ""}
{"index": "s701873368", "label": 3379, "func": ""}
{"index": "s709636079", "label": 2843, "func": ""}
{"index": "s779671602", "label": 2843, "func": ""}
{"index": "s256681041", "label": 2843, "func": ""}
{"index": "s641272479", "label": 2843, "func": ""}
{"index": "s422244498", "label": 2843, "func": ""}
{"index": "s573746330", "label": 2843, "func": ""}
{"index": "s622491833", "label": 2843, "func": ""}
{"index": "s254586097", "label": 2843, "func": ""}
{"index": "s966284599", "label": 2843, "func": ""}
{"index": "s771678115", "label": 2843, "func": ""}
{"index": "s266410575", "label": 2721, "func": ""}
{"index": "s791262993", "label": 2721, "func": ""}
{"index": "s428345352", "label": 2721, "func": ""}
{"index": "s287201561", "label": 2721, "func": ""}
{"index": "s481971394", "label": 2721, "func": ""}
{"index": "s367555388", "label": 2721, "func": ""}
{"index": "s620148412", "label": 2721, "func": ""}
{"index": "s823331835", "label": 2721, "func": ""}
{"index": "s277037312", "label": 2721, "func": ""}
{"index": "s664772538", "label": 2721, "func": ""}
{"index": "s235637266", "label": 1731, "func": ""}
{"index": "s586015833", "label": 1731, "func": ""}
{"index": "s321751958", "label": 1731, "func": ""}
{"index": "s240463053", "label": 1731, "func": ""}
{"index": "s010686028", "label": 1731, "func": ""}
{"index": "s734941587", "label": 1731, "func": ""}
{"index": "s334249737", "label": 1731, "func": ""}
{"index": "s489233177", "label": 1731, "func": ""}
{"index": "s226022654", "label": 1731, "func": ""}
{"index": "s761698268", "label": 1731, "func": ""}
{"index": "s180032672", "label": 1184, "func": ""}
{"index": "s868287868", "label": 1184, "func": ""}
{"index": "s254444180", "label": 1184, "func": ""}
{"index": "s202463871", "label": 1184, "func": ""}
{"index": "s348153303", "label": 4051, "func": ""}
{"index": "s795243426", "label": 4051, "func": ""}
{"index": "s619742274", "label": 4051, "func": ""}
{"index": "s194798141", "label": 3631, "func": ""}
{"index": "s374541753", "label": 3631, "func": ""}
{"index": "s037436535", "label": 3631, "func": ""}
{"index": "s581627587", "label": 3631, "func": ""}
{"index": "s584349366", "label": 3631, "func": ""}
{"index": "s135835473", "label": 3631, "func": ""}
{"index": "s176705298", "label": 3631, "func": ""}
{"index": "s443764872", "label": 3631, "func": ""}
{"index": "s628666926", "label": 3631, "func": ""}
{"index": "s233689850", "label": 3631, "func": ""}
{"index": "s588096684", "label": 3968, "func": ""}
{"index": "s874311456", "label": 3968, "func": ""}
{"index": "s069579829", "label": 3968, "func": ""}
{"index": "s693773876", "label": 3968, "func": ""}
{"index": "s888578674", "label": 3968, "func": ""}
{"index": "s466939194", "label": 3968, "func": ""}
{"index": "s573546948", "label": 3968, "func": ""}
{"index": "s515912504", "label": 3968, "func": ""}
{"index": "s453307701", "label": 2563, "func": ""}
{"index": "s638112715", "label": 2563, "func": ""}
{"index": "s368472941", "label": 2563, "func": ""}
{"index": "s563641918", "label": 2563, "func": ""}
{"index": "s298745801", "label": 2563, "func": ""}
{"index": "s618575931", "label": 2563, "func": ""}
{"index": "s589156456", "label": 2563, "func": ""}
{"index": "s494996188", "label": 2563, "func": ""}
{"index": "s805047289", "label": 186, "func": ""}
{"index": "s962875742", "label": 186, "func": ""}
{"index": "s822024212", "label": 186, "func": ""}
{"index": "s502850994", "label": 186, "func": ""}
{"index": "s791830316", "label": 186, "func": ""}
{"index": "s601028864", "label": 186, "func": ""}
{"index": "s011172401", "label": 186, "func": ""}
{"index": "s596463262", "label": 186, "func": ""}
{"index": "s006289865", "label": 186, "func": ""}
{"index": "s835305980", "label": 186, "func": ""}
{"index": "s110366935", "label": 2965, "func": ""}
{"index": "s483496063", "label": 2965, "func": ""}
{"index": "s884528399", "label": 2965, "func": ""}
{"index": "s382306636", "label": 2965, "func": ""}
{"index": "s317878870", "label": 225, "func": ""}
{"index": "s244707895", "label": 225, "func": ""}
{"index": "s482892251", "label": 225, "func": ""}
{"index": "s850981742", "label": 225, "func": ""}
{"index": "s743015335", "label": 225, "func": ""}
{"index": "s956437780", "label": 225, "func": ""}
{"index": "s806387229", "label": 225, "func": ""}
{"index": "s869562624", "label": 225, "func": ""}
{"index": "s468403777", "label": 225, "func": ""}
{"index": "s380152678", "label": 225, "func": ""}
{"index": "s117211113", "label": 3051, "func": ""}
{"index": "s714838559", "label": 3051, "func": ""}
{"index": "s273841939", "label": 3051, "func": ""}
{"index": "s455901713", "label": 3051, "func": ""}
{"index": "s561880792", "label": 3051, "func": ""}
{"index": "s165307232", "label": 3051, "func": ""}
{"index": "s434414698", "label": 3051, "func": ""}
{"index": "s791153654", "label": 3051, "func": ""}
{"index": "s227654057", "label": 3051, "func": ""}
{"index": "s868164547", "label": 2912, "func": ""}
{"index": "s071065129", "label": 2912, "func": ""}
{"index": "s295226748", "label": 2912, "func": ""}
{"index": "s949555365", "label": 2912, "func": ""}
{"index": "s769153567", "label": 2912, "func": ""}
{"index": "s071934631", "label": 2912, "func": ""}
{"index": "s541027671", "label": 2912, "func": ""}
{"index": "s085163932", "label": 2912, "func": ""}
{"index": "s548546840", "label": 2912, "func": ""}
{"index": "s250011379", "label": 2912, "func": ""}
{"index": "s147194670", "label": 3521, "func": ""}
{"index": "s081034248", "label": 2252, "func": ""}
{"index": "s307505866", "label": 2252, "func": ""}
{"index": "s212336965", "label": 2464, "func": ""}
{"index": "s445073454", "label": 2464, "func": ""}
{"index": "s504828919", "label": 2464, "func": ""}
{"index": "s840112949", "label": 2464, "func": ""}
{"index": "s242755059", "label": 2464, "func": ""}
{"index": "s745534923", "label": 2464, "func": ""}
{"index": "s600957861", "label": 2464, "func": ""}
{"index": "s357562630", "label": 2464, "func": ""}
{"index": "s339988774", "label": 2464, "func": ""}
{"index": "s438057470", "label": 2464, "func": ""}
{"index": "s662120679", "label": 1440, "func": ""}
{"index": "s947202233", "label": 1440, "func": ""}
{"index": "s785952875", "label": 1440, "func": ""}
{"index": "s886554413", "label": 1440, "func": ""}
{"index": "s441797636", "label": 784, "func": ""}
{"index": "s493660123", "label": 784, "func": ""}
{"index": "s393787812", "label": 784, "func": ""}
{"index": "s024843330", "label": 784, "func": ""}
{"index": "s646867773", "label": 784, "func": ""}
{"index": "s668439445", "label": 784, "func": ""}
{"index": "s318965774", "label": 3500, "func": ""}
{"index": "s297198126", "label": 3387, "func": ""}
{"index": "s963829870", "label": 3387, "func": ""}
{"index": "s617691648", "label": 3387, "func": ""}
{"index": "s003725948", "label": 3387, "func": ""}
{"index": "s916815304", "label": 3387, "func": ""}
{"index": "s996841603", "label": 3387, "func": ""}
{"index": "s405757032", "label": 3387, "func": ""}
{"index": "s214585854", "label": 3387, "func": ""}
{"index": "s847708630", "label": 3387, "func": ""}
{"index": "s246943628", "label": 3387, "func": ""}
{"index": "s107492647", "label": 3795, "func": ""}
{"index": "s973642041", "label": 3795, "func": ""}
{"index": "s601675967", "label": 3795, "func": ""}
{"index": "s280805404", "label": 3795, "func": ""}
{"index": "s095695991", "label": 3795, "func": ""}
{"index": "s601321480", "label": 3795, "func": ""}
{"index": "s752489408", "label": 3795, "func": ""}
{"index": "s503487870", "label": 3795, "func": ""}
{"index": "s653018722", "label": 3795, "func": ""}
{"index": "s292001937", "label": 3795, "func": ""}
{"index": "s204063734", "label": 2763, "func": ""}
{"index": "s073316700", "label": 2763, "func": ""}
{"index": "s341844305", "label": 2763, "func": ""}
{"index": "s353673641", "label": 2763, "func": ""}
{"index": "s202542375", "label": 2763, "func": ""}
{"index": "s864673446", "label": 2763, "func": ""}
{"index": "s389302992", "label": 2763, "func": ""}
{"index": "s282491232", "label": 2763, "func": ""}
{"index": "s156830769", "label": 2763, "func": ""}
{"index": "s541063644", "label": 2763, "func": ""}
{"index": "s344312266", "label": 3064, "func": ""}
{"index": "s488479081", "label": 3064, "func": ""}
{"index": "s795482281", "label": 3064, "func": ""}
{"index": "s071154380", "label": 3064, "func": ""}
{"index": "s244142048", "label": 3064, "func": ""}
{"index": "s577446198", "label": 3064, "func": ""}
{"index": "s039503285", "label": 3064, "func": ""}
{"index": "s921617150", "label": 3064, "func": ""}
{"index": "s793862696", "label": 3064, "func": ""}
{"index": "s050580261", "label": 3064, "func": ""}
{"index": "s642309804", "label": 2458, "func": ""}
{"index": "s475959072", "label": 2458, "func": ""}
{"index": "s168391333", "label": 2458, "func": ""}
{"index": "s618242844", "label": 2458, "func": ""}
{"index": "s418821869", "label": 2458, "func": ""}
{"index": "s829971657", "label": 2458, "func": ""}
{"index": "s127733937", "label": 2458, "func": ""}
{"index": "s241759031", "label": 2458, "func": ""}
{"index": "s626665993", "label": 2458, "func": ""}
{"index": "s826226466", "label": 3682, "func": ""}
{"index": "s366247349", "label": 3682, "func": ""}
{"index": "s016313907", "label": 3682, "func": ""}
{"index": "s170813795", "label": 3682, "func": ""}
{"index": "s880109895", "label": 3682, "func": ""}
{"index": "s790715121", "label": 3682, "func": ""}
{"index": "s748076471", "label": 3682, "func": ""}
{"index": "s981582322", "label": 3682, "func": ""}
{"index": "s299998725", "label": 3682, "func": ""}
{"index": "s004244379", "label": 3682, "func": ""}
{"index": "s499390948", "label": 2869, "func": ""}
{"index": "s916825548", "label": 2869, "func": ""}
{"index": "s519209651", "label": 2869, "func": ""}
{"index": "s039024906", "label": 2869, "func": ""}
{"index": "s573028600", "label": 2869, "func": ""}
{"index": "s262639282", "label": 2346, "func": ""}
{"index": "s424549734", "label": 2346, "func": ""}
{"index": "s883440033", "label": 2346, "func": ""}
{"index": "s056414481", "label": 2346, "func": ""}
{"index": "s384346523", "label": 2346, "func": ""}
{"index": "s117057266", "label": 2346, "func": ""}
{"index": "s770858934", "label": 2346, "func": ""}
{"index": "s440125460", "label": 2346, "func": ""}
{"index": "s494650223", "label": 2346, "func": ""}
{"index": "s398243642", "label": 2346, "func": ""}
{"index": "s589455687", "label": 2857, "func": ""}
{"index": "s094714982", "label": 2857, "func": ""}
{"index": "s475926561", "label": 2857, "func": ""}
{"index": "s494412509", "label": 2857, "func": ""}
{"index": "s883703598", "label": 2857, "func": ""}
{"index": "s520983484", "label": 2857, "func": ""}
{"index": "s837858499", "label": 2857, "func": ""}
{"index": "s835918930", "label": 2857, "func": ""}
{"index": "s100365745", "label": 2857, "func": ""}
{"index": "s156458326", "label": 838, "func": ""}
{"index": "s761620190", "label": 838, "func": ""}
{"index": "s240047209", "label": 838, "func": ""}
{"index": "s000683766", "label": 838, "func": ""}
{"index": "s286419327", "label": 838, "func": ""}
{"index": "s668307709", "label": 2884, "func": ""}
{"index": "s852012375", "label": 2884, "func": ""}
{"index": "s319581593", "label": 2884, "func": ""}
{"index": "s626556441", "label": 2884, "func": ""}
{"index": "s067557542", "label": 2884, "func": ""}
{"index": "s633891236", "label": 2884, "func": ""}
{"index": "s947014692", "label": 2884, "func": ""}
{"index": "s480562554", "label": 2884, "func": ""}
{"index": "s386690275", "label": 2884, "func": ""}
{"index": "s849111635", "label": 2884, "func": ""}
{"index": "s450245782", "label": 1006, "func": ""}
{"index": "s216825446", "label": 1006, "func": ""}
{"index": "s298092486", "label": 1006, "func": ""}
{"index": "s593567807", "label": 1006, "func": ""}
{"index": "s715453399", "label": 1006, "func": ""}
{"index": "s241558236", "label": 1006, "func": ""}
{"index": "s914835951", "label": 1006, "func": ""}
{"index": "s008350634", "label": 1006, "func": ""}
{"index": "s832395220", "label": 3455, "func": ""}
{"index": "s878506067", "label": 3455, "func": ""}
{"index": "s249657634", "label": 3455, "func": ""}
{"index": "s820540608", "label": 3455, "func": ""}
{"index": "s263852427", "label": 3455, "func": ""}
{"index": "s319732423", "label": 3455, "func": ""}
{"index": "s569155959", "label": 3455, "func": ""}
{"index": "s064447125", "label": 3455, "func": ""}
{"index": "s022070056", "label": 3455, "func": ""}
{"index": "s455756630", "label": 3455, "func": ""}
{"index": "s863164998", "label": 3385, "func": ""}
{"index": "s486433395", "label": 3385, "func": ""}
{"index": "s130683548", "label": 3385, "func": ""}
{"index": "s431468789", "label": 3385, "func": ""}
{"index": "s590760530", "label": 3385, "func": ""}
{"index": "s735876062", "label": 3385, "func": ""}
{"index": "s182610208", "label": 3385, "func": ""}
{"index": "s729830452", "label": 3385, "func": ""}
{"index": "s586069640", "label": 3385, "func": ""}
{"index": "s945505416", "label": 3385, "func": ""}
{"index": "s544024984", "label": 862, "func": ""}
{"index": "s751013523", "label": 862, "func": ""}
{"index": "s451155150", "label": 614, "func": ""}
{"index": "s726108457", "label": 614, "func": ""}
{"index": "s928691468", "label": 614, "func": ""}
{"index": "s014641153", "label": 614, "func": ""}
{"index": "s588368413", "label": 614, "func": ""}
{"index": "s186646827", "label": 614, "func": ""}
{"index": "s731262411", "label": 614, "func": ""}
{"index": "s030529599", "label": 614, "func": ""}
{"index": "s658689223", "label": 1904, "func": ""}
{"index": "s841278198", "label": 1904, "func": ""}
{"index": "s358240984", "label": 377, "func": ""}
{"index": "s435496137", "label": 377, "func": ""}
{"index": "s075605186", "label": 377, "func": ""}
{"index": "s395814446", "label": 377, "func": ""}
{"index": "s847797686", "label": 377, "func": ""}
{"index": "s568905756", "label": 377, "func": ""}
{"index": "s084524840", "label": 377, "func": ""}
{"index": "s598010162", "label": 377, "func": ""}
{"index": "s396599605", "label": 377, "func": ""}
{"index": "s258755738", "label": 377, "func": ""}
{"index": "s077460562", "label": 457, "func": ""}
{"index": "s810386501", "label": 457, "func": ""}
{"index": "s465201216", "label": 457, "func": ""}
{"index": "s849621932", "label": 457, "func": ""}
{"index": "s676778096", "label": 457, "func": ""}
{"index": "s116717103", "label": 457, "func": ""}
{"index": "s864786239", "label": 457, "func": ""}
{"index": "s803675968", "label": 457, "func": ""}
{"index": "s648164659", "label": 457, "func": ""}
{"index": "s563222389", "label": 457, "func": ""}
{"index": "s267558007", "label": 3972, "func": ""}
{"index": "s554898694", "label": 3972, "func": ""}
{"index": "s231695649", "label": 3972, "func": ""}
{"index": "s398318740", "label": 3972, "func": ""}
{"index": "s751211051", "label": 3972, "func": ""}
{"index": "s566023250", "label": 3972, "func": ""}
{"index": "s739095929", "label": 3972, "func": ""}
{"index": "s143387363", "label": 3972, "func": ""}
{"index": "s790043011", "label": 3972, "func": ""}
{"index": "s256445067", "label": 3972, "func": ""}
{"index": "s180746842", "label": 2466, "func": ""}
{"index": "s233449032", "label": 2466, "func": ""}
{"index": "s985453200", "label": 2466, "func": ""}
{"index": "s467283965", "label": 2466, "func": ""}
{"index": "s911582353", "label": 2466, "func": ""}
{"index": "s799887668", "label": 2466, "func": ""}
{"index": "s444559399", "label": 2466, "func": ""}
{"index": "s013423947", "label": 2466, "func": ""}
{"index": "s065103004", "label": 2466, "func": ""}
{"index": "s286119488", "label": 2466, "func": ""}
{"index": "s496804651", "label": 2718, "func": ""}
{"index": "s490529844", "label": 2718, "func": ""}
{"index": "s788537411", "label": 2718, "func": ""}
{"index": "s285448807", "label": 2718, "func": ""}
{"index": "s378229448", "label": 2718, "func": ""}
{"index": "s327604345", "label": 2718, "func": ""}
{"index": "s224065855", "label": 2718, "func": ""}
{"index": "s742594984", "label": 2718, "func": ""}
{"index": "s867856044", "label": 2718, "func": ""}
{"index": "s230599564", "label": 2718, "func": ""}
{"index": "s262123993", "label": 3373, "func": ""}
{"index": "s329407602", "label": 3373, "func": ""}
{"index": "s707482484", "label": 3373, "func": ""}
{"index": "s092159223", "label": 3373, "func": ""}
{"index": "s992259866", "label": 3373, "func": ""}
{"index": "s166653016", "label": 3373, "func": ""}
{"index": "s828389963", "label": 3373, "func": ""}
{"index": "s485840264", "label": 3373, "func": ""}
{"index": "s130680113", "label": 3373, "func": ""}
{"index": "s718423192", "label": 3373, "func": ""}
{"index": "s969242802", "label": 1129, "func": ""}
{"index": "s762214033", "label": 1129, "func": ""}
{"index": "s375299253", "label": 3083, "func": ""}
{"index": "s851132317", "label": 3083, "func": ""}
{"index": "s128196585", "label": 3083, "func": ""}
{"index": "s543133028", "label": 3083, "func": ""}
{"index": "s719810101", "label": 1641, "func": ""}
{"index": "s961491387", "label": 1641, "func": ""}
{"index": "s669343956", "label": 2774, "func": ""}
{"index": "s957941016", "label": 2774, "func": ""}
{"index": "s262378419", "label": 2774, "func": ""}
{"index": "s457256070", "label": 2774, "func": ""}
{"index": "s884154120", "label": 2774, "func": ""}
{"index": "s229498991", "label": 2774, "func": ""}
{"index": "s762560741", "label": 2774, "func": ""}
{"index": "s364814575", "label": 2774, "func": ""}
{"index": "s565680406", "label": 2774, "func": ""}
{"index": "s976460498", "label": 2774, "func": ""}
{"index": "s506241133", "label": 1335, "func": ""}
{"index": "s014055964", "label": 1335, "func": ""}
{"index": "s947943939", "label": 1335, "func": ""}
{"index": "s566749123", "label": 3255, "func": ""}
{"index": "s503913982", "label": 3255, "func": ""}
{"index": "s486333447", "label": 3255, "func": ""}
{"index": "s888797810", "label": 3255, "func": ""}
{"index": "s152312412", "label": 3255, "func": ""}
{"index": "s736555045", "label": 3255, "func": ""}
{"index": "s965440034", "label": 3255, "func": ""}
{"index": "s648854988", "label": 3255, "func": ""}
{"index": "s003424792", "label": 3255, "func": ""}
{"index": "s254979044", "label": 3255, "func": ""}
{"index": "s688217172", "label": 47, "func": ""}
{"index": "s795395121", "label": 47, "func": ""}
{"index": "s564142037", "label": 47, "func": ""}
{"index": "s939087697", "label": 47, "func": ""}
{"index": "s372881966", "label": 47, "func": ""}
{"index": "s197150296", "label": 47, "func": ""}
{"index": "s126097846", "label": 47, "func": ""}
{"index": "s393888110", "label": 47, "func": ""}
{"index": "s364697028", "label": 47, "func": ""}
{"index": "s362702450", "label": 47, "func": ""}
{"index": "s011634007", "label": 965, "func": ""}
{"index": "s404262077", "label": 965, "func": ""}
{"index": "s210016165", "label": 4034, "func": ""}
{"index": "s339936694", "label": 4034, "func": ""}
{"index": "s183895900", "label": 4034, "func": ""}
{"index": "s264849875", "label": 4034, "func": ""}
{"index": "s087797924", "label": 4034, "func": ""}
{"index": "s724345981", "label": 4034, "func": ""}
{"index": "s912387011", "label": 4034, "func": ""}
{"index": "s555991509", "label": 4034, "func": ""}
{"index": "s496079524", "label": 4034, "func": ""}
{"index": "s650224082", "label": 4034, "func": ""}
{"index": "s541564219", "label": 2573, "func": ""}
{"index": "s358286993", "label": 2573, "func": ""}
{"index": "s920948375", "label": 2573, "func": ""}
{"index": "s334972275", "label": 2573, "func": ""}
{"index": "s917731379", "label": 2573, "func": ""}
{"index": "s286229852", "label": 2573, "func": ""}
{"index": "s749956889", "label": 2573, "func": ""}
{"index": "s844347555", "label": 2573, "func": ""}
{"index": "s824376830", "label": 2573, "func": ""}
{"index": "s227581101", "label": 2573, "func": ""}
{"index": "s662173288", "label": 3103, "func": ""}
{"index": "s475691344", "label": 3103, "func": ""}
{"index": "s011207534", "label": 3103, "func": ""}
{"index": "s402316476", "label": 3103, "func": ""}
{"index": "s249150698", "label": 3103, "func": ""}
{"index": "s513689835", "label": 3103, "func": ""}
{"index": "s237562593", "label": 3103, "func": ""}
{"index": "s417707611", "label": 3103, "func": ""}
{"index": "s907683835", "label": 3103, "func": ""}
{"index": "s532704341", "label": 3103, "func": ""}
{"index": "s913656964", "label": 3970, "func": ""}
{"index": "s938890366", "label": 3970, "func": ""}
{"index": "s377902653", "label": 3970, "func": ""}
{"index": "s177313751", "label": 3970, "func": ""}
{"index": "s923622477", "label": 3970, "func": ""}
{"index": "s118105911", "label": 3970, "func": ""}
{"index": "s106733969", "label": 3970, "func": ""}
{"index": "s908013611", "label": 3970, "func": ""}
{"index": "s106763493", "label": 3970, "func": ""}
{"index": "s161680311", "label": 3970, "func": ""}
{"index": "s085658619", "label": 2561, "func": ""}
{"index": "s439590504", "label": 2561, "func": ""}
{"index": "s372776633", "label": 2561, "func": ""}
{"index": "s655992245", "label": 2561, "func": ""}
{"index": "s185345263", "label": 2561, "func": ""}
{"index": "s067801392", "label": 2561, "func": ""}
{"index": "s147428026", "label": 2561, "func": ""}
{"index": "s381975205", "label": 2561, "func": ""}
{"index": "s638426521", "label": 3948, "func": ""}
{"index": "s136650709", "label": 3948, "func": ""}
{"index": "s784781585", "label": 3948, "func": ""}
{"index": "s559311335", "label": 3948, "func": ""}
{"index": "s994317240", "label": 3948, "func": ""}
{"index": "s259083612", "label": 3948, "func": ""}
{"index": "s919049917", "label": 3948, "func": ""}
{"index": "s312502688", "label": 3948, "func": ""}
{"index": "s091482868", "label": 3948, "func": ""}
{"index": "s733721102", "label": 3948, "func": ""}
{"index": "s677240028", "label": 3899, "func": ""}
{"index": "s719578184", "label": 3109, "func": ""}
{"index": "s822559828", "label": 3109, "func": ""}
{"index": "s332962914", "label": 3109, "func": ""}
{"index": "s242803180", "label": 3109, "func": ""}
{"index": "s541729332", "label": 3109, "func": ""}
{"index": "s169468046", "label": 3109, "func": ""}
{"index": "s937107108", "label": 3109, "func": ""}
{"index": "s136388164", "label": 3109, "func": ""}
{"index": "s401633496", "label": 3109, "func": ""}
{"index": "s897752495", "label": 3109, "func": ""}
{"index": "s842311727", "label": 3830, "func": ""}
{"index": "s364425599", "label": 3830, "func": ""}
{"index": "s067667351", "label": 3830, "func": ""}
{"index": "s674653375", "label": 3830, "func": ""}
{"index": "s505118208", "label": 3830, "func": ""}
{"index": "s223055597", "label": 3830, "func": ""}
{"index": "s832423647", "label": 3830, "func": ""}
{"index": "s966291997", "label": 3830, "func": ""}
{"index": "s604530994", "label": 3830, "func": ""}
{"index": "s529251700", "label": 3830, "func": ""}
{"index": "s637146034", "label": 800, "func": ""}
{"index": "s089248056", "label": 2834, "func": ""}
{"index": "s394731410", "label": 2834, "func": ""}
{"index": "s730157819", "label": 2834, "func": ""}
{"index": "s408410955", "label": 2834, "func": ""}
{"index": "s468360620", "label": 2834, "func": ""}
{"index": "s053130899", "label": 2834, "func": ""}
{"index": "s919045359", "label": 2834, "func": ""}
{"index": "s022225381", "label": 2834, "func": ""}
{"index": "s280031818", "label": 2834, "func": ""}
{"index": "s099849351", "label": 2834, "func": ""}
{"index": "s717034405", "label": 1548, "func": ""}
{"index": "s932570323", "label": 1548, "func": ""}
{"index": "s370922894", "label": 239, "func": ""}
{"index": "s745915572", "label": 239, "func": ""}
{"index": "s770551780", "label": 239, "func": ""}
{"index": "s815620814", "label": 239, "func": ""}
{"index": "s359312101", "label": 239, "func": ""}
{"index": "s898452339", "label": 239, "func": ""}
{"index": "s449829383", "label": 239, "func": ""}
{"index": "s080355061", "label": 239, "func": ""}
{"index": "s861080743", "label": 239, "func": ""}
{"index": "s743202867", "label": 239, "func": ""}
{"index": "s553524409", "label": 2367, "func": ""}
{"index": "s884142069", "label": 2367, "func": ""}
{"index": "s283113928", "label": 2367, "func": ""}
{"index": "s290970290", "label": 2367, "func": ""}
{"index": "s453599814", "label": 2367, "func": ""}
{"index": "s808101269", "label": 2367, "func": ""}
{"index": "s108174763", "label": 2367, "func": ""}
{"index": "s384133356", "label": 1480, "func": ""}
{"index": "s112151196", "label": 1480, "func": ""}
{"index": "s480847274", "label": 1480, "func": ""}
{"index": "s721020788", "label": 1480, "func": ""}
{"index": "s366785836", "label": 1480, "func": ""}
{"index": "s532064740", "label": 1480, "func": ""}
{"index": "s182269788", "label": 1480, "func": ""}
{"index": "s971863424", "label": 1480, "func": ""}
{"index": "s531795554", "label": 1480, "func": ""}
{"index": "s857680051", "label": 1480, "func": ""}
{"index": "s416495767", "label": 2997, "func": ""}
{"index": "s841006662", "label": 2997, "func": ""}
{"index": "s670081655", "label": 2997, "func": ""}
{"index": "s936265051", "label": 2997, "func": ""}
{"index": "s032279104", "label": 2997, "func": ""}
{"index": "s674075782", "label": 2997, "func": ""}
{"index": "s896310125", "label": 2997, "func": ""}
{"index": "s934566583", "label": 2997, "func": ""}
{"index": "s251573337", "label": 2997, "func": ""}
{"index": "s844254039", "label": 2997, "func": ""}
{"index": "s111078134", "label": 1955, "func": ""}
{"index": "s943287709", "label": 1955, "func": ""}
{"index": "s531044421", "label": 2875, "func": ""}
{"index": "s886032121", "label": 2875, "func": ""}
{"index": "s677530519", "label": 2875, "func": ""}
{"index": "s747747032", "label": 2875, "func": ""}
{"index": "s546434563", "label": 2875, "func": ""}
{"index": "s034253775", "label": 3438, "func": ""}
{"index": "s076257507", "label": 3438, "func": ""}
{"index": "s816171568", "label": 3438, "func": ""}
{"index": "s580486578", "label": 3438, "func": ""}
{"index": "s737450503", "label": 3438, "func": ""}
{"index": "s798059907", "label": 3438, "func": ""}
{"index": "s288721931", "label": 3438, "func": ""}
{"index": "s644658294", "label": 3438, "func": ""}
{"index": "s993571922", "label": 3438, "func": ""}
{"index": "s092538331", "label": 3438, "func": ""}
{"index": "s308196955", "label": 3388, "func": ""}
{"index": "s908299625", "label": 3388, "func": ""}
{"index": "s615658908", "label": 3388, "func": ""}
{"index": "s298629639", "label": 3388, "func": ""}
{"index": "s465579153", "label": 3388, "func": ""}
{"index": "s791723031", "label": 3388, "func": ""}
{"index": "s543675574", "label": 3388, "func": ""}
{"index": "s933854340", "label": 3388, "func": ""}
{"index": "s399837175", "label": 3388, "func": ""}
{"index": "s689316307", "label": 3388, "func": ""}
{"index": "s884317894", "label": 3392, "func": ""}
{"index": "s629435267", "label": 3392, "func": ""}
{"index": "s278387217", "label": 3392, "func": ""}
{"index": "s126902492", "label": 3392, "func": ""}
{"index": "s074901538", "label": 2730, "func": ""}
{"index": "s976232494", "label": 2730, "func": ""}
{"index": "s517350376", "label": 2730, "func": ""}
{"index": "s839446146", "label": 2730, "func": ""}
{"index": "s690536518", "label": 2730, "func": ""}
{"index": "s188953507", "label": 2730, "func": ""}
{"index": "s182208804", "label": 2730, "func": ""}
{"index": "s030717898", "label": 2730, "func": ""}
{"index": "s664476021", "label": 2730, "func": ""}
{"index": "s105418758", "label": 2730, "func": ""}
{"index": "s814267295", "label": 788, "func": ""}
{"index": "s368705894", "label": 788, "func": ""}
{"index": "s102915470", "label": 788, "func": ""}
{"index": "s581871672", "label": 788, "func": ""}
{"index": "s367365742", "label": 788, "func": ""}
{"index": "s068865460", "label": 788, "func": ""}
{"index": "s046687269", "label": 788, "func": ""}
{"index": "s594211520", "label": 788, "func": ""}
{"index": "s067213528", "label": 788, "func": ""}
{"index": "s354268627", "label": 788, "func": ""}
{"index": "s190704614", "label": 470, "func": ""}
{"index": "s144134621", "label": 470, "func": ""}
{"index": "s473017534", "label": 470, "func": ""}
{"index": "s044744030", "label": 470, "func": ""}
{"index": "s187892445", "label": 470, "func": ""}
{"index": "s797231291", "label": 470, "func": ""}
{"index": "s453836109", "label": 470, "func": ""}
{"index": "s265399970", "label": 470, "func": ""}
{"index": "s794180016", "label": 470, "func": ""}
{"index": "s005102669", "label": 470, "func": ""}
{"index": "s156455878", "label": 2858, "func": ""}
{"index": "s863239802", "label": 2858, "func": ""}
{"index": "s035078155", "label": 90, "func": ""}
{"index": "s172705357", "label": 90, "func": ""}
{"index": "s567740723", "label": 90, "func": ""}
{"index": "s532000277", "label": 90, "func": ""}
{"index": "s465575130", "label": 90, "func": ""}
{"index": "s422021445", "label": 90, "func": ""}
{"index": "s830355728", "label": 90, "func": ""}
{"index": "s088394160", "label": 90, "func": ""}
{"index": "s556972041", "label": 90, "func": ""}
{"index": "s336066542", "label": 90, "func": ""}
{"index": "s024223244", "label": 2689, "func": ""}
{"index": "s759696251", "label": 2689, "func": ""}
{"index": "s156394244", "label": 2689, "func": ""}
{"index": "s037239658", "label": 2689, "func": ""}
{"index": "s993436185", "label": 2689, "func": ""}
{"index": "s231984506", "label": 2689, "func": ""}
{"index": "s854866890", "label": 2689, "func": ""}
{"index": "s447948846", "label": 2689, "func": ""}
{"index": "s499629936", "label": 2689, "func": ""}
{"index": "s715488058", "label": 2689, "func": ""}
{"index": "s523636111", "label": 3090, "func": ""}
{"index": "s269155605", "label": 3090, "func": ""}
{"index": "s125217483", "label": 3090, "func": ""}
{"index": "s724028178", "label": 3090, "func": ""}
{"index": "s290521181", "label": 3090, "func": ""}
{"index": "s015346614", "label": 3090, "func": ""}
{"index": "s576103287", "label": 3090, "func": ""}
{"index": "s013582824", "label": 3090, "func": ""}
{"index": "s227216724", "label": 3090, "func": ""}
{"index": "s534207877", "label": 3090, "func": ""}
{"index": "s312408659", "label": 2762, "func": ""}
{"index": "s762550548", "label": 2762, "func": ""}
{"index": "s889166470", "label": 2762, "func": ""}
{"index": "s635651614", "label": 2762, "func": ""}
{"index": "s651945700", "label": 2762, "func": ""}
{"index": "s231947243", "label": 2762, "func": ""}
{"index": "s324299034", "label": 2762, "func": ""}
{"index": "s620696416", "label": 2762, "func": ""}
{"index": "s507573409", "label": 2762, "func": ""}
{"index": "s429173077", "label": 2762, "func": ""}
{"index": "s639316900", "label": 0, "func": ""}
{"index": "s743730970", "label": 0, "func": ""}
{"index": "s878864277", "label": 0, "func": ""}
{"index": "s316952267", "label": 0, "func": ""}
{"index": "s992266589", "label": 0, "func": ""}
{"index": "s295091196", "label": 0, "func": ""}
{"index": "s283788938", "label": 0, "func": ""}
{"index": "s607173981", "label": 0, "func": ""}
{"index": "s080795834", "label": 0, "func": ""}
{"index": "s730159767", "label": 0, "func": ""}
{"index": "s666311813", "label": 3691, "func": ""}
{"index": "s100816448", "label": 3691, "func": ""}
{"index": "s583027776", "label": 3691, "func": ""}
{"index": "s729256189", "label": 3266, "func": ""}
{"index": "s525002432", "label": 3266, "func": ""}
{"index": "s593701723", "label": 3266, "func": ""}
{"index": "s539619744", "label": 3266, "func": ""}
{"index": "s939780107", "label": 3266, "func": ""}
{"index": "s460355440", "label": 3266, "func": ""}
{"index": "s427822697", "label": 3266, "func": ""}
{"index": "s955778385", "label": 3266, "func": ""}
{"index": "s038400148", "label": 3266, "func": ""}
{"index": "s845643201", "label": 3266, "func": ""}
{"index": "s216115981", "label": 134, "func": ""}
{"index": "s005368022", "label": 134, "func": ""}
{"index": "s173202979", "label": 134, "func": ""}
{"index": "s329329611", "label": 134, "func": ""}
{"index": "s066277552", "label": 134, "func": ""}
{"index": "s597055384", "label": 134, "func": ""}
{"index": "s063469349", "label": 134, "func": ""}
{"index": "s432995023", "label": 134, "func": ""}
{"index": "s077224208", "label": 134, "func": ""}
{"index": "s646582404", "label": 134, "func": ""}
{"index": "s821344670", "label": 750, "func": ""}
{"index": "s028904178", "label": 750, "func": ""}
{"index": "s681298067", "label": 750, "func": ""}
{"index": "s598378320", "label": 750, "func": ""}
{"index": "s327071336", "label": 750, "func": ""}
{"index": "s928882176", "label": 750, "func": ""}
{"index": "s623338176", "label": 750, "func": ""}
{"index": "s010052175", "label": 750, "func": ""}
{"index": "s068657635", "label": 750, "func": ""}
{"index": "s537992124", "label": 2380, "func": ""}
{"index": "s320236598", "label": 2380, "func": ""}
{"index": "s790268451", "label": 2380, "func": ""}
{"index": "s535352156", "label": 2380, "func": ""}
{"index": "s983236389", "label": 2380, "func": ""}
{"index": "s850774782", "label": 2380, "func": ""}
{"index": "s124757408", "label": 2380, "func": ""}
{"index": "s082982056", "label": 2380, "func": ""}
{"index": "s994418981", "label": 2380, "func": ""}
{"index": "s280814744", "label": 2380, "func": ""}
{"index": "s228719725", "label": 3107, "func": ""}
{"index": "s326060883", "label": 3107, "func": ""}
{"index": "s067101009", "label": 3107, "func": ""}
{"index": "s428501658", "label": 3107, "func": ""}
{"index": "s036848989", "label": 3107, "func": ""}
{"index": "s970499817", "label": 3107, "func": ""}
{"index": "s281143461", "label": 3107, "func": ""}
{"index": "s690498065", "label": 3107, "func": ""}
{"index": "s535818588", "label": 3107, "func": ""}
{"index": "s251697570", "label": 3107, "func": ""}
{"index": "s777890119", "label": 703, "func": ""}
{"index": "s452416376", "label": 703, "func": ""}
{"index": "s377483872", "label": 703, "func": ""}
{"index": "s985574987", "label": 703, "func": ""}
{"index": "s904809710", "label": 703, "func": ""}
{"index": "s446060847", "label": 3427, "func": ""}
{"index": "s543970905", "label": 3427, "func": ""}
{"index": "s635630677", "label": 3427, "func": ""}
{"index": "s064630208", "label": 3427, "func": ""}
{"index": "s450183749", "label": 3427, "func": ""}
{"index": "s222084706", "label": 3427, "func": ""}
{"index": "s114074941", "label": 3427, "func": ""}
{"index": "s797859889", "label": 3427, "func": ""}
{"index": "s588458737", "label": 3427, "func": ""}
{"index": "s089656986", "label": 3427, "func": ""}
{"index": "s926376423", "label": 775, "func": ""}
{"index": "s755940668", "label": 775, "func": ""}
{"index": "s930275242", "label": 775, "func": ""}
{"index": "s516680889", "label": 775, "func": ""}
{"index": "s367176134", "label": 775, "func": ""}
{"index": "s696926891", "label": 775, "func": ""}
{"index": "s689952795", "label": 775, "func": ""}
{"index": "s132474975", "label": 775, "func": ""}
{"index": "s738394453", "label": 775, "func": ""}
{"index": "s990190364", "label": 775, "func": ""}
{"index": "s329288037", "label": 1326, "func": ""}
{"index": "s223808681", "label": 1097, "func": ""}
{"index": "s390399555", "label": 1283, "func": ""}
{"index": "s412321634", "label": 1283, "func": ""}
{"index": "s966453983", "label": 1283, "func": ""}
{"index": "s662536306", "label": 1283, "func": ""}
{"index": "s145354337", "label": 1283, "func": ""}
{"index": "s865681194", "label": 1283, "func": ""}
{"index": "s786853838", "label": 1283, "func": ""}
{"index": "s116515762", "label": 1283, "func": ""}
{"index": "s069560130", "label": 1283, "func": ""}
{"index": "s296939613", "label": 1283, "func": ""}
{"index": "s479596799", "label": 1659, "func": ""}
{"index": "s701176204", "label": 1659, "func": ""}
{"index": "s245483538", "label": 1659, "func": ""}
{"index": "s624226559", "label": 1659, "func": ""}
{"index": "s086882716", "label": 1659, "func": ""}
{"index": "s596712442", "label": 1659, "func": ""}
{"index": "s506959217", "label": 1659, "func": ""}
{"index": "s308237840", "label": 1659, "func": ""}
{"index": "s246071579", "label": 3694, "func": ""}
{"index": "s579614193", "label": 3694, "func": ""}
{"index": "s282415145", "label": 3694, "func": ""}
{"index": "s375969171", "label": 3694, "func": ""}
{"index": "s316961800", "label": 3694, "func": ""}
{"index": "s671486502", "label": 3694, "func": ""}
{"index": "s910989718", "label": 3694, "func": ""}
{"index": "s007113245", "label": 3694, "func": ""}
{"index": "s868588197", "label": 3694, "func": ""}
{"index": "s256727909", "label": 3694, "func": ""}
{"index": "s009901776", "label": 3834, "func": ""}
{"index": "s511363655", "label": 3834, "func": ""}
{"index": "s037836504", "label": 3834, "func": ""}
{"index": "s463798780", "label": 3834, "func": ""}
{"index": "s208468730", "label": 3834, "func": ""}
{"index": "s817509605", "label": 3834, "func": ""}
{"index": "s929249439", "label": 3834, "func": ""}
{"index": "s506936022", "label": 3834, "func": ""}
{"index": "s216106884", "label": 3834, "func": ""}
{"index": "s858240069", "label": 3834, "func": ""}
{"index": "s616912082", "label": 3181, "func": ""}
{"index": "s114424117", "label": 3181, "func": ""}
{"index": "s444558340", "label": 3181, "func": ""}
{"index": "s964050932", "label": 3181, "func": ""}
{"index": "s436314583", "label": 3181, "func": ""}
{"index": "s742264414", "label": 3181, "func": ""}
{"index": "s729736721", "label": 3181, "func": ""}
{"index": "s128272807", "label": 3181, "func": ""}
{"index": "s313640394", "label": 3181, "func": ""}
{"index": "s457697652", "label": 3181, "func": ""}
{"index": "s149561129", "label": 3040, "func": ""}
{"index": "s320840062", "label": 3040, "func": ""}
{"index": "s309296806", "label": 3040, "func": ""}
{"index": "s408020735", "label": 3040, "func": ""}
{"index": "s014806494", "label": 3040, "func": ""}
{"index": "s615640641", "label": 3040, "func": ""}
{"index": "s084967724", "label": 3040, "func": ""}
{"index": "s437803862", "label": 3040, "func": ""}
{"index": "s135760511", "label": 3040, "func": ""}
{"index": "s438673689", "label": 3040, "func": ""}
{"index": "s133490240", "label": 1268, "func": ""}
{"index": "s968688368", "label": 1268, "func": ""}
{"index": "s101513838", "label": 1268, "func": ""}
{"index": "s455890922", "label": 1268, "func": ""}
{"index": "s538727926", "label": 1268, "func": ""}
{"index": "s249685457", "label": 1268, "func": ""}
{"index": "s852165768", "label": 1268, "func": ""}
{"index": "s362640892", "label": 1268, "func": ""}
{"index": "s543070278", "label": 1268, "func": ""}
{"index": "s028914477", "label": 1268, "func": ""}
{"index": "s750750196", "label": 3680, "func": ""}
{"index": "s902411265", "label": 3680, "func": ""}
{"index": "s650664852", "label": 3680, "func": ""}
{"index": "s774060552", "label": 3680, "func": ""}
{"index": "s376325765", "label": 3680, "func": ""}
{"index": "s800106744", "label": 3680, "func": ""}
{"index": "s393141807", "label": 3680, "func": ""}
{"index": "s718665210", "label": 3680, "func": ""}
{"index": "s895378957", "label": 3680, "func": ""}
{"index": "s619693468", "label": 3680, "func": ""}
{"index": "s600393339", "label": 3894, "func": ""}
{"index": "s832580892", "label": 3894, "func": ""}
{"index": "s472938123", "label": 2888, "func": ""}
{"index": "s376534835", "label": 2888, "func": ""}
{"index": "s719437437", "label": 2888, "func": ""}
{"index": "s136351214", "label": 2888, "func": ""}
{"index": "s079701950", "label": 2888, "func": ""}
{"index": "s994861030", "label": 2888, "func": ""}
{"index": "s343795201", "label": 2888, "func": ""}
{"index": "s873452695", "label": 2888, "func": ""}
{"index": "s477072135", "label": 2888, "func": ""}
{"index": "s227770092", "label": 2888, "func": ""}
{"index": "s450736498", "label": 429, "func": ""}
{"index": "s639793726", "label": 429, "func": ""}
{"index": "s771654643", "label": 429, "func": ""}
{"index": "s049105076", "label": 429, "func": ""}
{"index": "s401651949", "label": 429, "func": ""}
{"index": "s500769562", "label": 429, "func": ""}
{"index": "s795468243", "label": 429, "func": ""}
{"index": "s180000808", "label": 429, "func": ""}
{"index": "s910437271", "label": 429, "func": ""}
{"index": "s916556435", "label": 429, "func": ""}
{"index": "s941017700", "label": 2436, "func": ""}
{"index": "s003069797", "label": 2436, "func": ""}
{"index": "s766422645", "label": 2436, "func": ""}
{"index": "s575133689", "label": 2436, "func": ""}
{"index": "s339030508", "label": 2436, "func": ""}
{"index": "s523449582", "label": 2436, "func": ""}
{"index": "s455154624", "label": 2436, "func": ""}
{"index": "s462566666", "label": 2436, "func": ""}
{"index": "s804985087", "label": 2436, "func": ""}
{"index": "s745169974", "label": 2436, "func": ""}
{"index": "s829098923", "label": 3785, "func": ""}
{"index": "s223988062", "label": 3785, "func": ""}
{"index": "s956756710", "label": 3785, "func": ""}
{"index": "s974596561", "label": 3785, "func": ""}
{"index": "s089844647", "label": 3785, "func": ""}
{"index": "s112821012", "label": 3785, "func": ""}
{"index": "s569664293", "label": 3785, "func": ""}
{"index": "s881682419", "label": 3785, "func": ""}
{"index": "s147391414", "label": 3785, "func": ""}
{"index": "s818483467", "label": 3785, "func": ""}
{"index": "s377692580", "label": 3067, "func": ""}
{"index": "s694580329", "label": 3067, "func": ""}
{"index": "s092027196", "label": 3067, "func": ""}
{"index": "s142071096", "label": 3067, "func": ""}
{"index": "s152253537", "label": 3067, "func": ""}
{"index": "s190155762", "label": 3067, "func": ""}
{"index": "s379456101", "label": 3067, "func": ""}
{"index": "s820632920", "label": 3067, "func": ""}
{"index": "s620012554", "label": 3067, "func": ""}
{"index": "s805176763", "label": 3067, "func": ""}
{"index": "s809500897", "label": 541, "func": ""}
{"index": "s176919678", "label": 3343, "func": ""}
{"index": "s826123247", "label": 3343, "func": ""}
{"index": "s023144797", "label": 3343, "func": ""}
{"index": "s337869181", "label": 3343, "func": ""}
{"index": "s755151208", "label": 3343, "func": ""}
{"index": "s005131941", "label": 3343, "func": ""}
{"index": "s329567577", "label": 3343, "func": ""}
{"index": "s858130002", "label": 3343, "func": ""}
{"index": "s628117993", "label": 3343, "func": ""}
{"index": "s455552915", "label": 3343, "func": ""}
{"index": "s011127262", "label": 859, "func": ""}
{"index": "s176213352", "label": 859, "func": ""}
{"index": "s952212831", "label": 859, "func": ""}
{"index": "s899464081", "label": 859, "func": ""}
{"index": "s261918225", "label": 859, "func": ""}
{"index": "s937596262", "label": 859, "func": ""}
{"index": "s284810428", "label": 859, "func": ""}
{"index": "s850216961", "label": 859, "func": ""}
{"index": "s369847610", "label": 859, "func": ""}
{"index": "s460850473", "label": 859, "func": ""}
{"index": "s741576633", "label": 2660, "func": ""}
{"index": "s596058410", "label": 2660, "func": ""}
{"index": "s800843436", "label": 2660, "func": ""}
{"index": "s709434501", "label": 2660, "func": ""}
{"index": "s501089277", "label": 2660, "func": ""}
{"index": "s989703949", "label": 2660, "func": ""}
{"index": "s072257274", "label": 2660, "func": ""}
{"index": "s791143304", "label": 2660, "func": ""}
{"index": "s318917487", "label": 2660, "func": ""}
{"index": "s334365198", "label": 2660, "func": ""}
{"index": "s112144007", "label": 2493, "func": ""}
{"index": "s409920335", "label": 2493, "func": ""}
{"index": "s117845245", "label": 2493, "func": ""}
{"index": "s739933064", "label": 2493, "func": ""}
{"index": "s556045628", "label": 2493, "func": ""}
{"index": "s207818224", "label": 2493, "func": ""}
{"index": "s900749413", "label": 2493, "func": ""}
{"index": "s671820917", "label": 2493, "func": ""}
{"index": "s291011521", "label": 2493, "func": ""}
{"index": "s207347469", "label": 2493, "func": ""}
{"index": "s106048161", "label": 802, "func": ""}
{"index": "s313977374", "label": 802, "func": ""}
{"index": "s657872021", "label": 802, "func": ""}
{"index": "s121074745", "label": 3480, "func": ""}
{"index": "s014460197", "label": 3480, "func": ""}
{"index": "s113606190", "label": 3480, "func": ""}
{"index": "s820939917", "label": 3480, "func": ""}
{"index": "s713636910", "label": 3480, "func": ""}
{"index": "s567439279", "label": 3480, "func": ""}
{"index": "s110561580", "label": 3480, "func": ""}
{"index": "s420545820", "label": 3480, "func": ""}
{"index": "s189466279", "label": 3480, "func": ""}
{"index": "s324313306", "label": 3480, "func": ""}
{"index": "s360058738", "label": 2444, "func": ""}
{"index": "s269247996", "label": 2444, "func": ""}
{"index": "s514510450", "label": 2444, "func": ""}
{"index": "s178250810", "label": 2444, "func": ""}
{"index": "s239556706", "label": 2444, "func": ""}
{"index": "s771232688", "label": 2444, "func": ""}
{"index": "s036994324", "label": 2444, "func": ""}
{"index": "s354147856", "label": 2444, "func": ""}
{"index": "s307761495", "label": 2444, "func": ""}
{"index": "s099969875", "label": 2444, "func": ""}
{"index": "s254769760", "label": 3515, "func": ""}
{"index": "s161824380", "label": 3708, "func": ""}
{"index": "s543977716", "label": 3708, "func": ""}
{"index": "s263139333", "label": 3708, "func": ""}
{"index": "s772076368", "label": 3626, "func": ""}
{"index": "s886608847", "label": 3626, "func": ""}
{"index": "s179538729", "label": 3626, "func": ""}
{"index": "s992303070", "label": 3626, "func": ""}
{"index": "s327954443", "label": 3626, "func": ""}
{"index": "s328457984", "label": 3626, "func": ""}
{"index": "s962812675", "label": 3626, "func": ""}
{"index": "s337777244", "label": 3626, "func": ""}
{"index": "s586235690", "label": 3626, "func": ""}
{"index": "s549957690", "label": 3626, "func": ""}
{"index": "s153652302", "label": 2176, "func": ""}
{"index": "s187826740", "label": 2176, "func": ""}
{"index": "s035455047", "label": 3573, "func": ""}
{"index": "s558625999", "label": 3573, "func": ""}
{"index": "s096410924", "label": 3573, "func": ""}
{"index": "s298243215", "label": 3573, "func": ""}
{"index": "s754021094", "label": 3573, "func": ""}
{"index": "s867493945", "label": 3573, "func": ""}
{"index": "s530009982", "label": 3573, "func": ""}
{"index": "s895090420", "label": 3573, "func": ""}
{"index": "s444133550", "label": 3573, "func": ""}
{"index": "s647786959", "label": 3573, "func": ""}
{"index": "s017251529", "label": 1772, "func": ""}
{"index": "s307201883", "label": 1772, "func": ""}
{"index": "s791073115", "label": 1772, "func": ""}
{"index": "s224314827", "label": 1772, "func": ""}
{"index": "s437829971", "label": 1772, "func": ""}
{"index": "s412674744", "label": 1772, "func": ""}
{"index": "s609315129", "label": 1772, "func": ""}
{"index": "s123700158", "label": 1772, "func": ""}
{"index": "s643556515", "label": 1772, "func": ""}
{"index": "s963614607", "label": 1772, "func": ""}
{"index": "s654780150", "label": 2591, "func": ""}
{"index": "s138300766", "label": 1622, "func": ""}
{"index": "s650580491", "label": 1622, "func": ""}
{"index": "s724241195", "label": 1622, "func": ""}
{"index": "s714613948", "label": 1622, "func": ""}
{"index": "s055220139", "label": 1622, "func": ""}
{"index": "s835405044", "label": 1622, "func": ""}
{"index": "s228006044", "label": 1622, "func": ""}
{"index": "s255025980", "label": 1622, "func": ""}
{"index": "s746329522", "label": 1622, "func": ""}
{"index": "s396254289", "label": 1622, "func": ""}
{"index": "s270446679", "label": 3310, "func": ""}
{"index": "s757371217", "label": 3310, "func": ""}
{"index": "s531823426", "label": 3310, "func": ""}
{"index": "s625270368", "label": 3310, "func": ""}
{"index": "s043536706", "label": 3310, "func": ""}
{"index": "s090378078", "label": 3310, "func": ""}
{"index": "s721525095", "label": 3310, "func": ""}
{"index": "s528777586", "label": 3310, "func": ""}
{"index": "s007762606", "label": 3310, "func": ""}
{"index": "s053574831", "label": 3310, "func": ""}
{"index": "s343883956", "label": 3920, "func": ""}
{"index": "s259710073", "label": 3920, "func": ""}
{"index": "s455397501", "label": 3920, "func": ""}
{"index": "s475936051", "label": 3920, "func": ""}
{"index": "s670917197", "label": 3920, "func": ""}
{"index": "s602118395", "label": 3920, "func": ""}
{"index": "s038276475", "label": 3920, "func": ""}
{"index": "s040760116", "label": 3920, "func": ""}
{"index": "s216154878", "label": 3920, "func": ""}
{"index": "s285200468", "label": 3920, "func": ""}
{"index": "s399743015", "label": 2398, "func": ""}
{"index": "s118566429", "label": 2398, "func": ""}
{"index": "s860478995", "label": 2398, "func": ""}
{"index": "s051928326", "label": 2398, "func": ""}
{"index": "s578588111", "label": 2398, "func": ""}
{"index": "s479739815", "label": 2398, "func": ""}
{"index": "s829491775", "label": 2398, "func": ""}
{"index": "s219653553", "label": 2398, "func": ""}
{"index": "s779642872", "label": 2398, "func": ""}
{"index": "s414724206", "label": 2398, "func": ""}
{"index": "s915646448", "label": 2221, "func": ""}
{"index": "s130533988", "label": 2221, "func": ""}
{"index": "s485768874", "label": 196, "func": ""}
{"index": "s959847768", "label": 196, "func": ""}
{"index": "s774888589", "label": 196, "func": ""}
{"index": "s524184640", "label": 196, "func": ""}
{"index": "s384929432", "label": 196, "func": ""}
{"index": "s852417277", "label": 196, "func": ""}
{"index": "s172449595", "label": 196, "func": ""}
{"index": "s218077290", "label": 196, "func": ""}
{"index": "s179737939", "label": 196, "func": ""}
{"index": "s749883801", "label": 196, "func": ""}
{"index": "s513793052", "label": 3297, "func": ""}
{"index": "s040796251", "label": 3297, "func": ""}
{"index": "s023121060", "label": 3297, "func": ""}
{"index": "s448212863", "label": 3297, "func": ""}
{"index": "s830094481", "label": 3297, "func": ""}
{"index": "s616679521", "label": 3297, "func": ""}
{"index": "s617995479", "label": 3297, "func": ""}
{"index": "s115291096", "label": 3297, "func": ""}
{"index": "s566257306", "label": 3297, "func": ""}
{"index": "s795709167", "label": 3297, "func": ""}
{"index": "s600327300", "label": 46, "func": ""}
{"index": "s140682988", "label": 46, "func": ""}
{"index": "s147871182", "label": 46, "func": ""}
{"index": "s269162664", "label": 46, "func": ""}
{"index": "s960167979", "label": 46, "func": ""}
{"index": "s202206552", "label": 46, "func": ""}
{"index": "s322091280", "label": 46, "func": ""}
{"index": "s169378065", "label": 46, "func": ""}
{"index": "s334196607", "label": 46, "func": ""}
{"index": "s466639885", "label": 46, "func": ""}
{"index": "s185277483", "label": 3656, "func": ""}
{"index": "s073290659", "label": 1994, "func": ""}
{"index": "s521421247", "label": 3791, "func": ""}
{"index": "s074782356", "label": 3791, "func": ""}
{"index": "s535858600", "label": 3791, "func": ""}
{"index": "s188627186", "label": 3791, "func": ""}
{"index": "s363540780", "label": 3791, "func": ""}
{"index": "s384929120", "label": 3791, "func": ""}
{"index": "s012115678", "label": 3791, "func": ""}
{"index": "s418344035", "label": 3791, "func": ""}
{"index": "s870464043", "label": 3080, "func": ""}
{"index": "s815964890", "label": 3080, "func": ""}
{"index": "s374624109", "label": 3080, "func": ""}
{"index": "s202577440", "label": 3080, "func": ""}
{"index": "s751675303", "label": 3080, "func": ""}
{"index": "s059522135", "label": 3080, "func": ""}
{"index": "s993312737", "label": 3080, "func": ""}
{"index": "s614730995", "label": 3080, "func": ""}
{"index": "s616095133", "label": 3080, "func": ""}
{"index": "s656760371", "label": 3080, "func": ""}
{"index": "s689969819", "label": 144, "func": ""}
{"index": "s130494609", "label": 144, "func": ""}
{"index": "s421963425", "label": 144, "func": ""}
{"index": "s019260377", "label": 144, "func": ""}
{"index": "s078133419", "label": 144, "func": ""}
{"index": "s680110741", "label": 144, "func": ""}
{"index": "s702945740", "label": 144, "func": ""}
{"index": "s020571941", "label": 144, "func": ""}
{"index": "s625778482", "label": 144, "func": ""}
{"index": "s730862066", "label": 144, "func": ""}
{"index": "s093954809", "label": 1373, "func": ""}
{"index": "s201449128", "label": 1373, "func": ""}
{"index": "s795341767", "label": 1373, "func": ""}
{"index": "s962827275", "label": 3280, "func": ""}
{"index": "s227457837", "label": 3280, "func": ""}
{"index": "s334689660", "label": 3280, "func": ""}
{"index": "s823217148", "label": 3280, "func": ""}
{"index": "s843920341", "label": 3280, "func": ""}
{"index": "s893166334", "label": 3280, "func": ""}
{"index": "s020437171", "label": 3280, "func": ""}
{"index": "s530939172", "label": 3280, "func": ""}
{"index": "s557133030", "label": 3280, "func": ""}
{"index": "s593739234", "label": 3280, "func": ""}
{"index": "s573098041", "label": 2847, "func": ""}
{"index": "s214846716", "label": 2847, "func": ""}
{"index": "s532189638", "label": 2847, "func": ""}
{"index": "s643488865", "label": 2847, "func": ""}
{"index": "s316955291", "label": 2847, "func": ""}
{"index": "s817073264", "label": 2847, "func": ""}
{"index": "s365712560", "label": 2847, "func": ""}
{"index": "s618226385", "label": 2847, "func": ""}
{"index": "s480639883", "label": 2847, "func": ""}
{"index": "s809496017", "label": 2847, "func": ""}
{"index": "s010767685", "label": 649, "func": ""}
{"index": "s193123706", "label": 3913, "func": ""}
{"index": "s243815097", "label": 3469, "func": ""}
{"index": "s406974702", "label": 3469, "func": ""}
{"index": "s768458506", "label": 3469, "func": ""}
{"index": "s475750061", "label": 3469, "func": ""}
{"index": "s180793392", "label": 3469, "func": ""}
{"index": "s162091511", "label": 3469, "func": ""}
{"index": "s443580012", "label": 3469, "func": ""}
{"index": "s584740599", "label": 3469, "func": ""}
{"index": "s549723770", "label": 3469, "func": ""}
{"index": "s374449004", "label": 3469, "func": ""}
{"index": "s979725343", "label": 3194, "func": ""}
{"index": "s025788791", "label": 3194, "func": ""}
{"index": "s516323007", "label": 3194, "func": ""}
{"index": "s148482268", "label": 3194, "func": ""}
{"index": "s495652205", "label": 3194, "func": ""}
{"index": "s145048961", "label": 3194, "func": ""}
{"index": "s098501577", "label": 3194, "func": ""}
{"index": "s538821578", "label": 3194, "func": ""}
{"index": "s212213774", "label": 3194, "func": ""}
{"index": "s028287483", "label": 3194, "func": ""}
{"index": "s924901952", "label": 832, "func": ""}
{"index": "s681726364", "label": 832, "func": ""}
{"index": "s416346317", "label": 832, "func": ""}
{"index": "s672288036", "label": 832, "func": ""}
{"index": "s000887196", "label": 3252, "func": ""}
{"index": "s874649861", "label": 3252, "func": ""}
{"index": "s123231349", "label": 3252, "func": ""}
{"index": "s051553852", "label": 3252, "func": ""}
{"index": "s185167895", "label": 3252, "func": ""}
{"index": "s027653364", "label": 3252, "func": ""}
{"index": "s100606186", "label": 3252, "func": ""}
{"index": "s765429067", "label": 3252, "func": ""}
{"index": "s932413503", "label": 3252, "func": ""}
{"index": "s324923296", "label": 3252, "func": ""}
{"index": "s247115065", "label": 1805, "func": ""}
{"index": "s709708410", "label": 500, "func": ""}
{"index": "s240175840", "label": 500, "func": ""}
{"index": "s975525552", "label": 500, "func": ""}
{"index": "s629603547", "label": 500, "func": ""}
{"index": "s565244274", "label": 500, "func": ""}
{"index": "s109907338", "label": 500, "func": ""}
{"index": "s198700794", "label": 500, "func": ""}
{"index": "s752775492", "label": 500, "func": ""}
{"index": "s027791101", "label": 500, "func": ""}
{"index": "s697589683", "label": 500, "func": ""}
{"index": "s123702427", "label": 1112, "func": ""}
{"index": "s247440951", "label": 1112, "func": ""}
{"index": "s658409539", "label": 1112, "func": ""}
{"index": "s409404771", "label": 1112, "func": ""}
{"index": "s282650382", "label": 2552, "func": ""}
{"index": "s484694356", "label": 2552, "func": ""}
{"index": "s213512849", "label": 2552, "func": ""}
{"index": "s468722578", "label": 2552, "func": ""}
{"index": "s885650489", "label": 2552, "func": ""}
{"index": "s741444208", "label": 2552, "func": ""}
{"index": "s901471215", "label": 2552, "func": ""}
{"index": "s127568732", "label": 2552, "func": ""}
{"index": "s401082878", "label": 2552, "func": ""}
{"index": "s880574605", "label": 2552, "func": ""}
{"index": "s178919698", "label": 695, "func": ""}
{"index": "s967699842", "label": 695, "func": ""}
{"index": "s381732571", "label": 695, "func": ""}
{"index": "s700391130", "label": 695, "func": ""}
{"index": "s828005320", "label": 695, "func": ""}
{"index": "s779613974", "label": 695, "func": ""}
{"index": "s034680041", "label": 695, "func": ""}
{"index": "s598169251", "label": 695, "func": ""}
{"index": "s428613653", "label": 695, "func": ""}
{"index": "s369678731", "label": 695, "func": ""}
{"index": "s021303304", "label": 3065, "func": ""}
{"index": "s453391327", "label": 3065, "func": ""}
{"index": "s584667917", "label": 2435, "func": ""}
{"index": "s848629248", "label": 2435, "func": ""}
{"index": "s009303995", "label": 2435, "func": ""}
{"index": "s143124304", "label": 2435, "func": ""}
{"index": "s707275853", "label": 2435, "func": ""}
{"index": "s027624641", "label": 2435, "func": ""}
{"index": "s921834820", "label": 2435, "func": ""}
{"index": "s448602045", "label": 2435, "func": ""}
{"index": "s286777872", "label": 2435, "func": ""}
{"index": "s746552441", "label": 2435, "func": ""}
{"index": "s026699931", "label": 3326, "func": ""}
{"index": "s183912527", "label": 3326, "func": ""}
{"index": "s265651443", "label": 3326, "func": ""}
{"index": "s402659853", "label": 3326, "func": ""}
{"index": "s019826438", "label": 3326, "func": ""}
{"index": "s840589465", "label": 3326, "func": ""}
{"index": "s009518409", "label": 3326, "func": ""}
{"index": "s412805349", "label": 3326, "func": ""}
{"index": "s342538112", "label": 3326, "func": ""}
{"index": "s258834190", "label": 3326, "func": ""}
{"index": "s728020604", "label": 2687, "func": ""}
{"index": "s493894925", "label": 2687, "func": ""}
{"index": "s240081298", "label": 2687, "func": ""}
{"index": "s997286268", "label": 2687, "func": ""}
{"index": "s436796574", "label": 2687, "func": ""}
{"index": "s272200797", "label": 2687, "func": ""}
{"index": "s708157121", "label": 2687, "func": ""}
{"index": "s728550985", "label": 2687, "func": ""}
{"index": "s823373622", "label": 2687, "func": ""}
{"index": "s060472470", "label": 2687, "func": ""}
{"index": "s051574503", "label": 2114, "func": ""}
{"index": "s538472083", "label": 3768, "func": ""}
{"index": "s825806369", "label": 3768, "func": ""}
{"index": "s950872828", "label": 3768, "func": ""}
{"index": "s938652593", "label": 3768, "func": ""}
{"index": "s168329765", "label": 3768, "func": ""}
{"index": "s315754440", "label": 3768, "func": ""}
{"index": "s932984646", "label": 3768, "func": ""}
{"index": "s117820129", "label": 3768, "func": ""}
{"index": "s522820274", "label": 3768, "func": ""}
{"index": "s644751346", "label": 3768, "func": ""}
{"index": "s781030443", "label": 194, "func": ""}
{"index": "s338067101", "label": 194, "func": ""}
{"index": "s727255787", "label": 194, "func": ""}
{"index": "s608170126", "label": 194, "func": ""}
{"index": "s530655777", "label": 194, "func": ""}
{"index": "s760869096", "label": 194, "func": ""}
{"index": "s075304177", "label": 3890, "func": ""}
{"index": "s199865447", "label": 3890, "func": ""}
{"index": "s419929062", "label": 3890, "func": ""}
{"index": "s925231844", "label": 3890, "func": ""}
{"index": "s162763740", "label": 3890, "func": ""}
{"index": "s643596612", "label": 3890, "func": ""}
{"index": "s155014386", "label": 895, "func": ""}
{"index": "s676940612", "label": 895, "func": ""}
{"index": "s991237863", "label": 895, "func": ""}
{"index": "s876611523", "label": 895, "func": ""}
{"index": "s210866111", "label": 895, "func": ""}
{"index": "s483162849", "label": 895, "func": ""}
{"index": "s054146596", "label": 895, "func": ""}
{"index": "s244104811", "label": 895, "func": ""}
{"index": "s670568834", "label": 895, "func": ""}
{"index": "s394941498", "label": 895, "func": ""}
{"index": "s891279886", "label": 761, "func": ""}
{"index": "s997681334", "label": 761, "func": ""}
{"index": "s909357641", "label": 761, "func": ""}
{"index": "s406728431", "label": 761, "func": ""}
{"index": "s253203192", "label": 761, "func": ""}
{"index": "s439332908", "label": 761, "func": ""}
{"index": "s532216404", "label": 761, "func": ""}
{"index": "s382035147", "label": 761, "func": ""}
{"index": "s777979808", "label": 761, "func": ""}
{"index": "s444445389", "label": 761, "func": ""}
{"index": "s321374553", "label": 2250, "func": ""}
{"index": "s709922786", "label": 2250, "func": ""}
{"index": "s751386089", "label": 2250, "func": ""}
{"index": "s456616380", "label": 2250, "func": ""}
{"index": "s391428384", "label": 2250, "func": ""}
{"index": "s449609050", "label": 2250, "func": ""}
{"index": "s224018557", "label": 831, "func": ""}
{"index": "s043504611", "label": 831, "func": ""}
{"index": "s932802914", "label": 831, "func": ""}
{"index": "s480788832", "label": 831, "func": ""}
{"index": "s085148698", "label": 831, "func": ""}
{"index": "s965818088", "label": 831, "func": ""}
{"index": "s820458090", "label": 3769, "func": ""}
{"index": "s969392164", "label": 3769, "func": ""}
{"index": "s746003974", "label": 3769, "func": ""}
{"index": "s736004973", "label": 3769, "func": ""}
{"index": "s873058314", "label": 3769, "func": ""}
{"index": "s715325528", "label": 3640, "func": ""}
{"index": "s314178042", "label": 3640, "func": ""}
{"index": "s076793327", "label": 3640, "func": ""}
{"index": "s115460503", "label": 3640, "func": ""}
{"index": "s950050765", "label": 3640, "func": ""}
{"index": "s580931269", "label": 3640, "func": ""}
{"index": "s511722649", "label": 3640, "func": ""}
{"index": "s053899015", "label": 3640, "func": ""}
{"index": "s360450869", "label": 3640, "func": ""}
{"index": "s823192621", "label": 3640, "func": ""}
{"index": "s504825146", "label": 2661, "func": ""}
{"index": "s620667756", "label": 2661, "func": ""}
{"index": "s759031519", "label": 2661, "func": ""}
{"index": "s274034384", "label": 2661, "func": ""}
{"index": "s040996164", "label": 2661, "func": ""}
{"index": "s324794424", "label": 2661, "func": ""}
{"index": "s753764279", "label": 2661, "func": ""}
{"index": "s076900750", "label": 2661, "func": ""}
{"index": "s513184632", "label": 2661, "func": ""}
{"index": "s578405229", "label": 2661, "func": ""}
{"index": "s550744863", "label": 3570, "func": ""}
{"index": "s838965784", "label": 3570, "func": ""}
{"index": "s853046986", "label": 3570, "func": ""}
{"index": "s678244908", "label": 3570, "func": ""}
{"index": "s344649882", "label": 3570, "func": ""}
{"index": "s458769059", "label": 3570, "func": ""}
{"index": "s512212135", "label": 3570, "func": ""}
{"index": "s956956798", "label": 3570, "func": ""}
{"index": "s163127362", "label": 3570, "func": ""}
{"index": "s729633512", "label": 3570, "func": ""}
{"index": "s408405724", "label": 3163, "func": ""}
{"index": "s149712306", "label": 3163, "func": ""}
{"index": "s264927074", "label": 3163, "func": ""}
{"index": "s079050386", "label": 3163, "func": ""}
{"index": "s645159550", "label": 3163, "func": ""}
{"index": "s691212334", "label": 3163, "func": ""}
{"index": "s725124852", "label": 3163, "func": ""}
{"index": "s765007706", "label": 3163, "func": ""}
{"index": "s097503769", "label": 3163, "func": ""}
{"index": "s232100465", "label": 3163, "func": ""}
{"index": "s102608956", "label": 3598, "func": ""}
{"index": "s980646245", "label": 3598, "func": ""}
{"index": "s269510528", "label": 3598, "func": ""}
{"index": "s800788968", "label": 3598, "func": ""}
{"index": "s065640661", "label": 3598, "func": ""}
{"index": "s045618679", "label": 3598, "func": ""}
{"index": "s010018941", "label": 3598, "func": ""}
{"index": "s916450572", "label": 3598, "func": ""}
{"index": "s273906058", "label": 3598, "func": ""}
{"index": "s097248341", "label": 3598, "func": ""}
{"index": "s295370958", "label": 2727, "func": ""}
{"index": "s919383796", "label": 2727, "func": ""}
{"index": "s435637213", "label": 2727, "func": ""}
{"index": "s936708184", "label": 2727, "func": ""}
{"index": "s208181715", "label": 2727, "func": ""}
{"index": "s020968227", "label": 2727, "func": ""}
{"index": "s923640590", "label": 2727, "func": ""}
{"index": "s965187491", "label": 2727, "func": ""}
{"index": "s975658074", "label": 2727, "func": ""}
{"index": "s312487410", "label": 2727, "func": ""}
{"index": "s732427022", "label": 1293, "func": ""}
{"index": "s707482350", "label": 1293, "func": ""}
{"index": "s390867245", "label": 1293, "func": ""}
{"index": "s435600719", "label": 1293, "func": ""}
{"index": "s170592272", "label": 1293, "func": ""}
{"index": "s237473605", "label": 1293, "func": ""}
{"index": "s862943692", "label": 1293, "func": ""}
{"index": "s961990081", "label": 1293, "func": ""}
{"index": "s935516984", "label": 1293, "func": ""}
{"index": "s898351330", "label": 1293, "func": ""}
{"index": "s275454886", "label": 2652, "func": ""}
{"index": "s970071869", "label": 3287, "func": ""}
{"index": "s957126855", "label": 3287, "func": ""}
{"index": "s715124560", "label": 3287, "func": ""}
{"index": "s526044892", "label": 3287, "func": ""}
{"index": "s478655425", "label": 3287, "func": ""}
{"index": "s977944607", "label": 3287, "func": ""}
{"index": "s501679527", "label": 3287, "func": ""}
{"index": "s556260252", "label": 3287, "func": ""}
{"index": "s994982566", "label": 3287, "func": ""}
{"index": "s217163052", "label": 3287, "func": ""}
{"index": "s271842530", "label": 132, "func": ""}
{"index": "s860721462", "label": 132, "func": ""}
{"index": "s506945011", "label": 132, "func": ""}
{"index": "s961910677", "label": 132, "func": ""}
{"index": "s296414381", "label": 132, "func": ""}
{"index": "s980618605", "label": 132, "func": ""}
{"index": "s835657700", "label": 486, "func": ""}
{"index": "s975797870", "label": 486, "func": ""}
{"index": "s355653743", "label": 486, "func": ""}
{"index": "s573588535", "label": 486, "func": ""}
{"index": "s971455944", "label": 486, "func": ""}
{"index": "s574243922", "label": 486, "func": ""}
{"index": "s726961199", "label": 244, "func": ""}
{"index": "s196945781", "label": 244, "func": ""}
{"index": "s701963966", "label": 244, "func": ""}
{"index": "s810754107", "label": 244, "func": ""}
{"index": "s751327271", "label": 244, "func": ""}
{"index": "s883995739", "label": 244, "func": ""}
{"index": "s618717561", "label": 244, "func": ""}
{"index": "s269879986", "label": 244, "func": ""}
{"index": "s647698668", "label": 244, "func": ""}
{"index": "s692067489", "label": 244, "func": ""}
{"index": "s600815010", "label": 3184, "func": ""}
{"index": "s854018684", "label": 3184, "func": ""}
{"index": "s675326911", "label": 3184, "func": ""}
{"index": "s012424255", "label": 3184, "func": ""}
{"index": "s486263663", "label": 3184, "func": ""}
{"index": "s566009789", "label": 3184, "func": ""}
{"index": "s695517854", "label": 3184, "func": ""}
{"index": "s837805597", "label": 3184, "func": ""}
{"index": "s697331368", "label": 3184, "func": ""}
{"index": "s596268755", "label": 3184, "func": ""}
{"index": "s433631625", "label": 2778, "func": ""}
{"index": "s145110755", "label": 2778, "func": ""}
{"index": "s311856748", "label": 2778, "func": ""}
{"index": "s191029920", "label": 2778, "func": ""}
{"index": "s074249778", "label": 2778, "func": ""}
{"index": "s700963463", "label": 2778, "func": ""}
{"index": "s721242643", "label": 2778, "func": ""}
{"index": "s981435738", "label": 2778, "func": ""}
{"index": "s606673028", "label": 2778, "func": ""}
{"index": "s319988608", "label": 2778, "func": ""}
{"index": "s729936053", "label": 433, "func": ""}
{"index": "s608807617", "label": 433, "func": ""}
{"index": "s139682073", "label": 433, "func": ""}
{"index": "s260968849", "label": 433, "func": ""}
{"index": "s195190626", "label": 433, "func": ""}
{"index": "s933794715", "label": 433, "func": ""}
{"index": "s370252106", "label": 433, "func": ""}
{"index": "s786433596", "label": 433, "func": ""}
{"index": "s852957842", "label": 433, "func": ""}
{"index": "s563823386", "label": 433, "func": ""}
{"index": "s112797660", "label": 2952, "func": ""}
{"index": "s367037475", "label": 2952, "func": ""}
{"index": "s673636565", "label": 2952, "func": ""}
{"index": "s487853371", "label": 2952, "func": ""}
{"index": "s184349892", "label": 2952, "func": ""}
{"index": "s134110740", "label": 2952, "func": ""}
{"index": "s361570630", "label": 2952, "func": ""}
{"index": "s300819927", "label": 2952, "func": ""}
{"index": "s629367187", "label": 2952, "func": ""}
{"index": "s636994811", "label": 2952, "func": ""}
{"index": "s137959201", "label": 3931, "func": ""}
{"index": "s467484049", "label": 17, "func": ""}
{"index": "s646438543", "label": 17, "func": ""}
{"index": "s827802530", "label": 17, "func": ""}
{"index": "s576673994", "label": 17, "func": ""}
{"index": "s502980484", "label": 17, "func": ""}
{"index": "s093915678", "label": 17, "func": ""}
{"index": "s639538000", "label": 17, "func": ""}
{"index": "s449720661", "label": 17, "func": ""}
{"index": "s518280699", "label": 17, "func": ""}
{"index": "s280290867", "label": 17, "func": ""}
{"index": "s365213853", "label": 3210, "func": ""}
{"index": "s542780881", "label": 3210, "func": ""}
{"index": "s698621801", "label": 3210, "func": ""}
{"index": "s470967398", "label": 3210, "func": ""}
{"index": "s451695472", "label": 3210, "func": ""}
{"index": "s530735564", "label": 3210, "func": ""}
{"index": "s590495374", "label": 3210, "func": ""}
{"index": "s456306737", "label": 3210, "func": ""}
{"index": "s060297566", "label": 3210, "func": ""}
{"index": "s349992174", "label": 3210, "func": ""}
{"index": "s135439423", "label": 2624, "func": ""}
{"index": "s419250172", "label": 2624, "func": ""}
{"index": "s173629930", "label": 2624, "func": ""}
{"index": "s317697349", "label": 2624, "func": ""}
{"index": "s235826165", "label": 2624, "func": ""}
{"index": "s224949241", "label": 2624, "func": ""}
{"index": "s398120459", "label": 2624, "func": ""}
{"index": "s137474062", "label": 2624, "func": ""}
{"index": "s763993884", "label": 2624, "func": ""}
{"index": "s693815411", "label": 2624, "func": ""}
{"index": "s174447437", "label": 3729, "func": ""}
{"index": "s470643088", "label": 3729, "func": ""}
{"index": "s310202533", "label": 3729, "func": ""}
{"index": "s399077643", "label": 3729, "func": ""}
{"index": "s258782701", "label": 3729, "func": ""}
{"index": "s317762692", "label": 3729, "func": ""}
{"index": "s958401371", "label": 3729, "func": ""}
{"index": "s580831229", "label": 3729, "func": ""}
{"index": "s738310818", "label": 3729, "func": ""}
{"index": "s361917507", "label": 3729, "func": ""}
{"index": "s688850930", "label": 2931, "func": ""}
{"index": "s175905001", "label": 2931, "func": ""}
{"index": "s700523192", "label": 2931, "func": ""}
{"index": "s947738948", "label": 1624, "func": ""}
{"index": "s118986626", "label": 1419, "func": ""}
{"index": "s609952018", "label": 1419, "func": ""}
{"index": "s514086821", "label": 1419, "func": ""}
{"index": "s701990913", "label": 1419, "func": ""}
{"index": "s157078149", "label": 1419, "func": ""}
{"index": "s148538325", "label": 2536, "func": ""}
{"index": "s259381272", "label": 2536, "func": ""}
{"index": "s907605475", "label": 2536, "func": ""}
{"index": "s963484683", "label": 2536, "func": ""}
{"index": "s438666159", "label": 2536, "func": ""}
{"index": "s024843469", "label": 2536, "func": ""}
{"index": "s133254000", "label": 2536, "func": ""}
{"index": "s170227597", "label": 2536, "func": ""}
{"index": "s742712141", "label": 2536, "func": ""}
{"index": "s332850905", "label": 2536, "func": ""}
{"index": "s167011970", "label": 510, "func": ""}
{"index": "s852036657", "label": 510, "func": ""}
{"index": "s419417766", "label": 510, "func": ""}
{"index": "s289606634", "label": 510, "func": ""}
{"index": "s717352891", "label": 510, "func": ""}
{"index": "s914378888", "label": 510, "func": ""}
{"index": "s766862187", "label": 510, "func": ""}
{"index": "s072830363", "label": 510, "func": ""}
{"index": "s282073949", "label": 510, "func": ""}
{"index": "s493786484", "label": 510, "func": ""}
{"index": "s218950987", "label": 2253, "func": ""}
{"index": "s084564342", "label": 2253, "func": ""}
{"index": "s513895095", "label": 2253, "func": ""}
{"index": "s814150178", "label": 2253, "func": ""}
{"index": "s872707110", "label": 2253, "func": ""}
{"index": "s169005121", "label": 2253, "func": ""}
{"index": "s411557649", "label": 2474, "func": ""}
{"index": "s674042200", "label": 2474, "func": ""}
{"index": "s710536201", "label": 3829, "func": ""}
{"index": "s952410529", "label": 3829, "func": ""}
{"index": "s959325946", "label": 3829, "func": ""}
{"index": "s814003354", "label": 3829, "func": ""}
{"index": "s102322659", "label": 3829, "func": ""}
{"index": "s073171515", "label": 3829, "func": ""}
{"index": "s475570572", "label": 3829, "func": ""}
{"index": "s481453604", "label": 3829, "func": ""}
{"index": "s920006514", "label": 3829, "func": ""}
{"index": "s121629206", "label": 3829, "func": ""}
{"index": "s718368044", "label": 2706, "func": ""}
{"index": "s305493605", "label": 2706, "func": ""}
{"index": "s342947036", "label": 2706, "func": ""}
{"index": "s032534741", "label": 2706, "func": ""}
{"index": "s913129817", "label": 2706, "func": ""}
{"index": "s014574252", "label": 2706, "func": ""}
{"index": "s678369834", "label": 2706, "func": ""}
{"index": "s175952629", "label": 2706, "func": ""}
{"index": "s457847735", "label": 2706, "func": ""}
{"index": "s196353834", "label": 2706, "func": ""}
{"index": "s608810140", "label": 2542, "func": ""}
{"index": "s844549074", "label": 2542, "func": ""}
{"index": "s688136666", "label": 2542, "func": ""}
{"index": "s415463068", "label": 2542, "func": ""}
{"index": "s880129172", "label": 2542, "func": ""}
{"index": "s782027806", "label": 2542, "func": ""}
{"index": "s001371120", "label": 2542, "func": ""}
{"index": "s409735886", "label": 2542, "func": ""}
{"index": "s322522366", "label": 92, "func": ""}
{"index": "s900454215", "label": 92, "func": ""}
{"index": "s177659955", "label": 92, "func": ""}
{"index": "s594611168", "label": 92, "func": ""}
{"index": "s646259688", "label": 92, "func": ""}
{"index": "s889678777", "label": 92, "func": ""}
{"index": "s631276147", "label": 92, "func": ""}
{"index": "s227684414", "label": 92, "func": ""}
{"index": "s357147077", "label": 92, "func": ""}
{"index": "s479799892", "label": 92, "func": ""}
{"index": "s390333164", "label": 3988, "func": ""}
{"index": "s868963073", "label": 3988, "func": ""}
{"index": "s584766197", "label": 3988, "func": ""}
{"index": "s157606179", "label": 3988, "func": ""}
{"index": "s556575458", "label": 3988, "func": ""}
{"index": "s645694335", "label": 3988, "func": ""}
{"index": "s154651225", "label": 3988, "func": ""}
{"index": "s144020998", "label": 3988, "func": ""}
{"index": "s844423814", "label": 3988, "func": ""}
{"index": "s337580069", "label": 3988, "func": ""}
{"index": "s852356620", "label": 1829, "func": ""}
{"index": "s878398452", "label": 2736, "func": ""}
{"index": "s345937684", "label": 2736, "func": ""}
{"index": "s893087558", "label": 2736, "func": ""}
{"index": "s920902607", "label": 2736, "func": ""}
{"index": "s616250201", "label": 2736, "func": ""}
{"index": "s076320815", "label": 2736, "func": ""}
{"index": "s436459422", "label": 2736, "func": ""}
{"index": "s567072912", "label": 2736, "func": ""}
{"index": "s198452220", "label": 2736, "func": ""}
{"index": "s778412019", "label": 2736, "func": ""}
{"index": "s927773611", "label": 3402, "func": ""}
{"index": "s102579633", "label": 3402, "func": ""}
{"index": "s798665643", "label": 3402, "func": ""}
{"index": "s947957684", "label": 3402, "func": ""}
{"index": "s341376044", "label": 3402, "func": ""}
{"index": "s690346181", "label": 3402, "func": ""}
{"index": "s015867958", "label": 3402, "func": ""}
{"index": "s572230758", "label": 3402, "func": ""}
{"index": "s578082487", "label": 3402, "func": ""}
{"index": "s243583227", "label": 3402, "func": ""}
{"index": "s591448533", "label": 3780, "func": ""}
{"index": "s275659879", "label": 3780, "func": ""}
{"index": "s609757465", "label": 3780, "func": ""}
{"index": "s251554695", "label": 3780, "func": ""}
{"index": "s606724510", "label": 3780, "func": ""}
{"index": "s122047716", "label": 3780, "func": ""}
{"index": "s209805684", "label": 3780, "func": ""}
{"index": "s751461483", "label": 3780, "func": ""}
{"index": "s449229874", "label": 3780, "func": ""}
{"index": "s275664160", "label": 3780, "func": ""}
{"index": "s343046280", "label": 926, "func": ""}
{"index": "s245859308", "label": 926, "func": ""}
{"index": "s157547085", "label": 926, "func": ""}
{"index": "s497945960", "label": 926, "func": ""}
{"index": "s283459032", "label": 926, "func": ""}
{"index": "s752422013", "label": 926, "func": ""}
{"index": "s802885202", "label": 926, "func": ""}
{"index": "s027698906", "label": 926, "func": ""}
{"index": "s327122902", "label": 926, "func": ""}
{"index": "s582761103", "label": 926, "func": ""}
{"index": "s751171001", "label": 3856, "func": ""}
{"index": "s459807133", "label": 3856, "func": ""}
{"index": "s895244261", "label": 3856, "func": ""}
{"index": "s467398942", "label": 3856, "func": ""}
{"index": "s840699925", "label": 3856, "func": ""}
{"index": "s180841188", "label": 3856, "func": ""}
{"index": "s036779120", "label": 3856, "func": ""}
{"index": "s829911907", "label": 3856, "func": ""}
{"index": "s507462381", "label": 3856, "func": ""}
{"index": "s373156603", "label": 3856, "func": ""}
{"index": "s212733802", "label": 1895, "func": ""}
{"index": "s336015760", "label": 2659, "func": ""}
