code
stringlengths 82
54.1k
| code_codestyle
int64 0
699
| style_context
stringlengths 111
35.6k
| style_context_codestyle
int64 0
699
| label
int64 0
1
|
|---|---|---|---|---|
from collections import defaultdict
from math import ceil, sqrt
def _lowercase( __a : int = 100_0000 , __a : int = 10 ):
a__ =defaultdict(__a )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
a__ =max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
a__ =1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(__a , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 20 |
from __future__ import annotations
from collections import namedtuple
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> tuple:
_UpperCAmelCase = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( lowerCamelCase=None , lowerCamelCase=None ):
return field(default_factory=lambda: default , metadata=lowerCamelCase )
@dataclass
class __A :
UpperCamelCase = list_field(
default=[] , metadata={
"""help""": (
"""Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
""" of all available models"""
)
} , )
UpperCamelCase = list_field(
default=[8] , metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""} )
UpperCamelCase = list_field(
default=[8, 32, 128, 512] , metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """Use FP16 to accelerate inference."""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """Benchmark training of model"""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """Verbose memory tracing"""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""} , )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={
"""help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
} , )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """Trace memory line by line"""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """Save result to a CSV file"""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """Save all print statements in a log file"""} )
UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """Whether to print environment information"""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
""" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
""" for debugging / testing and on TPU."""
)
} , )
UpperCamelCase = field(
default=F"""inference_time_{round(time() )}.csv""" , metadata={"""help""": """CSV filename used if saving time results to csv."""} , )
UpperCamelCase = field(
default=F"""inference_memory_{round(time() )}.csv""" , metadata={"""help""": """CSV filename used if saving memory results to csv."""} , )
UpperCamelCase = field(
default=F"""train_time_{round(time() )}.csv""" , metadata={"""help""": """CSV filename used if saving time results to csv for training."""} , )
UpperCamelCase = field(
default=F"""train_memory_{round(time() )}.csv""" , metadata={"""help""": """CSV filename used if saving memory results to csv for training."""} , )
UpperCamelCase = field(
default=F"""env_info_{round(time() )}.csv""" , metadata={"""help""": """CSV filename used if saving environment information."""} , )
UpperCamelCase = field(
default=F"""log_{round(time() )}.csv""" , metadata={"""help""": """Log filename used if print statements are saved in log."""} , )
UpperCamelCase = field(default=3 , metadata={"""help""": """Times an experiment will be run."""} )
UpperCamelCase = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
""" model weights."""
)
} , )
def A__ ( self :List[Any] ):
'''simple docstring'''
warnings.warn(
f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
""" are deprecated in general and it is advised to use external Benchmarking libraries """
""" to benchmark Transformer models.""" , __snake_case , )
def A__ ( self :Dict ):
'''simple docstring'''
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def A__ ( self :Union[str, Any] ):
'''simple docstring'''
if len(self.models ) <= 0:
raise ValueError(
"""Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
""" bert-base-cased` or `args.models = ['bert-base-cased'].""" )
return self.models
@property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
if not self.multi_process:
return False
elif self.is_tpu:
logger.info("""Multiprocessing is currently not possible on TPU.""" )
return False
else:
return True
| 21 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __lowerCamelCase ( _lowerCAmelCase ) -> Any:
_UpperCAmelCase = {}
_UpperCAmelCase = job["started_at"]
_UpperCAmelCase = job["completed_at"]
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
_UpperCAmelCase = start
_UpperCAmelCase = end
_UpperCAmelCase = duration_in_min
return job_info
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=None ) -> str:
_UpperCAmelCase = None
if token is not None:
_UpperCAmelCase = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
_UpperCAmelCase = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
_UpperCAmelCase = requests.get(_lowerCAmelCase , headers=_lowerCAmelCase ).json()
_UpperCAmelCase = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
_UpperCAmelCase = math.ceil((result["total_count"] - 100) / 100 )
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = requests.get(url + F'''&page={i + 2}''' , headers=_lowerCAmelCase ).json()
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = get_job_time(args.workflow_run_id)
__lowerCAmelCase = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v["duration"]}''')
| 684 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
lowercase_ = 42
lowercase_ = None
lowercase_ = None
def snake_case_ ():
'''simple docstring'''
_a = Node(1 )
_a = Node(2 )
_a = Node(3 )
_a = Node(4 )
_a = Node(5 )
return tree
def snake_case_ (UpperCamelCase : Node | None ):
'''simple docstring'''
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def snake_case_ (UpperCamelCase : Node | None ):
'''simple docstring'''
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def snake_case_ (UpperCamelCase : Node | None ):
'''simple docstring'''
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def snake_case_ (UpperCamelCase : Node | None ):
'''simple docstring'''
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def snake_case_ (UpperCamelCase : Node | None ):
'''simple docstring'''
_a = []
if root is None:
return output
_a = deque([root] )
while process_queue:
_a = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def snake_case_ (UpperCamelCase : Node | None , UpperCamelCase : int ):
'''simple docstring'''
_a = []
def populate_output(UpperCamelCase : Node | None , UpperCamelCase : int ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(UpperCamelCase , UpperCamelCase )
return output
def snake_case_ (UpperCamelCase : Node | None , UpperCamelCase : int ):
'''simple docstring'''
_a = []
def populate_output(UpperCamelCase : Node | None , UpperCamelCase : int ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(UpperCamelCase , UpperCamelCase )
return output
def snake_case_ (UpperCamelCase : Node | None ):
'''simple docstring'''
if root is None:
return []
_a = []
_a = 0
_a = height(UpperCamelCase )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(UpperCamelCase , UpperCamelCase ) )
_a = 1
else:
output.append(get_nodes_from_right_to_left(UpperCamelCase , UpperCamelCase ) )
_a = 0
return output
def snake_case_ (): # Main function for testing.
'''simple docstring'''
_a = make_tree()
print(f'In-order Traversal: {inorder(UpperCamelCase )}' )
print(f'Pre-order Traversal: {preorder(UpperCamelCase )}' )
print(f'Post-order Traversal: {postorder(UpperCamelCase )}' , '''\n''' )
print(f'Height of Tree: {height(UpperCamelCase )}' , '''\n''' )
print('''Complete Level Order Traversal: ''' )
print(level_order(UpperCamelCase ) , '''\n''' )
print('''Level-wise order Traversal: ''' )
for level in range(1 , height(UpperCamelCase ) + 1 ):
print(f'Level {level}:' , get_nodes_from_left_to_right(UpperCamelCase , level=UpperCamelCase ) )
print('''\nZigZag order Traversal: ''' )
print(zigzag(UpperCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 22 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__lowerCAmelCase = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 1_3_1_0_7_2,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
return torch.atana(_lowerCAmelCase , _lowerCAmelCase ) / math.pi * 2
def __lowerCamelCase ( _lowerCAmelCase ) -> Union[str, Any]:
_UpperCAmelCase = torch.sin(t * math.pi / 2 ) ** 2
_UpperCAmelCase = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(_lowerCAmelCase , _lowerCAmelCase )
class __SCREAMING_SNAKE_CASE ( lowercase):
pass
class __SCREAMING_SNAKE_CASE ( nn.Module):
def __init__( self : str , __UpperCamelCase : Optional[int] ):
super().__init__()
_UpperCAmelCase = DiffusionAttnUnetaD(__UpperCamelCase , n_attn_layers=4 )
_UpperCAmelCase = deepcopy(self.diffusion )
_UpperCAmelCase = torch.quasirandom.SobolEngine(1 , scramble=__UpperCamelCase )
def __lowerCamelCase ( _lowerCAmelCase ) -> int:
_UpperCAmelCase = MODELS_MAP[model_name]["url"]
os.system(F'''wget {url} ./''' )
return F'''./{model_name}.ckpt'''
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
__lowerCAmelCase = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
__lowerCAmelCase = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
__lowerCAmelCase = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
__lowerCAmelCase = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
if name.startswith("skip" ):
return name.replace("skip" , RES_CONV_MAP["skip"] )
# name has to be of format main.{digit}
if not name.startswith("main." ):
raise ValueError(F'''ResConvBlock error with {name}''' )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[Any]:
for key, value in ATTN_MAP.items():
if name.startswith(_lowerCAmelCase ) and not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return name.replace(_lowerCAmelCase , _lowerCAmelCase )
elif name.startswith(_lowerCAmelCase ):
return [name.replace(_lowerCAmelCase , _lowerCAmelCase ) for v in value]
raise ValueError(F'''Attn error with {name}''' )
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=13 ) -> List[Any]:
_UpperCAmelCase = input_string
if string.split("." )[0] == "timestep_embed":
return string.replace("timestep_embed" , "time_proj" )
_UpperCAmelCase = 0
if string.startswith("net.3." ):
depth += 1
_UpperCAmelCase = string[6:]
elif string.startswith("net." ):
_UpperCAmelCase = string[4:]
while string.startswith("main.7." ):
depth += 1
_UpperCAmelCase = string[7:]
if string.startswith("main." ):
_UpperCAmelCase = string[5:]
# mid block
if string[:2].isdigit():
_UpperCAmelCase = string[:2]
_UpperCAmelCase = string[2:]
else:
_UpperCAmelCase = string[0]
_UpperCAmelCase = string[1:]
if depth == max_depth:
_UpperCAmelCase = MID_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = "mid_block"
elif depth > 0 and int(_lowerCAmelCase ) < 7:
_UpperCAmelCase = DOWN_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''down_blocks.{depth}'''
elif depth > 0 and int(_lowerCAmelCase ) > 7:
_UpperCAmelCase = UP_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - depth - 1}'''
elif depth == 0:
_UpperCAmelCase = DEPTH_0_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - 1}''' if int(_lowerCAmelCase ) > 3 else "down_blocks.0"
if not string_left.startswith("." ):
raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.''' )
_UpperCAmelCase = string_left[1:]
if "resnets" in new_layer:
_UpperCAmelCase = convert_resconv_naming(_lowerCAmelCase )
elif "attentions" in new_layer:
_UpperCAmelCase = convert_attn_naming(_lowerCAmelCase )
_UpperCAmelCase = new_string_left
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = prefix + "." + new_layer + "." + string_left
else:
_UpperCAmelCase = [prefix + "." + new_layer + "." + s for s in string_left]
return new_string
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[int]:
_UpperCAmelCase = {}
for k, v in state_dict.items():
if k.endswith("kernel" ):
# up- and downsample layers, don't have trainable weights
continue
_UpperCAmelCase = rename(_lowerCAmelCase )
# check if we need to transform from Conv => Linear for attention
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = transform_conv_attns(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_UpperCAmelCase = v
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
if len(_lowerCAmelCase ) == 1:
if len(v.shape ) == 3:
# weight
_UpperCAmelCase = v[:, :, 0]
else:
# bias
_UpperCAmelCase = v
else:
# qkv matrices
_UpperCAmelCase = v.shape[0]
_UpperCAmelCase = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase ) -> Tuple:
_UpperCAmelCase = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
_UpperCAmelCase = args.model_path.split("/" )[-1].split("." )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
_UpperCAmelCase = download(_lowerCAmelCase )
_UpperCAmelCase = MODELS_MAP[model_name]["sample_rate"]
_UpperCAmelCase = MODELS_MAP[model_name]["sample_size"]
_UpperCAmelCase = Object()
_UpperCAmelCase = sample_size
_UpperCAmelCase = sample_rate
_UpperCAmelCase = 0
_UpperCAmelCase = UNetaDModel(sample_size=_lowerCAmelCase , sample_rate=_lowerCAmelCase )
_UpperCAmelCase = diffusers_model.state_dict()
_UpperCAmelCase = DiffusionUncond(_lowerCAmelCase )
orig_model.load_state_dict(torch.load(args.model_path , map_location=_lowerCAmelCase )["state_dict"] )
_UpperCAmelCase = orig_model.diffusion_ema.eval()
_UpperCAmelCase = orig_model.state_dict()
_UpperCAmelCase = rename_orig_weights(_lowerCAmelCase )
_UpperCAmelCase = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
_UpperCAmelCase = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(_lowerCAmelCase ) == 0, F'''Problem with {renamed_minus_diffusers}'''
assert all(k.endswith("kernel" ) for k in list(_lowerCAmelCase ) ), F'''Problem with {diffusers_minus_renamed}'''
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
if key == "time_proj.weight":
_UpperCAmelCase = value.squeeze()
_UpperCAmelCase = value
diffusers_model.load_state_dict(_lowerCAmelCase )
_UpperCAmelCase = 100
_UpperCAmelCase = 33
_UpperCAmelCase = IPNDMScheduler(num_train_timesteps=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(_lowerCAmelCase )
_UpperCAmelCase = torch.randn([1, 2, config.sample_size] , generator=_lowerCAmelCase ).to(_lowerCAmelCase )
_UpperCAmelCase = torch.linspace(1 , 0 , steps + 1 , device=_lowerCAmelCase )[:-1]
_UpperCAmelCase = get_crash_schedule(_lowerCAmelCase )
_UpperCAmelCase = DanceDiffusionPipeline(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(33 )
_UpperCAmelCase = pipe(num_inference_steps=_lowerCAmelCase , generator=_lowerCAmelCase ).audios
_UpperCAmelCase = sampling.iplms_sample(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {} )
_UpperCAmelCase = generated.clamp(-1 , 1 )
_UpperCAmelCase = (generated - audio).abs().sum()
_UpperCAmelCase = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("Diff sum" , _lowerCAmelCase )
print("Diff max" , _lowerCAmelCase )
assert diff_max < 1E-3, F'''Diff max: {diff_max} is too much :-/'''
print(F'''Conversion for {model_name} successful!''' )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
__lowerCAmelCase = parser.parse_args()
main(args)
| 684 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
snake_case__ : Union[str, Any] = """__DUMMY_TRANSFORMERS_USER__"""
snake_case__ : Optional[int] = """Dummy User"""
snake_case__ : Any = """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"""
snake_case__ : List[Any] = """https://hub-ci.huggingface.co"""
snake_case__ : Dict = CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
snake_case__ : Dict = CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
snake_case__ : Optional[int] = Path("""~/.huggingface/hub_ci_token""").expanduser()
@pytest.fixture
def _snake_case (__lowercase):
monkeypatch.setattr(
'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE' , __lowercase)
@pytest.fixture
def _snake_case (__lowercase):
monkeypatch.setattr('datasets.config.HF_ENDPOINT' , __lowercase)
monkeypatch.setattr('datasets.config.HUB_DATASETS_URL' , __lowercase)
@pytest.fixture
def _snake_case (__lowercase):
monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token' , __lowercase)
@pytest.fixture
def _snake_case (__lowercase , __lowercase):
HfFolder.save_token(__lowercase)
yield
HfFolder.delete_token()
@pytest.fixture(scope='session')
def _snake_case ():
return HfApi(endpoint=__lowercase)
@pytest.fixture(scope='session')
def _snake_case (__lowercase):
UpperCamelCase_ = HfFolder.get_token()
HfFolder.save_token(__lowercase)
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(__lowercase)
@pytest.fixture
def _snake_case (__lowercase):
def _cleanup_repo(__lowercase):
hf_api.delete_repo(__lowercase , token=__lowercase , repo_type='dataset')
return _cleanup_repo
@pytest.fixture
def _snake_case (__lowercase):
@contextmanager
def _temporary_repo(__lowercase):
try:
yield repo_id
finally:
cleanup_repo(__lowercase)
return _temporary_repo
@pytest.fixture(scope='session')
def _snake_case (__lowercase , __lowercase , __lowercase):
UpperCamelCase_ = f"""repo_txt_data-{int(time.time() * 10e3)}"""
UpperCamelCase_ = f"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(__lowercase , token=__lowercase , repo_type='dataset' , private=__lowercase)
hf_api.upload_file(
token=__lowercase , path_or_fileobj=str(__lowercase) , path_in_repo='data/text_data.txt' , repo_id=__lowercase , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(__lowercase , token=__lowercase , repo_type='dataset')
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _snake_case (__lowercase , __lowercase , __lowercase):
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope='session')
def _snake_case (__lowercase , __lowercase , __lowercase):
UpperCamelCase_ = f"""repo_zipped_txt_data-{int(time.time() * 10e3)}"""