{"index": "s954858534", "label": 2659, "func": ""}
{"index": "s442139731", "label": 2659, "func": ""}
{"index": "s922750523", "label": 2659, "func": ""}
{"index": "s887537836", "label": 2659, "func": ""}
{"index": "s161471825", "label": 2659, "func": ""}
{"index": "s600521201", "label": 2659, "func": ""}
{"index": "s664429901", "label": 2659, "func": ""}
{"index": "s008188779", "label": 2659, "func": ""}
{"index": "s127964344", "label": 2659, "func": ""}
{"index": "s340202531", "label": 2366, "func": ""}
{"index": "s976670121", "label": 2366, "func": ""}
{"index": "s352367101", "label": 2366, "func": ""}
{"index": "s147941038", "label": 2366, "func": ""}
{"index": "s888942091", "label": 2366, "func": ""}
{"index": "s079916787", "label": 2366, "func": ""}
{"index": "s567526766", "label": 2366, "func": ""}
{"index": "s939708905", "label": 2366, "func": ""}
{"index": "s764457746", "label": 2772, "func": ""}
{"index": "s235671736", "label": 2772, "func": ""}
{"index": "s042836132", "label": 2772, "func": ""}
{"index": "s837115319", "label": 2772, "func": ""}
{"index": "s422104440", "label": 2772, "func": ""}
{"index": "s196194813", "label": 2772, "func": ""}
{"index": "s975506438", "label": 2772, "func": ""}
{"index": "s013063467", "label": 2772, "func": ""}
{"index": "s632704974", "label": 2772, "func": ""}
{"index": "s840547165", "label": 2772, "func": ""}
{"index": "s917119705", "label": 2971, "func": ""}
{"index": "s612488687", "label": 2971, "func": ""}
{"index": "s389318659", "label": 2971, "func": ""}
{"index": "s524762820", "label": 2971, "func": ""}
{"index": "s514730405", "label": 2971, "func": ""}
{"index": "s239159193", "label": 2971, "func": ""}
{"index": "s025408588", "label": 2971, "func": ""}
{"index": "s490468080", "label": 2971, "func": ""}
{"index": "s312202000", "label": 2971, "func": ""}
{"index": "s172049542", "label": 2971, "func": ""}
{"index": "s445598069", "label": 3818, "func": ""}
{"index": "s332484474", "label": 3818, "func": ""}
{"index": "s629652486", "label": 3818, "func": ""}
{"index": "s891916860", "label": 3818, "func": ""}
{"index": "s982790194", "label": 3818, "func": ""}
{"index": "s831803177", "label": 3818, "func": ""}
{"index": "s786954780", "label": 3818, "func": ""}
{"index": "s976878305", "label": 3818, "func": ""}
{"index": "s243430244", "label": 3818, "func": ""}
{"index": "s959446328", "label": 3818, "func": ""}
{"index": "s404264443", "label": 3777, "func": ""}
{"index": "s282335730", "label": 3777, "func": ""}
{"index": "s645311053", "label": 3777, "func": ""}
{"index": "s930736772", "label": 3777, "func": ""}
{"index": "s845661817", "label": 3777, "func": ""}
{"index": "s720277642", "label": 3777, "func": ""}
{"index": "s861731021", "label": 3777, "func": ""}
{"index": "s308755612", "label": 3777, "func": ""}
{"index": "s008839261", "label": 3777, "func": ""}
{"index": "s993369622", "label": 3777, "func": ""}
{"index": "s632026788", "label": 1561, "func": ""}
{"index": "s143850843", "label": 1561, "func": ""}
{"index": "s451291886", "label": 699, "func": ""}
{"index": "s523027915", "label": 699, "func": ""}
{"index": "s126083848", "label": 699, "func": ""}
{"index": "s832720988", "label": 699, "func": ""}
{"index": "s912213023", "label": 699, "func": ""}
{"index": "s756910026", "label": 699, "func": ""}
{"index": "s344154894", "label": 1028, "func": ""}
{"index": "s998680993", "label": 3929, "func": ""}
{"index": "s560650239", "label": 3929, "func": ""}
{"index": "s517614780", "label": 3929, "func": ""}
{"index": "s635657720", "label": 3929, "func": ""}
{"index": "s574176778", "label": 3929, "func": ""}
{"index": "s286909722", "label": 3929, "func": ""}
{"index": "s793859369", "label": 3929, "func": ""}
{"index": "s106374258", "label": 503, "func": ""}
{"index": "s253005826", "label": 503, "func": ""}
{"index": "s301436736", "label": 503, "func": ""}
{"index": "s432753628", "label": 503, "func": ""}
{"index": "s679521852", "label": 503, "func": ""}
{"index": "s757693928", "label": 503, "func": ""}
{"index": "s495849562", "label": 503, "func": ""}
{"index": "s864755913", "label": 650, "func": ""}
{"index": "s043578115", "label": 650, "func": ""}
{"index": "s561806023", "label": 650, "func": ""}
{"index": "s580490489", "label": 650, "func": ""}
{"index": "s968694597", "label": 2385, "func": ""}
{"index": "s875286039", "label": 2385, "func": ""}
{"index": "s250403510", "label": 2385, "func": ""}
{"index": "s016790193", "label": 2385, "func": ""}
{"index": "s900043221", "label": 2385, "func": ""}
{"index": "s245516405", "label": 2385, "func": ""}
{"index": "s075299255", "label": 2385, "func": ""}
{"index": "s453749685", "label": 2385, "func": ""}
{"index": "s480454831", "label": 2385, "func": ""}
{"index": "s768944643", "label": 2385, "func": ""}
{"index": "s903674380", "label": 284, "func": ""}
{"index": "s228722233", "label": 284, "func": ""}
{"index": "s418293881", "label": 284, "func": ""}
{"index": "s054075025", "label": 284, "func": ""}
{"index": "s033311228", "label": 2585, "func": ""}
{"index": "s267169544", "label": 2585, "func": ""}
{"index": "s330285905", "label": 2585, "func": ""}
{"index": "s177249891", "label": 2585, "func": ""}
{"index": "s391719468", "label": 2585, "func": ""}
{"index": "s485512912", "label": 2585, "func": ""}
{"index": "s407491249", "label": 2585, "func": ""}
{"index": "s378050378", "label": 2585, "func": ""}
{"index": "s796794572", "label": 2585, "func": ""}
{"index": "s562691198", "label": 2585, "func": ""}
{"index": "s573941390", "label": 1763, "func": ""}
{"index": "s200272060", "label": 637, "func": ""}
{"index": "s879037778", "label": 637, "func": ""}
{"index": "s527848462", "label": 637, "func": ""}
{"index": "s901434853", "label": 637, "func": ""}
{"index": "s385814386", "label": 637, "func": ""}
{"index": "s547268523", "label": 637, "func": ""}
{"index": "s652685873", "label": 637, "func": ""}
{"index": "s768278808", "label": 637, "func": ""}
{"index": "s260583381", "label": 637, "func": ""}
{"index": "s838914377", "label": 637, "func": ""}
{"index": "s217039982", "label": 3776, "func": ""}
{"index": "s352992560", "label": 3776, "func": ""}
{"index": "s885548230", "label": 3776, "func": ""}
{"index": "s304213045", "label": 3776, "func": ""}
{"index": "s688655679", "label": 3776, "func": ""}
{"index": "s367828106", "label": 3776, "func": ""}
{"index": "s809070841", "label": 3776, "func": ""}
{"index": "s694877822", "label": 3776, "func": ""}
{"index": "s637383745", "label": 3776, "func": ""}
{"index": "s092603682", "label": 3776, "func": ""}
{"index": "s028286680", "label": 2289, "func": ""}
{"index": "s902399463", "label": 2289, "func": ""}
{"index": "s257840351", "label": 2289, "func": ""}
{"index": "s562757448", "label": 2289, "func": ""}
{"index": "s449587591", "label": 2289, "func": ""}
{"index": "s454053872", "label": 2289, "func": ""}
{"index": "s979681747", "label": 2289, "func": ""}
{"index": "s295451425", "label": 2289, "func": ""}
{"index": "s766531115", "label": 2289, "func": ""}
{"index": "s306700893", "label": 2289, "func": ""}
{"index": "s505990242", "label": 2882, "func": ""}
{"index": "s127855471", "label": 2882, "func": ""}
{"index": "s807481121", "label": 2882, "func": ""}
{"index": "s760546843", "label": 2882, "func": ""}
{"index": "s957838008", "label": 2882, "func": ""}
{"index": "s623676379", "label": 2882, "func": ""}
{"index": "s856568538", "label": 2882, "func": ""}
{"index": "s020481247", "label": 2882, "func": ""}
{"index": "s286490871", "label": 2882, "func": ""}
{"index": "s272575314", "label": 2882, "func": ""}
{"index": "s999097364", "label": 2175, "func": ""}
{"index": "s292455335", "label": 2175, "func": ""}
{"index": "s073037166", "label": 2175, "func": ""}
{"index": "s765969432", "label": 4003, "func": ""}
{"index": "s702004145", "label": 4003, "func": ""}
{"index": "s571270498", "label": 4003, "func": ""}
{"index": "s199828457", "label": 4003, "func": ""}
{"index": "s868317058", "label": 4003, "func": ""}
{"index": "s101850059", "label": 4003, "func": ""}
{"index": "s089115066", "label": 4003, "func": ""}
{"index": "s288113428", "label": 4003, "func": ""}
{"index": "s410157975", "label": 4003, "func": ""}
{"index": "s986662468", "label": 4003, "func": ""}
{"index": "s057887465", "label": 415, "func": ""}
{"index": "s155582573", "label": 3153, "func": ""}
{"index": "s127631226", "label": 3153, "func": ""}
{"index": "s076714532", "label": 3153, "func": ""}
{"index": "s524617449", "label": 3153, "func": ""}
{"index": "s389836097", "label": 3153, "func": ""}
{"index": "s736045517", "label": 3153, "func": ""}
{"index": "s016845620", "label": 3153, "func": ""}
{"index": "s341385466", "label": 295, "func": ""}
{"index": "s247664581", "label": 295, "func": ""}
{"index": "s328745872", "label": 295, "func": ""}
{"index": "s147920161", "label": 295, "func": ""}
{"index": "s801003512", "label": 295, "func": ""}
{"index": "s583846444", "label": 295, "func": ""}
{"index": "s531341309", "label": 3846, "func": ""}
{"index": "s844794976", "label": 3846, "func": ""}
{"index": "s628375880", "label": 3846, "func": ""}
{"index": "s109052042", "label": 3846, "func": ""}
{"index": "s604118334", "label": 3846, "func": ""}
{"index": "s280710423", "label": 3846, "func": ""}
{"index": "s899211479", "label": 3846, "func": ""}
{"index": "s255454540", "label": 3846, "func": ""}
{"index": "s144341691", "label": 3846, "func": ""}
{"index": "s537321999", "label": 3846, "func": ""}
{"index": "s007163791", "label": 143, "func": ""}
{"index": "s750484045", "label": 143, "func": ""}
{"index": "s544941511", "label": 143, "func": ""}
{"index": "s007953493", "label": 143, "func": ""}
{"index": "s843283998", "label": 143, "func": ""}
{"index": "s194476548", "label": 143, "func": ""}
{"index": "s996185332", "label": 143, "func": ""}
{"index": "s192198879", "label": 143, "func": ""}
{"index": "s690755330", "label": 143, "func": ""}
{"index": "s870386297", "label": 143, "func": ""}
{"index": "s035230557", "label": 2666, "func": ""}
{"index": "s305527847", "label": 2666, "func": ""}
{"index": "s051761489", "label": 2666, "func": ""}
{"index": "s558028163", "label": 2666, "func": ""}
{"index": "s662529585", "label": 4009, "func": ""}
{"index": "s966207361", "label": 1295, "func": ""}
{"index": "s382357706", "label": 1295, "func": ""}
{"index": "s077068772", "label": 1295, "func": ""}
{"index": "s831921092", "label": 1295, "func": ""}
{"index": "s470450783", "label": 1295, "func": ""}
{"index": "s026769746", "label": 1295, "func": ""}
{"index": "s511872048", "label": 1295, "func": ""}
{"index": "s918490081", "label": 1295, "func": ""}
{"index": "s074717594", "label": 1295, "func": ""}
{"index": "s844205840", "label": 2825, "func": ""}
{"index": "s169939967", "label": 2825, "func": ""}
{"index": "s804654846", "label": 2825, "func": ""}
{"index": "s854493643", "label": 2825, "func": ""}
{"index": "s110944054", "label": 2825, "func": ""}
{"index": "s929446871", "label": 2825, "func": ""}
{"index": "s658751172", "label": 2825, "func": ""}
{"index": "s929565893", "label": 2825, "func": ""}
{"index": "s710707333", "label": 546, "func": ""}
{"index": "s238219352", "label": 546, "func": ""}
{"index": "s864512669", "label": 2932, "func": ""}
{"index": "s794913417", "label": 208, "func": ""}
{"index": "s043239944", "label": 208, "func": ""}
{"index": "s492402424", "label": 208, "func": ""}
{"index": "s629900628", "label": 208, "func": ""}
{"index": "s573734066", "label": 208, "func": ""}
{"index": "s910415762", "label": 208, "func": ""}
{"index": "s455941943", "label": 208, "func": ""}
{"index": "s218724863", "label": 208, "func": ""}
{"index": "s209330846", "label": 208, "func": ""}
{"index": "s820158061", "label": 208, "func": ""}
{"index": "s335362391", "label": 3796, "func": ""}
{"index": "s161002895", "label": 3796, "func": ""}
{"index": "s651571330", "label": 3796, "func": ""}
{"index": "s868161014", "label": 3796, "func": ""}
{"index": "s591366097", "label": 3796, "func": ""}
{"index": "s174908287", "label": 3796, "func": ""}
{"index": "s111021292", "label": 3796, "func": ""}
{"index": "s885644361", "label": 3796, "func": ""}
{"index": "s867452895", "label": 3796, "func": ""}
{"index": "s112720125", "label": 3796, "func": ""}
{"index": "s147125338", "label": 845, "func": ""}
{"index": "s539817541", "label": 845, "func": ""}
{"index": "s033106884", "label": 845, "func": ""}
{"index": "s143296820", "label": 845, "func": ""}
{"index": "s848101441", "label": 845, "func": ""}
{"index": "s291248381", "label": 845, "func": ""}
{"index": "s930957524", "label": 845, "func": ""}
{"index": "s849116967", "label": 3624, "func": ""}
{"index": "s480088588", "label": 3624, "func": ""}
{"index": "s233703283", "label": 3624, "func": ""}
{"index": "s680171199", "label": 3624, "func": ""}
{"index": "s769775505", "label": 3624, "func": ""}
{"index": "s533243936", "label": 3624, "func": ""}
{"index": "s488006654", "label": 3624, "func": ""}
{"index": "s581592640", "label": 3624, "func": ""}
{"index": "s387472682", "label": 3624, "func": ""}
{"index": "s735617415", "label": 3624, "func": ""}
{"index": "s297547249", "label": 2583, "func": ""}
{"index": "s064422300", "label": 2583, "func": ""}
{"index": "s300535580", "label": 2583, "func": ""}
{"index": "s170127879", "label": 2583, "func": ""}
{"index": "s519233596", "label": 2583, "func": ""}
{"index": "s705344363", "label": 2583, "func": ""}
{"index": "s397983010", "label": 2583, "func": ""}
{"index": "s626694681", "label": 2583, "func": ""}
{"index": "s229000846", "label": 2583, "func": ""}
{"index": "s117634583", "label": 2583, "func": ""}
{"index": "s810155318", "label": 539, "func": ""}
{"index": "s341326288", "label": 539, "func": ""}
{"index": "s092744692", "label": 3105, "func": ""}
{"index": "s042684447", "label": 3105, "func": ""}
{"index": "s345868293", "label": 3105, "func": ""}
{"index": "s134143458", "label": 3105, "func": ""}
{"index": "s590045598", "label": 3105, "func": ""}
{"index": "s503933039", "label": 3105, "func": ""}
{"index": "s142671634", "label": 3105, "func": ""}
{"index": "s171019065", "label": 3105, "func": ""}
{"index": "s657232131", "label": 3105, "func": ""}
{"index": "s961353530", "label": 3105, "func": ""}
{"index": "s610629953", "label": 3014, "func": ""}
{"index": "s110903822", "label": 3014, "func": ""}
{"index": "s738569791", "label": 3014, "func": ""}
{"index": "s799439210", "label": 3014, "func": ""}
{"index": "s908996453", "label": 3014, "func": ""}
{"index": "s416884462", "label": 3014, "func": ""}
{"index": "s875423472", "label": 3014, "func": ""}
{"index": "s648513648", "label": 3014, "func": ""}
{"index": "s289590676", "label": 3014, "func": ""}
{"index": "s223736168", "label": 3014, "func": ""}
{"index": "s326256260", "label": 3547, "func": ""}
{"index": "s000271983", "label": 3547, "func": ""}
{"index": "s720016866", "label": 3547, "func": ""}
{"index": "s814078658", "label": 3547, "func": ""}
{"index": "s630277786", "label": 3547, "func": ""}
{"index": "s287083248", "label": 3547, "func": ""}
{"index": "s453633354", "label": 3547, "func": ""}
{"index": "s200538208", "label": 3547, "func": ""}
{"index": "s929752434", "label": 3547, "func": ""}
{"index": "s684950604", "label": 3547, "func": ""}
{"index": "s956826475", "label": 2461, "func": ""}
{"index": "s351644891", "label": 2461, "func": ""}
{"index": "s561759198", "label": 2461, "func": ""}
{"index": "s556324875", "label": 2461, "func": ""}
{"index": "s386418824", "label": 2461, "func": ""}
{"index": "s275904969", "label": 2461, "func": ""}
{"index": "s434767864", "label": 2461, "func": ""}
{"index": "s603216239", "label": 2461, "func": ""}
{"index": "s943298488", "label": 2461, "func": ""}
{"index": "s386452183", "label": 2461, "func": ""}
{"index": "s341518671", "label": 616, "func": ""}
{"index": "s047463993", "label": 616, "func": ""}
{"index": "s884131222", "label": 616, "func": ""}
{"index": "s523592816", "label": 616, "func": ""}
{"index": "s699147313", "label": 616, "func": ""}
{"index": "s137488394", "label": 616, "func": ""}
{"index": "s800510791", "label": 616, "func": ""}
{"index": "s681846736", "label": 616, "func": ""}
{"index": "s446933420", "label": 616, "func": ""}
{"index": "s608215200", "label": 616, "func": ""}
{"index": "s047753080", "label": 1516, "func": ""}
{"index": "s980036710", "label": 1516, "func": ""}
{"index": "s561767358", "label": 1516, "func": ""}
{"index": "s060123439", "label": 1516, "func": ""}
{"index": "s753010453", "label": 1516, "func": ""}
{"index": "s370597267", "label": 1516, "func": ""}
{"index": "s571993681", "label": 1516, "func": ""}
{"index": "s577182256", "label": 1516, "func": ""}
{"index": "s331876815", "label": 1516, "func": ""}
{"index": "s630695005", "label": 1516, "func": ""}
{"index": "s567005855", "label": 1107, "func": ""}
{"index": "s149014768", "label": 1107, "func": ""}
{"index": "s326526824", "label": 2632, "func": ""}
{"index": "s622262669", "label": 2632, "func": ""}
{"index": "s284979964", "label": 2632, "func": ""}
{"index": "s617210886", "label": 2632, "func": ""}
{"index": "s728305868", "label": 2632, "func": ""}
{"index": "s142788707", "label": 2632, "func": ""}
{"index": "s092986890", "label": 2632, "func": ""}
{"index": "s461991900", "label": 2632, "func": ""}
{"index": "s000840945", "label": 2632, "func": ""}
{"index": "s575547284", "label": 2632, "func": ""}
{"index": "s205624032", "label": 2264, "func": ""}
{"index": "s223911252", "label": 2264, "func": ""}
{"index": "s949580201", "label": 2264, "func": ""}
{"index": "s674247580", "label": 2264, "func": ""}
{"index": "s806623780", "label": 2264, "func": ""}
{"index": "s156829425", "label": 2264, "func": ""}
{"index": "s647096566", "label": 2264, "func": ""}
{"index": "s293642901", "label": 2264, "func": ""}
{"index": "s364469069", "label": 2264, "func": ""}
{"index": "s078253975", "label": 2264, "func": ""}
{"index": "s853363541", "label": 1410, "func": ""}
{"index": "s073711999", "label": 1410, "func": ""}
{"index": "s605206732", "label": 1410, "func": ""}
{"index": "s731259426", "label": 1410, "func": ""}
{"index": "s948756001", "label": 658, "func": ""}
{"index": "s703550180", "label": 658, "func": ""}
{"index": "s878392615", "label": 658, "func": ""}
{"index": "s962653197", "label": 2935, "func": ""}
{"index": "s200831161", "label": 2935, "func": ""}
{"index": "s297840505", "label": 2935, "func": ""}
{"index": "s161403075", "label": 2935, "func": ""}
{"index": "s917439328", "label": 2935, "func": ""}
{"index": "s388997773", "label": 2935, "func": ""}
{"index": "s030453556", "label": 2935, "func": ""}
{"index": "s622654080", "label": 2935, "func": ""}
{"index": "s199779597", "label": 2935, "func": ""}
{"index": "s783177459", "label": 2935, "func": ""}
{"index": "s987843038", "label": 67, "func": ""}
{"index": "s469885503", "label": 67, "func": ""}
{"index": "s921854525", "label": 67, "func": ""}
{"index": "s398421487", "label": 67, "func": ""}
{"index": "s880549545", "label": 67, "func": ""}
{"index": "s628701933", "label": 67, "func": ""}
{"index": "s453210399", "label": 67, "func": ""}
{"index": "s916128380", "label": 67, "func": ""}
{"index": "s637348416", "label": 67, "func": ""}
{"index": "s975765097", "label": 67, "func": ""}
{"index": "s901425230", "label": 1804, "func": ""}
{"index": "s772025513", "label": 930, "func": ""}
{"index": "s847814737", "label": 930, "func": ""}
{"index": "s433236153", "label": 930, "func": ""}
{"index": "s580910528", "label": 930, "func": ""}
{"index": "s607969249", "label": 930, "func": ""}
{"index": "s023066278", "label": 930, "func": ""}
{"index": "s651471348", "label": 930, "func": ""}
{"index": "s520071661", "label": 930, "func": ""}
{"index": "s863724972", "label": 930, "func": ""}
{"index": "s237797243", "label": 2617, "func": ""}
{"index": "s261781820", "label": 2617, "func": ""}
{"index": "s095474494", "label": 2617, "func": ""}
{"index": "s033130509", "label": 2617, "func": ""}
{"index": "s925123414", "label": 2617, "func": ""}
{"index": "s761141266", "label": 2617, "func": ""}
{"index": "s763075457", "label": 2617, "func": ""}
{"index": "s304784417", "label": 2617, "func": ""}
{"index": "s388596484", "label": 2617, "func": ""}
{"index": "s110938018", "label": 2617, "func": ""}
{"index": "s155471831", "label": 33, "func": ""}
{"index": "s873669941", "label": 33, "func": ""}
{"index": "s904723599", "label": 33, "func": ""}
{"index": "s682659704", "label": 33, "func": ""}
{"index": "s039135536", "label": 33, "func": ""}
{"index": "s087093276", "label": 33, "func": ""}
{"index": "s459334075", "label": 33, "func": ""}
{"index": "s099468686", "label": 33, "func": ""}
{"index": "s152340422", "label": 33, "func": ""}
{"index": "s699182022", "label": 33, "func": ""}
{"index": "s977105945", "label": 2851, "func": ""}
{"index": "s132423530", "label": 2851, "func": ""}
{"index": "s049759848", "label": 2851, "func": ""}
{"index": "s777246577", "label": 2851, "func": ""}
{"index": "s814707427", "label": 2851, "func": ""}
{"index": "s820536770", "label": 2851, "func": ""}
{"index": "s609919660", "label": 2851, "func": ""}
{"index": "s531334965", "label": 2851, "func": ""}
{"index": "s219461574", "label": 2851, "func": ""}
{"index": "s060203406", "label": 2851, "func": ""}
{"index": "s415964859", "label": 618, "func": ""}
{"index": "s728627500", "label": 618, "func": ""}
{"index": "s782087734", "label": 618, "func": ""}
{"index": "s107529271", "label": 618, "func": ""}
{"index": "s812952089", "label": 618, "func": ""}
{"index": "s610656480", "label": 618, "func": ""}
{"index": "s034288667", "label": 618, "func": ""}
{"index": "s030559842", "label": 618, "func": ""}
{"index": "s860145351", "label": 618, "func": ""}
{"index": "s523016600", "label": 618, "func": ""}
{"index": "s443893428", "label": 3267, "func": ""}
{"index": "s761811396", "label": 3267, "func": ""}
{"index": "s209201283", "label": 3267, "func": ""}
{"index": "s910360790", "label": 3267, "func": ""}
{"index": "s155604961", "label": 3267, "func": ""}
{"index": "s631254543", "label": 3267, "func": ""}
{"index": "s512133721", "label": 3267, "func": ""}
{"index": "s475714191", "label": 3267, "func": ""}
{"index": "s625799340", "label": 3267, "func": ""}
{"index": "s467941720", "label": 3267, "func": ""}
{"index": "s378806006", "label": 3462, "func": ""}
{"index": "s618259551", "label": 118, "func": ""}
{"index": "s117637862", "label": 118, "func": ""}
{"index": "s450628803", "label": 118, "func": ""}
{"index": "s928851004", "label": 118, "func": ""}
{"index": "s913281817", "label": 118, "func": ""}
{"index": "s491720665", "label": 118, "func": ""}
{"index": "s990498433", "label": 118, "func": ""}
{"index": "s853502046", "label": 118, "func": ""}
{"index": "s973720367", "label": 118, "func": ""}
{"index": "s470712842", "label": 118, "func": ""}
{"index": "s047871135", "label": 2356, "func": ""}
{"index": "s080879131", "label": 2356, "func": ""}
{"index": "s399672454", "label": 2356, "func": ""}
{"index": "s939283875", "label": 2356, "func": ""}
{"index": "s017717074", "label": 2356, "func": ""}
{"index": "s974684064", "label": 2356, "func": ""}
{"index": "s326301971", "label": 2356, "func": ""}
{"index": "s915332186", "label": 2356, "func": ""}
{"index": "s389986205", "label": 2356, "func": ""}
{"index": "s225879784", "label": 2356, "func": ""}
{"index": "s642709913", "label": 3357, "func": ""}
{"index": "s387741188", "label": 3357, "func": ""}
{"index": "s278438850", "label": 3357, "func": ""}
{"index": "s149468612", "label": 3357, "func": ""}
{"index": "s306781537", "label": 3357, "func": ""}
{"index": "s156700782", "label": 3357, "func": ""}
{"index": "s662921597", "label": 3357, "func": ""}
{"index": "s561425473", "label": 3357, "func": ""}
{"index": "s718404743", "label": 3357, "func": ""}
{"index": "s755552729", "label": 3357, "func": ""}
{"index": "s573467600", "label": 3740, "func": ""}
{"index": "s358896171", "label": 3740, "func": ""}
{"index": "s492444037", "label": 3740, "func": ""}
{"index": "s239079967", "label": 3740, "func": ""}
{"index": "s726490236", "label": 3740, "func": ""}
{"index": "s632982110", "label": 3740, "func": ""}
{"index": "s172232101", "label": 3740, "func": ""}
{"index": "s095199107", "label": 3740, "func": ""}
{"index": "s261339969", "label": 3740, "func": ""}
{"index": "s530023664", "label": 3740, "func": ""}
{"index": "s895218463", "label": 887, "func": ""}
{"index": "s338730912", "label": 887, "func": ""}
{"index": "s629082150", "label": 887, "func": ""}
{"index": "s781768842", "label": 887, "func": ""}
{"index": "s218080088", "label": 887, "func": ""}
{"index": "s788657026", "label": 887, "func": ""}
{"index": "s234254066", "label": 887, "func": ""}
{"index": "s636518792", "label": 887, "func": ""}
{"index": "s097406204", "label": 887, "func": ""}
{"index": "s657533492", "label": 4044, "func": ""}
{"index": "s212497107", "label": 4044, "func": ""}
{"index": "s143759289", "label": 4044, "func": ""}
{"index": "s676613034", "label": 4044, "func": ""}
{"index": "s654669521", "label": 4044, "func": ""}
{"index": "s034506883", "label": 4044, "func": ""}
{"index": "s471627194", "label": 4044, "func": ""}
{"index": "s107348272", "label": 4044, "func": ""}
{"index": "s719682595", "label": 4044, "func": ""}
{"index": "s986869927", "label": 4044, "func": ""}
{"index": "s367863297", "label": 2885, "func": ""}
{"index": "s662086132", "label": 2885, "func": ""}
{"index": "s076438947", "label": 2885, "func": ""}
{"index": "s621642133", "label": 2885, "func": ""}
{"index": "s211823082", "label": 2885, "func": ""}
{"index": "s425158952", "label": 2885, "func": ""}
{"index": "s678634805", "label": 2885, "func": ""}
{"index": "s528266148", "label": 2885, "func": ""}
{"index": "s721668916", "label": 2885, "func": ""}
{"index": "s154437233", "label": 2885, "func": ""}
{"index": "s507031294", "label": 4008, "func": ""}
{"index": "s257461078", "label": 4008, "func": ""}
{"index": "s713084078", "label": 4008, "func": ""}
{"index": "s518416915", "label": 4008, "func": ""}
{"index": "s966246646", "label": 4008, "func": ""}
{"index": "s983790729", "label": 4008, "func": ""}
{"index": "s638266047", "label": 4008, "func": ""}
{"index": "s679543092", "label": 4008, "func": ""}
{"index": "s554774970", "label": 2586, "func": ""}
{"index": "s662467027", "label": 2586, "func": ""}
{"index": "s612328804", "label": 2586, "func": ""}
{"index": "s947718324", "label": 2586, "func": ""}
{"index": "s288480840", "label": 2586, "func": ""}
{"index": "s752665482", "label": 2586, "func": ""}
{"index": "s831844812", "label": 2586, "func": ""}
{"index": "s073315550", "label": 2586, "func": ""}
{"index": "s279516401", "label": 2586, "func": ""}
{"index": "s591977034", "label": 2586, "func": ""}
{"index": "s626011962", "label": 2680, "func": ""}
{"index": "s960280125", "label": 2680, "func": ""}
{"index": "s720380586", "label": 2680, "func": ""}
{"index": "s370632081", "label": 2680, "func": ""}
{"index": "s635773858", "label": 2680, "func": ""}
{"index": "s125672815", "label": 2680, "func": ""}
{"index": "s378018837", "label": 2680, "func": ""}
{"index": "s539986613", "label": 2680, "func": ""}
{"index": "s828266237", "label": 2680, "func": ""}
{"index": "s827921100", "label": 2680, "func": ""}
{"index": "s886653514", "label": 1438, "func": ""}
{"index": "s306340869", "label": 1438, "func": ""}
{"index": "s330673542", "label": 1438, "func": ""}
{"index": "s568971956", "label": 1438, "func": ""}
{"index": "s340831323", "label": 1438, "func": ""}
{"index": "s512528304", "label": 1438, "func": ""}
{"index": "s611272460", "label": 1438, "func": ""}
{"index": "s391976187", "label": 1438, "func": ""}
{"index": "s956430575", "label": 1438, "func": ""}
{"index": "s315600862", "label": 1438, "func": ""}
{"index": "s857162162", "label": 2677, "func": ""}
{"index": "s777322781", "label": 2677, "func": ""}
{"index": "s283938326", "label": 2677, "func": ""}
{"index": "s393069284", "label": 2677, "func": ""}
{"index": "s821688140", "label": 2677, "func": ""}
{"index": "s685304558", "label": 2677, "func": ""}
{"index": "s167683324", "label": 2677, "func": ""}
{"index": "s385542744", "label": 2677, "func": ""}
{"index": "s942073657", "label": 2677, "func": ""}
{"index": "s552932165", "label": 2677, "func": ""}
{"index": "s905500564", "label": 935, "func": ""}
{"index": "s309891620", "label": 935, "func": ""}
{"index": "s190545191", "label": 935, "func": ""}
{"index": "s355896888", "label": 935, "func": ""}
{"index": "s689395188", "label": 935, "func": ""}
{"index": "s736989842", "label": 935, "func": ""}
{"index": "s762182286", "label": 935, "func": ""}
{"index": "s104211661", "label": 935, "func": ""}
{"index": "s790462151", "label": 935, "func": ""}
{"index": "s355574602", "label": 935, "func": ""}
{"index": "s177125809", "label": 297, "func": ""}
{"index": "s889370276", "label": 297, "func": ""}
{"index": "s946799098", "label": 2062, "func": ""}
{"index": "s487582477", "label": 2062, "func": ""}
{"index": "s565303386", "label": 4025, "func": ""}
{"index": "s134341978", "label": 4025, "func": ""}
{"index": "s367138252", "label": 4025, "func": ""}
{"index": "s090129602", "label": 4025, "func": ""}
{"index": "s211413190", "label": 4025, "func": ""}
{"index": "s731720565", "label": 4025, "func": ""}
{"index": "s025334554", "label": 4025, "func": ""}
{"index": "s036922760", "label": 4025, "func": ""}
{"index": "s977405299", "label": 4025, "func": ""}
{"index": "s802400425", "label": 4025, "func": ""}
{"index": "s260565665", "label": 938, "func": ""}
{"index": "s925395688", "label": 938, "func": ""}
{"index": "s870457728", "label": 3151, "func": ""}
{"index": "s283004255", "label": 3151, "func": ""}
{"index": "s547519105", "label": 3151, "func": ""}
{"index": "s282299741", "label": 3151, "func": ""}
{"index": "s766069036", "label": 3151, "func": ""}
{"index": "s667509041", "label": 3151, "func": ""}
{"index": "s598867367", "label": 3151, "func": ""}
{"index": "s609607772", "label": 3151, "func": ""}
{"index": "s955148538", "label": 3151, "func": ""}
{"index": "s210260787", "label": 3151, "func": ""}
{"index": "s863681105", "label": 3903, "func": ""}
{"index": "s112453923", "label": 3903, "func": ""}
{"index": "s496871061", "label": 3952, "func": ""}
{"index": "s916817623", "label": 3952, "func": ""}
{"index": "s774224792", "label": 3952, "func": ""}
{"index": "s533948730", "label": 3952, "func": ""}
{"index": "s877211200", "label": 3952, "func": ""}
{"index": "s659843602", "label": 3952, "func": ""}
{"index": "s309634920", "label": 3952, "func": ""}
{"index": "s469301845", "label": 3952, "func": ""}
{"index": "s630753677", "label": 3952, "func": ""}
{"index": "s118656880", "label": 3952, "func": ""}
{"index": "s417085374", "label": 570, "func": ""}
{"index": "s355123333", "label": 570, "func": ""}
{"index": "s357331552", "label": 570, "func": ""}
{"index": "s149669573", "label": 570, "func": ""}
{"index": "s395547570", "label": 2829, "func": ""}
{"index": "s723160576", "label": 2829, "func": ""}
{"index": "s461087598", "label": 2829, "func": ""}
{"index": "s282310341", "label": 2829, "func": ""}
{"index": "s631108854", "label": 2829, "func": ""}
{"index": "s565645232", "label": 2829, "func": ""}
{"index": "s758661934", "label": 2829, "func": ""}
{"index": "s998849717", "label": 2829, "func": ""}
{"index": "s364530923", "label": 2829, "func": ""}
{"index": "s103999000", "label": 2829, "func": ""}
{"index": "s126654410", "label": 545, "func": ""}
{"index": "s127512001", "label": 545, "func": ""}
{"index": "s673663888", "label": 545, "func": ""}
{"index": "s587290795", "label": 545, "func": ""}
{"index": "s679647797", "label": 545, "func": ""}
{"index": "s454505461", "label": 545, "func": ""}
{"index": "s042353121", "label": 522, "func": ""}
{"index": "s430697986", "label": 522, "func": ""}
{"index": "s797383851", "label": 522, "func": ""}
{"index": "s503120947", "label": 522, "func": ""}
{"index": "s720527488", "label": 522, "func": ""}
{"index": "s737714476", "label": 522, "func": ""}
{"index": "s755710925", "label": 4037, "func": ""}
{"index": "s537068772", "label": 4037, "func": ""}
{"index": "s913835674", "label": 4037, "func": ""}
{"index": "s820487503", "label": 2247, "func": ""}
{"index": "s414249971", "label": 2247, "func": ""}
{"index": "s518107276", "label": 2247, "func": ""}
{"index": "s868963472", "label": 2247, "func": ""}
{"index": "s059678282", "label": 2247, "func": ""}
{"index": "s912488116", "label": 2247, "func": ""}
{"index": "s336489233", "label": 2247, "func": ""}
{"index": "s706741108", "label": 2247, "func": ""}
{"index": "s385414176", "label": 2247, "func": ""}
{"index": "s790803352", "label": 2247, "func": ""}
{"index": "s811482092", "label": 1248, "func": ""}
{"index": "s542050573", "label": 1554, "func": ""}
{"index": "s723965408", "label": 1554, "func": ""}
{"index": "s788868162", "label": 1554, "func": ""}
{"index": "s491335899", "label": 1554, "func": ""}
{"index": "s804529465", "label": 1554, "func": ""}
{"index": "s712636102", "label": 1554, "func": ""}
{"index": "s147582601", "label": 1554, "func": ""}
{"index": "s862535410", "label": 1554, "func": ""}
{"index": "s506888584", "label": 1554, "func": ""}
{"index": "s917432744", "label": 1554, "func": ""}
{"index": "s673005455", "label": 3730, "func": ""}
{"index": "s673212614", "label": 3730, "func": ""}
{"index": "s580875484", "label": 3730, "func": ""}
{"index": "s719405844", "label": 3730, "func": ""}
{"index": "s466479913", "label": 3730, "func": ""}
{"index": "s541829313", "label": 3730, "func": ""}
{"index": "s395584929", "label": 3730, "func": ""}
{"index": "s358817243", "label": 3730, "func": ""}
{"index": "s251268773", "label": 3730, "func": ""}
{"index": "s515810206", "label": 3730, "func": ""}
{"index": "s262966547", "label": 3004, "func": ""}
{"index": "s314280801", "label": 3004, "func": ""}
{"index": "s161063813", "label": 3004, "func": ""}
{"index": "s566789840", "label": 3004, "func": ""}
{"index": "s472086687", "label": 3004, "func": ""}
{"index": "s303012472", "label": 3004, "func": ""}
{"index": "s908931256", "label": 3004, "func": ""}
{"index": "s857875861", "label": 3004, "func": ""}
{"index": "s631290390", "label": 3004, "func": ""}
{"index": "s042715290", "label": 2124, "func": ""}
{"index": "s588514550", "label": 2124, "func": ""}
{"index": "s225757417", "label": 2124, "func": ""}
{"index": "s084549723", "label": 2124, "func": ""}
{"index": "s750234858", "label": 2124, "func": ""}
{"index": "s511166639", "label": 2124, "func": ""}
{"index": "s797951489", "label": 2124, "func": ""}
{"index": "s703390448", "label": 2124, "func": ""}
{"index": "s286122845", "label": 2124, "func": ""}
{"index": "s587382106", "label": 1734, "func": ""}
{"index": "s991284379", "label": 2970, "func": ""}
{"index": "s198591274", "label": 2970, "func": ""}
{"index": "s033252213", "label": 2970, "func": ""}
{"index": "s421696417", "label": 2970, "func": ""}
{"index": "s690380520", "label": 2970, "func": ""}
{"index": "s814063909", "label": 2970, "func": ""}
{"index": "s165315770", "label": 2970, "func": ""}
{"index": "s632041298", "label": 2970, "func": ""}
{"index": "s517512842", "label": 2970, "func": ""}
{"index": "s447271086", "label": 2970, "func": ""}
{"index": "s844560573", "label": 3167, "func": ""}
{"index": "s546973076", "label": 3167, "func": ""}
{"index": "s170687354", "label": 3167, "func": ""}
{"index": "s994977237", "label": 3167, "func": ""}
{"index": "s138055515", "label": 3167, "func": ""}
{"index": "s390903155", "label": 3167, "func": ""}
{"index": "s483150727", "label": 3167, "func": ""}
{"index": "s663149552", "label": 3167, "func": ""}
{"index": "s211323383", "label": 3167, "func": ""}
{"index": "s903310294", "label": 3167, "func": ""}
{"index": "s253837969", "label": 1873, "func": ""}
{"index": "s760826512", "label": 1873, "func": ""}
{"index": "s786712026", "label": 1873, "func": ""}
{"index": "s585460779", "label": 2280, "func": ""}
{"index": "s542066358", "label": 2280, "func": ""}
{"index": "s277677652", "label": 2280, "func": ""}
{"index": "s429365171", "label": 2280, "func": ""}
{"index": "s555688246", "label": 2280, "func": ""}
{"index": "s252147789", "label": 2280, "func": ""}
{"index": "s834413412", "label": 2280, "func": ""}
{"index": "s316179641", "label": 2280, "func": ""}
{"index": "s398677138", "label": 2280, "func": ""}
{"index": "s649968403", "label": 2280, "func": ""}
{"index": "s952006255", "label": 2783, "func": ""}
{"index": "s985965771", "label": 2783, "func": ""}
{"index": "s640599895", "label": 2783, "func": ""}
{"index": "s979021960", "label": 2783, "func": ""}
{"index": "s200775446", "label": 2783, "func": ""}
{"index": "s936900297", "label": 2783, "func": ""}
{"index": "s895835610", "label": 2783, "func": ""}
{"index": "s066456146", "label": 2783, "func": ""}
{"index": "s183992380", "label": 2783, "func": ""}
{"index": "s656688837", "label": 2783, "func": ""}
{"index": "s896165301", "label": 3784, "func": ""}
{"index": "s768633755", "label": 925, "func": ""}
{"index": "s570635607", "label": 925, "func": ""}
{"index": "s270611440", "label": 925, "func": ""}
{"index": "s296731042", "label": 925, "func": ""}
{"index": "s224469220", "label": 925, "func": ""}
{"index": "s751861124", "label": 925, "func": ""}
{"index": "s839024618", "label": 925, "func": ""}
{"index": "s464037410", "label": 925, "func": ""}
{"index": "s771420354", "label": 925, "func": ""}
{"index": "s588370470", "label": 925, "func": ""}
{"index": "s627915415", "label": 4045, "func": ""}
{"index": "s134872068", "label": 4045, "func": ""}
{"index": "s881144584", "label": 4045, "func": ""}
{"index": "s347350941", "label": 4045, "func": ""}
{"index": "s156880723", "label": 4045, "func": ""}
{"index": "s745338575", "label": 4045, "func": ""}
{"index": "s395173478", "label": 4045, "func": ""}
{"index": "s787588376", "label": 4045, "func": ""}
{"index": "s636871759", "label": 4045, "func": ""}
{"index": "s797377232", "label": 4045, "func": ""}
{"index": "s912609971", "label": 1924, "func": ""}
{"index": "s964259176", "label": 1924, "func": ""}
{"index": "s597107162", "label": 1924, "func": ""}
{"index": "s635325539", "label": 1924, "func": ""}
{"index": "s688112166", "label": 1924, "func": ""}
{"index": "s971829762", "label": 1924, "func": ""}
{"index": "s113757466", "label": 1924, "func": ""}
{"index": "s599788230", "label": 1924, "func": ""}
{"index": "s597680430", "label": 1924, "func": ""}
{"index": "s334240964", "label": 331, "func": ""}
{"index": "s068023725", "label": 331, "func": ""}
{"index": "s989199668", "label": 331, "func": ""}
{"index": "s051120088", "label": 331, "func": ""}
{"index": "s777226719", "label": 331, "func": ""}
{"index": "s197175382", "label": 331, "func": ""}
{"index": "s536655027", "label": 331, "func": ""}
{"index": "s835830248", "label": 331, "func": ""}
{"index": "s217659164", "label": 331, "func": ""}
{"index": "s551966621", "label": 331, "func": ""}
{"index": "s101705076", "label": 3996, "func": ""}
{"index": "s270673895", "label": 3996, "func": ""}
{"index": "s734055067", "label": 3996, "func": ""}
{"index": "s011441833", "label": 3148, "func": ""}
{"index": "s370457039", "label": 3148, "func": ""}
{"index": "s446744396", "label": 3148, "func": ""}
{"index": "s867407893", "label": 3148, "func": ""}
{"index": "s609359003", "label": 3148, "func": ""}
{"index": "s752278193", "label": 3148, "func": ""}
{"index": "s502489504", "label": 3148, "func": ""}
{"index": "s309393272", "label": 3148, "func": ""}
{"index": "s942756398", "label": 3148, "func": ""}
{"index": "s262747409", "label": 3148, "func": ""}
{"index": "s156027786", "label": 3496, "func": ""}
{"index": "s216275675", "label": 3496, "func": ""}
{"index": "s679276583", "label": 3496, "func": ""}
{"index": "s518172979", "label": 3496, "func": ""}
{"index": "s214723980", "label": 3496, "func": ""}
{"index": "s325416316", "label": 3496, "func": ""}
{"index": "s357424090", "label": 3496, "func": ""}
{"index": "s765728794", "label": 3496, "func": ""}
{"index": "s050323476", "label": 3496, "func": ""}
{"index": "s984003196", "label": 3496, "func": ""}
{"index": "s777245767", "label": 668, "func": ""}
{"index": "s571029527", "label": 668, "func": ""}
{"index": "s996512174", "label": 668, "func": ""}
{"index": "s490618786", "label": 1908, "func": ""}
{"index": "s419326195", "label": 1908, "func": ""}
{"index": "s469923932", "label": 2867, "func": ""}
{"index": "s416604953", "label": 2867, "func": ""}
{"index": "s474830014", "label": 2867, "func": ""}
{"index": "s561417602", "label": 2867, "func": ""}
{"index": "s102197637", "label": 2867, "func": ""}
{"index": "s279494207", "label": 2867, "func": ""}
{"index": "s407640724", "label": 2867, "func": ""}
{"index": "s176953184", "label": 3600, "func": ""}
{"index": "s313029817", "label": 3600, "func": ""}
{"index": "s560499758", "label": 3600, "func": ""}
{"index": "s477062068", "label": 3600, "func": ""}
{"index": "s756169994", "label": 3600, "func": ""}
{"index": "s023685830", "label": 3600, "func": ""}
{"index": "s800626005", "label": 3600, "func": ""}
{"index": "s141279410", "label": 3600, "func": ""}
{"index": "s410533403", "label": 3600, "func": ""}
{"index": "s445230091", "label": 3600, "func": ""}
{"index": "s350105770", "label": 3171, "func": ""}
{"index": "s730438659", "label": 3171, "func": ""}
{"index": "s503351667", "label": 3171, "func": ""}
{"index": "s491239299", "label": 3171, "func": ""}
{"index": "s714085300", "label": 3171, "func": ""}
{"index": "s375105826", "label": 3171, "func": ""}
{"index": "s460003589", "label": 3171, "func": ""}
{"index": "s520570544", "label": 3171, "func": ""}
{"index": "s361414327", "label": 3171, "func": ""}
{"index": "s518799210", "label": 3171, "func": ""}
{"index": "s758258750", "label": 431, "func": ""}
{"index": "s581562785", "label": 431, "func": ""}
{"index": "s757745013", "label": 431, "func": ""}
{"index": "s471236439", "label": 431, "func": ""}
{"index": "s455522676", "label": 431, "func": ""}
{"index": "s153230721", "label": 431, "func": ""}
{"index": "s643737418", "label": 431, "func": ""}
{"index": "s167485151", "label": 431, "func": ""}
{"index": "s124015057", "label": 431, "func": ""}
{"index": "s693306641", "label": 3821, "func": ""}
{"index": "s590502767", "label": 3821, "func": ""}
{"index": "s235857517", "label": 3821, "func": ""}
{"index": "s904619193", "label": 3821, "func": ""}
{"index": "s529045929", "label": 3821, "func": ""}
{"index": "s290642915", "label": 3821, "func": ""}
{"index": "s277537376", "label": 3821, "func": ""}
{"index": "s658620058", "label": 3821, "func": ""}
{"index": "s329056495", "label": 3821, "func": ""}
{"index": "s969557535", "label": 3821, "func": ""}
{"index": "s583597330", "label": 3848, "func": ""}
{"index": "s459999722", "label": 3848, "func": ""}
{"index": "s356805942", "label": 3848, "func": ""}
{"index": "s949772966", "label": 3848, "func": ""}
{"index": "s852266036", "label": 3848, "func": ""}
{"index": "s878009007", "label": 3848, "func": ""}
{"index": "s325148344", "label": 3848, "func": ""}
{"index": "s701291957", "label": 3848, "func": ""}
{"index": "s952435374", "label": 3848, "func": ""}
{"index": "s328467749", "label": 3848, "func": ""}
{"index": "s209120129", "label": 3097, "func": ""}
{"index": "s536123793", "label": 3097, "func": ""}
{"index": "s494089730", "label": 3097, "func": ""}
{"index": "s986862456", "label": 3097, "func": ""}
{"index": "s201577196", "label": 3097, "func": ""}
{"index": "s667177722", "label": 3097, "func": ""}
{"index": "s165832489", "label": 3097, "func": ""}
{"index": "s029889425", "label": 3097, "func": ""}
{"index": "s937518972", "label": 3477, "func": ""}
{"index": "s709151333", "label": 3477, "func": ""}
{"index": "s193303856", "label": 3477, "func": ""}
{"index": "s736294276", "label": 3477, "func": ""}
{"index": "s222317750", "label": 3477, "func": ""}
{"index": "s953331065", "label": 3477, "func": ""}
{"index": "s356858949", "label": 3477, "func": ""}
{"index": "s117891290", "label": 3477, "func": ""}
{"index": "s863882673", "label": 3477, "func": ""}
{"index": "s980552157", "label": 3477, "func": ""}
{"index": "s630340866", "label": 958, "func": ""}
{"index": "s294204994", "label": 622, "func": ""}
{"index": "s855805504", "label": 622, "func": ""}
{"index": "s763422316", "label": 622, "func": ""}
{"index": "s955329251", "label": 622, "func": ""}
{"index": "s904447491", "label": 622, "func": ""}
{"index": "s643931991", "label": 622, "func": ""}
{"index": "s096606515", "label": 622, "func": ""}
{"index": "s855148573", "label": 622, "func": ""}
{"index": "s709099768", "label": 622, "func": ""}
{"index": "s414281130", "label": 3364, "func": ""}
{"index": "s547888342", "label": 3364, "func": ""}
{"index": "s614685128", "label": 3364, "func": ""}
{"index": "s480757605", "label": 3364, "func": ""}
{"index": "s681166275", "label": 3364, "func": ""}
{"index": "s333419308", "label": 3364, "func": ""}
{"index": "s945736711", "label": 3364, "func": ""}
{"index": "s404871217", "label": 3364, "func": ""}
{"index": "s668220323", "label": 3364, "func": ""}
{"index": "s135228927", "label": 3364, "func": ""}
{"index": "s473462063", "label": 2619, "func": ""}
{"index": "s760412209", "label": 2619, "func": ""}
{"index": "s139005691", "label": 2619, "func": ""}
{"index": "s142312639", "label": 2619, "func": ""}
{"index": "s884894533", "label": 2619, "func": ""}
{"index": "s574325216", "label": 2619, "func": ""}
{"index": "s981373543", "label": 2619, "func": ""}
{"index": "s435705222", "label": 2619, "func": ""}
{"index": "s746935559", "label": 2619, "func": ""}
{"index": "s974136913", "label": 2619, "func": ""}
{"index": "s117546418", "label": 794, "func": ""}
{"index": "s249499748", "label": 794, "func": ""}
{"index": "s417883324", "label": 794, "func": ""}
{"index": "s802826862", "label": 794, "func": ""}
{"index": "s181474245", "label": 794, "func": ""}
{"index": "s808856613", "label": 794, "func": ""}
{"index": "s085031415", "label": 794, "func": ""}
{"index": "s719076944", "label": 794, "func": ""}
{"index": "s979800754", "label": 794, "func": ""}
{"index": "s121694574", "label": 187, "func": ""}
{"index": "s127803987", "label": 187, "func": ""}
{"index": "s588901257", "label": 187, "func": ""}
{"index": "s584271587", "label": 187, "func": ""}
{"index": "s073852537", "label": 187, "func": ""}
{"index": "s938371147", "label": 187, "func": ""}
{"index": "s569417824", "label": 187, "func": ""}
{"index": "s727807329", "label": 187, "func": ""}
{"index": "s417596479", "label": 187, "func": ""}
{"index": "s737709247", "label": 187, "func": ""}
{"index": "s443361226", "label": 1689, "func": ""}
{"index": "s213940452", "label": 1689, "func": ""}
{"index": "s284486101", "label": 3001, "func": ""}
{"index": "s030281689", "label": 3001, "func": ""}
{"index": "s343825547", "label": 3001, "func": ""}
{"index": "s741654725", "label": 3001, "func": ""}
{"index": "s087761335", "label": 3001, "func": ""}
{"index": "s260547954", "label": 3001, "func": ""}
{"index": "s015336256", "label": 3001, "func": ""}
{"index": "s327151826", "label": 3001, "func": ""}
{"index": "s840338032", "label": 3001, "func": ""}
{"index": "s320734654", "label": 3001, "func": ""}
{"index": "s511505926", "label": 3725, "func": ""}
{"index": "s409707100", "label": 3725, "func": ""}
{"index": "s907732523", "label": 3725, "func": ""}
{"index": "s833857968", "label": 3725, "func": ""}
{"index": "s062678057", "label": 3725, "func": ""}
{"index": "s497042363", "label": 3725, "func": ""}
{"index": "s354116143", "label": 3725, "func": ""}
{"index": "s856030261", "label": 3725, "func": ""}
{"index": "s391621699", "label": 3725, "func": ""}
{"index": "s375576236", "label": 3725, "func": ""}
{"index": "s956504324", "label": 1251, "func": ""}
{"index": "s999392114", "label": 2665, "func": ""}
{"index": "s753642270", "label": 2665, "func": ""}
{"index": "s524563473", "label": 2665, "func": ""}
{"index": "s475353210", "label": 2665, "func": ""}
{"index": "s101903580", "label": 2665, "func": ""}
{"index": "s612720905", "label": 2665, "func": ""}
{"index": "s643706124", "label": 2665, "func": ""}
{"index": "s080116739", "label": 2665, "func": ""}
{"index": "s286025685", "label": 2665, "func": ""}
{"index": "s275607231", "label": 2665, "func": ""}
{"index": "s358748988", "label": 3203, "func": ""}
{"index": "s725657795", "label": 3203, "func": ""}
{"index": "s252727630", "label": 3203, "func": ""}
{"index": "s917814763", "label": 3203, "func": ""}
{"index": "s686305165", "label": 3203, "func": ""}
{"index": "s461831027", "label": 3203, "func": ""}
{"index": "s223568958", "label": 3203, "func": ""}
{"index": "s068292994", "label": 3203, "func": ""}
{"index": "s940083150", "label": 3203, "func": ""}
{"index": "s409409885", "label": 3203, "func": ""}
{"index": "s011366006", "label": 3633, "func": ""}
{"index": "s203672004", "label": 3633, "func": ""}
{"index": "s851652308", "label": 3633, "func": ""}
{"index": "s163095591", "label": 3633, "func": ""}
{"index": "s176381511", "label": 3633, "func": ""}
{"index": "s991308676", "label": 3633, "func": ""}
{"index": "s613936093", "label": 3633, "func": ""}
{"index": "s370832188", "label": 3633, "func": ""}
{"index": "s038644071", "label": 3633, "func": ""}
{"index": "s453272496", "label": 3633, "func": ""}
{"index": "s341757509", "label": 1393, "func": ""}
{"index": "s153803329", "label": 1552, "func": ""}
{"index": "s687903808", "label": 1552, "func": ""}
{"index": "s522518813", "label": 1552, "func": ""}
{"index": "s761365985", "label": 1552, "func": ""}
{"index": "s661862796", "label": 1552, "func": ""}
{"index": "s736733924", "label": 1552, "func": ""}
{"index": "s417588595", "label": 1552, "func": ""}
{"index": "s254160106", "label": 1552, "func": ""}
{"index": "s860207221", "label": 1552, "func": ""}
{"index": "s561769356", "label": 1552, "func": ""}
{"index": "s992495794", "label": 2336, "func": ""}
{"index": "s154197384", "label": 3440, "func": ""}
{"index": "s079585306", "label": 3440, "func": ""}
{"index": "s355052570", "label": 3440, "func": ""}
{"index": "s461172543", "label": 3440, "func": ""}
{"index": "s536580196", "label": 3440, "func": ""}
{"index": "s498166114", "label": 3440, "func": ""}
{"index": "s107376987", "label": 3440, "func": ""}
{"index": "s516846780", "label": 3440, "func": ""}
{"index": "s277520673", "label": 3440, "func": ""}
{"index": "s269707054", "label": 3440, "func": ""}
{"index": "s461950567", "label": 1470, "func": ""}
{"index": "s052347030", "label": 1470, "func": ""}
{"index": "s675547474", "label": 1470, "func": ""}
{"index": "s423731455", "label": 1470, "func": ""}
{"index": "s570863685", "label": 1470, "func": ""}
{"index": "s282991382", "label": 1470, "func": ""}
{"index": "s479065423", "label": 3423, "func": ""}
{"index": "s405420450", "label": 3423, "func": ""}
{"index": "s880277888", "label": 3423, "func": ""}
{"index": "s137993009", "label": 3423, "func": ""}
{"index": "s291162275", "label": 3423, "func": ""}
{"index": "s251636344", "label": 3423, "func": ""}
{"index": "s235523457", "label": 3423, "func": ""}
{"index": "s458900677", "label": 3423, "func": ""}
{"index": "s438246421", "label": 3423, "func": ""}
{"index": "s634175827", "label": 3423, "func": ""}
{"index": "s740119103", "label": 2982, "func": ""}
{"index": "s620414751", "label": 2982, "func": ""}
{"index": "s983327485", "label": 2982, "func": ""}
{"index": "s979262770", "label": 2982, "func": ""}
{"index": "s602487560", "label": 2982, "func": ""}
{"index": "s529965144", "label": 2982, "func": ""}
{"index": "s715566020", "label": 2982, "func": ""}
{"index": "s115222493", "label": 2982, "func": ""}
{"index": "s944077828", "label": 2982, "func": ""}
{"index": "s625243663", "label": 2982, "func": ""}
{"index": "s111280588", "label": 3684, "func": ""}
{"index": "s713713764", "label": 3684, "func": ""}
{"index": "s853134235", "label": 3684, "func": ""}
{"index": "s162642476", "label": 3684, "func": ""}
{"index": "s049686874", "label": 3684, "func": ""}
{"index": "s112948279", "label": 3684, "func": ""}
{"index": "s960941844", "label": 3684, "func": ""}
{"index": "s783525378", "label": 3684, "func": ""}
{"index": "s777817476", "label": 3684, "func": ""}
{"index": "s422071871", "label": 3684, "func": ""}
{"index": "s827404538", "label": 532, "func": ""}
{"index": "s484347156", "label": 532, "func": ""}
{"index": "s094717242", "label": 532, "func": ""}
{"index": "s786568148", "label": 532, "func": ""}
{"index": "s446577061", "label": 532, "func": ""}
{"index": "s750731445", "label": 532, "func": ""}
{"index": "s390338757", "label": 532, "func": ""}
{"index": "s050611164", "label": 532, "func": ""}
{"index": "s313495643", "label": 532, "func": ""}
{"index": "s074519786", "label": 532, "func": ""}
{"index": "s113571943", "label": 1451, "func": ""}
{"index": "s296801404", "label": 1451, "func": ""}
{"index": "s590261332", "label": 1451, "func": ""}
{"index": "s190879368", "label": 1451, "func": ""}
{"index": "s918869753", "label": 1451, "func": ""}
{"index": "s443872005", "label": 1451, "func": ""}
{"index": "s522697494", "label": 171, "func": ""}
{"index": "s938326595", "label": 171, "func": ""}
{"index": "s408595491", "label": 171, "func": ""}
{"index": "s233995176", "label": 171, "func": ""}
{"index": "s319969328", "label": 2331, "func": ""}
{"index": "s779231404", "label": 2331, "func": ""}
{"index": "s689137429", "label": 2020, "func": ""}
{"index": "s366437375", "label": 2020, "func": ""}
{"index": "s227417591", "label": 3696, "func": ""}
{"index": "s556974287", "label": 3696, "func": ""}
{"index": "s484862722", "label": 3696, "func": ""}
{"index": "s215247031", "label": 3696, "func": ""}
{"index": "s688925959", "label": 3696, "func": ""}
{"index": "s691741956", "label": 3696, "func": ""}
{"index": "s176768041", "label": 3696, "func": ""}
{"index": "s472674252", "label": 3696, "func": ""}
{"index": "s829176292", "label": 3696, "func": ""}
{"index": "s005480115", "label": 3696, "func": ""}
{"index": "s788167550", "label": 711, "func": ""}
{"index": "s798683953", "label": 711, "func": ""}
{"index": "s081610565", "label": 711, "func": ""}
{"index": "s010215145", "label": 711, "func": ""}
{"index": "s612185901", "label": 711, "func": ""}
{"index": "s861991813", "label": 711, "func": ""}
{"index": "s435979813", "label": 711, "func": ""}
{"index": "s381597472", "label": 711, "func": ""}
{"index": "s386070571", "label": 711, "func": ""}
{"index": "s483634397", "label": 711, "func": ""}
{"index": "s541088536", "label": 1141, "func": ""}
{"index": "s655818048", "label": 2942, "func": ""}
{"index": "s127837842", "label": 2942, "func": ""}
{"index": "s142719744", "label": 2454, "func": ""}
{"index": "s951598663", "label": 2454, "func": ""}
{"index": "s520475793", "label": 2454, "func": ""}
{"index": "s509764865", "label": 2454, "func": ""}
{"index": "s829102175", "label": 2454, "func": ""}
{"index": "s931649144", "label": 2454, "func": ""}
{"index": "s292826456", "label": 2454, "func": ""}
{"index": "s848630233", "label": 2454, "func": ""}
{"index": "s871823217", "label": 2454, "func": ""}
{"index": "s613503307", "label": 2454, "func": ""}
{"index": "s984666888", "label": 716, "func": ""}
{"index": "s213446702", "label": 716, "func": ""}
{"index": "s161928519", "label": 716, "func": ""}
{"index": "s282896191", "label": 716, "func": ""}
{"index": "s889677294", "label": 716, "func": ""}
{"index": "s825364592", "label": 716, "func": ""}
{"index": "s034964594", "label": 716, "func": ""}
{"index": "s463865552", "label": 716, "func": ""}
{"index": "s462806581", "label": 716, "func": ""}
{"index": "s536415633", "label": 716, "func": ""}
{"index": "s718043779", "label": 3130, "func": ""}
{"index": "s005478716", "label": 3130, "func": ""}
{"index": "s898175168", "label": 3130, "func": ""}
{"index": "s717624057", "label": 3130, "func": ""}
{"index": "s089779135", "label": 3130, "func": ""}
{"index": "s591927054", "label": 3130, "func": ""}
{"index": "s728942996", "label": 3130, "func": ""}
{"index": "s443478993", "label": 3130, "func": ""}
{"index": "s848461049", "label": 3130, "func": ""}
{"index": "s953588776", "label": 3130, "func": ""}
{"index": "s985122859", "label": 3914, "func": ""}
{"index": "s479318011", "label": 3914, "func": ""}
{"index": "s093357489", "label": 3914, "func": ""}
{"index": "s840203561", "label": 3914, "func": ""}
{"index": "s661218875", "label": 3914, "func": ""}
{"index": "s546225875", "label": 1921, "func": ""}
{"index": "s316792565", "label": 1921, "func": ""}
{"index": "s605847206", "label": 2845, "func": ""}
{"index": "s513113527", "label": 2845, "func": ""}
{"index": "s221685876", "label": 2845, "func": ""}
{"index": "s394747255", "label": 2845, "func": ""}
{"index": "s720488045", "label": 2845, "func": ""}
{"index": "s558355503", "label": 2845, "func": ""}
{"index": "s052953228", "label": 2845, "func": ""}
{"index": "s536481699", "label": 2845, "func": ""}
{"index": "s418176515", "label": 2845, "func": ""}
{"index": "s528643718", "label": 2845, "func": ""}
{"index": "s644962841", "label": 2943, "func": ""}
{"index": "s873197198", "label": 2943, "func": ""}
{"index": "s950461387", "label": 2943, "func": ""}
{"index": "s655469673", "label": 2943, "func": ""}
{"index": "s953473376", "label": 2943, "func": ""}
{"index": "s139526155", "label": 357, "func": ""}
{"index": "s151969540", "label": 357, "func": ""}
{"index": "s928670157", "label": 357, "func": ""}
{"index": "s858915202", "label": 357, "func": ""}
{"index": "s359776426", "label": 357, "func": ""}
{"index": "s296565821", "label": 357, "func": ""}
{"index": "s666897855", "label": 357, "func": ""}
{"index": "s016846788", "label": 357, "func": ""}
{"index": "s907095710", "label": 357, "func": ""}
{"index": "s208333096", "label": 357, "func": ""}
{"index": "s353383340", "label": 574, "func": ""}
{"index": "s269864543", "label": 3471, "func": ""}
{"index": "s808221954", "label": 3471, "func": ""}
{"index": "s301109528", "label": 3471, "func": ""}
{"index": "s562775516", "label": 3471, "func": ""}
{"index": "s651890293", "label": 3471, "func": ""}
{"index": "s642323392", "label": 3471, "func": ""}
{"index": "s660471482", "label": 3471, "func": ""}
{"index": "s586161560", "label": 3471, "func": ""}
{"index": "s872348981", "label": 3471, "func": ""}
{"index": "s445332132", "label": 3471, "func": ""}
{"index": "s988135680", "label": 828, "func": ""}
{"index": "s717546123", "label": 828, "func": ""}
{"index": "s871525355", "label": 828, "func": ""}
{"index": "s795878583", "label": 828, "func": ""}
{"index": "s362184764", "label": 828, "func": ""}
{"index": "s976012268", "label": 828, "func": ""}
{"index": "s650364213", "label": 828, "func": ""}
{"index": "s704799794", "label": 828, "func": ""}
{"index": "s207906610", "label": 828, "func": ""}
{"index": "s500622954", "label": 828, "func": ""}
{"index": "s419594295", "label": 2781, "func": ""}
{"index": "s475931120", "label": 2781, "func": ""}
{"index": "s410732315", "label": 2781, "func": ""}
{"index": "s081604062", "label": 2781, "func": ""}
{"index": "s573305300", "label": 2781, "func": ""}
{"index": "s796827500", "label": 2781, "func": ""}
{"index": "s377257169", "label": 2781, "func": ""}
{"index": "s354878806", "label": 2781, "func": ""}
{"index": "s099131989", "label": 2781, "func": ""}
{"index": "s211861490", "label": 2781, "func": ""}
{"index": "s975934541", "label": 3162, "func": ""}
{"index": "s616594130", "label": 3162, "func": ""}
{"index": "s537213480", "label": 3162, "func": ""}
{"index": "s834966580", "label": 3162, "func": ""}
{"index": "s430563038", "label": 3162, "func": ""}
{"index": "s505384363", "label": 3162, "func": ""}
{"index": "s579733100", "label": 3162, "func": ""}
{"index": "s609733691", "label": 3162, "func": ""}
{"index": "s609520274", "label": 3162, "func": ""}
{"index": "s433333311", "label": 3162, "func": ""}
{"index": "s945292336", "label": 2239, "func": ""}
{"index": "s918888681", "label": 2239, "func": ""}
{"index": "s845342286", "label": 2239, "func": ""}
{"index": "s695560784", "label": 2239, "func": ""}
{"index": "s309346793", "label": 2239, "func": ""}
{"index": "s074569044", "label": 2239, "func": ""}
{"index": "s175015826", "label": 2239, "func": ""}
{"index": "s690894199", "label": 2239, "func": ""}
{"index": "s047370141", "label": 2239, "func": ""}
{"index": "s008177618", "label": 2239, "func": ""}
{"index": "s151837109", "label": 3214, "func": ""}
{"index": "s863040369", "label": 3214, "func": ""}
{"index": "s759276201", "label": 3214, "func": ""}
{"index": "s468487427", "label": 3214, "func": ""}
{"index": "s579531997", "label": 3214, "func": ""}
{"index": "s939520591", "label": 3214, "func": ""}
{"index": "s212589980", "label": 3214, "func": ""}
{"index": "s914145217", "label": 3214, "func": ""}
{"index": "s109844182", "label": 3214, "func": ""}
{"index": "s156485875", "label": 3214, "func": ""}
{"index": "s611719457", "label": 2760, "func": ""}
{"index": "s941793026", "label": 2760, "func": ""}
{"index": "s082192141", "label": 2760, "func": ""}
{"index": "s349999079", "label": 2760, "func": ""}
{"index": "s805861565", "label": 2760, "func": ""}
{"index": "s675572189", "label": 2760, "func": ""}
{"index": "s329576169", "label": 2760, "func": ""}
{"index": "s081785578", "label": 2760, "func": ""}
{"index": "s827505367", "label": 2760, "func": ""}
{"index": "s004705611", "label": 2760, "func": ""}
{"index": "s330990564", "label": 986, "func": ""}
{"index": "s271311130", "label": 805, "func": ""}
{"index": "s167041573", "label": 805, "func": ""}
{"index": "s245063342", "label": 805, "func": ""}
{"index": "s631998383", "label": 805, "func": ""}
{"index": "s236826153", "label": 805, "func": ""}
{"index": "s490635238", "label": 805, "func": ""}
{"index": "s599349721", "label": 2950, "func": ""}
{"index": "s281231773", "label": 2950, "func": ""}
{"index": "s967372950", "label": 2950, "func": ""}
{"index": "s225626972", "label": 2950, "func": ""}
{"index": "s773440349", "label": 4027, "func": ""}
{"index": "s687765496", "label": 4027, "func": ""}
{"index": "s130966638", "label": 4027, "func": ""}
{"index": "s387778408", "label": 4027, "func": ""}
{"index": "s002126689", "label": 4027, "func": ""}
{"index": "s662826082", "label": 4027, "func": ""}
{"index": "s224423490", "label": 4027, "func": ""}
{"index": "s655114345", "label": 4027, "func": ""}
{"index": "s233851660", "label": 4027, "func": ""}
{"index": "s014365719", "label": 886, "func": ""}
{"index": "s095983587", "label": 886, "func": ""}
{"index": "s847984332", "label": 886, "func": ""}
{"index": "s351848513", "label": 886, "func": ""}
{"index": "s733410331", "label": 886, "func": ""}
{"index": "s877478687", "label": 886, "func": ""}
{"index": "s691312489", "label": 886, "func": ""}
{"index": "s475343587", "label": 3411, "func": ""}
{"index": "s227562866", "label": 3411, "func": ""}
{"index": "s400700697", "label": 3411, "func": ""}
{"index": "s843490336", "label": 3411, "func": ""}
{"index": "s948684125", "label": 3411, "func": ""}
{"index": "s703222172", "label": 3411, "func": ""}
{"index": "s216408714", "label": 3411, "func": ""}
{"index": "s234632629", "label": 3411, "func": ""}
{"index": "s544961001", "label": 3411, "func": ""}
{"index": "s148565334", "label": 3411, "func": ""}
{"index": "s047229657", "label": 1337, "func": ""}
{"index": "s503945707", "label": 1337, "func": ""}
{"index": "s258401164", "label": 1337, "func": ""}
{"index": "s834572950", "label": 1337, "func": ""}
{"index": "s121950692", "label": 1337, "func": ""}
{"index": "s725416504", "label": 2696, "func": ""}
{"index": "s226450676", "label": 2696, "func": ""}
{"index": "s334259556", "label": 2696, "func": ""}
{"index": "s581482853", "label": 2696, "func": ""}
{"index": "s256404744", "label": 2696, "func": ""}
{"index": "s508790051", "label": 2696, "func": ""}
{"index": "s230221432", "label": 2696, "func": ""}
{"index": "s928692335", "label": 2696, "func": ""}
{"index": "s393796742", "label": 2696, "func": ""}
{"index": "s274263096", "label": 2696, "func": ""}
{"index": "s961396721", "label": 2860, "func": ""}
{"index": "s344511870", "label": 2860, "func": ""}
{"index": "s068965663", "label": 2860, "func": ""}
{"index": "s258956561", "label": 2860, "func": ""}
{"index": "s199279458", "label": 2860, "func": ""}
{"index": "s582813284", "label": 2860, "func": ""}
{"index": "s054593719", "label": 2860, "func": ""}
{"index": "s729581230", "label": 2860, "func": ""}
{"index": "s391323255", "label": 2860, "func": ""}
{"index": "s355764845", "label": 2860, "func": ""}
{"index": "s023612412", "label": 949, "func": ""}
{"index": "s516248080", "label": 949, "func": ""}
{"index": "s280785185", "label": 949, "func": ""}
{"index": "s467953129", "label": 949, "func": ""}
{"index": "s639542945", "label": 3309, "func": ""}
{"index": "s220769720", "label": 3309, "func": ""}
{"index": "s296968227", "label": 3309, "func": ""}
{"index": "s626418425", "label": 3309, "func": ""}
{"index": "s250571852", "label": 3309, "func": ""}
{"index": "s362823953", "label": 3309, "func": ""}
{"index": "s118805648", "label": 3309, "func": ""}
{"index": "s133580630", "label": 3309, "func": ""}
{"index": "s412530605", "label": 3309, "func": ""}
{"index": "s280165842", "label": 3309, "func": ""}
{"index": "s651607720", "label": 1660, "func": ""}
{"index": "s185752800", "label": 1854, "func": ""}
{"index": "s863433598", "label": 93, "func": ""}
{"index": "s706958959", "label": 93, "func": ""}
{"index": "s960316631", "label": 93, "func": ""}
{"index": "s816373058", "label": 93, "func": ""}
{"index": "s512859405", "label": 93, "func": ""}
{"index": "s750658951", "label": 93, "func": ""}
{"index": "s026131892", "label": 93, "func": ""}
{"index": "s366526258", "label": 93, "func": ""}
{"index": "s190212501", "label": 93, "func": ""}
{"index": "s306560903", "label": 93, "func": ""}
{"index": "s468512725", "label": 1502, "func": ""}
{"index": "s470421919", "label": 1502, "func": ""}
{"index": "s760844947", "label": 1502, "func": ""}
{"index": "s097333029", "label": 1502, "func": ""}
{"index": "s384708319", "label": 1502, "func": ""}
{"index": "s370098618", "label": 1502, "func": ""}
{"index": "s526416608", "label": 1502, "func": ""}
{"index": "s438669368", "label": 1502, "func": ""}
{"index": "s506152514", "label": 1502, "func": ""}
{"index": "s643775375", "label": 1502, "func": ""}
{"index": "s402082874", "label": 3826, "func": ""}
{"index": "s889870431", "label": 3826, "func": ""}
{"index": "s150668873", "label": 3826, "func": ""}
{"index": "s004458589", "label": 3826, "func": ""}
{"index": "s742276897", "label": 3826, "func": ""}
{"index": "s825083163", "label": 3826, "func": ""}
{"index": "s136680060", "label": 3826, "func": ""}
{"index": "s891100474", "label": 3826, "func": ""}
{"index": "s676043111", "label": 3826, "func": ""}
{"index": "s559859649", "label": 3826, "func": ""}
{"index": "s037811731", "label": 1918, "func": ""}
{"index": "s912247145", "label": 1918, "func": ""}
{"index": "s284681708", "label": 3149, "func": ""}
{"index": "s731606881", "label": 3149, "func": ""}
{"index": "s449507006", "label": 3149, "func": ""}
{"index": "s092672501", "label": 3149, "func": ""}
{"index": "s125103815", "label": 3149, "func": ""}
{"index": "s622243252", "label": 3149, "func": ""}
{"index": "s873706467", "label": 3149, "func": ""}
{"index": "s963750713", "label": 3149, "func": ""}
{"index": "s302340668", "label": 3149, "func": ""}
{"index": "s630787456", "label": 3149, "func": ""}
{"index": "s179185863", "label": 2352, "func": ""}
{"index": "s881072445", "label": 2352, "func": ""}
{"index": "s451883523", "label": 2352, "func": ""}
{"index": "s735470671", "label": 2352, "func": ""}
{"index": "s211390326", "label": 2352, "func": ""}
{"index": "s455333090", "label": 2352, "func": ""}
{"index": "s023678789", "label": 2352, "func": ""}
{"index": "s044363411", "label": 2352, "func": ""}
{"index": "s507399488", "label": 2352, "func": ""}
{"index": "s764797060", "label": 2352, "func": ""}
{"index": "s688348903", "label": 2340, "func": ""}
{"index": "s631916840", "label": 2340, "func": ""}
{"index": "s176042616", "label": 2340, "func": ""}
{"index": "s829950893", "label": 2603, "func": ""}
{"index": "s036736298", "label": 2603, "func": ""}
{"index": "s859109335", "label": 2603, "func": ""}
{"index": "s901458881", "label": 2603, "func": ""}
{"index": "s386855073", "label": 2603, "func": ""}
{"index": "s492519822", "label": 2603, "func": ""}
{"index": "s273114619", "label": 2603, "func": ""}
{"index": "s087837153", "label": 2603, "func": ""}
{"index": "s999584039", "label": 2603, "func": ""}
{"index": "s294806576", "label": 2603, "func": ""}
{"index": "s866686746", "label": 2676, "func": ""}
{"index": "s362280172", "label": 2676, "func": ""}
{"index": "s939982519", "label": 2676, "func": ""}
{"index": "s472684706", "label": 2676, "func": ""}
{"index": "s761910308", "label": 2676, "func": ""}
{"index": "s534763768", "label": 2676, "func": ""}
{"index": "s615839576", "label": 2676, "func": ""}
{"index": "s432591611", "label": 2676, "func": ""}
{"index": "s708663014", "label": 2676, "func": ""}
{"index": "s119279222", "label": 2676, "func": ""}
{"index": "s448565926", "label": 1065, "func": ""}
{"index": "s731971730", "label": 1065, "func": ""}
{"index": "s967879196", "label": 2029, "func": ""}
{"index": "s926598544", "label": 278, "func": ""}
{"index": "s221340871", "label": 278, "func": ""}
{"index": "s641768169", "label": 278, "func": ""}
{"index": "s261290138", "label": 278, "func": ""}
{"index": "s868675335", "label": 278, "func": ""}
{"index": "s103198966", "label": 278, "func": ""}
{"index": "s431424665", "label": 278, "func": ""}
{"index": "s296200193", "label": 278, "func": ""}
{"index": "s119104797", "label": 278, "func": ""}
{"index": "s348830562", "label": 278, "func": ""}
{"index": "s452464472", "label": 1074, "func": ""}
{"index": "s044247361", "label": 1074, "func": ""}
{"index": "s771276456", "label": 1074, "func": ""}
{"index": "s568308963", "label": 983, "func": ""}
{"index": "s923571773", "label": 769, "func": ""}
{"index": "s038691142", "label": 769, "func": ""}
{"index": "s789020973", "label": 769, "func": ""}
{"index": "s981560223", "label": 769, "func": ""}
{"index": "s204363044", "label": 769, "func": ""}
{"index": "s911874944", "label": 769, "func": ""}
{"index": "s035513643", "label": 769, "func": ""}
{"index": "s137755361", "label": 769, "func": ""}
{"index": "s232303771", "label": 769, "func": ""}
{"index": "s907190079", "label": 769, "func": ""}
{"index": "s735392495", "label": 2059, "func": ""}
{"index": "s419177385", "label": 3615, "func": ""}
{"index": "s110904814", "label": 3615, "func": ""}
{"index": "s254451824", "label": 3615, "func": ""}
{"index": "s071761664", "label": 3615, "func": ""}
{"index": "s564435288", "label": 3615, "func": ""}
{"index": "s840958596", "label": 3615, "func": ""}
{"index": "s162649176", "label": 776, "func": ""}
{"index": "s206951313", "label": 776, "func": ""}
{"index": "s192128290", "label": 776, "func": ""}
{"index": "s256133015", "label": 776, "func": ""}
{"index": "s647099861", "label": 776, "func": ""}
{"index": "s315456529", "label": 776, "func": ""}
{"index": "s796835371", "label": 776, "func": ""}
{"index": "s121491360", "label": 776, "func": ""}
{"index": "s587015430", "label": 776, "func": ""}
{"index": "s015015391", "label": 776, "func": ""}
{"index": "s284880170", "label": 3191, "func": ""}
{"index": "s051554270", "label": 3327, "func": ""}
{"index": "s947961839", "label": 3327, "func": ""}
{"index": "s121640575", "label": 3327, "func": ""}
{"index": "s476118790", "label": 3327, "func": ""}
{"index": "s034894904", "label": 3327, "func": ""}
{"index": "s127433182", "label": 3327, "func": ""}
{"index": "s963633553", "label": 3327, "func": ""}
{"index": "s555140920", "label": 3327, "func": ""}
{"index": "s177706995", "label": 3327, "func": ""}
{"index": "s055818551", "label": 3327, "func": ""}
{"index": "s376573578", "label": 1699, "func": ""}
{"index": "s779877528", "label": 1699, "func": ""}
{"index": "s507449809", "label": 3307, "func": ""}
{"index": "s587126131", "label": 3307, "func": ""}
{"index": "s471999987", "label": 3307, "func": ""}
{"index": "s129374921", "label": 3307, "func": ""}
{"index": "s318526774", "label": 3307, "func": ""}
{"index": "s423724064", "label": 3307, "func": ""}
{"index": "s614864724", "label": 3307, "func": ""}
{"index": "s045226032", "label": 3307, "func": ""}
{"index": "s154903008", "label": 3307, "func": ""}
{"index": "s874133287", "label": 3307, "func": ""}
{"index": "s017547042", "label": 807, "func": ""}
{"index": "s931664894", "label": 807, "func": ""}
{"index": "s853751982", "label": 807, "func": ""}
{"index": "s476244857", "label": 2347, "func": ""}
{"index": "s909513844", "label": 2347, "func": ""}
{"index": "s974326040", "label": 2347, "func": ""}
{"index": "s438296727", "label": 2479, "func": ""}
{"index": "s421364772", "label": 2479, "func": ""}
{"index": "s672425896", "label": 2479, "func": ""}
{"index": "s935478643", "label": 2479, "func": ""}
{"index": "s654998162", "label": 2479, "func": ""}
{"index": "s675417773", "label": 2479, "func": ""}
{"index": "s068972317", "label": 2479, "func": ""}
{"index": "s014073761", "label": 2479, "func": ""}
{"index": "s125081385", "label": 2479, "func": ""}
{"index": "s917713445", "label": 2479, "func": ""}
{"index": "s346958953", "label": 1651, "func": ""}
{"index": "s811315398", "label": 1651, "func": ""}
{"index": "s170466294", "label": 1651, "func": ""}
{"index": "s321739289", "label": 1909, "func": ""}
{"index": "s407426950", "label": 1909, "func": ""}
{"index": "s294140994", "label": 1909, "func": ""}
{"index": "s575291087", "label": 3263, "func": ""}
{"index": "s860293602", "label": 3263, "func": ""}
{"index": "s948207554", "label": 3263, "func": ""}
{"index": "s740279871", "label": 3263, "func": ""}
{"index": "s912560250", "label": 3263, "func": ""}
{"index": "s157393786", "label": 3263, "func": ""}
{"index": "s171345485", "label": 3263, "func": ""}
{"index": "s040612270", "label": 3263, "func": ""}
{"index": "s570783615", "label": 3263, "func": ""}
{"index": "s071596955", "label": 3263, "func": ""}
{"index": "s185257590", "label": 184, "func": ""}
{"index": "s903607545", "label": 184, "func": ""}
{"index": "s764445841", "label": 184, "func": ""}
{"index": "s618330231", "label": 184, "func": ""}
{"index": "s994912443", "label": 184, "func": ""}
{"index": "s586351709", "label": 184, "func": ""}
{"index": "s306775020", "label": 184, "func": ""}
{"index": "s663548634", "label": 184, "func": ""}
{"index": "s901860100", "label": 184, "func": ""}
{"index": "s275570837", "label": 184, "func": ""}
{"index": "s900709153", "label": 2388, "func": ""}
{"index": "s587471448", "label": 2388, "func": ""}
{"index": "s020217302", "label": 2388, "func": ""}
{"index": "s057729222", "label": 2388, "func": ""}
{"index": "s115365466", "label": 2388, "func": ""}
{"index": "s459600389", "label": 2388, "func": ""}
{"index": "s724871605", "label": 2388, "func": ""}
{"index": "s684994866", "label": 2388, "func": ""}
{"index": "s176002867", "label": 2388, "func": ""}
{"index": "s017156295", "label": 2388, "func": ""}
{"index": "s691381639", "label": 101, "func": ""}
{"index": "s485878418", "label": 101, "func": ""}
{"index": "s530628548", "label": 101, "func": ""}
{"index": "s578622276", "label": 101, "func": ""}
{"index": "s006199813", "label": 101, "func": ""}
{"index": "s248916500", "label": 101, "func": ""}
{"index": "s921917776", "label": 101, "func": ""}
{"index": "s704370576", "label": 101, "func": ""}
{"index": "s220426968", "label": 101, "func": ""}
{"index": "s173083463", "label": 101, "func": ""}
{"index": "s815822785", "label": 627, "func": ""}
{"index": "s527110765", "label": 627, "func": ""}
{"index": "s761388367", "label": 627, "func": ""}
{"index": "s064499823", "label": 627, "func": ""}
{"index": "s225039151", "label": 627, "func": ""}
{"index": "s687637981", "label": 627, "func": ""}
{"index": "s416908157", "label": 627, "func": ""}
{"index": "s067735172", "label": 627, "func": ""}
{"index": "s766800803", "label": 627, "func": ""}
{"index": "s671427654", "label": 627, "func": ""}
{"index": "s860061575", "label": 1316, "func": ""}
{"index": "s372083908", "label": 1316, "func": ""}
{"index": "s167237836", "label": 1316, "func": ""}
{"index": "s424620056", "label": 1316, "func": ""}
{"index": "s761253214", "label": 1316, "func": ""}
{"index": "s763309345", "label": 1316, "func": ""}
{"index": "s231635809", "label": 1316, "func": ""}
{"index": "s895201850", "label": 1316, "func": ""}
{"index": "s052012195", "label": 1316, "func": ""}
{"index": "s137841128", "label": 1316, "func": ""}
{"index": "s683536793", "label": 3031, "func": ""}
{"index": "s664333910", "label": 3031, "func": ""}
{"index": "s835557948", "label": 3031, "func": ""}
{"index": "s577674291", "label": 3031, "func": ""}
{"index": "s022953655", "label": 3031, "func": ""}
{"index": "s317452736", "label": 3031, "func": ""}
{"index": "s659928166", "label": 3031, "func": ""}
{"index": "s725476956", "label": 3031, "func": ""}
{"index": "s022945577", "label": 3031, "func": ""}
{"index": "s903263980", "label": 3031, "func": ""}
{"index": "s822275037", "label": 1138, "func": ""}
{"index": "s192289634", "label": 1138, "func": ""}
{"index": "s766435283", "label": 1138, "func": ""}
{"index": "s297555745", "label": 1138, "func": ""}
{"index": "s044920760", "label": 1138, "func": ""}
{"index": "s666780137", "label": 1138, "func": ""}
{"index": "s335285804", "label": 1138, "func": ""}
{"index": "s421939064", "label": 1138, "func": ""}
{"index": "s281418789", "label": 1138, "func": ""}
{"index": "s457194236", "label": 1138, "func": ""}
{"index": "s198588106", "label": 764, "func": ""}
{"index": "s872235427", "label": 764, "func": ""}
{"index": "s806515938", "label": 764, "func": ""}
{"index": "s224566516", "label": 764, "func": ""}
{"index": "s124856606", "label": 764, "func": ""}
{"index": "s114964141", "label": 764, "func": ""}
{"index": "s586938824", "label": 764, "func": ""}
{"index": "s293988184", "label": 764, "func": ""}
{"index": "s496111548", "label": 764, "func": ""}
{"index": "s481543443", "label": 764, "func": ""}
{"index": "s689159822", "label": 1931, "func": ""}
{"index": "s550292178", "label": 1931, "func": ""}
{"index": "s481442866", "label": 1931, "func": ""}
{"index": "s637408926", "label": 1931, "func": ""}
{"index": "s920218884", "label": 1931, "func": ""}
{"index": "s298151679", "label": 1931, "func": ""}
{"index": "s223111059", "label": 1931, "func": ""}
{"index": "s468378000", "label": 1931, "func": ""}
{"index": "s552109596", "label": 1349, "func": ""}
{"index": "s983990845", "label": 1349, "func": ""}
{"index": "s186723177", "label": 1349, "func": ""}
{"index": "s162717112", "label": 1349, "func": ""}
{"index": "s596069757", "label": 1349, "func": ""}
{"index": "s298586784", "label": 1349, "func": ""}
{"index": "s423182031", "label": 1349, "func": ""}
{"index": "s252223954", "label": 1349, "func": ""}
{"index": "s448757725", "label": 1349, "func": ""}
{"index": "s302464160", "label": 1349, "func": ""}
{"index": "s445757810", "label": 72, "func": ""}
{"index": "s791655230", "label": 72, "func": ""}
{"index": "s075029833", "label": 72, "func": ""}
{"index": "s174683543", "label": 72, "func": ""}
{"index": "s381178971", "label": 72, "func": ""}
{"index": "s470029693", "label": 72, "func": ""}
{"index": "s246823922", "label": 72, "func": ""}
{"index": "s217716386", "label": 72, "func": ""}
{"index": "s646660060", "label": 72, "func": ""}
{"index": "s895875075", "label": 72, "func": ""}
{"index": "s363799682", "label": 2956, "func": ""}
{"index": "s161931833", "label": 2956, "func": ""}
{"index": "s728338980", "label": 2956, "func": ""}
{"index": "s334554482", "label": 2956, "func": ""}
{"index": "s635005569", "label": 2956, "func": ""}
{"index": "s345143505", "label": 2956, "func": ""}
{"index": "s199635438", "label": 2956, "func": ""}
{"index": "s965710089", "label": 2956, "func": ""}
{"index": "s881209562", "label": 2956, "func": ""}
{"index": "s132179972", "label": 2956, "func": ""}
{"index": "s806846248", "label": 3353, "func": ""}
{"index": "s700487018", "label": 3353, "func": ""}
{"index": "s984968677", "label": 3353, "func": ""}
{"index": "s117833981", "label": 3353, "func": ""}
{"index": "s126461292", "label": 3353, "func": ""}
{"index": "s765447709", "label": 3353, "func": ""}
{"index": "s599620350", "label": 3353, "func": ""}
{"index": "s983043985", "label": 3353, "func": ""}
{"index": "s662014305", "label": 3353, "func": ""}
{"index": "s287639570", "label": 3353, "func": ""}
{"index": "s747522538", "label": 2238, "func": ""}
{"index": "s706262770", "label": 2238, "func": ""}
{"index": "s698390823", "label": 2238, "func": ""}
{"index": "s118132187", "label": 2238, "func": ""}
{"index": "s028906454", "label": 2238, "func": ""}
{"index": "s714579760", "label": 2238, "func": ""}
{"index": "s292421120", "label": 2238, "func": ""}
{"index": "s266709184", "label": 2238, "func": ""}
{"index": "s709725409", "label": 2238, "func": ""}
{"index": "s767414750", "label": 2238, "func": ""}
{"index": "s429518874", "label": 1629, "func": ""}
{"index": "s099191452", "label": 1629, "func": ""}
{"index": "s372100691", "label": 290, "func": ""}
{"index": "s524070421", "label": 290, "func": ""}
{"index": "s790219139", "label": 290, "func": ""}
{"index": "s368460513", "label": 290, "func": ""}
{"index": "s383952314", "label": 290, "func": ""}
{"index": "s427935076", "label": 290, "func": ""}
{"index": "s513647229", "label": 290, "func": ""}
{"index": "s455219612", "label": 290, "func": ""}
{"index": "s855341644", "label": 290, "func": ""}
{"index": "s183666026", "label": 290, "func": ""}
{"index": "s492472162", "label": 1532, "func": ""}
{"index": "s904171853", "label": 1532, "func": ""}
{"index": "s928311005", "label": 1532, "func": ""}
{"index": "s535011447", "label": 1532, "func": ""}
{"index": "s571932371", "label": 1532, "func": ""}
{"index": "s385148123", "label": 1532, "func": ""}
{"index": "s108161923", "label": 1532, "func": ""}
{"index": "s579020907", "label": 1532, "func": ""}
{"index": "s075577656", "label": 1532, "func": ""}
{"index": "s047462062", "label": 1532, "func": ""}
{"index": "s152021336", "label": 3372, "func": ""}
{"index": "s780794758", "label": 3372, "func": ""}
{"index": "s517421814", "label": 3372, "func": ""}
{"index": "s405423059", "label": 3372, "func": ""}
{"index": "s382789078", "label": 3372, "func": ""}
{"index": "s727070530", "label": 3372, "func": ""}
{"index": "s118882919", "label": 3372, "func": ""}
{"index": "s934968548", "label": 3372, "func": ""}
{"index": "s547273114", "label": 3372, "func": ""}
{"index": "s056888710", "label": 3372, "func": ""}
{"index": "s622555974", "label": 2360, "func": ""}
{"index": "s856497948", "label": 2360, "func": ""}
{"index": "s230398019", "label": 2360, "func": ""}
{"index": "s392607464", "label": 663, "func": ""}
{"index": "s417571512", "label": 663, "func": ""}
{"index": "s874243190", "label": 663, "func": ""}
{"index": "s918276563", "label": 663, "func": ""}
{"index": "s003492218", "label": 663, "func": ""}
{"index": "s804347443", "label": 663, "func": ""}
{"index": "s359315154", "label": 663, "func": ""}
{"index": "s661152208", "label": 2729, "func": ""}
{"index": "s330498818", "label": 2729, "func": ""}
{"index": "s198413736", "label": 2729, "func": ""}
{"index": "s213087409", "label": 2729, "func": ""}
{"index": "s061901737", "label": 2729, "func": ""}
{"index": "s970370925", "label": 2729, "func": ""}
{"index": "s425435067", "label": 2729, "func": ""}
{"index": "s023982615", "label": 2729, "func": ""}
{"index": "s419747227", "label": 2729, "func": ""}
{"index": "s433827421", "label": 2729, "func": ""}
{"index": "s236682984", "label": 220, "func": ""}
{"index": "s992756907", "label": 220, "func": ""}
{"index": "s648963575", "label": 220, "func": ""}
{"index": "s620958949", "label": 220, "func": ""}
{"index": "s217985741", "label": 220, "func": ""}
{"index": "s659118457", "label": 220, "func": ""}
{"index": "s676671631", "label": 220, "func": ""}
{"index": "s289306249", "label": 220, "func": ""}
{"index": "s671729291", "label": 220, "func": ""}
{"index": "s619816473", "label": 220, "func": ""}
{"index": "s517811967", "label": 52, "func": ""}
{"index": "s461038662", "label": 52, "func": ""}
{"index": "s549521598", "label": 52, "func": ""}
{"index": "s650652692", "label": 52, "func": ""}
{"index": "s605228279", "label": 52, "func": ""}
{"index": "s199771308", "label": 52, "func": ""}
{"index": "s374107302", "label": 52, "func": ""}
{"index": "s199523778", "label": 52, "func": ""}
{"index": "s915940682", "label": 52, "func": ""}
{"index": "s744209649", "label": 52, "func": ""}
{"index": "s395754595", "label": 1598, "func": ""}
{"index": "s796001074", "label": 2406, "func": ""}
{"index": "s175385828", "label": 2406, "func": ""}
{"index": "s241577014", "label": 2406, "func": ""}
{"index": "s539351050", "label": 2406, "func": ""}
{"index": "s665393155", "label": 2406, "func": ""}
{"index": "s283096170", "label": 2406, "func": ""}
{"index": "s603863251", "label": 2406, "func": ""}
{"index": "s555336648", "label": 2406, "func": ""}
{"index": "s179096191", "label": 2406, "func": ""}
{"index": "s020484280", "label": 2406, "func": ""}
{"index": "s349661946", "label": 3831, "func": ""}
{"index": "s504899791", "label": 3831, "func": ""}
{"index": "s848331999", "label": 3831, "func": ""}
{"index": "s740090059", "label": 3831, "func": ""}
{"index": "s236228316", "label": 3831, "func": ""}
{"index": "s280809256", "label": 3831, "func": ""}
{"index": "s246623268", "label": 3831, "func": ""}
{"index": "s970683628", "label": 3831, "func": ""}
{"index": "s311868410", "label": 3831, "func": ""}
{"index": "s950171195", "label": 3831, "func": ""}
{"index": "s986347279", "label": 3086, "func": ""}
{"index": "s512958194", "label": 3086, "func": ""}
{"index": "s474430934", "label": 3086, "func": ""}
{"index": "s597962306", "label": 3086, "func": ""}
{"index": "s160548216", "label": 3086, "func": ""}
{"index": "s872536787", "label": 3086, "func": ""}
{"index": "s626789064", "label": 3086, "func": ""}
{"index": "s409972796", "label": 3086, "func": ""}
{"index": "s288847032", "label": 3086, "func": ""}
{"index": "s964509544", "label": 3086, "func": ""}
{"index": "s874476813", "label": 1202, "func": ""}
{"index": "s677100045", "label": 1202, "func": ""}
{"index": "s329238876", "label": 1202, "func": ""}
{"index": "s689036379", "label": 1202, "func": ""}
{"index": "s566293293", "label": 1202, "func": ""}
{"index": "s784132832", "label": 257, "func": ""}
{"index": "s584039536", "label": 257, "func": ""}
{"index": "s253931468", "label": 257, "func": ""}
{"index": "s402630693", "label": 257, "func": ""}
{"index": "s293538023", "label": 257, "func": ""}
{"index": "s633798661", "label": 257, "func": ""}
{"index": "s228677094", "label": 257, "func": ""}
{"index": "s721624605", "label": 257, "func": ""}
{"index": "s755123957", "label": 257, "func": ""}
{"index": "s204780290", "label": 257, "func": ""}
{"index": "s967904348", "label": 609, "func": ""}
{"index": "s618758975", "label": 609, "func": ""}
{"index": "s754398310", "label": 609, "func": ""}
{"index": "s350429919", "label": 2414, "func": ""}
{"index": "s833793272", "label": 2414, "func": ""}
{"index": "s278655025", "label": 2414, "func": ""}
{"index": "s652882326", "label": 2414, "func": ""}
{"index": "s556503534", "label": 2414, "func": ""}
{"index": "s577343763", "label": 2414, "func": ""}
{"index": "s109297372", "label": 2414, "func": ""}
{"index": "s146072256", "label": 2414, "func": ""}
{"index": "s724016030", "label": 2414, "func": ""}
{"index": "s158608206", "label": 2414, "func": ""}
{"index": "s133594224", "label": 2767, "func": ""}
{"index": "s718815367", "label": 2767, "func": ""}
{"index": "s406965418", "label": 2767, "func": ""}
{"index": "s785662489", "label": 2767, "func": ""}
{"index": "s809155742", "label": 2767, "func": ""}
{"index": "s853220877", "label": 2767, "func": ""}
{"index": "s493148161", "label": 2767, "func": ""}
{"index": "s091590924", "label": 2767, "func": ""}
{"index": "s967706506", "label": 2767, "func": ""}
{"index": "s090642704", "label": 2767, "func": ""}
{"index": "s793828282", "label": 327, "func": ""}
{"index": "s519439890", "label": 3128, "func": ""}
{"index": "s645178425", "label": 3128, "func": ""}
{"index": "s396963437", "label": 3128, "func": ""}
{"index": "s593722322", "label": 3128, "func": ""}
{"index": "s604895891", "label": 3128, "func": ""}
{"index": "s597764082", "label": 3128, "func": ""}
{"index": "s988621568", "label": 3128, "func": ""}
{"index": "s382954664", "label": 3128, "func": ""}
{"index": "s470756399", "label": 3128, "func": ""}
{"index": "s578095708", "label": 3128, "func": ""}
{"index": "s316990507", "label": 1751, "func": ""}
{"index": "s676980380", "label": 1751, "func": ""}
{"index": "s820243208", "label": 1751, "func": ""}
{"index": "s918882100", "label": 1751, "func": ""}
{"index": "s149621293", "label": 1751, "func": ""}
{"index": "s352622746", "label": 1751, "func": ""}
{"index": "s300318551", "label": 1751, "func": ""}
{"index": "s020856736", "label": 1751, "func": ""}
{"index": "s946811993", "label": 1751, "func": ""}
{"index": "s717458104", "label": 1751, "func": ""}
{"index": "s987754544", "label": 483, "func": ""}
{"index": "s081925208", "label": 483, "func": ""}
{"index": "s699746235", "label": 483, "func": ""}
{"index": "s571231798", "label": 483, "func": ""}
{"index": "s966835667", "label": 483, "func": ""}
{"index": "s505716436", "label": 483, "func": ""}
{"index": "s487477237", "label": 483, "func": ""}
{"index": "s622156054", "label": 483, "func": ""}
{"index": "s599970295", "label": 483, "func": ""}
{"index": "s024454708", "label": 483, "func": ""}
{"index": "s423488387", "label": 717, "func": ""}
{"index": "s286217099", "label": 717, "func": ""}
{"index": "s473606572", "label": 717, "func": ""}
{"index": "s667277120", "label": 717, "func": ""}
{"index": "s337239563", "label": 717, "func": ""}
{"index": "s849184881", "label": 717, "func": ""}
{"index": "s268809496", "label": 717, "func": ""}
{"index": "s827550716", "label": 717, "func": ""}
{"index": "s179509250", "label": 717, "func": ""}
{"index": "s288352015", "label": 717, "func": ""}
{"index": "s001153912", "label": 325, "func": ""}
{"index": "s261668942", "label": 1370, "func": ""}
{"index": "s601610241", "label": 1370, "func": ""}
{"index": "s206687848", "label": 1370, "func": ""}
{"index": "s126112147", "label": 1370, "func": ""}
{"index": "s532981474", "label": 1370, "func": ""}
{"index": "s844596980", "label": 1370, "func": ""}
{"index": "s115588819", "label": 1370, "func": ""}
{"index": "s283318818", "label": 1370, "func": ""}
{"index": "s810227087", "label": 1370, "func": ""}
{"index": "s890705958", "label": 1370, "func": ""}
{"index": "s725856773", "label": 1979, "func": ""}
{"index": "s738397068", "label": 1290, "func": ""}
{"index": "s646639858", "label": 1290, "func": ""}
{"index": "s023838192", "label": 2098, "func": ""}
{"index": "s572381261", "label": 1713, "func": ""}
{"index": "s853197529", "label": 880, "func": ""}
{"index": "s898223524", "label": 880, "func": ""}
{"index": "s262065531", "label": 880, "func": ""}
{"index": "s064486643", "label": 880, "func": ""}
{"index": "s443348175", "label": 880, "func": ""}
{"index": "s880054971", "label": 880, "func": ""}
{"index": "s859066773", "label": 880, "func": ""}
{"index": "s560824203", "label": 880, "func": ""}
{"index": "s673039376", "label": 242, "func": ""}
{"index": "s163583744", "label": 242, "func": ""}
{"index": "s832857905", "label": 242, "func": ""}
{"index": "s527417248", "label": 242, "func": ""}
{"index": "s103888981", "label": 242, "func": ""}
{"index": "s130358872", "label": 242, "func": ""}
{"index": "s368238428", "label": 242, "func": ""}
{"index": "s343546529", "label": 242, "func": ""}
{"index": "s233396095", "label": 242, "func": ""}
{"index": "s182174164", "label": 242, "func": ""}
{"index": "s805408265", "label": 2392, "func": ""}
{"index": "s676260091", "label": 2392, "func": ""}
{"index": "s872225020", "label": 2392, "func": ""}
{"index": "s737818313", "label": 2392, "func": ""}
{"index": "s900412628", "label": 2392, "func": ""}
{"index": "s113015507", "label": 2392, "func": ""}
{"index": "s490936910", "label": 2392, "func": ""}
{"index": "s255950616", "label": 2392, "func": ""}
{"index": "s236809360", "label": 2392, "func": ""}
{"index": "s154548550", "label": 2392, "func": ""}
{"index": "s493878047", "label": 2559, "func": ""}
{"index": "s357680148", "label": 2559, "func": ""}
{"index": "s125500504", "label": 2559, "func": ""}
{"index": "s955516969", "label": 2559, "func": ""}
{"index": "s216204663", "label": 2559, "func": ""}
{"index": "s455584411", "label": 2559, "func": ""}
{"index": "s054745884", "label": 2559, "func": ""}
{"index": "s337314919", "label": 2559, "func": ""}
{"index": "s174137452", "label": 2559, "func": ""}
{"index": "s213954284", "label": 2559, "func": ""}
{"index": "s912939091", "label": 518, "func": ""}
{"index": "s399172763", "label": 518, "func": ""}
{"index": "s034898450", "label": 518, "func": ""}
{"index": "s376588770", "label": 518, "func": ""}
{"index": "s688200746", "label": 518, "func": ""}
{"index": "s020132016", "label": 518, "func": ""}
{"index": "s355134907", "label": 518, "func": ""}
{"index": "s448282036", "label": 518, "func": ""}
{"index": "s839870748", "label": 518, "func": ""}
{"index": "s011774567", "label": 518, "func": ""}
{"index": "s699392164", "label": 2027, "func": ""}
{"index": "s390725593", "label": 729, "func": ""}
{"index": "s122843499", "label": 729, "func": ""}
{"index": "s133791042", "label": 729, "func": ""}
{"index": "s082488820", "label": 729, "func": ""}
{"index": "s395451050", "label": 729, "func": ""}
{"index": "s823839173", "label": 729, "func": ""}
{"index": "s645034702", "label": 729, "func": ""}
{"index": "s557011372", "label": 729, "func": ""}
{"index": "s169150599", "label": 729, "func": ""}
{"index": "s431790580", "label": 729, "func": ""}
{"index": "s785042930", "label": 2695, "func": ""}
{"index": "s473445625", "label": 2695, "func": ""}
{"index": "s635229373", "label": 2695, "func": ""}
{"index": "s228283479", "label": 2695, "func": ""}
{"index": "s227437135", "label": 2695, "func": ""}
{"index": "s793642655", "label": 2695, "func": ""}
{"index": "s244809080", "label": 2695, "func": ""}
{"index": "s686890104", "label": 2695, "func": ""}
{"index": "s676884700", "label": 2695, "func": ""}
{"index": "s974536623", "label": 2695, "func": ""}
{"index": "s819342111", "label": 990, "func": ""}
{"index": "s210540144", "label": 990, "func": ""}
{"index": "s952986722", "label": 990, "func": ""}
{"index": "s149230279", "label": 990, "func": ""}
{"index": "s778806944", "label": 990, "func": ""}
{"index": "s253344635", "label": 990, "func": ""}
{"index": "s490199783", "label": 990, "func": ""}
{"index": "s106462500", "label": 990, "func": ""}
{"index": "s635077805", "label": 990, "func": ""}
{"index": "s813289996", "label": 990, "func": ""}
{"index": "s853907354", "label": 6, "func": ""}
{"index": "s324084295", "label": 6, "func": ""}
{"index": "s879020382", "label": 6, "func": ""}
{"index": "s802281945", "label": 6, "func": ""}
{"index": "s234983263", "label": 6, "func": ""}
{"index": "s164781278", "label": 6, "func": ""}
{"index": "s662238671", "label": 6, "func": ""}
{"index": "s459641780", "label": 6, "func": ""}
{"index": "s468513348", "label": 6, "func": ""}
{"index": "s427672045", "label": 6, "func": ""}
{"index": "s030356756", "label": 3995, "func": ""}
{"index": "s850756564", "label": 3995, "func": ""}
{"index": "s577623169", "label": 3995, "func": ""}
{"index": "s945913484", "label": 3995, "func": ""}
{"index": "s254538136", "label": 3995, "func": ""}
{"index": "s830242725", "label": 3995, "func": ""}
{"index": "s529631761", "label": 3995, "func": ""}
{"index": "s363450498", "label": 3995, "func": ""}
{"index": "s946551576", "label": 1645, "func": ""}
{"index": "s835928993", "label": 3216, "func": ""}
{"index": "s084094757", "label": 3216, "func": ""}
{"index": "s884257523", "label": 3216, "func": ""}
{"index": "s123531706", "label": 3216, "func": ""}
{"index": "s120384168", "label": 3216, "func": ""}
{"index": "s947899366", "label": 3216, "func": ""}
{"index": "s603757034", "label": 3216, "func": ""}
{"index": "s675435067", "label": 3216, "func": ""}
{"index": "s182818745", "label": 3216, "func": ""}
{"index": "s519481072", "label": 3216, "func": ""}
{"index": "s720665049", "label": 2278, "func": ""}
{"index": "s263245698", "label": 2278, "func": ""}
{"index": "s806048966", "label": 2278, "func": ""}
{"index": "s026119656", "label": 2278, "func": ""}
{"index": "s676171484", "label": 2278, "func": ""}
{"index": "s214259998", "label": 2278, "func": ""}
{"index": "s033166867", "label": 2278, "func": ""}
{"index": "s913987095", "label": 2278, "func": ""}
{"index": "s529033618", "label": 2278, "func": ""}
{"index": "s516302348", "label": 2278, "func": ""}
{"index": "s733660610", "label": 759, "func": ""}
{"index": "s574571141", "label": 759, "func": ""}
{"index": "s571187090", "label": 759, "func": ""}
{"index": "s023837829", "label": 759, "func": ""}
{"index": "s424027981", "label": 2321, "func": ""}
{"index": "s093276185", "label": 2321, "func": ""}
{"index": "s828812065", "label": 2321, "func": ""}
{"index": "s539468369", "label": 2321, "func": ""}
{"index": "s174906034", "label": 2321, "func": ""}
{"index": "s995797475", "label": 2321, "func": ""}
{"index": "s977226076", "label": 2321, "func": ""}
{"index": "s524784872", "label": 2321, "func": ""}
{"index": "s045900252", "label": 379, "func": ""}
{"index": "s485169371", "label": 379, "func": ""}
{"index": "s948392511", "label": 379, "func": ""}
{"index": "s564141372", "label": 379, "func": ""}
{"index": "s353108790", "label": 379, "func": ""}
{"index": "s077355156", "label": 379, "func": ""}
{"index": "s405824123", "label": 379, "func": ""}
{"index": "s333905989", "label": 2673, "func": ""}
{"index": "s167241224", "label": 3840, "func": ""}
{"index": "s751122857", "label": 3840, "func": ""}
{"index": "s819250577", "label": 3840, "func": ""}
{"index": "s344881037", "label": 3840, "func": ""}
{"index": "s555549532", "label": 3840, "func": ""}
{"index": "s781762040", "label": 3840, "func": ""}
{"index": "s056670196", "label": 3840, "func": ""}
{"index": "s888817678", "label": 3840, "func": ""}
{"index": "s339041513", "label": 3840, "func": ""}
{"index": "s201396143", "label": 3840, "func": ""}
{"index": "s616992928", "label": 126, "func": ""}
{"index": "s364476791", "label": 126, "func": ""}
{"index": "s676241114", "label": 126, "func": ""}
{"index": "s171707202", "label": 126, "func": ""}
{"index": "s944607466", "label": 126, "func": ""}
{"index": "s691794162", "label": 126, "func": ""}
{"index": "s232366654", "label": 126, "func": ""}
{"index": "s686042936", "label": 126, "func": ""}
{"index": "s099212173", "label": 126, "func": ""}
{"index": "s745171654", "label": 126, "func": ""}
{"index": "s833421015", "label": 3752, "func": ""}
{"index": "s536275919", "label": 3752, "func": ""}
{"index": "s187476076", "label": 3752, "func": ""}
{"index": "s511122725", "label": 3752, "func": ""}
{"index": "s647405825", "label": 3752, "func": ""}
{"index": "s446638421", "label": 3752, "func": ""}
{"index": "s800390273", "label": 3752, "func": ""}
{"index": "s091129715", "label": 3752, "func": ""}
{"index": "s022886324", "label": 3752, "func": ""}
{"index": "s879238143", "label": 3752, "func": ""}
{"index": "s097599288", "label": 603, "func": ""}
{"index": "s760915974", "label": 603, "func": ""}
{"index": "s493743131", "label": 603, "func": ""}
{"index": "s271937349", "label": 603, "func": ""}
{"index": "s816042389", "label": 603, "func": ""}
{"index": "s220788747", "label": 603, "func": ""}
{"index": "s913082532", "label": 603, "func": ""}
{"index": "s332341459", "label": 603, "func": ""}
{"index": "s895006064", "label": 603, "func": ""}
{"index": "s202192230", "label": 2567, "func": ""}
{"index": "s352433243", "label": 2567, "func": ""}
{"index": "s820079374", "label": 2567, "func": ""}
{"index": "s345361271", "label": 2567, "func": ""}
{"index": "s997499424", "label": 2567, "func": ""}
{"index": "s213924499", "label": 2567, "func": ""}
{"index": "s578067548", "label": 2567, "func": ""}
{"index": "s129890173", "label": 2567, "func": ""}
{"index": "s286818240", "label": 2567, "func": ""}
{"index": "s621061486", "label": 2567, "func": ""}
{"index": "s519258031", "label": 3549, "func": ""}
{"index": "s250461530", "label": 3549, "func": ""}
{"index": "s833981976", "label": 3549, "func": ""}
{"index": "s062002940", "label": 3549, "func": ""}
{"index": "s250531736", "label": 3549, "func": ""}
{"index": "s483296633", "label": 3549, "func": ""}
{"index": "s871430753", "label": 3549, "func": ""}
{"index": "s680856435", "label": 3549, "func": ""}
{"index": "s813989328", "label": 3549, "func": ""}
{"index": "s440167890", "label": 3549, "func": ""}
{"index": "s909371619", "label": 261, "func": ""}
{"index": "s213459528", "label": 261, "func": ""}
{"index": "s243226558", "label": 261, "func": ""}
{"index": "s478269330", "label": 261, "func": ""}
{"index": "s430594377", "label": 261, "func": ""}
{"index": "s689973987", "label": 261, "func": ""}
{"index": "s855927200", "label": 261, "func": ""}
{"index": "s694777110", "label": 261, "func": ""}
{"index": "s655473812", "label": 261, "func": ""}
{"index": "s871933733", "label": 261, "func": ""}
{"index": "s611874264", "label": 2622, "func": ""}
{"index": "s443161582", "label": 2622, "func": ""}
{"index": "s930425531", "label": 2622, "func": ""}
{"index": "s180655379", "label": 2622, "func": ""}
{"index": "s182875095", "label": 2622, "func": ""}
{"index": "s141052231", "label": 2622, "func": ""}
{"index": "s955223838", "label": 2622, "func": ""}
{"index": "s393480512", "label": 2622, "func": ""}
{"index": "s071279870", "label": 2622, "func": ""}
{"index": "s877868281", "label": 2622, "func": ""}
{"index": "s295805309", "label": 2268, "func": ""}
{"index": "s071193681", "label": 2268, "func": ""}
{"index": "s649969316", "label": 2268, "func": ""}
{"index": "s490652541", "label": 2268, "func": ""}
{"index": "s035725499", "label": 2268, "func": ""}
{"index": "s243219573", "label": 2268, "func": ""}
{"index": "s553749700", "label": 2268, "func": ""}
{"index": "s302689167", "label": 2268, "func": ""}
{"index": "s383726685", "label": 2268, "func": ""}
{"index": "s711787372", "label": 2268, "func": ""}
{"index": "s629740976", "label": 1588, "func": ""}
{"index": "s613446369", "label": 1588, "func": ""}
{"index": "s360098345", "label": 1146, "func": ""}
{"index": "s742690663", "label": 1146, "func": ""}
{"index": "s671012081", "label": 1146, "func": ""}
{"index": "s337947748", "label": 1146, "func": ""}
{"index": "s474840731", "label": 1146, "func": ""}
{"index": "s273394553", "label": 1146, "func": ""}
{"index": "s875019187", "label": 1146, "func": ""}
{"index": "s835275894", "label": 1146, "func": ""}
{"index": "s089709468", "label": 1146, "func": ""}
{"index": "s586259194", "label": 1146, "func": ""}
{"index": "s225816760", "label": 3160, "func": ""}
{"index": "s351069521", "label": 3160, "func": ""}
{"index": "s756265390", "label": 3160, "func": ""}
{"index": "s956101708", "label": 3160, "func": ""}
{"index": "s869746573", "label": 3160, "func": ""}
{"index": "s803085320", "label": 3160, "func": ""}
{"index": "s456214195", "label": 3160, "func": ""}
{"index": "s375165437", "label": 3160, "func": ""}
{"index": "s305401498", "label": 3160, "func": ""}
{"index": "s342385371", "label": 3160, "func": ""}
{"index": "s123997865", "label": 3502, "func": ""}
{"index": "s933349754", "label": 3502, "func": ""}
{"index": "s964993284", "label": 3502, "func": ""}
{"index": "s863933161", "label": 3502, "func": ""}
{"index": "s974236538", "label": 3502, "func": ""}
{"index": "s296345025", "label": 3502, "func": ""}
{"index": "s351358200", "label": 3502, "func": ""}
{"index": "s923334158", "label": 3502, "func": ""}
{"index": "s106660933", "label": 3502, "func": ""}
{"index": "s919583487", "label": 3502, "func": ""}
{"index": "s383762578", "label": 1308, "func": ""}
{"index": "s193462529", "label": 1308, "func": ""}
{"index": "s235287917", "label": 1308, "func": ""}
{"index": "s092214817", "label": 1308, "func": ""}
{"index": "s884398168", "label": 3820, "func": ""}
{"index": "s258084794", "label": 3820, "func": ""}
{"index": "s727038869", "label": 3820, "func": ""}
{"index": "s146029369", "label": 3820, "func": ""}
{"index": "s385934664", "label": 768, "func": ""}
{"index": "s502583872", "label": 768, "func": ""}
{"index": "s296736052", "label": 768, "func": ""}
{"index": "s958216024", "label": 768, "func": ""}
{"index": "s363070987", "label": 768, "func": ""}
{"index": "s348748850", "label": 768, "func": ""}
{"index": "s292948825", "label": 768, "func": ""}
{"index": "s314787164", "label": 768, "func": ""}
{"index": "s578328784", "label": 768, "func": ""}
{"index": "s482683774", "label": 768, "func": ""}
{"index": "s795487984", "label": 4031, "func": ""}
{"index": "s458206181", "label": 4031, "func": ""}
{"index": "s352579758", "label": 4031, "func": ""}
{"index": "s466880703", "label": 4031, "func": ""}
{"index": "s498411712", "label": 4031, "func": ""}
{"index": "s347253839", "label": 4031, "func": ""}
{"index": "s895360357", "label": 4031, "func": ""}
{"index": "s022465579", "label": 4031, "func": ""}
{"index": "s852495774", "label": 4031, "func": ""}
{"index": "s200234166", "label": 4031, "func": ""}
{"index": "s254167607", "label": 2812, "func": ""}
{"index": "s078053945", "label": 2812, "func": ""}
{"index": "s080677532", "label": 2812, "func": ""}
{"index": "s083150347", "label": 2812, "func": ""}
{"index": "s214250503", "label": 2812, "func": ""}
{"index": "s465314508", "label": 2812, "func": ""}
{"index": "s956721061", "label": 2812, "func": ""}
{"index": "s805458696", "label": 2812, "func": ""}
{"index": "s381741667", "label": 2812, "func": ""}
{"index": "s687896582", "label": 2812, "func": ""}
{"index": "s725742481", "label": 3812, "func": ""}
{"index": "s251109243", "label": 3812, "func": ""}
{"index": "s861312586", "label": 135, "func": ""}
{"index": "s727104965", "label": 135, "func": ""}
{"index": "s743758907", "label": 135, "func": ""}
{"index": "s755679499", "label": 135, "func": ""}
{"index": "s089228980", "label": 135, "func": ""}
{"index": "s484407519", "label": 135, "func": ""}
{"index": "s931218649", "label": 135, "func": ""}
{"index": "s550263046", "label": 135, "func": ""}
{"index": "s819080660", "label": 135, "func": ""}
{"index": "s799689048", "label": 135, "func": ""}
{"index": "s225111192", "label": 3642, "func": ""}
{"index": "s779820440", "label": 3642, "func": ""}
{"index": "s887041743", "label": 3642, "func": ""}
{"index": "s553829878", "label": 3642, "func": ""}
{"index": "s225958527", "label": 3642, "func": ""}
{"index": "s931137160", "label": 3642, "func": ""}
{"index": "s421360940", "label": 3196, "func": ""}
{"index": "s514153332", "label": 3196, "func": ""}
{"index": "s846421035", "label": 3196, "func": ""}
{"index": "s335660650", "label": 3196, "func": ""}
{"index": "s505285685", "label": 3196, "func": ""}
{"index": "s007833248", "label": 3196, "func": ""}
{"index": "s755027134", "label": 3196, "func": ""}
{"index": "s689072547", "label": 3196, "func": ""}
{"index": "s233723380", "label": 3196, "func": ""}
{"index": "s174896052", "label": 3196, "func": ""}
{"index": "s860046135", "label": 2499, "func": ""}
{"index": "s585231600", "label": 2499, "func": ""}
{"index": "s871874581", "label": 2499, "func": ""}
{"index": "s893143025", "label": 2499, "func": ""}
{"index": "s967878453", "label": 2499, "func": ""}
{"index": "s594987449", "label": 2499, "func": ""}
{"index": "s263334159", "label": 2499, "func": ""}
{"index": "s393979972", "label": 2499, "func": ""}
{"index": "s499932335", "label": 2499, "func": ""}
{"index": "s333259839", "label": 2499, "func": ""}
{"index": "s568021579", "label": 3804, "func": ""}
{"index": "s072398570", "label": 3804, "func": ""}
{"index": "s046165719", "label": 3804, "func": ""}
{"index": "s721528351", "label": 3804, "func": ""}
{"index": "s323815682", "label": 3804, "func": ""}
{"index": "s992409199", "label": 3804, "func": ""}
{"index": "s564138399", "label": 3804, "func": ""}
{"index": "s310153379", "label": 3804, "func": ""}
{"index": "s392990001", "label": 3804, "func": ""}
{"index": "s105572888", "label": 3804, "func": ""}
{"index": "s808916879", "label": 866, "func": ""}
{"index": "s839500452", "label": 866, "func": ""}
{"index": "s115228801", "label": 866, "func": ""}
{"index": "s678768074", "label": 1609, "func": ""}
{"index": "s521543171", "label": 1609, "func": ""}
{"index": "s133961912", "label": 1609, "func": ""}
{"index": "s802538036", "label": 1609, "func": ""}
{"index": "s415591214", "label": 4, "func": ""}
{"index": "s612251380", "label": 4, "func": ""}
{"index": "s790606676", "label": 4, "func": ""}
{"index": "s582703461", "label": 4, "func": ""}
{"index": "s332671917", "label": 4, "func": ""}
{"index": "s749331629", "label": 4, "func": ""}
{"index": "s489615988", "label": 4, "func": ""}
{"index": "s990077351", "label": 4, "func": ""}
{"index": "s111707024", "label": 4, "func": ""}
{"index": "s873795321", "label": 4, "func": ""}
{"index": "s833121276", "label": 3930, "func": ""}
{"index": "s885002247", "label": 730, "func": ""}
{"index": "s118646701", "label": 730, "func": ""}
{"index": "s961637768", "label": 730, "func": ""}
{"index": "s711619688", "label": 730, "func": ""}
{"index": "s558968544", "label": 730, "func": ""}
{"index": "s304868607", "label": 730, "func": ""}
{"index": "s960422386", "label": 730, "func": ""}
{"index": "s137885527", "label": 730, "func": ""}
{"index": "s052401239", "label": 730, "func": ""}
{"index": "s606819913", "label": 730, "func": ""}
{"index": "s518679856", "label": 3407, "func": ""}
{"index": "s900391439", "label": 3407, "func": ""}
{"index": "s347181050", "label": 3407, "func": ""}
{"index": "s712617578", "label": 3407, "func": ""}
{"index": "s518129498", "label": 3407, "func": ""}
{"index": "s340566094", "label": 3407, "func": ""}
{"index": "s531209138", "label": 3407, "func": ""}
{"index": "s811037604", "label": 3407, "func": ""}
{"index": "s693359507", "label": 3407, "func": ""}
{"index": "s751935352", "label": 3407, "func": ""}
{"index": "s837560484", "label": 1339, "func": ""}
{"index": "s285698224", "label": 1339, "func": ""}
{"index": "s655480519", "label": 1339, "func": ""}
{"index": "s731698696", "label": 1339, "func": ""}
{"index": "s618710462", "label": 2441, "func": ""}
{"index": "s442938194", "label": 2441, "func": ""}
{"index": "s461934228", "label": 2441, "func": ""}
{"index": "s233971493", "label": 2441, "func": ""}
{"index": "s686445396", "label": 2441, "func": ""}
{"index": "s378100711", "label": 2441, "func": ""}
{"index": "s145256498", "label": 2441, "func": ""}
{"index": "s371800732", "label": 2441, "func": ""}
{"index": "s190224451", "label": 2441, "func": ""}
{"index": "s842474760", "label": 2441, "func": ""}
{"index": "s770846246", "label": 1391, "func": ""}
{"index": "s209799011", "label": 1391, "func": ""}
{"index": "s339693853", "label": 1391, "func": ""}
{"index": "s896561591", "label": 1391, "func": ""}
{"index": "s869221524", "label": 2958, "func": ""}
{"index": "s283170106", "label": 2958, "func": ""}
{"index": "s115712629", "label": 2958, "func": ""}
{"index": "s975435697", "label": 2958, "func": ""}
{"index": "s196029204", "label": 2958, "func": ""}
{"index": "s975915122", "label": 2958, "func": ""}
{"index": "s032532003", "label": 2958, "func": ""}
{"index": "s018813402", "label": 2958, "func": ""}
{"index": "s935616472", "label": 2958, "func": ""}
{"index": "s079314430", "label": 2958, "func": ""}
{"index": "s445010585", "label": 3585, "func": ""}
{"index": "s196048067", "label": 3585, "func": ""}
{"index": "s050129804", "label": 3585, "func": ""}
{"index": "s970219432", "label": 3585, "func": ""}
{"index": "s525127995", "label": 3585, "func": ""}
{"index": "s151156691", "label": 3585, "func": ""}
{"index": "s572462044", "label": 3585, "func": ""}
{"index": "s712259406", "label": 3585, "func": ""}
{"index": "s350444316", "label": 3585, "func": ""}
{"index": "s164764980", "label": 3585, "func": ""}
{"index": "s542162302", "label": 2511, "func": ""}
{"index": "s226694565", "label": 2511, "func": ""}
{"index": "s225333883", "label": 2511, "func": ""}
{"index": "s014463374", "label": 2511, "func": ""}
{"index": "s723704085", "label": 2511, "func": ""}
{"index": "s453288358", "label": 2511, "func": ""}
{"index": "s781552654", "label": 2511, "func": ""}
{"index": "s801612740", "label": 2511, "func": ""}
{"index": "s146496178", "label": 2511, "func": ""}
{"index": "s999075197", "label": 2511, "func": ""}
{"index": "s976621205", "label": 718, "func": ""}
{"index": "s012557030", "label": 718, "func": ""}
{"index": "s211905817", "label": 718, "func": ""}
{"index": "s653969806", "label": 718, "func": ""}
{"index": "s226160075", "label": 718, "func": ""}
{"index": "s791572330", "label": 718, "func": ""}
{"index": "s678082443", "label": 718, "func": ""}
{"index": "s302194644", "label": 718, "func": ""}
{"index": "s856676637", "label": 718, "func": ""}
{"index": "s154102083", "label": 718, "func": ""}
{"index": "s920565321", "label": 313, "func": ""}
{"index": "s859845552", "label": 313, "func": ""}
{"index": "s015025757", "label": 313, "func": ""}
{"index": "s143799803", "label": 313, "func": ""}
{"index": "s056884650", "label": 313, "func": ""}
{"index": "s212901613", "label": 313, "func": ""}
{"index": "s415703800", "label": 313, "func": ""}
{"index": "s985076648", "label": 313, "func": ""}
{"index": "s042847548", "label": 313, "func": ""}
{"index": "s806957862", "label": 313, "func": ""}
{"index": "s495187675", "label": 2418, "func": ""}
{"index": "s535395511", "label": 2418, "func": ""}
{"index": "s925674397", "label": 2418, "func": ""}
{"index": "s659095799", "label": 2418, "func": ""}
{"index": "s970764440", "label": 2418, "func": ""}
{"index": "s827142304", "label": 2418, "func": ""}
{"index": "s700502644", "label": 2418, "func": ""}
{"index": "s824707899", "label": 2418, "func": ""}
{"index": "s447737338", "label": 2418, "func": ""}
{"index": "s129699319", "label": 2418, "func": ""}
{"index": "s056738859", "label": 3964, "func": ""}
{"index": "s719038730", "label": 3964, "func": ""}
{"index": "s061668153", "label": 3964, "func": ""}
{"index": "s226250414", "label": 3964, "func": ""}
{"index": "s236357423", "label": 3964, "func": ""}
{"index": "s880024270", "label": 3964, "func": ""}
{"index": "s758162555", "label": 3964, "func": ""}
{"index": "s578483799", "label": 3964, "func": ""}
{"index": "s394499728", "label": 3964, "func": ""}
{"index": "s154572488", "label": 3964, "func": ""}
{"index": "s341370309", "label": 2724, "func": ""}
{"index": "s740401466", "label": 2724, "func": ""}
{"index": "s471471626", "label": 2724, "func": ""}
{"index": "s113781874", "label": 2724, "func": ""}
{"index": "s811973313", "label": 2724, "func": ""}
{"index": "s846886776", "label": 2724, "func": ""}
{"index": "s367661151", "label": 2724, "func": ""}
{"index": "s582733834", "label": 2724, "func": ""}
{"index": "s731646666", "label": 2724, "func": ""}
{"index": "s179416995", "label": 2724, "func": ""}
{"index": "s036598420", "label": 2986, "func": ""}
{"index": "s622326742", "label": 2986, "func": ""}
{"index": "s262038535", "label": 2986, "func": ""}
{"index": "s526255498", "label": 2986, "func": ""}
{"index": "s026980476", "label": 2986, "func": ""}
{"index": "s604557253", "label": 2986, "func": ""}
{"index": "s095469785", "label": 2986, "func": ""}
{"index": "s731049505", "label": 2986, "func": ""}
{"index": "s915795639", "label": 2986, "func": ""}
{"index": "s009090659", "label": 2986, "func": ""}
{"index": "s504121015", "label": 2434, "func": ""}
{"index": "s028295092", "label": 2434, "func": ""}
{"index": "s904775793", "label": 2434, "func": ""}
{"index": "s332818115", "label": 2434, "func": ""}
{"index": "s218636555", "label": 2434, "func": ""}
{"index": "s720693822", "label": 2434, "func": ""}
{"index": "s653882992", "label": 2434, "func": ""}
{"index": "s838225858", "label": 2434, "func": ""}
{"index": "s661463414", "label": 2434, "func": ""}
{"index": "s484438020", "label": 2434, "func": ""}
{"index": "s930391319", "label": 1103, "func": ""}
{"index": "s584678452", "label": 1103, "func": ""}
{"index": "s612899944", "label": 1103, "func": ""}
{"index": "s952139201", "label": 1103, "func": ""}
{"index": "s877804331", "label": 1103, "func": ""}
{"index": "s031315623", "label": 1103, "func": ""}
{"index": "s946604420", "label": 1103, "func": ""}
{"index": "s820810044", "label": 1103, "func": ""}
{"index": "s060860989", "label": 1103, "func": ""}
{"index": "s779726791", "label": 1103, "func": ""}
{"index": "s555727591", "label": 2291, "func": ""}
{"index": "s861914098", "label": 2291, "func": ""}
{"index": "s121570830", "label": 2291, "func": ""}
{"index": "s493032818", "label": 2291, "func": ""}
{"index": "s636645954", "label": 2291, "func": ""}
{"index": "s407265090", "label": 2291, "func": ""}
{"index": "s683290871", "label": 2291, "func": ""}
{"index": "s334963596", "label": 2291, "func": ""}
{"index": "s507237228", "label": 2291, "func": ""}
{"index": "s419459224", "label": 2291, "func": ""}
{"index": "s550572412", "label": 3832, "func": ""}
{"index": "s181689532", "label": 3832, "func": ""}
{"index": "s169169324", "label": 3832, "func": ""}
{"index": "s975985064", "label": 3832, "func": ""}
{"index": "s007091415", "label": 3832, "func": ""}
{"index": "s094195389", "label": 3832, "func": ""}
{"index": "s195861516", "label": 3832, "func": ""}
{"index": "s183101887", "label": 3832, "func": ""}
{"index": "s654516673", "label": 3832, "func": ""}
{"index": "s085115482", "label": 3832, "func": ""}
{"index": "s082847158", "label": 3957, "func": ""}
{"index": "s982844875", "label": 3957, "func": ""}
{"index": "s681030175", "label": 3957, "func": ""}
{"index": "s394884977", "label": 3957, "func": ""}
{"index": "s855694980", "label": 3957, "func": ""}
{"index": "s055065150", "label": 3957, "func": ""}
{"index": "s442057317", "label": 3957, "func": ""}
{"index": "s740283027", "label": 3957, "func": ""}
{"index": "s138750393", "label": 3957, "func": ""}
{"index": "s063300562", "label": 3957, "func": ""}
{"index": "s691711431", "label": 587, "func": ""}
{"index": "s727814157", "label": 587, "func": ""}
{"index": "s123615661", "label": 587, "func": ""}
{"index": "s143232306", "label": 587, "func": ""}
{"index": "s599100304", "label": 587, "func": ""}
{"index": "s017837676", "label": 587, "func": ""}
{"index": "s846835315", "label": 587, "func": ""}
{"index": "s232001902", "label": 587, "func": ""}
{"index": "s224542625", "label": 587, "func": ""}
{"index": "s791581114", "label": 587, "func": ""}
{"index": "s541043822", "label": 2717, "func": ""}
{"index": "s513094494", "label": 2717, "func": ""}
{"index": "s847283138", "label": 2717, "func": ""}
{"index": "s523068145", "label": 2717, "func": ""}
{"index": "s823364111", "label": 2717, "func": ""}
{"index": "s425744827", "label": 2717, "func": ""}
{"index": "s619622319", "label": 2717, "func": ""}
{"index": "s565501215", "label": 2717, "func": ""}
{"index": "s275269879", "label": 2717, "func": ""}
{"index": "s819198054", "label": 2717, "func": ""}
{"index": "s296582586", "label": 633, "func": ""}
{"index": "s056556924", "label": 633, "func": ""}
{"index": "s716141875", "label": 633, "func": ""}
{"index": "s472297269", "label": 1269, "func": ""}
{"index": "s525168479", "label": 1269, "func": ""}
{"index": "s652384299", "label": 1269, "func": ""}
{"index": "s241687322", "label": 1269, "func": ""}
{"index": "s375593835", "label": 1269, "func": ""}
{"index": "s810000773", "label": 1269, "func": ""}
{"index": "s834135293", "label": 1269, "func": ""}
{"index": "s278067566", "label": 1269, "func": ""}
{"index": "s220255958", "label": 1269, "func": ""}
{"index": "s308183422", "label": 1269, "func": ""}
{"index": "s319962900", "label": 2920, "func": ""}
{"index": "s437918562", "label": 2920, "func": ""}
{"index": "s306551214", "label": 2920, "func": ""}
{"index": "s091552425", "label": 2920, "func": ""}
{"index": "s866735430", "label": 2920, "func": ""}
{"index": "s503045784", "label": 2920, "func": ""}
{"index": "s649744905", "label": 2920, "func": ""}
{"index": "s466014475", "label": 2920, "func": ""}
{"index": "s210724376", "label": 2920, "func": ""}
{"index": "s784379403", "label": 2920, "func": ""}
{"index": "s982466448", "label": 3676, "func": ""}
{"index": "s360187417", "label": 3676, "func": ""}
{"index": "s967449866", "label": 3676, "func": ""}
{"index": "s132367218", "label": 3676, "func": ""}
{"index": "s433454010", "label": 3676, "func": ""}
{"index": "s532415227", "label": 3676, "func": ""}
{"index": "s070839689", "label": 3676, "func": ""}
{"index": "s771660512", "label": 3676, "func": ""}
{"index": "s819447653", "label": 3676, "func": ""}
{"index": "s492769221", "label": 3676, "func": ""}
{"index": "s223596849", "label": 252, "func": ""}
{"index": "s448798990", "label": 252, "func": ""}
{"index": "s223169638", "label": 252, "func": ""}
{"index": "s067775123", "label": 252, "func": ""}
{"index": "s796603645", "label": 252, "func": ""}
{"index": "s113061069", "label": 252, "func": ""}
{"index": "s455726021", "label": 252, "func": ""}
{"index": "s353188603", "label": 252, "func": ""}
{"index": "s756011079", "label": 252, "func": ""}
{"index": "s885740050", "label": 252, "func": ""}
{"index": "s705916686", "label": 579, "func": ""}
{"index": "s832884469", "label": 654, "func": ""}
{"index": "s537631974", "label": 654, "func": ""}
{"index": "s440077132", "label": 654, "func": ""}
{"index": "s735215404", "label": 654, "func": ""}
{"index": "s975564093", "label": 654, "func": ""}
{"index": "s159091677", "label": 341, "func": ""}
{"index": "s774461287", "label": 341, "func": ""}
{"index": "s139244693", "label": 341, "func": ""}
{"index": "s629744130", "label": 341, "func": ""}
{"index": "s295447264", "label": 341, "func": ""}
{"index": "s650532824", "label": 341, "func": ""}
{"index": "s143493717", "label": 341, "func": ""}
{"index": "s010825951", "label": 341, "func": ""}
{"index": "s312255738", "label": 341, "func": ""}
{"index": "s478180853", "label": 341, "func": ""}
{"index": "s766146071", "label": 3435, "func": ""}
{"index": "s564373478", "label": 3435, "func": ""}
{"index": "s880203701", "label": 3435, "func": ""}
{"index": "s010540889", "label": 3435, "func": ""}
{"index": "s864667608", "label": 3435, "func": ""}
{"index": "s883234322", "label": 3435, "func": ""}
{"index": "s676249919", "label": 3435, "func": ""}
{"index": "s863663612", "label": 3435, "func": ""}
{"index": "s361345585", "label": 3435, "func": ""}
{"index": "s655097913", "label": 3435, "func": ""}
{"index": "s048813171", "label": 3809, "func": ""}
{"index": "s653056811", "label": 3809, "func": ""}
{"index": "s280638950", "label": 3809, "func": ""}
{"index": "s862487543", "label": 3809, "func": ""}
{"index": "s754603586", "label": 3809, "func": ""}
{"index": "s809055949", "label": 3809, "func": ""}
{"index": "s911377788", "label": 3809, "func": ""}
{"index": "s007202781", "label": 1422, "func": ""}
{"index": "s728396336", "label": 1422, "func": ""}
{"index": "s167733652", "label": 1422, "func": ""}
{"index": "s627484103", "label": 1422, "func": ""}
{"index": "s357153213", "label": 1422, "func": ""}
{"index": "s428379343", "label": 1422, "func": ""}
{"index": "s367053817", "label": 2610, "func": ""}
{"index": "s976849700", "label": 2610, "func": ""}
{"index": "s077410682", "label": 2610, "func": ""}
{"index": "s802740937", "label": 2610, "func": ""}
{"index": "s702809340", "label": 2610, "func": ""}
{"index": "s825768566", "label": 2610, "func": ""}
{"index": "s257695706", "label": 2610, "func": ""}
{"index": "s648320701", "label": 2610, "func": ""}
{"index": "s726462384", "label": 2610, "func": ""}
{"index": "s354308757", "label": 2610, "func": ""}
{"index": "s228249347", "label": 217, "func": ""}
{"index": "s369063738", "label": 217, "func": ""}
{"index": "s974650316", "label": 217, "func": ""}
{"index": "s326172331", "label": 217, "func": ""}
{"index": "s799942871", "label": 217, "func": ""}
{"index": "s944485743", "label": 217, "func": ""}
{"index": "s932798292", "label": 217, "func": ""}
{"index": "s188031790", "label": 217, "func": ""}
{"index": "s710846355", "label": 217, "func": ""}
{"index": "s101432102", "label": 217, "func": ""}
{"index": "s680562116", "label": 2807, "func": ""}
{"index": "s802762971", "label": 2807, "func": ""}
{"index": "s148758518", "label": 2807, "func": ""}
{"index": "s026233523", "label": 2807, "func": ""}
{"index": "s581761777", "label": 2807, "func": ""}
{"index": "s710815175", "label": 2807, "func": ""}
{"index": "s039540478", "label": 2807, "func": ""}
{"index": "s314508908", "label": 2807, "func": ""}
{"index": "s326878098", "label": 2807, "func": ""}
{"index": "s349157119", "label": 2807, "func": ""}
{"index": "s806251986", "label": 2332, "func": ""}
{"index": "s626444400", "label": 2332, "func": ""}
{"index": "s195850030", "label": 2831, "func": ""}
{"index": "s295623404", "label": 2831, "func": ""}
{"index": "s555500300", "label": 2831, "func": ""}
{"index": "s524453607", "label": 2831, "func": ""}
{"index": "s142969117", "label": 2831, "func": ""}
{"index": "s273890378", "label": 2831, "func": ""}
{"index": "s871668255", "label": 2831, "func": ""}
{"index": "s987626362", "label": 2831, "func": ""}
{"index": "s215961584", "label": 2831, "func": ""}
{"index": "s337923699", "label": 2831, "func": ""}
{"index": "s574291986", "label": 1336, "func": ""}
{"index": "s885154228", "label": 1336, "func": ""}
{"index": "s329996697", "label": 1336, "func": ""}
{"index": "s009280399", "label": 1336, "func": ""}
{"index": "s595298760", "label": 1336, "func": ""}
{"index": "s905546845", "label": 1336, "func": ""}
{"index": "s667022563", "label": 1336, "func": ""}
{"index": "s879486500", "label": 1336, "func": ""}
{"index": "s513697621", "label": 1336, "func": ""}
{"index": "s687222185", "label": 1336, "func": ""}
{"index": "s708375563", "label": 2055, "func": ""}
{"index": "s582786848", "label": 1215, "func": ""}
{"index": "s776484488", "label": 1215, "func": ""}
{"index": "s509765846", "label": 2473, "func": ""}
{"index": "s823961458", "label": 2473, "func": ""}
{"index": "s073672220", "label": 2473, "func": ""}
{"index": "s361806583", "label": 2473, "func": ""}
{"index": "s613211471", "label": 2473, "func": ""}
{"index": "s495241960", "label": 2473, "func": ""}
{"index": "s206784312", "label": 2473, "func": ""}
{"index": "s527974470", "label": 2473, "func": ""}
{"index": "s301165749", "label": 2473, "func": ""}
{"index": "s782144065", "label": 2473, "func": ""}
{"index": "s611573686", "label": 3157, "func": ""}
{"index": "s255712753", "label": 3157, "func": ""}
{"index": "s648821053", "label": 3157, "func": ""}
{"index": "s413638638", "label": 3157, "func": ""}
{"index": "s557090869", "label": 3157, "func": ""}
{"index": "s774855992", "label": 3157, "func": ""}
{"index": "s213207990", "label": 3157, "func": ""}
{"index": "s275597017", "label": 3157, "func": ""}
{"index": "s796537310", "label": 3157, "func": ""}
{"index": "s029297073", "label": 3157, "func": ""}
{"index": "s820340724", "label": 1901, "func": ""}
{"index": "s873870642", "label": 1901, "func": ""}
{"index": "s235597862", "label": 1901, "func": ""}
{"index": "s579243866", "label": 1901, "func": ""}
{"index": "s525280589", "label": 1901, "func": ""}
{"index": "s565471605", "label": 1638, "func": ""}
{"index": "s357120162", "label": 1638, "func": ""}
{"index": "s371830707", "label": 1581, "func": ""}
{"index": "s392351521", "label": 3888, "func": ""}
{"index": "s206675214", "label": 3888, "func": ""}
{"index": "s641843759", "label": 3888, "func": ""}
{"index": "s702108024", "label": 3888, "func": ""}
{"index": "s682497211", "label": 3888, "func": ""}
{"index": "s777149806", "label": 3888, "func": ""}
{"index": "s497126376", "label": 3888, "func": ""}
{"index": "s240815933", "label": 3888, "func": ""}
{"index": "s151640504", "label": 3888, "func": ""}
{"index": "s116455192", "label": 3182, "func": ""}
{"index": "s930377916", "label": 3182, "func": ""}
{"index": "s646763447", "label": 3182, "func": ""}
{"index": "s293334120", "label": 3182, "func": ""}
{"index": "s346219696", "label": 3182, "func": ""}
{"index": "s416028782", "label": 3182, "func": ""}
{"index": "s655774083", "label": 3182, "func": ""}
{"index": "s353511686", "label": 3182, "func": ""}
{"index": "s257463080", "label": 3182, "func": ""}
{"index": "s856594411", "label": 3182, "func": ""}
{"index": "s614818857", "label": 3662, "func": ""}
{"index": "s041218828", "label": 3662, "func": ""}
{"index": "s215392012", "label": 3662, "func": ""}
{"index": "s128447842", "label": 3662, "func": ""}
{"index": "s147087751", "label": 3662, "func": ""}
{"index": "s994913363", "label": 3662, "func": ""}
{"index": "s838761567", "label": 3662, "func": ""}
{"index": "s012511115", "label": 3662, "func": ""}
{"index": "s306388078", "label": 3662, "func": ""}
{"index": "s078218551", "label": 3662, "func": ""}
{"index": "s993543020", "label": 1498, "func": ""}
{"index": "s349426769", "label": 1498, "func": ""}
{"index": "s416658310", "label": 1367, "func": ""}
{"index": "s213178962", "label": 2402, "func": ""}
{"index": "s723176033", "label": 2402, "func": ""}
{"index": "s488119403", "label": 2402, "func": ""}
{"index": "s913369857", "label": 2402, "func": ""}
{"index": "s939576191", "label": 2402, "func": ""}
{"index": "s650281772", "label": 2402, "func": ""}
{"index": "s209385659", "label": 2402, "func": ""}
{"index": "s100138784", "label": 2402, "func": ""}
{"index": "s913126818", "label": 2402, "func": ""}
{"index": "s925831580", "label": 2402, "func": ""}
{"index": "s899825575", "label": 3444, "func": ""}
{"index": "s856259912", "label": 2420, "func": ""}
{"index": "s420637302", "label": 2420, "func": ""}
{"index": "s395706729", "label": 2420, "func": ""}
{"index": "s619865623", "label": 2420, "func": ""}
{"index": "s317615405", "label": 2420, "func": ""}
{"index": "s227183475", "label": 2420, "func": ""}
{"index": "s022969122", "label": 2420, "func": ""}
{"index": "s609535105", "label": 2420, "func": ""}
{"index": "s994720010", "label": 2420, "func": ""}
{"index": "s374536198", "label": 2420, "func": ""}
{"index": "s971749663", "label": 1721, "func": ""}
{"index": "s090062687", "label": 1721, "func": ""}
{"index": "s737379906", "label": 369, "func": ""}
{"index": "s679057249", "label": 2480, "func": ""}
{"index": "s704557573", "label": 2480, "func": ""}
{"index": "s574387025", "label": 2480, "func": ""}
{"index": "s386669019", "label": 2480, "func": ""}
{"index": "s739364507", "label": 2480, "func": ""}
{"index": "s861666737", "label": 2480, "func": ""}
{"index": "s232471634", "label": 2480, "func": ""}
{"index": "s132217130", "label": 2480, "func": ""}
{"index": "s736445574", "label": 2480, "func": ""}
{"index": "s427178914", "label": 2480, "func": ""}
{"index": "s294585382", "label": 3909, "func": ""}
{"index": "s589716438", "label": 3909, "func": ""}
{"index": "s268252296", "label": 3909, "func": ""}
{"index": "s865032697", "label": 3909, "func": ""}
{"index": "s890953881", "label": 3909, "func": ""}
{"index": "s462854995", "label": 3909, "func": ""}
{"index": "s761137965", "label": 3909, "func": ""}
{"index": "s303122865", "label": 3909, "func": ""}
{"index": "s754774923", "label": 3909, "func": ""}
{"index": "s229402329", "label": 3909, "func": ""}
{"index": "s821968155", "label": 3989, "func": ""}
{"index": "s680669225", "label": 3989, "func": ""}
{"index": "s194276427", "label": 3989, "func": ""}
{"index": "s946378497", "label": 2200, "func": ""}
{"index": "s648777490", "label": 2200, "func": ""}
{"index": "s771409511", "label": 332, "func": ""}
{"index": "s025086856", "label": 332, "func": ""}
{"index": "s798589501", "label": 332, "func": ""}
{"index": "s424150895", "label": 332, "func": ""}
{"index": "s424879113", "label": 332, "func": ""}
{"index": "s165518410", "label": 332, "func": ""}
{"index": "s060941340", "label": 332, "func": ""}
{"index": "s403649503", "label": 332, "func": ""}
{"index": "s849130668", "label": 332, "func": ""}
{"index": "s734461462", "label": 332, "func": ""}
{"index": "s884445462", "label": 1849, "func": ""}
{"index": "s177932526", "label": 2527, "func": ""}
{"index": "s258684641", "label": 2527, "func": ""}
{"index": "s479381837", "label": 2527, "func": ""}
{"index": "s623771963", "label": 2527, "func": ""}
{"index": "s277880383", "label": 2527, "func": ""}
{"index": "s114470868", "label": 2527, "func": ""}
{"index": "s710589250", "label": 2527, "func": ""}
{"index": "s299704126", "label": 2527, "func": ""}
{"index": "s771135175", "label": 2527, "func": ""}
{"index": "s470099206", "label": 2527, "func": ""}
{"index": "s879934550", "label": 4047, "func": ""}
{"index": "s588528742", "label": 4047, "func": ""}
{"index": "s112606578", "label": 4047, "func": ""}
{"index": "s039113818", "label": 4047, "func": ""}
{"index": "s890258367", "label": 4047, "func": ""}
{"index": "s789877861", "label": 4047, "func": ""}
{"index": "s963430566", "label": 4047, "func": ""}
{"index": "s394360269", "label": 4047, "func": ""}
{"index": "s202894456", "label": 4047, "func": ""}
{"index": "s407883521", "label": 4047, "func": ""}
{"index": "s966165717", "label": 88, "func": ""}
{"index": "s862039268", "label": 88, "func": ""}
{"index": "s503154319", "label": 88, "func": ""}
{"index": "s477692760", "label": 88, "func": ""}
{"index": "s405076285", "label": 88, "func": ""}
{"index": "s773999197", "label": 88, "func": ""}
{"index": "s606208055", "label": 88, "func": ""}
{"index": "s076515286", "label": 88, "func": ""}
{"index": "s082946950", "label": 88, "func": ""}
{"index": "s095978793", "label": 88, "func": ""}
{"index": "s517797651", "label": 1870, "func": ""}
{"index": "s905327195", "label": 1870, "func": ""}
{"index": "s722872010", "label": 303, "func": ""}
{"index": "s139754705", "label": 303, "func": ""}
{"index": "s463439345", "label": 303, "func": ""}
{"index": "s593995884", "label": 727, "func": ""}
{"index": "s023289376", "label": 727, "func": ""}
{"index": "s963702049", "label": 3602, "func": ""}
{"index": "s161566894", "label": 3602, "func": ""}
{"index": "s092094803", "label": 3602, "func": ""}
{"index": "s512240049", "label": 3602, "func": ""}
{"index": "s990077688", "label": 3602, "func": ""}
{"index": "s950069074", "label": 3602, "func": ""}
{"index": "s182705740", "label": 3602, "func": ""}
{"index": "s391749539", "label": 3602, "func": ""}
{"index": "s968002991", "label": 3602, "func": ""}
{"index": "s861102703", "label": 3602, "func": ""}
{"index": "s142859662", "label": 12, "func": ""}
{"index": "s338850104", "label": 12, "func": ""}
{"index": "s317714431", "label": 12, "func": ""}
{"index": "s543060457", "label": 12, "func": ""}
{"index": "s527675883", "label": 12, "func": ""}
{"index": "s473236136", "label": 12, "func": ""}
{"index": "s769254344", "label": 12, "func": ""}
{"index": "s989515307", "label": 12, "func": ""}
{"index": "s977295696", "label": 12, "func": ""}
{"index": "s572378722", "label": 12, "func": ""}
{"index": "s814317113", "label": 4033, "func": ""}
{"index": "s001125600", "label": 4033, "func": ""}
{"index": "s672547189", "label": 4033, "func": ""}
{"index": "s495795568", "label": 4033, "func": ""}
{"index": "s120078878", "label": 4033, "func": ""}
{"index": "s589767634", "label": 4033, "func": ""}
{"index": "s370816463", "label": 4033, "func": ""}
{"index": "s983464841", "label": 4033, "func": ""}
{"index": "s315990186", "label": 4033, "func": ""}
{"index": "s778114292", "label": 4033, "func": ""}
{"index": "s108267293", "label": 1139, "func": ""}
{"index": "s621417863", "label": 1139, "func": ""}
{"index": "s971131826", "label": 1139, "func": ""}
{"index": "s855402965", "label": 1139, "func": ""}
{"index": "s370111045", "label": 1139, "func": ""}
{"index": "s571801581", "label": 1139, "func": ""}
{"index": "s666505747", "label": 1139, "func": ""}
{"index": "s434383896", "label": 1139, "func": ""}
{"index": "s857400708", "label": 1139, "func": ""}
{"index": "s493046148", "label": 1139, "func": ""}
{"index": "s061557478", "label": 2455, "func": ""}
{"index": "s827850118", "label": 2455, "func": ""}
{"index": "s418566871", "label": 2455, "func": ""}
{"index": "s711607094", "label": 2455, "func": ""}
{"index": "s089895913", "label": 2455, "func": ""}
{"index": "s809453274", "label": 2455, "func": ""}
{"index": "s956512340", "label": 2455, "func": ""}
{"index": "s316987539", "label": 2455, "func": ""}
{"index": "s426734491", "label": 2455, "func": ""}
{"index": "s371376272", "label": 2455, "func": ""}
{"index": "s566398044", "label": 3006, "func": ""}
{"index": "s038535742", "label": 3006, "func": ""}
{"index": "s910447421", "label": 3006, "func": ""}
{"index": "s049481981", "label": 3006, "func": ""}
{"index": "s197645214", "label": 3006, "func": ""}
{"index": "s871130831", "label": 3006, "func": ""}
{"index": "s404128461", "label": 3006, "func": ""}
{"index": "s882258846", "label": 3006, "func": ""}
{"index": "s125479191", "label": 3006, "func": ""}
{"index": "s160113919", "label": 3006, "func": ""}
{"index": "s625954116", "label": 3285, "func": ""}
{"index": "s896532691", "label": 3285, "func": ""}
{"index": "s609503091", "label": 3285, "func": ""}
{"index": "s895680713", "label": 3285, "func": ""}
{"index": "s011432897", "label": 3285, "func": ""}
{"index": "s931725755", "label": 3285, "func": ""}
{"index": "s599936389", "label": 3285, "func": ""}
{"index": "s432722106", "label": 3285, "func": ""}
{"index": "s976685888", "label": 3285, "func": ""}
{"index": "s200738353", "label": 3285, "func": ""}
{"index": "s179167656", "label": 3629, "func": ""}
{"index": "s529665864", "label": 3629, "func": ""}
{"index": "s237640566", "label": 3629, "func": ""}
{"index": "s913343849", "label": 3629, "func": ""}
{"index": "s693631268", "label": 3629, "func": ""}
{"index": "s540401749", "label": 3629, "func": ""}
{"index": "s487722035", "label": 3629, "func": ""}
{"index": "s804524116", "label": 3629, "func": ""}
{"index": "s198751365", "label": 3629, "func": ""}
{"index": "s825989976", "label": 3629, "func": ""}
{"index": "s037387045", "label": 1324, "func": ""}
{"index": "s343323857", "label": 1324, "func": ""}
{"index": "s340105478", "label": 1324, "func": ""}
{"index": "s301033412", "label": 1324, "func": ""}
{"index": "s286401277", "label": 1324, "func": ""}
{"index": "s421388189", "label": 1324, "func": ""}
{"index": "s182631084", "label": 1324, "func": ""}
{"index": "s846042256", "label": 68, "func": ""}
{"index": "s437992600", "label": 68, "func": ""}
{"index": "s430272343", "label": 68, "func": ""}
{"index": "s757376907", "label": 68, "func": ""}
{"index": "s189663509", "label": 68, "func": ""}
{"index": "s500614571", "label": 68, "func": ""}
{"index": "s626919901", "label": 68, "func": ""}
{"index": "s031924466", "label": 68, "func": ""}
{"index": "s925388544", "label": 68, "func": ""}
{"index": "s606960694", "label": 68, "func": ""}
{"index": "s660106265", "label": 2485, "func": ""}
{"index": "s351806425", "label": 2485, "func": ""}
{"index": "s860332965", "label": 2485, "func": ""}
{"index": "s673346930", "label": 2485, "func": ""}
{"index": "s193651496", "label": 2485, "func": ""}
{"index": "s899477571", "label": 2485, "func": ""}
{"index": "s256519522", "label": 2485, "func": ""}
{"index": "s244133079", "label": 2485, "func": ""}
{"index": "s567281194", "label": 2485, "func": ""}
{"index": "s701060279", "label": 2485, "func": ""}
{"index": "s672496212", "label": 2929, "func": ""}
{"index": "s359462722", "label": 2929, "func": ""}
{"index": "s723583570", "label": 2929, "func": ""}
{"index": "s661207892", "label": 2929, "func": ""}
{"index": "s683815084", "label": 2929, "func": ""}
{"index": "s949313339", "label": 2929, "func": ""}
{"index": "s164736929", "label": 2929, "func": ""}
{"index": "s285867553", "label": 2929, "func": ""}
{"index": "s328727399", "label": 2929, "func": ""}
{"index": "s355419306", "label": 2929, "func": ""}
{"index": "s074169043", "label": 3476, "func": ""}
{"index": "s773251530", "label": 3476, "func": ""}
{"index": "s149879741", "label": 3476, "func": ""}
{"index": "s160458212", "label": 3476, "func": ""}
{"index": "s133440821", "label": 3476, "func": ""}
{"index": "s319930038", "label": 3476, "func": ""}
{"index": "s237015032", "label": 3476, "func": ""}
{"index": "s489549301", "label": 3476, "func": ""}
{"index": "s736073340", "label": 3476, "func": ""}
{"index": "s397805803", "label": 3476, "func": ""}
{"index": "s822157412", "label": 809, "func": ""}
{"index": "s576184021", "label": 809, "func": ""}
{"index": "s187131473", "label": 809, "func": ""}
{"index": "s161496860", "label": 809, "func": ""}
{"index": "s879042997", "label": 809, "func": ""}
{"index": "s530087913", "label": 809, "func": ""}
{"index": "s659489734", "label": 809, "func": ""}
{"index": "s429129369", "label": 3490, "func": ""}
{"index": "s383409034", "label": 3490, "func": ""}
{"index": "s631441387", "label": 3490, "func": ""}
{"index": "s925756750", "label": 3490, "func": ""}
{"index": "s160464018", "label": 3490, "func": ""}
{"index": "s761899730", "label": 3490, "func": ""}
{"index": "s795792549", "label": 3490, "func": ""}
{"index": "s170425213", "label": 3490, "func": ""}
{"index": "s505247041", "label": 3490, "func": ""}
{"index": "s756387418", "label": 3490, "func": ""}
{"index": "s606014300", "label": 610, "func": ""}
{"index": "s286498988", "label": 754, "func": ""}
{"index": "s412325290", "label": 754, "func": ""}
{"index": "s459551331", "label": 754, "func": ""}
{"index": "s827506912", "label": 754, "func": ""}
{"index": "s851435349", "label": 754, "func": ""}
{"index": "s932377951", "label": 754, "func": ""}
{"index": "s084380868", "label": 754, "func": ""}
{"index": "s576370392", "label": 754, "func": ""}
{"index": "s056420332", "label": 754, "func": ""}
{"index": "s112381755", "label": 754, "func": ""}
{"index": "s089325401", "label": 3240, "func": ""}
{"index": "s275194941", "label": 3240, "func": ""}
{"index": "s346342481", "label": 3240, "func": ""}
{"index": "s519145858", "label": 3240, "func": ""}
{"index": "s787816352", "label": 3240, "func": ""}
{"index": "s968895163", "label": 3240, "func": ""}
{"index": "s172085888", "label": 3240, "func": ""}
{"index": "s545746126", "label": 3240, "func": ""}
{"index": "s806294444", "label": 3240, "func": ""}
{"index": "s461013200", "label": 3240, "func": ""}
{"index": "s990418296", "label": 690, "func": ""}
{"index": "s229933533", "label": 690, "func": ""}
{"index": "s405176851", "label": 690, "func": ""}
{"index": "s275082339", "label": 690, "func": ""}
{"index": "s823750497", "label": 91, "func": ""}
{"index": "s770818513", "label": 91, "func": ""}
{"index": "s295385903", "label": 91, "func": ""}
{"index": "s141842929", "label": 91, "func": ""}
{"index": "s717715065", "label": 91, "func": ""}
{"index": "s425716661", "label": 91, "func": ""}
{"index": "s953973035", "label": 91, "func": ""}
{"index": "s896166200", "label": 91, "func": ""}
{"index": "s650535222", "label": 91, "func": ""}
{"index": "s960225888", "label": 91, "func": ""}
{"index": "s500980918", "label": 2596, "func": ""}
{"index": "s112444217", "label": 2596, "func": ""}
{"index": "s982400827", "label": 2596, "func": ""}
{"index": "s048236167", "label": 2596, "func": ""}
{"index": "s026481698", "label": 2596, "func": ""}
{"index": "s859517870", "label": 2596, "func": ""}
{"index": "s881713774", "label": 2596, "func": ""}
{"index": "s061700559", "label": 2596, "func": ""}
{"index": "s984544123", "label": 2596, "func": ""}
{"index": "s947021942", "label": 2596, "func": ""}
{"index": "s258440995", "label": 3262, "func": ""}
{"index": "s971432042", "label": 3262, "func": ""}
{"index": "s228561240", "label": 3262, "func": ""}
{"index": "s402083859", "label": 3262, "func": ""}
{"index": "s312004507", "label": 3262, "func": ""}
{"index": "s102548477", "label": 3262, "func": ""}
{"index": "s283374011", "label": 3262, "func": ""}
{"index": "s899363396", "label": 3262, "func": ""}
{"index": "s975146301", "label": 3262, "func": ""}
{"index": "s790118887", "label": 3262, "func": ""}
{"index": "s313961555", "label": 1106, "func": ""}
{"index": "s397227163", "label": 1106, "func": ""}
{"index": "s552171289", "label": 2386, "func": ""}
{"index": "s066453940", "label": 2386, "func": ""}
{"index": "s158911388", "label": 2386, "func": ""}
{"index": "s959405738", "label": 2386, "func": ""}
{"index": "s419788660", "label": 2386, "func": ""}
{"index": "s396501053", "label": 2386, "func": ""}
{"index": "s807855207", "label": 2386, "func": ""}
{"index": "s207137722", "label": 2386, "func": ""}
{"index": "s739933820", "label": 2386, "func": ""}
{"index": "s199315509", "label": 2386, "func": ""}
{"index": "s483676562", "label": 2962, "func": ""}
{"index": "s310933388", "label": 2962, "func": ""}
{"index": "s484189957", "label": 2962, "func": ""}
{"index": "s459085018", "label": 2962, "func": ""}
{"index": "s091983399", "label": 2962, "func": ""}
{"index": "s534549404", "label": 2962, "func": ""}
{"index": "s301650284", "label": 2962, "func": ""}
{"index": "s937500644", "label": 2962, "func": ""}
{"index": "s911381927", "label": 2962, "func": ""}
{"index": "s879299699", "label": 2962, "func": ""}
{"index": "s739274972", "label": 3238, "func": ""}
{"index": "s617389482", "label": 3238, "func": ""}
{"index": "s955071053", "label": 3238, "func": ""}
{"index": "s043359297", "label": 3238, "func": ""}
{"index": "s537610321", "label": 3238, "func": ""}
{"index": "s895908771", "label": 3238, "func": ""}
{"index": "s101099305", "label": 3238, "func": ""}
{"index": "s621652516", "label": 3238, "func": ""}
{"index": "s175294227", "label": 3238, "func": ""}
{"index": "s017524835", "label": 3238, "func": ""}
{"index": "s371335302", "label": 3047, "func": ""}
{"index": "s887928589", "label": 3047, "func": ""}
{"index": "s432787417", "label": 3047, "func": ""}
{"index": "s502835464", "label": 3047, "func": ""}
{"index": "s683707889", "label": 3047, "func": ""}
{"index": "s204100213", "label": 3047, "func": ""}
{"index": "s594656898", "label": 3047, "func": ""}
{"index": "s478756111", "label": 3047, "func": ""}
{"index": "s769689436", "label": 3047, "func": ""}
{"index": "s268440960", "label": 3047, "func": ""}
{"index": "s042568256", "label": 959, "func": ""}
{"index": "s673607408", "label": 684, "func": ""}
{"index": "s548027884", "label": 684, "func": ""}
{"index": "s501699473", "label": 684, "func": ""}
{"index": "s347107787", "label": 684, "func": ""}
{"index": "s762820424", "label": 684, "func": ""}
{"index": "s365975874", "label": 684, "func": ""}
{"index": "s749903402", "label": 684, "func": ""}
{"index": "s326007700", "label": 684, "func": ""}
{"index": "s295333111", "label": 684, "func": ""}
{"index": "s213147772", "label": 684, "func": ""}
{"index": "s026892177", "label": 3618, "func": ""}
{"index": "s162989373", "label": 3618, "func": ""}
{"index": "s919950019", "label": 3618, "func": ""}
{"index": "s817615525", "label": 3618, "func": ""}
{"index": "s278842958", "label": 3618, "func": ""}
{"index": "s588504537", "label": 3618, "func": ""}
{"index": "s528440888", "label": 3618, "func": ""}
{"index": "s401085609", "label": 3618, "func": ""}
{"index": "s609708819", "label": 3618, "func": ""}
{"index": "s637518988", "label": 3618, "func": ""}
{"index": "s832935740", "label": 3882, "func": ""}
{"index": "s466285036", "label": 3882, "func": ""}
{"index": "s993871711", "label": 2234, "func": ""}
{"index": "s754217704", "label": 2234, "func": ""}
{"index": "s942580063", "label": 2234, "func": ""}
{"index": "s527471652", "label": 2234, "func": ""}
{"index": "s578487316", "label": 2234, "func": ""}
{"index": "s335630615", "label": 2234, "func": ""}
{"index": "s720406551", "label": 2234, "func": ""}
{"index": "s840344224", "label": 2234, "func": ""}
{"index": "s454885222", "label": 2234, "func": ""}
{"index": "s501981004", "label": 2234, "func": ""}
{"index": "s216240066", "label": 676, "func": ""}
{"index": "s668538835", "label": 676, "func": ""}
{"index": "s586690932", "label": 676, "func": ""}
{"index": "s365855289", "label": 676, "func": ""}
{"index": "s602061206", "label": 676, "func": ""}
{"index": "s085920372", "label": 676, "func": ""}
{"index": "s924301776", "label": 676, "func": ""}
{"index": "s509532623", "label": 676, "func": ""}
{"index": "s391474875", "label": 676, "func": ""}
{"index": "s898220800", "label": 676, "func": ""}
{"index": "s959408907", "label": 1847, "func": ""}
{"index": "s014326012", "label": 1847, "func": ""}
{"index": "s463328141", "label": 1847, "func": ""}
{"index": "s222546242", "label": 905, "func": ""}
{"index": "s285372239", "label": 905, "func": ""}
{"index": "s511442295", "label": 905, "func": ""}
{"index": "s009223931", "label": 905, "func": ""}
{"index": "s935378906", "label": 905, "func": ""}
{"index": "s179152558", "label": 905, "func": ""}
{"index": "s735651639", "label": 905, "func": ""}
{"index": "s766681156", "label": 842, "func": ""}
{"index": "s783522547", "label": 842, "func": ""}
{"index": "s543809335", "label": 842, "func": ""}
{"index": "s909493548", "label": 2707, "func": ""}
{"index": "s451396213", "label": 2707, "func": ""}
{"index": "s148959734", "label": 2707, "func": ""}
{"index": "s654657145", "label": 2707, "func": ""}
{"index": "s408866149", "label": 2707, "func": ""}
{"index": "s264633430", "label": 2707, "func": ""}
{"index": "s554363382", "label": 2707, "func": ""}
{"index": "s833174540", "label": 2707, "func": ""}
{"index": "s471399702", "label": 2707, "func": ""}
{"index": "s369528285", "label": 2707, "func": ""}
{"index": "s086874518", "label": 1592, "func": ""}
{"index": "s113219975", "label": 2032, "func": ""}
{"index": "s974878782", "label": 2032, "func": ""}
{"index": "s975027366", "label": 3861, "func": ""}
{"index": "s142750336", "label": 3861, "func": ""}
{"index": "s714927380", "label": 3861, "func": ""}
{"index": "s558041923", "label": 3861, "func": ""}
{"index": "s757545205", "label": 3861, "func": ""}
{"index": "s591576441", "label": 3861, "func": ""}
{"index": "s679998339", "label": 3861, "func": ""}
{"index": "s967761397", "label": 3861, "func": ""}
{"index": "s460236187", "label": 3861, "func": ""}
{"index": "s479121493", "label": 3861, "func": ""}
{"index": "s820058525", "label": 2679, "func": ""}
{"index": "s525093729", "label": 2679, "func": ""}
{"index": "s634547961", "label": 2679, "func": ""}
{"index": "s329572558", "label": 2679, "func": ""}
{"index": "s237110519", "label": 2679, "func": ""}
{"index": "s417947239", "label": 2679, "func": ""}
{"index": "s986213895", "label": 2679, "func": ""}
{"index": "s043932283", "label": 2679, "func": ""}
{"index": "s413542698", "label": 2679, "func": ""}
{"index": "s674479142", "label": 2679, "func": ""}
{"index": "s140094106", "label": 3778, "func": ""}
{"index": "s905919751", "label": 3778, "func": ""}
{"index": "s445156404", "label": 3778, "func": ""}
{"index": "s006800014", "label": 3778, "func": ""}
{"index": "s205100986", "label": 3778, "func": ""}
{"index": "s336008784", "label": 3778, "func": ""}
{"index": "s045395673", "label": 3778, "func": ""}
{"index": "s879431927", "label": 3778, "func": ""}
{"index": "s713984840", "label": 3778, "func": ""}
{"index": "s763152163", "label": 3778, "func": ""}
{"index": "s216957699", "label": 3966, "func": ""}
{"index": "s148684144", "label": 3966, "func": ""}
{"index": "s595944346", "label": 3966, "func": ""}
{"index": "s536045025", "label": 3966, "func": ""}
{"index": "s058481191", "label": 3966, "func": ""}
{"index": "s680598452", "label": 3966, "func": ""}
{"index": "s917089222", "label": 3966, "func": ""}
{"index": "s528950610", "label": 3966, "func": ""}
{"index": "s751697017", "label": 3966, "func": ""}
{"index": "s474721474", "label": 3966, "func": ""}
{"index": "s040085993", "label": 1471, "func": ""}
{"index": "s689540093", "label": 1471, "func": ""}
{"index": "s226674101", "label": 1471, "func": ""}
{"index": "s566835001", "label": 3242, "func": ""}
{"index": "s010412005", "label": 3242, "func": ""}
{"index": "s401403275", "label": 3242, "func": ""}
{"index": "s645741614", "label": 3242, "func": ""}
{"index": "s661853632", "label": 3242, "func": ""}
{"index": "s649038520", "label": 3242, "func": ""}
{"index": "s795779489", "label": 3242, "func": ""}
{"index": "s751899172", "label": 3242, "func": ""}
{"index": "s119590113", "label": 3242, "func": ""}
{"index": "s337395051", "label": 3242, "func": ""}
{"index": "s082796813", "label": 2490, "func": ""}
{"index": "s739734581", "label": 2490, "func": ""}
{"index": "s674538502", "label": 2490, "func": ""}
{"index": "s301451006", "label": 2490, "func": ""}
{"index": "s142361986", "label": 2490, "func": ""}
{"index": "s813496558", "label": 2490, "func": ""}
{"index": "s396744059", "label": 2490, "func": ""}
{"index": "s893646255", "label": 2490, "func": ""}
{"index": "s897600039", "label": 2490, "func": ""}
{"index": "s496951280", "label": 2490, "func": ""}
{"index": "s960139684", "label": 2648, "func": ""}
{"index": "s858170355", "label": 2648, "func": ""}
{"index": "s324362223", "label": 2648, "func": ""}
{"index": "s080421579", "label": 2648, "func": ""}
{"index": "s574858781", "label": 2648, "func": ""}
{"index": "s230103754", "label": 2648, "func": ""}
{"index": "s916171806", "label": 2648, "func": ""}
{"index": "s188375053", "label": 2648, "func": ""}
{"index": "s788150506", "label": 2648, "func": ""}
{"index": "s016354158", "label": 2648, "func": ""}
{"index": "s077668437", "label": 3844, "func": ""}
{"index": "s667017335", "label": 3844, "func": ""}
{"index": "s848122198", "label": 3844, "func": ""}
{"index": "s470498524", "label": 3844, "func": ""}
{"index": "s728258194", "label": 3844, "func": ""}
{"index": "s426728680", "label": 3844, "func": ""}
{"index": "s749138599", "label": 3844, "func": ""}
{"index": "s479731884", "label": 3844, "func": ""}
{"index": "s399541968", "label": 3844, "func": ""}
{"index": "s911918785", "label": 3844, "func": ""}
{"index": "s610937560", "label": 3429, "func": ""}
{"index": "s337203081", "label": 3429, "func": ""}
{"index": "s766727903", "label": 3429, "func": ""}
{"index": "s076789182", "label": 3429, "func": ""}
{"index": "s697602174", "label": 3429, "func": ""}
{"index": "s529740439", "label": 2469, "func": ""}
{"index": "s090353743", "label": 2469, "func": ""}
{"index": "s759753859", "label": 2469, "func": ""}
{"index": "s876262422", "label": 2469, "func": ""}
{"index": "s235101555", "label": 2469, "func": ""}
{"index": "s830313039", "label": 2469, "func": ""}
{"index": "s038859874", "label": 2469, "func": ""}
{"index": "s649442290", "label": 2469, "func": ""}
{"index": "s804000880", "label": 2469, "func": ""}
{"index": "s879727023", "label": 2469, "func": ""}
{"index": "s857940872", "label": 771, "func": ""}
{"index": "s715695547", "label": 771, "func": ""}
{"index": "s116380449", "label": 771, "func": ""}
{"index": "s500582147", "label": 771, "func": ""}
{"index": "s131562450", "label": 771, "func": ""}
{"index": "s472719194", "label": 771, "func": ""}
{"index": "s258832833", "label": 771, "func": ""}
{"index": "s085274760", "label": 746, "func": ""}
{"index": "s101679852", "label": 746, "func": ""}
{"index": "s780248636", "label": 746, "func": ""}