UpperCamelCase_ = f"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(__lowercase , token=__lowercase , repo_type='dataset' , private=__lowercase)
hf_api.upload_file(
token=__lowercase , path_or_fileobj=str(__lowercase) , path_in_repo='data.zip' , repo_id=__lowercase , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(__lowercase , token=__lowercase , repo_type='dataset')
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _snake_case (__lowercase , __lowercase , __lowercase):
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope='session')
def _snake_case (__lowercase , __lowercase , __lowercase):
UpperCamelCase_ = f"""repo_zipped_img_data-{int(time.time() * 10e3)}"""
UpperCamelCase_ = f"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(__lowercase , token=__lowercase , repo_type='dataset' , private=__lowercase)
hf_api.upload_file(
token=__lowercase , path_or_fileobj=str(__lowercase) , path_in_repo='data.zip' , repo_id=__lowercase , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(__lowercase , token=__lowercase , repo_type='dataset')
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _snake_case (__lowercase , __lowercase , __lowercase):
return hf_private_dataset_repo_zipped_img_data_
| 23 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
__lowerCAmelCase = get_tests_dir("fixtures")
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 500
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=__UpperCamelCase ) as mock_head:
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase__ ( self : List[Any] ):
# This test is for deprecated behavior and can be removed in v5
_UpperCAmelCase = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json" )
def UpperCAmelCase__ ( self : Dict ):
with self.assertRaises(__UpperCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
_UpperCAmelCase = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants" )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants" , subfolder="feature_extractor" )
self.assertIsNotNone(__UpperCamelCase )
@is_staging_test
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
@classmethod
def UpperCAmelCase__ ( cls : str ):
_UpperCAmelCase = TOKEN
HfFolder.save_token(__UpperCamelCase )
@classmethod
def UpperCAmelCase__ ( cls : Optional[Any] ):
try:
delete_repo(token=cls._token , repo_id="test-image-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-image-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-image-processor" )
except HTTPError:
pass
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="test-image-processor" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("valid_org/test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="valid_org/test-image-processor-org" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : int ):
CustomImageProcessor.register_for_auto_class()
_UpperCAmelCase = CustomImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-dynamic-image-processor" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"} , )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
F'''{USER}/test-dynamic-image-processor''' , trust_remote_code=__UpperCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , "CustomImageProcessor" )
| 684 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class lowerCAmelCase ( __lowerCAmelCase):
__lowercase : jnp.ndarray
@flax_register_to_config
class lowerCAmelCase ( nn.Module , __lowerCAmelCase , __lowerCAmelCase):
__lowercase : int = 32
__lowercase : int = 4
__lowercase : int = 4
__lowercase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
__lowercase : Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
__lowercase : Union[bool, Tuple[bool]] = False
__lowercase : Tuple[int] = (320, 640, 1280, 1280)
__lowercase : int = 2
__lowercase : Union[int, Tuple[int]] = 8
__lowercase : Optional[Union[int, Tuple[int]]] = None
__lowercase : int = 1280
__lowercase : float = 0.0
__lowercase : bool = False
__lowercase : jnp.dtype = jnp.floataa
__lowercase : bool = True
__lowercase : int = 0
__lowercase : bool = False
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> FrozenDict:
'''simple docstring'''
__snake_case = (1, self.in_channels, self.sample_size, self.sample_size)
__snake_case = jnp.zeros(__SCREAMING_SNAKE_CASE , dtype=jnp.floataa )
__snake_case = jnp.ones((1,) , dtype=jnp.intaa )
__snake_case = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
__snake_case , __snake_case = jax.random.split(__SCREAMING_SNAKE_CASE )
__snake_case = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )["params"]
def lowerCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
__snake_case = self.block_out_channels
__snake_case = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
__snake_case = self.num_attention_heads or self.attention_head_dim
# input
__snake_case = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
__snake_case = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
__snake_case = FlaxTimestepEmbedding(__SCREAMING_SNAKE_CASE , dtype=self.dtype )
__snake_case = self.only_cross_attention
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__snake_case = (only_cross_attention,) * len(self.down_block_types )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__snake_case = (num_attention_heads,) * len(self.down_block_types )
# down
__snake_case = []
__snake_case = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
__snake_case = output_channel
__snake_case = block_out_channels[i]
__snake_case = i == len(__SCREAMING_SNAKE_CASE ) - 1
if down_block_type == "CrossAttnDownBlock2D":
__snake_case = FlaxCrossAttnDownBlockaD(
in_channels=__SCREAMING_SNAKE_CASE , out_channels=__SCREAMING_SNAKE_CASE , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
__snake_case = FlaxDownBlockaD(
in_channels=__SCREAMING_SNAKE_CASE , out_channels=__SCREAMING_SNAKE_CASE , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(__SCREAMING_SNAKE_CASE )
__snake_case = down_blocks
# mid
__snake_case = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
__snake_case = []
__snake_case = list(reversed(__SCREAMING_SNAKE_CASE ) )
__snake_case = list(reversed(__SCREAMING_SNAKE_CASE ) )
__snake_case = list(reversed(__SCREAMING_SNAKE_CASE ) )
__snake_case = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
__snake_case = output_channel
__snake_case = reversed_block_out_channels[i]
__snake_case = reversed_block_out_channels[min(i + 1 , len(__SCREAMING_SNAKE_CASE ) - 1 )]
__snake_case = i == len(__SCREAMING_SNAKE_CASE ) - 1
if up_block_type == "CrossAttnUpBlock2D":
__snake_case = FlaxCrossAttnUpBlockaD(
in_channels=__SCREAMING_SNAKE_CASE , out_channels=__SCREAMING_SNAKE_CASE , prev_output_channel=__SCREAMING_SNAKE_CASE , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
__snake_case = FlaxUpBlockaD(
in_channels=__SCREAMING_SNAKE_CASE , out_channels=__SCREAMING_SNAKE_CASE , prev_output_channel=__SCREAMING_SNAKE_CASE , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(__SCREAMING_SNAKE_CASE )
__snake_case = output_channel
__snake_case = up_blocks
# out
__snake_case = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
__snake_case = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE = True , __SCREAMING_SNAKE_CASE = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]:
'''simple docstring'''
if not isinstance(__SCREAMING_SNAKE_CASE , jnp.ndarray ):
__snake_case = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__SCREAMING_SNAKE_CASE , jnp.ndarray ) and len(timesteps.shape ) == 0:
__snake_case = timesteps.astype(dtype=jnp.floataa )
__snake_case = jnp.expand_dims(__SCREAMING_SNAKE_CASE , 0 )
__snake_case = self.time_proj(__SCREAMING_SNAKE_CASE )
__snake_case = self.time_embedding(__SCREAMING_SNAKE_CASE )
# 2. pre-process
__snake_case = jnp.transpose(__SCREAMING_SNAKE_CASE , (0, 2, 3, 1) )
__snake_case = self.conv_in(__SCREAMING_SNAKE_CASE )
# 3. down
__snake_case = (sample,)
for down_block in self.down_blocks:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__snake_case , __snake_case = down_block(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , deterministic=not train )
else:
__snake_case , __snake_case = down_block(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
__snake_case = ()
for down_block_res_sample, down_block_additional_residual in zip(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
__snake_case = new_down_block_res_samples
# 4. mid
__snake_case = self.mid_block(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
__snake_case = down_block_res_samples[-(self.layers_per_block + 1) :]
__snake_case = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__snake_case = up_block(
__SCREAMING_SNAKE_CASE , temb=__SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE , res_hidden_states_tuple=__SCREAMING_SNAKE_CASE , deterministic=not train , )
else:
__snake_case = up_block(__SCREAMING_SNAKE_CASE , temb=__SCREAMING_SNAKE_CASE , res_hidden_states_tuple=__SCREAMING_SNAKE_CASE , deterministic=not train )
# 6. post-process
__snake_case = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
__snake_case = nn.silu(__SCREAMING_SNAKE_CASE )
__snake_case = self.conv_out(__SCREAMING_SNAKE_CASE )
__snake_case = jnp.transpose(__SCREAMING_SNAKE_CASE , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=__SCREAMING_SNAKE_CASE )
| 24 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
return getitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
return setitem, k, v
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
return delitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase ) -> Optional[int]:
try:
return fun(_lowerCAmelCase , *_lowerCAmelCase ), None
except Exception as e:
return None, e
__lowerCAmelCase = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__lowerCAmelCase = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items" ),
pytest.param(_overwrite_items , id="overwrite items" ),
pytest.param(_delete_items , id="delete items" ),
pytest.param(_access_absent_items , id="access absent items" ),
pytest.param(_add_with_resize_up , id="add with resize up" ),
pytest.param(_add_with_resize_down , id="add with resize down" ),
) , )
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
_UpperCAmelCase = HashMap(initial_block_size=4 )
_UpperCAmelCase = {}
for _, (fun, *args) in enumerate(_lowerCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
assert my_res == py_res
assert str(_lowerCAmelCase ) == str(_lowerCAmelCase )
assert set(_lowerCAmelCase ) == set(_lowerCAmelCase )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
assert set(my.items() ) == set(py.items() )
def __lowerCamelCase ( ) -> List[Any]:
def is_public(_lowerCAmelCase ) -> bool:
return not name.startswith("_" )
_UpperCAmelCase = {name for name in dir({} ) if is_public(_lowerCAmelCase )}
_UpperCAmelCase = {name for name in dir(HashMap() ) if is_public(_lowerCAmelCase )}
assert dict_public_names > hash_public_names
| 684 | 0 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any] , a : List[Any] , a : List[Any]=13 , a : Union[str, Any]=7 , a : Optional[int]=True , a : Optional[int]=True , a : int=True , a : Any=True , a : Dict=99 , a : Tuple=32 , a : Optional[int]=5 , a : List[Any]=4 , a : Optional[int]=4 , a : List[str]="gelu" , a : Optional[int]=0.0 , a : int=0.1 , a : List[Any]=True , a : Union[str, Any]=512 , a : Tuple=16 , a : Union[str, Any]=2 , a : List[str]=0.02 , a : Any=3 , a : int=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Dict = seq_length
SCREAMING_SNAKE_CASE : str = is_training
SCREAMING_SNAKE_CASE : Optional[int] = use_input_mask
SCREAMING_SNAKE_CASE : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE : List[str] = use_labels
SCREAMING_SNAKE_CASE : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE : Tuple = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : str = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_multiple_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_dropout
SCREAMING_SNAKE_CASE : Any = attention_dropout
SCREAMING_SNAKE_CASE : str = weight_tying
SCREAMING_SNAKE_CASE : Any = max_position_embeddings
SCREAMING_SNAKE_CASE : int = type_vocab_size
SCREAMING_SNAKE_CASE : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : Any = num_labels
SCREAMING_SNAKE_CASE : List[Any] = num_choices
SCREAMING_SNAKE_CASE : List[str] = scope
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Optional[Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : Optional[int] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : List[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
return GPTNeoXJapaneseConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE : Optional[int] = True
return config, input_ids, input_mask, token_labels
def __UpperCamelCase ( self : List[str] , a : Union[str, Any] , a : Optional[Any] , a : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = GPTNeoXJapaneseModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : List[str] , a : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = True
SCREAMING_SNAKE_CASE : Dict = GPTNeoXJapaneseModel(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[Any] = model(a , attention_mask=a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Optional[Any] , a : Tuple , a : Union[str, Any] , a : Union[str, Any] , a : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = GPTNeoXJapaneseForCausalLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : Optional[int] , a : Tuple , a : Tuple , a : Dict ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : str = GPTNeoXJapaneseForCausalLM(config=a )
model.to(a )
model.eval()
# first forward pass
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , use_cache=a )
SCREAMING_SNAKE_CASE : Any = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE : Dict = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE : str = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE : Any = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat([input_mask, next_mask] , dim=-1 )
SCREAMING_SNAKE_CASE : List[Any] = model(a , attention_mask=a , output_hidden_states=a )
SCREAMING_SNAKE_CASE : Any = output_from_no_past["hidden_states"][0]
SCREAMING_SNAKE_CASE : Dict = model(
a , attention_mask=a , past_key_values=a , output_hidden_states=a , )["hidden_states"][0]
# select random slice
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE : List[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE : List[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a , a , atol=1e-3 ) )
def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[int] = config_and_inputs
SCREAMING_SNAKE_CASE : Any = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
lowerCamelCase__ =(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ =(
{'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
lowerCamelCase__ =False
lowerCamelCase__ =False
lowerCamelCase__ =False
lowerCamelCase__ =False
def __UpperCamelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = GPTNeoXJapaneseModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , hidden_size=37 )
def __UpperCamelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(a , a , a )
def __UpperCamelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(a , a , a )
def __UpperCamelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs_for_decoder()
SCREAMING_SNAKE_CASE : Dict = None
self.model_tester.create_and_check_model_as_decoder(a , a , a )
def __UpperCamelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(a , a , a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*a )
@slow
def __UpperCamelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = "abeja/gpt-neox-japanese-2.7b"
SCREAMING_SNAKE_CASE : Optional[int] = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]
SCREAMING_SNAKE_CASE : List[Any] = [
"データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
"100年後に必要とされる会社は、「人」が中心の会社です。",
"フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
"国境の長いトンネルを抜けると、そこは雪国だった。",
"美味しい日本食といえば、やっぱりお寿司ですよね。",
]
SCREAMING_SNAKE_CASE : List[str] = GPTNeoXJapaneseTokenizer.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = GPTNeoXJapaneseForCausalLM.from_pretrained(a )
SCREAMING_SNAKE_CASE : Optional[Any] = []
for prompt in prompts:
SCREAMING_SNAKE_CASE : str = tokenizer(a , return_tensors="pt" ).input_ids
SCREAMING_SNAKE_CASE : Any = model.generate(a , max_length=50 )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.batch_decode(a , skip_special_tokens=a )
predicted_outputs += generated_string
self.assertListEqual(a , a ) | 25 |
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
_UpperCAmelCase = len(_lowerCAmelCase )
for i in range(1 , _lowerCAmelCase ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase = 0
_UpperCAmelCase = i - 1
while low <= high:
_UpperCAmelCase = (low + high) // 2
if val < collection[mid]:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
for j in range(_lowerCAmelCase , _lowerCAmelCase , -1 ):
_UpperCAmelCase = collection[j - 1]
_UpperCAmelCase = val
return collection
if __name__ == "__main__":
__lowerCAmelCase = input("Enter numbers separated by a comma:\n").strip()
__lowerCAmelCase = [int(item) for item in user_input.split(",")]
print(binary_insertion_sort(unsorted))
| 684 | 0 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCamelCase = 16
__UpperCamelCase = 32
def _a ( _lowerCamelCase , _lowerCamelCase = 16 ) -> Dict:
"""simple docstring"""