{"index": "s716287908", "label": 746, "func": ""}
{"index": "s503041785", "label": 746, "func": ""}
{"index": "s836042530", "label": 746, "func": ""}
{"index": "s432657426", "label": 746, "func": ""}
{"index": "s658123113", "label": 746, "func": ""}
{"index": "s938535256", "label": 746, "func": ""}
{"index": "s564481740", "label": 746, "func": ""}
{"index": "s064649974", "label": 3892, "func": ""}
{"index": "s834079652", "label": 3892, "func": ""}
{"index": "s909412128", "label": 3892, "func": ""}
{"index": "s377240378", "label": 1715, "func": ""}
{"index": "s463365931", "label": 1715, "func": ""}
{"index": "s908071608", "label": 1715, "func": ""}
{"index": "s534716280", "label": 1715, "func": ""}
{"index": "s029217300", "label": 607, "func": ""}
{"index": "s006847033", "label": 607, "func": ""}
{"index": "s878132029", "label": 607, "func": ""}
{"index": "s958953006", "label": 607, "func": ""}
{"index": "s818660806", "label": 607, "func": ""}
{"index": "s124751693", "label": 607, "func": ""}
{"index": "s349822034", "label": 607, "func": ""}
{"index": "s157904123", "label": 607, "func": ""}
{"index": "s979744620", "label": 607, "func": ""}
{"index": "s775945947", "label": 607, "func": ""}
{"index": "s828295211", "label": 2240, "func": ""}
{"index": "s605546800", "label": 2240, "func": ""}
{"index": "s345949066", "label": 2240, "func": ""}
{"index": "s759705925", "label": 2240, "func": ""}
{"index": "s696759510", "label": 2240, "func": ""}
{"index": "s807581326", "label": 2240, "func": ""}
{"index": "s346142554", "label": 2240, "func": ""}
{"index": "s293669874", "label": 2240, "func": ""}
{"index": "s089835595", "label": 2240, "func": ""}
{"index": "s818483256", "label": 2240, "func": ""}
{"index": "s104242128", "label": 1657, "func": ""}
{"index": "s856729031", "label": 1657, "func": ""}
{"index": "s298221688", "label": 1657, "func": ""}
{"index": "s651403620", "label": 1657, "func": ""}
{"index": "s818569443", "label": 1657, "func": ""}
{"index": "s470728531", "label": 1657, "func": ""}
{"index": "s960204037", "label": 1657, "func": ""}
{"index": "s603409427", "label": 1657, "func": ""}
{"index": "s960927093", "label": 567, "func": ""}
{"index": "s285197229", "label": 2903, "func": ""}
{"index": "s398181294", "label": 2903, "func": ""}
{"index": "s019701954", "label": 2903, "func": ""}
{"index": "s134161086", "label": 2903, "func": ""}
{"index": "s645620811", "label": 2903, "func": ""}
{"index": "s920096809", "label": 2903, "func": ""}
{"index": "s348295569", "label": 2903, "func": ""}
{"index": "s092496165", "label": 2903, "func": ""}
{"index": "s848424184", "label": 2903, "func": ""}
{"index": "s980698312", "label": 2903, "func": ""}
{"index": "s964779596", "label": 63, "func": ""}
{"index": "s449318198", "label": 63, "func": ""}
{"index": "s717530952", "label": 63, "func": ""}
{"index": "s715024480", "label": 63, "func": ""}
{"index": "s080351704", "label": 63, "func": ""}
{"index": "s381451108", "label": 63, "func": ""}
{"index": "s952219548", "label": 63, "func": ""}
{"index": "s059522032", "label": 63, "func": ""}
{"index": "s919230571", "label": 63, "func": ""}
{"index": "s999419362", "label": 63, "func": ""}
{"index": "s903755684", "label": 2484, "func": ""}
{"index": "s030002710", "label": 2484, "func": ""}
{"index": "s245300112", "label": 2484, "func": ""}
{"index": "s305261805", "label": 2484, "func": ""}
{"index": "s387929651", "label": 2484, "func": ""}
{"index": "s999703257", "label": 2484, "func": ""}
{"index": "s820444587", "label": 2484, "func": ""}
{"index": "s259902235", "label": 2484, "func": ""}
{"index": "s163344013", "label": 2484, "func": ""}
{"index": "s402533034", "label": 2484, "func": ""}
{"index": "s365408739", "label": 1267, "func": ""}
{"index": "s434842311", "label": 1267, "func": ""}
{"index": "s682829924", "label": 1267, "func": ""}
{"index": "s733367378", "label": 1267, "func": ""}
{"index": "s158016583", "label": 1267, "func": ""}
{"index": "s630426899", "label": 1267, "func": ""}
{"index": "s287558356", "label": 1267, "func": ""}
{"index": "s435298882", "label": 1267, "func": ""}
{"index": "s332908121", "label": 1267, "func": ""}
{"index": "s441917593", "label": 1267, "func": ""}
{"index": "s485389305", "label": 1209, "func": ""}
{"index": "s086590614", "label": 3275, "func": ""}
{"index": "s768926641", "label": 3275, "func": ""}
{"index": "s952528131", "label": 3275, "func": ""}
{"index": "s325009197", "label": 3275, "func": ""}
{"index": "s981628863", "label": 3275, "func": ""}
{"index": "s970830363", "label": 3275, "func": ""}
{"index": "s882211386", "label": 3275, "func": ""}
{"index": "s451491329", "label": 3275, "func": ""}
{"index": "s769834638", "label": 3275, "func": ""}
{"index": "s776873068", "label": 3275, "func": ""}
{"index": "s940935396", "label": 3628, "func": ""}
{"index": "s999899587", "label": 3628, "func": ""}
{"index": "s016524564", "label": 3628, "func": ""}
{"index": "s526337017", "label": 3628, "func": ""}
{"index": "s818494392", "label": 3628, "func": ""}
{"index": "s489362084", "label": 3628, "func": ""}
{"index": "s437961461", "label": 3628, "func": ""}
{"index": "s417797567", "label": 3628, "func": ""}
{"index": "s638533176", "label": 3628, "func": ""}
{"index": "s748210027", "label": 3628, "func": ""}
{"index": "s369957612", "label": 1729, "func": ""}
{"index": "s369338448", "label": 1729, "func": ""}
{"index": "s341416617", "label": 2259, "func": ""}
{"index": "s132902621", "label": 2259, "func": ""}
{"index": "s554517265", "label": 2259, "func": ""}
{"index": "s219781371", "label": 2259, "func": ""}
{"index": "s550231801", "label": 2259, "func": ""}
{"index": "s062801434", "label": 2259, "func": ""}
{"index": "s258382312", "label": 2259, "func": ""}
{"index": "s371015648", "label": 2259, "func": ""}
{"index": "s113994242", "label": 2259, "func": ""}
{"index": "s817689887", "label": 2259, "func": ""}
{"index": "s304231884", "label": 3017, "func": ""}
{"index": "s934792016", "label": 3017, "func": ""}
{"index": "s668297206", "label": 3017, "func": ""}
{"index": "s930492325", "label": 3017, "func": ""}
{"index": "s953084609", "label": 3017, "func": ""}
{"index": "s326981639", "label": 3017, "func": ""}
{"index": "s697112855", "label": 3017, "func": ""}
{"index": "s244841995", "label": 3017, "func": ""}
{"index": "s459315465", "label": 3017, "func": ""}
{"index": "s494171790", "label": 3017, "func": ""}
{"index": "s620164001", "label": 2254, "func": ""}
{"index": "s399139057", "label": 2254, "func": ""}
{"index": "s382628514", "label": 2254, "func": ""}
{"index": "s423038321", "label": 1569, "func": ""}
{"index": "s088818171", "label": 1569, "func": ""}
{"index": "s201996320", "label": 1569, "func": ""}
{"index": "s738899408", "label": 1569, "func": ""}
{"index": "s336899317", "label": 3420, "func": ""}
{"index": "s192046046", "label": 3420, "func": ""}
{"index": "s359492159", "label": 3420, "func": ""}
{"index": "s581544430", "label": 3420, "func": ""}
{"index": "s155499902", "label": 3420, "func": ""}
{"index": "s574918242", "label": 3420, "func": ""}
{"index": "s663768534", "label": 3420, "func": ""}
{"index": "s884189549", "label": 3420, "func": ""}
{"index": "s857819829", "label": 3420, "func": ""}
{"index": "s284324148", "label": 3420, "func": ""}
{"index": "s557872506", "label": 1270, "func": ""}
{"index": "s739499129", "label": 1270, "func": ""}
{"index": "s172541013", "label": 1270, "func": ""}
{"index": "s294187948", "label": 1270, "func": ""}
{"index": "s449643533", "label": 1270, "func": ""}
{"index": "s046548090", "label": 1270, "func": ""}
{"index": "s579186543", "label": 1270, "func": ""}
{"index": "s663999325", "label": 1270, "func": ""}
{"index": "s329331057", "label": 1270, "func": ""}
{"index": "s843169307", "label": 1270, "func": ""}
{"index": "s279725889", "label": 1932, "func": ""}
{"index": "s661843504", "label": 1932, "func": ""}
{"index": "s851251927", "label": 2966, "func": ""}
{"index": "s214904553", "label": 473, "func": ""}
{"index": "s272355329", "label": 473, "func": ""}
{"index": "s107291775", "label": 473, "func": ""}
{"index": "s585251091", "label": 473, "func": ""}
{"index": "s993417444", "label": 473, "func": ""}
{"index": "s656156511", "label": 473, "func": ""}
{"index": "s332189780", "label": 473, "func": ""}
{"index": "s082279745", "label": 473, "func": ""}
{"index": "s541099005", "label": 473, "func": ""}
{"index": "s394466401", "label": 473, "func": ""}
{"index": "s859832905", "label": 3166, "func": ""}
{"index": "s341358749", "label": 3166, "func": ""}
{"index": "s365579191", "label": 3166, "func": ""}
{"index": "s741812681", "label": 3166, "func": ""}
{"index": "s535269255", "label": 3166, "func": ""}
{"index": "s947741910", "label": 3166, "func": ""}
{"index": "s928203490", "label": 3166, "func": ""}
{"index": "s263275020", "label": 3166, "func": ""}
{"index": "s727399423", "label": 3166, "func": ""}
{"index": "s407228976", "label": 3166, "func": ""}
{"index": "s739647643", "label": 48, "func": ""}
{"index": "s006085837", "label": 48, "func": ""}
{"index": "s360747801", "label": 48, "func": ""}
{"index": "s931978871", "label": 48, "func": ""}
{"index": "s151444787", "label": 48, "func": ""}
{"index": "s247065350", "label": 48, "func": ""}
{"index": "s139453668", "label": 48, "func": ""}
{"index": "s566080171", "label": 48, "func": ""}
{"index": "s842649227", "label": 48, "func": ""}
{"index": "s047213073", "label": 48, "func": ""}
{"index": "s944189746", "label": 480, "func": ""}
{"index": "s280082493", "label": 480, "func": ""}
{"index": "s313569130", "label": 480, "func": ""}
{"index": "s909210213", "label": 480, "func": ""}
{"index": "s522687268", "label": 480, "func": ""}
{"index": "s741097832", "label": 480, "func": ""}
{"index": "s713176804", "label": 480, "func": ""}
{"index": "s370964970", "label": 480, "func": ""}
{"index": "s593966814", "label": 480, "func": ""}
{"index": "s763201093", "label": 480, "func": ""}
{"index": "s870578716", "label": 3887, "func": ""}
{"index": "s260575609", "label": 169, "func": ""}
{"index": "s665918767", "label": 169, "func": ""}
{"index": "s363650849", "label": 169, "func": ""}
{"index": "s876008347", "label": 169, "func": ""}
{"index": "s637409170", "label": 169, "func": ""}
{"index": "s538124358", "label": 169, "func": ""}
{"index": "s233328563", "label": 169, "func": ""}
{"index": "s046369168", "label": 169, "func": ""}
{"index": "s328337757", "label": 169, "func": ""}
{"index": "s176460203", "label": 169, "func": ""}
{"index": "s824187411", "label": 3781, "func": ""}
{"index": "s376745528", "label": 3781, "func": ""}
{"index": "s261754282", "label": 3781, "func": ""}
{"index": "s343799579", "label": 3781, "func": ""}
{"index": "s535392971", "label": 3781, "func": ""}
{"index": "s849665835", "label": 3781, "func": ""}
{"index": "s616626801", "label": 3781, "func": ""}
{"index": "s654111698", "label": 3781, "func": ""}
{"index": "s446599709", "label": 3781, "func": ""}
{"index": "s666137405", "label": 3781, "func": ""}
{"index": "s419734347", "label": 197, "func": ""}
{"index": "s380268308", "label": 197, "func": ""}
{"index": "s541815734", "label": 197, "func": ""}
{"index": "s275533562", "label": 197, "func": ""}
{"index": "s940511620", "label": 197, "func": ""}
{"index": "s105673102", "label": 197, "func": ""}
{"index": "s792538884", "label": 197, "func": ""}
{"index": "s823035086", "label": 197, "func": ""}
{"index": "s831807161", "label": 197, "func": ""}
{"index": "s699768742", "label": 197, "func": ""}
{"index": "s968308015", "label": 3464, "func": ""}
{"index": "s824309905", "label": 3464, "func": ""}
{"index": "s279476258", "label": 3464, "func": ""}
{"index": "s814477555", "label": 3464, "func": ""}
{"index": "s898422103", "label": 3464, "func": ""}
{"index": "s242639670", "label": 3464, "func": ""}
{"index": "s467594690", "label": 3464, "func": ""}
{"index": "s151370346", "label": 3464, "func": ""}
{"index": "s116032592", "label": 3464, "func": ""}
{"index": "s993510291", "label": 3464, "func": ""}
{"index": "s533459739", "label": 2260, "func": ""}
{"index": "s803414511", "label": 2260, "func": ""}
{"index": "s251105822", "label": 2260, "func": ""}
{"index": "s640163137", "label": 2260, "func": ""}
{"index": "s491473167", "label": 2260, "func": ""}
{"index": "s051101742", "label": 2260, "func": ""}
{"index": "s280331335", "label": 2260, "func": ""}
{"index": "s208894142", "label": 2260, "func": ""}
{"index": "s821483223", "label": 2260, "func": ""}
{"index": "s470552218", "label": 2260, "func": ""}
{"index": "s142590570", "label": 2705, "func": ""}
{"index": "s716785044", "label": 2705, "func": ""}
{"index": "s959941295", "label": 2705, "func": ""}
{"index": "s885245214", "label": 2705, "func": ""}
{"index": "s641547520", "label": 2705, "func": ""}
{"index": "s290288581", "label": 2705, "func": ""}
{"index": "s179339052", "label": 2705, "func": ""}
{"index": "s187337967", "label": 2705, "func": ""}
{"index": "s065363057", "label": 2705, "func": ""}
{"index": "s416292189", "label": 2705, "func": ""}
{"index": "s184026022", "label": 601, "func": ""}
{"index": "s310756791", "label": 601, "func": ""}
{"index": "s568329327", "label": 3457, "func": ""}
{"index": "s684809397", "label": 3457, "func": ""}
{"index": "s646394391", "label": 3457, "func": ""}
{"index": "s691437298", "label": 3457, "func": ""}
{"index": "s783305182", "label": 3457, "func": ""}
{"index": "s435351860", "label": 3457, "func": ""}
{"index": "s465684730", "label": 3457, "func": ""}
{"index": "s240998236", "label": 3457, "func": ""}
{"index": "s557221032", "label": 3457, "func": ""}
{"index": "s802071951", "label": 3457, "func": ""}
{"index": "s988594960", "label": 3916, "func": ""}
{"index": "s903470456", "label": 2263, "func": ""}
{"index": "s764106847", "label": 2263, "func": ""}
{"index": "s527301188", "label": 2263, "func": ""}
{"index": "s148929156", "label": 2263, "func": ""}
{"index": "s456978019", "label": 2263, "func": ""}
{"index": "s679080549", "label": 2263, "func": ""}
{"index": "s836903895", "label": 2263, "func": ""}
{"index": "s505805675", "label": 2263, "func": ""}
{"index": "s025144760", "label": 2263, "func": ""}
{"index": "s105010174", "label": 2263, "func": ""}
{"index": "s759292443", "label": 3659, "func": ""}
{"index": "s405386590", "label": 3659, "func": ""}
{"index": "s188761486", "label": 3659, "func": ""}
{"index": "s134973006", "label": 3659, "func": ""}
{"index": "s744504574", "label": 3659, "func": ""}
{"index": "s523741784", "label": 3659, "func": ""}
{"index": "s020002702", "label": 3659, "func": ""}
{"index": "s047329273", "label": 3659, "func": ""}
{"index": "s873514986", "label": 3659, "func": ""}
{"index": "s313012628", "label": 3659, "func": ""}
{"index": "s851621772", "label": 285, "func": ""}
{"index": "s109975332", "label": 285, "func": ""}
{"index": "s497770097", "label": 2733, "func": ""}
{"index": "s943046657", "label": 2733, "func": ""}
{"index": "s718330489", "label": 2733, "func": ""}
{"index": "s550106517", "label": 2733, "func": ""}
{"index": "s033935498", "label": 2733, "func": ""}
{"index": "s668883874", "label": 2733, "func": ""}
{"index": "s869170640", "label": 2733, "func": ""}
{"index": "s606944631", "label": 2733, "func": ""}
{"index": "s353118373", "label": 2733, "func": ""}
{"index": "s916157533", "label": 2733, "func": ""}
{"index": "s579921573", "label": 3042, "func": ""}
{"index": "s524151261", "label": 3042, "func": ""}
{"index": "s420291473", "label": 3042, "func": ""}
{"index": "s606945580", "label": 3042, "func": ""}
{"index": "s405608269", "label": 3042, "func": ""}
{"index": "s819591701", "label": 3042, "func": ""}
{"index": "s706585124", "label": 3042, "func": ""}
{"index": "s538135989", "label": 3042, "func": ""}
{"index": "s254582734", "label": 3042, "func": ""}
{"index": "s183054497", "label": 3042, "func": ""}
{"index": "s364727143", "label": 829, "func": ""}
{"index": "s189334669", "label": 829, "func": ""}
{"index": "s766293264", "label": 829, "func": ""}
{"index": "s387138326", "label": 829, "func": ""}
{"index": "s295097746", "label": 829, "func": ""}
{"index": "s556328640", "label": 829, "func": ""}
{"index": "s567889563", "label": 829, "func": ""}
{"index": "s454842954", "label": 829, "func": ""}
{"index": "s330812397", "label": 829, "func": ""}
{"index": "s704139064", "label": 829, "func": ""}
{"index": "s744023072", "label": 899, "func": ""}
{"index": "s636643209", "label": 899, "func": ""}
{"index": "s570324582", "label": 899, "func": ""}
{"index": "s322135891", "label": 899, "func": ""}
{"index": "s315789557", "label": 899, "func": ""}
{"index": "s886906592", "label": 899, "func": ""}
{"index": "s217422863", "label": 899, "func": ""}
{"index": "s100216070", "label": 899, "func": ""}
{"index": "s709893253", "label": 899, "func": ""}
{"index": "s871201095", "label": 899, "func": ""}
{"index": "s464913273", "label": 3041, "func": ""}
{"index": "s875073325", "label": 3041, "func": ""}
{"index": "s073087695", "label": 3041, "func": ""}
{"index": "s519103299", "label": 3041, "func": ""}
{"index": "s594280652", "label": 3041, "func": ""}
{"index": "s790846823", "label": 3041, "func": ""}
{"index": "s264661898", "label": 3041, "func": ""}
{"index": "s936139924", "label": 3041, "func": ""}
{"index": "s129496317", "label": 3041, "func": ""}
{"index": "s636620941", "label": 3041, "func": ""}
{"index": "s526959432", "label": 354, "func": ""}
{"index": "s283780638", "label": 354, "func": ""}
{"index": "s812412301", "label": 354, "func": ""}
{"index": "s429084732", "label": 354, "func": ""}
{"index": "s268910101", "label": 354, "func": ""}
{"index": "s441490217", "label": 354, "func": ""}
{"index": "s277538421", "label": 354, "func": ""}
{"index": "s769694706", "label": 354, "func": ""}
{"index": "s751996781", "label": 354, "func": ""}
{"index": "s893943226", "label": 354, "func": ""}
{"index": "s620694214", "label": 749, "func": ""}
{"index": "s017053556", "label": 749, "func": ""}
{"index": "s315294816", "label": 749, "func": ""}
{"index": "s787701069", "label": 749, "func": ""}
{"index": "s593071292", "label": 749, "func": ""}
{"index": "s667520185", "label": 749, "func": ""}
{"index": "s375228534", "label": 749, "func": ""}
{"index": "s375049841", "label": 749, "func": ""}
{"index": "s035421800", "label": 749, "func": ""}
{"index": "s642354521", "label": 749, "func": ""}
{"index": "s495131387", "label": 3660, "func": ""}
{"index": "s524408551", "label": 3660, "func": ""}
{"index": "s707975084", "label": 3660, "func": ""}
{"index": "s998946109", "label": 3660, "func": ""}
{"index": "s754569054", "label": 3660, "func": ""}
{"index": "s588574810", "label": 3660, "func": ""}
{"index": "s734795210", "label": 3660, "func": ""}
{"index": "s715573278", "label": 3660, "func": ""}
{"index": "s948793376", "label": 3660, "func": ""}
{"index": "s984508426", "label": 3660, "func": ""}
{"index": "s783941872", "label": 2587, "func": ""}
{"index": "s379209380", "label": 2587, "func": ""}
{"index": "s362378540", "label": 2587, "func": ""}
{"index": "s442154919", "label": 2587, "func": ""}
{"index": "s074098299", "label": 2587, "func": ""}
{"index": "s635482849", "label": 2587, "func": ""}
{"index": "s192993755", "label": 2587, "func": ""}
{"index": "s698713633", "label": 2587, "func": ""}
{"index": "s100969547", "label": 2587, "func": ""}
{"index": "s969534356", "label": 2587, "func": ""}
{"index": "s043088306", "label": 3700, "func": ""}
{"index": "s003061587", "label": 3700, "func": ""}
{"index": "s820193369", "label": 3700, "func": ""}
{"index": "s918285507", "label": 3700, "func": ""}
{"index": "s463856149", "label": 3700, "func": ""}
{"index": "s297635711", "label": 3700, "func": ""}
{"index": "s224210314", "label": 3700, "func": ""}
{"index": "s920715066", "label": 3700, "func": ""}
{"index": "s315844032", "label": 3700, "func": ""}
{"index": "s473073801", "label": 3700, "func": ""}
{"index": "s726003563", "label": 456, "func": ""}
{"index": "s444596586", "label": 456, "func": ""}
{"index": "s449737011", "label": 456, "func": ""}
{"index": "s477328140", "label": 456, "func": ""}
{"index": "s585395753", "label": 456, "func": ""}
{"index": "s720396099", "label": 456, "func": ""}
{"index": "s689150433", "label": 456, "func": ""}
{"index": "s536536598", "label": 456, "func": ""}
{"index": "s375949510", "label": 456, "func": ""}
{"index": "s636887156", "label": 456, "func": ""}
{"index": "s152895613", "label": 1719, "func": ""}
{"index": "s963407070", "label": 1719, "func": ""}
{"index": "s734425827", "label": 1719, "func": ""}
{"index": "s114382842", "label": 1719, "func": ""}
{"index": "s986115202", "label": 1719, "func": ""}
{"index": "s975157919", "label": 1719, "func": ""}
{"index": "s239432998", "label": 1812, "func": ""}
{"index": "s446151711", "label": 1812, "func": ""}
{"index": "s059940345", "label": 1812, "func": ""}
{"index": "s849709547", "label": 1812, "func": ""}
{"index": "s982513954", "label": 1812, "func": ""}
{"index": "s646467307", "label": 2925, "func": ""}
{"index": "s805896380", "label": 2925, "func": ""}
{"index": "s519308038", "label": 2925, "func": ""}
{"index": "s970207827", "label": 2925, "func": ""}
{"index": "s775646660", "label": 2925, "func": ""}
{"index": "s264585731", "label": 2925, "func": ""}
{"index": "s608716619", "label": 2925, "func": ""}
{"index": "s478449358", "label": 2925, "func": ""}
{"index": "s870385816", "label": 2925, "func": ""}
{"index": "s515241286", "label": 2925, "func": ""}
{"index": "s209597562", "label": 565, "func": ""}
{"index": "s699958608", "label": 565, "func": ""}
{"index": "s008311470", "label": 565, "func": ""}
{"index": "s316153142", "label": 565, "func": ""}
{"index": "s384267734", "label": 565, "func": ""}
{"index": "s836953497", "label": 565, "func": ""}
{"index": "s950765993", "label": 565, "func": ""}
{"index": "s668710153", "label": 565, "func": ""}
{"index": "s526633209", "label": 565, "func": ""}
{"index": "s379155896", "label": 565, "func": ""}
{"index": "s481039307", "label": 2431, "func": ""}
{"index": "s544566547", "label": 2431, "func": ""}
{"index": "s108941225", "label": 2431, "func": ""}
{"index": "s554311923", "label": 2431, "func": ""}
{"index": "s627483394", "label": 2431, "func": ""}
{"index": "s706545727", "label": 2431, "func": ""}
{"index": "s628527292", "label": 2431, "func": ""}
{"index": "s100920475", "label": 2431, "func": ""}
{"index": "s521011724", "label": 2431, "func": ""}
{"index": "s989636320", "label": 2431, "func": ""}
{"index": "s006225375", "label": 1545, "func": ""}
{"index": "s221104350", "label": 1545, "func": ""}
{"index": "s720680436", "label": 1545, "func": ""}
{"index": "s462315883", "label": 1545, "func": ""}
{"index": "s167218997", "label": 1545, "func": ""}
{"index": "s790518970", "label": 2757, "func": ""}
{"index": "s250277485", "label": 2757, "func": ""}
{"index": "s801123121", "label": 2757, "func": ""}
{"index": "s679269651", "label": 2757, "func": ""}
{"index": "s995330179", "label": 2757, "func": ""}
{"index": "s472773417", "label": 2757, "func": ""}
{"index": "s264993584", "label": 2757, "func": ""}
{"index": "s304751900", "label": 2757, "func": ""}
{"index": "s676702085", "label": 2757, "func": ""}
{"index": "s827980257", "label": 2757, "func": ""}
{"index": "s927906530", "label": 423, "func": ""}
{"index": "s098208914", "label": 423, "func": ""}
{"index": "s496510715", "label": 423, "func": ""}
{"index": "s266960700", "label": 423, "func": ""}
{"index": "s108021547", "label": 423, "func": ""}
{"index": "s038312191", "label": 423, "func": ""}
{"index": "s181129684", "label": 423, "func": ""}
{"index": "s745293324", "label": 423, "func": ""}
{"index": "s528859195", "label": 423, "func": ""}
{"index": "s096025618", "label": 423, "func": ""}
{"index": "s644475627", "label": 2613, "func": ""}
{"index": "s206083497", "label": 2613, "func": ""}
{"index": "s128922886", "label": 2613, "func": ""}
{"index": "s321837780", "label": 2613, "func": ""}
{"index": "s796812455", "label": 2613, "func": ""}
{"index": "s953406314", "label": 2613, "func": ""}
{"index": "s468986549", "label": 2613, "func": ""}
{"index": "s805166812", "label": 2613, "func": ""}
{"index": "s029922512", "label": 2613, "func": ""}
{"index": "s113531043", "label": 2613, "func": ""}
{"index": "s348308578", "label": 811, "func": ""}
{"index": "s311691560", "label": 811, "func": ""}
{"index": "s860383153", "label": 811, "func": ""}
{"index": "s023318979", "label": 811, "func": ""}
{"index": "s642900131", "label": 811, "func": ""}
{"index": "s579467051", "label": 811, "func": ""}
{"index": "s490641948", "label": 811, "func": ""}
{"index": "s491040642", "label": 811, "func": ""}
{"index": "s772275377", "label": 811, "func": ""}
{"index": "s841266101", "label": 811, "func": ""}
{"index": "s098615540", "label": 404, "func": ""}
{"index": "s002554637", "label": 404, "func": ""}
{"index": "s268201365", "label": 404, "func": ""}
{"index": "s497827662", "label": 495, "func": ""}
{"index": "s767698537", "label": 495, "func": ""}
{"index": "s383107303", "label": 495, "func": ""}
{"index": "s065935326", "label": 495, "func": ""}
{"index": "s144511552", "label": 495, "func": ""}
{"index": "s977330874", "label": 495, "func": ""}
{"index": "s527387362", "label": 495, "func": ""}
{"index": "s842864983", "label": 495, "func": ""}
{"index": "s949474349", "label": 495, "func": ""}
{"index": "s480943358", "label": 495, "func": ""}
{"index": "s343319199", "label": 1478, "func": ""}
{"index": "s037135416", "label": 1478, "func": ""}
{"index": "s015785027", "label": 1478, "func": ""}
{"index": "s424904053", "label": 1478, "func": ""}
{"index": "s792107901", "label": 1478, "func": ""}
{"index": "s036647085", "label": 1478, "func": ""}
{"index": "s664477591", "label": 1478, "func": ""}
{"index": "s494616814", "label": 453, "func": ""}
{"index": "s717724107", "label": 453, "func": ""}
{"index": "s357050525", "label": 453, "func": ""}
{"index": "s045043690", "label": 453, "func": ""}
{"index": "s539237229", "label": 453, "func": ""}
{"index": "s414770879", "label": 453, "func": ""}
{"index": "s150414399", "label": 453, "func": ""}
{"index": "s213175133", "label": 453, "func": ""}
{"index": "s117869153", "label": 453, "func": ""}
{"index": "s521847988", "label": 453, "func": ""}
{"index": "s708544986", "label": 2712, "func": ""}
{"index": "s254561545", "label": 2712, "func": ""}
{"index": "s021811696", "label": 2712, "func": ""}
{"index": "s650723958", "label": 2712, "func": ""}
{"index": "s489351574", "label": 2712, "func": ""}
{"index": "s109808160", "label": 2712, "func": ""}
{"index": "s791954955", "label": 2712, "func": ""}
{"index": "s861827218", "label": 2712, "func": ""}
{"index": "s402246600", "label": 2712, "func": ""}
{"index": "s855044187", "label": 2712, "func": ""}
{"index": "s934512479", "label": 74, "func": ""}
{"index": "s750668571", "label": 74, "func": ""}
{"index": "s697409692", "label": 74, "func": ""}
{"index": "s784890410", "label": 74, "func": ""}
{"index": "s065177927", "label": 74, "func": ""}
{"index": "s570883718", "label": 74, "func": ""}
{"index": "s741564678", "label": 74, "func": ""}
{"index": "s924889913", "label": 74, "func": ""}
{"index": "s316330314", "label": 74, "func": ""}
{"index": "s873506858", "label": 74, "func": ""}
{"index": "s539533189", "label": 3872, "func": ""}
{"index": "s365095798", "label": 1307, "func": ""}
{"index": "s026993874", "label": 1307, "func": ""}
{"index": "s265520126", "label": 1307, "func": ""}
{"index": "s042113259", "label": 1307, "func": ""}
{"index": "s115061637", "label": 1307, "func": ""}
{"index": "s997409681", "label": 1307, "func": ""}
{"index": "s831059722", "label": 1307, "func": ""}
{"index": "s844219704", "label": 1307, "func": ""}
{"index": "s615079909", "label": 1307, "func": ""}
{"index": "s887661405", "label": 402, "func": ""}
{"index": "s861384626", "label": 402, "func": ""}
{"index": "s242624047", "label": 402, "func": ""}
{"index": "s993440103", "label": 402, "func": ""}
{"index": "s799494156", "label": 402, "func": ""}
{"index": "s352915237", "label": 402, "func": ""}
{"index": "s609056580", "label": 402, "func": ""}
{"index": "s664590161", "label": 402, "func": ""}
{"index": "s544861023", "label": 3881, "func": ""}
{"index": "s547587158", "label": 3434, "func": ""}
{"index": "s906987339", "label": 3434, "func": ""}
{"index": "s304635892", "label": 3434, "func": ""}
{"index": "s847125560", "label": 3434, "func": ""}
{"index": "s257321930", "label": 3434, "func": ""}
{"index": "s526164976", "label": 3434, "func": ""}
{"index": "s837516747", "label": 3434, "func": ""}
{"index": "s306420128", "label": 3434, "func": ""}
{"index": "s807388724", "label": 3434, "func": ""}
{"index": "s865403159", "label": 3434, "func": ""}
{"index": "s251269973", "label": 3666, "func": ""}
{"index": "s681419283", "label": 3666, "func": ""}
{"index": "s749141695", "label": 3666, "func": ""}
{"index": "s631283048", "label": 3666, "func": ""}
{"index": "s823122540", "label": 3666, "func": ""}
{"index": "s972417776", "label": 3666, "func": ""}
{"index": "s041032350", "label": 3666, "func": ""}
{"index": "s498741131", "label": 3666, "func": ""}
{"index": "s039838019", "label": 3666, "func": ""}
{"index": "s229021542", "label": 3666, "func": ""}
{"index": "s179179347", "label": 219, "func": ""}
{"index": "s637031618", "label": 219, "func": ""}
{"index": "s784525602", "label": 219, "func": ""}
{"index": "s064444655", "label": 219, "func": ""}
{"index": "s990992105", "label": 219, "func": ""}
{"index": "s697525543", "label": 219, "func": ""}
{"index": "s120783497", "label": 219, "func": ""}
{"index": "s837781924", "label": 219, "func": ""}
{"index": "s107889261", "label": 219, "func": ""}
{"index": "s424079563", "label": 219, "func": ""}
{"index": "s629694901", "label": 2871, "func": ""}
{"index": "s249818641", "label": 2871, "func": ""}
{"index": "s255592514", "label": 2871, "func": ""}
{"index": "s227046004", "label": 2871, "func": ""}
{"index": "s467291390", "label": 2871, "func": ""}
{"index": "s914374825", "label": 2871, "func": ""}
{"index": "s093410340", "label": 2871, "func": ""}
{"index": "s496750073", "label": 2871, "func": ""}
{"index": "s482033561", "label": 2871, "func": ""}
{"index": "s331478576", "label": 2871, "func": ""}
{"index": "s199747057", "label": 192, "func": ""}
{"index": "s110543785", "label": 192, "func": ""}
{"index": "s810078659", "label": 192, "func": ""}
{"index": "s326488037", "label": 3543, "func": ""}
{"index": "s027488840", "label": 3543, "func": ""}
{"index": "s905524560", "label": 3543, "func": ""}
{"index": "s213501804", "label": 3543, "func": ""}
{"index": "s416382615", "label": 3543, "func": ""}
{"index": "s505092954", "label": 3543, "func": ""}
{"index": "s171090698", "label": 3543, "func": ""}
{"index": "s700630894", "label": 3543, "func": ""}
{"index": "s345092014", "label": 3543, "func": ""}
{"index": "s425451829", "label": 3543, "func": ""}
{"index": "s367084760", "label": 2823, "func": ""}
{"index": "s422578352", "label": 2823, "func": ""}
{"index": "s068989572", "label": 2823, "func": ""}
{"index": "s214683675", "label": 2823, "func": ""}
{"index": "s450314161", "label": 2823, "func": ""}
{"index": "s850788690", "label": 2823, "func": ""}
{"index": "s451430956", "label": 2823, "func": ""}
{"index": "s304314462", "label": 2823, "func": ""}
{"index": "s979984067", "label": 2823, "func": ""}
{"index": "s169180503", "label": 2823, "func": ""}
{"index": "s295763391", "label": 1556, "func": ""}
{"index": "s350705204", "label": 1556, "func": ""}
{"index": "s991185179", "label": 1556, "func": ""}
{"index": "s262329471", "label": 1556, "func": ""}
{"index": "s770281005", "label": 1556, "func": ""}
{"index": "s662583977", "label": 1556, "func": ""}
{"index": "s855789852", "label": 1556, "func": ""}
{"index": "s688401122", "label": 1584, "func": ""}
{"index": "s382948619", "label": 3032, "func": ""}
{"index": "s998387284", "label": 3032, "func": ""}
{"index": "s431458110", "label": 3032, "func": ""}
{"index": "s481889815", "label": 3032, "func": ""}
{"index": "s489245254", "label": 3032, "func": ""}
{"index": "s911272692", "label": 3032, "func": ""}
{"index": "s582028762", "label": 3032, "func": ""}
{"index": "s403597432", "label": 3032, "func": ""}
{"index": "s340260432", "label": 3032, "func": ""}
{"index": "s915200229", "label": 3032, "func": ""}
{"index": "s560046288", "label": 1079, "func": ""}
{"index": "s720456085", "label": 1079, "func": ""}
{"index": "s433428850", "label": 34, "func": ""}
{"index": "s076970205", "label": 34, "func": ""}
{"index": "s249566040", "label": 34, "func": ""}
{"index": "s487046625", "label": 34, "func": ""}
{"index": "s119566380", "label": 34, "func": ""}
{"index": "s730689071", "label": 34, "func": ""}
{"index": "s760061513", "label": 34, "func": ""}
{"index": "s943807284", "label": 34, "func": ""}
{"index": "s639257288", "label": 34, "func": ""}
{"index": "s759428198", "label": 34, "func": ""}
{"index": "s141890368", "label": 403, "func": ""}
{"index": "s314472504", "label": 403, "func": ""}
{"index": "s877125419", "label": 403, "func": ""}
{"index": "s753734635", "label": 1223, "func": ""}
{"index": "s807154656", "label": 1223, "func": ""}
{"index": "s840696389", "label": 1223, "func": ""}
{"index": "s532058775", "label": 1223, "func": ""}
{"index": "s501843374", "label": 1223, "func": ""}
{"index": "s894070115", "label": 1223, "func": ""}
{"index": "s560822823", "label": 1223, "func": ""}
{"index": "s041689477", "label": 1223, "func": ""}
{"index": "s399885998", "label": 1223, "func": ""}
{"index": "s552259337", "label": 1223, "func": ""}
{"index": "s255380519", "label": 2518, "func": ""}
{"index": "s718609805", "label": 2518, "func": ""}
{"index": "s916572258", "label": 2518, "func": ""}
{"index": "s435320932", "label": 2518, "func": ""}
{"index": "s293405156", "label": 2518, "func": ""}
{"index": "s431743656", "label": 2518, "func": ""}
{"index": "s636054917", "label": 2518, "func": ""}
{"index": "s624826628", "label": 2609, "func": ""}
{"index": "s096245945", "label": 2609, "func": ""}
{"index": "s655282088", "label": 2609, "func": ""}
{"index": "s616011329", "label": 2609, "func": ""}
{"index": "s877785373", "label": 2609, "func": ""}
{"index": "s670135655", "label": 2609, "func": ""}
{"index": "s364398365", "label": 2609, "func": ""}
{"index": "s769107421", "label": 2609, "func": ""}
{"index": "s346690569", "label": 2609, "func": ""}
{"index": "s072862949", "label": 2609, "func": ""}
{"index": "s042774496", "label": 3576, "func": ""}
{"index": "s898437199", "label": 3576, "func": ""}
{"index": "s510602292", "label": 3576, "func": ""}
{"index": "s255196766", "label": 3576, "func": ""}
{"index": "s698200842", "label": 3576, "func": ""}
{"index": "s066667710", "label": 3576, "func": ""}
{"index": "s696507690", "label": 3576, "func": ""}
{"index": "s519314330", "label": 3576, "func": ""}
{"index": "s600197720", "label": 3576, "func": ""}
{"index": "s166875158", "label": 3576, "func": ""}
{"index": "s969892302", "label": 2715, "func": ""}
{"index": "s653085264", "label": 2715, "func": ""}
{"index": "s724885825", "label": 2715, "func": ""}
{"index": "s102431748", "label": 2715, "func": ""}
{"index": "s348898474", "label": 2715, "func": ""}
{"index": "s086137041", "label": 2715, "func": ""}
{"index": "s944094593", "label": 2715, "func": ""}
{"index": "s568609044", "label": 2715, "func": ""}
{"index": "s017795518", "label": 2715, "func": ""}
{"index": "s948051958", "label": 2715, "func": ""}
{"index": "s912008499", "label": 3938, "func": ""}
{"index": "s154392231", "label": 3938, "func": ""}
{"index": "s531954243", "label": 3938, "func": ""}
{"index": "s852086836", "label": 3938, "func": ""}
{"index": "s024795532", "label": 3938, "func": ""}
{"index": "s601718385", "label": 3938, "func": ""}
{"index": "s928146014", "label": 3938, "func": ""}
{"index": "s020610126", "label": 3938, "func": ""}
{"index": "s961244566", "label": 3938, "func": ""}
{"index": "s867038220", "label": 3938, "func": ""}
{"index": "s201167334", "label": 3697, "func": ""}
{"index": "s203513821", "label": 3697, "func": ""}
{"index": "s605777820", "label": 3697, "func": ""}
{"index": "s036273490", "label": 3697, "func": ""}
{"index": "s528861768", "label": 3697, "func": ""}
{"index": "s966936143", "label": 3697, "func": ""}
{"index": "s433098618", "label": 3697, "func": ""}
{"index": "s587622198", "label": 3697, "func": ""}
{"index": "s999886377", "label": 3697, "func": ""}
{"index": "s776980065", "label": 3697, "func": ""}
{"index": "s481430739", "label": 2443, "func": ""}
{"index": "s281462065", "label": 2443, "func": ""}
{"index": "s293425438", "label": 2443, "func": ""}
{"index": "s617787034", "label": 2443, "func": ""}
{"index": "s309867202", "label": 2443, "func": ""}
{"index": "s431061401", "label": 2443, "func": ""}
{"index": "s395587374", "label": 2443, "func": ""}
{"index": "s771734179", "label": 2443, "func": ""}
{"index": "s881566171", "label": 2443, "func": ""}
{"index": "s631454036", "label": 2443, "func": ""}
{"index": "s897736323", "label": 1533, "func": ""}
{"index": "s404739561", "label": 1533, "func": ""}
{"index": "s251639275", "label": 1533, "func": ""}
{"index": "s509440940", "label": 1533, "func": ""}
{"index": "s088345821", "label": 1533, "func": ""}
{"index": "s211584438", "label": 1533, "func": ""}
{"index": "s402210143", "label": 3246, "func": ""}
{"index": "s511798814", "label": 3246, "func": ""}
{"index": "s610746260", "label": 3246, "func": ""}
{"index": "s878132599", "label": 3246, "func": ""}
{"index": "s408314683", "label": 3246, "func": ""}
{"index": "s212190451", "label": 3246, "func": ""}
{"index": "s157483303", "label": 3246, "func": ""}
{"index": "s691923386", "label": 3246, "func": ""}
{"index": "s938048342", "label": 3246, "func": ""}
{"index": "s095997053", "label": 3246, "func": ""}
{"index": "s049123327", "label": 1429, "func": ""}
{"index": "s959120667", "label": 535, "func": ""}
{"index": "s027549560", "label": 535, "func": ""}
{"index": "s602494382", "label": 535, "func": ""}
{"index": "s490469202", "label": 985, "func": ""}
{"index": "s004372498", "label": 839, "func": ""}
{"index": "s755808720", "label": 839, "func": ""}
{"index": "s702080650", "label": 3034, "func": ""}
{"index": "s102306348", "label": 3034, "func": ""}
{"index": "s666332659", "label": 3034, "func": ""}
{"index": "s159335194", "label": 3034, "func": ""}
{"index": "s792106581", "label": 3034, "func": ""}
{"index": "s919675691", "label": 3034, "func": ""}
{"index": "s064905571", "label": 3034, "func": ""}
{"index": "s722263654", "label": 3034, "func": ""}
{"index": "s934458528", "label": 3034, "func": ""}
{"index": "s788143948", "label": 3034, "func": ""}
{"index": "s485054811", "label": 2612, "func": ""}
{"index": "s747135419", "label": 2612, "func": ""}
{"index": "s369878808", "label": 2612, "func": ""}
{"index": "s577700550", "label": 2612, "func": ""}
{"index": "s109804175", "label": 2612, "func": ""}
{"index": "s700408599", "label": 2612, "func": ""}
{"index": "s035854041", "label": 2612, "func": ""}
{"index": "s401653455", "label": 2612, "func": ""}
{"index": "s388395562", "label": 2612, "func": ""}
{"index": "s858150366", "label": 2612, "func": ""}
{"index": "s197861065", "label": 3828, "func": ""}
{"index": "s048681342", "label": 3828, "func": ""}
{"index": "s694367442", "label": 3828, "func": ""}
{"index": "s389112529", "label": 3828, "func": ""}
{"index": "s492027832", "label": 3828, "func": ""}
{"index": "s008619616", "label": 3828, "func": ""}
{"index": "s882583772", "label": 3828, "func": ""}
{"index": "s933367098", "label": 3828, "func": ""}
{"index": "s279921727", "label": 3828, "func": ""}
{"index": "s825470848", "label": 3828, "func": ""}
{"index": "s863111622", "label": 3718, "func": ""}
{"index": "s748423768", "label": 3718, "func": ""}
{"index": "s784846797", "label": 3718, "func": ""}
{"index": "s569549516", "label": 3718, "func": ""}
{"index": "s065019317", "label": 3718, "func": ""}
{"index": "s458001839", "label": 3718, "func": ""}
{"index": "s245871134", "label": 3718, "func": ""}
{"index": "s207615601", "label": 3718, "func": ""}
{"index": "s208241884", "label": 3718, "func": ""}
{"index": "s931776544", "label": 3718, "func": ""}
{"index": "s207456158", "label": 2604, "func": ""}
{"index": "s578019056", "label": 2604, "func": ""}
{"index": "s178194250", "label": 2604, "func": ""}
{"index": "s073835385", "label": 2604, "func": ""}
{"index": "s596109797", "label": 2604, "func": ""}
{"index": "s139032224", "label": 2604, "func": ""}
{"index": "s583605888", "label": 2604, "func": ""}
{"index": "s143783393", "label": 2604, "func": ""}
{"index": "s535331017", "label": 2604, "func": ""}
{"index": "s622978133", "label": 2604, "func": ""}
{"index": "s161188984", "label": 2399, "func": ""}
{"index": "s284859503", "label": 2399, "func": ""}
{"index": "s731188732", "label": 2399, "func": ""}
{"index": "s956058743", "label": 2399, "func": ""}
{"index": "s387184446", "label": 2399, "func": ""}
{"index": "s452530455", "label": 2399, "func": ""}
{"index": "s944288498", "label": 2399, "func": ""}
{"index": "s337327305", "label": 2399, "func": ""}
{"index": "s151644206", "label": 2399, "func": ""}
{"index": "s157390072", "label": 2399, "func": ""}
{"index": "s991814434", "label": 300, "func": ""}
{"index": "s526473450", "label": 300, "func": ""}
{"index": "s224820527", "label": 300, "func": ""}
{"index": "s316618916", "label": 300, "func": ""}
{"index": "s950208521", "label": 300, "func": ""}
{"index": "s591715415", "label": 300, "func": ""}
{"index": "s224479221", "label": 300, "func": ""}
{"index": "s829875422", "label": 300, "func": ""}
{"index": "s148075888", "label": 300, "func": ""}
{"index": "s760782506", "label": 300, "func": ""}
{"index": "s947381206", "label": 3546, "func": ""}
{"index": "s081988820", "label": 3546, "func": ""}
{"index": "s664114711", "label": 3546, "func": ""}
{"index": "s759583765", "label": 3546, "func": ""}
{"index": "s893415085", "label": 3546, "func": ""}
{"index": "s974912127", "label": 3546, "func": ""}
{"index": "s036196091", "label": 3546, "func": ""}
{"index": "s772540052", "label": 3546, "func": ""}
{"index": "s361448046", "label": 3546, "func": ""}
{"index": "s853724687", "label": 3546, "func": ""}
{"index": "s878198467", "label": 1985, "func": ""}
{"index": "s087911785", "label": 1985, "func": ""}
{"index": "s403439023", "label": 1985, "func": ""}
{"index": "s586756713", "label": 2801, "func": ""}
{"index": "s264445013", "label": 2801, "func": ""}
{"index": "s176486551", "label": 2801, "func": ""}
{"index": "s312146330", "label": 2801, "func": ""}
{"index": "s090593436", "label": 2801, "func": ""}
{"index": "s273238132", "label": 2801, "func": ""}
{"index": "s441555893", "label": 2801, "func": ""}
{"index": "s331953900", "label": 2801, "func": ""}
{"index": "s240172416", "label": 2801, "func": ""}
{"index": "s212376937", "label": 2801, "func": ""}
{"index": "s409992408", "label": 3289, "func": ""}
{"index": "s223482464", "label": 3289, "func": ""}
{"index": "s678843352", "label": 3289, "func": ""}
{"index": "s467889891", "label": 3289, "func": ""}
{"index": "s280782532", "label": 3289, "func": ""}
{"index": "s930902257", "label": 3289, "func": ""}
{"index": "s727705331", "label": 3289, "func": ""}
{"index": "s336410246", "label": 3289, "func": ""}
{"index": "s504104187", "label": 3289, "func": ""}
{"index": "s916437654", "label": 3289, "func": ""}
{"index": "s319109152", "label": 458, "func": ""}
{"index": "s031272820", "label": 458, "func": ""}
{"index": "s743389473", "label": 458, "func": ""}
{"index": "s379370937", "label": 458, "func": ""}
{"index": "s428728369", "label": 458, "func": ""}
{"index": "s800791020", "label": 458, "func": ""}
{"index": "s609798737", "label": 458, "func": ""}
{"index": "s786784845", "label": 458, "func": ""}
{"index": "s513338842", "label": 458, "func": ""}
{"index": "s951256676", "label": 458, "func": ""}
{"index": "s814948591", "label": 2669, "func": ""}
{"index": "s047485439", "label": 2669, "func": ""}
{"index": "s679737302", "label": 2669, "func": ""}
{"index": "s085438135", "label": 2669, "func": ""}
{"index": "s480121732", "label": 2669, "func": ""}
{"index": "s066357550", "label": 2669, "func": ""}
{"index": "s135941779", "label": 2669, "func": ""}
{"index": "s943806952", "label": 2669, "func": ""}
{"index": "s546636621", "label": 2669, "func": ""}
{"index": "s887535853", "label": 2669, "func": ""}
{"index": "s552722106", "label": 307, "func": ""}
{"index": "s152323472", "label": 3073, "func": ""}
{"index": "s627994511", "label": 3073, "func": ""}
{"index": "s482586716", "label": 3073, "func": ""}
{"index": "s700139723", "label": 3073, "func": ""}
{"index": "s788591131", "label": 3073, "func": ""}
{"index": "s399489245", "label": 3073, "func": ""}
{"index": "s602605112", "label": 3073, "func": ""}
{"index": "s332477365", "label": 3073, "func": ""}
{"index": "s399683599", "label": 3073, "func": ""}
{"index": "s852604622", "label": 3073, "func": ""}
{"index": "s567145878", "label": 3020, "func": ""}
{"index": "s170491904", "label": 3646, "func": ""}
{"index": "s595402486", "label": 3646, "func": ""}
{"index": "s931965947", "label": 3646, "func": ""}
{"index": "s060420144", "label": 3646, "func": ""}
{"index": "s105661855", "label": 3646, "func": ""}
{"index": "s613580382", "label": 3646, "func": ""}
{"index": "s140483588", "label": 3646, "func": ""}
{"index": "s136761379", "label": 3646, "func": ""}
{"index": "s606368295", "label": 3646, "func": ""}
{"index": "s879515106", "label": 3646, "func": ""}
{"index": "s844646260", "label": 2017, "func": ""}
{"index": "s094398299", "label": 2017, "func": ""}
{"index": "s347795702", "label": 2362, "func": ""}
{"index": "s209550132", "label": 2362, "func": ""}
{"index": "s890101693", "label": 2362, "func": ""}
{"index": "s971938302", "label": 2362, "func": ""}
{"index": "s551278575", "label": 2362, "func": ""}
{"index": "s565505284", "label": 2362, "func": ""}
{"index": "s393257911", "label": 2362, "func": ""}
{"index": "s732558577", "label": 2362, "func": ""}
{"index": "s978148010", "label": 2362, "func": ""}
{"index": "s690228264", "label": 2362, "func": ""}
{"index": "s841090730", "label": 2562, "func": ""}
{"index": "s975850423", "label": 2562, "func": ""}
{"index": "s663340507", "label": 2562, "func": ""}
{"index": "s303580753", "label": 2562, "func": ""}
{"index": "s902279762", "label": 2562, "func": ""}
{"index": "s870180426", "label": 2562, "func": ""}
{"index": "s438163390", "label": 2562, "func": ""}
{"index": "s119796680", "label": 2562, "func": ""}
{"index": "s858160620", "label": 2562, "func": ""}
{"index": "s917546787", "label": 2693, "func": ""}
{"index": "s318228367", "label": 2693, "func": ""}
{"index": "s220111845", "label": 2693, "func": ""}
{"index": "s121158302", "label": 2693, "func": ""}
{"index": "s669964740", "label": 2693, "func": ""}
{"index": "s507254920", "label": 2693, "func": ""}
{"index": "s174565604", "label": 2693, "func": ""}
{"index": "s252920098", "label": 2693, "func": ""}
{"index": "s121242269", "label": 2693, "func": ""}
{"index": "s210432586", "label": 2693, "func": ""}
{"index": "s690190281", "label": 3188, "func": ""}
{"index": "s455946645", "label": 3188, "func": ""}
{"index": "s282636663", "label": 2953, "func": ""}
{"index": "s760224377", "label": 2953, "func": ""}
{"index": "s540549010", "label": 2953, "func": ""}
{"index": "s272620434", "label": 2953, "func": ""}
{"index": "s473817396", "label": 2953, "func": ""}
{"index": "s728524165", "label": 2953, "func": ""}
{"index": "s122503408", "label": 2953, "func": ""}
{"index": "s047691055", "label": 2953, "func": ""}
{"index": "s439695501", "label": 2953, "func": ""}
{"index": "s007656563", "label": 2953, "func": ""}
{"index": "s886043161", "label": 3976, "func": ""}
{"index": "s592111777", "label": 3976, "func": ""}
{"index": "s095411383", "label": 3976, "func": ""}
{"index": "s732838592", "label": 3976, "func": ""}
{"index": "s273389024", "label": 3976, "func": ""}
{"index": "s762488881", "label": 3976, "func": ""}
{"index": "s779723362", "label": 3976, "func": ""}
{"index": "s973505770", "label": 606, "func": ""}
{"index": "s362147422", "label": 606, "func": ""}
{"index": "s593254080", "label": 606, "func": ""}
{"index": "s060355196", "label": 606, "func": ""}
{"index": "s869311983", "label": 606, "func": ""}
{"index": "s043271914", "label": 606, "func": ""}
{"index": "s036014554", "label": 606, "func": ""}
{"index": "s737391253", "label": 606, "func": ""}
{"index": "s577896644", "label": 606, "func": ""}
{"index": "s846444256", "label": 606, "func": ""}
{"index": "s221317588", "label": 2327, "func": ""}
{"index": "s379471205", "label": 2327, "func": ""}
{"index": "s362077677", "label": 2327, "func": ""}
{"index": "s427124769", "label": 2327, "func": ""}
{"index": "s192664172", "label": 2327, "func": ""}
{"index": "s589216332", "label": 2327, "func": ""}
{"index": "s836055683", "label": 2327, "func": ""}
{"index": "s208448165", "label": 2327, "func": ""}
{"index": "s301968536", "label": 2327, "func": ""}
{"index": "s772821773", "label": 2327, "func": ""}
{"index": "s329674861", "label": 1557, "func": ""}
{"index": "s292754056", "label": 1557, "func": ""}
{"index": "s205044583", "label": 1557, "func": ""}
{"index": "s473051375", "label": 1557, "func": ""}
{"index": "s277738835", "label": 1557, "func": ""}
{"index": "s458390657", "label": 1557, "func": ""}
{"index": "s995960223", "label": 3026, "func": ""}
{"index": "s217895139", "label": 3026, "func": ""}
{"index": "s061379070", "label": 3026, "func": ""}
{"index": "s631246579", "label": 3026, "func": ""}
{"index": "s510646420", "label": 3026, "func": ""}
{"index": "s283239128", "label": 3026, "func": ""}
{"index": "s755469703", "label": 3026, "func": ""}
{"index": "s774174007", "label": 3026, "func": ""}
{"index": "s469474777", "label": 3026, "func": ""}
{"index": "s122296981", "label": 3026, "func": ""}
{"index": "s135316251", "label": 2548, "func": ""}
{"index": "s304049479", "label": 2548, "func": ""}
{"index": "s833700375", "label": 2548, "func": ""}
{"index": "s905025050", "label": 2548, "func": ""}
{"index": "s703944226", "label": 2548, "func": ""}
{"index": "s585026836", "label": 2548, "func": ""}
{"index": "s052502589", "label": 2548, "func": ""}
{"index": "s551492720", "label": 2548, "func": ""}
{"index": "s634367042", "label": 2548, "func": ""}
{"index": "s848028670", "label": 2548, "func": ""}
{"index": "s500925435", "label": 3382, "func": ""}
{"index": "s983750386", "label": 3382, "func": ""}
{"index": "s738540174", "label": 3382, "func": ""}
{"index": "s478240863", "label": 3382, "func": ""}
{"index": "s014364631", "label": 3382, "func": ""}
{"index": "s685932683", "label": 3382, "func": ""}
{"index": "s893402012", "label": 3382, "func": ""}
{"index": "s854262602", "label": 3382, "func": ""}
{"index": "s448465248", "label": 3382, "func": ""}
{"index": "s855861561", "label": 3382, "func": ""}
{"index": "s777482503", "label": 1333, "func": ""}
{"index": "s832865914", "label": 1333, "func": ""}
{"index": "s149429613", "label": 1333, "func": ""}
{"index": "s581146124", "label": 1333, "func": ""}
{"index": "s421721272", "label": 1333, "func": ""}
{"index": "s466274300", "label": 1333, "func": ""}
{"index": "s243244344", "label": 1333, "func": ""}
{"index": "s272263868", "label": 1333, "func": ""}
{"index": "s047414123", "label": 1333, "func": ""}
{"index": "s823530442", "label": 1333, "func": ""}
{"index": "s151185961", "label": 2187, "func": ""}
{"index": "s786489767", "label": 3135, "func": ""}
{"index": "s083845234", "label": 3135, "func": ""}
{"index": "s666079630", "label": 3135, "func": ""}
{"index": "s983922524", "label": 3135, "func": ""}
{"index": "s318883660", "label": 3135, "func": ""}
{"index": "s105767175", "label": 3135, "func": ""}
{"index": "s846608406", "label": 3135, "func": ""}
{"index": "s261537517", "label": 3135, "func": ""}
{"index": "s353201976", "label": 3135, "func": ""}
{"index": "s900871097", "label": 3135, "func": ""}
{"index": "s481546960", "label": 2614, "func": ""}
{"index": "s997884388", "label": 2614, "func": ""}
{"index": "s944764365", "label": 2614, "func": ""}
{"index": "s404350289", "label": 2614, "func": ""}
{"index": "s936800814", "label": 2614, "func": ""}
{"index": "s913373939", "label": 2614, "func": ""}
{"index": "s869487283", "label": 2614, "func": ""}
{"index": "s596079951", "label": 2614, "func": ""}
{"index": "s019722771", "label": 2614, "func": ""}
{"index": "s352861773", "label": 2614, "func": ""}
{"index": "s176166517", "label": 692, "func": ""}
{"index": "s209965529", "label": 692, "func": ""}
{"index": "s542951799", "label": 692, "func": ""}
{"index": "s070414750", "label": 692, "func": ""}
{"index": "s188766483", "label": 692, "func": ""}
{"index": "s977506551", "label": 692, "func": ""}
{"index": "s242284189", "label": 400, "func": ""}
{"index": "s338508464", "label": 400, "func": ""}
{"index": "s918957882", "label": 400, "func": ""}
{"index": "s391043347", "label": 400, "func": ""}
{"index": "s901710596", "label": 400, "func": ""}
{"index": "s873410306", "label": 400, "func": ""}
{"index": "s282387509", "label": 400, "func": ""}
{"index": "s007198170", "label": 400, "func": ""}
{"index": "s960366236", "label": 400, "func": ""}
{"index": "s519934433", "label": 1016, "func": ""}
{"index": "s949008104", "label": 1016, "func": ""}
{"index": "s356876070", "label": 1604, "func": ""}
{"index": "s979104292", "label": 1604, "func": ""}
{"index": "s629500608", "label": 1604, "func": ""}
{"index": "s361944926", "label": 1604, "func": ""}
{"index": "s427560664", "label": 3612, "func": ""}
{"index": "s070616063", "label": 3612, "func": ""}
{"index": "s645528599", "label": 3612, "func": ""}
{"index": "s106888636", "label": 3612, "func": ""}
{"index": "s459078433", "label": 3612, "func": ""}
{"index": "s635432028", "label": 3612, "func": ""}
{"index": "s334429362", "label": 3612, "func": ""}
{"index": "s912315233", "label": 3612, "func": ""}
{"index": "s443160925", "label": 3612, "func": ""}
{"index": "s196017496", "label": 3612, "func": ""}
{"index": "s411189398", "label": 232, "func": ""}
{"index": "s202921858", "label": 232, "func": ""}
{"index": "s716975691", "label": 232, "func": ""}
{"index": "s449254695", "label": 232, "func": ""}
{"index": "s554545609", "label": 232, "func": ""}
{"index": "s044732858", "label": 232, "func": ""}
{"index": "s226096232", "label": 232, "func": ""}
{"index": "s834619660", "label": 232, "func": ""}
{"index": "s033006005", "label": 232, "func": ""}
{"index": "s935527579", "label": 232, "func": ""}
{"index": "s399178442", "label": 273, "func": ""}
{"index": "s823807125", "label": 273, "func": ""}
{"index": "s214405851", "label": 273, "func": ""}
{"index": "s772133264", "label": 273, "func": ""}
{"index": "s690053848", "label": 273, "func": ""}
{"index": "s782027175", "label": 273, "func": ""}
{"index": "s199909500", "label": 273, "func": ""}
{"index": "s829871309", "label": 273, "func": ""}
{"index": "s445316603", "label": 273, "func": ""}
{"index": "s143972780", "label": 273, "func": ""}
{"index": "s595181503", "label": 98, "func": ""}
{"index": "s001777336", "label": 98, "func": ""}
{"index": "s385240960", "label": 98, "func": ""}
{"index": "s462704970", "label": 98, "func": ""}
{"index": "s007382879", "label": 98, "func": ""}
{"index": "s318759424", "label": 98, "func": ""}
{"index": "s183544992", "label": 98, "func": ""}
{"index": "s887605476", "label": 98, "func": ""}
{"index": "s349882468", "label": 98, "func": ""}
{"index": "s026963504", "label": 98, "func": ""}
{"index": "s973625657", "label": 2963, "func": ""}
{"index": "s745760034", "label": 2963, "func": ""}
{"index": "s092475478", "label": 2963, "func": ""}
{"index": "s908309391", "label": 2963, "func": ""}
{"index": "s895880529", "label": 2963, "func": ""}
{"index": "s513628380", "label": 2963, "func": ""}
{"index": "s684878016", "label": 2963, "func": ""}
{"index": "s098555337", "label": 2963, "func": ""}
{"index": "s001587837", "label": 2963, "func": ""}
{"index": "s905938180", "label": 2963, "func": ""}
{"index": "s789862508", "label": 3743, "func": ""}
{"index": "s820257519", "label": 3924, "func": ""}
{"index": "s320624830", "label": 608, "func": ""}
{"index": "s253327134", "label": 608, "func": ""}
{"index": "s226097202", "label": 608, "func": ""}
{"index": "s455061474", "label": 3590, "func": ""}
{"index": "s188579735", "label": 3590, "func": ""}
{"index": "s003007184", "label": 3590, "func": ""}
{"index": "s408619385", "label": 3494, "func": ""}
{"index": "s499346305", "label": 3494, "func": ""}
{"index": "s911252696", "label": 3494, "func": ""}
{"index": "s900277624", "label": 3494, "func": ""}
{"index": "s645029876", "label": 3494, "func": ""}
{"index": "s903860206", "label": 3494, "func": ""}
{"index": "s414415563", "label": 3494, "func": ""}
{"index": "s611470059", "label": 3494, "func": ""}
{"index": "s355844560", "label": 3494, "func": ""}
{"index": "s032539230", "label": 3494, "func": ""}
{"index": "s362593119", "label": 493, "func": ""}
{"index": "s745045334", "label": 493, "func": ""}
{"index": "s504243286", "label": 493, "func": ""}
{"index": "s469384073", "label": 493, "func": ""}
{"index": "s885558067", "label": 493, "func": ""}
{"index": "s990846439", "label": 493, "func": ""}
{"index": "s672405945", "label": 209, "func": ""}
{"index": "s219227085", "label": 209, "func": ""}
{"index": "s362654461", "label": 209, "func": ""}
{"index": "s128111928", "label": 209, "func": ""}
{"index": "s315980927", "label": 209, "func": ""}
{"index": "s739023064", "label": 209, "func": ""}
{"index": "s959025318", "label": 209, "func": ""}
{"index": "s490336394", "label": 209, "func": ""}
{"index": "s291604319", "label": 209, "func": ""}
{"index": "s335558902", "label": 209, "func": ""}
{"index": "s006287295", "label": 2620, "func": ""}
{"index": "s811217631", "label": 2620, "func": ""}
{"index": "s172657204", "label": 2620, "func": ""}
{"index": "s355557790", "label": 2620, "func": ""}
{"index": "s296167973", "label": 2620, "func": ""}
{"index": "s196857641", "label": 2620, "func": ""}
{"index": "s211278010", "label": 2620, "func": ""}
{"index": "s581678688", "label": 2620, "func": ""}
{"index": "s729595652", "label": 2620, "func": ""}
{"index": "s921149752", "label": 2620, "func": ""}
{"index": "s128980552", "label": 1543, "func": ""}
{"index": "s740908043", "label": 1543, "func": ""}
{"index": "s262452009", "label": 1543, "func": ""}
{"index": "s905168048", "label": 3193, "func": ""}
{"index": "s431894548", "label": 3193, "func": ""}
{"index": "s469586838", "label": 3193, "func": ""}
{"index": "s361998430", "label": 3193, "func": ""}
{"index": "s317184694", "label": 3193, "func": ""}
{"index": "s233596469", "label": 3193, "func": ""}
{"index": "s855233400", "label": 3193, "func": ""}
{"index": "s332564051", "label": 3193, "func": ""}
{"index": "s776660180", "label": 3193, "func": ""}
{"index": "s784713077", "label": 3193, "func": ""}
{"index": "s175776125", "label": 3425, "func": ""}
{"index": "s136951528", "label": 3425, "func": ""}
{"index": "s680118366", "label": 3425, "func": ""}
{"index": "s473554005", "label": 3425, "func": ""}
{"index": "s501601016", "label": 3425, "func": ""}
{"index": "s443315152", "label": 3425, "func": ""}
{"index": "s896068389", "label": 3425, "func": ""}
{"index": "s136933391", "label": 3425, "func": ""}
{"index": "s417227046", "label": 3425, "func": ""}
{"index": "s355142238", "label": 3425, "func": ""}
{"index": "s899418607", "label": 268, "func": ""}
{"index": "s031605818", "label": 266, "func": ""}
{"index": "s865991527", "label": 266, "func": ""}
{"index": "s453699254", "label": 266, "func": ""}
{"index": "s650686893", "label": 266, "func": ""}
{"index": "s496652651", "label": 4011, "func": ""}
{"index": "s888507303", "label": 4011, "func": ""}
{"index": "s999632523", "label": 4011, "func": ""}
{"index": "s367001858", "label": 4011, "func": ""}
{"index": "s145971430", "label": 4011, "func": ""}
{"index": "s036613613", "label": 4011, "func": ""}
{"index": "s007331338", "label": 4011, "func": ""}
{"index": "s396291059", "label": 4011, "func": ""}
{"index": "s007775223", "label": 4011, "func": ""}
{"index": "s774081964", "label": 4011, "func": ""}
{"index": "s030401082", "label": 2317, "func": ""}
{"index": "s750910300", "label": 2317, "func": ""}
{"index": "s000378878", "label": 2317, "func": ""}
{"index": "s604194858", "label": 2317, "func": ""}
{"index": "s694779732", "label": 2317, "func": ""}
{"index": "s033473593", "label": 2317, "func": ""}
{"index": "s928685603", "label": 2317, "func": ""}
{"index": "s140215585", "label": 2317, "func": ""}
{"index": "s929563764", "label": 2317, "func": ""}
{"index": "s964259134", "label": 2317, "func": ""}
{"index": "s792469160", "label": 1361, "func": ""}
{"index": "s165872613", "label": 1361, "func": ""}
{"index": "s116518330", "label": 1361, "func": ""}
{"index": "s615839622", "label": 1361, "func": ""}
{"index": "s685434844", "label": 2708, "func": ""}
{"index": "s139376538", "label": 2708, "func": ""}
{"index": "s275748032", "label": 2708, "func": ""}
{"index": "s938644848", "label": 2708, "func": ""}
{"index": "s033234740", "label": 2708, "func": ""}
{"index": "s462327549", "label": 2708, "func": ""}
{"index": "s927512444", "label": 2708, "func": ""}
{"index": "s383191645", "label": 2708, "func": ""}
{"index": "s136774985", "label": 2708, "func": ""}
{"index": "s074123998", "label": 2708, "func": ""}
{"index": "s275051642", "label": 3587, "func": ""}
{"index": "s621842482", "label": 3587, "func": ""}
{"index": "s958848528", "label": 3587, "func": ""}
{"index": "s691084831", "label": 3587, "func": ""}
{"index": "s063752297", "label": 3587, "func": ""}
{"index": "s991330525", "label": 3587, "func": ""}
{"index": "s644711662", "label": 3587, "func": ""}
{"index": "s713823754", "label": 3587, "func": ""}
{"index": "s357867260", "label": 3587, "func": ""}
{"index": "s330592711", "label": 3587, "func": ""}
{"index": "s686074289", "label": 555, "func": ""}
{"index": "s597832486", "label": 555, "func": ""}
{"index": "s850974513", "label": 555, "func": ""}
{"index": "s719627010", "label": 555, "func": ""}
{"index": "s901639783", "label": 555, "func": ""}
{"index": "s075453748", "label": 555, "func": ""}
{"index": "s118892578", "label": 555, "func": ""}
{"index": "s029270223", "label": 555, "func": ""}
{"index": "s423221899", "label": 555, "func": ""}
{"index": "s756794056", "label": 555, "func": ""}
{"index": "s027127715", "label": 267, "func": ""}
{"index": "s648712882", "label": 267, "func": ""}
{"index": "s450183317", "label": 267, "func": ""}
{"index": "s325808213", "label": 267, "func": ""}
{"index": "s660668244", "label": 3567, "func": ""}
{"index": "s985254915", "label": 3567, "func": ""}
{"index": "s154140246", "label": 3567, "func": ""}
{"index": "s294258136", "label": 3567, "func": ""}
{"index": "s111794970", "label": 3567, "func": ""}
{"index": "s310239362", "label": 3567, "func": ""}
{"index": "s143060101", "label": 3567, "func": ""}
{"index": "s314161914", "label": 3567, "func": ""}
{"index": "s352966586", "label": 3567, "func": ""}
{"index": "s368399812", "label": 3567, "func": ""}
{"index": "s990917747", "label": 1619, "func": ""}
{"index": "s484375695", "label": 1619, "func": ""}
{"index": "s735298538", "label": 1619, "func": ""}
{"index": "s324103379", "label": 1619, "func": ""}
{"index": "s049618733", "label": 3705, "func": ""}
{"index": "s860505158", "label": 3705, "func": ""}
{"index": "s767340321", "label": 3705, "func": ""}
{"index": "s776111749", "label": 3705, "func": ""}
{"index": "s429905178", "label": 3705, "func": ""}
{"index": "s625653614", "label": 3705, "func": ""}
{"index": "s321382300", "label": 3705, "func": ""}
{"index": "s450905433", "label": 3705, "func": ""}
{"index": "s809175681", "label": 3705, "func": ""}
{"index": "s108784974", "label": 3705, "func": ""}
{"index": "s045284622", "label": 770, "func": ""}
{"index": "s007992718", "label": 770, "func": ""}
{"index": "s984936035", "label": 770, "func": ""}
{"index": "s106282797", "label": 770, "func": ""}
{"index": "s948464248", "label": 770, "func": ""}
{"index": "s239204937", "label": 770, "func": ""}
{"index": "s448361122", "label": 770, "func": ""}
{"index": "s587729701", "label": 770, "func": ""}
{"index": "s232697275", "label": 770, "func": ""}
{"index": "s704488246", "label": 770, "func": ""}
{"index": "s582948829", "label": 2788, "func": ""}
{"index": "s543190468", "label": 2788, "func": ""}
{"index": "s133059103", "label": 2788, "func": ""}
{"index": "s019844242", "label": 2788, "func": ""}
{"index": "s557104897", "label": 2788, "func": ""}
{"index": "s477724087", "label": 2788, "func": ""}
{"index": "s587443620", "label": 2788, "func": ""}
{"index": "s498868746", "label": 2788, "func": ""}
{"index": "s166199592", "label": 2788, "func": ""}
{"index": "s535287097", "label": 2788, "func": ""}
{"index": "s765697643", "label": 216, "func": ""}
{"index": "s485960283", "label": 216, "func": ""}
{"index": "s858104213", "label": 216, "func": ""}
{"index": "s861327319", "label": 216, "func": ""}
{"index": "s995525948", "label": 216, "func": ""}
{"index": "s059199069", "label": 216, "func": ""}
{"index": "s914384833", "label": 216, "func": ""}
{"index": "s244670670", "label": 216, "func": ""}
{"index": "s725957625", "label": 216, "func": ""}
{"index": "s971551328", "label": 216, "func": ""}
{"index": "s470016612", "label": 1282, "func": ""}
{"index": "s463938575", "label": 1460, "func": ""}
{"index": "s972702225", "label": 3669, "func": ""}
{"index": "s341035699", "label": 3519, "func": ""}
{"index": "s282736677", "label": 3519, "func": ""}
{"index": "s880278153", "label": 3519, "func": ""}
{"index": "s359337302", "label": 2544, "func": ""}
{"index": "s149416678", "label": 221, "func": ""}
{"index": "s921207187", "label": 221, "func": ""}
{"index": "s961500044", "label": 221, "func": ""}
{"index": "s015899375", "label": 221, "func": ""}
{"index": "s878968778", "label": 221, "func": ""}
{"index": "s441819110", "label": 221, "func": ""}
{"index": "s659309326", "label": 221, "func": ""}
{"index": "s841715171", "label": 221, "func": ""}
{"index": "s320855857", "label": 221, "func": ""}
{"index": "s769774901", "label": 221, "func": ""}
{"index": "s796820593", "label": 3865, "func": ""}
{"index": "s482490699", "label": 3865, "func": ""}
{"index": "s783061591", "label": 3865, "func": ""}
{"index": "s922809036", "label": 3865, "func": ""}
{"index": "s369717814", "label": 3865, "func": ""}
{"index": "s857951035", "label": 3865, "func": ""}
{"index": "s696680321", "label": 3865, "func": ""}
{"index": "s847237519", "label": 3865, "func": ""}
{"index": "s361491440", "label": 3865, "func": ""}
{"index": "s835652146", "label": 3865, "func": ""}
{"index": "s253826456", "label": 706, "func": ""}
{"index": "s995620153", "label": 706, "func": ""}
{"index": "s777751032", "label": 706, "func": ""}
{"index": "s484266000", "label": 706, "func": ""}
{"index": "s901226486", "label": 706, "func": ""}
{"index": "s603788886", "label": 706, "func": ""}
{"index": "s612500985", "label": 706, "func": ""}
{"index": "s179069785", "label": 706, "func": ""}
{"index": "s816751192", "label": 706, "func": ""}
{"index": "s734546008", "label": 706, "func": ""}
{"index": "s066725955", "label": 251, "func": ""}
{"index": "s439368010", "label": 251, "func": ""}
{"index": "s645740233", "label": 251, "func": ""}
{"index": "s575213562", "label": 251, "func": ""}
{"index": "s967051510", "label": 251, "func": ""}
{"index": "s846421911", "label": 251, "func": ""}
{"index": "s408736403", "label": 251, "func": ""}
{"index": "s822402706", "label": 251, "func": ""}
{"index": "s063295077", "label": 251, "func": ""}
{"index": "s238960589", "label": 251, "func": ""}
{"index": "s295741557", "label": 2694, "func": ""}
{"index": "s700534818", "label": 2694, "func": ""}
{"index": "s092620604", "label": 2694, "func": ""}
{"index": "s681589164", "label": 2694, "func": ""}
{"index": "s036682873", "label": 2694, "func": ""}
{"index": "s486935256", "label": 2694, "func": ""}
{"index": "s018650854", "label": 2694, "func": ""}
{"index": "s086399420", "label": 2694, "func": ""}
{"index": "s407227450", "label": 2694, "func": ""}
{"index": "s180462004", "label": 2694, "func": ""}
{"index": "s862098061", "label": 1537, "func": ""}
{"index": "s067729166", "label": 53, "func": ""}
{"index": "s695326177", "label": 53, "func": ""}
{"index": "s875765460", "label": 53, "func": ""}
{"index": "s242404395", "label": 53, "func": ""}
{"index": "s043849022", "label": 53, "func": ""}
{"index": "s329954180", "label": 53, "func": ""}
{"index": "s225717531", "label": 53, "func": ""}
{"index": "s239392069", "label": 53, "func": ""}
{"index": "s257037438", "label": 53, "func": ""}
{"index": "s825117243", "label": 53, "func": ""}
{"index": "s756920216", "label": 4028, "func": ""}
{"index": "s573299553", "label": 4028, "func": ""}
{"index": "s402683381", "label": 4028, "func": ""}
{"index": "s673922661", "label": 4028, "func": ""}
{"index": "s544273138", "label": 4028, "func": ""}
{"index": "s118801963", "label": 4028, "func": ""}
{"index": "s906273502", "label": 4028, "func": ""}
{"index": "s025753879", "label": 748, "func": ""}
{"index": "s196245340", "label": 748, "func": ""}
{"index": "s497482185", "label": 748, "func": ""}
{"index": "s317848961", "label": 748, "func": ""}
{"index": "s969267418", "label": 748, "func": ""}
{"index": "s907611377", "label": 748, "func": ""}
{"index": "s642504828", "label": 748, "func": ""}
{"index": "s599485894", "label": 748, "func": ""}
{"index": "s314652624", "label": 748, "func": ""}
{"index": "s285756418", "label": 748, "func": ""}
{"index": "s166276674", "label": 3224, "func": ""}
{"index": "s956411495", "label": 3224, "func": ""}
{"index": "s504506629", "label": 3224, "func": ""}
{"index": "s207405455", "label": 3224, "func": ""}
{"index": "s507460872", "label": 3224, "func": ""}
{"index": "s041999142", "label": 3224, "func": ""}
{"index": "s663279121", "label": 3224, "func": ""}
{"index": "s660882554", "label": 3224, "func": ""}
{"index": "s025993412", "label": 3224, "func": ""}
{"index": "s079317303", "label": 3224, "func": ""}
{"index": "s586023033", "label": 1522, "func": ""}
{"index": "s202722049", "label": 1522, "func": ""}
{"index": "s269272110", "label": 1522, "func": ""}
{"index": "s363010869", "label": 1522, "func": ""}
{"index": "s510374347", "label": 1522, "func": ""}
{"index": "s116781691", "label": 1522, "func": ""}
{"index": "s345828464", "label": 1522, "func": ""}
{"index": "s359908583", "label": 1522, "func": ""}
{"index": "s946407677", "label": 1522, "func": ""}
{"index": "s652791511", "label": 1522, "func": ""}
{"index": "s058117050", "label": 3179, "func": ""}
{"index": "s764017623", "label": 3179, "func": ""}
{"index": "s204168130", "label": 3179, "func": ""}
{"index": "s457099818", "label": 3179, "func": ""}
{"index": "s772114377", "label": 3179, "func": ""}
{"index": "s542736827", "label": 3179, "func": ""}
{"index": "s488824477", "label": 3179, "func": ""}
{"index": "s201529714", "label": 3179, "func": ""}
{"index": "s736344986", "label": 3179, "func": ""}
{"index": "s250184318", "label": 3179, "func": ""}
{"index": "s038189290", "label": 36, "func": ""}
{"index": "s118782743", "label": 36, "func": ""}
{"index": "s038181342", "label": 36, "func": ""}
{"index": "s988324026", "label": 36, "func": ""}
{"index": "s368417343", "label": 36, "func": ""}
{"index": "s898155288", "label": 36, "func": ""}
{"index": "s699542386", "label": 36, "func": ""}
{"index": "s731826892", "label": 36, "func": ""}
{"index": "s862235985", "label": 36, "func": ""}
{"index": "s283528977", "label": 36, "func": ""}
{"index": "s844695833", "label": 3898, "func": ""}
{"index": "s437160460", "label": 401, "func": ""}
{"index": "s594115788", "label": 401, "func": ""}
{"index": "s651962093", "label": 401, "func": ""}
{"index": "s563443534", "label": 401, "func": ""}
{"index": "s951471021", "label": 401, "func": ""}
{"index": "s899475053", "label": 401, "func": ""}
{"index": "s167575659", "label": 401, "func": ""}
{"index": "s477258268", "label": 1291, "func": ""}
{"index": "s928036462", "label": 1291, "func": ""}
{"index": "s301630615", "label": 3295, "func": ""}
{"index": "s232924360", "label": 3295, "func": ""}
{"index": "s039801539", "label": 3295, "func": ""}
{"index": "s710703996", "label": 3295, "func": ""}
{"index": "s511405546", "label": 3295, "func": ""}
{"index": "s682627150", "label": 3295, "func": ""}
{"index": "s609278194", "label": 3295, "func": ""}
{"index": "s089115082", "label": 3295, "func": ""}
{"index": "s792938360", "label": 3295, "func": ""}
{"index": "s885803924", "label": 3295, "func": ""}
{"index": "s161385029", "label": 3722, "func": ""}
{"index": "s196511076", "label": 3722, "func": ""}
{"index": "s860454520", "label": 3722, "func": ""}
{"index": "s387180441", "label": 3722, "func": ""}
{"index": "s666134896", "label": 3722, "func": ""}
{"index": "s946591960", "label": 3722, "func": ""}
{"index": "s319406208", "label": 3722, "func": ""}
{"index": "s111862474", "label": 3722, "func": ""}
{"index": "s801549790", "label": 3722, "func": ""}
{"index": "s576230294", "label": 3722, "func": ""}
{"index": "s896743498", "label": 3441, "func": ""}
{"index": "s667423698", "label": 3441, "func": ""}
{"index": "s579269173", "label": 3441, "func": ""}
{"index": "s300973192", "label": 3441, "func": ""}
{"index": "s054637162", "label": 3441, "func": ""}
{"index": "s770531622", "label": 3441, "func": ""}
{"index": "s648757774", "label": 3441, "func": ""}
{"index": "s554182366", "label": 3441, "func": ""}
{"index": "s374856913", "label": 3441, "func": ""}
{"index": "s325234030", "label": 3056, "func": ""}
{"index": "s084879859", "label": 3056, "func": ""}
{"index": "s345159293", "label": 228, "func": ""}
{"index": "s910915329", "label": 228, "func": ""}
{"index": "s683453469", "label": 228, "func": ""}
{"index": "s220617293", "label": 228, "func": ""}
{"index": "s748778637", "label": 228, "func": ""}
{"index": "s924586725", "label": 228, "func": ""}
{"index": "s939124759", "label": 228, "func": ""}
{"index": "s346687799", "label": 228, "func": ""}
{"index": "s662192854", "label": 228, "func": ""}
{"index": "s045744670", "label": 228, "func": ""}
{"index": "s809427233", "label": 2949, "func": ""}
{"index": "s440539722", "label": 2949, "func": ""}
{"index": "s874940791", "label": 2949, "func": ""}
{"index": "s960983442", "label": 2949, "func": ""}
{"index": "s413061597", "label": 2949, "func": ""}
{"index": "s927870172", "label": 2949, "func": ""}
{"index": "s250066395", "label": 2949, "func": ""}
{"index": "s672164078", "label": 2949, "func": ""}
{"index": "s897460683", "label": 2949, "func": ""}
{"index": "s515728466", "label": 2949, "func": ""}
{"index": "s520339096", "label": 1743, "func": ""}
{"index": "s280265475", "label": 1743, "func": ""}
{"index": "s578106966", "label": 1743, "func": ""}
{"index": "s060658329", "label": 3689, "func": ""}
{"index": "s185355947", "label": 3689, "func": ""}
{"index": "s154386307", "label": 3689, "func": ""}
{"index": "s848420845", "label": 3689, "func": ""}
{"index": "s641340144", "label": 3689, "func": ""}
{"index": "s351884284", "label": 3689, "func": ""}
{"index": "s136682926", "label": 3689, "func": ""}
{"index": "s141426509", "label": 3689, "func": ""}
{"index": "s875635645", "label": 3689, "func": ""}
{"index": "s936007877", "label": 3689, "func": ""}
{"index": "s319925306", "label": 29, "func": ""}
{"index": "s758304802", "label": 29, "func": ""}
{"index": "s371865651", "label": 29, "func": ""}
{"index": "s329775071", "label": 29, "func": ""}
{"index": "s715688858", "label": 29, "func": ""}
{"index": "s259925782", "label": 29, "func": ""}
{"index": "s450265077", "label": 29, "func": ""}
{"index": "s314983505", "label": 29, "func": ""}
{"index": "s746429401", "label": 29, "func": ""}
{"index": "s209796301", "label": 29, "func": ""}
{"index": "s526006961", "label": 2947, "func": ""}
{"index": "s879895289", "label": 2947, "func": ""}
{"index": "s060415760", "label": 2947, "func": ""}
{"index": "s695471899", "label": 2947, "func": ""}
{"index": "s643670566", "label": 2947, "func": ""}
{"index": "s734907450", "label": 2947, "func": ""}
{"index": "s284402945", "label": 2947, "func": ""}
{"index": "s057654521", "label": 2947, "func": ""}
{"index": "s137727768", "label": 2947, "func": ""}
{"index": "s310360172", "label": 2947, "func": ""}
{"index": "s666167536", "label": 3651, "func": ""}
{"index": "s952373487", "label": 3651, "func": ""}
{"index": "s051731179", "label": 3651, "func": ""}
{"index": "s724489882", "label": 3651, "func": ""}
{"index": "s301390357", "label": 3651, "func": ""}
{"index": "s603671543", "label": 3651, "func": ""}
{"index": "s755392816", "label": 3651, "func": ""}
{"index": "s786270956", "label": 3651, "func": ""}
{"index": "s879872921", "label": 3651, "func": ""}
{"index": "s858053724", "label": 3651, "func": ""}
{"index": "s635227958", "label": 476, "func": ""}
{"index": "s113477660", "label": 476, "func": ""}
{"index": "s243115862", "label": 214, "func": ""}
{"index": "s965423890", "label": 214, "func": ""}
{"index": "s544512350", "label": 214, "func": ""}
{"index": "s176193578", "label": 214, "func": ""}
{"index": "s270997474", "label": 214, "func": ""}
{"index": "s160063667", "label": 214, "func": ""}
{"index": "s964864659", "label": 214, "func": ""}
{"index": "s776885614", "label": 214, "func": ""}
{"index": "s383381773", "label": 214, "func": ""}
{"index": "s608726604", "label": 214, "func": ""}
{"index": "s270109467", "label": 79, "func": ""}
{"index": "s773607414", "label": 79, "func": ""}
{"index": "s416830477", "label": 79, "func": ""}
{"index": "s822174958", "label": 79, "func": ""}
{"index": "s198551663", "label": 79, "func": ""}
{"index": "s474870122", "label": 79, "func": ""}
{"index": "s487478085", "label": 79, "func": ""}
{"index": "s475020777", "label": 79, "func": ""}
{"index": "s224461001", "label": 79, "func": ""}
{"index": "s193369618", "label": 79, "func": ""}
{"index": "s019306799", "label": 1853, "func": ""}
{"index": "s295784113", "label": 1853, "func": ""}
{"index": "s539207479", "label": 1853, "func": ""}
{"index": "s059421400", "label": 1853, "func": ""}
{"index": "s054451990", "label": 3138, "func": ""}
{"index": "s432514608", "label": 3138, "func": ""}
{"index": "s161062998", "label": 3138, "func": ""}
{"index": "s015646596", "label": 3138, "func": ""}
{"index": "s226392120", "label": 3138, "func": ""}
{"index": "s341626686", "label": 3138, "func": ""}
{"index": "s940513306", "label": 3138, "func": ""}
{"index": "s767434197", "label": 3138, "func": ""}
{"index": "s279302939", "label": 3138, "func": ""}
{"index": "s772781555", "label": 3138, "func": ""}
{"index": "s725354373", "label": 733, "func": ""}
{"index": "s038266105", "label": 3737, "func": ""}
{"index": "s044213540", "label": 3737, "func": ""}
{"index": "s057863015", "label": 3737, "func": ""}
{"index": "s542975541", "label": 3737, "func": ""}
{"index": "s159298747", "label": 3737, "func": ""}
{"index": "s388358740", "label": 3737, "func": ""}
{"index": "s218066160", "label": 3737, "func": ""}
{"index": "s300011795", "label": 3737, "func": ""}
{"index": "s478266531", "label": 3737, "func": ""}
{"index": "s955339404", "label": 3737, "func": ""}
{"index": "s223221193", "label": 2861, "func": ""}
{"index": "s671670393", "label": 2861, "func": ""}
{"index": "s168124214", "label": 2861, "func": ""}
{"index": "s215979508", "label": 2861, "func": ""}
{"index": "s642981952", "label": 2861, "func": ""}
{"index": "s044801463", "label": 2861, "func": ""}
{"index": "s421341652", "label": 2861, "func": ""}
{"index": "s806377859", "label": 2861, "func": ""}
{"index": "s615697316", "label": 2861, "func": ""}
{"index": "s627137046", "label": 2861, "func": ""}
{"index": "s078568946", "label": 4048, "func": ""}
{"index": "s961294411", "label": 4048, "func": ""}
{"index": "s027586806", "label": 4048, "func": ""}
{"index": "s683023376", "label": 4048, "func": ""}
{"index": "s222676540", "label": 4048, "func": ""}
{"index": "s430934902", "label": 4048, "func": ""}
{"index": "s725657065", "label": 4048, "func": ""}
{"index": "s582762535", "label": 4048, "func": ""}
{"index": "s422590106", "label": 4048, "func": ""}
{"index": "s296911285", "label": 4048, "func": ""}
{"index": "s867311661", "label": 3774, "func": ""}
{"index": "s450141646", "label": 3774, "func": ""}
{"index": "s849908379", "label": 3774, "func": ""}
{"index": "s327133561", "label": 3774, "func": ""}
{"index": "s892665066", "label": 3774, "func": ""}
{"index": "s865692530", "label": 3774, "func": ""}
{"index": "s616987974", "label": 3774, "func": ""}
{"index": "s221967691", "label": 3774, "func": ""}
{"index": "s180401671", "label": 3774, "func": ""}
{"index": "s698537507", "label": 3774, "func": ""}
{"index": "s812179111", "label": 1769, "func": ""}
{"index": "s641347532", "label": 1769, "func": ""}
{"index": "s982968035", "label": 2116, "func": ""}
{"index": "s038878820", "label": 450, "func": ""}
{"index": "s264146684", "label": 450, "func": ""}
{"index": "s928204045", "label": 450, "func": ""}
{"index": "s122615027", "label": 450, "func": ""}
{"index": "s220322070", "label": 450, "func": ""}
{"index": "s350603474", "label": 450, "func": ""}
{"index": "s776630474", "label": 450, "func": ""}
{"index": "s484508821", "label": 450, "func": ""}
{"index": "s465372588", "label": 450, "func": ""}
{"index": "s450681779", "label": 450, "func": ""}
{"index": "s693690559", "label": 2749, "func": ""}
{"index": "s642714475", "label": 2749, "func": ""}
{"index": "s914187505", "label": 2749, "func": ""}
{"index": "s446750397", "label": 2749, "func": ""}
{"index": "s815283386", "label": 2749, "func": ""}
{"index": "s687552562", "label": 2749, "func": ""}
{"index": "s854251385", "label": 2749, "func": ""}
{"index": "s002875240", "label": 2749, "func": ""}
{"index": "s171676654", "label": 2749, "func": ""}
{"index": "s497308257", "label": 2749, "func": ""}
{"index": "s379859393", "label": 1499, "func": ""}
{"index": "s813472495", "label": 1499, "func": ""}
{"index": "s863292045", "label": 1499, "func": ""}
{"index": "s013263686", "label": 1499, "func": ""}
{"index": "s464625036", "label": 1499, "func": ""}
{"index": "s862783276", "label": 1499, "func": ""}
{"index": "s211401941", "label": 1499, "func": ""}
{"index": "s906076582", "label": 3592, "func": ""}
{"index": "s703141884", "label": 3592, "func": ""}
{"index": "s500951433", "label": 3592, "func": ""}
{"index": "s567039792", "label": 3592, "func": ""}
{"index": "s241025467", "label": 3592, "func": ""}
{"index": "s415322810", "label": 3592, "func": ""}
{"index": "s835803921", "label": 3592, "func": ""}
{"index": "s105893583", "label": 3592, "func": ""}
{"index": "s301091648", "label": 3592, "func": ""}
{"index": "s134792114", "label": 3592, "func": ""}
{"index": "s612068907", "label": 3568, "func": ""}
{"index": "s518596463", "label": 3568, "func": ""}
{"index": "s526838751", "label": 3568, "func": ""}
{"index": "s860807980", "label": 3568, "func": ""}
{"index": "s297187236", "label": 3568, "func": ""}
{"index": "s303098738", "label": 3568, "func": ""}
{"index": "s920348369", "label": 3568, "func": ""}
{"index": "s446044113", "label": 3568, "func": ""}
{"index": "s865309477", "label": 3568, "func": ""}
{"index": "s975655797", "label": 3568, "func": ""}
{"index": "s649166820", "label": 1925, "func": ""}
{"index": "s410714276", "label": 1925, "func": ""}
{"index": "s310527384", "label": 1925, "func": ""}
{"index": "s567081225", "label": 1925, "func": ""}
{"index": "s898792213", "label": 3247, "func": ""}
{"index": "s522792537", "label": 3247, "func": ""}
{"index": "s799866590", "label": 3247, "func": ""}
{"index": "s922345652", "label": 3247, "func": ""}
{"index": "s011660268", "label": 847, "func": ""}
{"index": "s525636256", "label": 847, "func": ""}
{"index": "s805016077", "label": 847, "func": ""}
{"index": "s815899682", "label": 847, "func": ""}
{"index": "s371950574", "label": 847, "func": ""}
{"index": "s349754609", "label": 847, "func": ""}
{"index": "s069171652", "label": 847, "func": ""}
{"index": "s683308006", "label": 3969, "func": ""}
{"index": "s202540565", "label": 3673, "func": ""}
{"index": "s677012408", "label": 3673, "func": ""}
{"index": "s758062704", "label": 3673, "func": ""}
{"index": "s484535988", "label": 3673, "func": ""}
{"index": "s656593106", "label": 3673, "func": ""}
{"index": "s165151603", "label": 3673, "func": ""}
{"index": "s710150798", "label": 3673, "func": ""}
{"index": "s459023590", "label": 3673, "func": ""}
{"index": "s873668445", "label": 3673, "func": ""}
{"index": "s296378323", "label": 3673, "func": ""}
{"index": "s626039941", "label": 205, "func": ""}
{"index": "s239712419", "label": 205, "func": ""}
{"index": "s533947821", "label": 205, "func": ""}
{"index": "s636901330", "label": 205, "func": ""}
{"index": "s829525754", "label": 205, "func": ""}
{"index": "s714491788", "label": 205, "func": ""}
{"index": "s715666457", "label": 205, "func": ""}
{"index": "s536693921", "label": 205, "func": ""}
{"index": "s296425218", "label": 205, "func": ""}
{"index": "s684553699", "label": 205, "func": ""}
{"index": "s880741594", "label": 3864, "func": ""}
{"index": "s332766384", "label": 3864, "func": ""}
{"index": "s224294059", "label": 3864, "func": ""}
{"index": "s610921309", "label": 3864, "func": ""}
{"index": "s159410722", "label": 3864, "func": ""}
{"index": "s855053395", "label": 3864, "func": ""}
{"index": "s699299712", "label": 3864, "func": ""}
{"index": "s688470201", "label": 3864, "func": ""}
{"index": "s643367125", "label": 3864, "func": ""}
{"index": "s217508284", "label": 3864, "func": ""}
{"index": "s642986884", "label": 1496, "func": ""}
{"index": "s940398118", "label": 1496, "func": ""}
{"index": "s487453226", "label": 2497, "func": ""}
{"index": "s529287665", "label": 2497, "func": ""}
{"index": "s872946312", "label": 2497, "func": ""}
{"index": "s229752044", "label": 2497, "func": ""}
{"index": "s113733711", "label": 2497, "func": ""}
{"index": "s731589362", "label": 2497, "func": ""}
{"index": "s603127969", "label": 2497, "func": ""}
{"index": "s549380220", "label": 2497, "func": ""}
{"index": "s921491861", "label": 2497, "func": ""}
{"index": "s221841523", "label": 2497, "func": ""}
{"index": "s249017442", "label": 296, "func": ""}
{"index": "s498370370", "label": 296, "func": ""}
{"index": "s767410547", "label": 296, "func": ""}
{"index": "s219975172", "label": 296, "func": ""}
{"index": "s765003364", "label": 296, "func": ""}
{"index": "s498929755", "label": 296, "func": ""}
{"index": "s486160424", "label": 296, "func": ""}
{"index": "s077898557", "label": 2732, "func": ""}
{"index": "s268710894", "label": 2732, "func": ""}
{"index": "s751441753", "label": 2732, "func": ""}
{"index": "s182343282", "label": 2732, "func": ""}
{"index": "s493974957", "label": 2732, "func": ""}
{"index": "s763661975", "label": 2732, "func": ""}
{"index": "s653263149", "label": 2732, "func": ""}
{"index": "s699077585", "label": 2732, "func": ""}
{"index": "s370004846", "label": 2732, "func": ""}
{"index": "s934347629", "label": 2732, "func": ""}
{"index": "s327471242", "label": 2125, "func": ""}
{"index": "s529699747", "label": 2125, "func": ""}
{"index": "s925601527", "label": 2125, "func": ""}
{"index": "s076916723", "label": 2125, "func": ""}
{"index": "s681300235", "label": 2125, "func": ""}
{"index": "s701708497", "label": 2125, "func": ""}
{"index": "s530611558", "label": 2922, "func": ""}
{"index": "s561235763", "label": 2922, "func": ""}
{"index": "s127143179", "label": 2922, "func": ""}
{"index": "s438643219", "label": 2922, "func": ""}
{"index": "s405028572", "label": 2922, "func": ""}
{"index": "s609379961", "label": 2922, "func": ""}
{"index": "s040598042", "label": 2922, "func": ""}
{"index": "s339593921", "label": 2922, "func": ""}
{"index": "s095018412", "label": 2922, "func": ""}
{"index": "s504408682", "label": 2922, "func": ""}
{"index": "s044020163", "label": 2030, "func": ""}
{"index": "s810698477", "label": 2030, "func": ""}
{"index": "s204468540", "label": 2030, "func": ""}
{"index": "s016452112", "label": 2030, "func": ""}
{"index": "s132071237", "label": 2030, "func": ""}
{"index": "s161051499", "label": 81, "func": ""}
{"index": "s027196272", "label": 81, "func": ""}
{"index": "s724321674", "label": 81, "func": ""}
{"index": "s895910202", "label": 81, "func": ""}
{"index": "s358776884", "label": 81, "func": ""}
{"index": "s523862018", "label": 81, "func": ""}
{"index": "s774033703", "label": 81, "func": ""}
{"index": "s398343212", "label": 81, "func": ""}
{"index": "s816104758", "label": 81, "func": ""}
{"index": "s498192188", "label": 81, "func": ""}
{"index": "s209996130", "label": 1070, "func": ""}
{"index": "s206054506", "label": 1315, "func": ""}
{"index": "s990826807", "label": 1315, "func": ""}
{"index": "s162791270", "label": 1315, "func": ""}
{"index": "s127902099", "label": 1315, "func": ""}
{"index": "s912962545", "label": 1315, "func": ""}
{"index": "s182704737", "label": 1315, "func": ""}
{"index": "s360165298", "label": 1315, "func": ""}
{"index": "s115393467", "label": 1315, "func": ""}
{"index": "s422040589", "label": 1315, "func": ""}
{"index": "s606235180", "label": 1315, "func": ""}
{"index": "s976497616", "label": 2130, "func": ""}
{"index": "s172927561", "label": 1846, "func": ""}
{"index": "s949932136", "label": 1846, "func": ""}
{"index": "s867239595", "label": 1846, "func": ""}
{"index": "s518377043", "label": 1846, "func": ""}
{"index": "s024814924", "label": 1846, "func": ""}
{"index": "s411539015", "label": 1846, "func": ""}
{"index": "s841627716", "label": 1846, "func": ""}
{"index": "s985593485", "label": 1846, "func": ""}
{"index": "s244624104", "label": 8, "func": ""}
{"index": "s534517723", "label": 8, "func": ""}
{"index": "s066253024", "label": 8, "func": ""}
{"index": "s758307829", "label": 8, "func": ""}
{"index": "s212205114", "label": 8, "func": ""}
{"index": "s631930006", "label": 8, "func": ""}
{"index": "s800858248", "label": 8, "func": ""}
{"index": "s355854975", "label": 8, "func": ""}
{"index": "s090961788", "label": 8, "func": ""}
{"index": "s721651370", "label": 8, "func": ""}
{"index": "s159737442", "label": 2053, "func": ""}
{"index": "s552836042", "label": 2419, "func": ""}
{"index": "s675152064", "label": 2419, "func": ""}
{"index": "s626705701", "label": 2419, "func": ""}
{"index": "s101507861", "label": 2419, "func": ""}
{"index": "s661715845", "label": 2419, "func": ""}
{"index": "s411182265", "label": 2419, "func": ""}
{"index": "s950735209", "label": 2419, "func": ""}
{"index": "s478837318", "label": 2419, "func": ""}
{"index": "s587697905", "label": 2419, "func": ""}
{"index": "s618740254", "label": 2419, "func": ""}
{"index": "s878476679", "label": 2805, "func": ""}
{"index": "s383503882", "label": 2805, "func": ""}
{"index": "s652074494", "label": 2805, "func": ""}
{"index": "s919245815", "label": 2805, "func": ""}
{"index": "s389129811", "label": 2805, "func": ""}
{"index": "s635427016", "label": 2805, "func": ""}
{"index": "s901128775", "label": 2805, "func": ""}
{"index": "s654714690", "label": 2805, "func": ""}
{"index": "s893599862", "label": 2805, "func": ""}
{"index": "s686631290", "label": 2805, "func": ""}
{"index": "s386420802", "label": 3922, "func": ""}
{"index": "s724577801", "label": 3922, "func": ""}
{"index": "s309019811", "label": 3922, "func": ""}
{"index": "s559992575", "label": 3922, "func": ""}
{"index": "s475014645", "label": 3922, "func": ""}
{"index": "s597347821", "label": 2703, "func": ""}
{"index": "s619110195", "label": 2703, "func": ""}
{"index": "s099728590", "label": 2703, "func": ""}
{"index": "s552095201", "label": 2703, "func": ""}
{"index": "s318806700", "label": 2703, "func": ""}
{"index": "s144327928", "label": 2703, "func": ""}
{"index": "s466485780", "label": 2703, "func": ""}
{"index": "s405318209", "label": 2703, "func": ""}
{"index": "s179594407", "label": 2703, "func": ""}
{"index": "s580418514", "label": 2703, "func": ""}
{"index": "s379294500", "label": 969, "func": ""}
{"index": "s834671462", "label": 969, "func": ""}
{"index": "s050186330", "label": 3711, "func": ""}
{"index": "s628603753", "label": 3711, "func": ""}
{"index": "s961268789", "label": 3711, "func": ""}
{"index": "s489956241", "label": 3711, "func": ""}
{"index": "s635951651", "label": 3711, "func": ""}
{"index": "s730999491", "label": 3711, "func": ""}
{"index": "s386012243", "label": 3711, "func": ""}
{"index": "s147792145", "label": 3711, "func": ""}
{"index": "s045646304", "label": 3711, "func": ""}
{"index": "s035917856", "label": 3711, "func": ""}
{"index": "s807195092", "label": 3312, "func": ""}
{"index": "s789582030", "label": 3312, "func": ""}
{"index": "s642492771", "label": 3312, "func": ""}
{"index": "s507848380", "label": 3312, "func": ""}
{"index": "s557796942", "label": 3312, "func": ""}
{"index": "s184243961", "label": 3312, "func": ""}
{"index": "s260892161", "label": 3312, "func": ""}
{"index": "s422288604", "label": 3312, "func": ""}
{"index": "s346622142", "label": 3312, "func": ""}
{"index": "s839209943", "label": 3312, "func": ""}
{"index": "s628225826", "label": 2216, "func": ""}
{"index": "s485017631", "label": 2216, "func": ""}
{"index": "s146082027", "label": 2216, "func": ""}
{"index": "s294415834", "label": 3716, "func": ""}
{"index": "s073402161", "label": 3716, "func": ""}
{"index": "s778362680", "label": 3716, "func": ""}
{"index": "s386547947", "label": 3716, "func": ""}
{"index": "s940832033", "label": 3716, "func": ""}
{"index": "s342933646", "label": 3716, "func": ""}
{"index": "s038220260", "label": 3716, "func": ""}
{"index": "s441332940", "label": 3716, "func": ""}
{"index": "s641259290", "label": 3716, "func": ""}
{"index": "s690526837", "label": 3716, "func": ""}
{"index": "s797489906", "label": 3657, "func": ""}
{"index": "s222424034", "label": 3657, "func": ""}
{"index": "s969652818", "label": 3657, "func": ""}
{"index": "s696399873", "label": 3657, "func": ""}
{"index": "s724523778", "label": 3657, "func": ""}
{"index": "s192941778", "label": 3657, "func": ""}
{"index": "s807056330", "label": 3657, "func": ""}
{"index": "s438394339", "label": 3657, "func": ""}
{"index": "s547913844", "label": 3657, "func": ""}
{"index": "s069059015", "label": 3657, "func": ""}
{"index": "s348337104", "label": 163, "func": ""}
{"index": "s928013710", "label": 163, "func": ""}
{"index": "s945987514", "label": 163, "func": ""}
{"index": "s251675584", "label": 163, "func": ""}
{"index": "s599571859", "label": 163, "func": ""}
{"index": "s337509413", "label": 163, "func": ""}
{"index": "s386848566", "label": 163, "func": ""}
{"index": "s250595470", "label": 163, "func": ""}
{"index": "s727189174", "label": 163, "func": ""}
{"index": "s199950705", "label": 163, "func": ""}
{"index": "s735662878", "label": 1981, "func": ""}
{"index": "s088441653", "label": 1981, "func": ""}
{"index": "s062035361", "label": 1981, "func": ""}
{"index": "s792324230", "label": 1981, "func": ""}
{"index": "s664759723", "label": 1981, "func": ""}
{"index": "s682094165", "label": 1981, "func": ""}
{"index": "s792750208", "label": 1981, "func": ""}
{"index": "s858233476", "label": 1981, "func": ""}
{"index": "s476440955", "label": 1981, "func": ""}
{"index": "s379891906", "label": 1981, "func": ""}
{"index": "s359715860", "label": 2337, "func": ""}
{"index": "s805812445", "label": 3125, "func": ""}
{"index": "s858359939", "label": 3125, "func": ""}
{"index": "s090522713", "label": 3125, "func": ""}
{"index": "s713751702", "label": 3125, "func": ""}
{"index": "s874825255", "label": 3125, "func": ""}
{"index": "s774041149", "label": 3125, "func": ""}
{"index": "s348752094", "label": 3125, "func": ""}
{"index": "s883441267", "label": 3125, "func": ""}
{"index": "s482897301", "label": 3125, "func": ""}
{"index": "s530208153", "label": 3125, "func": ""}
{"index": "s773621895", "label": 3400, "func": ""}
{"index": "s742414145", "label": 3400, "func": ""}
{"index": "s128944079", "label": 3400, "func": ""}
{"index": "s681629106", "label": 3400, "func": ""}
{"index": "s686313589", "label": 3400, "func": ""}
{"index": "s917507476", "label": 3400, "func": ""}
{"index": "s120418361", "label": 3400, "func": ""}
{"index": "s696375404", "label": 3400, "func": ""}
{"index": "s570063002", "label": 3400, "func": ""}
{"index": "s098207673", "label": 3400, "func": ""}
{"index": "s218324153", "label": 780, "func": ""}
{"index": "s413498677", "label": 780, "func": ""}
{"index": "s808975036", "label": 780, "func": ""}
{"index": "s960810553", "label": 780, "func": ""}
{"index": "s620168106", "label": 780, "func": ""}
{"index": "s915960770", "label": 780, "func": ""}
{"index": "s380503642", "label": 780, "func": ""}
{"index": "s037038935", "label": 780, "func": ""}
{"index": "s701917271", "label": 780, "func": ""}
{"index": "s019286956", "label": 780, "func": ""}
{"index": "s177128259", "label": 61, "func": ""}
{"index": "s405835959", "label": 61, "func": ""}
{"index": "s926301238", "label": 61, "func": ""}
{"index": "s966226369", "label": 61, "func": ""}
{"index": "s515689012", "label": 61, "func": ""}
{"index": "s673458061", "label": 61, "func": ""}
{"index": "s676291487", "label": 61, "func": ""}
{"index": "s184965524", "label": 61, "func": ""}
{"index": "s568345239", "label": 61, "func": ""}
{"index": "s860647219", "label": 61, "func": ""}
{"index": "s009497807", "label": 321, "func": ""}
{"index": "s636858469", "label": 321, "func": ""}
{"index": "s330001926", "label": 321, "func": ""}
{"index": "s722665404", "label": 321, "func": ""}
{"index": "s563759050", "label": 3671, "func": ""}
{"index": "s746083509", "label": 3671, "func": ""}
{"index": "s026004032", "label": 3671, "func": ""}
{"index": "s540740847", "label": 3671, "func": ""}
{"index": "s866770969", "label": 3671, "func": ""}
{"index": "s767751827", "label": 3671, "func": ""}
{"index": "s501378564", "label": 3671, "func": ""}
{"index": "s514695576", "label": 3671, "func": ""}
{"index": "s053302979", "label": 3671, "func": ""}
{"index": "s025814917", "label": 3671, "func": ""}
{"index": "s638540882", "label": 80, "func": ""}
{"index": "s340821511", "label": 80, "func": ""}
{"index": "s510069806", "label": 80, "func": ""}
{"index": "s208472838", "label": 80, "func": ""}
{"index": "s550170838", "label": 80, "func": ""}
{"index": "s853953365", "label": 80, "func": ""}
{"index": "s631260537", "label": 80, "func": ""}
{"index": "s023658806", "label": 80, "func": ""}
{"index": "s537275960", "label": 80, "func": ""}
{"index": "s584957768", "label": 80, "func": ""}
{"index": "s918545691", "label": 3855, "func": ""}
{"index": "s983405154", "label": 3855, "func": ""}
{"index": "s337375703", "label": 3855, "func": ""}
{"index": "s368411842", "label": 3855, "func": ""}
{"index": "s591894622", "label": 3855, "func": ""}
{"index": "s379449363", "label": 3855, "func": ""}
{"index": "s385048501", "label": 3855, "func": ""}
{"index": "s689163307", "label": 3855, "func": ""}
{"index": "s774075111", "label": 3855, "func": ""}
{"index": "s884416312", "label": 3855, "func": ""}
{"index": "s179200616", "label": 3803, "func": ""}
{"index": "s047757900", "label": 3803, "func": ""}
{"index": "s339303966", "label": 3803, "func": ""}
{"index": "s393705103", "label": 3803, "func": ""}
{"index": "s500280979", "label": 3803, "func": ""}
{"index": "s974385887", "label": 3803, "func": ""}
{"index": "s100376948", "label": 3803, "func": ""}
{"index": "s641846238", "label": 3803, "func": ""}
{"index": "s298223912", "label": 3803, "func": ""}
{"index": "s070968011", "label": 3803, "func": ""}
{"index": "s065879312", "label": 2832, "func": ""}
{"index": "s408973718", "label": 2832, "func": ""}
{"index": "s668733483", "label": 2832, "func": ""}
{"index": "s953920749", "label": 2832, "func": ""}
{"index": "s452689826", "label": 2832, "func": ""}
{"index": "s130927048", "label": 2832, "func": ""}
{"index": "s025605900", "label": 2832, "func": ""}
{"index": "s195371468", "label": 2832, "func": ""}
{"index": "s011268935", "label": 2832, "func": ""}
{"index": "s314451688", "label": 2832, "func": ""}
{"index": "s206837472", "label": 211, "func": ""}
{"index": "s539131775", "label": 211, "func": ""}
{"index": "s819584866", "label": 211, "func": ""}
{"index": "s373135798", "label": 211, "func": ""}
{"index": "s441965105", "label": 211, "func": ""}
{"index": "s291748532", "label": 211, "func": ""}
{"index": "s958225443", "label": 211, "func": ""}
{"index": "s432116655", "label": 211, "func": ""}
{"index": "s427907829", "label": 211, "func": ""}
{"index": "s075132495", "label": 211, "func": ""}
{"index": "s196440355", "label": 992, "func": ""}
{"index": "s153470327", "label": 992, "func": ""}
{"index": "s574125225", "label": 992, "func": ""}
{"index": "s646120101", "label": 992, "func": ""}
{"index": "s118740590", "label": 992, "func": ""}
{"index": "s643323735", "label": 992, "func": ""}
{"index": "s364933430", "label": 992, "func": ""}
{"index": "s102757297", "label": 992, "func": ""}
{"index": "s640145126", "label": 3744, "func": ""}
{"index": "s543257635", "label": 3744, "func": ""}
{"index": "s751810573", "label": 2599, "func": ""}
{"index": "s942905486", "label": 2599, "func": ""}
{"index": "s904628975", "label": 2599, "func": ""}
{"index": "s944394126", "label": 2599, "func": ""}
{"index": "s813914475", "label": 2599, "func": ""}
{"index": "s403126891", "label": 2599, "func": ""}
{"index": "s800070748", "label": 2599, "func": ""}
{"index": "s071217326", "label": 2599, "func": ""}
{"index": "s024603683", "label": 2599, "func": ""}
{"index": "s935913167", "label": 2599, "func": ""}
{"index": "s306641064", "label": 1254, "func": ""}
{"index": "s114025895", "label": 1254, "func": ""}
{"index": "s420523189", "label": 1254, "func": ""}
{"index": "s907526979", "label": 2498, "func": ""}
{"index": "s447110712", "label": 2498, "func": ""}
{"index": "s102723271", "label": 2498, "func": ""}
{"index": "s937882580", "label": 2498, "func": ""}
{"index": "s035269528", "label": 2498, "func": ""}
{"index": "s564065618", "label": 2498, "func": ""}
{"index": "s547100610", "label": 2498, "func": ""}
{"index": "s924103316", "label": 2498, "func": ""}
{"index": "s833253761", "label": 2498, "func": ""}
{"index": "s774165477", "label": 2498, "func": ""}
{"index": "s509281223", "label": 2741, "func": ""}
{"index": "s240258455", "label": 2741, "func": ""}
{"index": "s724312333", "label": 2741, "func": ""}
{"index": "s731732923", "label": 2741, "func": ""}
{"index": "s359366925", "label": 2741, "func": ""}
{"index": "s040750370", "label": 2741, "func": ""}
{"index": "s461510923", "label": 2741, "func": ""}
{"index": "s234207398", "label": 2741, "func": ""}
{"index": "s470093906", "label": 2741, "func": ""}
{"index": "s736453675", "label": 2741, "func": ""}
{"index": "s232008989", "label": 1650, "func": ""}
{"index": "s094935335", "label": 1650, "func": ""}
{"index": "s970137834", "label": 1650, "func": ""}
{"index": "s275550575", "label": 889, "func": ""}
{"index": "s106325979", "label": 889, "func": ""}
{"index": "s865279627", "label": 889, "func": ""}
{"index": "s094391868", "label": 889, "func": ""}
{"index": "s270537083", "label": 889, "func": ""}
{"index": "s904859693", "label": 889, "func": ""}
{"index": "s290008139", "label": 889, "func": ""}
{"index": "s925288068", "label": 889, "func": ""}
{"index": "s029350014", "label": 889, "func": ""}
{"index": "s102972442", "label": 889, "func": ""}
{"index": "s753354511", "label": 1742, "func": ""}
{"index": "s544952988", "label": 909, "func": ""}
{"index": "s018652122", "label": 909, "func": ""}
{"index": "s130086984", "label": 909, "func": ""}
{"index": "s188919667", "label": 909, "func": ""}
{"index": "s142354620", "label": 909, "func": ""}
{"index": "s523107363", "label": 909, "func": ""}
{"index": "s474679385", "label": 909, "func": ""}
{"index": "s605433207", "label": 909, "func": ""}
{"index": "s724609023", "label": 909, "func": ""}
{"index": "s964356831", "label": 909, "func": ""}
{"index": "s930575121", "label": 2373, "func": ""}
{"index": "s491399091", "label": 2373, "func": ""}
{"index": "s067738030", "label": 2373, "func": ""}
{"index": "s481838469", "label": 2373, "func": ""}
{"index": "s608926996", "label": 2373, "func": ""}
{"index": "s833279996", "label": 2373, "func": ""}
{"index": "s011526009", "label": 2373, "func": ""}
{"index": "s574361019", "label": 2373, "func": ""}
{"index": "s958496865", "label": 2373, "func": ""}
{"index": "s294255275", "label": 2373, "func": ""}
{"index": "s535984067", "label": 3770, "func": ""}
{"index": "s964788223", "label": 3770, "func": ""}
{"index": "s359709294", "label": 3770, "func": ""}
{"index": "s708349899", "label": 3770, "func": ""}
{"index": "s614756871", "label": 2456, "func": ""}
{"index": "s078731805", "label": 2456, "func": ""}
{"index": "s630981211", "label": 2456, "func": ""}
{"index": "s505574078", "label": 2456, "func": ""}
{"index": "s225048728", "label": 2456, "func": ""}
{"index": "s134858450", "label": 2456, "func": ""}
{"index": "s378928650", "label": 2456, "func": ""}
{"index": "s125837785", "label": 2456, "func": ""}
{"index": "s889317932", "label": 2456, "func": ""}
{"index": "s006186188", "label": 2456, "func": ""}
{"index": "s636446151", "label": 1408, "func": ""}
{"index": "s424917398", "label": 2446, "func": ""}
{"index": "s823636072", "label": 2446, "func": ""}
{"index": "s256183058", "label": 2446, "func": ""}
{"index": "s247161598", "label": 2446, "func": ""}
{"index": "s922330896", "label": 2446, "func": ""}
{"index": "s058660415", "label": 2446, "func": ""}
{"index": "s034662744", "label": 2446, "func": ""}
{"index": "s219580401", "label": 2446, "func": ""}
{"index": "s621305491", "label": 2446, "func": ""}
{"index": "s496376343", "label": 2446, "func": ""}
{"index": "s671478858", "label": 3228, "func": ""}
{"index": "s625973491", "label": 3228, "func": ""}
{"index": "s712862057", "label": 3228, "func": ""}
{"index": "s803149233", "label": 3228, "func": ""}
{"index": "s873598310", "label": 3228, "func": ""}
{"index": "s564436891", "label": 3228, "func": ""}
{"index": "s677046829", "label": 3228, "func": ""}
{"index": "s086879895", "label": 3228, "func": ""}
{"index": "s453906400", "label": 3228, "func": ""}
{"index": "s590556091", "label": 3228, "func": ""}
{"index": "s698291739", "label": 2798, "func": ""}
{"index": "s240331933", "label": 2798, "func": ""}
{"index": "s861351350", "label": 2798, "func": ""}
{"index": "s180813899", "label": 2798, "func": ""}
{"index": "s085977415", "label": 2798, "func": ""}
{"index": "s099531478", "label": 2798, "func": ""}
{"index": "s512216844", "label": 2798, "func": ""}
{"index": "s201736428", "label": 2798, "func": ""}
{"index": "s159321028", "label": 2798, "func": ""}
{"index": "s772123303", "label": 2798, "func": ""}
{"index": "s409859231", "label": 2964, "func": ""}
{"index": "s010995429", "label": 2964, "func": ""}
{"index": "s526759260", "label": 2964, "func": ""}
{"index": "s598537704", "label": 2964, "func": ""}
{"index": "s443580419", "label": 2964, "func": ""}
{"index": "s375531421", "label": 2964, "func": ""}
{"index": "s327454647", "label": 2964, "func": ""}
{"index": "s342021592", "label": 2964, "func": ""}
{"index": "s623122918", "label": 2964, "func": ""}
{"index": "s597917467", "label": 2964, "func": ""}
{"index": "s754725610", "label": 3185, "func": ""}
{"index": "s956281545", "label": 3185, "func": ""}
{"index": "s820490630", "label": 3185, "func": ""}
{"index": "s932193713", "label": 3185, "func": ""}
{"index": "s691686043", "label": 3185, "func": ""}
{"index": "s661415985", "label": 3185, "func": ""}
{"index": "s139141053", "label": 3185, "func": ""}
{"index": "s928003391", "label": 3185, "func": ""}
{"index": "s139099951", "label": 3185, "func": ""}
{"index": "s332911305", "label": 3185, "func": ""}
{"index": "s945993644", "label": 4043, "func": ""}
{"index": "s386112435", "label": 4043, "func": ""}
{"index": "s756609321", "label": 4043, "func": ""}
{"index": "s537685325", "label": 4043, "func": ""}
{"index": "s268193796", "label": 4043, "func": ""}
{"index": "s794430661", "label": 4043, "func": ""}
{"index": "s940416522", "label": 4043, "func": ""}
{"index": "s398340909", "label": 4043, "func": ""}
{"index": "s681388658", "label": 4043, "func": ""}
{"index": "s204629451", "label": 4043, "func": ""}
{"index": "s190903077", "label": 2432, "func": ""}
{"index": "s028311456", "label": 2432, "func": ""}
{"index": "s880014237", "label": 2432, "func": ""}
{"index": "s280856904", "label": 2432, "func": ""}
{"index": "s934462546", "label": 2432, "func": ""}
{"index": "s391307213", "label": 2432, "func": ""}
{"index": "s743462774", "label": 2432, "func": ""}
{"index": "s599473209", "label": 2432, "func": ""}
{"index": "s694965864", "label": 2432, "func": ""}
{"index": "s485964596", "label": 2432, "func": ""}
{"index": "s949970263", "label": 2650, "func": ""}
{"index": "s022933612", "label": 822, "func": ""}
{"index": "s153999229", "label": 822, "func": ""}
{"index": "s210940593", "label": 822, "func": ""}
{"index": "s283650036", "label": 822, "func": ""}
{"index": "s524110998", "label": 822, "func": ""}
{"index": "s172044462", "label": 822, "func": ""}
{"index": "s880436547", "label": 1105, "func": ""}
{"index": "s442410517", "label": 2223, "func": ""}
{"index": "s126449104", "label": 2261, "func": ""}
{"index": "s941737021", "label": 2261, "func": ""}
{"index": "s446106198", "label": 2261, "func": ""}
{"index": "s275100665", "label": 2261, "func": ""}
{"index": "s371896890", "label": 2261, "func": ""}
{"index": "s275454614", "label": 2261, "func": ""}
{"index": "s116955112", "label": 2261, "func": ""}
{"index": "s294993719", "label": 2261, "func": ""}
{"index": "s607578302", "label": 2261, "func": ""}
{"index": "s736432205", "label": 2261, "func": ""}
{"index": "s684698560", "label": 626, "func": ""}
{"index": "s077777324", "label": 188, "func": ""}
{"index": "s404532587", "label": 188, "func": ""}
{"index": "s335326966", "label": 188, "func": ""}
{"index": "s597278815", "label": 188, "func": ""}
{"index": "s441968541", "label": 188, "func": ""}
{"index": "s857029672", "label": 188, "func": ""}
{"index": "s263201481", "label": 188, "func": ""}
{"index": "s202165015", "label": 188, "func": ""}
{"index": "s034845847", "label": 188, "func": ""}
{"index": "s631425566", "label": 188, "func": ""}
{"index": "s470076883", "label": 2597, "func": ""}
{"index": "s670017859", "label": 2597, "func": ""}
{"index": "s004362519", "label": 2597, "func": ""}
{"index": "s675817698", "label": 2597, "func": ""}
{"index": "s349692609", "label": 2597, "func": ""}
{"index": "s870326586", "label": 2597, "func": ""}
{"index": "s988366805", "label": 2597, "func": ""}
{"index": "s839987742", "label": 2597, "func": ""}
{"index": "s784454132", "label": 2597, "func": ""}
{"index": "s868178279", "label": 2597, "func": ""}
{"index": "s712032645", "label": 2064, "func": ""}
{"index": "s194555699", "label": 2064, "func": ""}
{"index": "s905084910", "label": 2452, "func": ""}
{"index": "s463810863", "label": 2452, "func": ""}
{"index": "s691547728", "label": 2452, "func": ""}
{"index": "s608054397", "label": 2452, "func": ""}
{"index": "s770971287", "label": 2452, "func": ""}
{"index": "s840924369", "label": 2452, "func": ""}
{"index": "s652206150", "label": 2452, "func": ""}
{"index": "s838553038", "label": 2452, "func": ""}
{"index": "s812147604", "label": 2452, "func": ""}
{"index": "s374015851", "label": 2452, "func": ""}
{"index": "s967620210", "label": 1773, "func": ""}
{"index": "s624028325", "label": 1773, "func": ""}
{"index": "s686810952", "label": 1350, "func": ""}
{"index": "s631280425", "label": 1350, "func": ""}
{"index": "s130228587", "label": 1350, "func": ""}
{"index": "s386832304", "label": 3891, "func": ""}
{"index": "s791187067", "label": 3891, "func": ""}
{"index": "s392486851", "label": 3891, "func": ""}
{"index": "s830101512", "label": 2524, "func": ""}
{"index": "s540134572", "label": 2524, "func": ""}
{"index": "s030279835", "label": 2524, "func": ""}
{"index": "s887948806", "label": 2524, "func": ""}
{"index": "s055734330", "label": 2524, "func": ""}
{"index": "s414163403", "label": 2524, "func": ""}
{"index": "s590574686", "label": 2524, "func": ""}
{"index": "s569817041", "label": 2524, "func": ""}
{"index": "s965032288", "label": 2524, "func": ""}
{"index": "s913825990", "label": 2524, "func": ""}
{"index": "s763257841", "label": 1573, "func": ""}
{"index": "s789909364", "label": 1573, "func": ""}
{"index": "s128386560", "label": 1573, "func": ""}
{"index": "s685626970", "label": 1573, "func": ""}
{"index": "s436616203", "label": 1523, "func": ""}
{"index": "s576324858", "label": 1523, "func": ""}
{"index": "s948151520", "label": 1523, "func": ""}
{"index": "s808265268", "label": 1523, "func": ""}
{"index": "s240838453", "label": 1523, "func": ""}
{"index": "s152760086", "label": 1523, "func": ""}
{"index": "s383800085", "label": 1523, "func": ""}
{"index": "s389295596", "label": 1523, "func": ""}
{"index": "s572919938", "label": 1523, "func": ""}
{"index": "s359581677", "label": 1523, "func": ""}
{"index": "s963947268", "label": 2381, "func": ""}
{"index": "s125907973", "label": 2381, "func": ""}
{"index": "s330131647", "label": 2381, "func": ""}
{"index": "s728035457", "label": 2381, "func": ""}
{"index": "s925817174", "label": 2381, "func": ""}
{"index": "s041394860", "label": 2381, "func": ""}
{"index": "s749445719", "label": 2381, "func": ""}
{"index": "s869808015", "label": 2381, "func": ""}
{"index": "s987375214", "label": 2381, "func": ""}
{"index": "s208456617", "label": 2381, "func": ""}
{"index": "s123179142", "label": 3448, "func": ""}
{"index": "s937787753", "label": 3448, "func": ""}
{"index": "s571325194", "label": 3448, "func": ""}
{"index": "s364488346", "label": 3448, "func": ""}
{"index": "s582387203", "label": 3448, "func": ""}
{"index": "s015050873", "label": 3448, "func": ""}
{"index": "s226001913", "label": 3448, "func": ""}
{"index": "s970014615", "label": 3448, "func": ""}
{"index": "s170035682", "label": 3448, "func": ""}
{"index": "s971079759", "label": 3448, "func": ""}
{"index": "s811610016", "label": 1334, "func": ""}
{"index": "s163532121", "label": 1334, "func": ""}
{"index": "s874949321", "label": 1334, "func": ""}
{"index": "s103936105", "label": 1334, "func": ""}
{"index": "s618928028", "label": 1334, "func": ""}
{"index": "s871381876", "label": 1334, "func": ""}
{"index": "s517341549", "label": 1334, "func": ""}
{"index": "s817405856", "label": 1334, "func": ""}
{"index": "s968407686", "label": 1334, "func": ""}
{"index": "s261577728", "label": 1334, "func": ""}
{"index": "s368960035", "label": 301, "func": ""}
{"index": "s173556716", "label": 301, "func": ""}
{"index": "s526182712", "label": 301, "func": ""}
{"index": "s103068428", "label": 301, "func": ""}
{"index": "s866352090", "label": 301, "func": ""}
{"index": "s896534871", "label": 1768, "func": ""}
{"index": "s762756057", "label": 1768, "func": ""}
{"index": "s822852697", "label": 1768, "func": ""}
{"index": "s258013783", "label": 1768, "func": ""}
{"index": "s451235321", "label": 1768, "func": ""}
{"index": "s646919629", "label": 1768, "func": ""}
{"index": "s456588084", "label": 1768, "func": ""}
{"index": "s032163193", "label": 1157, "func": ""}
{"index": "s900096749", "label": 1792, "func": ""}
{"index": "s632961466", "label": 2635, "func": ""}
{"index": "s073955987", "label": 2635, "func": ""}
{"index": "s258835723", "label": 2635, "func": ""}
{"index": "s523663931", "label": 2635, "func": ""}
{"index": "s247524811", "label": 2635, "func": ""}
{"index": "s802341874", "label": 2521, "func": ""}
{"index": "s422644164", "label": 2521, "func": ""}
{"index": "s603327625", "label": 2521, "func": ""}
{"index": "s482187895", "label": 2026, "func": ""}
{"index": "s298665271", "label": 3268, "func": ""}
{"index": "s558221529", "label": 3268, "func": ""}
{"index": "s056332926", "label": 3268, "func": ""}
{"index": "s358935582", "label": 3268, "func": ""}
{"index": "s263052033", "label": 3268, "func": ""}
{"index": "s541591571", "label": 3268, "func": ""}
{"index": "s710134605", "label": 3268, "func": ""}
{"index": "s478970327", "label": 3268, "func": ""}
{"index": "s675650673", "label": 3268, "func": ""}
{"index": "s468486317", "label": 3268, "func": ""}
{"index": "s894698596", "label": 378, "func": ""}
{"index": "s419584516", "label": 378, "func": ""}
{"index": "s364300779", "label": 378, "func": ""}
{"index": "s307731101", "label": 378, "func": ""}
{"index": "s805587836", "label": 378, "func": ""}
{"index": "s767235304", "label": 378, "func": ""}
{"index": "s277372968", "label": 378, "func": ""}
{"index": "s454295484", "label": 378, "func": ""}
{"index": "s741147050", "label": 378, "func": ""}
{"index": "s821657528", "label": 378, "func": ""}
{"index": "s017538939", "label": 2972, "func": ""}
{"index": "s876098891", "label": 2972, "func": ""}
{"index": "s108644392", "label": 2972, "func": ""}
{"index": "s829326601", "label": 2972, "func": ""}
{"index": "s688697249", "label": 2972, "func": ""}
{"index": "s625506923", "label": 2972, "func": ""}
{"index": "s577708315", "label": 2972, "func": ""}
{"index": "s941131510", "label": 2972, "func": ""}
{"index": "s823561822", "label": 2972, "func": ""}
{"index": "s948147361", "label": 2972, "func": ""}
{"index": "s062646431", "label": 38, "func": ""}
{"index": "s925271958", "label": 38, "func": ""}
{"index": "s103581088", "label": 38, "func": ""}
{"index": "s096856154", "label": 38, "func": ""}
{"index": "s331136474", "label": 38, "func": ""}
{"index": "s147223157", "label": 38, "func": ""}
{"index": "s062497618", "label": 38, "func": ""}
{"index": "s438376236", "label": 38, "func": ""}
{"index": "s339257452", "label": 38, "func": ""}
{"index": "s175137198", "label": 38, "func": ""}
{"index": "s115674750", "label": 1601, "func": ""}
{"index": "s165624626", "label": 1601, "func": ""}
{"index": "s205306084", "label": 1601, "func": ""}
{"index": "s367338310", "label": 1601, "func": ""}
{"index": "s990318966", "label": 1601, "func": ""}
{"index": "s142624700", "label": 1601, "func": ""}
{"index": "s025401139", "label": 1601, "func": ""}
{"index": "s036363091", "label": 1601, "func": ""}
{"index": "s690174245", "label": 1601, "func": ""}
{"index": "s837996624", "label": 1601, "func": ""}
{"index": "s593261743", "label": 2262, "func": ""}
{"index": "s529439124", "label": 2262, "func": ""}
{"index": "s095668580", "label": 2262, "func": ""}
{"index": "s900109809", "label": 2262, "func": ""}
{"index": "s634176545", "label": 2262, "func": ""}
{"index": "s604241279", "label": 2262, "func": ""}
{"index": "s419316087", "label": 2262, "func": ""}
{"index": "s942281379", "label": 2262, "func": ""}
{"index": "s922568191", "label": 2262, "func": ""}
{"index": "s276920027", "label": 2262, "func": ""}
{"index": "s672218262", "label": 50, "func": ""}
{"index": "s287367043", "label": 50, "func": ""}
{"index": "s635560554", "label": 50, "func": ""}
{"index": "s998488368", "label": 50, "func": ""}
{"index": "s376949051", "label": 50, "func": ""}
{"index": "s741396124", "label": 50, "func": ""}
{"index": "s782700894", "label": 50, "func": ""}
{"index": "s225094669", "label": 50, "func": ""}
{"index": "s771934268", "label": 50, "func": ""}
{"index": "s321291612", "label": 50, "func": ""}
{"index": "s401380103", "label": 3037, "func": ""}
{"index": "s746861391", "label": 3037, "func": ""}
{"index": "s589157259", "label": 3037, "func": ""}
{"index": "s231815581", "label": 3037, "func": ""}
{"index": "s487620660", "label": 3037, "func": ""}
{"index": "s773726054", "label": 3037, "func": ""}
{"index": "s981889029", "label": 3037, "func": ""}
{"index": "s242935663", "label": 3037, "func": ""}
{"index": "s849827084", "label": 3037, "func": ""}
{"index": "s379108901", "label": 3037, "func": ""}
{"index": "s378326278", "label": 1479, "func": ""}
{"index": "s949412749", "label": 1479, "func": ""}
{"index": "s487896424", "label": 1479, "func": ""}
{"index": "s428118965", "label": 1479, "func": ""}
{"index": "s111029829", "label": 1479, "func": ""}
{"index": "s415411626", "label": 1479, "func": ""}
{"index": "s657989696", "label": 4038, "func": ""}
{"index": "s211039829", "label": 4038, "func": ""}
{"index": "s975346954", "label": 4038, "func": ""}
{"index": "s428663286", "label": 714, "func": ""}
{"index": "s262998986", "label": 714, "func": ""}
{"index": "s112537305", "label": 714, "func": ""}
{"index": "s302206094", "label": 714, "func": ""}
{"index": "s780001048", "label": 714, "func": ""}
{"index": "s341929713", "label": 714, "func": ""}
{"index": "s457300213", "label": 714, "func": ""}
{"index": "s771544946", "label": 714, "func": ""}
{"index": "s526588391", "label": 714, "func": ""}
{"index": "s025537968", "label": 714, "func": ""}
{"index": "s237923066", "label": 2580, "func": ""}
{"index": "s119627539", "label": 2580, "func": ""}
{"index": "s220712655", "label": 2580, "func": ""}
{"index": "s930829993", "label": 2580, "func": ""}
{"index": "s899242584", "label": 2580, "func": ""}
{"index": "s010360071", "label": 2580, "func": ""}
{"index": "s360568370", "label": 2580, "func": ""}
{"index": "s863877906", "label": 2580, "func": ""}
{"index": "s605361164", "label": 2580, "func": ""}
{"index": "s646560737", "label": 2580, "func": ""}
{"index": "s212191691", "label": 315, "func": ""}
{"index": "s932903410", "label": 315, "func": ""}
{"index": "s200183037", "label": 315, "func": ""}
{"index": "s339665865", "label": 1969, "func": ""}
{"index": "s309409421", "label": 3944, "func": ""}
{"index": "s488968208", "label": 3944, "func": ""}
{"index": "s535583173", "label": 3944, "func": ""}
{"index": "s299833588", "label": 3944, "func": ""}
{"index": "s380767494", "label": 3944, "func": ""}
{"index": "s565136482", "label": 3944, "func": ""}
{"index": "s378329480", "label": 3944, "func": ""}
{"index": "s409255866", "label": 3944, "func": ""}
{"index": "s660558078", "label": 3944, "func": ""}
{"index": "s087574475", "label": 3944, "func": ""}
{"index": "s258407519", "label": 3569, "func": ""}
{"index": "s451133703", "label": 3569, "func": ""}
{"index": "s665748257", "label": 3569, "func": ""}
{"index": "s518735934", "label": 3569, "func": ""}
{"index": "s918168007", "label": 3569, "func": ""}
{"index": "s111028632", "label": 3569, "func": ""}
{"index": "s919460911", "label": 3569, "func": ""}
{"index": "s672315004", "label": 3569, "func": ""}
{"index": "s267998261", "label": 3569, "func": ""}
{"index": "s876594944", "label": 3569, "func": ""}
{"index": "s768018467", "label": 477, "func": ""}
{"index": "s208419837", "label": 477, "func": ""}
{"index": "s900095618", "label": 477, "func": ""}
{"index": "s582549641", "label": 477, "func": ""}
{"index": "s209619188", "label": 477, "func": ""}
{"index": "s994682700", "label": 477, "func": ""}
{"index": "s572155103", "label": 477, "func": ""}
{"index": "s026934774", "label": 477, "func": ""}
{"index": "s874905171", "label": 477, "func": ""}
{"index": "s409555388", "label": 477, "func": ""}
{"index": "s302784382", "label": 2302, "func": ""}
{"index": "s516219304", "label": 3775, "func": ""}
{"index": "s564795480", "label": 3775, "func": ""}
{"index": "s847316336", "label": 3775, "func": ""}
{"index": "s561451209", "label": 3775, "func": ""}
{"index": "s403921765", "label": 3775, "func": ""}
{"index": "s136878408", "label": 3775, "func": ""}
{"index": "s268348632", "label": 3775, "func": ""}
{"index": "s305133918", "label": 3775, "func": ""}
{"index": "s435071814", "label": 3775, "func": ""}
{"index": "s739093366", "label": 3775, "func": ""}
{"index": "s615486963", "label": 1681, "func": ""}
{"index": "s199996779", "label": 1681, "func": ""}
{"index": "s650484700", "label": 3055, "func": ""}
{"index": "s709108707", "label": 3055, "func": ""}
{"index": "s189984217", "label": 3055, "func": ""}
{"index": "s235429960", "label": 3055, "func": ""}
{"index": "s559186372", "label": 3055, "func": ""}
{"index": "s307916187", "label": 3055, "func": ""}
{"index": "s559393296", "label": 3055, "func": ""}
{"index": "s849221011", "label": 3055, "func": ""}
{"index": "s607846698", "label": 3055, "func": ""}
{"index": "s214258355", "label": 3055, "func": ""}
{"index": "s867444116", "label": 1536, "func": ""}
{"index": "s809476780", "label": 1536, "func": ""}
{"index": "s785370032", "label": 2271, "func": ""}
{"index": "s854540616", "label": 2271, "func": ""}
{"index": "s683895272", "label": 2271, "func": ""}
{"index": "s021211481", "label": 2271, "func": ""}
{"index": "s964466169", "label": 2271, "func": ""}
{"index": "s277388838", "label": 2271, "func": ""}
{"index": "s081271614", "label": 2271, "func": ""}
{"index": "s073507173", "label": 2271, "func": ""}
{"index": "s263006506", "label": 2271, "func": ""}
{"index": "s937101624", "label": 2271, "func": ""}
{"index": "s626236776", "label": 258, "func": ""}
{"index": "s728323526", "label": 258, "func": ""}
{"index": "s657604252", "label": 258, "func": ""}
{"index": "s834670080", "label": 258, "func": ""}
{"index": "s866449819", "label": 258, "func": ""}
{"index": "s070956386", "label": 258, "func": ""}
{"index": "s665082953", "label": 258, "func": ""}
{"index": "s603777471", "label": 258, "func": ""}
{"index": "s149922201", "label": 258, "func": ""}
{"index": "s867528774", "label": 258, "func": ""}
{"index": "s823504228", "label": 376, "func": ""}
{"index": "s464871907", "label": 376, "func": ""}
{"index": "s450161891", "label": 376, "func": ""}
{"index": "s026207812", "label": 376, "func": ""}
{"index": "s049634442", "label": 376, "func": ""}
{"index": "s305663242", "label": 376, "func": ""}
{"index": "s878655505", "label": 376, "func": ""}
{"index": "s255335854", "label": 376, "func": ""}
{"index": "s450418042", "label": 376, "func": ""}
{"index": "s975699291", "label": 376, "func": ""}
{"index": "s871856267", "label": 3403, "func": ""}
{"index": "s729359592", "label": 3403, "func": ""}
{"index": "s502219657", "label": 3403, "func": ""}
{"index": "s242364429", "label": 3403, "func": ""}
{"index": "s793655321", "label": 3403, "func": ""}
{"index": "s278649564", "label": 3403, "func": ""}
{"index": "s889277866", "label": 3403, "func": ""}
{"index": "s613530593", "label": 3403, "func": ""}
{"index": "s030125243", "label": 3403, "func": ""}
{"index": "s765143096", "label": 3403, "func": ""}
{"index": "s055713796", "label": 1304, "func": ""}
{"index": "s482773496", "label": 1304, "func": ""}
{"index": "s455723468", "label": 1304, "func": ""}
{"index": "s521202878", "label": 1304, "func": ""}
{"index": "s994340146", "label": 1304, "func": ""}
{"index": "s840904857", "label": 1304, "func": ""}
{"index": "s517850450", "label": 1304, "func": ""}
{"index": "s899685254", "label": 1304, "func": ""}
{"index": "s881566424", "label": 1304, "func": ""}
{"index": "s106807711", "label": 1304, "func": ""}
{"index": "s055754403", "label": 556, "func": ""}
{"index": "s838106162", "label": 556, "func": ""}
{"index": "s968810811", "label": 556, "func": ""}
{"index": "s016877670", "label": 3665, "func": ""}
{"index": "s244684555", "label": 3665, "func": ""}
{"index": "s887539026", "label": 3665, "func": ""}
{"index": "s714761658", "label": 3665, "func": ""}
{"index": "s997400842", "label": 3665, "func": ""}
{"index": "s500603608", "label": 3665, "func": ""}
{"index": "s624104678", "label": 3665, "func": ""}
{"index": "s164102128", "label": 3665, "func": ""}
{"index": "s470769518", "label": 3665, "func": ""}
{"index": "s829853323", "label": 3665, "func": ""}
{"index": "s969356066", "label": 3965, "func": ""}
{"index": "s506059041", "label": 3965, "func": ""}
{"index": "s111366391", "label": 3965, "func": ""}
{"index": "s729238649", "label": 3965, "func": ""}
{"index": "s436419670", "label": 3965, "func": ""}
{"index": "s400771509", "label": 3965, "func": ""}
{"index": "s610660383", "label": 3965, "func": ""}
{"index": "s494791485", "label": 3965, "func": ""}
{"index": "s410271185", "label": 3965, "func": ""}
{"index": "s626312502", "label": 3965, "func": ""}
{"index": "s115921316", "label": 1193, "func": ""}
{"index": "s451969477", "label": 1193, "func": ""}
{"index": "s880039382", "label": 1193, "func": ""}
{"index": "s766210151", "label": 1193, "func": ""}
{"index": "s282335524", "label": 1193, "func": ""}
{"index": "s629769434", "label": 2794, "func": ""}
{"index": "s142281297", "label": 2794, "func": ""}
{"index": "s547451419", "label": 2794, "func": ""}
{"index": "s484565134", "label": 2794, "func": ""}
{"index": "s559125224", "label": 2794, "func": ""}
{"index": "s341846391", "label": 2794, "func": ""}
{"index": "s291904224", "label": 2794, "func": ""}
{"index": "s117036962", "label": 2794, "func": ""}
{"index": "s839603220", "label": 2794, "func": ""}
{"index": "s873360545", "label": 2794, "func": ""}
{"index": "s318224011", "label": 708, "func": ""}
{"index": "s290989748", "label": 708, "func": ""}
{"index": "s675443142", "label": 708, "func": ""}
{"index": "s390632003", "label": 708, "func": ""}
{"index": "s091504014", "label": 708, "func": ""}
{"index": "s136137274", "label": 708, "func": ""}
{"index": "s002455364", "label": 708, "func": ""}
{"index": "s991695270", "label": 708, "func": ""}
{"index": "s182733537", "label": 708, "func": ""}
{"index": "s296983458", "label": 708, "func": ""}
{"index": "s895489459", "label": 3681, "func": ""}
{"index": "s428055908", "label": 3681, "func": ""}
{"index": "s174974341", "label": 3681, "func": ""}
{"index": "s594289632", "label": 3681, "func": ""}
{"index": "s524769641", "label": 3681, "func": ""}
{"index": "s300558225", "label": 3681, "func": ""}
{"index": "s610928585", "label": 3681, "func": ""}
{"index": "s209232378", "label": 3681, "func": ""}
{"index": "s265131600", "label": 3681, "func": ""}
{"index": "s257060928", "label": 3681, "func": ""}
{"index": "s478939662", "label": 51, "func": ""}
{"index": "s609863463", "label": 51, "func": ""}
{"index": "s780180348", "label": 51, "func": ""}
{"index": "s267391388", "label": 51, "func": ""}
{"index": "s646884135", "label": 51, "func": ""}
{"index": "s689948816", "label": 51, "func": ""}
{"index": "s978051586", "label": 51, "func": ""}
{"index": "s818673807", "label": 51, "func": ""}
{"index": "s619700868", "label": 51, "func": ""}
{"index": "s110663283", "label": 51, "func": ""}
{"index": "s153968631", "label": 2069, "func": ""}
{"index": "s447237110", "label": 3529, "func": ""}
{"index": "s302783202", "label": 2940, "func": ""}
{"index": "s058375372", "label": 2940, "func": ""}
{"index": "s463525959", "label": 2940, "func": ""}
{"index": "s388764829", "label": 2940, "func": ""}
{"index": "s288804402", "label": 2940, "func": ""}
{"index": "s994439825", "label": 2940, "func": ""}
{"index": "s157304210", "label": 2940, "func": ""}
{"index": "s825919127", "label": 2940, "func": ""}
{"index": "s232949835", "label": 2940, "func": ""}
{"index": "s103357630", "label": 3715, "func": ""}
{"index": "s265695673", "label": 3715, "func": ""}
{"index": "s433082612", "label": 3715, "func": ""}
{"index": "s279006238", "label": 3715, "func": ""}
{"index": "s027013913", "label": 3715, "func": ""}
{"index": "s875201407", "label": 3715, "func": ""}
{"index": "s810755273", "label": 3715, "func": ""}
{"index": "s044571613", "label": 3715, "func": ""}
{"index": "s410692697", "label": 3715, "func": ""}
{"index": "s525954618", "label": 3715, "func": ""}
{"index": "s293034692", "label": 236, "func": ""}
{"index": "s041380019", "label": 236, "func": ""}
{"index": "s053422997", "label": 236, "func": ""}
{"index": "s928077422", "label": 236, "func": ""}
{"index": "s206829274", "label": 236, "func": ""}
{"index": "s517123278", "label": 236, "func": ""}
{"index": "s882370348", "label": 236, "func": ""}
{"index": "s758852140", "label": 236, "func": ""}
{"index": "s478437957", "label": 236, "func": ""}
{"index": "s457829264", "label": 236, "func": ""}
{"index": "s773110917", "label": 974, "func": ""}
{"index": "s645568639", "label": 2112, "func": ""}
{"index": "s600591906", "label": 2588, "func": ""}
{"index": "s812306195", "label": 2588, "func": ""}
{"index": "s082356256", "label": 2588, "func": ""}
{"index": "s252444057", "label": 2588, "func": ""}
{"index": "s705970356", "label": 2588, "func": ""}
{"index": "s576954419", "label": 2588, "func": ""}
{"index": "s284352737", "label": 2588, "func": ""}
{"index": "s169506228", "label": 2588, "func": ""}
{"index": "s314004571", "label": 2588, "func": ""}
{"index": "s364361477", "label": 2588, "func": ""}
{"index": "s580054346", "label": 3701, "func": ""}
{"index": "s296121372", "label": 3701, "func": ""}
{"index": "s683621943", "label": 3701, "func": ""}
{"index": "s043870934", "label": 3701, "func": ""}
{"index": "s871453118", "label": 3701, "func": ""}
{"index": "s867954519", "label": 3701, "func": ""}
{"index": "s551116804", "label": 3701, "func": ""}
{"index": "s581272532", "label": 3701, "func": ""}
{"index": "s725261336", "label": 3701, "func": ""}
{"index": "s489884017", "label": 3701, "func": ""}
{"index": "s937244290", "label": 2873, "func": ""}
{"index": "s120625481", "label": 2873, "func": ""}
{"index": "s980978102", "label": 2873, "func": ""}
{"index": "s652203819", "label": 2873, "func": ""}
{"index": "s405263724", "label": 2873, "func": ""}
{"index": "s111072124", "label": 2873, "func": ""}
{"index": "s527400952", "label": 2873, "func": ""}
{"index": "s558801628", "label": 2873, "func": ""}
{"index": "s592726933", "label": 2873, "func": ""}
{"index": "s883496900", "label": 2873, "func": ""}
{"index": "s734761343", "label": 3413, "func": ""}
{"index": "s092183204", "label": 3413, "func": ""}
{"index": "s998093833", "label": 3413, "func": ""}
{"index": "s579621203", "label": 3413, "func": ""}
{"index": "s646139798", "label": 3413, "func": ""}
{"index": "s950619300", "label": 3413, "func": ""}
{"index": "s060473732", "label": 506, "func": ""}
{"index": "s854355994", "label": 506, "func": ""}
{"index": "s810460753", "label": 506, "func": ""}
{"index": "s078626376", "label": 506, "func": ""}
{"index": "s422799122", "label": 506, "func": ""}
{"index": "s508714822", "label": 506, "func": ""}
{"index": "s805980345", "label": 506, "func": ""}
{"index": "s320239645", "label": 506, "func": ""}
{"index": "s513634009", "label": 506, "func": ""}
{"index": "s489759061", "label": 506, "func": ""}
{"index": "s807311102", "label": 203, "func": ""}
{"index": "s663155198", "label": 203, "func": ""}
{"index": "s321439843", "label": 203, "func": ""}
{"index": "s124238340", "label": 203, "func": ""}
{"index": "s174278932", "label": 203, "func": ""}
{"index": "s067254956", "label": 203, "func": ""}
{"index": "s930521673", "label": 203, "func": ""}
{"index": "s123374895", "label": 203, "func": ""}
{"index": "s676961120", "label": 203, "func": ""}
{"index": "s650680040", "label": 203, "func": ""}
{"index": "s488090291", "label": 3936, "func": ""}
{"index": "s464855665", "label": 3936, "func": ""}
{"index": "s075074029", "label": 3936, "func": ""}
{"index": "s454730327", "label": 3936, "func": ""}
{"index": "s264324707", "label": 3936, "func": ""}
{"index": "s667922553", "label": 4022, "func": ""}
{"index": "s910525370", "label": 4022, "func": ""}
{"index": "s444687182", "label": 4022, "func": ""}
{"index": "s045597562", "label": 4022, "func": ""}
{"index": "s368707309", "label": 4022, "func": ""}
{"index": "s376404064", "label": 2957, "func": ""}
{"index": "s165106706", "label": 2957, "func": ""}
{"index": "s304314050", "label": 2957, "func": ""}
{"index": "s471343505", "label": 2957, "func": ""}
{"index": "s071361012", "label": 2957, "func": ""}
{"index": "s529467745", "label": 2957, "func": ""}
{"index": "s534448694", "label": 2957, "func": ""}
{"index": "s525507524", "label": 2957, "func": ""}
{"index": "s456993426", "label": 2957, "func": ""}
{"index": "s331044014", "label": 2957, "func": ""}
{"index": "s969498582", "label": 3805, "func": ""}
{"index": "s847656411", "label": 3805, "func": ""}
{"index": "s190267849", "label": 3805, "func": ""}
{"index": "s840376648", "label": 3805, "func": ""}
{"index": "s830338912", "label": 3805, "func": ""}
{"index": "s460580498", "label": 3805, "func": ""}
{"index": "s698848490", "label": 3805, "func": ""}
{"index": "s658101216", "label": 3805, "func": ""}
{"index": "s105662056", "label": 3805, "func": ""}
{"index": "s117668461", "label": 3805, "func": ""}
{"index": "s130584512", "label": 3597, "func": ""}
{"index": "s746180093", "label": 3597, "func": ""}
{"index": "s431725221", "label": 3597, "func": ""}
{"index": "s741065407", "label": 3597, "func": ""}
{"index": "s220030181", "label": 3597, "func": ""}
{"index": "s372658119", "label": 3597, "func": ""}
{"index": "s827100252", "label": 3597, "func": ""}
{"index": "s598991346", "label": 3597, "func": ""}
{"index": "s188688667", "label": 3597, "func": ""}
{"index": "s355958397", "label": 3597, "func": ""}
{"index": "s243316504", "label": 3637, "func": ""}
{"index": "s824906658", "label": 3637, "func": ""}
{"index": "s102901005", "label": 3637, "func": ""}
{"index": "s129073499", "label": 3637, "func": ""}
{"index": "s942466965", "label": 3637, "func": ""}
{"index": "s899391046", "label": 3637, "func": ""}
{"index": "s436137650", "label": 3637, "func": ""}
{"index": "s196724703", "label": 3637, "func": ""}
{"index": "s841436424", "label": 3637, "func": ""}
{"index": "s694778458", "label": 3637, "func": ""}
{"index": "s647027035", "label": 3027, "func": ""}
{"index": "s334404661", "label": 3027, "func": ""}
{"index": "s354984333", "label": 3027, "func": ""}
{"index": "s606179301", "label": 3027, "func": ""}
{"index": "s606724970", "label": 3027, "func": ""}
{"index": "s953997557", "label": 3027, "func": ""}
{"index": "s107346935", "label": 3027, "func": ""}
{"index": "s715878373", "label": 3027, "func": ""}
{"index": "s854314647", "label": 3027, "func": ""}
{"index": "s228654881", "label": 3027, "func": ""}
{"index": "s825763009", "label": 808, "func": ""}
{"index": "s766118859", "label": 808, "func": ""}
{"index": "s528264923", "label": 808, "func": ""}
{"index": "s110009706", "label": 2344, "func": ""}
{"index": "s985915129", "label": 2344, "func": ""}
{"index": "s941758600", "label": 2344, "func": ""}
{"index": "s643303625", "label": 2344, "func": ""}
{"index": "s551642143", "label": 2344, "func": ""}
{"index": "s201484689", "label": 2344, "func": ""}
{"index": "s149653732", "label": 2344, "func": ""}
{"index": "s986590498", "label": 2344, "func": ""}
{"index": "s932271883", "label": 2344, "func": ""}
{"index": "s801627888", "label": 2344, "func": ""}
{"index": "s051449980", "label": 3703, "func": ""}
{"index": "s859448103", "label": 3703, "func": ""}
{"index": "s371163273", "label": 3703, "func": ""}
{"index": "s641318883", "label": 3703, "func": ""}
{"index": "s442170883", "label": 3703, "func": ""}
{"index": "s051870443", "label": 3703, "func": ""}
{"index": "s095383413", "label": 3703, "func": ""}
{"index": "s975961488", "label": 3703, "func": ""}
{"index": "s770012906", "label": 3703, "func": ""}
{"index": "s264753494", "label": 3703, "func": ""}
{"index": "s527869589", "label": 3381, "func": ""}
{"index": "s714415571", "label": 3381, "func": ""}
{"index": "s220500978", "label": 3381, "func": ""}
{"index": "s952898178", "label": 3381, "func": ""}
{"index": "s581763966", "label": 3381, "func": ""}
{"index": "s994114634", "label": 3381, "func": ""}
{"index": "s372930452", "label": 3381, "func": ""}
{"index": "s290551215", "label": 3381, "func": ""}
{"index": "s365845015", "label": 3381, "func": ""}
{"index": "s620750677", "label": 3381, "func": ""}
{"index": "s585985254", "label": 3223, "func": ""}
{"index": "s358584425", "label": 3223, "func": ""}
{"index": "s904694756", "label": 3223, "func": ""}
{"index": "s886292367", "label": 3223, "func": ""}
{"index": "s961224507", "label": 3223, "func": ""}
{"index": "s346647414", "label": 3223, "func": ""}
{"index": "s159337937", "label": 3223, "func": ""}
{"index": "s062753505", "label": 3223, "func": ""}
{"index": "s778887632", "label": 3223, "func": ""}
{"index": "s959864482", "label": 3223, "func": ""}
{"index": "s562400979", "label": 1080, "func": ""}
{"index": "s164493218", "label": 1080, "func": ""}
{"index": "s063032211", "label": 2833, "func": ""}
{"index": "s744494515", "label": 2833, "func": ""}
{"index": "s722231218", "label": 2833, "func": ""}
{"index": "s609919096", "label": 2833, "func": ""}
{"index": "s692134145", "label": 2833, "func": ""}
{"index": "s009616129", "label": 2833, "func": ""}
{"index": "s982027641", "label": 2833, "func": ""}
{"index": "s492306646", "label": 2833, "func": ""}
{"index": "s106612682", "label": 2833, "func": ""}
{"index": "s707710981", "label": 2833, "func": ""}
{"index": "s515598600", "label": 344, "func": ""}
{"index": "s204818841", "label": 344, "func": ""}
{"index": "s202353864", "label": 344, "func": ""}
{"index": "s863843179", "label": 3437, "func": ""}
{"index": "s332368021", "label": 3437, "func": ""}
{"index": "s337715088", "label": 3437, "func": ""}
{"index": "s834698992", "label": 3437, "func": ""}
{"index": "s129410051", "label": 3437, "func": ""}
{"index": "s394979494", "label": 3437, "func": ""}
{"index": "s334709887", "label": 3437, "func": ""}
{"index": "s148855076", "label": 3437, "func": ""}
{"index": "s130892619", "label": 3437, "func": ""}
{"index": "s666989953", "label": 3437, "func": ""}
{"index": "s515067763", "label": 282, "func": ""}
{"index": "s245394824", "label": 282, "func": ""}
{"index": "s370501785", "label": 282, "func": ""}
{"index": "s728991863", "label": 282, "func": ""}
{"index": "s992865792", "label": 282, "func": ""}
{"index": "s493274724", "label": 282, "func": ""}
{"index": "s001334789", "label": 282, "func": ""}
{"index": "s175418257", "label": 282, "func": ""}
{"index": "s138948589", "label": 282, "func": ""}
{"index": "s403241873", "label": 282, "func": ""}
{"index": "s359056562", "label": 1192, "func": ""}
{"index": "s038000548", "label": 1192, "func": ""}
{"index": "s930637945", "label": 1192, "func": ""}
{"index": "s689008334", "label": 1192, "func": ""}
{"index": "s522839309", "label": 302, "func": ""}
{"index": "s550362008", "label": 763, "func": ""}
{"index": "s670293534", "label": 763, "func": ""}
{"index": "s763762804", "label": 763, "func": ""}
{"index": "s977188971", "label": 763, "func": ""}
{"index": "s988254602", "label": 763, "func": ""}
{"index": "s055717213", "label": 763, "func": ""}
{"index": "s021331430", "label": 763, "func": ""}
{"index": "s431340572", "label": 763, "func": ""}
{"index": "s158756303", "label": 763, "func": ""}
{"index": "s474483481", "label": 763, "func": ""}
{"index": "s548366738", "label": 2713, "func": ""}
{"index": "s632516658", "label": 2713, "func": ""}
{"index": "s751786133", "label": 2713, "func": ""}
{"index": "s578484428", "label": 2713, "func": ""}
{"index": "s349738971", "label": 2713, "func": ""}
{"index": "s282220046", "label": 2713, "func": ""}
{"index": "s480322825", "label": 2713, "func": ""}
{"index": "s178078590", "label": 2713, "func": ""}
{"index": "s964679649", "label": 2713, "func": ""}
{"index": "s351373483", "label": 2713, "func": ""}
{"index": "s364639169", "label": 1616, "func": ""}
{"index": "s796529875", "label": 1616, "func": ""}
{"index": "s053620101", "label": 1616, "func": ""}
{"index": "s504254010", "label": 3555, "func": ""}
{"index": "s867522002", "label": 3555, "func": ""}
{"index": "s579768649", "label": 3555, "func": ""}
{"index": "s309978717", "label": 3555, "func": ""}
{"index": "s621734915", "label": 3555, "func": ""}
{"index": "s555221007", "label": 3555, "func": ""}
{"index": "s152782796", "label": 3555, "func": ""}
{"index": "s217220775", "label": 3555, "func": ""}
{"index": "s203281785", "label": 3555, "func": ""}
{"index": "s213558147", "label": 3555, "func": ""}
{"index": "s095423497", "label": 1182, "func": ""}
{"index": "s458170381", "label": 1182, "func": ""}
{"index": "s412424611", "label": 2657, "func": ""}
{"index": "s817252580", "label": 2657, "func": ""}
{"index": "s142183364", "label": 2657, "func": ""}
{"index": "s452280759", "label": 2657, "func": ""}
{"index": "s278641797", "label": 2657, "func": ""}
{"index": "s033624729", "label": 2657, "func": ""}
{"index": "s135699110", "label": 2657, "func": ""}
{"index": "s101743461", "label": 2657, "func": ""}
{"index": "s315693208", "label": 2657, "func": ""}
{"index": "s798831105", "label": 2657, "func": ""}
{"index": "s052414349", "label": 540, "func": ""}
{"index": "s136336764", "label": 2863, "func": ""}
{"index": "s410671266", "label": 2863, "func": ""}
{"index": "s994425499", "label": 2863, "func": ""}
{"index": "s750179639", "label": 2863, "func": ""}
{"index": "s004530402", "label": 2863, "func": ""}
{"index": "s762023557", "label": 2863, "func": ""}
{"index": "s541073005", "label": 2863, "func": ""}
{"index": "s097080407", "label": 2863, "func": ""}
{"index": "s096479556", "label": 2863, "func": ""}
{"index": "s299831794", "label": 2863, "func": ""}
{"index": "s722219992", "label": 2282, "func": ""}
{"index": "s397269087", "label": 2282, "func": ""}
{"index": "s097207888", "label": 2282, "func": ""}
{"index": "s533750672", "label": 2282, "func": ""}
{"index": "s792706340", "label": 2282, "func": ""}
{"index": "s677480692", "label": 2282, "func": ""}
{"index": "s543273287", "label": 2282, "func": ""}
{"index": "s672048069", "label": 2282, "func": ""}
{"index": "s012348243", "label": 2282, "func": ""}
{"index": "s018069767", "label": 2282, "func": ""}
{"index": "s176433005", "label": 3346, "func": ""}
{"index": "s610392981", "label": 3346, "func": ""}
{"index": "s699761030", "label": 3346, "func": ""}
{"index": "s788292654", "label": 3346, "func": ""}
{"index": "s991360946", "label": 3346, "func": ""}
{"index": "s779902175", "label": 3346, "func": ""}
{"index": "s718344865", "label": 3346, "func": ""}
{"index": "s816296319", "label": 3346, "func": ""}
{"index": "s922497586", "label": 3346, "func": ""}
{"index": "s495422809", "label": 3346, "func": ""}
{"index": "s484483687", "label": 620, "func": ""}
{"index": "s534298309", "label": 620, "func": ""}
{"index": "s382678205", "label": 620, "func": ""}
{"index": "s669418443", "label": 620, "func": ""}
{"index": "s766209815", "label": 620, "func": ""}
{"index": "s028800239", "label": 620, "func": ""}
{"index": "s535370505", "label": 3096, "func": ""}
{"index": "s182415428", "label": 3096, "func": ""}
{"index": "s818995583", "label": 3096, "func": ""}
{"index": "s261944804", "label": 3096, "func": ""}
{"index": "s901794113", "label": 3096, "func": ""}
{"index": "s825200793", "label": 3096, "func": ""}
{"index": "s695171680", "label": 3096, "func": ""}
{"index": "s257586831", "label": 3096, "func": ""}
{"index": "s636029188", "label": 3096, "func": ""}
{"index": "s233588287", "label": 3096, "func": ""}
{"index": "s023367516", "label": 2995, "func": ""}
{"index": "s452979318", "label": 2995, "func": ""}
{"index": "s334668768", "label": 2995, "func": ""}
{"index": "s607330685", "label": 2995, "func": ""}
{"index": "s072851515", "label": 2995, "func": ""}
{"index": "s463311113", "label": 2995, "func": ""}
{"index": "s918837966", "label": 2995, "func": ""}
{"index": "s960432824", "label": 2995, "func": ""}
{"index": "s091705965", "label": 2995, "func": ""}
{"index": "s764444413", "label": 2995, "func": ""}
{"index": "s502334836", "label": 3129, "func": ""}
{"index": "s347982740", "label": 3129, "func": ""}
{"index": "s150486413", "label": 3129, "func": ""}
{"index": "s665942800", "label": 3129, "func": ""}
{"index": "s798236418", "label": 3129, "func": ""}
{"index": "s938432793", "label": 3129, "func": ""}
{"index": "s120887965", "label": 3129, "func": ""}
{"index": "s957039681", "label": 3129, "func": ""}
{"index": "s811819797", "label": 3129, "func": ""}
{"index": "s736731605", "label": 3129, "func": ""}
{"index": "s330830401", "label": 709, "func": ""}
{"index": "s222884797", "label": 709, "func": ""}
{"index": "s720727509", "label": 709, "func": ""}
{"index": "s978152879", "label": 709, "func": ""}
{"index": "s892908635", "label": 709, "func": ""}
{"index": "s822900084", "label": 709, "func": ""}
{"index": "s599303607", "label": 709, "func": ""}
{"index": "s299781460", "label": 3763, "func": ""}
{"index": "s611524194", "label": 3763, "func": ""}
{"index": "s197312622", "label": 3763, "func": ""}
{"index": "s443078497", "label": 3763, "func": ""}
{"index": "s963474603", "label": 3763, "func": ""}
{"index": "s716721829", "label": 3763, "func": ""}
{"index": "s986800417", "label": 3763, "func": ""}
{"index": "s931850662", "label": 3763, "func": ""}
{"index": "s737211975", "label": 3763, "func": ""}
{"index": "s367209749", "label": 3763, "func": ""}
{"index": "s164995268", "label": 1910, "func": ""}
{"index": "s864935356", "label": 1910, "func": ""}
{"index": "s606467534", "label": 1910, "func": ""}
{"index": "s731377319", "label": 1521, "func": ""}
{"index": "s147693651", "label": 1521, "func": ""}
{"index": "s304139346", "label": 1521, "func": ""}
{"index": "s074756640", "label": 1521, "func": ""}
{"index": "s893139135", "label": 1521, "func": ""}
{"index": "s275654138", "label": 1521, "func": ""}
{"index": "s174443620", "label": 1521, "func": ""}
{"index": "s792655136", "label": 1521, "func": ""}
{"index": "s418965928", "label": 1521, "func": ""}
{"index": "s964938161", "label": 1521, "func": ""}
{"index": "s052455014", "label": 3355, "func": ""}
{"index": "s667232007", "label": 3355, "func": ""}
{"index": "s369701344", "label": 3355, "func": ""}
{"index": "s027996606", "label": 3355, "func": ""}
{"index": "s849030405", "label": 3355, "func": ""}
{"index": "s157438449", "label": 3355, "func": ""}
{"index": "s515546459", "label": 3355, "func": ""}
{"index": "s963423663", "label": 3355, "func": ""}
{"index": "s288842912", "label": 3355, "func": ""}
{"index": "s390843159", "label": 3355, "func": ""}
{"index": "s472259886", "label": 657, "func": ""}
{"index": "s978844670", "label": 657, "func": ""}
{"index": "s272876132", "label": 657, "func": ""}
{"index": "s867715744", "label": 657, "func": ""}
{"index": "s964750012", "label": 657, "func": ""}
{"index": "s790041237", "label": 657, "func": ""}
{"index": "s546821866", "label": 657, "func": ""}
{"index": "s736782959", "label": 657, "func": ""}
{"index": "s764296643", "label": 657, "func": ""}
{"index": "s525944470", "label": 657, "func": ""}
{"index": "s048846911", "label": 1007, "func": ""}
{"index": "s118541869", "label": 1007, "func": ""}
{"index": "s726321392", "label": 1007, "func": ""}
{"index": "s860171517", "label": 148, "func": ""}
{"index": "s849640610", "label": 148, "func": ""}
{"index": "s420271093", "label": 148, "func": ""}
{"index": "s283034584", "label": 148, "func": ""}
{"index": "s264525169", "label": 148, "func": ""}
{"index": "s284238274", "label": 148, "func": ""}
{"index": "s997346319", "label": 148, "func": ""}
{"index": "s496002993", "label": 148, "func": ""}
{"index": "s317908497", "label": 148, "func": ""}
{"index": "s638056954", "label": 148, "func": ""}
{"index": "s116231226", "label": 2376, "func": ""}
{"index": "s754311397", "label": 2376, "func": ""}
{"index": "s153404016", "label": 2376, "func": ""}
{"index": "s016492656", "label": 2376, "func": ""}
{"index": "s967587416", "label": 2376, "func": ""}
{"index": "s393465731", "label": 2376, "func": ""}
{"index": "s122950913", "label": 2376, "func": ""}
{"index": "s094771405", "label": 2376, "func": ""}
{"index": "s498590076", "label": 2376, "func": ""}
{"index": "s623831669", "label": 2376, "func": ""}
{"index": "s673027428", "label": 3819, "func": ""}
{"index": "s365366412", "label": 3819, "func": ""}
{"index": "s724757458", "label": 3819, "func": ""}
{"index": "s690039552", "label": 3819, "func": ""}
{"index": "s100224520", "label": 3819, "func": ""}
{"index": "s559971954", "label": 3819, "func": ""}
{"index": "s790307468", "label": 3819, "func": ""}
{"index": "s401173382", "label": 3819, "func": ""}
{"index": "s213957760", "label": 3819, "func": ""}
{"index": "s128154801", "label": 3819, "func": ""}
{"index": "s735934838", "label": 1380, "func": ""}
{"index": "s050266556", "label": 1380, "func": ""}
{"index": "s256898344", "label": 1380, "func": ""}
{"index": "s016199142", "label": 1380, "func": ""}
{"index": "s697891987", "label": 1380, "func": ""}
{"index": "s459294433", "label": 1380, "func": ""}
{"index": "s560377542", "label": 1380, "func": ""}
{"index": "s093173372", "label": 1380, "func": ""}
{"index": "s725856243", "label": 1380, "func": ""}
{"index": "s276158564", "label": 1380, "func": ""}
{"index": "s263961038", "label": 1549, "func": ""}
{"index": "s588941753", "label": 1549, "func": ""}
{"index": "s842523170", "label": 1549, "func": ""}
{"index": "s392618295", "label": 1549, "func": ""}
{"index": "s240797742", "label": 1549, "func": ""}
{"index": "s728599065", "label": 1549, "func": ""}
{"index": "s970912157", "label": 1549, "func": ""}
{"index": "s745424557", "label": 1549, "func": ""}
{"index": "s141488785", "label": 1549, "func": ""}
{"index": "s312792877", "label": 571, "func": ""}
{"index": "s683548119", "label": 571, "func": ""}
{"index": "s547342142", "label": 3308, "func": ""}
{"index": "s605671827", "label": 3308, "func": ""}
{"index": "s199388170", "label": 3308, "func": ""}
{"index": "s212341994", "label": 3308, "func": ""}
{"index": "s536838064", "label": 3308, "func": ""}
{"index": "s917951134", "label": 3308, "func": ""}
{"index": "s293941730", "label": 3308, "func": ""}
{"index": "s847150558", "label": 3308, "func": ""}
{"index": "s581276843", "label": 3308, "func": ""}
{"index": "s835949230", "label": 3308, "func": ""}
{"index": "s806089536", "label": 11, "func": ""}
{"index": "s414148352", "label": 11, "func": ""}
{"index": "s779496541", "label": 11, "func": ""}
{"index": "s198789286", "label": 11, "func": ""}
{"index": "s736515521", "label": 11, "func": ""}
{"index": "s643161551", "label": 11, "func": ""}
{"index": "s808259015", "label": 11, "func": ""}
{"index": "s632025906", "label": 11, "func": ""}
{"index": "s375011851", "label": 11, "func": ""}
{"index": "s839733909", "label": 11, "func": ""}
{"index": "s959467675", "label": 3431, "func": ""}
{"index": "s048169673", "label": 3431, "func": ""}
{"index": "s090202814", "label": 3370, "func": ""}
{"index": "s024506289", "label": 3370, "func": ""}
{"index": "s255194964", "label": 3370, "func": ""}
{"index": "s604612513", "label": 3370, "func": ""}
{"index": "s739505626", "label": 3370, "func": ""}
{"index": "s197861101", "label": 3370, "func": ""}
{"index": "s376460103", "label": 3370, "func": ""}
{"index": "s141045468", "label": 3370, "func": ""}
{"index": "s047826548", "label": 3370, "func": ""}
{"index": "s728176333", "label": 3370, "func": ""}
{"index": "s007394971", "label": 13, "func": ""}
{"index": "s911557088", "label": 13, "func": ""}
{"index": "s495997418", "label": 13, "func": ""}
{"index": "s188082630", "label": 13, "func": ""}
{"index": "s436290519", "label": 13, "func": ""}
{"index": "s682359573", "label": 13, "func": ""}
{"index": "s873223740", "label": 13, "func": ""}
{"index": "s099352749", "label": 13, "func": ""}
{"index": "s929254030", "label": 13, "func": ""}
{"index": "s572408934", "label": 13, "func": ""}
{"index": "s339601165", "label": 3091, "func": ""}
{"index": "s195610269", "label": 3091, "func": ""}
{"index": "s446913440", "label": 3091, "func": ""}
{"index": "s063120975", "label": 3091, "func": ""}
{"index": "s357693321", "label": 3091, "func": ""}
{"index": "s646129726", "label": 3091, "func": ""}
{"index": "s278766679", "label": 3081, "func": ""}
{"index": "s630235568", "label": 3081, "func": ""}
{"index": "s761584960", "label": 3081, "func": ""}