__snake_case : Optional[Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
__snake_case : Dict = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(_lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
__snake_case : List[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=_lowerCamelCase , max_length=_lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__snake_case : Optional[Any] = datasets.map(
_lowerCamelCase , batched=_lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__snake_case : int = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(_lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__snake_case : Optional[int] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__snake_case : int = 16
elif accelerator.mixed_precision != "no":
__snake_case : Any = 8
else:
__snake_case : List[Any] = None
return tokenizer.pad(
_lowerCamelCase , padding="""longest""" , max_length=_lowerCamelCase , pad_to_multiple_of=_lowerCamelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
__snake_case : List[str] = DataLoader(
tokenized_datasets["""train"""] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase )
__snake_case : Dict = DataLoader(
tokenized_datasets["""validation"""] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__UpperCamelCase = mocked_dataloaders # noqa: F811
def _a ( _lowerCamelCase , _lowerCamelCase ) -> Tuple:
"""simple docstring"""
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , _lowerCamelCase ) == "1":
__snake_case : Optional[int] = 2
# Initialize accelerator
__snake_case : List[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__snake_case : Union[str, Any] = config["""lr"""]
__snake_case : Optional[int] = int(config["""num_epochs"""] )
__snake_case : Optional[int] = int(config["""seed"""] )
__snake_case : List[Any] = int(config["""batch_size"""] )
__snake_case : int = evaluate.load("""glue""" , """mrpc""" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=_lowerCamelCase )
def inner_training_loop(_lowerCamelCase ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(_lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__snake_case : str = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=_lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__snake_case : Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
__snake_case : int = AdamW(params=model.parameters() , lr=_lowerCamelCase )
__snake_case , __snake_case : Optional[int] = get_dataloaders(_lowerCamelCase , _lowerCamelCase )
# Instantiate scheduler
__snake_case : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=_lowerCamelCase , num_warmup_steps=100 , num_training_steps=(len(_lowerCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : int = accelerator.prepare(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# Now we train the model
for epoch in range(_lowerCamelCase ):
model.train()
for step, batch in enumerate(_lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__snake_case : Optional[int] = model(**_lowerCamelCase )
__snake_case : Optional[Any] = outputs.loss
accelerator.backward(_lowerCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__snake_case : Optional[Any] = model(**_lowerCamelCase )
__snake_case : List[Any] = outputs.logits.argmax(dim=-1 )
__snake_case , __snake_case : Optional[int] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=_lowerCamelCase , references=_lowerCamelCase , )
__snake_case : Any = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , _lowerCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def _a ( ) -> Optional[int]:
"""simple docstring"""
__snake_case : Optional[Any] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=_lowerCamelCase , default=_lowerCamelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
__snake_case : Any = parser.parse_args()
__snake_case : Any = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(_lowerCamelCase , _lowerCamelCase )
if __name__ == "__main__":
main()
| 26 |
__lowerCAmelCase = 2_5_6
# Modulus to hash a string
__lowerCAmelCase = 1_0_0_0_0_0_3
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
_UpperCAmelCase = len(_lowerCAmelCase )
_UpperCAmelCase = len(_lowerCAmelCase )
if p_len > t_len:
return False
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 1
# Calculating the hash of pattern and substring of text
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_UpperCAmelCase = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_UpperCAmelCase = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_UpperCAmelCase = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def __lowerCamelCase ( ) -> None:
_UpperCAmelCase = "abc1abc12"
_UpperCAmelCase = "alskfjaldsabc1abc1abc12k23adsfabcabc"
_UpperCAmelCase = "alskfjaldsk23adsfabcabc"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase ) and not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 2)
_UpperCAmelCase = "ABABX"
_UpperCAmelCase = "ABABZABABYABABX"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 3)
_UpperCAmelCase = "AAAB"
_UpperCAmelCase = "ABAAAAAB"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 4)
_UpperCAmelCase = "abcdabcy"
_UpperCAmelCase = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 5)
_UpperCAmelCase = "Lü"
_UpperCAmelCase = "Lüsai"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
_UpperCAmelCase = "Lue"
assert not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 684 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self ):
_A = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=snake_case_ ).to(snake_case_ )
_A = AutoTokenizer.from_pretrained('google/mt5-small' )
_A = tokenizer('Hello there' , return_tensors='pt' ).input_ids
_A = tokenizer('Hi I am' , return_tensors='pt' ).input_ids
_A = model(input_ids.to(snake_case_ ) , labels=labels.to(snake_case_ ) ).loss
_A = -(labels.shape[-1] * loss.item())
_A = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 27 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__lowerCAmelCase = random.Random()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=1.0 , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> List[str]:
if rng is None:
_UpperCAmelCase = global_rng
_UpperCAmelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def __init__( self : Optional[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any]=7 , __UpperCamelCase : Union[str, Any]=400 , __UpperCamelCase : List[Any]=2_000 , __UpperCamelCase : Optional[Any]=10 , __UpperCamelCase : Optional[int]=160 , __UpperCamelCase : Any=8 , __UpperCamelCase : List[Any]=0.0 , __UpperCamelCase : Dict=4_000 , __UpperCamelCase : Optional[int]=False , __UpperCamelCase : Tuple=True , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = min_seq_length
_UpperCAmelCase = max_seq_length
_UpperCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_UpperCAmelCase = padding_value
_UpperCAmelCase = sampling_rate
_UpperCAmelCase = return_attention_mask
_UpperCAmelCase = do_normalize
_UpperCAmelCase = feature_size
_UpperCAmelCase = chunk_length
_UpperCAmelCase = hop_length
def UpperCAmelCase__ ( self : Optional[Any] ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : Tuple=False , __UpperCamelCase : Dict=False ):
def _flatten(__UpperCamelCase : Any ):
return list(itertools.chain(*__UpperCamelCase ) )
if equal_length:
_UpperCAmelCase = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_UpperCAmelCase = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : str = WhisperFeatureExtractor if is_speech_available() else None
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = WhisperFeatureExtractionTester(self )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = feat_extract_first.save_pretrained(__UpperCamelCase )[0]
check_json_file_has_correct_format(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = os.path.join(__UpperCamelCase , "feat_extract.json" )
feat_extract_first.to_json_file(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_json_file(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
# Test feature size
_UpperCAmelCase = feature_extractor(__UpperCamelCase , padding="max_length" , return_tensors="np" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
_UpperCAmelCase = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_features
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test batched
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
_UpperCAmelCase = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_UpperCAmelCase = np.asarray(__UpperCamelCase )
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test truncation required
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
_UpperCAmelCase = [x[: feature_extractor.n_samples] for x in speech_inputs]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs_truncated]
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
import torch
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = np.random.rand(100 , 32 ).astype(np.floataa )
_UpperCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Tuple ):
_UpperCAmelCase = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
_UpperCAmelCase = ds.sort("id" ).select(range(__UpperCamelCase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def UpperCAmelCase__ ( self : Tuple ):
# fmt: off
_UpperCAmelCase = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
_UpperCAmelCase = self._load_datasamples(1 )
_UpperCAmelCase = WhisperFeatureExtractor()
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="pt" ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __UpperCamelCase , atol=1e-4 ) )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = self._load_datasamples(1 )[0]
_UpperCAmelCase = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
_UpperCAmelCase = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__UpperCamelCase )[0]
self.assertTrue(np.all(np.mean(__UpperCamelCase ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase ) - 1 ) < 1e-3 ) )
| 684 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
self.test()
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Optional[int] = False
while not completed:
if counter == 1:
self.reset()
SCREAMING_SNAKE_CASE : str = self.advance()
if not self.does_advance(A ):
raise Exception(
'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.' )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = self.update(A )
counter += 1
if counter > 10_000:
raise Exception('update() does not fulfill the constraint.' )
if self.remaining() != 0:
raise Exception('Custom Constraint is not defined correctly.' )
@abstractmethod
def UpperCamelCase_ ( self ):
'''simple docstring'''
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
@abstractmethod
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
@abstractmethod
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
@abstractmethod
def UpperCamelCase_ ( self ):
'''simple docstring'''
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
@abstractmethod
def UpperCamelCase_ ( self ):
'''simple docstring'''
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
@abstractmethod
def UpperCamelCase_ ( self, A=False ):
'''simple docstring'''
raise NotImplementedError(
F"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
super(A, self ).__init__()
if not isinstance(A, A ) or len(A ) == 0:
raise ValueError(F"`token_ids` has to be a non-empty list, but is {token_ids}." )
if any((not isinstance(A, A ) or token_id < 0) for token_id in token_ids ):
raise ValueError(F"Each list in `token_ids` has to be a list of positive integers, but is {token_ids}." )
SCREAMING_SNAKE_CASE : Tuple = token_ids
SCREAMING_SNAKE_CASE : Tuple = len(self.token_ids )
SCREAMING_SNAKE_CASE : Optional[int] = -1 # the index of the currently fulfilled step
SCREAMING_SNAKE_CASE : Any = False
def UpperCamelCase_ ( self ):
'''simple docstring'''
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if not isinstance(A, A ):
raise ValueError(F"`token_id` has to be an `int`, but is {token_id} of type {type(A )}" )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if not isinstance(A, A ):
raise ValueError(F"`token_id` has to be an `int`, but is {token_id} of type {type(A )}" )
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : str = False
SCREAMING_SNAKE_CASE : int = False
if self.does_advance(A ):
self.fulfilled_idx += 1
SCREAMING_SNAKE_CASE : int = True
if self.fulfilled_idx == (self.seqlen - 1):
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = completed
else:
# failed to make progress.
SCREAMING_SNAKE_CASE : int = True
self.reset()
return stepped, completed, reset
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : Dict = 0
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self.seqlen - (self.fulfilled_idx + 1)
def UpperCamelCase_ ( self, A=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = PhrasalConstraint(self.token_ids )
if stateful:
SCREAMING_SNAKE_CASE : str = self.seqlen
SCREAMING_SNAKE_CASE : List[Any] = self.fulfilled_idx
SCREAMING_SNAKE_CASE : List[Any] = self.completed
return new_constraint
class _a :
'''simple docstring'''
def __init__( self, A, A=True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = max([len(A ) for one in nested_token_ids] )
SCREAMING_SNAKE_CASE : Optional[int] = {}
for token_ids in nested_token_ids:
SCREAMING_SNAKE_CASE : Dict = root
for tidx, token_id in enumerate(A ):
if token_id not in level:
SCREAMING_SNAKE_CASE : Dict = {}
SCREAMING_SNAKE_CASE : Optional[Any] = level[token_id]
if no_subsets and self.has_subsets(A, A ):
raise ValueError(
'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
F" {nested_token_ids}." )
SCREAMING_SNAKE_CASE : Any = root
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.trie
for current_token in current_seq:
SCREAMING_SNAKE_CASE : Optional[Any] = start[current_token]
SCREAMING_SNAKE_CASE : Optional[Any] = list(start.keys() )
return next_tokens
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.next_tokens(A )
return len(A ) == 0
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = list(root.values() )
if len(A ) == 0:
return 1
else:
return sum([self.count_leaves(A ) for nn in next_nodes] )
def UpperCamelCase_ ( self, A, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.count_leaves(A )
return len(A ) != leaf_count
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
super(A, self ).__init__()
if not isinstance(A, A ) or len(A ) == 0:
raise ValueError(F"`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}." )
if any(not isinstance(A, A ) for token_ids in nested_token_ids ):
raise ValueError(F"`nested_token_ids` has to be a list of lists, but is {nested_token_ids}." )
if any(
any((not isinstance(A, A ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
F"Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}." )
SCREAMING_SNAKE_CASE : Dict = DisjunctiveTrie(A )
SCREAMING_SNAKE_CASE : int = nested_token_ids
SCREAMING_SNAKE_CASE : int = self.trie.max_height
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : List[str] = False
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.trie.next_tokens(self.current_seq )
if len(A ) == 0:
return None
else:
return token_list
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if not isinstance(A, A ):
raise ValueError(F"`token_id` is supposed to be type `int`, but is {token_id} of type {type(A )}" )
SCREAMING_SNAKE_CASE : List[str] = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if not isinstance(A, A ):
raise ValueError(F"`token_id` is supposed to be type `int`, but is {token_id} of type {type(A )}" )
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : Optional[int] = False
SCREAMING_SNAKE_CASE : Optional[Any] = False
if self.does_advance(A ):
self.current_seq.append(A )
SCREAMING_SNAKE_CASE : Tuple = True
else:
SCREAMING_SNAKE_CASE : Dict = True
self.reset()
SCREAMING_SNAKE_CASE : int = self.trie.reached_leaf(self.current_seq )
SCREAMING_SNAKE_CASE : List[str] = completed
return stepped, completed, reset
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CASE : Optional[int] = []
def UpperCamelCase_ ( self ):
'''simple docstring'''
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def UpperCamelCase_ ( self, A=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = DisjunctiveConstraint(self.token_ids )
if stateful:
SCREAMING_SNAKE_CASE : Tuple = self.seqlen
SCREAMING_SNAKE_CASE : Dict = self.current_seq
SCREAMING_SNAKE_CASE : str = self.completed
return new_constraint
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = constraints
# max # of steps required to fulfill a given constraint
SCREAMING_SNAKE_CASE : List[str] = max([c.seqlen for c in constraints] )
SCREAMING_SNAKE_CASE : str = len(A )
SCREAMING_SNAKE_CASE : Any = False
self.init_state()
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : Any = None
SCREAMING_SNAKE_CASE : str = [constraint.copy(stateful=A ) for constraint in self.constraints]
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
SCREAMING_SNAKE_CASE : List[str] = constraint.advance()
if isinstance(A, A ):
token_list.append(A )
elif isinstance(A, A ):
token_list.extend(A )
else:
SCREAMING_SNAKE_CASE : List[Any] = self.inprogress_constraint.advance()
if isinstance(A, A ):
token_list.append(A )
elif isinstance(A, A ):
token_list.extend(A )
if len(A ) == 0:
return None
else:
return token_list
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = self.add(A )
# the entire list of constraints are fulfilled
if self.completed:
break
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if not isinstance(A, A ):
raise ValueError(F"`token_id` should be an `int`, but is `{token_id}`." )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = False, False
if self.completed:
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Dict = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = self.inprogress_constraint.update(A )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=A ) )
SCREAMING_SNAKE_CASE : Dict = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
SCREAMING_SNAKE_CASE : Any = None
if len(self.pending_constraints ) == 0:
# we're done!
SCREAMING_SNAKE_CASE : Optional[int] = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(A ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = pending_constraint.update(A )
if not stepped:
raise Exception(
'`constraint.update(token_id)` is not yielding incremental progress, '
'even though `constraint.does_advance(token_id)` is true.' )
if complete:
self.complete_constraints.append(A )
SCREAMING_SNAKE_CASE : Optional[Any] = None
if not complete and stepped:
SCREAMING_SNAKE_CASE : Union[str, Any] = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
SCREAMING_SNAKE_CASE : Any = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
SCREAMING_SNAKE_CASE : Dict = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def UpperCamelCase_ ( self, A=True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
SCREAMING_SNAKE_CASE : int = [
constraint.copy(stateful=A ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
SCREAMING_SNAKE_CASE : List[str] = self.inprogress_constraint.copy(stateful=A )
SCREAMING_SNAKE_CASE : Optional[Any] = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 28 |
# Copyright 2023 The HuggingFace Inc. 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.
import re
from ..utils import cached_file
# docstyle-ignore
__lowerCAmelCase = "\nHuman: <<task>>\n\nAssistant: "
__lowerCAmelCase = "huggingface-tools/default-prompts"
__lowerCAmelCase = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase="run" ) -> Union[str, Any]:
if prompt_or_repo_id is None:
_UpperCAmelCase = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , _lowerCAmelCase ) is not None:
return prompt_or_repo_id
_UpperCAmelCase = cached_file(
_lowerCAmelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(_lowerCAmelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 684 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
A_ = """ \"\"\"
Output class for the scheduler's step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"\"\"
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
"""
class __lowerCamelCase ( unittest.TestCase ):
def UpperCAmelCase__ ( self ):
lowerCamelCase_ = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
lowerCamelCase_ = self.diffusers_dir
shutil.copy(
os.path.join(UpperCAmelCase , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def UpperCAmelCase__ ( self ):
lowerCamelCase_ = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def UpperCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None ):
lowerCamelCase_ = comment + f"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
lowerCamelCase_ = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
lowerCamelCase_ = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
lowerCamelCase_ = black.format_str(UpperCAmelCase , mode=UpperCAmelCase )
lowerCamelCase_ = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(UpperCAmelCase , '''w''' , newline='''\n''' ) as f:
f.write(UpperCAmelCase )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(UpperCAmelCase ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=UpperCAmelCase )
with open(UpperCAmelCase , '''r''' ) as f:
self.assertTrue(f.read() , UpperCAmelCase )
def UpperCAmelCase__ ( self ):
lowerCamelCase_ = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase__ ( self ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , UpperCAmelCase , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , UpperCAmelCase ) , )
# Copy consistency with a really long name
lowerCamelCase_ = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
f"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}" , f"{long_class_name}SchedulerOutput" , re.sub('''Bert''' , UpperCAmelCase , UpperCAmelCase ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , UpperCAmelCase , overwrite_result=re.sub('''DDPM''' , '''Test''' , UpperCAmelCase ) , )
| 29 |
from itertools import permutations
def __lowerCamelCase ( _lowerCAmelCase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(_lowerCAmelCase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def __lowerCamelCase ( _lowerCAmelCase = 10 ) -> int:
return sum(
int("".join(map(_lowerCAmelCase , _lowerCAmelCase ) ) )
for num in permutations(range(_lowerCAmelCase ) )
if is_substring_divisible(_lowerCAmelCase ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 684 | 0 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=False , ):
'''simple docstring'''
output_path.parent.mkdir(parents=_lowercase , exist_ok=_lowercase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_lowercase , _lowercase , f=output_path.as_posix() , input_names=_lowercase , output_names=_lowercase , dynamic_axes=_lowercase , do_constant_folding=_lowercase , use_external_data_format=_lowercase , enable_onnx_checker=_lowercase , opset_version=_lowercase , )
else:
export(
_lowercase , _lowercase , f=output_path.as_posix() , input_names=_lowercase , output_names=_lowercase , dynamic_axes=_lowercase , do_constant_folding=_lowercase , opset_version=_lowercase , )
@torch.no_grad()
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase = False ):
'''simple docstring'''
UpperCAmelCase_ : Any = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
UpperCAmelCase_ : Tuple = '''cuda'''
elif fpaa and not torch.cuda.is_available():
raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
else:
UpperCAmelCase_ : Optional[int] = '''cpu'''
UpperCAmelCase_ : List[str] = Path(_lowercase )
# VAE DECODER
UpperCAmelCase_ : Optional[Any] = AutoencoderKL.from_pretrained(model_path + '''/vae''' )
UpperCAmelCase_ : Union[str, Any] = vae_decoder.config.latent_channels
# forward only through the decoder part
UpperCAmelCase_ : Optional[Any] = vae_decoder.decode
onnx_export(
_lowercase , model_args=(
torch.randn(1 , _lowercase , 25 , 25 ).to(device=_lowercase , dtype=_lowercase ),
False,
) , output_path=output_path / '''vae_decoder''' / '''model.onnx''' , ordered_input_names=['''latent_sample''', '''return_dict'''] , output_names=['''sample'''] , dynamic_axes={
'''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=_lowercase , )
del vae_decoder
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument(
'--model_path',
type=str,
required=True,
help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
)
parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--opset',
default=14,
type=int,
help='The version of the ONNX operator set to use.',
)
parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')
__a = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('SD: Done: ONNX') | 30 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
__lowerCAmelCase = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
__lowerCAmelCase = {"facebook/blenderbot-3B": 1_2_8}
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : Optional[int] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : str = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : List[Any] = ["""input_ids""", """attention_mask"""]
__SCREAMING_SNAKE_CASE : List[str] = BlenderbotTokenizer
def __init__( self : Tuple , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]="replace" , __UpperCamelCase : Tuple="<s>" , __UpperCamelCase : str="</s>" , __UpperCamelCase : Dict="</s>" , __UpperCamelCase : Union[str, Any]="<s>" , __UpperCamelCase : Union[str, Any]="<unk>" , __UpperCamelCase : Tuple="<pad>" , __UpperCamelCase : Optional[int]="<mask>" , __UpperCamelCase : Union[str, Any]=False , __UpperCamelCase : List[str]=True , **__UpperCamelCase : int , ):
super().__init__(
__UpperCamelCase , __UpperCamelCase , tokenizer_file=__UpperCamelCase , errors=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = getattr(__UpperCamelCase , pre_tok_state.pop("type" ) )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = pre_tok_class(**__UpperCamelCase )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = "post_processor"
_UpperCAmelCase = getattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
if tokenizer_component_instance:
_UpperCAmelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
_UpperCAmelCase = tuple(state["sep"] )
if "cls" in state:
_UpperCAmelCase = tuple(state["cls"] )
_UpperCAmelCase = False
if state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = True
if state.get("trim_offsets" , __UpperCamelCase ) != trim_offsets:
_UpperCAmelCase = trim_offsets
_UpperCAmelCase = True
if changes_to_apply:
_UpperCAmelCase = getattr(__UpperCamelCase , state.pop("type" ) )
_UpperCAmelCase = component_class(**__UpperCamelCase )
setattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def UpperCAmelCase__ ( self : Union[str, Any] ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else value
_UpperCAmelCase = value
def UpperCAmelCase__ ( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : List[Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : Tuple , *__UpperCamelCase : int , **__UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : str , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None ):
_UpperCAmelCase = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : "Conversation" ):
_UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text )
else:
# Generated responses should contain them already.
inputs.append(__UpperCamelCase )
_UpperCAmelCase = " ".join(__UpperCamelCase )
_UpperCAmelCase = self.encode(__UpperCamelCase )
if len(__UpperCamelCase ) > self.model_max_length:
_UpperCAmelCase = input_ids[-self.model_max_length :]
logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids
| 684 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : Dict = {
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'LongformerConfig',
'LongformerOnnxConfig',
],
'tokenization_longformer': ['LongformerTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : List[Any] = ['LongformerTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : Any = [
'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'LongformerForMaskedLM',
'LongformerForMultipleChoice',
'LongformerForQuestionAnswering',
'LongformerForSequenceClassification',
'LongformerForTokenClassification',
'LongformerModel',
'LongformerPreTrainedModel',
'LongformerSelfAttention',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ : int = [
'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFLongformerForMaskedLM',
'TFLongformerForMultipleChoice',
'TFLongformerForQuestionAnswering',
'TFLongformerForSequenceClassification',
'TFLongformerForTokenClassification',
'TFLongformerModel',
'TFLongformerPreTrainedModel',
'TFLongformerSelfAttention',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
lowerCamelCase__ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 31 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
_UpperCAmelCase = WavaVecaForSequenceClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["projector.weight"]
_UpperCAmelCase = downstream_dict["projector.bias"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.weight"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
_UpperCAmelCase = WavaVecaForAudioFrameClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["model.linear.weight"]
_UpperCAmelCase = downstream_dict["model.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = WavaVecaForXVector.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["connector.weight"]
_UpperCAmelCase = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_UpperCAmelCase = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
_UpperCAmelCase = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
_UpperCAmelCase = downstream_dict["objective.W"]
return model
@torch.no_grad()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = torch.load(_lowerCAmelCase , map_location="cpu" )
_UpperCAmelCase = checkpoint["Downstream"]
_UpperCAmelCase = WavaVecaConfig.from_pretrained(_lowerCAmelCase )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , do_normalize=_lowerCAmelCase )
_UpperCAmelCase = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
_UpperCAmelCase = convert_classification(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForAudioFrameClassification" ):
_UpperCAmelCase = convert_diarization(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForXVector" ):
_UpperCAmelCase = convert_xvector(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
_UpperCAmelCase = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(_lowerCAmelCase )
hf_model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
__lowerCAmelCase = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 684 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 32 |
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
_UpperCAmelCase = []
_UpperCAmelCase = set({"(", "[", "{"} )
_UpperCAmelCase = set({")", "]", "}"} )
_UpperCAmelCase = {"{": "}", "[": "]", "(": ")"}
for i in range(len(_lowerCAmelCase ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(_lowerCAmelCase ) == 0 or (len(_lowerCAmelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(_lowerCAmelCase ) == 0
def __lowerCamelCase ( ) -> str:
_UpperCAmelCase = input("Enter sequence of brackets: " )
if is_balanced(_lowerCAmelCase ):
print(_lowerCAmelCase , "is balanced" )
else:
print(_lowerCAmelCase , "is not balanced" )
if __name__ == "__main__":
main()
| 684 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __magic_name__ (snake_case_ ,unittest.TestCase ):
'''simple docstring'''
__lowercase : Tuple = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:Optional[int]=0 ):
snake_case__ = floats_tensor((1, 3, 1_28, 1_28) , rng=random.Random(_a ) )
snake_case__ = np.random.RandomState(_a )
snake_case__ = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''strength''': 0.75,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self:int ):
snake_case__ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=_a )
snake_case__ = self.get_dummy_inputs()
snake_case__ = pipe(**_a ).images
snake_case__ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 1_28, 1_28, 3)
snake_case__ = np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def SCREAMING_SNAKE_CASE__ ( self:Tuple ):
snake_case__ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
snake_case__ = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_a )
pipe.set_progress_bar_config(disable=_a )
snake_case__ = self.get_dummy_inputs()
snake_case__ = pipe(**_a ).images
snake_case__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
snake_case__ = np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def SCREAMING_SNAKE_CASE__ ( self:Union[str, Any] ):
snake_case__ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
snake_case__ = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
# warmup pass to apply optimizations
snake_case__ = pipe(**self.get_dummy_inputs() )
snake_case__ = self.get_dummy_inputs()
snake_case__ = pipe(**_a ).images
snake_case__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
snake_case__ = np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def SCREAMING_SNAKE_CASE__ ( self:str ):
snake_case__ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
snake_case__ = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
snake_case__ = self.get_dummy_inputs()
snake_case__ = pipe(**_a ).images
snake_case__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
snake_case__ = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def SCREAMING_SNAKE_CASE__ ( self:List[str] ):
snake_case__ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
snake_case__ = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
snake_case__ = self.get_dummy_inputs()
snake_case__ = pipe(**_a ).images
snake_case__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
snake_case__ = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def SCREAMING_SNAKE_CASE__ ( self:str ):
snake_case__ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
snake_case__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
snake_case__ = self.get_dummy_inputs()
snake_case__ = pipe(**_a ).images
snake_case__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
snake_case__ = np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __magic_name__ (unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self:List[Any] ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE__ ( self:Tuple ):
snake_case__ = ort.SessionOptions()
snake_case__ = False
return options
def SCREAMING_SNAKE_CASE__ ( self:Optional[Any] ):
snake_case__ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
snake_case__ = init_image.resize((7_68, 5_12) )
# using the PNDM scheduler by default
snake_case__ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''onnx''' , safety_checker=_a , feature_extractor=_a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_a )
snake_case__ = '''A fantasy landscape, trending on artstation'''
snake_case__ = np.random.RandomState(0 )
snake_case__ = pipe(
prompt=_a , image=_a , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=_a , output_type='''np''' , )
snake_case__ = output.images
snake_case__ = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
snake_case__ = np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def SCREAMING_SNAKE_CASE__ ( self:int ):
snake_case__ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
snake_case__ = init_image.resize((7_68, 5_12) )
snake_case__ = LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , subfolder='''scheduler''' , revision='''onnx''' )
snake_case__ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , scheduler=_a , safety_checker=_a , feature_extractor=_a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_a )
snake_case__ = '''A fantasy landscape, trending on artstation'''
snake_case__ = np.random.RandomState(0 )
snake_case__ = pipe(
prompt=_a , image=_a , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=_a , output_type='''np''' , )
snake_case__ = output.images
snake_case__ = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
snake_case__ = np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 33 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = equationa
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = equationa
# Calculate the determinants of the matrices
_UpperCAmelCase = aa * ba - aa * ba
_UpperCAmelCase = ca * ba - ca * ba
_UpperCAmelCase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
_UpperCAmelCase = determinant_x / determinant
_UpperCAmelCase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 684 | 0 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument(
'--original_config_file',
type=str,
required=True,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--image_size',
default=512,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
def __snake_case ( _lowercase ):
"""simple docstring"""
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(f'could not parse string as bool {string}' )
parser.add_argument(
'--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
)
parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
SCREAMING_SNAKE_CASE_ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) | 34 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
# Initialise PyTorch model
_UpperCAmelCase = RemBertConfig.from_json_file(_lowerCAmelCase )
print("Building PyTorch model from configuration: {}".format(str(_lowerCAmelCase ) ) )
_UpperCAmelCase = RemBertModel(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Save pytorch-model
print("Save PyTorch model to {}".format(_lowerCAmelCase ) )
torch.save(model.state_dict() , _lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--rembert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained RemBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 684 | 0 |
a_ :int = 6_55_21
def a ( A__ ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : str = 0
for plain_chr in plain_text:
SCREAMING_SNAKE_CASE__ : Any = (a + ord(A__ )) % MOD_ADLER
SCREAMING_SNAKE_CASE__ : Optional[int] = (b + a) % MOD_ADLER
return (b << 1_6) | a
| 35 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def UpperCAmelCase__ ( *__UpperCamelCase : Dict , **__UpperCamelCase : Optional[int] ):
pass
@is_pipeline_test
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
__SCREAMING_SNAKE_CASE : List[Any] = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
_UpperCAmelCase = [
{
"image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"question": "How many cats are there?",
},
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"question": "How many cats are there?",
},
]
return vqa_pipeline, examples
def UpperCAmelCase__ ( self : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[int] ):
_UpperCAmelCase = vqa_pipeline(__UpperCamelCase , top_k=1 )
self.assertEqual(
__UpperCamelCase , [
[{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}],
[{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}],
] , )
@require_torch
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
_UpperCAmelCase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_UpperCAmelCase = "How many cats are there?"