{"index": "s487584403", "label": 3081, "func": ""}
{"index": "s681774531", "label": 3081, "func": ""}
{"index": "s397722433", "label": 3081, "func": ""}
{"index": "s346716976", "label": 3081, "func": ""}
{"index": "s568052757", "label": 3081, "func": ""}
{"index": "s148508329", "label": 3081, "func": ""}
{"index": "s172498536", "label": 3081, "func": ""}
{"index": "s212240583", "label": 914, "func": ""}
{"index": "s583626977", "label": 914, "func": ""}
{"index": "s165607128", "label": 914, "func": ""}
{"index": "s544629575", "label": 914, "func": ""}
{"index": "s578518244", "label": 914, "func": ""}
{"index": "s862824907", "label": 914, "func": ""}
{"index": "s131014214", "label": 914, "func": ""}
{"index": "s851130023", "label": 914, "func": ""}
{"index": "s012195009", "label": 914, "func": ""}
{"index": "s216763828", "label": 914, "func": ""}
{"index": "s052053195", "label": 2981, "func": ""}
{"index": "s707545671", "label": 2981, "func": ""}
{"index": "s641474401", "label": 2981, "func": ""}
{"index": "s997223143", "label": 2981, "func": ""}
{"index": "s438173737", "label": 2981, "func": ""}
{"index": "s164571751", "label": 2981, "func": ""}
{"index": "s452061825", "label": 2981, "func": ""}
{"index": "s063338285", "label": 2981, "func": ""}
{"index": "s471250044", "label": 2981, "func": ""}
{"index": "s357860026", "label": 2981, "func": ""}
{"index": "s997914674", "label": 2298, "func": ""}
{"index": "s081995410", "label": 2298, "func": ""}
{"index": "s850148249", "label": 2298, "func": ""}
{"index": "s265159764", "label": 2298, "func": ""}
{"index": "s718648491", "label": 2298, "func": ""}
{"index": "s908440824", "label": 2298, "func": ""}
{"index": "s988371167", "label": 2298, "func": ""}
{"index": "s300037372", "label": 2298, "func": ""}
{"index": "s033424619", "label": 2298, "func": ""}
{"index": "s217998181", "label": 2298, "func": ""}
{"index": "s964914911", "label": 1815, "func": ""}
{"index": "s479270967", "label": 1815, "func": ""}
{"index": "s659714556", "label": 1815, "func": ""}
{"index": "s984636864", "label": 1319, "func": ""}
{"index": "s898284762", "label": 1319, "func": ""}
{"index": "s228772949", "label": 816, "func": ""}
{"index": "s055358269", "label": 816, "func": ""}
{"index": "s113883389", "label": 816, "func": ""}
{"index": "s996871982", "label": 816, "func": ""}
{"index": "s789145043", "label": 816, "func": ""}
{"index": "s090880735", "label": 816, "func": ""}
{"index": "s533838860", "label": 816, "func": ""}
{"index": "s418837705", "label": 816, "func": ""}
{"index": "s800931720", "label": 816, "func": ""}
{"index": "s119527588", "label": 816, "func": ""}
{"index": "s436674379", "label": 3734, "func": ""}
{"index": "s074831754", "label": 3734, "func": ""}
{"index": "s182324346", "label": 3734, "func": ""}
{"index": "s137393615", "label": 3734, "func": ""}
{"index": "s258120191", "label": 3734, "func": ""}
{"index": "s735696940", "label": 3734, "func": ""}
{"index": "s496922164", "label": 3734, "func": ""}
{"index": "s990806326", "label": 3734, "func": ""}
{"index": "s848064419", "label": 3734, "func": ""}
{"index": "s332590285", "label": 3734, "func": ""}
{"index": "s739885248", "label": 624, "func": ""}
{"index": "s264017023", "label": 624, "func": ""}
{"index": "s620480005", "label": 624, "func": ""}
{"index": "s406836202", "label": 624, "func": ""}
{"index": "s697513999", "label": 624, "func": ""}
{"index": "s677664012", "label": 624, "func": ""}
{"index": "s428916054", "label": 624, "func": ""}
{"index": "s814082815", "label": 1917, "func": ""}
{"index": "s301947302", "label": 1917, "func": ""}
{"index": "s979372001", "label": 1967, "func": ""}
{"index": "s345755668", "label": 1967, "func": ""}
{"index": "s484400983", "label": 1967, "func": ""}
{"index": "s995680713", "label": 1967, "func": ""}
{"index": "s023726667", "label": 1967, "func": ""}
{"index": "s279424719", "label": 3057, "func": ""}
{"index": "s719617269", "label": 3057, "func": ""}
{"index": "s458515967", "label": 3057, "func": ""}
{"index": "s745885348", "label": 3057, "func": ""}
{"index": "s861066625", "label": 3456, "func": ""}
{"index": "s359229497", "label": 3456, "func": ""}
{"index": "s052684342", "label": 3456, "func": ""}
{"index": "s256774263", "label": 3456, "func": ""}
{"index": "s584753427", "label": 3456, "func": ""}
{"index": "s205297909", "label": 3456, "func": ""}
{"index": "s261059528", "label": 3456, "func": ""}
{"index": "s102291260", "label": 3456, "func": ""}
{"index": "s393568094", "label": 3456, "func": ""}
{"index": "s802815395", "label": 3456, "func": ""}
{"index": "s440180891", "label": 883, "func": ""}
{"index": "s135773201", "label": 883, "func": ""}
{"index": "s296034076", "label": 883, "func": ""}
{"index": "s660602835", "label": 883, "func": ""}
{"index": "s086271370", "label": 883, "func": ""}
{"index": "s433798904", "label": 883, "func": ""}
{"index": "s214894392", "label": 883, "func": ""}
{"index": "s633534363", "label": 199, "func": ""}
{"index": "s654444950", "label": 199, "func": ""}
{"index": "s239462573", "label": 199, "func": ""}
{"index": "s371435525", "label": 199, "func": ""}
{"index": "s904953326", "label": 199, "func": ""}
{"index": "s795922849", "label": 199, "func": ""}
{"index": "s679006614", "label": 199, "func": ""}
{"index": "s566601537", "label": 199, "func": ""}
{"index": "s859928105", "label": 199, "func": ""}
{"index": "s281327218", "label": 199, "func": ""}
{"index": "s111467036", "label": 2513, "func": ""}
{"index": "s151540766", "label": 2513, "func": ""}
{"index": "s591599429", "label": 2513, "func": ""}
{"index": "s217660578", "label": 2513, "func": ""}
{"index": "s806682340", "label": 2513, "func": ""}
{"index": "s695256225", "label": 2513, "func": ""}
{"index": "s769579114", "label": 2513, "func": ""}
{"index": "s648801885", "label": 2513, "func": ""}
{"index": "s700952630", "label": 2513, "func": ""}
{"index": "s185734743", "label": 2513, "func": ""}
{"index": "s620825185", "label": 1872, "func": ""}
{"index": "s669821534", "label": 1872, "func": ""}
{"index": "s739402407", "label": 1872, "func": ""}
{"index": "s166013748", "label": 1872, "func": ""}
{"index": "s490748173", "label": 1872, "func": ""}
{"index": "s617042832", "label": 1872, "func": ""}
{"index": "s048321370", "label": 1872, "func": ""}
{"index": "s466838310", "label": 2684, "func": ""}
{"index": "s985894959", "label": 2684, "func": ""}
{"index": "s504545113", "label": 2684, "func": ""}
{"index": "s197397213", "label": 2684, "func": ""}
{"index": "s397979427", "label": 2684, "func": ""}
{"index": "s346357972", "label": 2684, "func": ""}
{"index": "s707997542", "label": 2684, "func": ""}
{"index": "s708373866", "label": 2684, "func": ""}
{"index": "s923908175", "label": 2684, "func": ""}
{"index": "s114860556", "label": 2684, "func": ""}
{"index": "s747908534", "label": 3601, "func": ""}
{"index": "s901358032", "label": 3601, "func": ""}
{"index": "s801521084", "label": 3601, "func": ""}
{"index": "s127269828", "label": 3601, "func": ""}
{"index": "s237445487", "label": 3601, "func": ""}
{"index": "s497536168", "label": 3601, "func": ""}
{"index": "s647617346", "label": 3601, "func": ""}
{"index": "s144377469", "label": 3601, "func": ""}
{"index": "s336294268", "label": 3601, "func": ""}
{"index": "s541230011", "label": 3601, "func": ""}
{"index": "s075720521", "label": 2425, "func": ""}
{"index": "s803266484", "label": 2425, "func": ""}
{"index": "s692678343", "label": 2425, "func": ""}
{"index": "s462075674", "label": 2425, "func": ""}
{"index": "s412794265", "label": 2425, "func": ""}
{"index": "s004173564", "label": 2425, "func": ""}
{"index": "s930315276", "label": 2425, "func": ""}
{"index": "s392621971", "label": 2425, "func": ""}
{"index": "s643943290", "label": 2425, "func": ""}
{"index": "s047587690", "label": 3799, "func": ""}
{"index": "s185733755", "label": 3799, "func": ""}
{"index": "s116960541", "label": 3799, "func": ""}
{"index": "s345603102", "label": 3799, "func": ""}
{"index": "s503114199", "label": 3799, "func": ""}
{"index": "s166322859", "label": 3799, "func": ""}
{"index": "s768081927", "label": 3799, "func": ""}
{"index": "s542721418", "label": 3799, "func": ""}
{"index": "s832596506", "label": 3799, "func": ""}
{"index": "s971582407", "label": 3799, "func": ""}
{"index": "s916497442", "label": 1534, "func": ""}
{"index": "s337765132", "label": 1534, "func": ""}
{"index": "s950583327", "label": 723, "func": ""}
{"index": "s015370180", "label": 723, "func": ""}
{"index": "s558239767", "label": 723, "func": ""}
{"index": "s445870715", "label": 723, "func": ""}
{"index": "s905929795", "label": 723, "func": ""}
{"index": "s682047001", "label": 723, "func": ""}
{"index": "s261591890", "label": 723, "func": ""}
{"index": "s185313430", "label": 723, "func": ""}
{"index": "s883075290", "label": 723, "func": ""}
{"index": "s445368635", "label": 723, "func": ""}
{"index": "s004069550", "label": 1938, "func": ""}
{"index": "s351432063", "label": 1938, "func": ""}
{"index": "s476482045", "label": 1938, "func": ""}
{"index": "s753612059", "label": 2992, "func": ""}
{"index": "s500225736", "label": 2992, "func": ""}
{"index": "s748557533", "label": 2992, "func": ""}
{"index": "s670770643", "label": 2992, "func": ""}
{"index": "s949349650", "label": 2992, "func": ""}
{"index": "s271186384", "label": 2992, "func": ""}
{"index": "s254928020", "label": 2992, "func": ""}
{"index": "s219456930", "label": 2992, "func": ""}
{"index": "s970316106", "label": 2992, "func": ""}
{"index": "s363028106", "label": 2992, "func": ""}
{"index": "s875856580", "label": 430, "func": ""}
{"index": "s123258213", "label": 430, "func": ""}
{"index": "s736641421", "label": 430, "func": ""}
{"index": "s971105627", "label": 430, "func": ""}
{"index": "s585622196", "label": 430, "func": ""}
{"index": "s214324941", "label": 430, "func": ""}
{"index": "s083281242", "label": 430, "func": ""}
{"index": "s987572966", "label": 430, "func": ""}
{"index": "s257427435", "label": 430, "func": ""}
{"index": "s628792487", "label": 430, "func": ""}
{"index": "s793616169", "label": 3206, "func": ""}
{"index": "s495814826", "label": 3206, "func": ""}
{"index": "s465503912", "label": 3206, "func": ""}
{"index": "s640061053", "label": 3206, "func": ""}
{"index": "s887433365", "label": 3206, "func": ""}
{"index": "s142279642", "label": 3206, "func": ""}
{"index": "s445030876", "label": 3206, "func": ""}
{"index": "s987286269", "label": 3206, "func": ""}
{"index": "s571619335", "label": 3206, "func": ""}
{"index": "s689459367", "label": 3206, "func": ""}
{"index": "s384109880", "label": 2447, "func": ""}
{"index": "s982077826", "label": 2447, "func": ""}
{"index": "s807001109", "label": 2447, "func": ""}
{"index": "s859453647", "label": 2447, "func": ""}
{"index": "s567324332", "label": 2447, "func": ""}
{"index": "s834947412", "label": 2447, "func": ""}
{"index": "s927681861", "label": 2447, "func": ""}
{"index": "s514724510", "label": 2447, "func": ""}
{"index": "s512387774", "label": 2447, "func": ""}
{"index": "s496476403", "label": 2447, "func": ""}
{"index": "s193321039", "label": 1542, "func": ""}
{"index": "s900126142", "label": 1542, "func": ""}
{"index": "s718646281", "label": 1542, "func": ""}
{"index": "s645562957", "label": 1542, "func": ""}
{"index": "s697596444", "label": 1542, "func": ""}
{"index": "s144833739", "label": 1542, "func": ""}
{"index": "s848977714", "label": 1542, "func": ""}
{"index": "s634736790", "label": 1542, "func": ""}
{"index": "s036367715", "label": 1542, "func": ""}
{"index": "s930162807", "label": 3607, "func": ""}
{"index": "s382744338", "label": 3607, "func": ""}
{"index": "s767425801", "label": 3607, "func": ""}
{"index": "s010831582", "label": 3607, "func": ""}
{"index": "s253659554", "label": 3607, "func": ""}
{"index": "s569435878", "label": 3607, "func": ""}
{"index": "s184079394", "label": 3607, "func": ""}
{"index": "s645738373", "label": 3607, "func": ""}
{"index": "s310631047", "label": 3607, "func": ""}
{"index": "s762854123", "label": 3607, "func": ""}
{"index": "s192989894", "label": 231, "func": ""}
{"index": "s934321685", "label": 231, "func": ""}
{"index": "s405541716", "label": 231, "func": ""}
{"index": "s697905543", "label": 231, "func": ""}
{"index": "s152861567", "label": 231, "func": ""}
{"index": "s575335060", "label": 231, "func": ""}
{"index": "s384587257", "label": 231, "func": ""}
{"index": "s339873051", "label": 231, "func": ""}
{"index": "s596757959", "label": 231, "func": ""}
{"index": "s046930429", "label": 231, "func": ""}
{"index": "s696443405", "label": 584, "func": ""}
{"index": "s061861296", "label": 3315, "func": ""}
{"index": "s382224848", "label": 3315, "func": ""}
{"index": "s288954101", "label": 3315, "func": ""}
{"index": "s337817048", "label": 3315, "func": ""}
{"index": "s856312019", "label": 3315, "func": ""}
{"index": "s660870966", "label": 3315, "func": ""}
{"index": "s048573700", "label": 3315, "func": ""}
{"index": "s841503631", "label": 3315, "func": ""}
{"index": "s270786798", "label": 3315, "func": ""}
{"index": "s030097057", "label": 3315, "func": ""}
{"index": "s363586947", "label": 222, "func": ""}
{"index": "s131019470", "label": 222, "func": ""}
{"index": "s945474447", "label": 222, "func": ""}
{"index": "s418926837", "label": 222, "func": ""}
{"index": "s654712129", "label": 222, "func": ""}
{"index": "s013155970", "label": 222, "func": ""}
{"index": "s982800500", "label": 222, "func": ""}
{"index": "s763554732", "label": 222, "func": ""}
{"index": "s520362852", "label": 222, "func": ""}
{"index": "s217128072", "label": 222, "func": ""}
{"index": "s917146769", "label": 605, "func": ""}
{"index": "s213147776", "label": 605, "func": ""}
{"index": "s180149820", "label": 605, "func": ""}
{"index": "s028645587", "label": 605, "func": ""}
{"index": "s165031696", "label": 605, "func": ""}
{"index": "s358194144", "label": 605, "func": ""}
{"index": "s086023986", "label": 605, "func": ""}
{"index": "s710572731", "label": 605, "func": ""}
{"index": "s422397460", "label": 605, "func": ""}
{"index": "s640261274", "label": 605, "func": ""}
{"index": "s089201080", "label": 2197, "func": ""}
{"index": "s573952829", "label": 2379, "func": ""}
{"index": "s067430493", "label": 2379, "func": ""}
{"index": "s401352384", "label": 2379, "func": ""}
{"index": "s728199604", "label": 2379, "func": ""}
{"index": "s556245167", "label": 2379, "func": ""}
{"index": "s138517036", "label": 2379, "func": ""}
{"index": "s157815436", "label": 2379, "func": ""}
{"index": "s220577338", "label": 2379, "func": ""}
{"index": "s188967238", "label": 2379, "func": ""}
{"index": "s963726930", "label": 2379, "func": ""}
{"index": "s137157894", "label": 1329, "func": ""}
{"index": "s849391062", "label": 1329, "func": ""}
{"index": "s760442891", "label": 1329, "func": ""}
{"index": "s252565235", "label": 2248, "func": ""}
{"index": "s234400615", "label": 2248, "func": ""}
{"index": "s510053441", "label": 2248, "func": ""}
{"index": "s268530917", "label": 2248, "func": ""}
{"index": "s350776588", "label": 2248, "func": ""}
{"index": "s709065121", "label": 2248, "func": ""}
{"index": "s706156528", "label": 2248, "func": ""}
{"index": "s484337496", "label": 2248, "func": ""}
{"index": "s505289178", "label": 2248, "func": ""}
{"index": "s914895931", "label": 2248, "func": ""}
{"index": "s811079668", "label": 2426, "func": ""}
{"index": "s789054327", "label": 2426, "func": ""}
{"index": "s291664196", "label": 2426, "func": ""}
{"index": "s398839747", "label": 2426, "func": ""}
{"index": "s989131131", "label": 2426, "func": ""}
{"index": "s251586951", "label": 2426, "func": ""}
{"index": "s216496041", "label": 2426, "func": ""}
{"index": "s912145307", "label": 2426, "func": ""}
{"index": "s206197994", "label": 3378, "func": ""}
{"index": "s094444581", "label": 3378, "func": ""}
{"index": "s313021720", "label": 3378, "func": ""}
{"index": "s947225297", "label": 3378, "func": ""}
{"index": "s330004766", "label": 3378, "func": ""}
{"index": "s474391804", "label": 3378, "func": ""}
{"index": "s391519774", "label": 3378, "func": ""}
{"index": "s263636661", "label": 3378, "func": ""}
{"index": "s731833824", "label": 3378, "func": ""}
{"index": "s992460765", "label": 3378, "func": ""}
{"index": "s406645243", "label": 3668, "func": ""}
{"index": "s012948885", "label": 3668, "func": ""}
{"index": "s971799053", "label": 3668, "func": ""}
{"index": "s697947092", "label": 3668, "func": ""}
{"index": "s283099588", "label": 3668, "func": ""}
{"index": "s791854475", "label": 3668, "func": ""}
{"index": "s201931451", "label": 3668, "func": ""}
{"index": "s746455707", "label": 3668, "func": ""}
{"index": "s506881693", "label": 3668, "func": ""}
{"index": "s570167340", "label": 3668, "func": ""}
{"index": "s374061427", "label": 3486, "func": ""}
{"index": "s287444187", "label": 3486, "func": ""}
{"index": "s540571478", "label": 3486, "func": ""}
{"index": "s175379552", "label": 3486, "func": ""}
{"index": "s955471692", "label": 3486, "func": ""}
{"index": "s227903939", "label": 3486, "func": ""}
{"index": "s670441232", "label": 3486, "func": ""}
{"index": "s448211435", "label": 3486, "func": ""}
{"index": "s006054162", "label": 3486, "func": ""}
{"index": "s373938958", "label": 3486, "func": ""}
{"index": "s840300289", "label": 1145, "func": ""}
{"index": "s719433314", "label": 1145, "func": ""}
{"index": "s202244292", "label": 1145, "func": ""}
{"index": "s195351328", "label": 1145, "func": ""}
{"index": "s775675873", "label": 1145, "func": ""}
{"index": "s918087249", "label": 1145, "func": ""}
{"index": "s798373481", "label": 632, "func": ""}
{"index": "s418190539", "label": 632, "func": ""}
{"index": "s050944909", "label": 632, "func": ""}
{"index": "s842898507", "label": 632, "func": ""}
{"index": "s496505536", "label": 632, "func": ""}
{"index": "s806876801", "label": 632, "func": ""}
{"index": "s201600945", "label": 632, "func": ""}
{"index": "s890610314", "label": 632, "func": ""}
{"index": "s775715728", "label": 632, "func": ""}
{"index": "s940019430", "label": 632, "func": ""}
{"index": "s403597154", "label": 1140, "func": ""}
{"index": "s182081601", "label": 1140, "func": ""}
{"index": "s788337515", "label": 1140, "func": ""}
{"index": "s183004594", "label": 1140, "func": ""}
{"index": "s220048673", "label": 1140, "func": ""}
{"index": "s677432897", "label": 1140, "func": ""}
{"index": "s921071848", "label": 1140, "func": ""}
{"index": "s899351675", "label": 1140, "func": ""}
{"index": "s454628792", "label": 1140, "func": ""}
{"index": "s857355360", "label": 1140, "func": ""}
{"index": "s578929039", "label": 200, "func": ""}
{"index": "s983328805", "label": 200, "func": ""}
{"index": "s472310682", "label": 200, "func": ""}
{"index": "s757713580", "label": 200, "func": ""}
{"index": "s574380631", "label": 200, "func": ""}
{"index": "s916969419", "label": 200, "func": ""}
{"index": "s178726530", "label": 200, "func": ""}
{"index": "s655845104", "label": 200, "func": ""}
{"index": "s354713157", "label": 200, "func": ""}
{"index": "s308469206", "label": 200, "func": ""}
{"index": "s448247906", "label": 679, "func": ""}
{"index": "s956625258", "label": 679, "func": ""}
{"index": "s409193614", "label": 2647, "func": ""}
{"index": "s933977751", "label": 2647, "func": ""}
{"index": "s855629658", "label": 2647, "func": ""}
{"index": "s683288636", "label": 2647, "func": ""}
{"index": "s256851644", "label": 2647, "func": ""}
{"index": "s225110247", "label": 2647, "func": ""}
{"index": "s725767171", "label": 2647, "func": ""}
{"index": "s963435694", "label": 2647, "func": ""}
{"index": "s390493192", "label": 2647, "func": ""}
{"index": "s462362466", "label": 2647, "func": ""}
{"index": "s868376309", "label": 2038, "func": ""}
{"index": "s712417177", "label": 2038, "func": ""}
{"index": "s259135806", "label": 2038, "func": ""}
{"index": "s848080086", "label": 195, "func": ""}
{"index": "s758735408", "label": 195, "func": ""}
{"index": "s973749949", "label": 195, "func": ""}
{"index": "s271320431", "label": 195, "func": ""}
{"index": "s428309474", "label": 195, "func": ""}
{"index": "s172639650", "label": 195, "func": ""}
{"index": "s766840853", "label": 195, "func": ""}
{"index": "s476034179", "label": 195, "func": ""}
{"index": "s070944760", "label": 195, "func": ""}
{"index": "s520231668", "label": 195, "func": ""}
{"index": "s002867572", "label": 3342, "func": ""}
{"index": "s996801479", "label": 3342, "func": ""}
{"index": "s214363884", "label": 3342, "func": ""}
{"index": "s332128985", "label": 3342, "func": ""}
{"index": "s246206963", "label": 3342, "func": ""}
{"index": "s004858184", "label": 3342, "func": ""}
{"index": "s975914172", "label": 3342, "func": ""}
{"index": "s902836651", "label": 3342, "func": ""}
{"index": "s500266501", "label": 3342, "func": ""}
{"index": "s092681198", "label": 3342, "func": ""}
{"index": "s535489664", "label": 2598, "func": ""}
{"index": "s674258339", "label": 2598, "func": ""}
{"index": "s077050466", "label": 2598, "func": ""}
{"index": "s026997268", "label": 2598, "func": ""}
{"index": "s582931606", "label": 2598, "func": ""}
{"index": "s141781950", "label": 2598, "func": ""}
{"index": "s971765289", "label": 2598, "func": ""}
{"index": "s229077828", "label": 2598, "func": ""}
{"index": "s445395071", "label": 2598, "func": ""}
{"index": "s127125319", "label": 2598, "func": ""}
{"index": "s480811941", "label": 697, "func": ""}
{"index": "s957172688", "label": 697, "func": ""}
{"index": "s500275800", "label": 697, "func": ""}
{"index": "s152514519", "label": 697, "func": ""}
{"index": "s227399965", "label": 697, "func": ""}
{"index": "s911818167", "label": 697, "func": ""}
{"index": "s678483792", "label": 697, "func": ""}
{"index": "s070840396", "label": 697, "func": ""}
{"index": "s756876505", "label": 697, "func": ""}
{"index": "s428731516", "label": 3897, "func": ""}
{"index": "s691085984", "label": 3012, "func": ""}
{"index": "s930527400", "label": 3012, "func": ""}
{"index": "s316255845", "label": 3012, "func": ""}
{"index": "s216959667", "label": 3012, "func": ""}
{"index": "s311457104", "label": 3012, "func": ""}
{"index": "s478363937", "label": 3012, "func": ""}
{"index": "s200465165", "label": 3012, "func": ""}
{"index": "s494845891", "label": 3012, "func": ""}
{"index": "s629628464", "label": 3012, "func": ""}
{"index": "s837265819", "label": 3012, "func": ""}
{"index": "s022217157", "label": 514, "func": ""}
{"index": "s518797864", "label": 514, "func": ""}
{"index": "s137987580", "label": 514, "func": ""}
{"index": "s008727667", "label": 514, "func": ""}
{"index": "s745002283", "label": 514, "func": ""}
{"index": "s123823731", "label": 514, "func": ""}
{"index": "s872077075", "label": 514, "func": ""}
{"index": "s095651760", "label": 514, "func": ""}
{"index": "s562331198", "label": 514, "func": ""}
{"index": "s606242633", "label": 514, "func": ""}
{"index": "s637503367", "label": 3508, "func": ""}
{"index": "s582013488", "label": 3508, "func": ""}
{"index": "s000538723", "label": 3460, "func": ""}
{"index": "s951314148", "label": 3460, "func": ""}
{"index": "s086551581", "label": 3460, "func": ""}
{"index": "s147632231", "label": 3460, "func": ""}
{"index": "s669786031", "label": 3460, "func": ""}
{"index": "s846159732", "label": 3460, "func": ""}
{"index": "s242340481", "label": 3460, "func": ""}
{"index": "s212847230", "label": 3460, "func": ""}
{"index": "s549757394", "label": 3460, "func": ""}
{"index": "s881307767", "label": 3460, "func": ""}
{"index": "s527567359", "label": 1360, "func": ""}
{"index": "s604622867", "label": 1360, "func": ""}
{"index": "s404840162", "label": 1360, "func": ""}
{"index": "s134795258", "label": 1360, "func": ""}
{"index": "s174482053", "label": 1360, "func": ""}
{"index": "s758389942", "label": 1360, "func": ""}
{"index": "s334623781", "label": 1360, "func": ""}
{"index": "s862745695", "label": 1360, "func": ""}
{"index": "s113709851", "label": 1360, "func": ""}
{"index": "s804041468", "label": 1360, "func": ""}
{"index": "s296674813", "label": 2365, "func": ""}
{"index": "s298505608", "label": 2365, "func": ""}
{"index": "s088010985", "label": 2608, "func": ""}
{"index": "s426792518", "label": 2608, "func": ""}
{"index": "s598688009", "label": 2608, "func": ""}
{"index": "s575497763", "label": 2608, "func": ""}
{"index": "s756942128", "label": 2608, "func": ""}
{"index": "s501226295", "label": 2608, "func": ""}
{"index": "s718817912", "label": 2608, "func": ""}
{"index": "s753570935", "label": 2608, "func": ""}
{"index": "s032722984", "label": 2608, "func": ""}
{"index": "s157132827", "label": 2608, "func": ""}
{"index": "s997534148", "label": 1130, "func": ""}
{"index": "s736418943", "label": 1130, "func": ""}
{"index": "s642391768", "label": 1130, "func": ""}
{"index": "s249976470", "label": 1130, "func": ""}
{"index": "s721980789", "label": 1130, "func": ""}
{"index": "s656712888", "label": 1130, "func": ""}
{"index": "s139195128", "label": 1130, "func": ""}
{"index": "s315441268", "label": 1130, "func": ""}
{"index": "s624748951", "label": 1130, "func": ""}
{"index": "s972706874", "label": 1130, "func": ""}
{"index": "s759866399", "label": 3142, "func": ""}
{"index": "s744356368", "label": 3142, "func": ""}
{"index": "s893925479", "label": 3142, "func": ""}
{"index": "s560091123", "label": 3142, "func": ""}
{"index": "s043939740", "label": 3142, "func": ""}
{"index": "s756539465", "label": 3142, "func": ""}
{"index": "s577437056", "label": 3142, "func": ""}
{"index": "s399748630", "label": 3142, "func": ""}
{"index": "s118423855", "label": 3142, "func": ""}
{"index": "s759155207", "label": 3142, "func": ""}
{"index": "s818016613", "label": 642, "func": ""}
{"index": "s459109734", "label": 642, "func": ""}
{"index": "s604276833", "label": 642, "func": ""}
{"index": "s214608731", "label": 642, "func": ""}
{"index": "s552593011", "label": 642, "func": ""}
{"index": "s720336082", "label": 642, "func": ""}
{"index": "s787619745", "label": 642, "func": ""}
{"index": "s844126629", "label": 3949, "func": ""}
{"index": "s407998885", "label": 3949, "func": ""}
{"index": "s945502556", "label": 3949, "func": ""}
{"index": "s711804076", "label": 3949, "func": ""}
{"index": "s038914296", "label": 3949, "func": ""}
{"index": "s480215224", "label": 3949, "func": ""}
{"index": "s317527964", "label": 3949, "func": ""}
{"index": "s602803575", "label": 1214, "func": ""}
{"index": "s220878608", "label": 1214, "func": ""}
{"index": "s240401738", "label": 30, "func": ""}
{"index": "s742884137", "label": 30, "func": ""}
{"index": "s301409221", "label": 30, "func": ""}
{"index": "s761147351", "label": 30, "func": ""}
{"index": "s284865205", "label": 30, "func": ""}
{"index": "s751477476", "label": 30, "func": ""}
{"index": "s036296788", "label": 30, "func": ""}
{"index": "s707134030", "label": 30, "func": ""}
{"index": "s845647470", "label": 30, "func": ""}
{"index": "s709733324", "label": 30, "func": ""}
{"index": "s090359104", "label": 2852, "func": ""}
{"index": "s948601535", "label": 2852, "func": ""}
{"index": "s251500737", "label": 2852, "func": ""}
{"index": "s168866908", "label": 2852, "func": ""}
{"index": "s876598225", "label": 2852, "func": ""}
{"index": "s888512038", "label": 2852, "func": ""}
{"index": "s125422535", "label": 2852, "func": ""}
{"index": "s119911911", "label": 2852, "func": ""}
{"index": "s753013084", "label": 2852, "func": ""}
{"index": "s099544349", "label": 2852, "func": ""}
{"index": "s446988718", "label": 213, "func": ""}
{"index": "s332236769", "label": 213, "func": ""}
{"index": "s822582940", "label": 213, "func": ""}
{"index": "s590168766", "label": 213, "func": ""}
{"index": "s260529863", "label": 213, "func": ""}
{"index": "s436103996", "label": 213, "func": ""}
{"index": "s460702365", "label": 213, "func": ""}
{"index": "s542829970", "label": 3925, "func": ""}
{"index": "s639418657", "label": 3661, "func": ""}
{"index": "s021175129", "label": 3661, "func": ""}
{"index": "s197397917", "label": 3661, "func": ""}
{"index": "s898880211", "label": 3661, "func": ""}
{"index": "s803805210", "label": 3661, "func": ""}
{"index": "s578935174", "label": 3661, "func": ""}
{"index": "s643530012", "label": 3661, "func": ""}
{"index": "s247146321", "label": 3661, "func": ""}
{"index": "s604901666", "label": 3661, "func": ""}
{"index": "s907280482", "label": 3661, "func": ""}
{"index": "s051924500", "label": 511, "func": ""}
{"index": "s219277998", "label": 511, "func": ""}
{"index": "s290147497", "label": 511, "func": ""}
{"index": "s513946701", "label": 511, "func": ""}
{"index": "s798365703", "label": 511, "func": ""}
{"index": "s431541380", "label": 511, "func": ""}
{"index": "s323595230", "label": 511, "func": ""}
{"index": "s143771222", "label": 511, "func": ""}
{"index": "s516674681", "label": 511, "func": ""}
{"index": "s499798611", "label": 511, "func": ""}
{"index": "s805797443", "label": 2357, "func": ""}
{"index": "s749469808", "label": 2357, "func": ""}
{"index": "s417233624", "label": 2357, "func": ""}
{"index": "s392127272", "label": 2357, "func": ""}
{"index": "s867376553", "label": 2357, "func": ""}
{"index": "s184404423", "label": 2357, "func": ""}
{"index": "s369167017", "label": 2357, "func": ""}
{"index": "s276072146", "label": 2357, "func": ""}
{"index": "s823422075", "label": 2357, "func": ""}
{"index": "s907156634", "label": 3054, "func": ""}
{"index": "s141467487", "label": 3054, "func": ""}
{"index": "s634884684", "label": 3054, "func": ""}
{"index": "s399300745", "label": 3054, "func": ""}
{"index": "s299243691", "label": 3054, "func": ""}
{"index": "s386520132", "label": 3054, "func": ""}
{"index": "s666395601", "label": 3054, "func": ""}
{"index": "s225456871", "label": 3054, "func": ""}
{"index": "s499803692", "label": 3054, "func": ""}
{"index": "s196726830", "label": 3054, "func": ""}
{"index": "s598227291", "label": 2529, "func": ""}
{"index": "s885757235", "label": 2529, "func": ""}
{"index": "s403810364", "label": 2529, "func": ""}
{"index": "s810332136", "label": 2529, "func": ""}
{"index": "s976819804", "label": 2529, "func": ""}
{"index": "s534514596", "label": 2529, "func": ""}
{"index": "s858365097", "label": 2529, "func": ""}
{"index": "s984606667", "label": 2529, "func": ""}
{"index": "s235783903", "label": 2529, "func": ""}
{"index": "s238048438", "label": 2529, "func": ""}
{"index": "s623955341", "label": 2553, "func": ""}
{"index": "s587457746", "label": 2553, "func": ""}
{"index": "s461644158", "label": 2553, "func": ""}
{"index": "s221158156", "label": 2553, "func": ""}
{"index": "s384417483", "label": 2553, "func": ""}
{"index": "s533411447", "label": 2553, "func": ""}
{"index": "s955503226", "label": 2553, "func": ""}
{"index": "s405765260", "label": 2553, "func": ""}
{"index": "s439725995", "label": 2553, "func": ""}
{"index": "s062190000", "label": 2553, "func": ""}
{"index": "s570271610", "label": 1811, "func": ""}
{"index": "s186546556", "label": 1811, "func": ""}
{"index": "s456445432", "label": 1811, "func": ""}
{"index": "s050552588", "label": 1811, "func": ""}
{"index": "s187210603", "label": 1811, "func": ""}
{"index": "s511349508", "label": 3798, "func": ""}
{"index": "s134825154", "label": 3798, "func": ""}
{"index": "s045239750", "label": 3798, "func": ""}
{"index": "s334902394", "label": 3798, "func": ""}
{"index": "s367806240", "label": 3798, "func": ""}
{"index": "s239671243", "label": 3798, "func": ""}
{"index": "s023290049", "label": 3798, "func": ""}
{"index": "s461883228", "label": 3798, "func": ""}
{"index": "s235627462", "label": 3798, "func": ""}
{"index": "s475881109", "label": 3798, "func": ""}
{"index": "s011046928", "label": 1128, "func": ""}
{"index": "s646725862", "label": 1128, "func": ""}
{"index": "s211622128", "label": 1128, "func": ""}
{"index": "s981277627", "label": 1128, "func": ""}
{"index": "s674137028", "label": 1128, "func": ""}
{"index": "s069001472", "label": 1128, "func": ""}
{"index": "s991908000", "label": 1128, "func": ""}
{"index": "s628686669", "label": 1128, "func": ""}
{"index": "s898618630", "label": 1128, "func": ""}
{"index": "s569659751", "label": 1128, "func": ""}
{"index": "s752289607", "label": 1747, "func": ""}
{"index": "s207202956", "label": 1747, "func": ""}
{"index": "s331159330", "label": 1756, "func": ""}
{"index": "s342475970", "label": 311, "func": ""}
{"index": "s230571352", "label": 311, "func": ""}
{"index": "s250548217", "label": 311, "func": ""}
{"index": "s677514728", "label": 311, "func": ""}
{"index": "s990273450", "label": 311, "func": ""}
{"index": "s465332466", "label": 311, "func": ""}
{"index": "s641787064", "label": 311, "func": ""}
{"index": "s662405701", "label": 311, "func": ""}
{"index": "s425101231", "label": 311, "func": ""}
{"index": "s052746009", "label": 311, "func": ""}
{"index": "s299687479", "label": 2267, "func": ""}
{"index": "s418065756", "label": 2267, "func": ""}
{"index": "s811086009", "label": 2267, "func": ""}
{"index": "s685470273", "label": 2267, "func": ""}
{"index": "s777479410", "label": 2267, "func": ""}
{"index": "s812998536", "label": 2267, "func": ""}
{"index": "s057521623", "label": 2267, "func": ""}
{"index": "s610896130", "label": 2267, "func": ""}
{"index": "s486010266", "label": 2267, "func": ""}
{"index": "s245398899", "label": 2267, "func": ""}
{"index": "s873198464", "label": 2859, "func": ""}
{"index": "s273064662", "label": 2859, "func": ""}
{"index": "s979893055", "label": 2859, "func": ""}
{"index": "s607154609", "label": 2859, "func": ""}
{"index": "s578497201", "label": 2859, "func": ""}
{"index": "s903340838", "label": 2859, "func": ""}
{"index": "s600900818", "label": 2859, "func": ""}
{"index": "s092957124", "label": 2859, "func": ""}
{"index": "s661055062", "label": 2859, "func": ""}
{"index": "s075106508", "label": 2859, "func": ""}
{"index": "s211855897", "label": 2633, "func": ""}
{"index": "s001472356", "label": 2633, "func": ""}
{"index": "s179034084", "label": 2633, "func": ""}
{"index": "s739145511", "label": 2633, "func": ""}
{"index": "s734352579", "label": 2633, "func": ""}
{"index": "s889544408", "label": 2633, "func": ""}
{"index": "s791480431", "label": 2633, "func": ""}
{"index": "s258007689", "label": 2633, "func": ""}
{"index": "s658419704", "label": 2633, "func": ""}
{"index": "s553851210", "label": 2633, "func": ""}
{"index": "s224661121", "label": 281, "func": ""}
{"index": "s447010197", "label": 281, "func": ""}
{"index": "s619618408", "label": 281, "func": ""}
{"index": "s247774152", "label": 281, "func": ""}
{"index": "s870928185", "label": 281, "func": ""}
{"index": "s677299746", "label": 281, "func": ""}
{"index": "s379937130", "label": 4020, "func": ""}
{"index": "s952509540", "label": 4020, "func": ""}
{"index": "s056962098", "label": 4020, "func": ""}
{"index": "s938054866", "label": 4020, "func": ""}
{"index": "s258673648", "label": 4020, "func": ""}
{"index": "s265675730", "label": 4020, "func": ""}
{"index": "s394676631", "label": 4020, "func": ""}
{"index": "s855606174", "label": 4020, "func": ""}
{"index": "s707741134", "label": 4020, "func": ""}
{"index": "s419839206", "label": 4020, "func": ""}
{"index": "s122749152", "label": 1078, "func": ""}
{"index": "s630918861", "label": 1078, "func": ""}
{"index": "s205422219", "label": 1971, "func": ""}
{"index": "s740867294", "label": 1971, "func": ""}
{"index": "s468444875", "label": 1971, "func": ""}
{"index": "s486962480", "label": 316, "func": ""}
{"index": "s100947415", "label": 491, "func": ""}
{"index": "s204890253", "label": 491, "func": ""}
{"index": "s900865757", "label": 491, "func": ""}
{"index": "s369681609", "label": 491, "func": ""}
{"index": "s578045816", "label": 491, "func": ""}
{"index": "s003455330", "label": 491, "func": ""}
{"index": "s419643111", "label": 491, "func": ""}
{"index": "s508818246", "label": 491, "func": ""}
{"index": "s489614100", "label": 491, "func": ""}
{"index": "s699851766", "label": 491, "func": ""}
{"index": "s703897200", "label": 3016, "func": ""}
{"index": "s877578854", "label": 3016, "func": ""}
{"index": "s067801683", "label": 3016, "func": ""}
{"index": "s009267906", "label": 3016, "func": ""}
{"index": "s032523412", "label": 3016, "func": ""}
{"index": "s832037364", "label": 3016, "func": ""}
{"index": "s910014223", "label": 987, "func": ""}
{"index": "s478491092", "label": 3958, "func": ""}
{"index": "s408647159", "label": 3958, "func": ""}
{"index": "s754744701", "label": 3958, "func": ""}
{"index": "s605064742", "label": 3958, "func": ""}
{"index": "s433888733", "label": 3958, "func": ""}
{"index": "s756845547", "label": 3958, "func": ""}
{"index": "s941107429", "label": 3958, "func": ""}
{"index": "s374764909", "label": 3958, "func": ""}
{"index": "s208555375", "label": 3958, "func": ""}
{"index": "s712847844", "label": 3958, "func": ""}
{"index": "s383767705", "label": 3686, "func": ""}
{"index": "s624381056", "label": 3686, "func": ""}
{"index": "s471545448", "label": 3686, "func": ""}
{"index": "s466963077", "label": 3686, "func": ""}
{"index": "s963507420", "label": 3686, "func": ""}
{"index": "s834119140", "label": 3094, "func": ""}
{"index": "s153700642", "label": 352, "func": ""}
{"index": "s759003748", "label": 352, "func": ""}
{"index": "s762562319", "label": 352, "func": ""}
{"index": "s520876811", "label": 352, "func": ""}
{"index": "s346420469", "label": 352, "func": ""}
{"index": "s033975843", "label": 352, "func": ""}
{"index": "s837412473", "label": 352, "func": ""}
{"index": "s168347839", "label": 352, "func": ""}
{"index": "s327380342", "label": 352, "func": ""}
{"index": "s967543943", "label": 352, "func": ""}
{"index": "s461527389", "label": 1358, "func": ""}
{"index": "s692998366", "label": 1358, "func": ""}
{"index": "s982420181", "label": 1358, "func": ""}
{"index": "s852316252", "label": 1358, "func": ""}
{"index": "s662884664", "label": 138, "func": ""}
{"index": "s220426261", "label": 138, "func": ""}
{"index": "s325927790", "label": 138, "func": ""}
{"index": "s395217853", "label": 138, "func": ""}
{"index": "s555116653", "label": 138, "func": ""}
{"index": "s205134022", "label": 138, "func": ""}
{"index": "s672013116", "label": 138, "func": ""}
{"index": "s997888839", "label": 138, "func": ""}
{"index": "s948059167", "label": 138, "func": ""}
{"index": "s026837619", "label": 138, "func": ""}
{"index": "s199877730", "label": 2761, "func": ""}
{"index": "s390396256", "label": 2761, "func": ""}
{"index": "s659229363", "label": 2761, "func": ""}
{"index": "s279024370", "label": 2761, "func": ""}
{"index": "s917886594", "label": 2761, "func": ""}
{"index": "s482730648", "label": 2761, "func": ""}
{"index": "s124570181", "label": 2761, "func": ""}
{"index": "s455578358", "label": 2761, "func": ""}
{"index": "s956599009", "label": 2761, "func": ""}
{"index": "s158470885", "label": 2761, "func": ""}
{"index": "s882880021", "label": 3507, "func": ""}
{"index": "s023989906", "label": 3507, "func": ""}
{"index": "s267606722", "label": 3507, "func": ""}
{"index": "s994175424", "label": 3507, "func": ""}
{"index": "s998381806", "label": 3507, "func": ""}
{"index": "s805303013", "label": 3610, "func": ""}
{"index": "s433808608", "label": 3610, "func": ""}
{"index": "s088144453", "label": 3610, "func": ""}
{"index": "s791841079", "label": 3610, "func": ""}
{"index": "s265516987", "label": 3610, "func": ""}
{"index": "s132084210", "label": 3610, "func": ""}
{"index": "s003888533", "label": 3610, "func": ""}
{"index": "s690007244", "label": 3610, "func": ""}
{"index": "s229212679", "label": 3610, "func": ""}
{"index": "s686152256", "label": 3610, "func": ""}
{"index": "s607477315", "label": 2204, "func": ""}
{"index": "s895380088", "label": 2204, "func": ""}
{"index": "s160358824", "label": 2998, "func": ""}
{"index": "s523493787", "label": 2998, "func": ""}
{"index": "s161394678", "label": 2998, "func": ""}
{"index": "s740369223", "label": 2998, "func": ""}
{"index": "s258000073", "label": 2998, "func": ""}
{"index": "s929076585", "label": 2998, "func": ""}
{"index": "s042096416", "label": 2998, "func": ""}
{"index": "s267107525", "label": 2998, "func": ""}
{"index": "s720772308", "label": 2998, "func": ""}
{"index": "s417057193", "label": 2998, "func": ""}
{"index": "s289145115", "label": 849, "func": ""}
{"index": "s805518523", "label": 2242, "func": ""}
{"index": "s912576241", "label": 2242, "func": ""}
{"index": "s959848464", "label": 2242, "func": ""}
{"index": "s762389569", "label": 2242, "func": ""}
{"index": "s375301272", "label": 2242, "func": ""}
{"index": "s013612708", "label": 2242, "func": ""}
{"index": "s340006553", "label": 2242, "func": ""}
{"index": "s376963832", "label": 2242, "func": ""}
{"index": "s439137378", "label": 2242, "func": ""}
{"index": "s571843373", "label": 2242, "func": ""}
{"index": "s372020660", "label": 1493, "func": ""}
{"index": "s875124517", "label": 1493, "func": ""}
{"index": "s605814903", "label": 924, "func": ""}
{"index": "s423807407", "label": 924, "func": ""}
{"index": "s739849216", "label": 924, "func": ""}
{"index": "s548812392", "label": 924, "func": ""}
{"index": "s480716995", "label": 924, "func": ""}
{"index": "s760553625", "label": 924, "func": ""}
{"index": "s687941211", "label": 924, "func": ""}
{"index": "s958767895", "label": 924, "func": ""}
{"index": "s744155093", "label": 924, "func": ""}
{"index": "s394302868", "label": 924, "func": ""}
{"index": "s457126351", "label": 3172, "func": ""}
{"index": "s881696177", "label": 3172, "func": ""}
{"index": "s866905624", "label": 3172, "func": ""}
{"index": "s796713650", "label": 3172, "func": ""}
{"index": "s577028823", "label": 3172, "func": ""}
{"index": "s568619792", "label": 3172, "func": ""}
{"index": "s667655838", "label": 3172, "func": ""}
{"index": "s752755544", "label": 3172, "func": ""}
{"index": "s954915491", "label": 3172, "func": ""}
{"index": "s786891062", "label": 3172, "func": ""}
{"index": "s756070723", "label": 3245, "func": ""}
{"index": "s705070042", "label": 3245, "func": ""}
{"index": "s434137437", "label": 3245, "func": ""}
{"index": "s985644948", "label": 3245, "func": ""}
{"index": "s322754175", "label": 3245, "func": ""}
{"index": "s723008953", "label": 3245, "func": ""}
{"index": "s613953638", "label": 3245, "func": ""}
{"index": "s425032122", "label": 2303, "func": ""}
{"index": "s447848830", "label": 3526, "func": ""}
{"index": "s087473998", "label": 3526, "func": ""}
{"index": "s214675685", "label": 3526, "func": ""}
{"index": "s587970652", "label": 3526, "func": ""}
{"index": "s615682845", "label": 3526, "func": ""}
{"index": "s186673443", "label": 3683, "func": ""}
{"index": "s790233372", "label": 3683, "func": ""}
{"index": "s659580950", "label": 3683, "func": ""}
{"index": "s367791149", "label": 3683, "func": ""}
{"index": "s565793768", "label": 3683, "func": ""}
{"index": "s025713213", "label": 3683, "func": ""}
{"index": "s747623945", "label": 3683, "func": ""}
{"index": "s775817497", "label": 3683, "func": ""}
{"index": "s780964831", "label": 3683, "func": ""}
{"index": "s012480265", "label": 3683, "func": ""}
{"index": "s131039521", "label": 32, "func": ""}
{"index": "s581258735", "label": 32, "func": ""}
{"index": "s191300745", "label": 32, "func": ""}
{"index": "s947569047", "label": 32, "func": ""}
{"index": "s369282772", "label": 32, "func": ""}
{"index": "s388673103", "label": 32, "func": ""}
{"index": "s281003179", "label": 32, "func": ""}
{"index": "s704177701", "label": 32, "func": ""}
{"index": "s304190439", "label": 32, "func": ""}
{"index": "s197280106", "label": 32, "func": ""}
{"index": "s523189169", "label": 3325, "func": ""}
{"index": "s662164331", "label": 3325, "func": ""}
{"index": "s554345060", "label": 3325, "func": ""}
{"index": "s458853809", "label": 3325, "func": ""}
{"index": "s110733265", "label": 3325, "func": ""}
{"index": "s632927160", "label": 3325, "func": ""}
{"index": "s853577116", "label": 3325, "func": ""}
{"index": "s038840420", "label": 3325, "func": ""}
{"index": "s677860638", "label": 3325, "func": ""}
{"index": "s225375610", "label": 3325, "func": ""}
{"index": "s114539553", "label": 3416, "func": ""}
{"index": "s728294376", "label": 3416, "func": ""}
{"index": "s542496355", "label": 3416, "func": ""}
{"index": "s433105440", "label": 3416, "func": ""}
{"index": "s817172435", "label": 3416, "func": ""}
{"index": "s366119960", "label": 3416, "func": ""}
{"index": "s522442695", "label": 3416, "func": ""}
{"index": "s755886370", "label": 3416, "func": ""}
{"index": "s988188585", "label": 3416, "func": ""}
{"index": "s408637761", "label": 3416, "func": ""}
{"index": "s239818019", "label": 3062, "func": ""}
{"index": "s261372645", "label": 3062, "func": ""}
{"index": "s472703998", "label": 3062, "func": ""}
{"index": "s837999232", "label": 3062, "func": ""}
{"index": "s977231133", "label": 3062, "func": ""}
{"index": "s595610024", "label": 3062, "func": ""}
{"index": "s168455712", "label": 3062, "func": ""}
{"index": "s380004867", "label": 3062, "func": ""}
{"index": "s663392440", "label": 3062, "func": ""}
{"index": "s710248518", "label": 3062, "func": ""}
{"index": "s732937290", "label": 1539, "func": ""}
{"index": "s668848492", "label": 1539, "func": ""}
{"index": "s021231258", "label": 1539, "func": ""}
{"index": "s500426951", "label": 1539, "func": ""}
{"index": "s316390373", "label": 1539, "func": ""}
{"index": "s752576315", "label": 1539, "func": ""}
{"index": "s765245004", "label": 1539, "func": ""}
{"index": "s141647660", "label": 1539, "func": ""}
{"index": "s120308002", "label": 1539, "func": ""}
{"index": "s126275268", "label": 1539, "func": ""}
{"index": "s268698415", "label": 2748, "func": ""}
{"index": "s698344874", "label": 2748, "func": ""}
{"index": "s681271542", "label": 2748, "func": ""}
{"index": "s798601779", "label": 2748, "func": ""}
{"index": "s521157265", "label": 2748, "func": ""}
{"index": "s567821909", "label": 2748, "func": ""}
{"index": "s417797191", "label": 2748, "func": ""}
{"index": "s708842291", "label": 2748, "func": ""}
{"index": "s366465377", "label": 2748, "func": ""}
{"index": "s708620114", "label": 2748, "func": ""}
{"index": "s932191459", "label": 2531, "func": ""}
{"index": "s054105807", "label": 2531, "func": ""}
{"index": "s135660944", "label": 2531, "func": ""}
{"index": "s286672601", "label": 2531, "func": ""}
{"index": "s285311779", "label": 2531, "func": ""}
{"index": "s184734964", "label": 2531, "func": ""}
{"index": "s008260003", "label": 2531, "func": ""}
{"index": "s954278594", "label": 2531, "func": ""}
{"index": "s806158395", "label": 2531, "func": ""}
{"index": "s476519689", "label": 2531, "func": ""}
{"index": "s273443924", "label": 1423, "func": ""}
{"index": "s660097808", "label": 1423, "func": ""}
{"index": "s872723900", "label": 2319, "func": ""}
{"index": "s286029153", "label": 2319, "func": ""}
{"index": "s473744488", "label": 2319, "func": ""}
{"index": "s170954863", "label": 2319, "func": ""}
{"index": "s934435263", "label": 2319, "func": ""}
{"index": "s416490268", "label": 2319, "func": ""}
{"index": "s744293796", "label": 2319, "func": ""}
{"index": "s761266977", "label": 2319, "func": ""}
{"index": "s016976308", "label": 2319, "func": ""}
{"index": "s453175360", "label": 2319, "func": ""}
{"index": "s037433177", "label": 1544, "func": ""}
{"index": "s551685326", "label": 1544, "func": ""}
{"index": "s337820994", "label": 2921, "func": ""}
{"index": "s936196652", "label": 2921, "func": ""}
{"index": "s652019777", "label": 2921, "func": ""}
{"index": "s088067751", "label": 2921, "func": ""}
{"index": "s503395092", "label": 2921, "func": ""}
{"index": "s252652105", "label": 2921, "func": ""}
{"index": "s447546604", "label": 2921, "func": ""}
{"index": "s264766316", "label": 2921, "func": ""}
{"index": "s990862269", "label": 2921, "func": ""}
{"index": "s934002332", "label": 2921, "func": ""}
{"index": "s569479341", "label": 19, "func": ""}
{"index": "s129685055", "label": 19, "func": ""}
{"index": "s126995880", "label": 19, "func": ""}
{"index": "s709000583", "label": 19, "func": ""}
{"index": "s097944700", "label": 19, "func": ""}
{"index": "s864052067", "label": 19, "func": ""}
{"index": "s331692364", "label": 19, "func": ""}
{"index": "s103544693", "label": 19, "func": ""}
{"index": "s732309872", "label": 19, "func": ""}
{"index": "s206081770", "label": 19, "func": ""}
{"index": "s000327579", "label": 957, "func": ""}
{"index": "s444969667", "label": 957, "func": ""}
{"index": "s376955350", "label": 957, "func": ""}
{"index": "s847433176", "label": 957, "func": ""}
{"index": "s148851456", "label": 957, "func": ""}
{"index": "s968272597", "label": 957, "func": ""}
{"index": "s090401371", "label": 3484, "func": ""}
{"index": "s175819498", "label": 3484, "func": ""}
{"index": "s779462982", "label": 3390, "func": ""}
{"index": "s387632041", "label": 3390, "func": ""}
{"index": "s824372280", "label": 3390, "func": ""}
{"index": "s791677724", "label": 3390, "func": ""}
{"index": "s877696220", "label": 3390, "func": ""}
{"index": "s403014240", "label": 3390, "func": ""}
{"index": "s831359028", "label": 3390, "func": ""}
{"index": "s472470650", "label": 3390, "func": ""}
{"index": "s478183541", "label": 3390, "func": ""}
{"index": "s546684128", "label": 3390, "func": ""}
{"index": "s464284192", "label": 4018, "func": ""}
{"index": "s340357664", "label": 4018, "func": ""}
{"index": "s785894575", "label": 4018, "func": ""}
{"index": "s565568682", "label": 892, "func": ""}
{"index": "s120998475", "label": 892, "func": ""}
{"index": "s903628436", "label": 892, "func": ""}
{"index": "s296653102", "label": 237, "func": ""}
{"index": "s887796832", "label": 237, "func": ""}
{"index": "s364368365", "label": 237, "func": ""}
{"index": "s665940464", "label": 237, "func": ""}
{"index": "s825783707", "label": 2541, "func": ""}
{"index": "s913571366", "label": 2541, "func": ""}
{"index": "s687382214", "label": 2541, "func": ""}
{"index": "s099788728", "label": 2541, "func": ""}
{"index": "s163018177", "label": 2541, "func": ""}
{"index": "s130697056", "label": 2541, "func": ""}
{"index": "s754577469", "label": 2541, "func": ""}
{"index": "s299673801", "label": 2541, "func": ""}
{"index": "s955348992", "label": 2541, "func": ""}
{"index": "s524505162", "label": 2541, "func": ""}
{"index": "s083526646", "label": 1362, "func": ""}
{"index": "s881157598", "label": 1362, "func": ""}
{"index": "s825922657", "label": 1362, "func": ""}
{"index": "s546897339", "label": 1378, "func": ""}
{"index": "s042875091", "label": 1378, "func": ""}
{"index": "s878150553", "label": 1378, "func": ""}
{"index": "s115633375", "label": 1378, "func": ""}
{"index": "s092585755", "label": 1378, "func": ""}
{"index": "s610211345", "label": 1378, "func": ""}
{"index": "s887203657", "label": 1378, "func": ""}
{"index": "s237286452", "label": 1378, "func": ""}
{"index": "s228709248", "label": 1378, "func": ""}
{"index": "s704527140", "label": 3356, "func": ""}
{"index": "s146115567", "label": 3356, "func": ""}
{"index": "s455987431", "label": 3356, "func": ""}
{"index": "s500621504", "label": 3356, "func": ""}
{"index": "s667496649", "label": 3356, "func": ""}
{"index": "s259417483", "label": 3356, "func": ""}
{"index": "s347208192", "label": 3356, "func": ""}
{"index": "s014639168", "label": 3356, "func": ""}
{"index": "s184545145", "label": 3356, "func": ""}
{"index": "s495895193", "label": 3356, "func": ""}
{"index": "s078310587", "label": 850, "func": ""}
{"index": "s214632769", "label": 850, "func": ""}
{"index": "s604068491", "label": 850, "func": ""}
{"index": "s526228864", "label": 2678, "func": ""}
{"index": "s161841170", "label": 2678, "func": ""}
{"index": "s737294188", "label": 2678, "func": ""}
{"index": "s000373715", "label": 2678, "func": ""}
{"index": "s709510096", "label": 2678, "func": ""}
{"index": "s639263905", "label": 2678, "func": ""}
{"index": "s285320907", "label": 2678, "func": ""}
{"index": "s108392768", "label": 2678, "func": ""}
{"index": "s804962028", "label": 2678, "func": ""}
{"index": "s426457541", "label": 2678, "func": ""}
{"index": "s431257547", "label": 2556, "func": ""}
{"index": "s391931276", "label": 2556, "func": ""}
{"index": "s080122047", "label": 2556, "func": ""}
{"index": "s735056322", "label": 2556, "func": ""}
{"index": "s157036805", "label": 2556, "func": ""}
{"index": "s181207036", "label": 2556, "func": ""}
{"index": "s403639868", "label": 2556, "func": ""}
{"index": "s505311912", "label": 2556, "func": ""}
{"index": "s434945534", "label": 2556, "func": ""}
{"index": "s769816334", "label": 2556, "func": ""}
{"index": "s906732115", "label": 897, "func": ""}
{"index": "s878950870", "label": 897, "func": ""}
{"index": "s944956603", "label": 897, "func": ""}
{"index": "s191215248", "label": 897, "func": ""}
{"index": "s753008537", "label": 897, "func": ""}
{"index": "s639285644", "label": 897, "func": ""}
{"index": "s089315755", "label": 897, "func": ""}
{"index": "s963387015", "label": 3599, "func": ""}
{"index": "s797356977", "label": 3599, "func": ""}
{"index": "s100218225", "label": 3599, "func": ""}
{"index": "s504051418", "label": 3599, "func": ""}
{"index": "s812717751", "label": 3599, "func": ""}
{"index": "s822985173", "label": 3599, "func": ""}
{"index": "s330185416", "label": 3599, "func": ""}
{"index": "s820436259", "label": 3599, "func": ""}
{"index": "s259944735", "label": 3599, "func": ""}
{"index": "s410634670", "label": 3599, "func": ""}
{"index": "s878805555", "label": 105, "func": ""}
{"index": "s313377872", "label": 105, "func": ""}
{"index": "s547577075", "label": 105, "func": ""}
{"index": "s931610957", "label": 105, "func": ""}
{"index": "s727364786", "label": 105, "func": ""}
{"index": "s268142151", "label": 105, "func": ""}
{"index": "s918364501", "label": 105, "func": ""}
{"index": "s267048057", "label": 105, "func": ""}
{"index": "s972922428", "label": 105, "func": ""}
{"index": "s784969265", "label": 105, "func": ""}
{"index": "s540004375", "label": 2618, "func": ""}
{"index": "s103102105", "label": 2618, "func": ""}
{"index": "s929535897", "label": 2618, "func": ""}
{"index": "s013833498", "label": 2618, "func": ""}
{"index": "s931958440", "label": 2618, "func": ""}
{"index": "s218676523", "label": 2618, "func": ""}
{"index": "s900674413", "label": 2618, "func": ""}
{"index": "s366704141", "label": 2618, "func": ""}
{"index": "s437741878", "label": 2618, "func": ""}
{"index": "s353628548", "label": 2618, "func": ""}
{"index": "s268721989", "label": 3827, "func": ""}
{"index": "s229629347", "label": 3827, "func": ""}
{"index": "s091324471", "label": 3827, "func": ""}
{"index": "s902787973", "label": 3827, "func": ""}
{"index": "s854640185", "label": 3827, "func": ""}
{"index": "s124112335", "label": 3827, "func": ""}
{"index": "s421415548", "label": 3827, "func": ""}
{"index": "s254719462", "label": 3827, "func": ""}
{"index": "s974264859", "label": 3827, "func": ""}
{"index": "s444045502", "label": 3827, "func": ""}
{"index": "s456676630", "label": 3630, "func": ""}
{"index": "s114759325", "label": 3630, "func": ""}
{"index": "s605788503", "label": 3630, "func": ""}
{"index": "s368139324", "label": 3630, "func": ""}
{"index": "s509725069", "label": 3630, "func": ""}
{"index": "s779702563", "label": 3630, "func": ""}
{"index": "s563692004", "label": 3630, "func": ""}
{"index": "s499619044", "label": 3506, "func": ""}
{"index": "s387765929", "label": 3990, "func": ""}
{"index": "s216116383", "label": 3990, "func": ""}
{"index": "s751264872", "label": 3990, "func": ""}
{"index": "s217128061", "label": 3990, "func": ""}
{"index": "s072867694", "label": 3993, "func": ""}
{"index": "s974716411", "label": 3993, "func": ""}
{"index": "s578500399", "label": 3993, "func": ""}
{"index": "s799110981", "label": 3993, "func": ""}
{"index": "s355807742", "label": 3993, "func": ""}
{"index": "s145483198", "label": 3993, "func": ""}
{"index": "s034661148", "label": 3993, "func": ""}
{"index": "s189635654", "label": 3993, "func": ""}
{"index": "s254209865", "label": 3993, "func": ""}
{"index": "s870304593", "label": 3993, "func": ""}
{"index": "s157864251", "label": 3284, "func": ""}
{"index": "s186070349", "label": 3284, "func": ""}
{"index": "s829849707", "label": 3284, "func": ""}
{"index": "s187335920", "label": 3284, "func": ""}
{"index": "s384447171", "label": 3284, "func": ""}
{"index": "s996322685", "label": 3284, "func": ""}
{"index": "s347291455", "label": 3284, "func": ""}
{"index": "s597108415", "label": 3284, "func": ""}
{"index": "s389649408", "label": 3284, "func": ""}
{"index": "s680345552", "label": 3284, "func": ""}
{"index": "s669587015", "label": 687, "func": ""}
{"index": "s267069724", "label": 687, "func": ""}
{"index": "s677194509", "label": 687, "func": ""}
{"index": "s968574043", "label": 687, "func": ""}
{"index": "s023025683", "label": 687, "func": ""}
{"index": "s611439120", "label": 687, "func": ""}
{"index": "s507715022", "label": 687, "func": ""}
{"index": "s440372532", "label": 687, "func": ""}
{"index": "s383157510", "label": 687, "func": ""}
{"index": "s750811523", "label": 687, "func": ""}
{"index": "s897516085", "label": 3871, "func": ""}
{"index": "s243657740", "label": 3871, "func": ""}
{"index": "s265050269", "label": 3871, "func": ""}
{"index": "s784986274", "label": 3871, "func": ""}
{"index": "s490151984", "label": 3340, "func": ""}
{"index": "s099542007", "label": 3340, "func": ""}
{"index": "s372484876", "label": 3340, "func": ""}
{"index": "s373858145", "label": 3340, "func": ""}
{"index": "s582398867", "label": 3340, "func": ""}
{"index": "s189690898", "label": 3340, "func": ""}
{"index": "s249043803", "label": 3340, "func": ""}
{"index": "s696114919", "label": 3340, "func": ""}
{"index": "s407474849", "label": 3340, "func": ""}
{"index": "s717673272", "label": 3340, "func": ""}
{"index": "s079152570", "label": 3044, "func": ""}
{"index": "s186454359", "label": 3044, "func": ""}
{"index": "s520853573", "label": 3044, "func": ""}
{"index": "s093753369", "label": 3044, "func": ""}
{"index": "s323670902", "label": 3044, "func": ""}
{"index": "s207706212", "label": 3044, "func": ""}
{"index": "s181997965", "label": 3044, "func": ""}
{"index": "s214710322", "label": 3044, "func": ""}
{"index": "s560019998", "label": 3044, "func": ""}
{"index": "s831706624", "label": 3044, "func": ""}
{"index": "s225836641", "label": 3823, "func": ""}
{"index": "s899623334", "label": 3823, "func": ""}
{"index": "s522516640", "label": 3823, "func": ""}
{"index": "s889912385", "label": 3823, "func": ""}
{"index": "s677996843", "label": 3823, "func": ""}
{"index": "s047916024", "label": 3823, "func": ""}
{"index": "s646085727", "label": 3823, "func": ""}
{"index": "s670929697", "label": 1613, "func": ""}
{"index": "s402459604", "label": 1613, "func": ""}
{"index": "s634217464", "label": 1613, "func": ""}
{"index": "s009302547", "label": 1613, "func": ""}
{"index": "s483000274", "label": 1613, "func": ""}
{"index": "s745865309", "label": 1613, "func": ""}
{"index": "s661680522", "label": 1613, "func": ""}
{"index": "s374170059", "label": 1613, "func": ""}
{"index": "s486221541", "label": 1613, "func": ""}
{"index": "s690400925", "label": 1613, "func": ""}
{"index": "s773380186", "label": 2753, "func": ""}
{"index": "s061933306", "label": 2753, "func": ""}
{"index": "s397124128", "label": 2753, "func": ""}
{"index": "s514373012", "label": 2753, "func": ""}
{"index": "s390024641", "label": 2753, "func": ""}
{"index": "s704528780", "label": 2753, "func": ""}
{"index": "s079511336", "label": 2753, "func": ""}
{"index": "s239133435", "label": 2753, "func": ""}
{"index": "s356561999", "label": 2753, "func": ""}
{"index": "s760963433", "label": 2753, "func": ""}
{"index": "s556527007", "label": 2547, "func": ""}
{"index": "s809101976", "label": 2547, "func": ""}
{"index": "s253052489", "label": 2547, "func": ""}
{"index": "s176095211", "label": 2547, "func": ""}
{"index": "s629288041", "label": 2547, "func": ""}
{"index": "s447136230", "label": 2547, "func": ""}
{"index": "s642390928", "label": 2547, "func": ""}
{"index": "s333145690", "label": 2547, "func": ""}
{"index": "s128950374", "label": 2547, "func": ""}
{"index": "s271788011", "label": 2547, "func": ""}
{"index": "s915672354", "label": 3371, "func": ""}
{"index": "s678681616", "label": 3371, "func": ""}
{"index": "s569636109", "label": 3371, "func": ""}
{"index": "s537435397", "label": 3371, "func": ""}
{"index": "s359745462", "label": 3371, "func": ""}
{"index": "s263758136", "label": 3371, "func": ""}
{"index": "s635459911", "label": 3371, "func": ""}
{"index": "s006646818", "label": 3371, "func": ""}
{"index": "s772745595", "label": 3371, "func": ""}
{"index": "s913403736", "label": 3371, "func": ""}
{"index": "s286725533", "label": 747, "func": ""}
{"index": "s344310059", "label": 747, "func": ""}
{"index": "s712964917", "label": 747, "func": ""}
{"index": "s512809819", "label": 747, "func": ""}
{"index": "s367968028", "label": 747, "func": ""}
{"index": "s753413078", "label": 747, "func": ""}
{"index": "s379212663", "label": 747, "func": ""}
{"index": "s273868122", "label": 747, "func": ""}
{"index": "s748712386", "label": 747, "func": ""}
{"index": "s310500682", "label": 747, "func": ""}
{"index": "s833885928", "label": 2555, "func": ""}
{"index": "s336326035", "label": 2555, "func": ""}
{"index": "s577685684", "label": 2555, "func": ""}
{"index": "s235995540", "label": 2555, "func": ""}
{"index": "s153419944", "label": 2555, "func": ""}
{"index": "s630983638", "label": 2555, "func": ""}
{"index": "s000049456", "label": 2555, "func": ""}
{"index": "s690515325", "label": 2555, "func": ""}
{"index": "s315093775", "label": 2555, "func": ""}
{"index": "s516932115", "label": 2555, "func": ""}
{"index": "s377521831", "label": 2560, "func": ""}
{"index": "s208092382", "label": 2560, "func": ""}
{"index": "s773283839", "label": 2560, "func": ""}
{"index": "s339466025", "label": 2560, "func": ""}
{"index": "s284001942", "label": 2560, "func": ""}
{"index": "s101758291", "label": 2560, "func": ""}
{"index": "s971571013", "label": 1134, "func": ""}
{"index": "s681014357", "label": 1134, "func": ""}
{"index": "s531285350", "label": 1134, "func": ""}
{"index": "s918346960", "label": 1134, "func": ""}
{"index": "s938263874", "label": 1134, "func": ""}
{"index": "s742813716", "label": 1134, "func": ""}
{"index": "s710274188", "label": 1134, "func": ""}
{"index": "s099516705", "label": 1134, "func": ""}
{"index": "s681099509", "label": 1134, "func": ""}
{"index": "s279429941", "label": 3419, "func": ""}
{"index": "s051726728", "label": 3419, "func": ""}
{"index": "s722309004", "label": 3419, "func": ""}
{"index": "s241480353", "label": 3419, "func": ""}
{"index": "s801356834", "label": 3419, "func": ""}
{"index": "s140029294", "label": 3419, "func": ""}
{"index": "s391922234", "label": 3419, "func": ""}
{"index": "s541286067", "label": 3419, "func": ""}
{"index": "s324659946", "label": 3419, "func": ""}
{"index": "s414974836", "label": 3419, "func": ""}
{"index": "s715973582", "label": 2192, "func": ""}
{"index": "s790325539", "label": 3386, "func": ""}
{"index": "s987656005", "label": 3386, "func": ""}
{"index": "s749650570", "label": 3386, "func": ""}
{"index": "s238830359", "label": 3386, "func": ""}
{"index": "s939842046", "label": 3386, "func": ""}
{"index": "s002352397", "label": 3386, "func": ""}
{"index": "s470617323", "label": 3386, "func": ""}
{"index": "s990364472", "label": 3386, "func": ""}
{"index": "s709938764", "label": 3386, "func": ""}
{"index": "s074187335", "label": 3386, "func": ""}
{"index": "s059640509", "label": 2866, "func": ""}
{"index": "s902545437", "label": 2866, "func": ""}
{"index": "s991821815", "label": 2866, "func": ""}
{"index": "s236343925", "label": 2866, "func": ""}
{"index": "s601924697", "label": 2866, "func": ""}
{"index": "s802748132", "label": 2866, "func": ""}
{"index": "s353305688", "label": 2866, "func": ""}
{"index": "s892768410", "label": 2866, "func": ""}
{"index": "s084324474", "label": 2866, "func": ""}
{"index": "s064981217", "label": 2866, "func": ""}
{"index": "s891102115", "label": 3292, "func": ""}
{"index": "s176185570", "label": 3292, "func": ""}
{"index": "s108458145", "label": 3292, "func": ""}
{"index": "s437760766", "label": 3292, "func": ""}
{"index": "s957142102", "label": 3292, "func": ""}
{"index": "s284062007", "label": 3292, "func": ""}
{"index": "s261077230", "label": 3292, "func": ""}
{"index": "s891597514", "label": 3292, "func": ""}
{"index": "s978763179", "label": 3292, "func": ""}
{"index": "s470182850", "label": 3292, "func": ""}
{"index": "s651822978", "label": 318, "func": ""}
{"index": "s780915705", "label": 1610, "func": ""}
{"index": "s802806202", "label": 1610, "func": ""}
{"index": "s200336192", "label": 1610, "func": ""}
{"index": "s778984569", "label": 1610, "func": ""}
{"index": "s186331741", "label": 3850, "func": ""}
{"index": "s705964703", "label": 3850, "func": ""}
{"index": "s648766712", "label": 3850, "func": ""}
{"index": "s758769065", "label": 3850, "func": ""}
{"index": "s433891713", "label": 2320, "func": ""}
{"index": "s239811708", "label": 2320, "func": ""}
{"index": "s402407761", "label": 2320, "func": ""}
{"index": "s969594135", "label": 2320, "func": ""}
{"index": "s357161108", "label": 2320, "func": ""}
{"index": "s807514543", "label": 2320, "func": ""}
{"index": "s690219368", "label": 2320, "func": ""}
{"index": "s405054535", "label": 2320, "func": ""}
{"index": "s411137264", "label": 2320, "func": ""}
{"index": "s147540015", "label": 474, "func": ""}
{"index": "s205226984", "label": 474, "func": ""}
{"index": "s201122586", "label": 474, "func": ""}
{"index": "s362699498", "label": 474, "func": ""}
{"index": "s797689448", "label": 474, "func": ""}
{"index": "s280559689", "label": 474, "func": ""}
{"index": "s061735404", "label": 474, "func": ""}
{"index": "s888199100", "label": 474, "func": ""}
{"index": "s863013829", "label": 474, "func": ""}
{"index": "s279829954", "label": 474, "func": ""}
{"index": "s593055641", "label": 345, "func": ""}
{"index": "s957389809", "label": 345, "func": ""}
{"index": "s335870642", "label": 3692, "func": ""}
{"index": "s934048613", "label": 1482, "func": ""}
{"index": "s895401583", "label": 1482, "func": ""}
{"index": "s833739296", "label": 1880, "func": ""}
{"index": "s924708190", "label": 1880, "func": ""}
{"index": "s998867740", "label": 1880, "func": ""}
{"index": "s583861459", "label": 1880, "func": ""}
{"index": "s153546623", "label": 1880, "func": ""}
{"index": "s888544290", "label": 1880, "func": ""}
{"index": "s403456040", "label": 1880, "func": ""}
{"index": "s014580590", "label": 1880, "func": ""}
{"index": "s721091559", "label": 1880, "func": ""}
{"index": "s820139366", "label": 1880, "func": ""}
{"index": "s004296120", "label": 3319, "func": ""}
{"index": "s565252349", "label": 3319, "func": ""}
{"index": "s847475226", "label": 3319, "func": ""}
{"index": "s826637286", "label": 3319, "func": ""}
{"index": "s752663832", "label": 3319, "func": ""}
{"index": "s354064102", "label": 3319, "func": ""}
{"index": "s732864629", "label": 3319, "func": ""}
{"index": "s331541229", "label": 3319, "func": ""}
{"index": "s831758233", "label": 3319, "func": ""}
{"index": "s271301291", "label": 3319, "func": ""}
{"index": "s940831195", "label": 3491, "func": ""}
{"index": "s652606904", "label": 3491, "func": ""}
{"index": "s263763737", "label": 3491, "func": ""}
{"index": "s473839577", "label": 3620, "func": ""}
{"index": "s643372132", "label": 3620, "func": ""}
{"index": "s449679991", "label": 176, "func": ""}
{"index": "s790777685", "label": 176, "func": ""}
{"index": "s433396657", "label": 176, "func": ""}
{"index": "s919061957", "label": 176, "func": ""}
{"index": "s379477452", "label": 176, "func": ""}
{"index": "s239492413", "label": 176, "func": ""}
{"index": "s551520449", "label": 176, "func": ""}
{"index": "s038116939", "label": 176, "func": ""}
{"index": "s750219798", "label": 176, "func": ""}
{"index": "s224282430", "label": 176, "func": ""}
{"index": "s102661727", "label": 1427, "func": ""}
{"index": "s488421620", "label": 1427, "func": ""}
{"index": "s967791574", "label": 3474, "func": ""}
{"index": "s760161010", "label": 3474, "func": ""}
{"index": "s186602302", "label": 3474, "func": ""}
{"index": "s754420733", "label": 3474, "func": ""}
{"index": "s263818175", "label": 3474, "func": ""}
{"index": "s261697248", "label": 3474, "func": ""}
{"index": "s836591852", "label": 3474, "func": ""}
{"index": "s074163951", "label": 3474, "func": ""}
{"index": "s905557682", "label": 3474, "func": ""}
{"index": "s777737654", "label": 3474, "func": ""}
{"index": "s678919584", "label": 1110, "func": ""}
{"index": "s407874851", "label": 1110, "func": ""}
{"index": "s605515345", "label": 1110, "func": ""}
{"index": "s172492048", "label": 1110, "func": ""}
{"index": "s032241433", "label": 1110, "func": ""}
{"index": "s188096022", "label": 1110, "func": ""}
{"index": "s897882799", "label": 1110, "func": ""}
{"index": "s159932968", "label": 1110, "func": ""}
{"index": "s105829336", "label": 1110, "func": ""}
{"index": "s770213064", "label": 1110, "func": ""}
{"index": "s941559464", "label": 643, "func": ""}
{"index": "s583293716", "label": 643, "func": ""}
{"index": "s730654191", "label": 643, "func": ""}
{"index": "s099043689", "label": 643, "func": ""}
{"index": "s167516037", "label": 643, "func": ""}
{"index": "s405493680", "label": 643, "func": ""}
{"index": "s879342306", "label": 643, "func": ""}
{"index": "s389175268", "label": 643, "func": ""}
{"index": "s123567158", "label": 643, "func": ""}
{"index": "s828619590", "label": 801, "func": ""}
{"index": "s934446945", "label": 801, "func": ""}
{"index": "s917699498", "label": 801, "func": ""}
{"index": "s466765612", "label": 1435, "func": ""}
{"index": "s255524170", "label": 3536, "func": ""}
{"index": "s551013888", "label": 3536, "func": ""}
{"index": "s575623902", "label": 3536, "func": ""}
{"index": "s344157423", "label": 3536, "func": ""}
{"index": "s686230388", "label": 3536, "func": ""}
{"index": "s215817223", "label": 3536, "func": ""}
{"index": "s067428033", "label": 3536, "func": ""}
{"index": "s984876851", "label": 3536, "func": ""}
{"index": "s885156791", "label": 3536, "func": ""}
{"index": "s754729146", "label": 790, "func": ""}
{"index": "s633986337", "label": 790, "func": ""}
{"index": "s488614988", "label": 790, "func": ""}
{"index": "s856720330", "label": 790, "func": ""}
{"index": "s504658681", "label": 790, "func": ""}
{"index": "s502708627", "label": 790, "func": ""}
{"index": "s154305557", "label": 790, "func": ""}
{"index": "s540608735", "label": 790, "func": ""}
{"index": "s070674093", "label": 790, "func": ""}
{"index": "s827935433", "label": 994, "func": ""}
{"index": "s908986702", "label": 2453, "func": ""}
{"index": "s680251886", "label": 2453, "func": ""}
{"index": "s632256750", "label": 2453, "func": ""}
{"index": "s253780386", "label": 2453, "func": ""}
{"index": "s007117809", "label": 2453, "func": ""}
{"index": "s318312105", "label": 2453, "func": ""}
{"index": "s960428793", "label": 2453, "func": ""}
{"index": "s967329641", "label": 2453, "func": ""}
{"index": "s690309237", "label": 2453, "func": ""}
{"index": "s666677865", "label": 2453, "func": ""}
{"index": "s682830058", "label": 2293, "func": ""}
{"index": "s093644114", "label": 2293, "func": ""}
{"index": "s376261259", "label": 2293, "func": ""}
{"index": "s168021248", "label": 2293, "func": ""}
{"index": "s172420537", "label": 2293, "func": ""}
{"index": "s281363584", "label": 2293, "func": ""}
{"index": "s993481113", "label": 2293, "func": ""}
{"index": "s088641860", "label": 2293, "func": ""}
{"index": "s883686404", "label": 2293, "func": ""}
{"index": "s815528249", "label": 2293, "func": ""}
{"index": "s769049559", "label": 2202, "func": ""}
{"index": "s668296272", "label": 2202, "func": ""}
{"index": "s875976790", "label": 2202, "func": ""}
{"index": "s395977836", "label": 2296, "func": ""}
{"index": "s795598469", "label": 2296, "func": ""}
{"index": "s581640491", "label": 2296, "func": ""}
{"index": "s483295978", "label": 2296, "func": ""}
{"index": "s415810596", "label": 2296, "func": ""}
{"index": "s014929791", "label": 2296, "func": ""}
{"index": "s206567132", "label": 2296, "func": ""}
{"index": "s013186667", "label": 2296, "func": ""}
{"index": "s801183136", "label": 2296, "func": ""}
{"index": "s183427960", "label": 2296, "func": ""}
{"index": "s138642176", "label": 1274, "func": ""}
{"index": "s366658507", "label": 1274, "func": ""}
{"index": "s205625249", "label": 2830, "func": ""}
{"index": "s295166632", "label": 2830, "func": ""}
{"index": "s313733622", "label": 2830, "func": ""}
{"index": "s780252630", "label": 2830, "func": ""}
{"index": "s089826785", "label": 2830, "func": ""}
{"index": "s947943070", "label": 2830, "func": ""}
{"index": "s672387517", "label": 2830, "func": ""}
{"index": "s943969052", "label": 2830, "func": ""}
{"index": "s833196271", "label": 2830, "func": ""}
{"index": "s814348023", "label": 2830, "func": ""}
{"index": "s902912856", "label": 2802, "func": ""}
{"index": "s231324425", "label": 2802, "func": ""}
{"index": "s770170179", "label": 2802, "func": ""}
{"index": "s116694455", "label": 2802, "func": ""}
{"index": "s299044551", "label": 2802, "func": ""}
{"index": "s174246244", "label": 2802, "func": ""}
{"index": "s057090188", "label": 2802, "func": ""}
{"index": "s390073644", "label": 2802, "func": ""}
{"index": "s296259069", "label": 2802, "func": ""}
{"index": "s722608816", "label": 2802, "func": ""}
{"index": "s317325987", "label": 693, "func": ""}
{"index": "s251097820", "label": 693, "func": ""}
{"index": "s366774728", "label": 693, "func": ""}
{"index": "s606366885", "label": 693, "func": ""}
{"index": "s074662348", "label": 693, "func": ""}
{"index": "s643376784", "label": 693, "func": ""}
{"index": "s834824344", "label": 693, "func": ""}
{"index": "s020975011", "label": 693, "func": ""}
{"index": "s968702500", "label": 693, "func": ""}
{"index": "s689494362", "label": 3070, "func": ""}
{"index": "s618933545", "label": 3070, "func": ""}
{"index": "s177875421", "label": 3070, "func": ""}
{"index": "s991582971", "label": 3070, "func": ""}
{"index": "s460510670", "label": 3070, "func": ""}
{"index": "s834504380", "label": 1558, "func": ""}
{"index": "s163783073", "label": 1558, "func": ""}
{"index": "s774951967", "label": 1558, "func": ""}
{"index": "s070771533", "label": 1558, "func": ""}
{"index": "s248271358", "label": 1558, "func": ""}
{"index": "s320270256", "label": 1558, "func": ""}
{"index": "s363069146", "label": 1558, "func": ""}
{"index": "s528381406", "label": 1558, "func": ""}
{"index": "s501710035", "label": 1558, "func": ""}
{"index": "s569192200", "label": 1558, "func": ""}
{"index": "s855645717", "label": 2883, "func": ""}
{"index": "s513377833", "label": 2883, "func": ""}
{"index": "s743758824", "label": 2883, "func": ""}
{"index": "s918259192", "label": 2883, "func": ""}
{"index": "s314552302", "label": 2883, "func": ""}
{"index": "s057072318", "label": 2883, "func": ""}
{"index": "s751806959", "label": 2883, "func": ""}
{"index": "s100982246", "label": 2883, "func": ""}
{"index": "s833857007", "label": 2883, "func": ""}
{"index": "s266309309", "label": 2883, "func": ""}
{"index": "s060251534", "label": 412, "func": ""}
{"index": "s852342783", "label": 3910, "func": ""}
{"index": "s492559314", "label": 3910, "func": ""}
{"index": "s432971452", "label": 3910, "func": ""}
{"index": "s537544320", "label": 3910, "func": ""}
{"index": "s693502558", "label": 3910, "func": ""}
{"index": "s645075446", "label": 3910, "func": ""}
{"index": "s707348206", "label": 3910, "func": ""}
{"index": "s294659168", "label": 3910, "func": ""}
{"index": "s309188310", "label": 3910, "func": ""}
{"index": "s238520168", "label": 3910, "func": ""}
{"index": "s400222801", "label": 2775, "func": ""}
{"index": "s890483600", "label": 2775, "func": ""}
{"index": "s999847605", "label": 2775, "func": ""}
{"index": "s274511306", "label": 2775, "func": ""}
{"index": "s053421143", "label": 2775, "func": ""}
{"index": "s849438051", "label": 2775, "func": ""}
{"index": "s915814957", "label": 2775, "func": ""}
{"index": "s422669661", "label": 2775, "func": ""}
{"index": "s335071670", "label": 2775, "func": ""}
{"index": "s536896265", "label": 2775, "func": ""}
{"index": "s467902565", "label": 2290, "func": ""}
{"index": "s688882259", "label": 2290, "func": ""}
{"index": "s228299354", "label": 2290, "func": ""}
{"index": "s673792524", "label": 2290, "func": ""}
{"index": "s370590570", "label": 2290, "func": ""}
{"index": "s767056463", "label": 2290, "func": ""}
{"index": "s525519255", "label": 2290, "func": ""}
{"index": "s236850196", "label": 2290, "func": ""}
{"index": "s306713968", "label": 2290, "func": ""}
{"index": "s829857945", "label": 2290, "func": ""}
{"index": "s097012877", "label": 1634, "func": ""}
{"index": "s065327632", "label": 1634, "func": ""}
{"index": "s184820067", "label": 1634, "func": ""}
{"index": "s794520117", "label": 1634, "func": ""}
{"index": "s282671072", "label": 1634, "func": ""}
{"index": "s868173908", "label": 1634, "func": ""}
{"index": "s711232887", "label": 1634, "func": ""}
{"index": "s976252410", "label": 1634, "func": ""}
{"index": "s042766987", "label": 1634, "func": ""}
{"index": "s887557593", "label": 1634, "func": ""}
{"index": "s109481721", "label": 223, "func": ""}
{"index": "s703414401", "label": 223, "func": ""}
{"index": "s776616224", "label": 223, "func": ""}
{"index": "s104142746", "label": 223, "func": ""}
{"index": "s661743644", "label": 223, "func": ""}
{"index": "s147693008", "label": 223, "func": ""}
{"index": "s888170925", "label": 223, "func": ""}
{"index": "s385823710", "label": 223, "func": ""}
{"index": "s829536874", "label": 223, "func": ""}
{"index": "s066636123", "label": 223, "func": ""}
{"index": "s218157364", "label": 2255, "func": ""}
{"index": "s192133031", "label": 2255, "func": ""}
{"index": "s356738910", "label": 2255, "func": ""}
{"index": "s777983102", "label": 2255, "func": ""}
{"index": "s744656829", "label": 2255, "func": ""}
{"index": "s610900444", "label": 2255, "func": ""}
{"index": "s423975271", "label": 2255, "func": ""}
{"index": "s881762026", "label": 2255, "func": ""}
{"index": "s083328474", "label": 2255, "func": ""}
{"index": "s293664707", "label": 2255, "func": ""}
{"index": "s216002124", "label": 1273, "func": ""}
{"index": "s381741746", "label": 1273, "func": ""}
{"index": "s449601313", "label": 1273, "func": ""}
{"index": "s184285525", "label": 1273, "func": ""}
{"index": "s594739701", "label": 1273, "func": ""}
{"index": "s431376650", "label": 1273, "func": ""}
{"index": "s223061848", "label": 1273, "func": ""}
{"index": "s908068784", "label": 1273, "func": ""}
{"index": "s744860099", "label": 1273, "func": ""}
{"index": "s295754955", "label": 1273, "func": ""}
{"index": "s300353564", "label": 3724, "func": ""}
{"index": "s729570257", "label": 3724, "func": ""}
{"index": "s206947987", "label": 3724, "func": ""}
{"index": "s743272681", "label": 3724, "func": ""}
{"index": "s376697815", "label": 3724, "func": ""}
{"index": "s929564225", "label": 3724, "func": ""}
{"index": "s021147384", "label": 3724, "func": ""}
{"index": "s054709230", "label": 3724, "func": ""}
{"index": "s854256740", "label": 3724, "func": ""}
{"index": "s280936388", "label": 3724, "func": ""}
{"index": "s257590479", "label": 230, "func": ""}
{"index": "s197886138", "label": 230, "func": ""}
{"index": "s219702936", "label": 230, "func": ""}
{"index": "s525269882", "label": 230, "func": ""}
{"index": "s348834022", "label": 230, "func": ""}
{"index": "s468190323", "label": 230, "func": ""}
{"index": "s043182283", "label": 230, "func": ""}
{"index": "s705509304", "label": 230, "func": ""}
{"index": "s110339283", "label": 230, "func": ""}
{"index": "s077515450", "label": 230, "func": ""}
{"index": "s699831472", "label": 1, "func": ""}
{"index": "s305146775", "label": 1, "func": ""}
{"index": "s847856888", "label": 1, "func": ""}
{"index": "s990633130", "label": 1, "func": ""}
{"index": "s383061006", "label": 1, "func": ""}
{"index": "s482380768", "label": 1, "func": ""}
{"index": "s118127379", "label": 1, "func": ""}
{"index": "s576287231", "label": 1, "func": ""}
{"index": "s967858671", "label": 1, "func": ""}
{"index": "s380961127", "label": 1, "func": ""}
{"index": "s375537141", "label": 2472, "func": ""}
{"index": "s845286302", "label": 2472, "func": ""}
{"index": "s067081874", "label": 2472, "func": ""}
{"index": "s354940324", "label": 2472, "func": ""}
{"index": "s749360065", "label": 2472, "func": ""}
{"index": "s306818692", "label": 2472, "func": ""}
{"index": "s955893910", "label": 2472, "func": ""}
{"index": "s124374119", "label": 2472, "func": ""}
{"index": "s529859734", "label": 2472, "func": ""}
{"index": "s608517086", "label": 2538, "func": ""}
{"index": "s354511220", "label": 2538, "func": ""}
{"index": "s159719057", "label": 2538, "func": ""}
{"index": "s855169055", "label": 2538, "func": ""}
{"index": "s772025290", "label": 2538, "func": ""}
{"index": "s275124221", "label": 2538, "func": ""}
{"index": "s383944115", "label": 2538, "func": ""}
{"index": "s116829613", "label": 2538, "func": ""}
{"index": "s927385281", "label": 2538, "func": ""}
{"index": "s396766978", "label": 2538, "func": ""}
{"index": "s285828203", "label": 796, "func": ""}
{"index": "s702664526", "label": 796, "func": ""}
{"index": "s444748410", "label": 796, "func": ""}
{"index": "s627889666", "label": 796, "func": ""}
{"index": "s959435020", "label": 796, "func": ""}
{"index": "s701400016", "label": 796, "func": ""}
{"index": "s808667741", "label": 796, "func": ""}
{"index": "s026657009", "label": 796, "func": ""}
{"index": "s161094295", "label": 796, "func": ""}
{"index": "s110913838", "label": 796, "func": ""}
{"index": "s454388988", "label": 3991, "func": ""}
{"index": "s339006655", "label": 3991, "func": ""}
{"index": "s853978417", "label": 3991, "func": ""}
{"index": "s610476199", "label": 3991, "func": ""}
{"index": "s338378083", "label": 3991, "func": ""}
{"index": "s781085387", "label": 3991, "func": ""}
{"index": "s013162105", "label": 3991, "func": ""}
{"index": "s414312222", "label": 3991, "func": ""}
{"index": "s386616358", "label": 3991, "func": ""}
{"index": "s116057260", "label": 3959, "func": ""}
{"index": "s161972142", "label": 3959, "func": ""}
{"index": "s808760863", "label": 3959, "func": ""}
{"index": "s383084773", "label": 3959, "func": ""}
{"index": "s767393366", "label": 3959, "func": ""}
{"index": "s324895901", "label": 3959, "func": ""}
{"index": "s643315388", "label": 3959, "func": ""}
{"index": "s783240864", "label": 3959, "func": ""}
{"index": "s673209677", "label": 3959, "func": ""}
{"index": "s690850407", "label": 3959, "func": ""}
{"index": "s189255973", "label": 2697, "func": ""}
{"index": "s887140057", "label": 2697, "func": ""}
{"index": "s266679183", "label": 2697, "func": ""}
{"index": "s316062923", "label": 2697, "func": ""}
{"index": "s636022207", "label": 2697, "func": ""}
{"index": "s867547090", "label": 2697, "func": ""}
{"index": "s151949861", "label": 2697, "func": ""}
{"index": "s235026756", "label": 2697, "func": ""}
{"index": "s803121421", "label": 2697, "func": ""}
{"index": "s869115378", "label": 2697, "func": ""}
{"index": "s161734706", "label": 3822, "func": ""}
{"index": "s619597640", "label": 3822, "func": ""}
{"index": "s730403124", "label": 3822, "func": ""}
{"index": "s067093542", "label": 3822, "func": ""}
{"index": "s595188329", "label": 3822, "func": ""}
{"index": "s776215850", "label": 3822, "func": ""}
{"index": "s725312320", "label": 3822, "func": ""}
{"index": "s902716338", "label": 3822, "func": ""}
{"index": "s737329564", "label": 3822, "func": ""}
{"index": "s690884701", "label": 3822, "func": ""}
{"index": "s245794111", "label": 1224, "func": ""}
{"index": "s900854341", "label": 1224, "func": ""}
{"index": "s019982101", "label": 1224, "func": ""}
{"index": "s715519499", "label": 1224, "func": ""}
{"index": "s940608611", "label": 1224, "func": ""}
{"index": "s505075287", "label": 1224, "func": ""}
{"index": "s672220671", "label": 1224, "func": ""}
{"index": "s929024541", "label": 1224, "func": ""}
{"index": "s221872291", "label": 1224, "func": ""}
{"index": "s425108387", "label": 1224, "func": ""}
{"index": "s845555922", "label": 335, "func": ""}
{"index": "s920839040", "label": 824, "func": ""}
{"index": "s109795979", "label": 824, "func": ""}
{"index": "s008833781", "label": 824, "func": ""}
{"index": "s473646159", "label": 824, "func": ""}
{"index": "s316041914", "label": 824, "func": ""}
{"index": "s290693677", "label": 824, "func": ""}
{"index": "s059071974", "label": 824, "func": ""}
{"index": "s348649082", "label": 824, "func": ""}
{"index": "s402021841", "label": 824, "func": ""}
{"index": "s547094732", "label": 824, "func": ""}
{"index": "s299356816", "label": 3221, "func": ""}
{"index": "s048001156", "label": 3221, "func": ""}
{"index": "s630275324", "label": 3221, "func": ""}
{"index": "s432180165", "label": 3221, "func": ""}
{"index": "s502687611", "label": 3221, "func": ""}
{"index": "s189184371", "label": 3221, "func": ""}
{"index": "s304728173", "label": 3221, "func": ""}
{"index": "s163185623", "label": 3221, "func": ""}
{"index": "s236163250", "label": 3221, "func": ""}
{"index": "s513277164", "label": 3221, "func": ""}
{"index": "s784478694", "label": 2792, "func": ""}
{"index": "s708576485", "label": 2792, "func": ""}
{"index": "s902527790", "label": 2792, "func": ""}
{"index": "s508137293", "label": 2792, "func": ""}
{"index": "s905449993", "label": 2792, "func": ""}
{"index": "s846237911", "label": 2792, "func": ""}
{"index": "s077478973", "label": 2792, "func": ""}
{"index": "s791274287", "label": 2792, "func": ""}
{"index": "s255919608", "label": 2792, "func": ""}
{"index": "s178156552", "label": 2792, "func": ""}
{"index": "s806245916", "label": 1998, "func": ""}
{"index": "s578795572", "label": 1998, "func": ""}
{"index": "s026158459", "label": 1998, "func": ""}
{"index": "s435966175", "label": 2057, "func": ""}
{"index": "s602714230", "label": 2256, "func": ""}
{"index": "s322771368", "label": 2256, "func": ""}
{"index": "s500516152", "label": 2256, "func": ""}
{"index": "s678628792", "label": 2256, "func": ""}
{"index": "s120135117", "label": 2256, "func": ""}
{"index": "s922746035", "label": 2256, "func": ""}
{"index": "s678964049", "label": 2256, "func": ""}
{"index": "s314803890", "label": 2256, "func": ""}
{"index": "s933085471", "label": 2256, "func": ""}
{"index": "s027631844", "label": 2256, "func": ""}
{"index": "s854335001", "label": 1678, "func": ""}
{"index": "s299792443", "label": 1678, "func": ""}
{"index": "s334194697", "label": 1678, "func": ""}
{"index": "s749037906", "label": 1678, "func": ""}
{"index": "s889312902", "label": 1277, "func": ""}
{"index": "s917291719", "label": 1277, "func": ""}
{"index": "s159984488", "label": 1277, "func": ""}
{"index": "s093507532", "label": 2758, "func": ""}
{"index": "s942871500", "label": 2758, "func": ""}
{"index": "s620424535", "label": 2758, "func": ""}
{"index": "s295165173", "label": 2758, "func": ""}
{"index": "s518451192", "label": 2758, "func": ""}
{"index": "s154093280", "label": 2758, "func": ""}
{"index": "s374666024", "label": 2758, "func": ""}
{"index": "s803803770", "label": 2758, "func": ""}
{"index": "s849450602", "label": 2758, "func": ""}
{"index": "s344213390", "label": 2758, "func": ""}
{"index": "s179640771", "label": 2570, "func": ""}
{"index": "s414696863", "label": 2570, "func": ""}
{"index": "s230893061", "label": 2570, "func": ""}
{"index": "s534127084", "label": 2570, "func": ""}
{"index": "s469608614", "label": 2570, "func": ""}
{"index": "s280284970", "label": 2570, "func": ""}
{"index": "s380741620", "label": 2570, "func": ""}
{"index": "s666977452", "label": 2570, "func": ""}
{"index": "s169507027", "label": 2570, "func": ""}
{"index": "s958307757", "label": 2570, "func": ""}
{"index": "s542072167", "label": 753, "func": ""}
{"index": "s645194946", "label": 753, "func": ""}
{"index": "s964838517", "label": 753, "func": ""}
{"index": "s608846105", "label": 753, "func": ""}
{"index": "s026494902", "label": 753, "func": ""}
{"index": "s583177191", "label": 753, "func": ""}
{"index": "s549533118", "label": 753, "func": ""}
{"index": "s127972684", "label": 753, "func": ""}
{"index": "s073474974", "label": 753, "func": ""}
{"index": "s418684099", "label": 753, "func": ""}
{"index": "s593160224", "label": 3349, "func": ""}
{"index": "s814482304", "label": 858, "func": ""}
{"index": "s331335965", "label": 858, "func": ""}
{"index": "s213392503", "label": 858, "func": ""}
{"index": "s516004547", "label": 858, "func": ""}
{"index": "s533127375", "label": 858, "func": ""}
{"index": "s335343257", "label": 858, "func": ""}
{"index": "s575744451", "label": 73, "func": ""}
{"index": "s047357729", "label": 73, "func": ""}
{"index": "s794780528", "label": 73, "func": ""}
{"index": "s611615253", "label": 73, "func": ""}
{"index": "s566262843", "label": 73, "func": ""}
{"index": "s546117409", "label": 73, "func": ""}
{"index": "s679090139", "label": 73, "func": ""}
{"index": "s175509368", "label": 73, "func": ""}
{"index": "s148730529", "label": 73, "func": ""}
{"index": "s245401772", "label": 73, "func": ""}
{"index": "s601939403", "label": 2692, "func": ""}
{"index": "s033355530", "label": 2692, "func": ""}
{"index": "s111149062", "label": 2692, "func": ""}
{"index": "s343391877", "label": 2692, "func": ""}
{"index": "s169666306", "label": 2692, "func": ""}
{"index": "s494237688", "label": 2692, "func": ""}
{"index": "s819519882", "label": 2692, "func": ""}
{"index": "s275694261", "label": 2692, "func": ""}
{"index": "s637332404", "label": 2692, "func": ""}
{"index": "s718914119", "label": 2692, "func": ""}
{"index": "s723594956", "label": 1172, "func": ""}
{"index": "s502373590", "label": 1172, "func": ""}
{"index": "s283949404", "label": 1172, "func": ""}
{"index": "s807873139", "label": 1172, "func": ""}
{"index": "s653130587", "label": 2565, "func": ""}
{"index": "s838662622", "label": 2565, "func": ""}
{"index": "s405229308", "label": 2565, "func": ""}
{"index": "s650726686", "label": 2565, "func": ""}
{"index": "s114506233", "label": 2396, "func": ""}
{"index": "s707584101", "label": 2396, "func": ""}
{"index": "s476016762", "label": 2396, "func": ""}
{"index": "s631892276", "label": 2396, "func": ""}
{"index": "s187341089", "label": 2396, "func": ""}
{"index": "s057520778", "label": 2396, "func": ""}
{"index": "s794381557", "label": 2396, "func": ""}
{"index": "s700306911", "label": 2396, "func": ""}
{"index": "s635613641", "label": 2396, "func": ""}
{"index": "s754976375", "label": 2396, "func": ""}
{"index": "s944287188", "label": 272, "func": ""}
{"index": "s278074720", "label": 272, "func": ""}
{"index": "s272409798", "label": 272, "func": ""}
{"index": "s651842412", "label": 272, "func": ""}
{"index": "s657553498", "label": 272, "func": ""}
{"index": "s779365963", "label": 272, "func": ""}
{"index": "s564132191", "label": 272, "func": ""}
{"index": "s704414934", "label": 272, "func": ""}
{"index": "s339051191", "label": 272, "func": ""}
{"index": "s875114949", "label": 272, "func": ""}
{"index": "s856735082", "label": 1095, "func": ""}
{"index": "s450459247", "label": 1095, "func": ""}
{"index": "s213498784", "label": 1095, "func": ""}
{"index": "s148432983", "label": 1095, "func": ""}
{"index": "s392188035", "label": 1095, "func": ""}
{"index": "s446201713", "label": 1095, "func": ""}
{"index": "s629806233", "label": 1095, "func": ""}
{"index": "s015714590", "label": 1095, "func": ""}
{"index": "s389422258", "label": 1095, "func": ""}
{"index": "s025006459", "label": 1095, "func": ""}
{"index": "s941441989", "label": 621, "func": ""}
{"index": "s923523869", "label": 621, "func": ""}
{"index": "s339813435", "label": 621, "func": ""}
{"index": "s172678621", "label": 621, "func": ""}
{"index": "s345945446", "label": 621, "func": ""}
{"index": "s481324302", "label": 621, "func": ""}
{"index": "s472957477", "label": 621, "func": ""}
{"index": "s753752739", "label": 621, "func": ""}
{"index": "s800137754", "label": 621, "func": ""}
{"index": "s632706780", "label": 621, "func": ""}
{"index": "s902893789", "label": 241, "func": ""}
{"index": "s537850770", "label": 241, "func": ""}
{"index": "s204228119", "label": 241, "func": ""}
{"index": "s109348535", "label": 241, "func": ""}
{"index": "s860403553", "label": 241, "func": ""}
{"index": "s134973831", "label": 241, "func": ""}
{"index": "s597780427", "label": 241, "func": ""}
{"index": "s598158943", "label": 241, "func": ""}
{"index": "s047058852", "label": 241, "func": ""}
{"index": "s076251620", "label": 241, "func": ""}
{"index": "s080567771", "label": 204, "func": ""}
{"index": "s125158088", "label": 333, "func": ""}
{"index": "s786054885", "label": 333, "func": ""}
{"index": "s021876671", "label": 333, "func": ""}
{"index": "s173073312", "label": 333, "func": ""}
{"index": "s343328890", "label": 333, "func": ""}
{"index": "s784385691", "label": 333, "func": ""}
{"index": "s798311613", "label": 333, "func": ""}
{"index": "s631125295", "label": 333, "func": ""}
{"index": "s249064725", "label": 333, "func": ""}
{"index": "s276556136", "label": 333, "func": ""}
{"index": "s122785616", "label": 3215, "func": ""}
{"index": "s934193326", "label": 3215, "func": ""}
{"index": "s515187344", "label": 3215, "func": ""}
{"index": "s275319930", "label": 3215, "func": ""}
{"index": "s841876486", "label": 3215, "func": ""}
{"index": "s274734328", "label": 3215, "func": ""}
{"index": "s366374312", "label": 3215, "func": ""}
{"index": "s804218015", "label": 3215, "func": ""}
{"index": "s226117387", "label": 3215, "func": ""}
{"index": "s061667649", "label": 3215, "func": ""}
{"index": "s335458886", "label": 4041, "func": ""}
{"index": "s337508989", "label": 4041, "func": ""}
{"index": "s320540474", "label": 4041, "func": ""}
{"index": "s064325671", "label": 4041, "func": ""}
{"index": "s052759055", "label": 4041, "func": ""}
{"index": "s035585829", "label": 4041, "func": ""}
{"index": "s584500253", "label": 4041, "func": ""}
{"index": "s861011732", "label": 4041, "func": ""}
{"index": "s526618833", "label": 4041, "func": ""}
{"index": "s649150025", "label": 4041, "func": ""}
{"index": "s706936841", "label": 1294, "func": ""}
{"index": "s027157916", "label": 1294, "func": ""}
{"index": "s819128273", "label": 1294, "func": ""}
{"index": "s323372365", "label": 1294, "func": ""}
{"index": "s570839546", "label": 1294, "func": ""}
{"index": "s038525225", "label": 1294, "func": ""}
{"index": "s592885202", "label": 1294, "func": ""}
{"index": "s515737590", "label": 1294, "func": ""}
{"index": "s096141898", "label": 131, "func": ""}
{"index": "s963280832", "label": 131, "func": ""}
{"index": "s264004038", "label": 131, "func": ""}
{"index": "s780721604", "label": 131, "func": ""}
{"index": "s023980504", "label": 131, "func": ""}
{"index": "s086256086", "label": 131, "func": ""}
{"index": "s376801943", "label": 131, "func": ""}
{"index": "s176188860", "label": 131, "func": ""}
{"index": "s951628417", "label": 131, "func": ""}
{"index": "s138678381", "label": 131, "func": ""}
{"index": "s956097683", "label": 1127, "func": ""}
{"index": "s800515274", "label": 1127, "func": ""}
{"index": "s496245171", "label": 1127, "func": ""}
{"index": "s461563148", "label": 1127, "func": ""}
{"index": "s460436913", "label": 1127, "func": ""}
{"index": "s055356613", "label": 1127, "func": ""}
{"index": "s015044607", "label": 1127, "func": ""}
{"index": "s030592271", "label": 1127, "func": ""}
{"index": "s426749395", "label": 1127, "func": ""}
{"index": "s084430097", "label": 1127, "func": ""}
{"index": "s452838860", "label": 3404, "func": ""}
{"index": "s390256777", "label": 3404, "func": ""}
{"index": "s617669996", "label": 3404, "func": ""}
{"index": "s106876180", "label": 3404, "func": ""}
{"index": "s736872765", "label": 3404, "func": ""}
{"index": "s336076428", "label": 3404, "func": ""}
{"index": "s435257082", "label": 3404, "func": ""}
{"index": "s460777459", "label": 3404, "func": ""}
{"index": "s548237763", "label": 3404, "func": ""}
{"index": "s486611014", "label": 3404, "func": ""}
{"index": "s259281267", "label": 2334, "func": ""}
{"index": "s815101307", "label": 3039, "func": ""}
{"index": "s712216463", "label": 3039, "func": ""}
{"index": "s950147273", "label": 3039, "func": ""}
{"index": "s426045351", "label": 3039, "func": ""}
{"index": "s810370190", "label": 3039, "func": ""}
{"index": "s756517183", "label": 3039, "func": ""}
{"index": "s069707769", "label": 3039, "func": ""}
{"index": "s221631803", "label": 3039, "func": ""}
{"index": "s382449218", "label": 3039, "func": ""}
{"index": "s784545973", "label": 3039, "func": ""}
{"index": "s761564681", "label": 2191, "func": ""}
{"index": "s680583306", "label": 2191, "func": ""}
{"index": "s081493571", "label": 2191, "func": ""}
{"index": "s928933999", "label": 2838, "func": ""}
{"index": "s949421378", "label": 2838, "func": ""}
{"index": "s319277456", "label": 2838, "func": ""}
{"index": "s753305875", "label": 2838, "func": ""}
{"index": "s048434899", "label": 2838, "func": ""}
{"index": "s385582908", "label": 2838, "func": ""}
{"index": "s300112586", "label": 2838, "func": ""}
{"index": "s722172841", "label": 2838, "func": ""}
{"index": "s610718405", "label": 2838, "func": ""}
{"index": "s551401758", "label": 2838, "func": ""}
{"index": "s321223412", "label": 2471, "func": ""}
{"index": "s348847525", "label": 2471, "func": ""}
{"index": "s209396063", "label": 2471, "func": ""}
{"index": "s153440950", "label": 2471, "func": ""}
{"index": "s284067558", "label": 2471, "func": ""}
{"index": "s171202895", "label": 2471, "func": ""}
{"index": "s068110784", "label": 2471, "func": ""}
{"index": "s228054858", "label": 2471, "func": ""}
{"index": "s202306029", "label": 2471, "func": ""}
{"index": "s429646058", "label": 2471, "func": ""}
{"index": "s747105109", "label": 4049, "func": ""}
{"index": "s037125937", "label": 4049, "func": ""}
{"index": "s603613603", "label": 4049, "func": ""}
{"index": "s896781319", "label": 4049, "func": ""}
{"index": "s743567384", "label": 4049, "func": ""}
{"index": "s779386466", "label": 4049, "func": ""}
{"index": "s296892154", "label": 4049, "func": ""}
{"index": "s035821919", "label": 4049, "func": ""}
{"index": "s889489373", "label": 4049, "func": ""}
{"index": "s725443343", "label": 4049, "func": ""}
{"index": "s132773137", "label": 2704, "func": ""}
{"index": "s780658449", "label": 2704, "func": ""}
{"index": "s671315026", "label": 2704, "func": ""}
{"index": "s098552354", "label": 2704, "func": ""}
{"index": "s182137661", "label": 2776, "func": ""}
{"index": "s750599839", "label": 2776, "func": ""}
{"index": "s992955548", "label": 2776, "func": ""}
{"index": "s043257812", "label": 2776, "func": ""}
{"index": "s887219758", "label": 2776, "func": ""}
{"index": "s532910960", "label": 2776, "func": ""}
{"index": "s129635393", "label": 2776, "func": ""}
{"index": "s321416269", "label": 2776, "func": ""}
{"index": "s173792206", "label": 3251, "func": ""}
{"index": "s948813724", "label": 3251, "func": ""}
{"index": "s566394988", "label": 3251, "func": ""}
{"index": "s634972605", "label": 3251, "func": ""}
{"index": "s688485528", "label": 3251, "func": ""}
{"index": "s862326560", "label": 3251, "func": ""}
{"index": "s639439816", "label": 3251, "func": ""}
{"index": "s242928802", "label": 3251, "func": ""}
{"index": "s449752433", "label": 3251, "func": ""}
{"index": "s400650839", "label": 3251, "func": ""}
{"index": "s397378420", "label": 3987, "func": ""}
{"index": "s712305318", "label": 3987, "func": ""}
{"index": "s298409245", "label": 3987, "func": ""}
{"index": "s719283018", "label": 3987, "func": ""}
{"index": "s217897732", "label": 3987, "func": ""}
{"index": "s102586189", "label": 3987, "func": ""}
{"index": "s045826240", "label": 3987, "func": ""}
{"index": "s666832990", "label": 3987, "func": ""}
{"index": "s496641701", "label": 3987, "func": ""}
{"index": "s161916559", "label": 3987, "func": ""}
{"index": "s505115259", "label": 3688, "func": ""}
{"index": "s790893926", "label": 3688, "func": ""}
{"index": "s877991774", "label": 3688, "func": ""}
{"index": "s099985500", "label": 3688, "func": ""}
{"index": "s389259314", "label": 3688, "func": ""}
{"index": "s294504207", "label": 3688, "func": ""}
{"index": "s259505077", "label": 3688, "func": ""}
{"index": "s952695756", "label": 3688, "func": ""}
{"index": "s821142624", "label": 3688, "func": ""}
{"index": "s639385776", "label": 3688, "func": ""}
{"index": "s996989908", "label": 2854, "func": ""}
{"index": "s763006270", "label": 2854, "func": ""}
{"index": "s247383314", "label": 2854, "func": ""}
{"index": "s558976541", "label": 2854, "func": ""}
{"index": "s082323744", "label": 2854, "func": ""}
{"index": "s999213946", "label": 2854, "func": ""}
{"index": "s906912467", "label": 2854, "func": ""}
{"index": "s298742064", "label": 2854, "func": ""}
{"index": "s510849587", "label": 2854, "func": ""}
{"index": "s648714856", "label": 2854, "func": ""}
{"index": "s340105354", "label": 2651, "func": ""}
{"index": "s146901423", "label": 2651, "func": ""}
{"index": "s321780949", "label": 2651, "func": ""}
{"index": "s460176992", "label": 2651, "func": ""}
{"index": "s212248691", "label": 2651, "func": ""}
{"index": "s154397586", "label": 2651, "func": ""}
{"index": "s235275403", "label": 2476, "func": ""}
{"index": "s415532971", "label": 2476, "func": ""}
{"index": "s319067325", "label": 2476, "func": ""}
{"index": "s464067214", "label": 2476, "func": ""}
{"index": "s293177790", "label": 2476, "func": ""}
{"index": "s582757781", "label": 2476, "func": ""}
{"index": "s685158757", "label": 2476, "func": ""}
{"index": "s090858219", "label": 2476, "func": ""}
{"index": "s545966607", "label": 2476, "func": ""}
{"index": "s010941288", "label": 2476, "func": ""}
{"index": "s302711208", "label": 391, "func": ""}
{"index": "s936909043", "label": 391, "func": ""}
{"index": "s730454992", "label": 263, "func": ""}
{"index": "s626457450", "label": 263, "func": ""}
{"index": "s686477568", "label": 263, "func": ""}
{"index": "s885369931", "label": 263, "func": ""}
{"index": "s728413357", "label": 263, "func": ""}
{"index": "s024759522", "label": 263, "func": ""}
{"index": "s218176804", "label": 263, "func": ""}
{"index": "s470514938", "label": 263, "func": ""}
{"index": "s908772292", "label": 263, "func": ""}
{"index": "s948526611", "label": 263, "func": ""}
{"index": "s681088647", "label": 2899, "func": ""}
{"index": "s728406998", "label": 2899, "func": ""}
{"index": "s048813155", "label": 2899, "func": ""}
{"index": "s973122420", "label": 2899, "func": ""}
{"index": "s602001764", "label": 2899, "func": ""}
{"index": "s201588567", "label": 2899, "func": ""}
{"index": "s398245489", "label": 2899, "func": ""}
{"index": "s267384387", "label": 2899, "func": ""}
{"index": "s519556183", "label": 2899, "func": ""}
{"index": "s224933521", "label": 2899, "func": ""}
{"index": "s149336098", "label": 2325, "func": ""}
{"index": "s970470739", "label": 2325, "func": ""}
{"index": "s345319784", "label": 56, "func": ""}
{"index": "s287798679", "label": 56, "func": ""}
{"index": "s221201964", "label": 56, "func": ""}
{"index": "s885679212", "label": 56, "func": ""}
{"index": "s611066592", "label": 56, "func": ""}
{"index": "s947413717", "label": 56, "func": ""}
{"index": "s360039421", "label": 56, "func": ""}
{"index": "s744592423", "label": 56, "func": ""}
{"index": "s949938124", "label": 56, "func": ""}
{"index": "s166108403", "label": 56, "func": ""}
{"index": "s643527174", "label": 2412, "func": ""}
{"index": "s394466552", "label": 2412, "func": ""}
{"index": "s795231410", "label": 2412, "func": ""}
{"index": "s956755406", "label": 2412, "func": ""}
{"index": "s384737494", "label": 2412, "func": ""}
{"index": "s733897761", "label": 2412, "func": ""}
{"index": "s061035277", "label": 2412, "func": ""}
{"index": "s534102777", "label": 2412, "func": ""}
{"index": "s623368815", "label": 2412, "func": ""}
{"index": "s162849832", "label": 2412, "func": ""}
{"index": "s882785939", "label": 120, "func": ""}
{"index": "s229423531", "label": 120, "func": ""}
{"index": "s590703065", "label": 120, "func": ""}
{"index": "s336752382", "label": 120, "func": ""}
{"index": "s362084462", "label": 120, "func": ""}
{"index": "s289797678", "label": 120, "func": ""}
{"index": "s884729963", "label": 120, "func": ""}
{"index": "s677395460", "label": 120, "func": ""}
{"index": "s208927589", "label": 120, "func": ""}
{"index": "s675825462", "label": 120, "func": ""}
{"index": "s851164328", "label": 167, "func": ""}
{"index": "s089978668", "label": 167, "func": ""}
{"index": "s856227605", "label": 167, "func": ""}
{"index": "s127755888", "label": 167, "func": ""}
{"index": "s786301150", "label": 167, "func": ""}
{"index": "s655086056", "label": 167, "func": ""}
{"index": "s090854873", "label": 167, "func": ""}
{"index": "s885316327", "label": 167, "func": ""}
{"index": "s171377488", "label": 167, "func": ""}
{"index": "s588971163", "label": 167, "func": ""}
{"index": "s974726733", "label": 2889, "func": ""}
{"index": "s874297817", "label": 2889, "func": ""}
{"index": "s346498119", "label": 2889, "func": ""}
{"index": "s070924674", "label": 2889, "func": ""}
{"index": "s391383013", "label": 2889, "func": ""}
{"index": "s848579378", "label": 2889, "func": ""}
{"index": "s827088082", "label": 2889, "func": ""}
{"index": "s581043048", "label": 2889, "func": ""}
{"index": "s323566313", "label": 2889, "func": ""}
{"index": "s603462363", "label": 2889, "func": ""}
{"index": "s557464741", "label": 734, "func": ""}
{"index": "s346467936", "label": 734, "func": ""}
{"index": "s447716171", "label": 734, "func": ""}
{"index": "s859549949", "label": 734, "func": ""}
{"index": "s661558657", "label": 734, "func": ""}
{"index": "s136191585", "label": 734, "func": ""}
{"index": "s949732303", "label": 734, "func": ""}
{"index": "s816365180", "label": 734, "func": ""}
{"index": "s867794217", "label": 734, "func": ""}
{"index": "s955489700", "label": 734, "func": ""}
{"index": "s157699469", "label": 3712, "func": ""}
{"index": "s061193226", "label": 3712, "func": ""}
{"index": "s843459267", "label": 3712, "func": ""}
{"index": "s558623698", "label": 3712, "func": ""}
{"index": "s841646233", "label": 3712, "func": ""}
{"index": "s083468473", "label": 3712, "func": ""}
{"index": "s883736369", "label": 3712, "func": ""}
{"index": "s096004489", "label": 3712, "func": ""}
{"index": "s343668238", "label": 3712, "func": ""}
{"index": "s776624096", "label": 3712, "func": ""}
{"index": "s227269494", "label": 2839, "func": ""}
{"index": "s682226970", "label": 2839, "func": ""}
{"index": "s996966214", "label": 2839, "func": ""}
{"index": "s286028169", "label": 2839, "func": ""}
{"index": "s997031111", "label": 2839, "func": ""}
{"index": "s554208302", "label": 2839, "func": ""}
{"index": "s858269282", "label": 2839, "func": ""}
{"index": "s609314490", "label": 2839, "func": ""}
{"index": "s570595944", "label": 2839, "func": ""}
{"index": "s972197920", "label": 2839, "func": ""}
{"index": "s660760345", "label": 634, "func": ""}
{"index": "s814485987", "label": 634, "func": ""}
{"index": "s873419362", "label": 634, "func": ""}
{"index": "s814146846", "label": 634, "func": ""}
{"index": "s588981447", "label": 634, "func": ""}
{"index": "s368406600", "label": 634, "func": ""}
{"index": "s531338562", "label": 588, "func": ""}
{"index": "s488090694", "label": 588, "func": ""}
{"index": "s737154624", "label": 588, "func": ""}
{"index": "s401948718", "label": 588, "func": ""}
{"index": "s782413178", "label": 588, "func": ""}
{"index": "s937915685", "label": 588, "func": ""}
{"index": "s392230673", "label": 588, "func": ""}
{"index": "s048836312", "label": 588, "func": ""}
{"index": "s846005592", "label": 588, "func": ""}
{"index": "s314057277", "label": 1831, "func": ""}
{"index": "s638507762", "label": 2372, "func": ""}
{"index": "s685502369", "label": 2372, "func": ""}
{"index": "s367368923", "label": 2372, "func": ""}
{"index": "s802604584", "label": 2372, "func": ""}
{"index": "s231477639", "label": 2372, "func": ""}
{"index": "s018461203", "label": 2372, "func": ""}
{"index": "s777991561", "label": 2372, "func": ""}
{"index": "s854249792", "label": 2448, "func": ""}
{"index": "s666547286", "label": 2448, "func": ""}
{"index": "s889143291", "label": 2448, "func": ""}
{"index": "s942844669", "label": 2448, "func": ""}
{"index": "s117787150", "label": 2448, "func": ""}
{"index": "s161012456", "label": 2448, "func": ""}
{"index": "s358992742", "label": 2448, "func": ""}
{"index": "s965734228", "label": 2448, "func": ""}
{"index": "s267887413", "label": 2448, "func": ""}
{"index": "s106532000", "label": 2448, "func": ""}
{"index": "s430175138", "label": 2549, "func": ""}
{"index": "s294306325", "label": 2549, "func": ""}
{"index": "s507200915", "label": 2549, "func": ""}
{"index": "s963249852", "label": 2549, "func": ""}
{"index": "s135404507", "label": 2549, "func": ""}
{"index": "s111080933", "label": 2549, "func": ""}
{"index": "s586183290", "label": 2549, "func": ""}
{"index": "s497441900", "label": 2549, "func": ""}
{"index": "s539177262", "label": 2549, "func": ""}
{"index": "s785505308", "label": 2549, "func": ""}
{"index": "s739301605", "label": 1418, "func": ""}
{"index": "s184521893", "label": 1418, "func": ""}
{"index": "s015069009", "label": 1418, "func": ""}
{"index": "s564583673", "label": 1418, "func": ""}
{"index": "s709299103", "label": 1418, "func": ""}
{"index": "s805488281", "label": 1418, "func": ""}
{"index": "s412625498", "label": 1418, "func": ""}
{"index": "s639822103", "label": 1418, "func": ""}
{"index": "s485083509", "label": 1418, "func": ""}
{"index": "s682861808", "label": 9, "func": ""}
{"index": "s992837028", "label": 9, "func": ""}
{"index": "s879743548", "label": 9, "func": ""}
{"index": "s534012794", "label": 9, "func": ""}
{"index": "s661196395", "label": 9, "func": ""}
{"index": "s065275805", "label": 9, "func": ""}
{"index": "s575286588", "label": 9, "func": ""}
{"index": "s739624963", "label": 9, "func": ""}
{"index": "s847637895", "label": 9, "func": ""}
{"index": "s369516512", "label": 9, "func": ""}
{"index": "s028387857", "label": 3227, "func": ""}
{"index": "s425520379", "label": 3227, "func": ""}
{"index": "s217478071", "label": 3227, "func": ""}
{"index": "s705196899", "label": 3227, "func": ""}
{"index": "s790753878", "label": 3227, "func": ""}
{"index": "s235671128", "label": 3227, "func": ""}
{"index": "s113845116", "label": 3227, "func": ""}
{"index": "s762847398", "label": 3227, "func": ""}
{"index": "s310472097", "label": 3227, "func": ""}
{"index": "s139728617", "label": 3227, "func": ""}
{"index": "s994165229", "label": 2835, "func": ""}
{"index": "s939833441", "label": 2835, "func": ""}
{"index": "s807833649", "label": 2835, "func": ""}
{"index": "s277931393", "label": 2835, "func": ""}
{"index": "s293205351", "label": 2835, "func": ""}
{"index": "s807182273", "label": 2835, "func": ""}
{"index": "s148409134", "label": 2835, "func": ""}
{"index": "s278393473", "label": 2835, "func": ""}
{"index": "s698632578", "label": 2835, "func": ""}
{"index": "s316592993", "label": 2835, "func": ""}
{"index": "s997809512", "label": 1852, "func": ""}
{"index": "s071741991", "label": 1852, "func": ""}
{"index": "s889925881", "label": 1852, "func": ""}
{"index": "s552587758", "label": 1852, "func": ""}
{"index": "s914015840", "label": 2750, "func": ""}
{"index": "s970664214", "label": 2750, "func": ""}
{"index": "s452882404", "label": 2750, "func": ""}
{"index": "s284711084", "label": 2750, "func": ""}
{"index": "s946616559", "label": 2750, "func": ""}
{"index": "s964496315", "label": 2750, "func": ""}
{"index": "s995940045", "label": 2750, "func": ""}
{"index": "s983608965", "label": 2750, "func": ""}
{"index": "s330573057", "label": 1096, "func": ""}
{"index": "s369968984", "label": 1096, "func": ""}
{"index": "s465107134", "label": 1096, "func": ""}
{"index": "s744073669", "label": 1096, "func": ""}
{"index": "s890146438", "label": 1096, "func": ""}
{"index": "s148689092", "label": 1096, "func": ""}
{"index": "s484923732", "label": 1096, "func": ""}
{"index": "s919495953", "label": 1096, "func": ""}
{"index": "s157902394", "label": 1096, "func": ""}
{"index": "s548491992", "label": 1096, "func": ""}
{"index": "s995090694", "label": 3704, "func": ""}
{"index": "s598998779", "label": 3704, "func": ""}
{"index": "s426460910", "label": 3704, "func": ""}
{"index": "s390025620", "label": 3704, "func": ""}
{"index": "s691203606", "label": 3704, "func": ""}
{"index": "s684645787", "label": 3704, "func": ""}
{"index": "s563159505", "label": 3704, "func": ""}
{"index": "s277068215", "label": 3704, "func": ""}
{"index": "s920158402", "label": 3963, "func": ""}
{"index": "s679348375", "label": 3963, "func": ""}
{"index": "s885134725", "label": 3963, "func": ""}
{"index": "s567042562", "label": 3963, "func": ""}
{"index": "s446058464", "label": 3963, "func": ""}
{"index": "s282872461", "label": 3963, "func": ""}
{"index": "s742630388", "label": 3963, "func": ""}
{"index": "s069549435", "label": 3963, "func": ""}
{"index": "s105862053", "label": 3963, "func": ""}
{"index": "s394264950", "label": 3963, "func": ""}
{"index": "s583514309", "label": 1514, "func": ""}
{"index": "s980350398", "label": 1514, "func": ""}
{"index": "s963183791", "label": 1514, "func": ""}
{"index": "s733109628", "label": 1514, "func": ""}
{"index": "s968257120", "label": 1514, "func": ""}
{"index": "s900047881", "label": 1514, "func": ""}
{"index": "s557169858", "label": 1514, "func": ""}
{"index": "s366611749", "label": 1514, "func": ""}
{"index": "s532150396", "label": 1514, "func": ""}
{"index": "s368934143", "label": 1514, "func": ""}
{"index": "s546336371", "label": 2269, "func": ""}
{"index": "s540629570", "label": 2269, "func": ""}
{"index": "s681050788", "label": 2269, "func": ""}
{"index": "s352945221", "label": 2269, "func": ""}
{"index": "s100924005", "label": 2269, "func": ""}
{"index": "s655253584", "label": 2269, "func": ""}
{"index": "s783624773", "label": 2269, "func": ""}
{"index": "s490333827", "label": 2269, "func": ""}
{"index": "s595156204", "label": 2269, "func": ""}
{"index": "s250010393", "label": 2269, "func": ""}
{"index": "s222685601", "label": 3127, "func": ""}
{"index": "s522278304", "label": 3127, "func": ""}
{"index": "s752745919", "label": 3127, "func": ""}
{"index": "s335029062", "label": 3127, "func": ""}
{"index": "s940864109", "label": 3127, "func": ""}
{"index": "s068174830", "label": 3127, "func": ""}
{"index": "s520983437", "label": 3127, "func": ""}
{"index": "s912110273", "label": 3127, "func": ""}
{"index": "s133698030", "label": 3127, "func": ""}
{"index": "s261785433", "label": 3127, "func": ""}
{"index": "s884104519", "label": 2681, "func": ""}
{"index": "s237258757", "label": 2681, "func": ""}
{"index": "s855352426", "label": 2681, "func": ""}
{"index": "s929210352", "label": 2681, "func": ""}
{"index": "s341543548", "label": 2681, "func": ""}
{"index": "s299397682", "label": 2681, "func": ""}
{"index": "s298922647", "label": 2681, "func": ""}
{"index": "s808376737", "label": 2681, "func": ""}
{"index": "s871405861", "label": 2681, "func": ""}
{"index": "s831818207", "label": 2681, "func": ""}
{"index": "s006652199", "label": 292, "func": ""}
{"index": "s049393811", "label": 292, "func": ""}
{"index": "s947318357", "label": 292, "func": ""}
{"index": "s529698025", "label": 292, "func": ""}
{"index": "s794598573", "label": 292, "func": ""}
{"index": "s368464147", "label": 292, "func": ""}
{"index": "s219676832", "label": 292, "func": ""}
{"index": "s372355840", "label": 292, "func": ""}
{"index": "s810703214", "label": 292, "func": ""}
{"index": "s710886042", "label": 292, "func": ""}
{"index": "s967000227", "label": 3439, "func": ""}
{"index": "s658475223", "label": 3439, "func": ""}
{"index": "s911930971", "label": 3439, "func": ""}
{"index": "s694206531", "label": 3439, "func": ""}
{"index": "s085078237", "label": 3439, "func": ""}
{"index": "s470172308", "label": 3439, "func": ""}
{"index": "s713815366", "label": 3439, "func": ""}
{"index": "s685204042", "label": 3439, "func": ""}
{"index": "s796911853", "label": 3439, "func": ""}
{"index": "s042924267", "label": 3439, "func": ""}
{"index": "s965814539", "label": 1411, "func": ""}
{"index": "s897373387", "label": 1411, "func": ""}
{"index": "s326338419", "label": 2111, "func": ""}
{"index": "s323898682", "label": 2111, "func": ""}
{"index": "s042188331", "label": 3617, "func": ""}
{"index": "s330004592", "label": 3617, "func": ""}
{"index": "s011737450", "label": 3617, "func": ""}
{"index": "s288327365", "label": 3617, "func": ""}
{"index": "s134520634", "label": 3617, "func": ""}
{"index": "s939293558", "label": 3617, "func": ""}
{"index": "s524319485", "label": 3617, "func": ""}
{"index": "s226552036", "label": 3617, "func": ""}
{"index": "s192352367", "label": 3617, "func": ""}
{"index": "s561850772", "label": 3617, "func": ""}
{"index": "s978561714", "label": 146, "func": ""}
{"index": "s201870453", "label": 146, "func": ""}
{"index": "s236492518", "label": 146, "func": ""}
{"index": "s772646276", "label": 146, "func": ""}
{"index": "s145142466", "label": 146, "func": ""}
{"index": "s790894530", "label": 146, "func": ""}
{"index": "s830129035", "label": 146, "func": ""}
{"index": "s580702910", "label": 146, "func": ""}
{"index": "s454006894", "label": 146, "func": ""}
{"index": "s810557282", "label": 146, "func": ""}
{"index": "s486551538", "label": 3672, "func": ""}
{"index": "s987506375", "label": 3672, "func": ""}
{"index": "s211347089", "label": 3672, "func": ""}
{"index": "s332162195", "label": 3672, "func": ""}
{"index": "s981839579", "label": 3672, "func": ""}
{"index": "s769058280", "label": 3672, "func": ""}
{"index": "s698380829", "label": 3672, "func": ""}
{"index": "s345774916", "label": 3672, "func": ""}
{"index": "s613737822", "label": 3672, "func": ""}
{"index": "s750108906", "label": 3672, "func": ""}
{"index": "s362459373", "label": 1867, "func": ""}
{"index": "s304589728", "label": 1867, "func": ""}
{"index": "s332883680", "label": 1867, "func": ""}
{"index": "s188700544", "label": 1867, "func": ""}
{"index": "s339291033", "label": 1867, "func": ""}
{"index": "s212655890", "label": 1767, "func": ""}
{"index": "s526654312", "label": 1767, "func": ""}
{"index": "s570692091", "label": 1767, "func": ""}
{"index": "s277095257", "label": 1767, "func": ""}
{"index": "s342816266", "label": 1767, "func": ""}
{"index": "s634302685", "label": 1767, "func": ""}
{"index": "s816323629", "label": 2819, "func": ""}
{"index": "s886741724", "label": 2819, "func": ""}
{"index": "s804849245", "label": 2819, "func": ""}
{"index": "s139632630", "label": 2819, "func": ""}
{"index": "s968181393", "label": 2819, "func": ""}
{"index": "s221656531", "label": 2819, "func": ""}
{"index": "s736182753", "label": 2819, "func": ""}
{"index": "s508824926", "label": 2819, "func": ""}
{"index": "s550441918", "label": 2819, "func": ""}
{"index": "s155363918", "label": 2819, "func": ""}
{"index": "s121990813", "label": 182, "func": ""}
{"index": "s443156096", "label": 182, "func": ""}
{"index": "s460182709", "label": 182, "func": ""}
{"index": "s071387099", "label": 182, "func": ""}
{"index": "s045227144", "label": 1213, "func": ""}
{"index": "s289747135", "label": 1213, "func": ""}
{"index": "s929044976", "label": 1213, "func": ""}
{"index": "s045420891", "label": 1213, "func": ""}
{"index": "s594518113", "label": 1213, "func": ""}
{"index": "s874601941", "label": 1213, "func": ""}
{"index": "s359798786", "label": 1213, "func": ""}
{"index": "s631410464", "label": 707, "func": ""}
{"index": "s058204557", "label": 707, "func": ""}
{"index": "s395023968", "label": 707, "func": ""}
{"index": "s233590946", "label": 707, "func": ""}
{"index": "s644087702", "label": 707, "func": ""}
{"index": "s608117162", "label": 707, "func": ""}
{"index": "s945523984", "label": 707, "func": ""}
{"index": "s948016705", "label": 707, "func": ""}
{"index": "s669657920", "label": 707, "func": ""}
{"index": "s785613136", "label": 707, "func": ""}
{"index": "s192679187", "label": 1210, "func": ""}
{"index": "s254989025", "label": 1210, "func": ""}
{"index": "s244783412", "label": 3401, "func": ""}
{"index": "s387807898", "label": 3401, "func": ""}
{"index": "s623254960", "label": 3401, "func": ""}
{"index": "s803965058", "label": 3401, "func": ""}
{"index": "s297103690", "label": 3401, "func": ""}
{"index": "s129913449", "label": 3401, "func": ""}
{"index": "s273148593", "label": 3401, "func": ""}
{"index": "s486866928", "label": 3401, "func": ""}
{"index": "s971119363", "label": 3401, "func": ""}
{"index": "s540554322", "label": 3401, "func": ""}
{"index": "s050731721", "label": 625, "func": ""}
{"index": "s185135458", "label": 1933, "func": ""}
{"index": "s506741425", "label": 1933, "func": ""}
{"index": "s103090320", "label": 767, "func": ""}
{"index": "s055362875", "label": 767, "func": ""}
{"index": "s923709211", "label": 767, "func": ""}
{"index": "s032029554", "label": 767, "func": ""}
{"index": "s807112793", "label": 767, "func": ""}
{"index": "s875156431", "label": 767, "func": ""}
{"index": "s142559393", "label": 767, "func": ""}
{"index": "s795438505", "label": 767, "func": ""}
{"index": "s504325294", "label": 767, "func": ""}
{"index": "s931484812", "label": 767, "func": ""}
{"index": "s904801782", "label": 3384, "func": ""}
{"index": "s382221736", "label": 3384, "func": ""}
{"index": "s136413460", "label": 1863, "func": ""}
{"index": "s537661648", "label": 1863, "func": ""}
{"index": "s510772354", "label": 505, "func": ""}
{"index": "s182127508", "label": 505, "func": ""}
{"index": "s197179597", "label": 505, "func": ""}
{"index": "s399804861", "label": 505, "func": ""}
{"index": "s951834609", "label": 505, "func": ""}
{"index": "s149680409", "label": 505, "func": ""}
{"index": "s739201049", "label": 505, "func": ""}
{"index": "s377661261", "label": 505, "func": ""}
{"index": "s381697097", "label": 505, "func": ""}
{"index": "s752671750", "label": 505, "func": ""}
{"index": "s232087954", "label": 3511, "func": ""}
{"index": "s120042298", "label": 3511, "func": ""}
{"index": "s608580894", "label": 2880, "func": ""}
{"index": "s078996996", "label": 2880, "func": ""}
{"index": "s213784444", "label": 2880, "func": ""}
{"index": "s957434166", "label": 2880, "func": ""}
{"index": "s835139200", "label": 2880, "func": ""}
{"index": "s647104282", "label": 2880, "func": ""}
{"index": "s326906460", "label": 2880, "func": ""}
{"index": "s390003494", "label": 2880, "func": ""}
{"index": "s535172932", "label": 2880, "func": ""}
{"index": "s842935528", "label": 2880, "func": ""}
{"index": "s451085128", "label": 3578, "func": ""}
{"index": "s045752232", "label": 3578, "func": ""}
{"index": "s969266126", "label": 3578, "func": ""}
{"index": "s968577718", "label": 3578, "func": ""}
{"index": "s374827253", "label": 3578, "func": ""}
{"index": "s059766329", "label": 3578, "func": ""}
{"index": "s366190569", "label": 3578, "func": ""}
{"index": "s377177730", "label": 3578, "func": ""}
{"index": "s997485056", "label": 3578, "func": ""}
{"index": "s906896734", "label": 3578, "func": ""}
{"index": "s096113475", "label": 774, "func": ""}
{"index": "s570025395", "label": 774, "func": ""}
{"index": "s948309365", "label": 774, "func": ""}
{"index": "s194906287", "label": 774, "func": ""}
{"index": "s166159157", "label": 774, "func": ""}
{"index": "s415380494", "label": 774, "func": ""}
{"index": "s241626907", "label": 774, "func": ""}
{"index": "s747555509", "label": 774, "func": ""}
{"index": "s595477954", "label": 774, "func": ""}
{"index": "s132275953", "label": 774, "func": ""}
{"index": "s978159321", "label": 867, "func": ""}
{"index": "s641794845", "label": 867, "func": ""}
{"index": "s831937773", "label": 867, "func": ""}
{"index": "s922075652", "label": 97, "func": ""}
{"index": "s908978654", "label": 97, "func": ""}
{"index": "s124776353", "label": 97, "func": ""}
{"index": "s467588466", "label": 97, "func": ""}
{"index": "s227619501", "label": 97, "func": ""}
{"index": "s244507218", "label": 97, "func": ""}
{"index": "s501752972", "label": 97, "func": ""}
{"index": "s740968185", "label": 97, "func": ""}
{"index": "s017471399", "label": 97, "func": ""}
{"index": "s080180994", "label": 97, "func": ""}
{"index": "s123119217", "label": 2463, "func": ""}
{"index": "s536211912", "label": 2463, "func": ""}
{"index": "s788584612", "label": 2463, "func": ""}
{"index": "s701314552", "label": 2463, "func": ""}
{"index": "s052646168", "label": 2463, "func": ""}
{"index": "s627274178", "label": 2463, "func": ""}
{"index": "s075761190", "label": 2463, "func": ""}
{"index": "s504158735", "label": 2463, "func": ""}
{"index": "s800903417", "label": 2463, "func": ""}
{"index": "s906105903", "label": 2463, "func": ""}
{"index": "s206951696", "label": 1564, "func": ""}
{"index": "s248760216", "label": 1564, "func": ""}
{"index": "s112987777", "label": 1564, "func": ""}
{"index": "s069339795", "label": 2826, "func": ""}
{"index": "s494896974", "label": 1590, "func": ""}
{"index": "s949613140", "label": 1126, "func": ""}
{"index": "s935562576", "label": 1126, "func": ""}
{"index": "s206004954", "label": 1126, "func": ""}
{"index": "s762749186", "label": 1126, "func": ""}
{"index": "s116622273", "label": 1126, "func": ""}
{"index": "s210516728", "label": 1126, "func": ""}
{"index": "s716198025", "label": 1126, "func": ""}
{"index": "s778480677", "label": 1126, "func": ""}
{"index": "s519907185", "label": 1126, "func": ""}
{"index": "s356416182", "label": 1126, "func": ""}
{"index": "s007525901", "label": 1428, "func": ""}
{"index": "s800296510", "label": 1428, "func": ""}
{"index": "s394937651", "label": 1428, "func": ""}
{"index": "s116435566", "label": 1428, "func": ""}
{"index": "s663675930", "label": 1428, "func": ""}
{"index": "s104705597", "label": 1428, "func": ""}
{"index": "s429070676", "label": 1428, "func": ""}
{"index": "s703899996", "label": 1428, "func": ""}
{"index": "s483649861", "label": 1428, "func": ""}
{"index": "s932017704", "label": 1428, "func": ""}
{"index": "s002919087", "label": 99, "func": ""}
{"index": "s008063046", "label": 99, "func": ""}
{"index": "s203848648", "label": 99, "func": ""}
{"index": "s091600398", "label": 99, "func": ""}
{"index": "s954588036", "label": 99, "func": ""}
{"index": "s674924079", "label": 99, "func": ""}
{"index": "s766095660", "label": 99, "func": ""}
{"index": "s191224763", "label": 99, "func": ""}
{"index": "s266593864", "label": 99, "func": ""}
{"index": "s041883515", "label": 99, "func": ""}
{"index": "s722061314", "label": 3293, "func": ""}
{"index": "s141365773", "label": 3293, "func": ""}
{"index": "s631255592", "label": 3293, "func": ""}
{"index": "s568036264", "label": 3293, "func": ""}
{"index": "s145843199", "label": 3293, "func": ""}
{"index": "s028988598", "label": 3293, "func": ""}
{"index": "s919793568", "label": 3293, "func": ""}
{"index": "s797377794", "label": 3293, "func": ""}
{"index": "s813236913", "label": 3293, "func": ""}
{"index": "s425815096", "label": 3293, "func": ""}
{"index": "s629321407", "label": 173, "func": ""}
{"index": "s274174014", "label": 173, "func": ""}
{"index": "s902108958", "label": 173, "func": ""}
{"index": "s183386704", "label": 173, "func": ""}
{"index": "s732337616", "label": 173, "func": ""}
{"index": "s010546775", "label": 173, "func": ""}
{"index": "s671747241", "label": 173, "func": ""}
{"index": "s476568831", "label": 173, "func": ""}
{"index": "s433711909", "label": 173, "func": ""}
{"index": "s299374632", "label": 173, "func": ""}
{"index": "s768746385", "label": 390, "func": ""}
{"index": "s059512064", "label": 390, "func": ""}
{"index": "s878158201", "label": 390, "func": ""}
{"index": "s332330884", "label": 3087, "func": ""}
{"index": "s969930166", "label": 3087, "func": ""}
{"index": "s823957291", "label": 3087, "func": ""}
{"index": "s124302821", "label": 3087, "func": ""}
{"index": "s449048819", "label": 3087, "func": ""}
{"index": "s715767333", "label": 3087, "func": ""}
{"index": "s009910243", "label": 3087, "func": ""}
{"index": "s520662331", "label": 3087, "func": ""}
{"index": "s435750223", "label": 3087, "func": ""}
{"index": "s494888190", "label": 3087, "func": ""}
{"index": "s572701295", "label": 2251, "func": ""}
{"index": "s664609321", "label": 2251, "func": ""}
{"index": "s627565642", "label": 2251, "func": ""}
{"index": "s646646379", "label": 2251, "func": ""}
{"index": "s175586305", "label": 2251, "func": ""}
{"index": "s416943190", "label": 2251, "func": ""}
{"index": "s639609735", "label": 2251, "func": ""}
{"index": "s886539471", "label": 2251, "func": ""}
{"index": "s953001853", "label": 2251, "func": ""}
{"index": "s717303514", "label": 2251, "func": ""}
{"index": "s362668441", "label": 2926, "func": ""}
{"index": "s061426026", "label": 2926, "func": ""}
{"index": "s320803830", "label": 2926, "func": ""}
{"index": "s088769149", "label": 2926, "func": ""}
{"index": "s477499578", "label": 2926, "func": ""}
{"index": "s603979029", "label": 2926, "func": ""}
{"index": "s516554784", "label": 2926, "func": ""}
{"index": "s618204843", "label": 2926, "func": ""}
{"index": "s292277819", "label": 2926, "func": ""}
{"index": "s946637672", "label": 2926, "func": ""}
{"index": "s015388329", "label": 1164, "func": ""}
{"index": "s023472143", "label": 1164, "func": ""}
{"index": "s705820593", "label": 1164, "func": ""}
{"index": "s269388479", "label": 2530, "func": ""}
{"index": "s686434800", "label": 2530, "func": ""}
{"index": "s773630756", "label": 2530, "func": ""}
{"index": "s351527598", "label": 2530, "func": ""}
{"index": "s804902869", "label": 2530, "func": ""}
{"index": "s463873238", "label": 2530, "func": ""}
{"index": "s909687386", "label": 2530, "func": ""}
{"index": "s810664202", "label": 2530, "func": ""}
{"index": "s701336912", "label": 2530, "func": ""}
{"index": "s863518479", "label": 2530, "func": ""}
{"index": "s033384074", "label": 3043, "func": ""}
{"index": "s593264000", "label": 3043, "func": ""}
{"index": "s783681297", "label": 3043, "func": ""}
{"index": "s502199286", "label": 3043, "func": ""}
{"index": "s052671351", "label": 3043, "func": ""}
{"index": "s144866550", "label": 3043, "func": ""}
{"index": "s275546643", "label": 3043, "func": ""}
{"index": "s321146865", "label": 3043, "func": ""}
{"index": "s573716178", "label": 3043, "func": ""}
{"index": "s906346867", "label": 3043, "func": ""}
{"index": "s822697760", "label": 773, "func": ""}
{"index": "s251360410", "label": 773, "func": ""}
{"index": "s526505089", "label": 773, "func": ""}
{"index": "s797654041", "label": 773, "func": ""}
{"index": "s031532289", "label": 773, "func": ""}
{"index": "s313831536", "label": 773, "func": ""}
{"index": "s063870791", "label": 773, "func": ""}
{"index": "s927012894", "label": 773, "func": ""}
{"index": "s053054967", "label": 773, "func": ""}
{"index": "s796206435", "label": 773, "func": ""}
{"index": "s303555925", "label": 150, "func": ""}
{"index": "s257192266", "label": 150, "func": ""}
{"index": "s829145755", "label": 150, "func": ""}
{"index": "s394283405", "label": 150, "func": ""}
{"index": "s554042931", "label": 150, "func": ""}
{"index": "s486759798", "label": 150, "func": ""}
{"index": "s667351626", "label": 150, "func": ""}
{"index": "s332873798", "label": 150, "func": ""}
{"index": "s136994390", "label": 150, "func": ""}
{"index": "s705272044", "label": 150, "func": ""}
{"index": "s916617722", "label": 3960, "func": ""}
{"index": "s234168361", "label": 3960, "func": ""}
{"index": "s248376852", "label": 3960, "func": ""}
{"index": "s305634144", "label": 3960, "func": ""}
{"index": "s320095712", "label": 3960, "func": ""}
{"index": "s157403117", "label": 3960, "func": ""}
{"index": "s272268754", "label": 3960, "func": ""}
{"index": "s464907110", "label": 3960, "func": ""}
{"index": "s374081398", "label": 1824, "func": ""}
{"index": "s444308769", "label": 3588, "func": ""}
{"index": "s418414093", "label": 3588, "func": ""}
{"index": "s375081023", "label": 3588, "func": ""}
{"index": "s946178617", "label": 3588, "func": ""}
{"index": "s348837627", "label": 3588, "func": ""}
{"index": "s799130212", "label": 3588, "func": ""}
{"index": "s009093126", "label": 3588, "func": ""}
{"index": "s918725709", "label": 3588, "func": ""}
{"index": "s291933348", "label": 3588, "func": ""}
{"index": "s891182869", "label": 3588, "func": ""}
{"index": "s331464904", "label": 451, "func": ""}
{"index": "s213376998", "label": 451, "func": ""}
{"index": "s838461329", "label": 451, "func": ""}
{"index": "s895881206", "label": 451, "func": ""}
{"index": "s881835494", "label": 451, "func": ""}
{"index": "s916606764", "label": 451, "func": ""}
{"index": "s930312439", "label": 451, "func": ""}
{"index": "s145252140", "label": 451, "func": ""}
{"index": "s748739788", "label": 451, "func": ""}
{"index": "s706455402", "label": 451, "func": ""}
{"index": "s472727129", "label": 1868, "func": ""}
{"index": "s927076744", "label": 1868, "func": ""}
{"index": "s088094461", "label": 1868, "func": ""}
{"index": "s640080509", "label": 1868, "func": ""}
{"index": "s394167569", "label": 1868, "func": ""}
{"index": "s598187001", "label": 1868, "func": ""}
{"index": "s899413894", "label": 2287, "func": ""}
{"index": "s176216313", "label": 2287, "func": ""}
{"index": "s811246491", "label": 2287, "func": ""}
{"index": "s072982481", "label": 2287, "func": ""}
{"index": "s919733101", "label": 2287, "func": ""}
{"index": "s238262663", "label": 2287, "func": ""}
{"index": "s794389949", "label": 2287, "func": ""}
{"index": "s174420950", "label": 2287, "func": ""}
{"index": "s949173315", "label": 2287, "func": ""}
{"index": "s693410777", "label": 2287, "func": ""}
{"index": "s269692316", "label": 3591, "func": ""}
{"index": "s759369341", "label": 3591, "func": ""}
{"index": "s443637659", "label": 3591, "func": ""}
{"index": "s459980311", "label": 3591, "func": ""}
{"index": "s182825269", "label": 3591, "func": ""}
{"index": "s503760610", "label": 3591, "func": ""}
{"index": "s114286111", "label": 3591, "func": ""}
{"index": "s343471125", "label": 3591, "func": ""}
{"index": "s051127805", "label": 3591, "func": ""}
{"index": "s178396848", "label": 3591, "func": ""}
{"index": "s467069501", "label": 1983, "func": ""}
{"index": "s033603260", "label": 1983, "func": ""}
{"index": "s200761687", "label": 1983, "func": ""}
{"index": "s651764461", "label": 1983, "func": ""}
{"index": "s428170933", "label": 2797, "func": ""}
{"index": "s402743593", "label": 2797, "func": ""}
{"index": "s088173456", "label": 2797, "func": ""}
{"index": "s245922089", "label": 2797, "func": ""}
{"index": "s142040459", "label": 2797, "func": ""}
{"index": "s508164267", "label": 2797, "func": ""}
{"index": "s671226115", "label": 2797, "func": ""}
{"index": "s205800621", "label": 2797, "func": ""}
{"index": "s170881099", "label": 2797, "func": ""}
{"index": "s625274592", "label": 2797, "func": ""}
{"index": "s076506879", "label": 2734, "func": ""}
{"index": "s164195057", "label": 2734, "func": ""}
{"index": "s436727453", "label": 2734, "func": ""}
{"index": "s147886656", "label": 2734, "func": ""}
{"index": "s366044346", "label": 2734, "func": ""}
{"index": "s209722890", "label": 2734, "func": ""}
{"index": "s802400450", "label": 2734, "func": ""}
{"index": "s345476393", "label": 2734, "func": ""}
{"index": "s172780629", "label": 2734, "func": ""}
{"index": "s932686902", "label": 2734, "func": ""}
{"index": "s873768679", "label": 2408, "func": ""}
{"index": "s996951841", "label": 2408, "func": ""}
{"index": "s838214878", "label": 2408, "func": ""}
{"index": "s095100274", "label": 2408, "func": ""}
{"index": "s282758048", "label": 2408, "func": ""}
{"index": "s293437460", "label": 2408, "func": ""}
{"index": "s616501603", "label": 2408, "func": ""}
{"index": "s819648988", "label": 2408, "func": ""}
{"index": "s591525216", "label": 2408, "func": ""}
{"index": "s602338009", "label": 2408, "func": ""}
{"index": "s843302385", "label": 1839, "func": ""}
{"index": "s591722712", "label": 1839, "func": ""}
{"index": "s519720182", "label": 1839, "func": ""}
{"index": "s369299478", "label": 1839, "func": ""}
{"index": "s999248064", "label": 1839, "func": ""}
{"index": "s543165486", "label": 1839, "func": ""}
{"index": "s979472434", "label": 1839, "func": ""}
{"index": "s122862678", "label": 1839, "func": ""}
{"index": "s268508779", "label": 1839, "func": ""}
{"index": "s035253266", "label": 1839, "func": ""}
{"index": "s920291577", "label": 2791, "func": ""}
{"index": "s592896640", "label": 2791, "func": ""}
{"index": "s408686801", "label": 2791, "func": ""}
{"index": "s703193459", "label": 2791, "func": ""}
{"index": "s642926210", "label": 2791, "func": ""}
{"index": "s767709610", "label": 2791, "func": ""}
{"index": "s391059702", "label": 2791, "func": ""}
{"index": "s906156940", "label": 2791, "func": ""}
{"index": "s207752486", "label": 2791, "func": ""}
{"index": "s608663248", "label": 2791, "func": ""}
{"index": "s680681128", "label": 2780, "func": ""}
{"index": "s078738131", "label": 2780, "func": ""}
{"index": "s070845474", "label": 2780, "func": ""}
{"index": "s750196256", "label": 2780, "func": ""}
{"index": "s997778930", "label": 2780, "func": ""}
{"index": "s274492695", "label": 2780, "func": ""}
{"index": "s037135534", "label": 2780, "func": ""}
{"index": "s539992934", "label": 2780, "func": ""}
{"index": "s058870954", "label": 2780, "func": ""}
{"index": "s261171845", "label": 2780, "func": ""}
{"index": "s587922168", "label": 380, "func": ""}
{"index": "s093411554", "label": 380, "func": ""}
{"index": "s940427869", "label": 2430, "func": ""}
{"index": "s355960340", "label": 2430, "func": ""}
{"index": "s298478204", "label": 2430, "func": ""}
{"index": "s188683047", "label": 2430, "func": ""}
{"index": "s509683820", "label": 2430, "func": ""}
{"index": "s265952335", "label": 2430, "func": ""}
{"index": "s673869459", "label": 2430, "func": ""}
{"index": "s266636357", "label": 2430, "func": ""}
{"index": "s768526974", "label": 2430, "func": ""}
{"index": "s268072970", "label": 455, "func": ""}
{"index": "s762804331", "label": 455, "func": ""}
{"index": "s875156482", "label": 455, "func": ""}
{"index": "s980991316", "label": 455, "func": ""}
{"index": "s618036807", "label": 455, "func": ""}
{"index": "s826890738", "label": 455, "func": ""}
{"index": "s589353743", "label": 455, "func": ""}
{"index": "s117681237", "label": 455, "func": ""}
{"index": "s964297241", "label": 455, "func": ""}
{"index": "s139108774", "label": 455, "func": ""}
{"index": "s061491069", "label": 389, "func": ""}
{"index": "s433619182", "label": 389, "func": ""}
{"index": "s591786574", "label": 389, "func": ""}
{"index": "s413414696", "label": 3564, "func": ""}
{"index": "s242689355", "label": 3564, "func": ""}
{"index": "s006213501", "label": 3564, "func": ""}
{"index": "s017212916", "label": 3564, "func": ""}
{"index": "s825646663", "label": 3564, "func": ""}
{"index": "s229048019", "label": 3564, "func": ""}
{"index": "s783923273", "label": 3564, "func": ""}
{"index": "s444476046", "label": 3564, "func": ""}
{"index": "s942836258", "label": 3564, "func": ""}
{"index": "s455733909", "label": 3564, "func": ""}
{"index": "s840016966", "label": 3483, "func": ""}
{"index": "s176215460", "label": 3483, "func": ""}
{"index": "s876123273", "label": 3483, "func": ""}
{"index": "s602095039", "label": 3483, "func": ""}
{"index": "s719964599", "label": 3483, "func": ""}
{"index": "s975284667", "label": 3483, "func": ""}
{"index": "s361563430", "label": 3483, "func": ""}
{"index": "s065429292", "label": 3483, "func": ""}
{"index": "s222495083", "label": 3483, "func": ""}
{"index": "s341971270", "label": 3483, "func": ""}
{"index": "s535587039", "label": 3088, "func": ""}
{"index": "s254215864", "label": 3088, "func": ""}
{"index": "s468991144", "label": 3088, "func": ""}
{"index": "s225777293", "label": 3088, "func": ""}
{"index": "s791647838", "label": 3088, "func": ""}
{"index": "s997338060", "label": 3088, "func": ""}
{"index": "s678171703", "label": 3088, "func": ""}
{"index": "s157758833", "label": 3088, "func": ""}
{"index": "s832300120", "label": 3088, "func": ""}
{"index": "s903398621", "label": 3088, "func": ""}
{"index": "s678145902", "label": 1706, "func": ""}
{"index": "s870670135", "label": 970, "func": ""}
{"index": "s616312234", "label": 1527, "func": ""}
{"index": "s485593093", "label": 548, "func": ""}
|
CodeBERT/UniXcoder/downstream-tasks/zero-shot-search/dataset/java.jsonl/0
|
{
"file_path": "CodeBERT/UniXcoder/downstream-tasks/zero-shot-search/dataset/java.jsonl",
"repo_id": "CodeBERT",
"token_count": 512704
}
| 218 |
import re
from tqdm import tqdm
from multiset import Multiset
from functools import lru_cache
import random
import json
import pdb
import torch
import torch.nn.functional as F
import numpy as np
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
pipeline,
)
import time
class BaseCase:
def __init__(self, ground_truth, preds):
self.question = ""
self.ground_truth = ground_truth
self.preds = preds
self.correct_preds_num = 0.0
class GSM8KCase(BaseCase):
def __init__(self, ground_truth, preds):
super().__init__(ground_truth, preds)
self.entailment_batch_size = 512
def do_step_labeling(self, model=None, tokenizer=None):
# 将ground_truth标记为true
self.ground_truth.is_correct = True
for step in self.ground_truth.steps:
self.ground_truth.step_labels[step] = 1
# 先预存正样本集合
positive_preds = [self.ground_truth]
for i, pred in enumerate(self.preds):
if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():
positive_preds.append(pred)
# 再对所有样本的所有step打标签
for i, pred in enumerate(self.preds):
if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():
pred.is_correct = True
for step in pred.steps:
pred.step_labels[step] = 1
else:
for k, step in enumerate(pred.steps):
ans = GSM8KExample.match(
pred.steps[:k+1],
positive_preds,
model=model,
tokenizer=tokenizer,
)
pred.step_labels[step] = ans
class TextEntailmentCase(BaseCase):
def __init__(self, ground_truth, preds, entailment_batch_size=512):
super().__init__(ground_truth, preds)
self.entailment_results = {}
self.entailment_batch_size = entailment_batch_size
def do_step_labeling(self, model=None, tokenizer=None):
# 将ground_truth标记为true
self.ground_truth.is_correct = True
for step in self.ground_truth.steps:
self.ground_truth.step_labels[step] = 1
# 先预存正样本集合
positive_preds = [self.ground_truth]
for i, pred in enumerate(self.preds):
if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():
positive_preds.append(pred)
# 将所有待NLI的文本预存起来
self.collect_entailment_texts(positive_preds)
# print("Number of entailment result keys:", len(self.entailment_results.keys()))
# 预处理所有NLI结果
self.preprocess_entailment(model=model, tokenizer=tokenizer)
# 再对所有样本的所有step打标签
for i, pred in enumerate(self.preds):
if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():
pred.is_correct = True
for step in pred.steps:
pred.step_labels[step] = 1
else:
for k, step in enumerate(pred.steps):
ans = TextEntailmentExample.match(
pred.steps[:k+1],
positive_preds,
model=model,
tokenizer=tokenizer,
entailment_result_dict=self.entailment_results,
)
pred.step_labels[step] = ans
def collect_entailment_texts(self, positive_preds):
for i, pred in enumerate(self.preds):
if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():
pass
else:
for pp in positive_preds:
for k, step in enumerate(pred.steps):
if k >= len(pp.steps):
continue
pp_step = pp.steps[k].strip()
text1 = f"premise: {pp_step} hypothesis: {step}"
text2 = f"premise: {step} hypothesis: {pp_step}"
self.entailment_results[text1] = -1
self.entailment_results[text2] = -1
def preprocess_entailment(self, model, tokenizer):
text_all = list(self.entailment_results.keys())
text_batch, results_batch = [], []
for i in range(0, len(text_all), self.entailment_batch_size):
text_batch = text_all[i : min(len(text_all), i + self.entailment_batch_size)]
batch_results = entailment_batch(text_batch, model, tokenizer)
for sc in batch_results:
results_batch.append(sc)
for text, result in zip(text_batch, results_batch):
self.entailment_results[text] = 1 if result else 0
class BaseExample:
inf = "-99999999"
def __init__(self, content):
self.content = content.strip()
self.steps = self.get_steps()
self.step_labels = {}
self.sequence_labels = []
self.is_correct= False
# Only for GSM8K dataset use
def init_equations(self):
raise NotImplementedError
def get_steps(self):
return [x+"%%" if x != self.content.split("%%")[-1] else x for i, x in enumerate(self.content.split("%%"))]
def get_final_answer(self):
ans = ""
if "####" in self.content:
ans = self.content.split("####")[-1].strip().replace("%%", "").replace(" ", "")
else:
ans = BaseExample.inf
return clean_ans(ans)
def label_to_string(self):
return "".join(str(self.labels[k]) for k in self.labels.keys())
class GSM8KExample(BaseExample):
def __init__(self, content):
super().__init__(content)
self.equations = self.init_equations()
self.verifier_score = 0.0
# 按'<<xxx>>'的格式将公式提取出来
def init_equations(self):
return [x for x in re.findall("<<.+>>[0-9\.]+", self.content) if "=" in x]
def get_step_answer(step):
expression = re.findall("<<.+>>[0-9\.]+", step)
if len(expression == 0):
ans = BaseExample.inf
else:
ans = expression[-1].split(">>")[-1].strip()
return clean_ans(ans)
@staticmethod
@lru_cache(maxsize=4096)
def get_answer(s):
ans = ""
if "####" in s:
ans = s.split("####")[-1].replace("%%", "").replace(" ", "").strip()
else:
expression = re.findall("<<.+>>[0-9\.]+", s)
if len(expression) == 0:
ans = GSM8KExample.inf
else:
ans = expression[-1].split(">>")[-1].strip()
return clean_ans(ans)
@staticmethod
def match(steps, positive_examples, model=None, tokenizer=None):
curr_set = Multiset([GSM8KExample.get_answer(x) for x in steps])
for positive_example in positive_examples:
golden_set = Multiset([GSM8KExample.get_answer(x) for x in positive_example.steps])
if GSM8KExample.inf in curr_set:
curr_set.remove(GSM8KExample.inf)
if GSM8KExample.inf in golden_set:
golden_set.remove(GSM8KExample.inf)
if len(curr_set) == 0:
return 0
if curr_set.issubset(golden_set):
return 1
return 0
def get_sequence_labels(question, pred):
sequence_labels = []
if pred.is_correct:
sequence_labels.append(("[CLS]", "SOLUTION-CORRECT"))
else:
sequence_labels.append(("[CLS]", "SOLUTION-INCORRECT"))
# add step tokens
for s in pred.steps:
token_list = [x for x in re.split("(>>| )", s) if x != ' ']
for token in token_list:
if token == ">>":
if pred.step_labels[s] == 1:
sequence_labels.append((token, "STEP-CORRECT"))
else:
sequence_labels.append((token, "STEP-INCORRECT"))
else:
sequence_labels.append((token, "O"))
# add a split symbol
sequence_labels.append(("&&", "O"))
# add question tokens
for token in question.split(" "):
sequence_labels.append((token, "O"))
return sequence_labels
class TextEntailmentExample(BaseExample):
def __init__(self, content):
super().__init__(content)
@staticmethod
def match(steps, positive_examples, model, tokenizer, entailment_result_dict):
for pp in positive_examples:
if TextEntailmentExample.match_per_example(pp, steps, entailment_result_dict):
return 1
return 0
@staticmethod
def match_per_example(pp, steps, entailment_result_dict):
for k, step in enumerate(steps):
if k >= len(pp.steps):
continue
# print("step:", step)
# print("pp.steps[k]:", pp.steps[k])
pp_step = pp.steps[k].strip()
text1 = f"premise: {step} hypothesis: {pp_step}"
text2 = f"premise: {pp_step} hypothesis: {step}"
if entailment_result_dict[text1] == 0 or entailment_result_dict[text2] == 0:
# error_case = 'No, Christmas trees are not dissimilar to deciduous trees.%%Both Christmas trees and deciduous trees are types of trees.%%Both Christmas trees and deciduous trees have leaves.%%So the answer is no.#### no'
# if error_case in text1 or error_case in text2:
# print("text1:", text1)
# print("text2:", text2)
# pdb.set_trace()
return 0
return 1
def get_sequence_labels(question, pred):
sequence_labels = []
if pred.is_correct:
sequence_labels.append(("[CLS]", "SOLUTION-CORRECT"))
else:
sequence_labels.append(("[CLS]", "SOLUTION-INCORRECT"))
# add step tokens
for s in pred.steps:
token_list = [x for x in re.split("(%%| )", s) if x != ' ']
for token in token_list:
if token == "":
continue
if token == "%%":
if pred.step_labels[s] == 1:
sequence_labels.append((token, "STEP-CORRECT"))
else:
sequence_labels.append((token, "STEP-INCORRECT"))
else:
sequence_labels.append((token, "O"))
# add a split symbol
sequence_labels.append(("&&", "O"))
# add question tokens
for token in question.split(" "):
sequence_labels.append((token, "O"))
return sequence_labels
@torch.no_grad()
def entailment_batch(text, model, tokenizer):
inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt").to("cuda")
labels = torch.tensor([1] * len(text)).to("cuda")
outputs = model(**inputs, labels=labels)
logits = outputs.logits
ans_list = torch.argmax(F.softmax(logits, dim=-1), dim=-1).tolist()
ans_list = [x == model.config.label2id["ENTAILMENT"] for x in ans_list]
return ans_list
@torch.no_grad()
def entailment(premise, hypothesis, model, tokenizer):
text = f"premise: {premise} hypothesis: {hypothesis}"
inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt").to(model.device)
labels = torch.tensor([1]).to(model.device)
outputs = model(**inputs, labels=labels)
logits = outputs.logits
ans = torch.argmax(F.softmax(logits, dim=-1)).item() == model.config.label2id["ENTAILMENT"]
return ans
def convert_eval_sequences_to_cases(eval_sequences, pred_num_per_case, case_class, example_class):
cases = []
for i in range(0, len(eval_sequences), pred_num_per_case + 1):
case = case_class("", [])
# question, grount_truth = eval_sequences[i].split("&&")[0], eval_sequences[i].split("&&")[1]
question, grount_truth = eval_sequences[i].split("&&")[1], eval_sequences[i].split("&&")[0]
case.ground_truth = example_class(grount_truth)
case.question = question
for j in range(i+1, i+pred_num_per_case+1):
# case.preds.append(GSM8KExample(eval_sequences[j].split("&&")[1]))
case.preds.append(example_class(eval_sequences[j].split("&&")[0]))
cases.append(case)
# if example_class.__name__ == "TextEntailmentExample":
# cases = post_process_answer_clutrr(cases)
return cases
def post_process_answer_clutrr_mapping(cases):
print("before loading pipeline")
classifier = pipeline("zero-shot-classification", device=0)
print("after loading pipeline")
print("post processing")
candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']
for case_idx, case in tqdm(enumerate(cases)):
gt_ans = case.ground_truth.get_final_answer()
# skip StrategyQA task
if gt_ans == "yes" or gt_ans == "no":
break
for pred in case.preds:
pred_ans = pred.get_final_answer()
if pred_ans != BaseExample.inf and pred_ans != gt_ans:
outputs = classifier(pred_ans, candidate_labels)
logits = outputs["scores"]
labels = outputs["labels"]
candidate_index = np.argmax(logits)
most_similar_answer = labels[candidate_index]
body = pred.content.split("####")[0]
pred.content = body + "####" + most_similar_answer
# pdb.set_trace()
return cases
def post_process_answer_clutrr_cutoff(cases):
candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']
for case_idx, case in tqdm(enumerate(cases)):
gt_ans = case.ground_truth.get_final_answer()
# skip StrategyQA task
if gt_ans == "yes" or gt_ans == "no":
break
for pred in case.preds:
pred_ans = pred.get_final_answer()
if pred_ans not in candidate_labels:
body = pred.content.split("####")[0]
pred.content = body + "####" + BaseExample.inf
return cases
def random_1_hit(gt_ans, preds):
idx = random.randint(0, len(preds)-1)
# random 1 acc
pred0_ans = preds[idx].get_final_answer()
return 1 if pred0_ans == gt_ans else 0
def recall_hit(gt_ans, preds):
for pred in preds:
if pred.get_final_answer() == gt_ans:
return 1
return 0
def voting_hit(gt_ans, preds):
# voting acc
answers = {}
for pred in preds:
if pred.get_final_answer() not in answers:
answers[pred.get_final_answer()] = 0
answers[pred.get_final_answer()] += 1
answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)
for i in range(len(answers)):
ans, ans_cnt = answers[i][0], answers[i][1]
if ans != GSM8KExample.inf:
return 1 if ans == gt_ans else 0
return 0
def weighted_voting_hit(gt_ans, preds):
# voting acc
answers = {}
for pred in preds:
if pred.get_final_answer() not in answers:
answers[pred.get_final_answer()] = 0
answers[pred.get_final_answer()] += pred.verifier_score
answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)
for i in range(len(answers)):
ans, ans_cnt = answers[i][0], answers[i][1]
if ans != GSM8KExample.inf:
return 1 if ans == gt_ans else 0
return 0
def verification_hit(gt_ans, preds):
preds = sorted(preds, key=lambda x : x.verifier_score, reverse=True)
for pred in preds:
ans = pred.get_final_answer()
if ans != GSM8KExample.inf:
return 1 if ans == gt_ans else 0
return 0
def compute_top1_and_recall(data, rand_k=100):
total_random_hit_cnt = 0
total_vote_cnt = 0
total_recall_cnt = 0
for i, x in enumerate(data):
gt_ans = x.ground_truth.get_final_answer()
slice = x.preds if rand_k >= len(x.preds) else random.sample(x.preds, rand_k)
total_random_hit_cnt += random_1_hit(gt_ans, slice)
total_vote_cnt += voting_hit(gt_ans, slice)
total_recall_cnt += recall_hit(gt_ans, slice)
result = {
"random_top1": total_random_hit_cnt / len(data),
"voting_top1_accuracy": total_vote_cnt / len(data),
"recall": total_recall_cnt / len(data),
}
return result
def compute_results(data, rand_k=100):
total_random_hit_cnt = 0
total_recall_cnt = 0
total_vote_cnt = 0
total_weighted_vote_cnt = 0
total_verification_cnt = 0
for i, x in enumerate(data):
gt_ans = x.ground_truth.get_final_answer()
slice = x.preds if rand_k == len(x.preds) else random.sample(x.preds, rand_k)
total_random_hit_cnt += random_1_hit(gt_ans, slice)
total_vote_cnt += voting_hit(gt_ans, slice)
total_recall_cnt += recall_hit(gt_ans, slice)
total_weighted_vote_cnt += weighted_voting_hit(gt_ans, slice)
total_verification_cnt += verification_hit(gt_ans, slice)
result = {
"random_top1": total_random_hit_cnt / len(data),
f"recall@{rand_k}": total_recall_cnt / len(data),
f"verifier_top1_accuracy@{rand_k}": total_verification_cnt / len(data),
f"voting_top1_accuracy@{rand_k}": total_vote_cnt / len(data),
f"weighted_voting_top1_accuracy@{rand_k}": total_weighted_vote_cnt / len(data),
}
return result
def compute_results_avg(data, rand_k=100, repeat_time=5):
sum_result_dict = {
"random_top1": 0,
f"recall@{rand_k}": 0,
f"verifier_top1_accuracy@{rand_k}": 0,
f"voting_top1_accuracy@{rand_k}": 0,
f"weighted_voting_top1_accuracy@{rand_k}": 0,
}
for i in tqdm(range(repeat_time)):
for k in sum_result_dict:
result_dict = compute_results(data, rand_k=rand_k)
sum_result_dict[k] += result_dict[k]
for k in sum_result_dict:
sum_result_dict[k] = sum_result_dict[k] / repeat_time if repeat_time != 1 else sum_result_dict[k]
sum_result_dict[k] = round(sum_result_dict[k], 8)
return sum_result_dict
def dedup(li):
s = set()
new_li = []
for x in li:
if str(x) not in s:
new_li.append(x)
s.add(str(x))
return new_li
def print_stat(data):
cnt = 0
for x in data:
if x["output"] == "correct":
cnt += 1
print(cnt, len(data) - cnt, len(data))
def clean_ans(s):
s = str(s)
if s and len(s) > 0 and s[-1] == '.':
s = s[:-1]
return s.lower() # for CLUTRR and strategyQA use
|
CodeT/DIVERSE/code/src/utils.py/0
|
{
"file_path": "CodeT/DIVERSE/code/src/utils.py",
"repo_id": "CodeT",
"token_count": 9365
}
| 219 |
# CodeT
This repository contains projects that aims to equip large-scale pretrained language models with better programming and reasoning skills.