_UpperCAmelCase = vqa_pipeline(image=__UpperCamelCase , question="How many cats are there?" , top_k=2 )
self.assertEqual(
__UpperCamelCase , [{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}, {"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}] )
_UpperCAmelCase = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
__UpperCamelCase , [{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}, {"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}] )
@slow
@require_torch
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="dandelin/vilt-b32-finetuned-vqa" )
_UpperCAmelCase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_UpperCAmelCase = "How many cats are there?"
_UpperCAmelCase = vqa_pipeline(image=__UpperCamelCase , question=__UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
_UpperCAmelCase = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
_UpperCAmelCase = vqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2 , )
@require_tf
@unittest.skip("Visual question answering not implemented in TF" )
def UpperCAmelCase__ ( self : Optional[int] ):
pass
| 684 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowercase ( __A : List[Any] ) -> Any:
'''simple docstring'''
snake_case : Optional[Any] = 384
if "tiny" in model_name:
snake_case : Optional[Any] = [3, 3, 9, 3]
snake_case : Optional[int] = [96, 192, 384, 768]
if "small" in model_name:
snake_case : Union[str, Any] = [3, 3, 27, 3]
snake_case : int = [96, 192, 384, 768]
if "base" in model_name:
snake_case : Optional[Any] = [3, 3, 27, 3]
snake_case : int = [128, 256, 512, 1024]
snake_case : Optional[int] = 512
if "large" in model_name:
snake_case : Optional[Any] = [3, 3, 27, 3]
snake_case : Any = [192, 384, 768, 1536]
snake_case : int = 768
if "xlarge" in model_name:
snake_case : Tuple = [3, 3, 27, 3]
snake_case : Any = [256, 512, 1024, 2048]
snake_case : Optional[int] = 1024
# set label information
snake_case : Optional[int] = 150
snake_case : str = """huggingface/label-files"""
snake_case : str = """ade20k-id2label.json"""
snake_case : Any = json.load(open(hf_hub_download(__A , __A , repo_type="""dataset""" ) , """r""" ) )
snake_case : Dict = {int(__A ): v for k, v in idalabel.items()}
snake_case : Any = {v: k for k, v in idalabel.items()}
snake_case : int = ConvNextConfig(
depths=__A , hidden_sizes=__A , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
snake_case : Tuple = UperNetConfig(
backbone_config=__A , auxiliary_in_channels=__A , num_labels=__A , idalabel=__A , labelaid=__A , )
return config
def lowercase ( __A : List[str] ) -> Any:
'''simple docstring'''
snake_case : Union[str, Any] = []
# fmt: off
# stem
rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""") )
rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"""backbone.stages.{i}.{j}.gamma""", f"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.norm.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.norm.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""") )
if i > 0:
rename_keys.append((f"""backbone.downsample_layers.{i}.0.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""") )
rename_keys.append((f"""backbone.downsample_layers.{i}.0.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""") )
rename_keys.append((f"""backbone.downsample_layers.{i}.1.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""") )
rename_keys.append((f"""backbone.downsample_layers.{i}.1.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.bias""") )
rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""") )
rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""") )
# decode head
rename_keys.extend(
[
("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
] )
# fmt: on
return rename_keys
def lowercase ( __A : Union[str, Any] , __A : List[Any] , __A : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : Optional[Any] = dct.pop(__A )
snake_case : List[Any] = val
def lowercase ( __A : str , __A : Dict , __A : List[str] ) -> List[Any]:
'''simple docstring'''
snake_case : Tuple = {
"""upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
"""upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
"""upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
"""upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
"""upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
}
snake_case : List[str] = model_name_to_url[model_name]
snake_case : Optional[int] = torch.hub.load_state_dict_from_url(__A , map_location="""cpu""" )["""state_dict"""]
snake_case : Optional[int] = get_upernet_config(__A )
snake_case : Optional[Any] = UperNetForSemanticSegmentation(__A )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
snake_case : Tuple = state_dict.pop(__A )
if "bn" in key:
snake_case : Dict = key.replace("""bn""" , """batch_norm""" )
snake_case : Union[str, Any] = val
# rename keys
snake_case : str = create_rename_keys(__A )
for src, dest in rename_keys:
rename_key(__A , __A , __A )
model.load_state_dict(__A )
# verify on image
snake_case : Optional[int] = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
snake_case : List[Any] = Image.open(requests.get(__A , stream=__A ).raw ).convert("""RGB""" )
snake_case : List[str] = SegformerImageProcessor()
snake_case : Optional[int] = processor(__A , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
snake_case : Optional[Any] = model(__A )
if model_name == "upernet-convnext-tiny":
snake_case : Any = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
elif model_name == "upernet-convnext-small":
snake_case : List[Any] = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
elif model_name == "upernet-convnext-base":
snake_case : Union[str, Any] = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
elif model_name == "upernet-convnext-large":
snake_case : Optional[Any] = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
elif model_name == "upernet-convnext-xlarge":
snake_case : Tuple = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
print("""Logits:""" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , __A , atol=1E-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
print(f"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(__A )
if push_to_hub:
print(f"""Pushing model and processor for {model_name} to hub""" )
model.push_to_hub(f"""openmmlab/{model_name}""" )
processor.push_to_hub(f"""openmmlab/{model_name}""" )
if __name__ == "__main__":
__lowercase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''upernet-convnext-tiny''',
type=str,
choices=[f'''upernet-convnext-{size}''' for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
help='''Name of the ConvNext UperNet model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
__lowercase : Tuple = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 684 | 0 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( A__ ):
"""simple docstring"""
_lowercase = (PNDMScheduler,)
_lowercase = (('num_inference_steps', 5_0),)
def _UpperCamelCase( self : int , **lowerCamelCase__ : str ):
a__ : Optional[int] = {
"num_train_timesteps": 1_000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCamelCase__ )
return config
def _UpperCamelCase( self : str , lowerCamelCase__ : Any=0 , **lowerCamelCase__ : Tuple ):
a__ : List[str] = dict(self.forward_default_kwargs )
a__ : Any = kwargs.pop("num_inference_steps" , lowerCamelCase__ )
a__ : Union[str, Any] = self.dummy_sample
a__ : Optional[int] = 0.1 * sample
a__ : List[Any] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
a__ : List[Any] = self.get_scheduler_config(**lowerCamelCase__ )
a__ : str = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(lowerCamelCase__ )
# copy over dummy past residuals
a__ : Optional[Any] = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCamelCase__ )
a__ : Tuple = scheduler_class.from_pretrained(lowerCamelCase__ )
new_scheduler.set_timesteps(lowerCamelCase__ )
# copy over dummy past residuals
a__ : Optional[Any] = dummy_past_residuals[:]
a__ : int = scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
a__ : Optional[Any] = new_scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
a__ : Optional[Any] = scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
a__ : Dict = new_scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _UpperCamelCase( self : Tuple ):
pass
def _UpperCamelCase( self : Optional[int] , lowerCamelCase__ : List[Any]=0 , **lowerCamelCase__ : List[Any] ):
a__ : List[Any] = dict(self.forward_default_kwargs )
a__ : List[Any] = kwargs.pop("num_inference_steps" , lowerCamelCase__ )
a__ : int = self.dummy_sample
a__ : List[str] = 0.1 * sample
a__ : Union[str, Any] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
a__ : List[str] = self.get_scheduler_config()
a__ : List[str] = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(lowerCamelCase__ )
# copy over dummy past residuals (must be after setting timesteps)
a__ : Optional[int] = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCamelCase__ )
a__ : Optional[Any] = scheduler_class.from_pretrained(lowerCamelCase__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCamelCase__ )
# copy over dummy past residual (must be after setting timesteps)
a__ : Optional[Any] = dummy_past_residuals[:]
a__ : List[str] = scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
a__ : List[Any] = new_scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
a__ : Union[str, Any] = scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
a__ : str = new_scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _UpperCamelCase( self : int , **lowerCamelCase__ : Tuple ):
a__ : Union[str, Any] = self.scheduler_classes[0]
a__ : Dict = self.get_scheduler_config(**lowerCamelCase__ )
a__ : Optional[Any] = scheduler_class(**lowerCamelCase__ )
a__ : Any = 10
a__ : List[str] = self.dummy_model()
a__ : Tuple = self.dummy_sample_deter
scheduler.set_timesteps(lowerCamelCase__ )
for i, t in enumerate(scheduler.prk_timesteps ):
a__ : Tuple = model(lowerCamelCase__ , lowerCamelCase__ )
a__ : str = scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
a__ : str = model(lowerCamelCase__ , lowerCamelCase__ )
a__ : Optional[int] = scheduler.step_plms(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ).prev_sample
return sample
def _UpperCamelCase( self : str ):
a__ : Optional[Any] = dict(self.forward_default_kwargs )
a__ : Tuple = kwargs.pop("num_inference_steps" , lowerCamelCase__ )
for scheduler_class in self.scheduler_classes:
a__ : int = self.get_scheduler_config()
a__ : List[Any] = scheduler_class(**lowerCamelCase__ )
a__ : Dict = self.dummy_sample
a__ : Tuple = 0.1 * sample
if num_inference_steps is not None and hasattr(lowerCamelCase__ , "set_timesteps" ):
scheduler.set_timesteps(lowerCamelCase__ )
elif num_inference_steps is not None and not hasattr(lowerCamelCase__ , "set_timesteps" ):
a__ : str = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a__ : int = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
a__ : Dict = dummy_past_residuals[:]
a__ : str = scheduler.step_prk(lowerCamelCase__ , 0 , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
a__ : Union[str, Any] = scheduler.step_prk(lowerCamelCase__ , 1 , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
a__ : Dict = scheduler.step_plms(lowerCamelCase__ , 0 , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
a__ : str = scheduler.step_plms(lowerCamelCase__ , 1 , lowerCamelCase__ , **lowerCamelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _UpperCamelCase( self : Optional[int] ):
for timesteps in [100, 1_000]:
self.check_over_configs(num_train_timesteps=lowerCamelCase__ )
def _UpperCamelCase( self : Union[str, Any] ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowerCamelCase__ )
a__ : str = self.scheduler_classes[0]
a__ : str = self.get_scheduler_config(steps_offset=1 )
a__ : Optional[Any] = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def _UpperCamelCase( self : List[str] ):
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCamelCase__ , beta_end=lowerCamelCase__ )
def _UpperCamelCase( self : List[str] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCamelCase__ )
def _UpperCamelCase( self : Tuple ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase__ )
def _UpperCamelCase( self : Tuple ):
for t in [1, 5, 10]:
self.check_over_forward(time_step=lowerCamelCase__ )
def _UpperCamelCase( self : Optional[int] ):
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=lowerCamelCase__ )
def _UpperCamelCase( self : List[str] ):
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
a__ : Optional[int] = 27
for scheduler_class in self.scheduler_classes:
a__ : int = self.dummy_sample
a__ : Optional[int] = 0.1 * sample
a__ : str = self.get_scheduler_config()
a__ : List[str] = scheduler_class(**lowerCamelCase__ )
scheduler.set_timesteps(lowerCamelCase__ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
a__ : Dict = scheduler.step_prk(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ).prev_sample
def _UpperCamelCase( self : Optional[Any] ):
with self.assertRaises(lowerCamelCase__ ):
a__ : Union[str, Any] = self.scheduler_classes[0]
a__ : Optional[Any] = self.get_scheduler_config()
a__ : Union[str, Any] = scheduler_class(**lowerCamelCase__ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def _UpperCamelCase( self : Any ):
a__ : Union[str, Any] = self.full_loop()
a__ : str = torch.sum(torch.abs(lowerCamelCase__ ) )
a__ : Dict = torch.mean(torch.abs(lowerCamelCase__ ) )
assert abs(result_sum.item() - 198.1318 ) < 1E-2
assert abs(result_mean.item() - 0.2580 ) < 1E-3
def _UpperCamelCase( self : Tuple ):
a__ : Dict = self.full_loop(prediction_type="v_prediction" )
a__ : Optional[int] = torch.sum(torch.abs(lowerCamelCase__ ) )
a__ : int = torch.mean(torch.abs(lowerCamelCase__ ) )
assert abs(result_sum.item() - 67.3986 ) < 1E-2
assert abs(result_mean.item() - 0.0878 ) < 1E-3
def _UpperCamelCase( self : Tuple ):
# We specify different beta, so that the first alpha is 0.99
a__ : Tuple = self.full_loop(set_alpha_to_one=lowerCamelCase__ , beta_start=0.01 )
a__ : Tuple = torch.sum(torch.abs(lowerCamelCase__ ) )
a__ : Tuple = torch.mean(torch.abs(lowerCamelCase__ ) )
assert abs(result_sum.item() - 230.0399 ) < 1E-2
assert abs(result_mean.item() - 0.2995 ) < 1E-3
def _UpperCamelCase( self : List[Any] ):
# We specify different beta, so that the first alpha is 0.99
a__ : List[str] = self.full_loop(set_alpha_to_one=lowerCamelCase__ , beta_start=0.01 )
a__ : Optional[int] = torch.sum(torch.abs(lowerCamelCase__ ) )
a__ : Union[str, Any] = torch.mean(torch.abs(lowerCamelCase__ ) )
assert abs(result_sum.item() - 186.9482 ) < 1E-2
assert abs(result_mean.item() - 0.2434 ) < 1E-3
| 37 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : str = (UniPCMultistepScheduler,)
__SCREAMING_SNAKE_CASE : Dict = (("""num_inference_steps""", 25),)
def UpperCAmelCase__ ( self : str , **__UpperCamelCase : Any ):
_UpperCAmelCase = {
"num_train_timesteps": 1_000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__UpperCamelCase )
return config
def UpperCAmelCase__ ( self : int , __UpperCamelCase : Any=0 , **__UpperCamelCase : Any ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any]=0 , **__UpperCamelCase : List[Any] ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Dict=None , **__UpperCamelCase : Optional[Any] ):
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 10
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(__UpperCamelCase , "set_timesteps" ):
scheduler.set_timesteps(__UpperCamelCase )
elif num_inference_steps is not None and not hasattr(__UpperCamelCase , "set_timesteps" ):
_UpperCAmelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
_UpperCAmelCase = scheduler.timesteps[5]
_UpperCAmelCase = scheduler.timesteps[6]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCAmelCase__ ( self : Union[str, Any] ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = UniPCMultistepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def UpperCAmelCase__ ( self : str ):
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def UpperCAmelCase__ ( self : int ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase__ ( self : Optional[int] ):
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = self.full_loop(prediction_type="v_prediction" )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1014 ) < 1e-3
def UpperCAmelCase__ ( self : Tuple ):
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 10
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
def UpperCAmelCase__ ( self : str , **__UpperCamelCase : Optional[Any] ):
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 684 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : int ) -> int:
'''simple docstring'''
if not isinstance(__magic_name__ , __magic_name__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
snake_case__ : List[str] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __UpperCamelCase : List[Any]=0 ): # a graph with Node 0,1,...,N-1
_UpperCAmelCase = n
_UpperCAmelCase = [
[math.inf for j in range(0 , __UpperCamelCase )] for i in range(0 , __UpperCamelCase )
] # adjacency matrix for weight
_UpperCAmelCase = [
[math.inf for j in range(0 , __UpperCamelCase )] for i in range(0 , __UpperCamelCase )
] # dp[i][j] stores minimum distance from i to j
def UpperCAmelCase__ ( self : str , __UpperCamelCase : Tuple , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] ):
_UpperCAmelCase = w
def UpperCAmelCase__ ( self : Dict ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCAmelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCAmelCase__ ( self : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Any ):
return self.dp[u][v]
if __name__ == "__main__":
__lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 684 | 0 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class snake_case_ :
'''simple docstring'''
@staticmethod
def snake_case__( *_UpperCamelCase : List[str] , **_UpperCamelCase : Dict ) ->List[str]:
pass
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def snake_case__( self : List[str] , _UpperCamelCase : List[str] , _UpperCamelCase : int , _UpperCamelCase : Optional[int] ) ->int:
snake_case_ = DepthEstimationPipeline(model=_UpperCamelCase , image_processor=_UpperCamelCase )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def snake_case__( self : Dict , _UpperCamelCase : List[Any] , _UpperCamelCase : Tuple ) ->Optional[int]:
snake_case_ = depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )} , _UpperCamelCase )
import datasets
snake_case_ = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case_ = depth_estimator(
[
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
] )
self.assertEqual(
[
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
] , _UpperCamelCase , )
@require_tf
@unittest.skip('''Depth estimation is not implemented in TF''' )
def snake_case__( self : Optional[Any] ) ->Union[str, Any]:
pass
@slow
@require_torch
def snake_case__( self : Any ) ->List[Any]:
snake_case_ = '''Intel/dpt-large'''
snake_case_ = pipeline('''depth-estimation''' , model=_UpperCamelCase )
snake_case_ = depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
snake_case_ = hashimage(outputs['''depth'''] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() ) , 2.662 )
@require_torch
def snake_case__( self : Optional[int] ) ->str:
# This is highly irregular to have no small tests.
self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' ) | 39 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowercase , lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : Dict = VQModel
__SCREAMING_SNAKE_CASE : Optional[int] = """sample"""
@property
def UpperCAmelCase__ ( self : List[str] , __UpperCamelCase : Optional[int]=(32, 32) ):
_UpperCAmelCase = 4
_UpperCAmelCase = 3
_UpperCAmelCase = floats_tensor((batch_size, num_channels) + sizes ).to(__UpperCamelCase )
return {"sample": image}
@property
def UpperCAmelCase__ ( self : Tuple ):
return (3, 32, 32)
@property
def UpperCAmelCase__ ( self : str ):
return (3, 32, 32)
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 3,
}
_UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Dict ):
pass
def UpperCAmelCase__ ( self : str ):
pass
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = VQModel.from_pretrained("fusing/vqgan-dummy" , output_loading_info=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(__UpperCamelCase )
_UpperCAmelCase = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = VQModel.from_pretrained("fusing/vqgan-dummy" )
model.to(__UpperCamelCase ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
_UpperCAmelCase = torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size )
_UpperCAmelCase = image.to(__UpperCamelCase )
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase ).sample
_UpperCAmelCase = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
_UpperCAmelCase = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] )
# fmt: on
self.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
| 684 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : Any = "yolos"
def __init__( self, SCREAMING_SNAKE_CASE_=768, SCREAMING_SNAKE_CASE_=12, SCREAMING_SNAKE_CASE_=12, SCREAMING_SNAKE_CASE_=3072, SCREAMING_SNAKE_CASE_="gelu", SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=1e-12, SCREAMING_SNAKE_CASE_=[512, 864], SCREAMING_SNAKE_CASE_=16, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=100, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=5, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=5, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.1, **SCREAMING_SNAKE_CASE_, ) -> Optional[int]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Tuple = num_attention_heads
UpperCamelCase : Optional[Any] = intermediate_size
UpperCamelCase : Any = hidden_act
UpperCamelCase : List[Any] = hidden_dropout_prob
UpperCamelCase : Union[str, Any] = attention_probs_dropout_prob
UpperCamelCase : Optional[int] = initializer_range
UpperCamelCase : Tuple = layer_norm_eps
UpperCamelCase : Tuple = image_size
UpperCamelCase : int = patch_size
UpperCamelCase : List[str] = num_channels
UpperCamelCase : List[str] = qkv_bias
UpperCamelCase : Tuple = num_detection_tokens
UpperCamelCase : Tuple = use_mid_position_embeddings
UpperCamelCase : Tuple = auxiliary_loss
# Hungarian matcher
UpperCamelCase : Any = class_cost
UpperCamelCase : Optional[int] = bbox_cost
UpperCamelCase : str = giou_cost
# Loss coefficients
UpperCamelCase : List[str] = bbox_loss_coefficient
UpperCamelCase : Optional[int] = giou_loss_coefficient
UpperCamelCase : Optional[int] = eos_coefficient
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : List[Any] = version.parse("1.11" )
@property
def snake_case_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def snake_case_ ( self ) -> float:
return 1e-4
@property
def snake_case_ ( self ) -> int:
return 12
| 40 |
import requests
__lowerCAmelCase = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def __lowerCamelCase ( _lowerCAmelCase ) -> None:
# fetching a list of articles in json format
_UpperCAmelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"] , 1 ):
print(F'''{i}.) {article["title"]}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 684 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase_ (lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DDIMPipeline
SCREAMING_SNAKE_CASE : Any = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
SCREAMING_SNAKE_CASE : int = PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'latents',
'callback',
'callback_steps',
}
SCREAMING_SNAKE_CASE : Union[str, Any] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
SCREAMING_SNAKE_CASE : Union[str, Any] = False
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
torch.manual_seed(0 )
__lowercase = UNetaDModel(
block_out_channels=(3_2, 6_4) ,layers_per_block=2 ,sample_size=3_2 ,in_channels=3 ,out_channels=3 ,down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,)
__lowercase = DDIMScheduler()
__lowercase = {'''unet''': unet, '''scheduler''': scheduler}
return components
def SCREAMING_SNAKE_CASE ( self : Tuple ,lowercase__ : str ,lowercase__ : int=0 ):
if str(lowercase__ ).startswith('''mps''' ):
__lowercase = torch.manual_seed(lowercase__ )
else:
__lowercase = torch.Generator(device=lowercase__ ).manual_seed(lowercase__ )
__lowercase = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = '''cpu'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowercase__ )
pipe.to(lowercase__ )
pipe.set_progress_bar_config(disable=lowercase__ )
__lowercase = self.get_dummy_inputs(lowercase__ )
__lowercase = pipe(**lowercase__ ).images
__lowercase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape ,(1, 3_2, 3_2, 3) )
__lowercase = np.array(
[1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4] )
__lowercase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase__ ,1e-3 )
def SCREAMING_SNAKE_CASE ( self : Dict ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def SCREAMING_SNAKE_CASE ( self : str ):
super().test_save_load_local(expected_max_difference=3e-3 )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def SCREAMING_SNAKE_CASE ( self : int ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = '''google/ddpm-cifar10-32'''
__lowercase = UNetaDModel.from_pretrained(lowercase__ )
__lowercase = DDIMScheduler()
__lowercase = DDIMPipeline(unet=lowercase__ ,scheduler=lowercase__ )
ddim.to(lowercase__ )
ddim.set_progress_bar_config(disable=lowercase__ )
__lowercase = torch.manual_seed(0 )
__lowercase = ddim(generator=lowercase__ ,eta=0.0 ,output_type='''numpy''' ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__lowercase = np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = '''google/ddpm-ema-bedroom-256'''
__lowercase = UNetaDModel.from_pretrained(lowercase__ )
__lowercase = DDIMScheduler.from_pretrained(lowercase__ )
__lowercase = DDIMPipeline(unet=lowercase__ ,scheduler=lowercase__ )
ddpm.to(lowercase__ )
ddpm.set_progress_bar_config(disable=lowercase__ )
__lowercase = torch.manual_seed(0 )
__lowercase = ddpm(generator=lowercase__ ,output_type='''numpy''' ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_5_6, 2_5_6, 3)
__lowercase = np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 41 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Any ):
_UpperCAmelCase = 10
def UpperCAmelCase__ ( self : Optional[int] ):
_UpperCAmelCase = [1, 2, 3, 4]
_UpperCAmelCase = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = "It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this."
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , [] )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = ""
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , [] )
self.assertEqual(__UpperCamelCase , [] )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = (
"It was the year of Our Lord one thousand seven hundred and "
"seventy-five\n\nSpiritual revelations were conceded to England "
"at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
)
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
_UpperCAmelCase = [
"It was the year of Our Lord one thousand seven hundred and seventy-five.",
"Spiritual revelations were conceded to England at that favoured period, as at this.",
]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ["It was the best of times."]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = torch.tensor([1, 2, 3, 4] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 0 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 23 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Optional[int] ):
_UpperCAmelCase = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 1 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = 101
_UpperCAmelCase = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
_UpperCAmelCase = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
_UpperCAmelCase = compute_token_type_ids(__UpperCamelCase , __UpperCamelCase )
np.testing.assert_array_equal(__UpperCamelCase , __UpperCamelCase )
| 684 | 0 |
'''simple docstring'''
A_ = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
"V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
"2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
"8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
"?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
"(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
A_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def _UpperCamelCase ( __UpperCamelCase ) -> str:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def _UpperCamelCase ( __UpperCamelCase ) -> str:
return "".join(REVERSE_DICT[char] for char in message.split() )
def _UpperCamelCase ( ) -> None:
lowerCamelCase_ = 'Morse code here!'
print(__UpperCamelCase )
lowerCamelCase_ = encrypt(__UpperCamelCase )
print(__UpperCamelCase )
lowerCamelCase_ = decrypt(__UpperCamelCase )
print(__UpperCamelCase )
if __name__ == "__main__":
main()
| 42 |
from __future__ import annotations
from collections import namedtuple
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> tuple:
_UpperCAmelCase = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase = logging.get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
lowercase__ = []
# fmt: off
# stem:
rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''') )
rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''') )
rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''') )
# backbone
rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''') )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight') )
rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowercase__ = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
# fmt: on
return rename_keys
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
lowercase__ = ''''''
else:
lowercase__ = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase__ = state_dict.pop(f'blocks.{i}.attn.qkv.weight' )
lowercase__ = state_dict.pop(f'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[
: config.hidden_size, :
]
lowercase__ = in_proj_bias[: config.hidden_size]
lowercase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase__ = in_proj_weight[
-config.hidden_size :, :
]
lowercase__ = in_proj_bias[-config.hidden_size :]
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = dct.pop(SCREAMING_SNAKE_CASE )
lowercase__ = val
def _a ( ):
"""simple docstring"""
lowercase__ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
lowercase__ = BitConfig(
global_padding='''same''' , layer_type='''bottleneck''' , depths=(3, 4, 9) , out_features=['''stage3'''] , embedding_dynamic_padding=SCREAMING_SNAKE_CASE , )
lowercase__ = ViTHybridConfig(backbone_config=SCREAMING_SNAKE_CASE , image_size=3_84 , num_labels=10_00 )
lowercase__ = False
# load original model from timm
lowercase__ = timm.create_model(SCREAMING_SNAKE_CASE , pretrained=SCREAMING_SNAKE_CASE )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
lowercase__ = timm_model.state_dict()
if base_model:
remove_classification_head_(SCREAMING_SNAKE_CASE )
lowercase__ = create_rename_keys(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
read_in_q_k_v(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ = '''huggingface/label-files'''
lowercase__ = '''imagenet-1k-id2label.json'''
lowercase__ = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ = {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
lowercase__ = ViTHybridModel(SCREAMING_SNAKE_CASE ).eval()
else:
lowercase__ = ViTHybridForImageClassification(SCREAMING_SNAKE_CASE ).eval()
model.load_state_dict(SCREAMING_SNAKE_CASE )
# create image processor
lowercase__ = create_transform(**resolve_data_config({} , model=SCREAMING_SNAKE_CASE ) )
lowercase__ = transform.transforms
lowercase__ = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
lowercase__ = ViTHybridImageProcessor(
do_resize=SCREAMING_SNAKE_CASE , size={'''shortest_edge''': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=SCREAMING_SNAKE_CASE , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} , do_normalize=SCREAMING_SNAKE_CASE , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
lowercase__ = prepare_img()
lowercase__ = transform(SCREAMING_SNAKE_CASE ).unsqueeze(0 )
lowercase__ = processor(SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# verify logits
with torch.no_grad():
lowercase__ = model(SCREAMING_SNAKE_CASE )
lowercase__ = outputs.logits
print('''Predicted class:''' , logits.argmax(-1 ).item() )
if base_model:
lowercase__ = timm_model.forward_features(SCREAMING_SNAKE_CASE )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(SCREAMING_SNAKE_CASE , outputs.pooler_output , atol=1E-3 )
else:
lowercase__ = timm_model(SCREAMING_SNAKE_CASE )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(SCREAMING_SNAKE_CASE , outputs.logits , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
print(f'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE )
print(f'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(SCREAMING_SNAKE_CASE )
if push_to_hub:
print(f'Pushing model and processor to the hub {vit_name}' )
model.push_to_hub(f'ybelkada/{vit_name}' )
processor.push_to_hub(f'ybelkada/{vit_name}' )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_r50_s16_384',
type=str,
help='Name of the hybrid ViT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
lowerCAmelCase = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 43 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __lowerCamelCase ( _lowerCAmelCase ) -> Any:
_UpperCAmelCase = {}
_UpperCAmelCase = job["started_at"]
_UpperCAmelCase = job["completed_at"]
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
_UpperCAmelCase = start
_UpperCAmelCase = end
_UpperCAmelCase = duration_in_min
return job_info
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=None ) -> str:
_UpperCAmelCase = None
if token is not None:
_UpperCAmelCase = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
_UpperCAmelCase = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
_UpperCAmelCase = requests.get(_lowerCAmelCase , headers=_lowerCAmelCase ).json()
_UpperCAmelCase = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
_UpperCAmelCase = math.ceil((result["total_count"] - 100) / 100 )
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = requests.get(url + F'''&page={i + 2}''' , headers=_lowerCAmelCase ).json()
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = get_job_time(args.workflow_run_id)
__lowerCAmelCase = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v["duration"]}''')
| 684 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCase_ ( self : Dict ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase , _lowerCamelCase : int = FlaxStableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2",revision="bf16",dtype=jnp.bfloataa,)
_lowerCamelCase : Optional[Any] = "A painting of a squirrel eating a burger"
_lowerCamelCase : Tuple = jax.device_count()
_lowerCamelCase : Dict = num_samples * [prompt]
_lowerCamelCase : int = sd_pipe.prepare_inputs(__A )
_lowerCamelCase : Union[str, Any] = replicate(__A )
_lowerCamelCase : Any = shard(__A )