These projects are presented by Microsoft Research Asia and Microsoft Azure AI.
## Projects
- [[CodeT]](./CodeT/): Code Generation with Generated Tests
- [[DIVERSE]](./DIVERSE/): On the Advance of Making Language Models Better Reasoners
- [[RepoCoder]](./RepoCoder/): Repository-Level Code Completion Through Iterative Retrieval and Generation
|
CodeT/README.md/0
|
{
"file_path": "CodeT/README.md",
"repo_id": "CodeT",
"token_count": 119
}
| 220 |
###
# PowerShell script to clean up the Codex CLI settings for PowerShell
#
# File/Content to be removed:
# 1. PowerShell profile (Remove file if the content only has Codex CLI setup; otherwise, wipe the Codex CLI setup content)
# 2. OpenAI configuration file (openaiapirc)
###
$RepoRoot = (Get-Location)
$openAIConfigPath = Join-Path $RepoRoot -ChildPath "src\openaiapirc"
function CleanUpOpenAiConfig()
{
if (Test-Path -Path $openAIConfigPath) {
Remove-Item $openAIConfigPath
Write-Host "Removed $openAIConfigPath"
}
}
function CleanUpProfileContent()
{
if (Test-Path -Path $PROFILE) {
# RegEx match setup code, replace with empty string.
(Get-Content -Path $PROFILE -Raw) -replace "(?ms)### Codex CLI setup - start.*?### Codex CLI setup - end", "" | Set-Content -Path $PROFILE
# Delete the file if its content is empty
if ([String]::IsNullOrWhiteSpace((Get-Content -Path $PROFILE))) {
Remove-Item $PROFILE
Write-Host "Removed $PROFILE"
}
}
}
CleanUpProfileContent
CleanUpOpenAiConfig
Write-Host -ForegroundColor Blue "Codex CLI PowerShell clean up completed. Please close this PowerShell session."
|
Codex-CLI/scripts/powershell_cleanup.ps1/0
|
{
"file_path": "Codex-CLI/scripts/powershell_cleanup.ps1",
"repo_id": "Codex-CLI",
"token_count": 424
}
| 221 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: face_list.py
Description: Face List section of the Cognitive Face API.
"""
from . import util
def add_face(image, face_list_id, user_data=None, target_face=None):
"""Add a face to a face list.
The input face is specified as an image with a `target_face` rectangle. It
returns a `persisted_face_id` representing the added face, and
`persisted_face_id` will not expire. Note `persisted_face_id` is different
from `face_id` which represents the detected face by `face.detect`.
Args:
image: A URL or a file path or a file-like object represents an image.
face_list_id: Valid character is letter in lower case or digit or '-'
or '_', maximum length is 64.
user_data: Optional parameter. User-specified data about the face list
for any purpose. The maximum length is 1KB.
target_face: Optional parameter. A face rectangle to specify the target
face to be added into the face list, in the format of
"left,top,width,height". E.g. "10,10,100,100". If there are more
than one faces in the image, `target_face` is required to specify
which face to add. No `target_face` means there is only one face
detected in the entire image.
Returns:
A new `persisted_face_id`.
"""
url = 'facelists/{}/persistedFaces'.format(face_list_id)
headers, data, json = util.parse_image(image)
params = {
'userData': user_data,
'targetFace': target_face,
}
return util.request(
'POST', url, headers=headers, params=params, json=json, data=data)
def create(face_list_id, name=None, user_data=None):
"""Create an empty face list with user-specified `face_list_id`, `name` and
an optional `user_data`. Up to 64 face lists are allowed to exist in one
subscription.
Args:
face_list_id: Valid character is letter in lower case or digit or '-'
or '_', maximum length is 64.
name: Name of the created face list, maximum length is 128.
user_data: Optional parameter. User-defined data for the face list.
Length should not exceed 16KB.
Returns:
An empty response body.
"""
name = name or face_list_id
url = 'facelists/{}'.format(face_list_id)
json = {
'name': name,
'userData': user_data,
}
return util.request('PUT', url, json=json)
def delete_face(face_list_id, persisted_face_id):
"""Delete an existing face from a face list (given by a `persisted_face_id`
and a `face_list_id`). Persisted image related to the face will also be
deleted.
Args:
face_list_id: Valid character is letter in lower case or digit or '-'
or '_', maximum length is 64.
persisted_face_id: `persisted_face_id` of an existing face. Valid
character is letter in lower case or digit or '-' or '_', maximum
length is 64.
Returns:
An empty response body.
"""
url = 'facelists/{}/persistedFaces/{}'.format(face_list_id,
persisted_face_id)
return util.request('DELETE', url)
def delete(face_list_id):
"""Delete an existing face list according to `face_list_id`. Persisted face
images in the face list will also be deleted.
Args:
face_list_id: Valid character is letter in lower case or digit or '-'
or '_', maximum length is 64.
Returns:
An empty response body.
"""
url = 'facelists/{}'.format(face_list_id)
return util.request('DELETE', url)
def get(face_list_id):
"""Retrieve a face list's information, including `face_list_id`, `name`,
`user_data` and faces in the face list. Face list simply represents a list
of faces, and could be treated as a searchable data source in
`face.find_similars`.
Args:
face_list_id: Valid character is letter in lower case or digit or '-'
or '_', maximum length is 64.
Returns:
The face list's information.
"""
url = 'facelists/{}'.format(face_list_id)
return util.request('GET', url)
def lists():
"""Retrieve information about all existing face lists. Only `face_list_id`,
`name` and `user_data` will be returned. Try `face_list.get` to retrieve
face information inside face list.
Returns:
An array of face list.
"""
url = 'facelists'
return util.request('GET', url)
def update(face_list_id, name=None, user_data=None):
"""Update information of a face list, including `name` and `user_data`.
Face List simply represents a list of persisted faces, and could be treated
as a searchable data source in `face.find_similars`.
Args:
face_list_id: Valid character is letter in lower case or digit or '-'
or '_', maximum length is 64.
name: Name of the created face list, maximum length is 128.
user_data: Optional parameter. User-defined data for the face list.
Length should not exceed 16KB.
Returns:
An empty response body.
"""
url = 'facelists/{}'.format(face_list_id)
json = {
'name': name,
'userData': user_data,
}
return util.request('PATCH', url, json=json)
|
Cognitive-Face-Python/cognitive_face/face_list.py/0
|
{
"file_path": "Cognitive-Face-Python/cognitive_face/face_list.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 2042
}
| 222 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: test_large_person_group_person_face.py
Description: Unittests for Large Person Group Person Face section of the
Cognitive Face API.
"""
import unittest
import cognitive_face as CF
from . import util
class TestLargePersonGroupPersonFace(unittest.TestCase):
"""Unittests for Large Person Group Person Face section."""
def test_face(self):
"""Unittests for `large_person_group_person_face.add`,
`large_person_group_person_face.update` and
`large_person_group_person_face.delete`.
"""
image = '{}PersonGroup/Family1-Dad/Family1-Dad3.jpg'.format(
util.BASE_URL_IMAGE)
res = CF.large_person_group_person_face.add(
image, util.DataStore.large_person_group_id,
util.DataStore.large_person_group_person_id['Dad'])
print(res)
self.assertIsInstance(res, dict)
util.wait()
persisted_face_id = res['persistedFaceId']
res = CF.large_person_group_person_face.update(
util.DataStore.large_person_group_id,
util.DataStore.large_person_group_person_id['Dad'],
persisted_face_id, 'TempUserData')
print(res)
self.assertIsInstance(res, dict)
util.wait()
res = CF.large_person_group_person_face.delete(
util.DataStore.large_person_group_id,
util.DataStore.large_person_group_person_id['Dad'],
persisted_face_id)
print(res)
self.assertIsInstance(res, dict)
util.wait()
def test_get(self):
"""Unittest for `large_person_group_person_face.get`."""
res = CF.large_person_group_person_face.get(
util.DataStore.large_person_group_id,
util.DataStore.large_person_group_person_id['Dad'],
util.DataStore.large_person_group_person_face_id['Dad'][0])
print(res)
self.assertIsInstance(res, dict)
util.wait()
|
Cognitive-Face-Python/cognitive_face/tests/test_large_person_group_person_face.py/0
|
{
"file_path": "Cognitive-Face-Python/cognitive_face/tests/test_large_person_group_person_face.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 883
}
| 223 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: view.py
Description: Subscription Panel for Python SDK sample.
"""
import wx
import wx.lib.agw.hyperlink as HL
import util
from view.base import MyPanel
class SubscriptionPanel(MyPanel):
"""Subscription Panel."""
def __init__(self, parent):
super(SubscriptionPanel, self).__init__(parent)
self.sizer = wx.BoxSizer(wx.VERTICAL)
label = (
'To use the service, make sure you have a valid '
'subscription key.\nNote that each service (Face, Emotion, '
'Speech, etc.) has its own subscription keys.\nAnd each '
'subscription key belongs to one specific endpoint.\nYou can use '
'the link below to get a key.\nWhen ready, paste your key '
'into the textbox below.')
style = wx.ALIGN_LEFT | wx.ST_ELLIPSIZE_END
self.static_text = wx.StaticText(self, label=label, style=style)
self.sizer.Add(self.static_text, flag=wx.EXPAND | wx.ALL, border=5)
label = 'Get Key'
url = 'https://www.microsoft.com/cognitive-services/en-us/sign-up'
colour_window = wx.SystemSettings.GetColour(wx.SYS_COLOUR_WINDOW)
flag = wx.ALIGN_LEFT | wx.ALL
self.link = HL.HyperLinkCtrl(self, label=label, URL=url)
self.link.SetBackgroundColour(colour_window)
self.sizer.Add(self.link, flag=flag, border=5)
subgridsizer = wx.GridSizer(rows=2, cols=2, hgap=5, vgap=5)
flag = wx.ALIGN_CENTER_VERTICAL | wx.ALL | wx.FIXED_MINSIZE
label = 'Subscription Key : '
self.subscription_key_label = wx.StaticText(self, label=label)
subgridsizer.Add(self.subscription_key_label, flag=flag, border=5)
flag = wx.EXPAND | wx.ALIGN_CENTER_VERTICAL | wx.ALL
subscription_key = util.SubscriptionKey.get()
self.subscription_key_text = wx.TextCtrl(self, size=(-1, -1))
self.subscription_key_text.SetValue(subscription_key)
subgridsizer.Add(self.subscription_key_text, 1, flag=flag, border=5)
flag = wx.ALIGN_CENTER_VERTICAL | wx.ALL | wx.FIXED_MINSIZE
label = 'Endpoint : '
self.endpoint_label = wx.StaticText(self, label=label)
subgridsizer.Add(self.endpoint_label, flag=flag, border=5)
flag = wx.EXPAND | wx.ALIGN_CENTER_VERTICAL | wx.ALL
endpoint = util.Endpoint.get()
self.endpoint_text = wx.TextCtrl(self, size=(-1, -1))
self.endpoint_text.SetValue(endpoint)
subgridsizer.Add(self.endpoint_text, 1, flag=flag, border=5)
flag = wx.EXPAND | wx.TOP | wx.BOTTOM
self.sizer.Add(subgridsizer, flag=flag, border=5)
subsizer = wx.BoxSizer()
flag = wx.ALIGN_CENTER_VERTICAL | wx.ALL | wx.FIXED_MINSIZE
self.btn_save = wx.Button(self, label='Save')
subsizer.Add(self.btn_save, flag=flag, border=5)
self.Bind(wx.EVT_BUTTON, self.OnSave, self.btn_save)
flag = wx.ALIGN_CENTER_VERTICAL | wx.ALL
self.btn_del = wx.Button(self, label='Delete')
subsizer.Add(self.btn_del, flag=flag, border=5)
self.Bind(wx.EVT_BUTTON, self.OnDelete, self.btn_del)
flag = wx.EXPAND | wx.TOP | wx.BOTTOM
self.sizer.Add(subsizer, flag=flag, border=5)
self.SetSizer(self.sizer)
def OnSave(self, evt):
"""Save the key and endpoint."""
util.SubscriptionKey.set(
self.subscription_key_text.GetValue())
util.Endpoint.set(self.endpoint_text.GetValue())
text = (
'Settings successfully saved on your disk.\nYou do not need to '
'paste the key next time.')
title = 'Settings'
style = wx.OK | wx.ICON_INFORMATION
wx.MessageBox(text, title, style)
def OnDelete(self, evt):
"""Delete the key and endpoint."""
util.SubscriptionKey.delete()
util.Endpoint.delete()
self.subscription_key_text.Clear()
self.endpoint_text.SetValue(util.Endpoint.get())
text = 'Settings successfully deleted from your disk.'
title = 'Settings'
style = wx.OK | wx.ICON_INFORMATION
wx.MessageBox(text, title, style)
|
Cognitive-Face-Python/sample/view/panel_subscription.py/0
|
{
"file_path": "Cognitive-Face-Python/sample/view/panel_subscription.py",
"repo_id": "Cognitive-Face-Python",
"token_count": 1897
}
| 224 |
NOTICES AND INFORMATION
Do Not Translate or Localize
This software incorporates material from third parties. Microsoft makes certain
open source code available at https://3rdpartysource.microsoft.com, or you may
send a check or money order for US $5.00, including the product name, the open
source component name, platform, and version number, to:
Source Code Compliance Team
Microsoft Corporation
One Microsoft Way
Redmond, WA 98052
USA
Notwithstanding any other terms, you may reverse engineer this software to the
extent required to debug changes to any libraries licensed under the GNU Lesser
General Public License.
=======================================================================
Component. PyTorch
Open Source License/Copyright Notice.
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
From Caffe2:
Copyright (c) 2016-present, Facebook Inc. All rights reserved.
All contributions by Facebook:
Copyright (c) 2016 Facebook Inc.
All contributions by Google:
Copyright (c) 2015 Google Inc.
All rights reserved.
All contributions by Yangqing Jia:
Copyright (c) 2015 Yangqing Jia
All rights reserved.
All contributions from Caffe:
Copyright(c) 2013, 2014, 2015, the respective contributors
All rights reserved.
All other contributions:
Copyright(c) 2015, 2016 the respective contributors
All rights reserved.
Caffe2 uses a copyright model similar to Caffe: each contributor holds
copyright over their contributions to Caffe2. The project versioning records
all such contribution and copyright details. If a contributor wants to further
mark their specific copyright on a particular contribution, they should
indicate their copyright solely in the commit message of the change when it is
committed.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
and IDIAP Research Institute nor the names of its contributors may be
used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
=======================================================================
Component. AllenNLP
Open Source License/Copyright Notice.
Apache License
Version 2.0, January 2004
https://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "{}"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright {yyyy} {name of copyright owner}
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
=======================================================================
Component. nltk
Open Source License/Copyright Notice.
Copyright (C) 2001-2019 NLTK Project
Licensed under the Apache License, Version 2.0 (the 'License');
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an 'AS IS' BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
=======================================================================
Component. transformers
Open Source License/Copyright Notice.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
=======================================================================
Component. Glove
Open Source License/Copyright Notice.
Preamble
The Open Data Commons – Public Domain Dedication & Licence is a document intended to allow you to freely share, modify, and use this work for any purpose and without any restrictions. This licence is intended for use on databases or their contents (“data”), either together or individually.
Many databases are covered by copyright. Some jurisdictions, mainly in Europe, have specific special rights that cover databases called the “sui generis” database right. Both of these sets of rights, as well as other legal rights used to protect databases and data, can create uncertainty or practical difficulty for those wishing to share databases and their underlying data but retain a limited amount of rights under a “some rights reserved” approach to licensing as outlined in the Science Commons Protocol for Implementing Open Access Data. As a result, this waiver and licence tries to the fullest extent possible to eliminate or fully license any rights that cover this database and data. Any Community Norms or similar statements of use of the database or data do not form a part of this document, and do not act as a contract for access or other terms of use for the database or data.
The position of the recipient of the work
Because this document places the database and its contents in or as close as possible within the public domain, there are no restrictions or requirements placed on the recipient by this document. Recipients may use this work commercially, use technical protection measures, combine this data or database with other databases or data, and share their changes and additions or keep them secret. It is not a requirement that recipients provide further users with a copy of this licence or attribute the original creator of the data or database as a source. The goal is to eliminate restrictions held by the original creator of the data and database on the use of it by others.
The position of the dedicator of the work
Copyright law, as with most other law under the banner of “intellectual property”, is inherently national law. This means that there exists several differences in how copyright and other IP rights can be relinquished, waived or licensed in the many legal jurisdictions of the world. This is despite much harmonisation of minimum levels of protection. The internet and other communication technologies span these many disparate legal jurisdictions and thus pose special difficulties for a document relinquishing and waiving intellectual property rights, including copyright and database rights, for use by the global community. Because of this feature of intellectual property law, this document first relinquishes the rights and waives the relevant rights and claims. It then goes on to license these same rights for jurisdictions or areas of law that may make it difficult to relinquish or waive rights or claims.
The purpose of this document is to enable rightsholders to place their work into the public domain. Unlike licences for free and open source software, free cultural works, or open content licences, rightsholders will not be able to “dual license” their work by releasing the same work under different licences. This is because they have allowed anyone to use the work in whatever way they choose. Rightsholders therefore can’t re-license it under copyright or database rights on different terms because they have nothing left to license. Doing so creates truly accessible data to build rich applications and advance the progress of science and the arts.
This document can cover either or both of the database and its contents (the data). Because databases can have a wide variety of content – not just factual data – rightsholders should use the Open Data Commons – Public Domain Dedication & Licence for an entire database and its contents only if everything can be placed under the terms of this document. Because even factual data can sometimes have intellectual property rights, rightsholders should use this licence to cover both the database and its factual data when making material available under this document; even if it is likely that the data would not be covered by copyright or database rights.
Rightsholders can also use this document to cover any copyright or database rights claims over only a database, and leave the contents to be covered by other licences or documents. They can do this because this document refers to the “Work”, which can be either – or both – the database and its contents. As a result, rightsholders need to clearly state what they are dedicating under this document when they dedicate it.
Just like any licence or other document dealing with intellectual property, rightsholders should be aware that one can only license what one owns. Please ensure that the rights have been cleared to make this material available under this document.
This document permanently and irrevocably makes the Work available to the public for any use of any kind, and it should not be used unless the rightsholder is prepared for this to happen.
Part I: Introduction
The Rightsholder (the Person holding rights or claims over the Work) agrees as follows:
1.0 Definitions of Capitalised Words
“Copyright” – Includes rights under copyright and under neighbouring rights and similarly related sets of rights under the law of the relevant jurisdiction under Section 6.4.
“Data” – The contents of the Database, which includes the information, independent works, or other material collected into the Database offered under the terms of this Document.
“Database” – A collection of Data arranged in a systematic or methodical way and individually accessible by electronic or other means offered under the terms of this Document.
“Database Right” – Means rights over Data resulting from the Chapter III (“sui generis”) rights in the Database Directive (Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases) and any future updates as well as any similar rights available in the relevant jurisdiction under Section 6.4.
“Document” – means this relinquishment and waiver of rights and claims and back up licence agreement.
“Person” – Means a natural or legal person or a body of persons corporate or incorporate.
“Use” – As a verb, means doing any act that is restricted by Copyright or Database Rights whether in the original medium or any other; and includes modifying the Work as may be technically necessary to use it in a different mode or format. This includes the right to sublicense the Work.
“Work” – Means either or both of the Database and Data offered under the terms of this Document.
“You” – the Person acquiring rights under the licence elements of this Document.
Words in the singular include the plural and vice versa.
2.0 What this document covers
2.1. Legal effect of this Document. This Document is:
a. A dedication to the public domain and waiver of Copyright and Database Rights over the Work; and
b. A licence of Copyright and Database Rights over the Work in jurisdictions that do not allow for relinquishment or waiver.
2.2. Legal rights covered.
a. Copyright. Any copyright or neighbouring rights in the Work. Copyright law varies between jurisdictions, but is likely to cover: the Database model or schema, which is the structure, arrangement, and organisation of the Database, and can also include the Database tables and table indexes; the data entry and output sheets; and the Field names of Data stored in the Database. Copyright may also cover the Data depending on the jurisdiction and type of Data; and
b. Database Rights. Database Rights only extend to the extraction and re-utilisation of the whole or a substantial part of the Data. Database Rights can apply even when there is no copyright over the Database. Database Rights can also apply when the Data is removed from the Database and is selected and arranged in a way that would not infringe any applicable copyright.
2.2 Rights not covered.
a. This Document does not apply to computer programs used in the making or operation of the Database;
b. This Document does not cover any patents over the Data or the Database. Please see Section 4.2 later in this Document for further details; and
c. This Document does not cover any trade marks associated with the Database. Please see Section 4.3 later in this Document for further details.
Users of this Database are cautioned that they may have to clear other rights or consult other licences.
2.3 Facts are free. The Rightsholder takes the position that factual information is not covered by Copyright. This Document however covers the Work in jurisdictions that may protect the factual information in the Work by Copyright, and to cover any information protected by Copyright that is contained in the Work.
Part II: Dedication to the public domain
3.0 Dedication, waiver, and licence of Copyright and Database Rights
3.1 Dedication of Copyright and Database Rights to the public domain. The Rightsholder by using this Document, dedicates the Work to the public domain for the benefit of the public and relinquishes all rights in Copyright and Database Rights over the Work.
a. The Rightsholder realises that once these rights are relinquished, that the Rightsholder has no further rights in Copyright and Database Rights over the Work, and that the Work is free and open for others to Use.
b. The Rightsholder intends for their relinquishment to cover all present and future rights in the Work under Copyright and Database Rights, whether they are vested or contingent rights, and that this relinquishment of rights covers all their heirs and successors.
The above relinquishment of rights applies worldwide and includes media and formats now known or created in the future.
3.2 Waiver of rights and claims in Copyright and Database Rights when Section 3.1 dedication inapplicable. If the dedication in Section 3.1 does not apply in the relevant jurisdiction under Section 6.4, the Rightsholder waives any rights and claims that the Rightsholder may have or acquire in the future over the Work in:
a. Copyright; and
b. Database Rights.
To the extent possible in the relevant jurisdiction, the above waiver of rights and claims applies worldwide and includes media and formats now known or created in the future. The Rightsholder agrees not to assert the above rights and waives the right to enforce them over the Work.
3.3 Licence of Copyright and Database Rights when Sections 3.1 and 3.2 inapplicable. If the dedication and waiver in Sections 3.1 and 3.2 does not apply in the relevant jurisdiction under Section 6.4, the Rightsholder and You agree as follows:
a. The Licensor grants to You a worldwide, royalty-free, non-exclusive, licence to Use the Work for the duration of any applicable Copyright and Database Rights. These rights explicitly include commercial use, and do not exclude any field of endeavour. To the extent possible in the relevant jurisdiction, these rights may be exercised in all media and formats whether now known or created in the future.
3.4 Moral rights. This section covers moral rights, including the right to be identified as the author of the Work or to object to treatment that would otherwise prejudice the author’s honour and reputation, or any other derogatory treatment:
a. For jurisdictions allowing waiver of moral rights, Licensor waives all moral rights that Licensor may have in the Work to the fullest extent possible by the law of the relevant jurisdiction under Section 6.4;
b. If waiver of moral rights under Section 3.4 a in the relevant jurisdiction is not possible, Licensor agrees not to assert any moral rights over the Work and waives all claims in moral rights to the fullest extent possible by the law of the relevant jurisdiction under Section 6.4; and
c. For jurisdictions not allowing waiver or an agreement not to assert moral rights under Section 3.4 a and b, the author may retain their moral rights over the copyrighted aspects of the Work.
Please note that some jurisdictions do not allow for the waiver of moral rights, and so moral rights may still subsist over the work in some jurisdictions.
4.0 Relationship to other rights
4.1 No other contractual conditions. The Rightsholder makes this Work available to You without any other contractual obligations, either express or implied. Any Community Norms statement associated with the Work is not a contract and does not form part of this Document.
4.2 Relationship to patents. This Document does not grant You a licence for any patents that the Rightsholder may own. Users of this Database are cautioned that they may have to clear other rights or consult other licences.
4.3 Relationship to trade marks. This Document does not grant You a licence for any trade marks that the Rightsholder may own or that the Rightsholder may use to cover the Work. Users of this Database are cautioned that they may have to clear other rights or consult other licences.
Part III: General provisions
5.0 Warranties, disclaimer, and limitation of liability
5.1 The Work is provided by the Rightsholder “as is” and without any warranty of any kind, either express or implied, whether of title, of accuracy or completeness, of the presence of absence of errors, of fitness for purpose, or otherwise. Some jurisdictions do not allow the exclusion of implied warranties, so this exclusion may not apply to You.
5.2 Subject to any liability that may not be excluded or limited by law, the Rightsholder is not liable for, and expressly excludes, all liability for loss or damage however and whenever caused to anyone by any use under this Document, whether by You or by anyone else, and whether caused by any fault on the part of the Rightsholder or not. This exclusion of liability includes, but is not limited to, any special, incidental, consequential, punitive, or exemplary damages. This exclusion applies even if the Rightsholder has been advised of the possibility of such damages.
5.3 If liability may not be excluded by law, it is limited to actual and direct financial loss to the extent it is caused by proved negligence on the part of the Rightsholder.
6.0 General
6.1 If any provision of this Document is held to be invalid or unenforceable, that must not affect the validity or enforceability of the remainder of the terms of this Document.
6.2 This Document is the entire agreement between the parties with respect to the Work covered here. It replaces any earlier understandings, agreements or representations with respect to the Work not specified here.
6.3 This Document does not affect any rights that You or anyone else may independently have under any applicable law to make any use of this Work, including (for jurisdictions where this Document is a licence) fair dealing, fair use, database exceptions, or any other legally recognised limitation or exception to infringement of copyright or other applicable laws.
6.4 This Document takes effect in the relevant jurisdiction in which the Document terms are sought to be enforced. If the rights waived or granted under applicable law in the relevant jurisdiction includes additional rights not waived or granted under this Document, these additional rights are included in this Document in order to meet the intent of this Document.
|
ContextualSP/NOTICE/0
|
{
"file_path": "ContextualSP/NOTICE",
"repo_id": "ContextualSP",
"token_count": 10725
}
| 225 |
export CUDA_VISIBLE_DEVICES=5
python t5_run_eval.py \
--model_name_or_path ./checkpoint/Mod/ControlExp_finetune_set1_seed1/checkpoint-50000 \
--subtask Mod \
--validation_file test \
--ebatch_size 16 \
--set set1
|
ContextualSP/abstraction_probing/code/t5_code/Mod_ControlExp_test.sh/0
|
{
"file_path": "ContextualSP/abstraction_probing/code/t5_code/Mod_ControlExp_test.sh",
"repo_id": "ContextualSP",
"token_count": 84
}
| 226 |
# Copyright (c) Microsoft. All rights reserved.
from enum import Enum
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import accuracy_score, f1_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from scipy.stats import pearsonr, spearmanr
from seqeval.metrics import classification_report
from data_utils.mrc_eval import squadv1_evaluate_func
from data_utils.mrc_eval import squadv2_evaluate_func
import seqeval
def compute_acc(predicts, labels):
return 100.0 * accuracy_score(labels, predicts)
def compute_f1(predicts, labels):
return 100.0 * f1_score(labels, predicts)
def compute_f1mac(predicts, labels):
return 100.0 * f1_score(labels, predicts, average="macro")
def compute_f1mic(predicts, labels):
return 100.0 * f1_score(labels, predicts, average="micro")
def compute_mcc(predicts, labels):
return 100.0 * matthews_corrcoef(labels, predicts)
def compute_pearson(predicts, labels):
pcof = pearsonr(labels, predicts)[0]
return 100.0 * pcof
def compute_spearman(predicts, labels):
scof = spearmanr(labels, predicts)[0]
return 100.0 * scof
def compute_auc(predicts, labels):
auc = roc_auc_score(labels, predicts)
return 100.0 * auc
def compute_cmat(predicts, labels):
# return str(confusion_matrix(labels, predicts))
return confusion_matrix(labels, predicts)
def compute_seqacc(predicts, labels, label_mapper):
y_true, y_pred = [], []
def trim(predict, label):
temp_1 = []
temp_2 = []
for j, m in enumerate(predict):
if j == 0:
continue
if label_mapper[label[j]] != "X":
temp_1.append(label_mapper[label[j]])
temp_2.append(label_mapper[m])
temp_1.pop()
temp_2.pop()
y_true.append(temp_1)
y_pred.append(temp_2)
for predict, label in zip(predicts, labels):
trim(predict, label)
report = classification_report(y_true, y_pred, digits=4)
return report
def compute_list_f1(predicts, labels, label_mapper):
def trim(predict, label):
temp_1 = []
temp_2 = []
for j, m in enumerate(predict):
if j == 0:
continue
if label_mapper[label[j]] != "X":
temp_1.append(label_mapper[label[j]])
temp_2.append(label_mapper[m])
temp_1.pop()
temp_2.pop()
return temp_1, temp_2
f1 = 0
for predict, label in zip(predicts, labels):
if label == [0] * len(label): # all 'O' i.e. empty list
if predict == [0] * len(predict):
f1 += 1.0
else:
y_true, y_pred = trim(predict, label)
f1 += seqeval.metrics.f1_score([y_true], [y_pred]) # , digits=4)
f1 = f1 / len(predicts)
return 100.0 * f1
def compute_emf1(predicts, labels):
return squadv1_evaluate_func(labels, predicts)
def compute_emf12(predicts, labels):
return squadv2_evaluate_func(labels, predicts)
class Metric(Enum):
ACC = 0
F1 = 1
MCC = 2
Pearson = 3
Spearman = 4
AUC = 5
SeqEval = 7
EmF1 = 8
F1MAC = 9
F1MIC = 10
CMAT = 11
EmF12 = 12
SeqEvalList = 13 # include empty list for clue
METRIC_FUNC = {
Metric.ACC: compute_acc,
Metric.F1: compute_f1,
Metric.MCC: compute_mcc,
Metric.Pearson: compute_pearson,
Metric.Spearman: compute_spearman,
Metric.AUC: compute_auc,
Metric.SeqEval: compute_seqacc,
Metric.SeqEvalList: compute_list_f1,
Metric.EmF1: compute_emf1,
Metric.F1MAC: compute_f1mac,
Metric.F1MIC: compute_f1mic,
Metric.CMAT: compute_cmat,
Metric.EmF12: compute_emf12,
}
def calc_metrics(metric_meta, golds, predictions, scores, label_mapper=None):
"""Label Mapper is used for NER/POS etc.
TODO: a better refactor, by xiaodl
"""
metrics = {}
for mm in metric_meta:
metric_name = mm.name
metric_func = METRIC_FUNC[mm]
if mm in (
Metric.ACC,
Metric.F1,
Metric.MCC,
Metric.F1MAC,
Metric.F1MIC,
Metric.CMAT,
):
metric = metric_func(predictions, golds)
elif mm in (Metric.SeqEval, Metric.SeqEvalList):
metric = metric_func(predictions, golds, label_mapper)
elif mm == Metric.EmF1 or mm == Metric.EmF12:
metric = metric_func(predictions, golds)
else:
if mm == Metric.AUC:
assert len(scores) == 2 * len(
golds
), "AUC is only valid for binary classification problem"
scores = scores[1::2]
metric = metric_func(scores, golds)
metrics[metric_name] = metric
return metrics
|
ContextualSP/adaptershare/data_utils/metrics.py/0
|
{
"file_path": "ContextualSP/adaptershare/data_utils/metrics.py",
"repo_id": "ContextualSP",
"token_count": 2246
}
| 227 |
# coding=utf-8
# Copyright (c) Microsoft. All rights reserved.
import yaml
import os
import numpy as np
import argparse
import json
import sys
from tqdm.auto import tqdm
from data_utils.task_def import TaskType, DataFormat
from data_utils.log_wrapper import create_logger
from experiments.exp_def import TaskDefs, EncoderModelType
from transformers import AutoTokenizer
DEBUG_MODE = False
MAX_SEQ_LEN = 384
DOC_STRIDE = 128
MAX_QUERY_LEN = 64
logger = create_logger(
__name__, to_disk=True, log_file="mt_dnn_clues_data_proc_{}.log".format(MAX_SEQ_LEN)
)
def load_json(path):
with open(path, "r", encoding="utf-8") as f:
return json.load(f)
def load_squad(path):
input_data = load_json(path)
print("version: {}".format(input_data["version"]))
version = input_data["version"]
input_data = input_data["data"]
examples = []
for entry in tqdm(input_data):
title = entry["title"]
for paragraph in entry["paragraphs"]:
context = paragraph["context"]
for qa in paragraph["qas"]:
qas_id = qa["id"]
question = qa["question"]
answer = qa["answers"]
is_impossible = qa.get("is_impossible", False)
example = {
"id": qas_id,
"context": context,
"question": question,
"answer": answer,
"is_impossible": is_impossible,
}
examples.append(example)
return examples
def flat_squad(path, is_training=True):
def qa_sample(uid, context, question, answers, is_impossible, answer_start=None):
if len(answers) > 0:
answer_text = [answer["text"].strip() for answer in answers]
answer_start = [answer["answer_start"] for answer in answers]
else:
answer_text = []
answer_start = []
answers = {"text": answer_text, "answer_start": answer_start}
example = {
"id": uid,
"context": context.strip(), # fix whitespace issues
"question": question.strip(), # fix whitespace issues
"answer": answers,
"is_impossible": is_impossible,
}
return example
input_data = load_squad(path)
examples = []
for entry in tqdm(input_data):
context = entry["context"]
if "qas" in entry:
for qa_entry in entry["qas"]:
question = qa_entry["question"]
if isinstance(question, list):
assert len(question) == 1
question = question[0]
uid = qa_entry["id"]
# remove dumplicated answers
answers = list(set(qa_entry.get("answer", [])))
if type(context) is dict:
entities = context["entities"]
context_text = context["text"]
assert type(answers) is list
temp_answers = []
ent_strs = []
for ent in entities:
ent_strs.append(context_text[ent["start"] : ent["end"] + 1])
for answer in answers:
positions = []
for ent in entities:
if context_text[ent["start"] : ent["end"] + 1] == answer:
positions.append(ent["start"])
temp_answers.append({"text": answer, "answer_start": positions})
answers = temp_answers
is_impossible = qa_entry.get("is_impossible", None)
example = qa_sample(uid, context_text, question, answers, is_impossible)
examples.append(example)
else:
question = entry["question"]
uid = entry["id"]
answers = entry.get("answer", [])
is_impossible = entry.get("is_impossible", None)
example = qa_sample(uid, context, question, answers, is_impossible)
examples.append(example)
return examples
def search_index(
input_ids,
sequence_ids,
offsets,
cls_index,
start_char,
end_char,
pad_on_right=False,
):
start_position, end_position = cls_index, cls_index
# Start token index of the current span in the text.
token_start_index = 0
while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
token_start_index += 1
# End token index of the current span in the text.
token_end_index = len(input_ids) - 1
while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
token_end_index -= 1
# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
if not (
offsets[token_start_index][0] <= start_char
and offsets[token_end_index][1] >= end_char
):
start_position = cls_index
end_position = cls_index
else:
# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
# Note: we could go after the last offset if the answer is the last word (edge case).
while (
token_start_index < len(offsets)
and offsets[token_start_index][0] <= start_char
):
token_start_index += 1
start_position = token_start_index - 1
while offsets[token_end_index][1] >= end_char:
token_end_index -= 1
end_position = token_end_index + 1
return start_position, end_position
def prepare_train_feature(
tokenizer,
samples,
output_path,
data_type=DataFormat.CLUE_CLASSIFICATION,
max_seq_length=384,
doc_stride=128,
pad_on_right=True,
pad_to_max_length=True,
label_mapper=None,
):
if not tokenizer.cls_token:
# cls_tok_id = tokenizer.eos_token_id
cls_tok_id = tokenizer.pad_token_id
prefix_pad = True
else:
cls_tok_id = tokenizer.cls_token_id
prefix_pad = False
if not tokenizer.sep_token:
sep_tok_id = tokenizer.eos_token_id
else:
sep_tok_id = tokenizer.sep_token_id
with open(output_path, "w", encoding="utf-8") as writer:
for sample in samples:
context = sample["context"]
question = sample["question"]
if pad_on_right and prefix_pad:
question = tokenizer.pad_token + question
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
question if pad_on_right else context,
context if pad_on_right else question,
truncation="only_second" if pad_on_right else "only_first",
max_length=max_seq_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length" if pad_to_max_length else False,
verbose=False,
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# The offset mappings will give us a map from token to character position in the original context. This will
# help us compute the start_positions and end_positions.
offset_mapping = tokenized_examples.pop("offset_mapping")
# Let's label those examples!
tokenized_examples["start_positions"] = []
tokenized_examples["end_positions"] = []
tokenized_examples["id"] = []
tokenized_examples["label"] = []
for i, offsets in enumerate(offset_mapping):
# We will label impossible answers with the index of the CLS token.
input_ids = tokenized_examples["input_ids"][i]
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
cls_index = input_ids.index(cls_tok_id)
# One example can give several spans, this is the index of the example containing this span of text.
# sample_index = sample_mapping[i]
answers = sample["answer"]
tokenized_examples["id"].append(sample["id"])
label = None
if sample["is_impossible"] is not None:
label = 1 if sample["is_impossible"] else 0
tokenized_examples["label"].append(label)
# If no answers are given, set the cls_index as answer.
if len(answers) == 0 or len(answers["answer_start"]) == 0:
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# Start/end character index of the answer in the text.
start_char = answers["answer_start"][0]
if type(start_char) is list:
start_position, end_position = [], []
for sc in start_char:
end_char = sc + len(answers["text"][0])
sp, ep = search_index(
input_ids,
sequence_ids,
offsets,
cls_index,
sc,
end_char,
pad_on_right=pad_on_right,
)
start_position.append(sp)
end_position.append(ep)
tokenized_examples["start_positions"].append(start_position)
tokenized_examples["end_positions"].append(end_position)
else:
end_char = start_char + len(answers["text"][0])
start_position, end_position = search_index(
input_ids,
sequence_ids,
offsets,
cls_index,
start_char,
end_char,
pad_on_right=pad_on_right,
)
tokenized_examples["start_positions"].append(start_position)
tokenized_examples["end_positions"].append(end_position)
tokenized_examples["label"].append(0)
for i in range(0, len(tokenized_examples["input_ids"])):
sample = {
"uid": tokenized_examples["id"][i],
"token_id": tokenized_examples["input_ids"][i],
"mask": tokenized_examples["attention_mask"][i],
"type_id": tokenized_examples["token_type_ids"][i]
if "token_type_ids" in tokenized_examples
else len(tokenized_examples["input_ids"][i]) * [0],
"start_position": tokenized_examples["start_positions"][i],
"end_position": tokenized_examples["end_positions"][i],
"label": tokenized_examples["label"][i],
}
writer.write("{}\n".format(json.dumps(sample)))
# Validation preprocessing
def prepare_validation_features(
tokenizer,
samples,
output_path,
max_seq_length=384,
doc_stride=128,
pad_on_right=True,
pad_to_max_length=True,
label_mapper=None,
):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
if not tokenizer.cls_token:
# cls_tok_id = tokenizer.eos_token_id
cls_tok_id = tokenizer.pad_token_id
prefix_pad = True
else:
cls_tok_id = tokenizer.cls_token_id
prefix_pad = False
if not tokenizer.sep_token:
sep_tok_id = tokenizer.eos_token_id
else:
sep_tok_id = tokenizer.sep_token_id
with open(output_path, "w", encoding="utf-8") as writer:
for sample in samples:
context = sample["context"]
question = sample["question"]
answer = sample.get("answer")
if pad_on_right and prefix_pad:
question = tokenizer.pad_token + question
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
question if pad_on_right else context,
context if pad_on_right else question,
truncation="only_second" if pad_on_right else "only_first",
max_length=max_seq_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length" if pad_to_max_length else False,
verbose=False,
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
tokenized_examples["start_positions"] = []
tokenized_examples["end_positions"] = []
tokenized_examples["id"] = []
tokenized_examples["label"] = []
tokenized_examples["null_ans_index"] = []
label = None
offset_mapping = tokenized_examples.pop("offset_mapping")
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
input_ids = tokenized_examples["input_ids"][i]
cls_index = input_ids.index(cls_tok_id)
sep_index = input_ids.index(sep_tok_id)
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
tokenized_examples["id"].append(sample["id"])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["id"].append(sample["id"])
if sample["is_impossible"] is not None:
label = 1 if sample["is_impossible"] else 0
answer["is_impossible"] = sample["is_impossible"]
tokenized_examples["label"].append(label)
tokenized_examples["null_ans_index"].append(cls_index)
# tokenized_examples["offset_mapping"] = offset_mapping
for i in range(0, len(tokenized_examples["input_ids"])):
sample = {
"uid": tokenized_examples["id"][i],
"token_id": tokenized_examples["input_ids"][i],
"mask": tokenized_examples["attention_mask"][i],
"type_id": tokenized_examples["token_type_ids"][i]
if "token_type_ids" in tokenized_examples
else len(tokenized_examples["input_ids"][i]) * [0],
"offset_mapping": offset_mapping[i],
"null_ans_index": tokenized_examples["null_ans_index"][i],
"context": context,
"answer": answer,
"label": tokenized_examples["label"][i],
}
writer.write("{}\n".format(json.dumps(sample)))
def parse_args():
parser = argparse.ArgumentParser(description="Preprocessing MRC dataset.")
parser.add_argument(
"--model",
type=str,
default="bert-base-uncased",
help="support all BERT, XLNET and ROBERTA family supported by HuggingFace Transformers",
)
parser.add_argument("--root_dir", type=str, default="data/canonical_data")
parser.add_argument("--cache_dir", type=str, default=".cache")
parser.add_argument("--model_revision", type=str, default=None)
parser.add_argument(
"--task_def", type=str, default="experiments/squad/squad_task_def.yml"
)
parser.add_argument("--max_seq_length", type=int, default=MAX_SEQ_LEN)
parser.add_argument("--doc_stride", type=int, default=DOC_STRIDE)
args = parser.parse_args()
return args
def main(args):
# hyper param
root = args.root_dir
assert os.path.exists(root)
suffix = args.model.split("/")[-1]
literal_model_type = suffix.split("-")[0].upper()
encoder_model = EncoderModelType[literal_model_type]
literal_model_type = literal_model_type.lower()
mt_dnn_suffix = literal_model_type
if "base" in args.model:
mt_dnn_suffix += "_base"
elif "large" in args.model:
mt_dnn_suffix += "_large"
# tokenizer
tokenizer = AutoTokenizer.from_pretrained(
args.model,
cache_dir=args.cache_dir,
use_fast=True,
from_slow=True,
revision=args.model_revision,
)
# Padding side determines if we do (question|context) or (context|question).
pad_on_right = tokenizer.padding_side == "right"
if "uncased" in args.model:
mt_dnn_suffix = "{}_uncased".format(mt_dnn_suffix)
else:
mt_dnn_suffix = "{}_cased".format(mt_dnn_suffix)
mt_dnn_root = os.path.join(root, mt_dnn_suffix)
if not os.path.isdir(mt_dnn_root):
os.mkdir(mt_dnn_root)
task_defs = TaskDefs(args.task_def)
for task in task_defs.get_task_names():
task_def = task_defs.get_task_def(task)
logger.info("Task %s" % task)
for split_name in task_def.split_names:
print(root)
file_path = os.path.join(root, "%s_%s.json" % (task, split_name))
print(file_path)
if not os.path.exists(file_path):
logger.warning("File %s doesnot exit")
sys.exit(1)
logger.warning("processing %s" % file_path)
is_training = True
if not "train" in split_name:
is_training = False
rows = flat_squad(file_path, is_training)
dump_path = os.path.join(mt_dnn_root, "%s_%s.json" % (task, split_name))
logger.info(dump_path)
if is_training:
prepare_train_feature(
tokenizer,
rows,
dump_path,
pad_on_right=pad_on_right,
label_mapper=task_def.label_vocab,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
)
else:
prepare_validation_features(
tokenizer,
rows,
dump_path,
pad_on_right=pad_on_right,
label_mapper=task_def.label_vocab,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
)
if __name__ == "__main__":
args = parse_args()
main(args)
|
ContextualSP/adaptershare/experiments/squad/squad_prepro.py/0
|
{
"file_path": "ContextualSP/adaptershare/experiments/squad/squad_prepro.py",
"repo_id": "ContextualSP",
"token_count": 9977
}
| 228 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import copy
from pytorch_pretrained_bert.modeling import BertEmbeddings, BertLayerNorm, BertConfig
from module.similarity import SelfAttnWrapper
from module.dropout_wrapper import DropoutWrapper
class SanLayer(nn.Module):
def __init__(self, num_hid, bidirect, dropout, rnn_type):
super().__init__()
assert isinstance(rnn_type, str)
rnn_type = rnn_type.upper()
assert rnn_type == "LSTM" or rnn_type == "GRU"
rnn_cls = getattr(nn, rnn_type)
self._rnn = rnn_cls(
num_hid,
num_hid,
1,
bidirectional=bidirect,
dropout=dropout,
batch_first=True,
)
self._layer_norm = BertLayerNorm(num_hid, eps=1e-12)
self.rnn_type = rnn_type
self.num_hid = num_hid
self.ndirections = 1 + int(bidirect)
def init_hidden(self, batch):
weight = next(self.parameters()).data
hid_shape = (self.ndirections, batch, self.num_hid)
if self.rnn_type == "LSTM":
return (weight.new(*hid_shape).zero_(), weight.new(*hid_shape).zero_())
else:
return weight.new(*hid_shape).zero_()
def forward(self, x, attention_mask):
# x: [batch, sequence, in_dim]
self._rnn.flatten_parameters()
batch = x.size(0)
hidden0 = self.init_hidden(batch)
tmp_output = self._rnn(x, hidden0)[0]
if self.ndirections > 1:
size = tmp_output.shape
tmp_output = tmp_output.view(size[0], size[1], self.num_hid, 2).max(-1)[0]
output = self._layer_norm(x + tmp_output)
return output
class SanEncoder(nn.Module):
def __init__(self, num_hid, nlayers, bidirect, dropout, rnn_type="LSTM"):
super().__init__()
layer = SanLayer(num_hid, bidirect, dropout, rnn_type)
self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(nlayers)])
def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
all_encoder_layers = []
for layer_module in self.layer:
hidden_states = layer_module(hidden_states, attention_mask)
if output_all_encoded_layers:
all_encoder_layers.append(hidden_states)
if not output_all_encoded_layers:
all_encoder_layers.append(hidden_states)
return all_encoder_layers
class SanPooler(nn.Module):
def __init__(self, hidden_size, dropout_p):
super().__init__()
my_dropout = DropoutWrapper(dropout_p, False)
self.self_att = SelfAttnWrapper(hidden_size, dropout=my_dropout)
self.dense = nn.Linear(hidden_size, hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states, attention_mask):
"""
Arguments:
hidden_states {FloatTensor} -- shape (batch, seq_len, hidden_size)
attention_mask {ByteTensor} -- 1 indicates padded token
"""
first_token_tensor = self.self_att(hidden_states, attention_mask)
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class SanModel(nn.Module):
def __init__(self, config: BertConfig):
super().__init__()
self.embeddings = BertEmbeddings(config)
self.encoder = SanEncoder(
config.hidden_size,
config.num_hidden_layers,
True,
config.hidden_dropout_prob,
)
self.pooler = SanPooler(config.hidden_size, config.hidden_dropout_prob)
self.config = config
def forward(
self,
input_ids,
token_type_ids=None,
attention_mask=None,
output_all_encoded_layers=True,
):
"""[summary]
Arguments:
input_ids {LongTensor} -- shape [batch_size, seq_len]
Keyword Arguments:
token_type_ids {LongTensor} -- shape [batch_size, seq_len]
attention_mask {LongTensor} -- 0 indicates padding tokens
Returns: Tuple of (sequence_output, pooled_output)
"""
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
embedding_output = self.embeddings(input_ids, token_type_ids)
encoded_layers = self.encoder(
embedding_output,
attention_mask,
output_all_encoded_layers=output_all_encoded_layers,
)
sequence_output = encoded_layers[-1]
pooled_output = self.pooler(sequence_output, attention_mask == 0)
if not output_all_encoded_layers:
encoded_layers = encoded_layers[-1]
return encoded_layers, pooled_output
|
ContextualSP/adaptershare/module/san_model.py/0
|
{
"file_path": "ContextualSP/adaptershare/module/san_model.py",
"repo_id": "ContextualSP",
"token_count": 2247
}
| 229 |
import os
import torch
import torch.nn as nn
from transformers import BertTokenizer
from collections import Counter
from models import *
from utils import *
def evaluate_squall(model: nn.Module, data_iter: DataLoader, enable_types: List[SQLTokenType], threshold: float):
eval_results, eval_logs = {}, []
for eval_type in enable_types:
eval_results[eval_type] = defaultdict(int)
statistics = Counter()
with torch.no_grad():
for model_input in data_iter:
# model_input.pop('column_labels', None)
model_output = model(**model_input)
example = model_input['example'][0]
gold_sql: SQLExpression = SQLExpression.from_json(example['sql'])
meta_index: MetaIndex = model_input['meta_index'][0]
question: Utterance = Utterance.from_json(example['question'])
schema: WTQSchema = WTQSchema.from_json(example['schema'])
eval_logs.append(
"table id: {}, query id: {} question: {}\n".format(schema.table_id, example['id'], question.text))
eval_logs.append("Schema: {}\n".format(schema.to_string()))
eval_logs.append("Gold SQL: {}\n".format(str(gold_sql)))
statistics['total_examples'] += 1
pred_sql = None
if 'hypotheses' in model_output:
hypothesis: SQLTokenHypothesis = model_output['hypotheses'][0]
pred_sql = hypothesis.to_sql(schema.table_id)
sql_correct = pred_sql is not None and pred_sql.sql == gold_sql.sql
eval_logs.append("Pred SQL: {}\n".format(str(pred_sql)))
statistics['LF_correct'] += sql_correct
gold_align_labels = get_wtq_alignments_from_labeling(example['align_labels'], question, schema)
pred_align_labels = get_wtq_alignments_from_prediction(model_output['alignment_weights'][0], question,
schema, meta_index, threshold=threshold)
eval_logs.append(
'Gold Align: {}\n'.format(" ".join([str(align_label) for align_label in gold_align_labels])))
eval_logs.append(
'Pred Align: {}\n'.format(" ".join([str(align_label) for align_label in pred_align_labels])))
linking_result = evaluate_linking(gold_align_labels, pred_align_labels, enable_types)
metrics = get_precision_recall_and_f1(linking_result)
eval_logs.append("SQL(LF) = {}\n".format(sql_correct))
for eval_type, eval_value in linking_result.items():
eval_results[eval_type]['tp'] += eval_value['tp']
eval_results[eval_type]['fn'] += eval_value['fn']
eval_results[eval_type]['fp'] += eval_value['fp']
eval_logs.append(
"{}: P = {:.3f}, R = {:.3f}, F1 = {:.3f}\n".format(str(eval_type), metrics[eval_type]['P'],
metrics[eval_type]['R'],
metrics[eval_type]['F1']))
eval_logs.append('\n')
LF_acc = statistics['LF_correct'] / statistics['total_examples']
print("SQL LF accuracy: {:.3f} ({}/{})".format(LF_acc, statistics['LF_correct'], statistics['total_examples']))
align_metrics = get_precision_recall_and_f1(eval_results)
eval_result_string = ''
out_eval_results = {'LF Accuracy': LF_acc}
for eval_type in enable_types:
print("{}: P = {:.3f}, R = {:.3f}, F1 = {:.3f}".format(str(eval_type), align_metrics[eval_type]['P'],
align_metrics[eval_type]['R'],
align_metrics[eval_type]['F1']))
eval_result_string += "{}_F1_{:.3f}".format(str(eval_type), align_metrics[eval_type]['F1'])
out_eval_results[str(eval_type)] = eval_results[eval_type]
out_eval_results[str(eval_type)]['P'] = align_metrics[eval_type]['P']
out_eval_results[str(eval_type)]['R'] = align_metrics[eval_type]['R']
out_eval_results[str(eval_type)]['F1'] = align_metrics[eval_type]['F1']
saved_path = args.checkpoint.replace('.pt', "eval_LF_acc_{:.3f}_align_thres{:.2f}_{}.txt".format(LF_acc, threshold,
eval_result_string))
open(saved_path, 'w', encoding='utf-8').writelines(eval_logs)
json.dump(out_eval_results, open(args.checkpoint.replace('.pt', "eval_thres{}_results.json".format(threshold)), 'w',
encoding='utf-8'))
print("Evaluate over")
def postprocess_with_gold_sql(identify_logits: Dict[SQLTokenType, torch.Tensor],
alignments: Dict[SQLTokenType, torch.Tensor], gold_sql: SQLExpression,
schema: SpiderSchema, values: List[ValueMatch]):
for tbl_idx in range(schema.num_tables):
is_found = False
for sql_token in gold_sql.tokens:
if sql_token.token_type == SQLTokenType.table and schema.id_map[sql_token.value] == tbl_idx:
is_found = True
break
if not is_found:
identify_logits[SQLTokenType.table][tbl_idx][1] = -1000
alignments[SQLTokenType.table][tbl_idx] = 0.0
for col_idx in range(schema.num_columns):
is_found = False
for sql_token in gold_sql.tokens:
if sql_token.token_type == SQLTokenType.column and schema.id_map[sql_token.value] == col_idx:
is_found = True
break
if not is_found:
identify_logits[SQLTokenType.column][col_idx][1] = -1000
alignments[SQLTokenType.column][col_idx] = 0.0
for val_idx in range(len(values)):
is_found = False
for sql_token in gold_sql.tokens:
if sql_token.token_type == SQLTokenType.column and sql_token.column_name == values[val_idx].column.lower():
is_found = True
break
if not is_found:
identify_logits[SQLTokenType.value][val_idx][1] = -1000
alignments[SQLTokenType.value][val_idx] = 0.0
return identify_logits, alignments
def evaluate_spider(model: nn.Module, data_iter: DataLoader, enable_types: List[SQLTokenType], args):
prefix = os.path.split(args.data_path)[-1]
assert prefix in ['train', 'dev']
print('Data Prefix: {}'.format(prefix))
eval_results, eval_logs = {}, []
for eval_type in enable_types:
eval_results[eval_type] = defaultdict(int)
statistics = Counter()
slsql_align_labels, slsql_align_labels_all = [], []
with torch.no_grad():
for model_input in data_iter:
model_output = model(**model_input)
example = model_input['example'][0]
meta_index: MetaIndex = model_input['meta_index'][0]
question: Utterance = Utterance.from_json(example['question'])
schema: SpiderSchema = SpiderSchema.from_json(example['schema'])
values = [ValueMatch.from_json(v) for v in example['values']]
sql: SQLExpression = SQLExpression.from_json(example['sql'])
eval_logs.append("Q: {}\n".format(question.text))
eval_logs.append("{}\n".format(schema.to_string()))
eval_logs.append("Gold SQL: {}\n".format(sql.sql))
statistics['total_examples'] += 1
gold_align_labels = get_spider_alignments_from_labeling(example['align_labels'], question, schema)
identify_logits = {SQLTokenType.table: model_output['table_logits'][0],
SQLTokenType.column: model_output['column_logits'][0],
SQLTokenType.value: model_output['value_logits'][0]}
tbl_align_weights, col_align_weights, val_align_weights = meta_index.split(
model_output['alignment_weights'][0].cpu())
align_weights = {SQLTokenType.table: tbl_align_weights, SQLTokenType.column: col_align_weights,
SQLTokenType.value: val_align_weights}
if args.with_gold_sql:
identify_logits, align_weights = postprocess_with_gold_sql(identify_logits, align_weights, sql, schema,
values)
pred_align_labels = greedy_link_spider(identify_logits, align_weights, question, schema, values,
threshold=args.threshold)
# pred_align_labels = generate_alignments_spider(align_weights, question, schema, values, threshold=args.threshold)
assert len(pred_align_labels) == len(question.tokens)
sql_align_label = [label.to_slsql(schema) for label in pred_align_labels]
slsql_align_labels += [sql_align_label]
# slsql_align_labels_all += [greedy_search_all_spider(identify_logits, align_weights, question, schema, values, threshold=args.threshold)]
# pred_align_labels = post_process_alignment_labels(pred_align_labels, gold_align_labels)
eval_logs.append(
'Gold Align: {}\n'.format(" ".join([str(align_label) for align_label in gold_align_labels])))
eval_logs.append(
'Pred Align: {}\n'.format(" ".join([str(align_label) for align_label in pred_align_labels])))
linking_result = evaluate_linking(gold_align_labels, pred_align_labels, enable_types)
metrics = get_precision_recall_and_f1(linking_result)
for eval_type, eval_value in linking_result.items():
eval_results[eval_type]['tp'] += eval_value['tp']
eval_results[eval_type]['fn'] += eval_value['fn']
eval_results[eval_type]['fp'] += eval_value['fp']
eval_logs.append(
"{}: P = {:.3f}, R = {:.3f}, F1 = {:.3f}\n".format(str(eval_type), metrics[eval_type]['P'],
metrics[eval_type]['R'],
metrics[eval_type]['F1']))
eval_logs.append('\n')
align_metrics = get_precision_recall_and_f1(eval_results)
eval_result_string = []
out_eval_results = {}
for eval_type in enable_types:
print("{}: P = {:.3f}, R = {:.3f}, F1 = {:.3f}".format(str(eval_type), align_metrics[eval_type]['P'],
align_metrics[eval_type]['R'],
align_metrics[eval_type]['F1']))
eval_result_string += ["{}_{:.3f}".format(eval_type.abbr, align_metrics[eval_type]['F1'])]
out_eval_results[str(eval_type)] = eval_results[eval_type]
out_eval_results[str(eval_type)]['P'] = align_metrics[eval_type]['P']
out_eval_results[str(eval_type)]['R'] = align_metrics[eval_type]['R']
out_eval_results[str(eval_type)]['F1'] = align_metrics[eval_type]['F1']
eval_saved_path = args.checkpoint.replace('.pt', "{}.threshold{:.2f}.{}.results.txt".format(prefix, args.threshold,
".".join(
eval_result_string)))
align_saved_path = args.checkpoint.replace('.pt', ".{}_align.json".format(prefix))
align_all_saved_path = args.checkpoint.replace('.pt', ".{}_align.all.json".format(prefix))
if args.with_gold_sql:
eval_saved_path = eval_saved_path.replace(".results.txt", ".gold_sql.results.txt")
align_saved_path = align_saved_path.replace(".json", '.gold_sql.json')
align_all_saved_path = align_all_saved_path.replace(".all.json", '.gold_sql.all.json')
open(eval_saved_path, 'w', encoding='utf-8').writelines(eval_logs)
save_json_objects(slsql_align_labels, align_saved_path)
save_json_objects(slsql_align_labels_all, align_all_saved_path)
print("Evaluate over")
def evaluate(args):
config, model, data_iter = load_model_and_data_iter(args)
if config['model'] in ['WTQAlignmentModel', 'WTQSeq2SeqModel']:
evaluate_squall(model, data_iter, [SQLTokenType.column], args.threshold)
elif config['model'] in ['SpiderAlignmentModel']:
evaluate_spider(model, data_iter, [SQLTokenType.table, SQLTokenType.column, SQLTokenType.value], args)
else:
raise NotImplementedError()
def load_model_and_data_iter(args):
ckpt_path = args.checkpoint
device = torch.device(args.device)
config = json.load(open(os.path.join(os.path.dirname(ckpt_path), 'config.json'), 'r', encoding='utf-8'))
config['checkpoint'] = ckpt_path
config['device'] = device
model = load_model_from_checkpoint(**config)
model.eval()
print('-------------------Config-------------------')
for key, val in config.items():
print(key, val)
print('load {} from {} over .'.format(config['model'], ckpt_path))
bert_version = config['bert_version']
tokenizer = BertTokenizer.from_pretrained(bert_version)
data_path = args.data_path + '.{}.json'.format(bert_version)
data_iter = get_data_iterator_func(config['model'])(data_path, tokenizer, 1, device, False, False, 512, None)
return config, model, data_iter
def parse_args():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-ckpt', '--checkpoint')
parser.add_argument('-data', '--data_path')
parser.add_argument('-dump_all', '--dump_all', action='store_true')
parser.add_argument('-gpu', '--device', default='cuda:0' if torch.cuda.is_available() else 'cpu')
parser.add_argument('-with_gold_sql', '--with_gold_sql', action='store_true')
parser.add_argument('-threshold', '--threshold', type=float)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
evaluate(args)
|
ContextualSP/awakening_latent_grounding/eval.py/0
|
{
"file_path": "ContextualSP/awakening_latent_grounding/eval.py",
"repo_id": "ContextualSP",
"token_count": 6808
}
| 230 |
# %%
import sys
sys.path.append("..")
from utils import *
# %%
data_dir = 'data/wtq_grounding'
bert_version = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(bert_version)
# tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
print('load Bert tokenizer over, vocab size = {}'.format(len(tokenizer)))
number_type = ['<c, date>',
'<c, number>',
'<c, score(score)>',
'<c, score>',
'<c, time>',
'<c, timespan(text)>',
'<c, timespan>',
'<c, unitnum>',
'<c,number(text)>',
'<c,number>',
'<c,numberspan>',
'<c,rank(text)>',
'<c,score(number)>',
'<c,score(score)>',
'<c,score>',
'<c,time(text)>',
'<c,time>',
'<c,timespan(text)>',
'<c,timespan>',
'<cn, timespan>',
'<cn,numberspan(text)>', '<cs, timespan>', '<d, number>', '<d, unitnum(unitnum)>',
'<d, unitnum>',
'<d, unitnum>(<d, unitnum>)', '<f, unitnum>',
'<f,date>',
'<f,number>', '<m, number>',
'<m,score>', '<n, date(text)>',
'<n, number>', '<n, timespan>',
'<n, unitnum(text)>',
'<n, unitnum>',
'<n,date>',
'<n,number>',
'<n,score(text)>', '<n,time(text)>',
'<n,time>',
'<n,timespan>',
'<nc,rank(text)>', '<s,date>',
'<s,score>', '<s,timespan>', '<t, number>', '<t,number>', '<t,unitnum>', '<w,score(number)>', ]
def get_ctype(raw_type: str):
if raw_type.startswith("unitnum") \
or raw_type.startswith("time") \
or raw_type.startswith("score") \
or raw_type.startswith("rank") \
or raw_type.startswith("percentage") \
or raw_type.startswith("number") \
or raw_type.startswith("fraction") \
or raw_type.startswith("date") \
or raw_type in number_type:
return "number"
else:
return "text"
# %%
@dataclass
class Column:
header: str
data_type: str
values: List[object]
@dataclass
class WTQTable:
table_id: str
columns: List[str]
types: List[str]
columns_internal: List[Column]
internal_to_header: List[int]
header_to_internals: List[List[int]]
def get_schema(self):
return WTQSchema(
table_id=self.table_id,
column_headers=self.columns,
column_names_internal=[x.header for x in self.columns_internal],
column_types_internal=[x.data_type for x in self.columns_internal],
internal_to_header=self.internal_to_header)
@classmethod
def from_dataset_json(cls, obj: Dict):
contents = obj['contents']
assert contents[0][0]['col'] == 'id'
assert contents[1][0]['col'] == 'agg'
headers = []
columns_internal = []
internal_to_header = []
header_to_internals = []
column_type = []
for i, content in enumerate(contents):
if i < 2:
continue
column_type.append(get_ctype(obj['types'][i]))
internal_ids = []
for col in content:
column = Column(header=col['col'], data_type=col['type'], values=col['data'])
internal_ids.append(len(columns_internal))
internal_to_header.append(len(headers))
columns_internal.append(column)
headers += [obj['headers'][i]]
header_to_internals += [internal_ids]
return WTQTable(
table_id=None,
columns=headers,
types=column_type,
columns_internal=columns_internal,
internal_to_header=internal_to_header,
header_to_internals=header_to_internals)
def load_wtq_tables(data_dir: str) -> Dict[str, WTQTable]:
wtq_tables = {}
for file_name in os.listdir(data_dir):
assert file_name.endswith('.json')
path = os.path.join(data_dir, file_name)
table = WTQTable.from_dataset_json(json.load(open(path, 'r', encoding='utf-8')))
table.table_id = file_name.replace('.json', '')
assert table.table_id not in wtq_tables
wtq_tables[table.table_id] = table
print('load {} tables from {} over.'.format(len(wtq_tables), data_dir))
return wtq_tables
def column_type_statistics(wtq_tables: List[WTQTable]):
types = {}
for table in wtq_tables:
for col in table.columns_internal:
types[col.data_type] = types.get(col.data_type, 0) + 1
for key, val in types.items():
print(key, val)
# Download from https://github.com/tzshi/squall/tree/main/tables/json and copy folder to '../data/squall/'
tables = load_wtq_tables(f'{data_dir}/json')
column_type_statistics(list(tables.values()))
# %%
wtq_type_mappings = {
'TEXT': 'text',
'REAL': 'real',
'INTEGER': 'integer',
'LIST TEXT': 'text',
'LIST REAL': 'real',
'LIST INTEGER': 'integer',
'EMPTY': 'text'
}
def _generate_labels_from_ralign(align_labels):
labels = set([])
for align_type, align_value, _ in align_labels:
if align_type != 'Column':
continue
if align_type == 'Column':
assert isinstance(align_value, str)
labels.add(align_value)
return list(labels)
def _generate_labels_from_sql_and_slign(query):
result = [['None', ""] for _ in range(len(query['nl']))]
for y_step, (ttype, value, span) in enumerate(query['sql']):
if ttype == 'Column':
for xs, ys in query['align']:
if y_step in ys:
for x in xs:
result[x] = ['Column', value]
return result
sql_value_suffix = defaultdict(int)
def _get_value_span(indices: List[int]) -> Tuple[int, int]:
assert len(indices) > 0
if len(indices) == 1:
return indices[0], indices[0]
assert len(indices) == 2, indices
return indices[0], indices[-1]
def parse_squall_sql(sql: Dict, schema: WTQSchema) -> SQLExpression:
tokens = []
for item in sql:
if item[0] == 'Keyword':
if item[1] == 'w':
tokens += [TableToken(table_name=schema.table_id)]
else:
tokens += [KeywordToken(keyword=item[1])]
elif item[0] == 'Column':
tokens += [ColumnToken(column_header=item[1], suffix_type="")]
elif item[0].startswith('Literal'):
suffix = item[0].replace('Literal.', '')
sql_value_suffix[suffix] += 1
span = _get_value_span(item[2])
if item[0] == 'Literal.String':
value = item[1]
tokens += [ValueToken(value=value, span=span, columns=None)]
elif item[0] == 'Literal.Number':
value = item[1]
tokens += [ValueToken(value=value, span=span, columns=None)]
else:
raise NotImplementedError(item[0])
else:
raise NotImplementedError("Not supported SQL type: {}".format(item[0]))
return SQLExpression(db_id=schema.table_id, tokens=tokens)
ngram_matchers: Dict[str, NGramMatcher] = {}
def generate_masking_ngrams(question: Utterance, schema: WTQSchema) -> List[Tuple[int, int, str]]:
if schema.table_id not in ngram_matchers:
column_tokens = []
for i, column in enumerate(schema.column_headers):
column_tokens.append((column, column.split(' ')))
ngram_matchers[schema.table_id] = NGramMatcher(column_tokens)
ngram_matcher = ngram_matchers[schema.table_id]
masking_ngrams = []
for tok_idx in range(len(question.tokens)):
masking_ngrams.append((tok_idx, tok_idx, question.tokens[tok_idx].token))
ngram_spans = set([])
for q_i, q_j, _, _, _ in ngram_matcher.match([token.token for token in question.tokens]):
ngram_spans.add((q_i, q_j))
for q_i, q_j in sorted(list(ngram_spans), key=lambda x: x[1] - x[0], reverse=True):
is_overlap = False
for q_i2, q_j2, ngram in masking_ngrams:
if q_i2 <= q_i and q_j2 >= q_j:
is_overlap = True
break
if not is_overlap:
ngram_ij = " ".join([x.token for x in question.tokens[q_i:q_j + 1]])
masking_ngrams.append((q_i, q_j, ngram_ij))
return masking_ngrams
column_suffix_dict = defaultdict(int)
def process_squall_test_set(query: Dict) -> Dict:
question_tokens = query['nl']
question_utterance = generate_utterance(tokenizer, None, question_tokens, None)
table = tables[query['tbl']]
schema = table.get_schema()
processed_columns_internal = []
for i, column in enumerate(table.columns_internal):
header = schema.column_headers[schema.internal_to_header[i]]
assert '_' not in header
suffix = re.sub('^c\d+', '', column.header).replace('_', ' ')
true_col_name = header + ' :' + suffix
column_suffix_dict[suffix] += 1
column_utterance = generate_utterance(tokenizer, true_col_name)
column_json = {
'index': i,
'id': column.header,
'data_type': wtq_type_mappings[column.data_type],
'utterance': column_utterance.to_json()
}
processed_columns_internal += [column_json]
processed_columns = []
for i, column in enumerate(table.columns):
column_utterance = generate_utterance(tokenizer, column)
column_json = {
'index': i,
'id': column,
'data_type': table.types[i],
'utterance': column_utterance.to_json()
}
processed_columns += [column_json]
masking_ngrams = generate_masking_ngrams(question_utterance, schema)
return {
'id': query['nt'],
'question': question_utterance.to_json(),
'columns': processed_columns,
'columns_internal': processed_columns_internal,
'masking_ngrams': masking_ngrams,
'schema': schema.to_json()
}
def process_squall_query(query: Dict) -> Dict:
question_tokens = query['nl']
question_utterance = generate_utterance(tokenizer, None, question_tokens, None)
table = tables[query['tbl']]
schema = table.get_schema()
processed_columns_internal = []
for col_index, column in enumerate(table.columns_internal):
header = schema.column_headers[schema.internal_to_header[col_index]]
assert '_' not in header
suffix = re.sub('^c\d+', '', column.header).replace('_', ' ')
true_col_name = header + ' :' + suffix
column_suffix_dict[suffix] += 1
column_utterance = generate_utterance(tokenizer, true_col_name)
column_json = {
'index': col_index,
'id': column.header,
'data_type': wtq_type_mappings[column.data_type],
'utterance': column_utterance.to_json()
}
processed_columns_internal += [column_json]
processed_columns = []
for col_index, column in enumerate(table.columns):
column_utterance = generate_utterance(tokenizer, column)
column_json = {
'index': col_index,
'id': column,
'data_type': table.types[col_index],
'utterance': column_utterance.to_json()
}
processed_columns += [column_json]
identify_labels_internal = _generate_labels_from_ralign(query['sql'])
identify_labels = {
"column": []
}
for col_name in identify_labels_internal:
header_id = schema.lookup_header_id_from_internal(col_name)
identify_labels["column"].append(schema.column_headers[header_id])
parsed_sql = parse_squall_sql(query['sql'], schema)
masking_ngrams = generate_masking_ngrams(question_utterance, schema)
return {
'id': query['nt'],
'question': question_utterance.to_json(),
'columns': processed_columns,
'columns_internal': processed_columns_internal,
'identify_labels': identify_labels,
'sql': parsed_sql.to_json(),
'align_labels': _generate_labels_from_sql_and_slign(query),
'masking_ngrams': masking_ngrams,
'schema': schema.to_json()
}
# %%
test_query = json.load(open(f'{data_dir}/test.json', 'r', encoding='utf-8'))
print('load test over, size = {}'.format(len(test_query)))
dev_queries = json.load(open(f'{data_dir}/dev.json', 'r', encoding='utf-8'))
print('load dev over, size = {}'.format(len(dev_queries)))
train_queries = json.load(open(f'{data_dir}/train.json', 'r', encoding='utf-8'))
print('load train over, size = {}'.format(len(train_queries)))
# %%
process_squall_query(dev_queries[0])
# %%
dev_processed = []
for query in tqdm(dev_queries):
try:
dev_processed += [process_squall_query(query)]
except Exception as ex:
print(ex)
save_json_objects(dev_processed, os.path.join(data_dir, 'dev.{}.json'.format(bert_version)))
print('process dev over')
# %%
train_processed = []
for query in tqdm(train_queries):
try:
processed_query = process_squall_query(query)
train_processed += [processed_query]
except Exception as ex:
print(ex)
save_json_objects(train_processed,
os.path.join(data_dir, 'train.{}.json'.format(bert_version)))
print('process train over, {}/{}'.format(len(train_processed), len(train_queries)))
process_squall_test_set(test_query[0])
test_processed = []
for query in tqdm(test_query):
try:
test_processed += [process_squall_test_set(query)]
except Exception as ex:
print(ex)
save_json_objects(test_processed, os.path.join(data_dir, 'test.{}.json'.format(bert_version)))
print('process test over')
test_iter = load_wtq_data_iterator(f'{data_dir}/test.{bert_version}.json', tokenizer, 1,
torch.device('cpu'), False, False, 300)
|
ContextualSP/awakening_latent_grounding/scripts/data_preprocess.squall.py/0
|
{
"file_path": "ContextualSP/awakening_latent_grounding/scripts/data_preprocess.squall.py",
"repo_id": "ContextualSP",
"token_count": 6736
}
| 231 |
#!/usr/bin/env bash
wget https://raw.githubusercontent.com/chin-gyou/dialogue-utterance-rewriter/master/corpus.txt
python ../../preprocess.py --dataset Rewrite
|
ContextualSP/incomplete_utterance_rewriting/dataset/Rewrite/download.sh/0
|
{
"file_path": "ContextualSP/incomplete_utterance_rewriting/dataset/Rewrite/download.sh",
"repo_id": "ContextualSP",
"token_count": 57
}
| 232 |
from allennlp.models.archival import load_archive
from allennlp.predictors.predictor import Predictor
# WARNING: Do not exclude these imports
from predictor import RewritePredictor
from data_reader import RewriteDatasetReader
from model import UnifiedFollowUp
class PredictManager:
def __init__(self, archive_file):
archive = load_archive(archive_file)
self.predictor = Predictor.from_archive(
archive, predictor_name="rewrite")
def predict_result(self, dialog_flatten: str):
# dialog_flatten is split by \t\t
dialog_snippets = dialog_flatten.split("\t\t")
param = {
"context": dialog_snippets[:-1],
"current": dialog_snippets[-1]
}
restate = self.predictor.predict_json(param)["predicted_tokens"]
return restate
if __name__ == '__main__':
manager = PredictManager("../pretrained_weights/multi_bert.tar.gz")
result = manager.predict_result("周 末 就 要 上 班 了 我 也 是 节 操 在 哪 里 年 终 奖 多 少 你 呢")
print(result)
|
ContextualSP/incomplete_utterance_rewriting/src/predict.py/0
|
{
"file_path": "ContextualSP/incomplete_utterance_rewriting/src/predict.py",
"repo_id": "ContextualSP",
"token_count": 447
}
| 233 |
# coding: utf-8
import time
import numpy as np
from scipy.optimize import linear_sum_assignment
from tqdm import tqdm
class BipartiteGraphSolver:
solver = linear_sum_assignment
@staticmethod
def find_min(cost_matrix):
row_ind, col_ind = BipartiteGraphSolver.solver(cost_matrix)
sum_cost = cost_matrix[row_ind, col_ind].sum()
return sum_cost, (row_ind, col_ind)
@staticmethod
def find_max(value_matrix):
cost_matrix = -value_matrix
sum_cost, (row_ind, col_ind) = BipartiteGraphSolver.find_min(cost_matrix)
return -sum_cost, (row_ind, col_ind)
if __name__ == '__main__':
# cost_mat = np.random.random((30, 50))
cost_mat = np.array([[1, 3, 5, 7], [2, 6, 5, 8], [7, 0, 3, 6]])
st_time = time.time()
for _ in tqdm(range(100)):
cost, (row_ind, col_ind) = BipartiteGraphSolver.find_max(cost_mat)
ed_time = time.time()
print(f'average calculation time = {(ed_time - st_time) / 10000}')
print(cost)
print(row_ind, col_ind)
a = list(range(100))
with tqdm(a) as tqdm_iter:
for idx, _ in enumerate(tqdm_iter):
print(f'{idx}: {_ ** 2}')
|
ContextualSP/interactive_text_to_sql/src/utils/algo_utils.py/0
|
{
"file_path": "ContextualSP/interactive_text_to_sql/src/utils/algo_utils.py",
"repo_id": "ContextualSP",
"token_count": 544
}
| 234 |
import json
import tensorflow as tf
from tensorflow.python.client import timeline
class ProfiledSession(tf.Session):
def __init__(self, *args, **kwargs):
super(ProfiledSession, self).__init__(*args, **kwargs)
def run(self, fetches, feed_dict=None):
"""like Session.run, but return a Timeline object in Chrome trace format (JSON).
Save the json to a file, go to chrome://tracing, and open the file.
Args:
fetches
feed_dict
Returns:
dict: a JSON dict
"""
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
super(ProfiledSession, self).run(fetches, feed_dict, options=options, run_metadata=run_metadata)
# Create the Timeline object, and write it to a json
tl = timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
return json.loads(ctf)
|
ContextualSP/lemon/executor/gtd/ml/profile.py/0
|
{
"file_path": "ContextualSP/lemon/executor/gtd/ml/profile.py",
"repo_id": "ContextualSP",
"token_count": 390
}
| 235 |
import pytest
from gtd.io import IntegerDirectories
class TestIntegerDirectories(object):
@pytest.fixture
def int_dirs(self, tmpdir):
tmpdir.mkdir('152_blah')
tmpdir.mkdir('153_woo')
tmpdir.mkdir('1_') # no suffix, should still match
tmpdir.mkdir('-1') # no suffix, should still match
tmpdir.mkdir('_10') # prefix is not integer, ignore
tmpdir.mkdir('.DS_Store')
tmpdir.mkdir('other')
return IntegerDirectories(str(tmpdir))
def test_keys(self, int_dirs):
assert list(int_dirs.keys()) == [-1, 1, 152, 153]
assert len(int_dirs) == 4
def test_largest_int(self, int_dirs):
assert int_dirs.largest_int == 153
def test_new_dir(self, tmpdir, int_dirs):
correct = str(tmpdir.join('154'))
assert int_dirs.new_dir() == correct
def test_new_dir_named(self, tmpdir, int_dirs):
correct = str(tmpdir.join('154')) + '_foobar'
assert int_dirs.new_dir('foobar') == correct
|
ContextualSP/lemon/executor/gtd/tests/test_io.py/0
|
{
"file_path": "ContextualSP/lemon/executor/gtd/tests/test_io.py",
"repo_id": "ContextualSP",
"token_count": 447
}
| 236 |
from abc import ABCMeta, abstractmethod, abstractproperty
from strongsup.predicate import Predicate
class Executor(object, metaclass=ABCMeta):
@abstractmethod
def execute(self, y_toks, old_denotation=None):
"""Return the intermediate denotation of the formula.
Args:
y_toks (list[Predicate]): the formula fragment to be executed
old_denotation (Denotation): If specified, continue execution
from this intermediate denotation.
Returns:
Denotation
The denotation is not finalized.
Throws:
Exception if the formula is malformed.
"""
raise NotImplementedError
def execute_predicate(self, predicate, old_denotation=None):
"""Return the denotation of the formula.
This method takes only a single Predicate object as the argument.
This allows more optimization to be performed.
Args:
predicate (Predicate): the next predicate
old_denotation (Denotation): If specified, continue execution
from this intermediate denotation.
Returns:
Denotation
The denotation is not finalized.
Throws:
Exception if the formula is malformed.
"""
# Default: call execute
return self.execute([predicate], old_denotation)
@abstractmethod
def finalize(self, denotation):
"""Given a Denotation, return its finalized form as list[Value].
Args:
denotation (Denotation)
Returns:
list[Value] or None
Raises:
ValueError if the denotation cannot be finalized
"""
raise NotImplementedError
class Denotation(object, metaclass=ABCMeta):
"""Intermediate denotation."""
@abstractproperty
def utterance_idx(self):
"""Current the utterance index (int).
Should be incremented every time an end-of-utterance predicate
or its equivalence is executed.
"""
raise NotImplementedError
|
ContextualSP/lemon/executor/strongsup/executor.py/0
|
{
"file_path": "ContextualSP/lemon/executor/strongsup/executor.py",
"repo_id": "ContextualSP",
"token_count": 820
}
| 237 |
import os
from dependency.data_directory import DataDirectory
from strongsup.domain import Domain
from strongsup.dataset import DatasetFromFile
from strongsup.rlong.path_checker import RLongPathChecker
from strongsup.rlong.predicate import RLongPredicateType
from strongsup.rlong.predicates_computer import get_fixed_predicates
class RLongDomain(Domain):
def load_datasets(self):
config = self.config.dataset
from strongsup.rlong.example_factory import RLongExampleFactory
train = DatasetFromFile(config.train_file, lambda filename:
RLongExampleFactory(filename, config.name,
config.train_num_steps,
config.train_slice_steps_from_middle).examples)
valid = DatasetFromFile(config.valid_file, lambda filename:
RLongExampleFactory(filename, config.name,
config.valid_num_steps,
config.valid_slice_steps_from_middle).examples)
final = DatasetFromFile(config.final_file, lambda filename:
RLongExampleFactory(filename, config.name,
config.final_num_steps,
config.final_slice_steps_from_middle).examples)
return train, valid, final
def _get_path_checker(self, prune_config):
return RLongPathChecker(prune_config)
@property
def fixed_predicates(self):
return get_fixed_predicates(self.config.dataset.name)
@property
def all_types(self):
return list(RLongPredicateType.ALL_TYPES)
|
ContextualSP/lemon/executor/strongsup/rlong/domain.py/0
|
{
"file_path": "ContextualSP/lemon/executor/strongsup/rlong/domain.py",
"repo_id": "ContextualSP",
"token_count": 636
}
| 238 |
from strongsup.path_checker import PathChecker
from strongsup.utils import EOU
class TablesPathChecker(PathChecker):
def __init__(self, config):
PathChecker.__init__(self, config)
self._max_stack_size = config.get('max_stack_size')
self._prune_idempotent = config.get('prune_idempotent')
def __call__(self, path):
"""Check whether the path should be added to the beam.
Args:
path (ParsePath)
Returns:
boolean
"""
if (self._max_stack_size
and len(path.denotation) > self._max_stack_size):
return False
if (self._prune_idempotent
and len(path) > 1
and path[-1].decision.name != EOU
and path[-2].denotation == path.denotation):
return False
return True
|
ContextualSP/lemon/executor/strongsup/tables/path_checker.py/0
|
{
"file_path": "ContextualSP/lemon/executor/strongsup/tables/path_checker.py",
"repo_id": "ContextualSP",
"token_count": 403
}
| 239 |
import pytest
import math
import tensorflow as tf
import numpy as np
from numpy.testing import assert_almost_equal
from gtd.utils import Bunch
from strongsup.example import Context
from strongsup.decoder import Decoder, DecoderConfig
from strongsup.predicate import Predicate
from strongsup.utils import EOS
from strongsup.tests.utils import PredicateGenerator, softmax
class DummyParseModel(object):
def __init__(self, logits):
self.logits = logits
def score(self, cases):
for case in cases:
case.choice_logits = self.logits[:len(case.choices)]
case.choice_probs = softmax(self.logits[:len(case.choices)])
class DummyExecutor(object):
def execute(self, y_toks, old_denotation=None):
new_denotation = (old_denotation or []) + [x.name for x in y_toks]
# Let's disallow some sequences
if new_denotation == ['a', 'c']:
raise ValueError
return new_denotation
class DummyContext(Context):
def __init__(self):
self._table_path = None
self._utterance = None
self._executor = DummyExecutor()
self._predicates = None
@property
def predicates(self):
return self._predicates
class TestSimpleDecode(object):
@pytest.fixture
def context(self):
context = DummyContext()
p = PredicateGenerator(context)
context._predicates = [p('a'), p('b'), p('c'), p(EOS)]
return context
@pytest.fixture
def parse_model(self):
return DummyParseModel([0, math.log(2), math.log(4), math.log(3)])
@pytest.fixture
def config(self):
return DecoderConfig(10, 3)
@pytest.fixture
def decoder(self, parse_model, config):
caching = False
return Decoder(parse_model, None, None, caching, config)
def test_initial_beam(self, decoder, context):
beam = decoder.initial_beam(context)
assert len(beam) == 1
assert len(beam[0]) == 0
assert beam[0].context == context
def test_advance(self, decoder, context):
beam = decoder.initial_beam(context)
new_beams = decoder.advance([beam])
assert len(new_beams) == 1
new_beam = new_beams[0]
assert len(new_beam) == 4
ranked = [' '.join([y.name for y in x.decisions]) for x in new_beam]
assert ranked == ['c', EOS, 'b', 'a']
def test_advance_twice(self, config, context):
logits = [math.log(1), math.log(2), math.log(4), math.log(3)]
parse_model = DummyParseModel(logits)
caching = False
decoder = Decoder(parse_model, None, None, caching, config)
beam = decoder.initial_beam(context)
new_beams = decoder.advance([beam])
parse_model.logits = [math.log(5), math.log(2), math.log(7), math.log(6)]
new_beams = decoder.advance(new_beams)
assert len(new_beams) == 1
new_beam = new_beams[0]
assert len(new_beam) == 10
ranked = [' '.join([y.name for y in x.decisions]) for x in new_beam]
assert ranked == [
EOS, 'c c', 'c ' + EOS, 'c a', 'b c',
'b ' + EOS, 'b a', 'c b', 'a ' + EOS, 'a a']
def test_normalized_path_probs(self):
beam = [Bunch(prob=0.01), Bunch(prob=0.5), Bunch(prob=0.2)]
assert_almost_equal(Decoder._normalized_path_probs(beam), [1./71, 50./71, 20./71], decimal=5)
# TODO Test predictions and train_step
|
ContextualSP/lemon/executor/strongsup/tests/test_decoder.py/0
|
{
"file_path": "ContextualSP/lemon/executor/strongsup/tests/test_decoder.py",
"repo_id": "ContextualSP",
"token_count": 1481
}
| 240 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.