_lowerCamelCase : Dict = jax.random.PRNGKey(0 )
_lowerCamelCase : List[str] = jax.random.split(__A,jax.device_count() )
_lowerCamelCase : List[Any] = sd_pipe(__A,__A,__A,num_inference_steps=2_5,jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)
_lowerCamelCase : Union[str, Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_lowerCamelCase : Optional[int] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
_lowerCamelCase : List[str] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_lowerCamelCase : int = jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Optional[Any] = "stabilityai/stable-diffusion-2"
_lowerCamelCase , _lowerCamelCase : int = FlaxDPMSolverMultistepScheduler.from_pretrained(__A,subfolder="scheduler" )
_lowerCamelCase , _lowerCamelCase : Dict = FlaxStableDiffusionPipeline.from_pretrained(
__A,scheduler=__A,revision="bf16",dtype=jnp.bfloataa,)
_lowerCamelCase : List[str] = scheduler_params
_lowerCamelCase : List[str] = "A painting of a squirrel eating a burger"
_lowerCamelCase : List[str] = jax.device_count()
_lowerCamelCase : Dict = num_samples * [prompt]
_lowerCamelCase : Tuple = sd_pipe.prepare_inputs(__A )
_lowerCamelCase : Any = replicate(__A )
_lowerCamelCase : Any = shard(__A )
_lowerCamelCase : Tuple = jax.random.PRNGKey(0 )
_lowerCamelCase : Optional[Any] = jax.random.split(__A,jax.device_count() )
_lowerCamelCase : Optional[Any] = sd_pipe(__A,__A,__A,num_inference_steps=2_5,jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)
_lowerCamelCase : Union[str, Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_lowerCamelCase : int = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
_lowerCamelCase : Tuple = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_lowerCamelCase : str = jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2 | 44 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__lowerCAmelCase = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 1_3_1_0_7_2,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
return torch.atana(_lowerCAmelCase , _lowerCAmelCase ) / math.pi * 2
def __lowerCamelCase ( _lowerCAmelCase ) -> Union[str, Any]:
_UpperCAmelCase = torch.sin(t * math.pi / 2 ) ** 2
_UpperCAmelCase = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(_lowerCAmelCase , _lowerCAmelCase )
class __SCREAMING_SNAKE_CASE ( lowercase):
pass
class __SCREAMING_SNAKE_CASE ( nn.Module):
def __init__( self : str , __UpperCamelCase : Optional[int] ):
super().__init__()
_UpperCAmelCase = DiffusionAttnUnetaD(__UpperCamelCase , n_attn_layers=4 )
_UpperCAmelCase = deepcopy(self.diffusion )
_UpperCAmelCase = torch.quasirandom.SobolEngine(1 , scramble=__UpperCamelCase )
def __lowerCamelCase ( _lowerCAmelCase ) -> int:
_UpperCAmelCase = MODELS_MAP[model_name]["url"]
os.system(F'''wget {url} ./''' )
return F'''./{model_name}.ckpt'''
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
__lowerCAmelCase = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
__lowerCAmelCase = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
__lowerCAmelCase = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
__lowerCAmelCase = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
if name.startswith("skip" ):
return name.replace("skip" , RES_CONV_MAP["skip"] )
# name has to be of format main.{digit}
if not name.startswith("main." ):
raise ValueError(F'''ResConvBlock error with {name}''' )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[Any]:
for key, value in ATTN_MAP.items():
if name.startswith(_lowerCAmelCase ) and not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return name.replace(_lowerCAmelCase , _lowerCAmelCase )
elif name.startswith(_lowerCAmelCase ):
return [name.replace(_lowerCAmelCase , _lowerCAmelCase ) for v in value]
raise ValueError(F'''Attn error with {name}''' )
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=13 ) -> List[Any]:
_UpperCAmelCase = input_string
if string.split("." )[0] == "timestep_embed":
return string.replace("timestep_embed" , "time_proj" )
_UpperCAmelCase = 0
if string.startswith("net.3." ):
depth += 1
_UpperCAmelCase = string[6:]
elif string.startswith("net." ):
_UpperCAmelCase = string[4:]
while string.startswith("main.7." ):
depth += 1
_UpperCAmelCase = string[7:]
if string.startswith("main." ):
_UpperCAmelCase = string[5:]
# mid block
if string[:2].isdigit():
_UpperCAmelCase = string[:2]
_UpperCAmelCase = string[2:]
else:
_UpperCAmelCase = string[0]
_UpperCAmelCase = string[1:]
if depth == max_depth:
_UpperCAmelCase = MID_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = "mid_block"
elif depth > 0 and int(_lowerCAmelCase ) < 7:
_UpperCAmelCase = DOWN_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''down_blocks.{depth}'''
elif depth > 0 and int(_lowerCAmelCase ) > 7:
_UpperCAmelCase = UP_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - depth - 1}'''
elif depth == 0:
_UpperCAmelCase = DEPTH_0_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - 1}''' if int(_lowerCAmelCase ) > 3 else "down_blocks.0"
if not string_left.startswith("." ):
raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.''' )
_UpperCAmelCase = string_left[1:]
if "resnets" in new_layer:
_UpperCAmelCase = convert_resconv_naming(_lowerCAmelCase )
elif "attentions" in new_layer:
_UpperCAmelCase = convert_attn_naming(_lowerCAmelCase )
_UpperCAmelCase = new_string_left
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = prefix + "." + new_layer + "." + string_left
else:
_UpperCAmelCase = [prefix + "." + new_layer + "." + s for s in string_left]
return new_string
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[int]:
_UpperCAmelCase = {}
for k, v in state_dict.items():
if k.endswith("kernel" ):
# up- and downsample layers, don't have trainable weights
continue
_UpperCAmelCase = rename(_lowerCAmelCase )
# check if we need to transform from Conv => Linear for attention
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = transform_conv_attns(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_UpperCAmelCase = v
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
if len(_lowerCAmelCase ) == 1:
if len(v.shape ) == 3:
# weight
_UpperCAmelCase = v[:, :, 0]
else:
# bias
_UpperCAmelCase = v
else:
# qkv matrices
_UpperCAmelCase = v.shape[0]
_UpperCAmelCase = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase ) -> Tuple:
_UpperCAmelCase = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
_UpperCAmelCase = args.model_path.split("/" )[-1].split("." )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
_UpperCAmelCase = download(_lowerCAmelCase )
_UpperCAmelCase = MODELS_MAP[model_name]["sample_rate"]
_UpperCAmelCase = MODELS_MAP[model_name]["sample_size"]
_UpperCAmelCase = Object()
_UpperCAmelCase = sample_size
_UpperCAmelCase = sample_rate
_UpperCAmelCase = 0
_UpperCAmelCase = UNetaDModel(sample_size=_lowerCAmelCase , sample_rate=_lowerCAmelCase )
_UpperCAmelCase = diffusers_model.state_dict()
_UpperCAmelCase = DiffusionUncond(_lowerCAmelCase )
orig_model.load_state_dict(torch.load(args.model_path , map_location=_lowerCAmelCase )["state_dict"] )
_UpperCAmelCase = orig_model.diffusion_ema.eval()
_UpperCAmelCase = orig_model.state_dict()
_UpperCAmelCase = rename_orig_weights(_lowerCAmelCase )
_UpperCAmelCase = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
_UpperCAmelCase = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(_lowerCAmelCase ) == 0, F'''Problem with {renamed_minus_diffusers}'''
assert all(k.endswith("kernel" ) for k in list(_lowerCAmelCase ) ), F'''Problem with {diffusers_minus_renamed}'''
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
if key == "time_proj.weight":
_UpperCAmelCase = value.squeeze()
_UpperCAmelCase = value
diffusers_model.load_state_dict(_lowerCAmelCase )
_UpperCAmelCase = 100
_UpperCAmelCase = 33
_UpperCAmelCase = IPNDMScheduler(num_train_timesteps=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(_lowerCAmelCase )
_UpperCAmelCase = torch.randn([1, 2, config.sample_size] , generator=_lowerCAmelCase ).to(_lowerCAmelCase )
_UpperCAmelCase = torch.linspace(1 , 0 , steps + 1 , device=_lowerCAmelCase )[:-1]
_UpperCAmelCase = get_crash_schedule(_lowerCAmelCase )
_UpperCAmelCase = DanceDiffusionPipeline(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(33 )
_UpperCAmelCase = pipe(num_inference_steps=_lowerCAmelCase , generator=_lowerCAmelCase ).audios
_UpperCAmelCase = sampling.iplms_sample(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {} )
_UpperCAmelCase = generated.clamp(-1 , 1 )
_UpperCAmelCase = (generated - audio).abs().sum()
_UpperCAmelCase = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("Diff sum" , _lowerCAmelCase )
print("Diff max" , _lowerCAmelCase )
assert diff_max < 1E-3, F'''Diff max: {diff_max} is too much :-/'''
print(F'''Conversion for {model_name} successful!''' )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
__lowerCAmelCase = parser.parse_args()
main(args)
| 684 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
UpperCamelCase = "\\n Text data.\n Second line of data."
UpperCamelCase = "file"
@pytest.fixture(scope="""session""" )
def A ( lowercase__ : List[str] ) -> Union[str, Any]:
UpperCamelCase__ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / (FILE_PATH + """.zstd""")
UpperCamelCase__ :Optional[Any] = bytes(lowercase__ , """utf-8""" )
with zstd.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture
def A ( lowercase__ : str ) -> int:
with open(os.path.join(tmpfs.local_root_dir , lowercase__ ) , """w""" ) as f:
f.write(lowercase__ )
return FILE_PATH
@pytest.mark.parametrize("""compression_format""" , ["""gzip""", """xz""", """zstd"""] )
def A ( lowercase__ : Optional[Any] , lowercase__ : Dict , lowercase__ : int , lowercase__ : List[str] , lowercase__ : Optional[int] , lowercase__ : Any ) -> Union[str, Any]:
UpperCamelCase__ :Optional[int] = {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
UpperCamelCase__ :List[Any] = input_paths[compression_format]
UpperCamelCase__ :Tuple = tmp_path / """cache"""
UpperCamelCase__ :Dict = DownloadConfig(cache_dir=lowercase__ , extract_compressed_file=lowercase__ )
UpperCamelCase__ :int = cached_path(lowercase__ , download_config=lowercase__ )
with open(lowercase__ ) as f:
UpperCamelCase__ :int = f.read()
with open(lowercase__ ) as f:
UpperCamelCase__ :Tuple = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("""default_extracted""" , [True, False] )
@pytest.mark.parametrize("""default_cache_dir""" , [True, False] )
def A ( lowercase__ : Union[str, Any] , lowercase__ : Dict , lowercase__ : Dict , lowercase__ : Any , lowercase__ : List[Any] ) -> List[str]:
UpperCamelCase__ :Dict = """custom_cache"""
UpperCamelCase__ :Union[str, Any] = """custom_extracted_dir"""
UpperCamelCase__ :Optional[int] = tmp_path / """custom_extracted_path"""
if default_extracted:
UpperCamelCase__ :Union[str, Any] = ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
else:
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR""" , lowercase__ )
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH""" , str(lowercase__ ) )
UpperCamelCase__ :Dict = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
UpperCamelCase__ :Optional[int] = xz_file
UpperCamelCase__ :Optional[Any] = (
DownloadConfig(extract_compressed_file=lowercase__ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=lowercase__ )
)
UpperCamelCase__ :str = cached_path(lowercase__ , download_config=lowercase__ )
assert Path(lowercase__ ).parent.parts[-2:] == expected
def A ( lowercase__ : List[str] ) -> Dict:
# absolute path
UpperCamelCase__ :Optional[Any] = str(Path(lowercase__ ).resolve() )
assert cached_path(lowercase__ ) == text_file
# relative path
UpperCamelCase__ :str = str(Path(lowercase__ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(lowercase__ ) == text_file
def A ( lowercase__ : Optional[Any] ) -> Tuple:
# absolute path
UpperCamelCase__ :Tuple = str(tmp_path.resolve() / """__missing_file__.txt""" )
with pytest.raises(lowercase__ ):
cached_path(lowercase__ )
# relative path
UpperCamelCase__ :Tuple = """./__missing_file__.txt"""
with pytest.raises(lowercase__ ):
cached_path(lowercase__ )
def A ( lowercase__ : str ) -> Optional[int]:
UpperCamelCase__ :Any = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(lowercase__ ) as f:
UpperCamelCase__ :Tuple = f.read()
assert output_file_content == FILE_CONTENT
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowercase__ )
def A ( ) -> Tuple:
with pytest.raises(lowercase__ ):
cached_path("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowercase__ )
def A ( lowercase__ : str ) -> Optional[int]:
UpperCamelCase__ :Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(lowercase__ ):
http_get("""https://huggingface.co""" , temp_file=lowercase__ )
with pytest.raises(lowercase__ ):
http_head("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowercase__ )
def A ( lowercase__ : List[Any] ) -> int:
UpperCamelCase__ :List[str] = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(lowercase__ ):
ftp_get("""ftp://huggingface.co""" , temp_file=lowercase__ )
with pytest.raises(lowercase__ ):
ftp_head("""ftp://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowercase__ )
def A ( lowercase__ : Optional[int] ) -> List[Any]:
UpperCamelCase__ :Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(lowercase__ ):
fsspec_get("""s3://huggingface.co""" , temp_file=lowercase__ )
with pytest.raises(lowercase__ ):
fsspec_head("""s3://huggingface.co""" ) | 45 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
__lowerCAmelCase = get_tests_dir("fixtures")
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 500
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=__UpperCamelCase ) as mock_head:
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase__ ( self : List[Any] ):
# This test is for deprecated behavior and can be removed in v5
_UpperCAmelCase = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json" )
def UpperCAmelCase__ ( self : Dict ):
with self.assertRaises(__UpperCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
_UpperCAmelCase = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants" )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants" , subfolder="feature_extractor" )
self.assertIsNotNone(__UpperCamelCase )
@is_staging_test
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
@classmethod
def UpperCAmelCase__ ( cls : str ):
_UpperCAmelCase = TOKEN
HfFolder.save_token(__UpperCamelCase )
@classmethod
def UpperCAmelCase__ ( cls : Optional[Any] ):
try:
delete_repo(token=cls._token , repo_id="test-image-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-image-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-image-processor" )
except HTTPError:
pass
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="test-image-processor" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("valid_org/test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="valid_org/test-image-processor-org" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : int ):
CustomImageProcessor.register_for_auto_class()
_UpperCAmelCase = CustomImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-dynamic-image-processor" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"} , )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
F'''{USER}/test-dynamic-image-processor''' , trust_remote_code=__UpperCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , "CustomImageProcessor" )
| 684 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : Dict = get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_sentencepiece
@require_tokenizers
class A_ ( _a , unittest.TestCase ):
lowerCAmelCase__ = SpeechTaTokenizer
lowerCAmelCase__ = False
lowerCAmelCase__ = True
def _lowercase ( self: List[Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase : str = SpeechTaTokenizer(__lowerCAmelCase )
_lowerCamelCase : Tuple = AddedToken("<mask>" ,lstrip=__lowerCAmelCase ,rstrip=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self: List[str] ,__lowerCAmelCase: str ):
'''simple docstring'''
_lowerCamelCase : Dict = "this is a test"
_lowerCamelCase : Optional[Any] = "this is a test"
return input_text, output_text
def _lowercase ( self: List[str] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: Any=False ,__lowerCAmelCase: str=20 ,__lowerCAmelCase: List[Any]=5 ):
'''simple docstring'''
_lowerCamelCase, _lowerCamelCase : List[str] = self.get_input_output_texts(__lowerCAmelCase )
_lowerCamelCase : Optional[int] = tokenizer.encode(__lowerCAmelCase ,add_special_tokens=__lowerCAmelCase )
_lowerCamelCase : Tuple = tokenizer.decode(__lowerCAmelCase ,clean_up_tokenization_spaces=__lowerCAmelCase )
return text, ids
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = "<pad>"
_lowerCamelCase : List[str] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase ) ,__lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase ) ,__lowerCAmelCase )
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"<s>" )
self.assertEqual(vocab_keys[1] ,"<pad>" )
self.assertEqual(vocab_keys[-4] ,"œ" )
self.assertEqual(vocab_keys[-2] ,"<mask>" )
self.assertEqual(vocab_keys[-1] ,"<ctc_blank>" )
self.assertEqual(len(__lowerCAmelCase ) ,81 )
def _lowercase ( self: Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,79 )
def _lowercase ( self: Any ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = self.get_tokenizers(do_lower_case=__lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_lowerCamelCase : Tuple = tokenizer.vocab_size
_lowerCamelCase : Optional[Any] = len(__lowerCAmelCase )
self.assertNotEqual(__lowerCAmelCase ,0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
_lowerCamelCase : Optional[int] = ["aaaaa bbbbbb", "cccccccccdddddddd"]
_lowerCamelCase : Any = tokenizer.add_tokens(__lowerCAmelCase )
_lowerCamelCase : Tuple = tokenizer.vocab_size
_lowerCamelCase : Union[str, Any] = len(__lowerCAmelCase )
self.assertNotEqual(__lowerCAmelCase ,0 )
self.assertEqual(__lowerCAmelCase ,__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase ,len(__lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase ,all_size + len(__lowerCAmelCase ) )
_lowerCamelCase : Any = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" ,add_special_tokens=__lowerCAmelCase )
self.assertGreaterEqual(len(__lowerCAmelCase ) ,4 )
self.assertGreater(tokens[0] ,tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] ,tokenizer.vocab_size - 1 )
_lowerCamelCase : List[Any] = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
_lowerCamelCase : str = tokenizer.add_special_tokens(__lowerCAmelCase )
_lowerCamelCase : int = tokenizer.vocab_size
_lowerCamelCase : str = len(__lowerCAmelCase )
self.assertNotEqual(__lowerCAmelCase ,0 )
self.assertEqual(__lowerCAmelCase ,__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase ,len(__lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase ,all_size_a + len(__lowerCAmelCase ) )
_lowerCamelCase : Optional[int] = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" ,add_special_tokens=__lowerCAmelCase )
self.assertGreaterEqual(len(__lowerCAmelCase ) ,6 )
self.assertGreater(tokens[0] ,tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] ,tokens[1] )
self.assertGreater(tokens[-3] ,tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] ,tokens[-4] )
self.assertEqual(tokens[0] ,tokenizer.eos_token_id )
self.assertEqual(tokens[-3] ,tokenizer.pad_token_id )
def _lowercase ( self: Any ):
'''simple docstring'''
pass
def _lowercase ( self: Tuple ):
'''simple docstring'''
pass
def _lowercase ( self: str ):
'''simple docstring'''
_lowerCamelCase : Tuple = self.get_tokenizer()
_lowerCamelCase : Optional[int] = tokenizer.tokenize("This is a test" )
# fmt: off
self.assertListEqual(__lowerCAmelCase ,[SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) ,[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] ,)
_lowerCamelCase : int = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
__lowerCAmelCase ,[SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
_lowerCamelCase : List[str] = tokenizer.convert_tokens_to_ids(__lowerCAmelCase )
# fmt: off
self.assertListEqual(__lowerCAmelCase ,[4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
_lowerCamelCase : Any = tokenizer.convert_ids_to_tokens(__lowerCAmelCase )
self.assertListEqual(
__lowerCAmelCase ,[SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
@slow
def _lowercase ( self: List[Any] ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
_lowerCamelCase : Tuple = {
"input_ids": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
"attention_mask": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCAmelCase ,model_name="microsoft/speecht5_asr" ,revision="c5ef64c71905caeccde0e4462ef3f9077224c524" ,sequences=__lowerCAmelCase ,) | 46 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
return getitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
return setitem, k, v
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
return delitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase ) -> Optional[int]:
try:
return fun(_lowerCAmelCase , *_lowerCAmelCase ), None
except Exception as e:
return None, e
__lowerCAmelCase = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__lowerCAmelCase = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items" ),
pytest.param(_overwrite_items , id="overwrite items" ),
pytest.param(_delete_items , id="delete items" ),
pytest.param(_access_absent_items , id="access absent items" ),
pytest.param(_add_with_resize_up , id="add with resize up" ),
pytest.param(_add_with_resize_down , id="add with resize down" ),
) , )
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
_UpperCAmelCase = HashMap(initial_block_size=4 )
_UpperCAmelCase = {}
for _, (fun, *args) in enumerate(_lowerCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
assert my_res == py_res
assert str(_lowerCAmelCase ) == str(_lowerCAmelCase )
assert set(_lowerCAmelCase ) == set(_lowerCAmelCase )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
assert set(my.items() ) == set(py.items() )
def __lowerCamelCase ( ) -> List[Any]:
def is_public(_lowerCAmelCase ) -> bool:
return not name.startswith("_" )
_UpperCAmelCase = {name for name in dir({} ) if is_public(_lowerCAmelCase )}
_UpperCAmelCase = {name for name in dir(HashMap() ) if is_public(_lowerCAmelCase )}
assert dict_public_names > hash_public_names
| 684 | 0 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = ['''model.decoder.embed_positions.weights''']
def UpperCAmelCase__ ( lowerCamelCase_ : Tuple ):
if "emb" in name:
__a : Any = name.replace('emb' , 'model.decoder.embed_tokens' )
if "transformer" in name:
__a : str = name.replace('transformer' , 'model.decoder' )
if "cross_attention" in name:
__a : List[Any] = name.replace('cross_attention' , 'encoder_attn' )
if "linear1" in name:
__a : List[Any] = name.replace('linear1' , 'fc1' )
if "linear2" in name:
__a : List[str] = name.replace('linear2' , 'fc2' )
if "norm1" in name:
__a : List[str] = name.replace('norm1' , 'self_attn_layer_norm' )
if "norm_cross" in name:
__a : List[Any] = name.replace('norm_cross' , 'encoder_attn_layer_norm' )
if "norm2" in name:
__a : str = name.replace('norm2' , 'final_layer_norm' )
if "out_norm" in name:
__a : int = name.replace('out_norm' , 'model.decoder.layer_norm' )
if "linears" in name:
__a : Any = name.replace('linears' , 'lm_heads' )
if "condition_provider.conditioners.description.output_proj" in name:
__a : List[Any] = name.replace('condition_provider.conditioners.description.output_proj' , 'enc_to_dec_proj' )
return name
def UpperCAmelCase__ ( lowerCamelCase_ : OrderedDict , lowerCamelCase_ : int ):
__a : Union[str, Any] = list(state_dict.keys() )
__a : Optional[int] = {}
for key in keys:
__a : Optional[int] = state_dict.pop(lowerCamelCase_ )
__a : List[Any] = rename_keys(lowerCamelCase_ )
if "in_proj_weight" in key:
# split fused qkv proj
__a : Optional[Any] = val[:hidden_size, :]
__a : Optional[Any] = val[hidden_size : 2 * hidden_size, :]
__a : str = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__a : List[str] = val
else:
__a : Any = val
return state_dict, enc_dec_proj_state_dict
def UpperCAmelCase__ ( lowerCamelCase_ : str ):
if checkpoint == "small":
# default config values
__a : Union[str, Any] = 1_0_2_4
__a : Any = 2_4
__a : Tuple = 1_6
elif checkpoint == "medium":
__a : Dict = 1_5_3_6
__a : Dict = 4_8
__a : Union[str, Any] = 2_4
elif checkpoint == "large":
__a : int = 2_0_4_8
__a : Dict = 4_8
__a : Union[str, Any] = 3_2
else:
raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
__a : str = MusicgenDecoderConfig(
hidden_size=lowerCamelCase_ , ffn_dim=hidden_size * 4 , num_hidden_layers=lowerCamelCase_ , num_attention_heads=lowerCamelCase_ , )
return config
@torch.no_grad()
def UpperCAmelCase__ ( lowerCamelCase_ : Tuple , lowerCamelCase_ : Optional[Any]=None , lowerCamelCase_ : int=None , lowerCamelCase_ : List[Any]="cpu" ):
__a : str = MusicGen.get_pretrained(lowerCamelCase_ , device=lowerCamelCase_ )
__a : str = decoder_config_from_checkpoint(lowerCamelCase_ )
__a : Tuple = fairseq_model.lm.state_dict()
__a , __a : int = rename_state_dict(
lowerCamelCase_ , hidden_size=decoder_config.hidden_size )
__a : int = TaEncoderModel.from_pretrained('t5-base' )
__a : List[Any] = EncodecModel.from_pretrained('facebook/encodec_32khz' )
__a : Tuple = MusicgenForCausalLM(lowerCamelCase_ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__a , __a : Optional[int] = decoder.load_state_dict(lowerCamelCase_ , strict=lowerCamelCase_ )
for key in missing_keys.copy():
if key.startswith(('text_encoder', 'audio_encoder') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
raise ValueError(f'''Missing key(s) in state_dict: {missing_keys}''' )
if len(lowerCamelCase_ ) > 0:
raise ValueError(f'''Unexpected key(s) in state_dict: {unexpected_keys}''' )
# init the composite model
__a : Union[str, Any] = MusicgenForConditionalGeneration(text_encoder=lowerCamelCase_ , audio_encoder=lowerCamelCase_ , decoder=lowerCamelCase_ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowerCamelCase_ )
# check we can do a forward pass
__a : Any = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__a : Tuple = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__a : Any = model(input_ids=lowerCamelCase_ , decoder_input_ids=lowerCamelCase_ ).logits
if logits.shape != (8, 1, 2_0_4_8):
raise ValueError('Incorrect shape for logits' )
# now construct the processor
__a : Any = AutoTokenizer.from_pretrained('t5-base' )
__a : Optional[Any] = AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz' , padding_side='left' )
__a : Union[str, Any] = MusicgenProcessor(feature_extractor=lowerCamelCase_ , tokenizer=lowerCamelCase_ )
# set the appropriate bos/pad token ids
__a : Tuple = 2_0_4_8
__a : int = 2_0_4_8
# set other default generation config params
__a : Union[str, Any] = int(3_0 * audio_encoder.config.frame_rate )
__a : List[Any] = True
__a : Any = 3.0
if pytorch_dump_folder is not None:
Path(lowerCamelCase_ ).mkdir(exist_ok=lowerCamelCase_ )
logger.info(f'''Saving model {checkpoint} to {pytorch_dump_folder}''' )
model.save_pretrained(lowerCamelCase_ )
processor.save_pretrained(lowerCamelCase_ )
if repo_id:
logger.info(f'''Pushing model {checkpoint} to {repo_id}''' )
model.push_to_hub(lowerCamelCase_ )
processor.push_to_hub(lowerCamelCase_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint''',
default='''small''',
type=str,
help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
)
parser.add_argument(
'''--pytorch_dump_folder''',
required=True,
default=None,
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
parser.add_argument(
'''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 47 |
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
_UpperCAmelCase = len(_lowerCAmelCase )
for i in range(1 , _lowerCAmelCase ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase = 0
_UpperCAmelCase = i - 1
while low <= high:
_UpperCAmelCase = (low + high) // 2
if val < collection[mid]:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
for j in range(_lowerCAmelCase , _lowerCAmelCase , -1 ):
_UpperCAmelCase = collection[j - 1]
_UpperCAmelCase = val
return collection
if __name__ == "__main__":
__lowerCAmelCase = input("Enter numbers separated by a comma:\n").strip()
__lowerCAmelCase = [int(item) for item in user_input.split(",")]
print(binary_insertion_sort(unsorted))
| 684 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Union[str, Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase__ : Optional[int] = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
"tokenizer_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
},
}
UpperCAmelCase__ : int = {
"facebook/bart-base": 10_24,
"facebook/bart-large": 10_24,
"facebook/bart-large-mnli": 10_24,
"facebook/bart-large-cnn": 10_24,
"facebook/bart-large-xsum": 10_24,
"yjernite/bart_eli5": 10_24,
}
class A ( SCREAMING_SNAKE_CASE__ ):
snake_case__ :Optional[int] = VOCAB_FILES_NAMES
snake_case__ :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
snake_case__ :int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ :List[Any] = ['input_ids', 'attention_mask']
snake_case__ :Union[str, Any] = BartTokenizer
def __init__( self : List[str] , __magic_name__ : Dict=None , __magic_name__ : Optional[int]=None , __magic_name__ : Tuple=None , __magic_name__ : List[Any]="replace" , __magic_name__ : Union[str, Any]="<s>" , __magic_name__ : Optional[int]="</s>" , __magic_name__ : Dict="</s>" , __magic_name__ : Union[str, Any]="<s>" , __magic_name__ : Optional[int]="<unk>" , __magic_name__ : str="<pad>" , __magic_name__ : Dict="<mask>" , __magic_name__ : Optional[int]=False , __magic_name__ : str=True , **__magic_name__ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
__magic_name__ , __magic_name__ , tokenizer_file=__magic_name__ , errors=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , sep_token=__magic_name__ , cls_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , mask_token=__magic_name__ , add_prefix_space=__magic_name__ , trim_offsets=__magic_name__ , **__magic_name__ , )
lowerCAmelCase__ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __magic_name__ ) != add_prefix_space:
lowerCAmelCase__ = getattr(__magic_name__ , pre_tok_state.pop("type" ) )
lowerCAmelCase__ = add_prefix_space
lowerCAmelCase__ = pre_tok_class(**__magic_name__ )
lowerCAmelCase__ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowerCAmelCase__ = "post_processor"
lowerCAmelCase__ = getattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
if tokenizer_component_instance:
lowerCAmelCase__ = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowerCAmelCase__ = tuple(state["sep"] )
if "cls" in state:
lowerCAmelCase__ = tuple(state["cls"] )
lowerCAmelCase__ = False
if state.get("add_prefix_space" , __magic_name__ ) != add_prefix_space:
lowerCAmelCase__ = add_prefix_space
lowerCAmelCase__ = True
if state.get("trim_offsets" , __magic_name__ ) != trim_offsets:
lowerCAmelCase__ = trim_offsets
lowerCAmelCase__ = True
if changes_to_apply:
lowerCAmelCase__ = getattr(__magic_name__ , state.pop("type" ) )
lowerCAmelCase__ = component_class(**__magic_name__ )
setattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
@property
def __SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def __SCREAMING_SNAKE_CASE ( self : Tuple , __magic_name__ : List[Any] ):
"""simple docstring"""
lowerCAmelCase__ = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else value
lowerCAmelCase__ = value
def __SCREAMING_SNAKE_CASE ( self : str , *__magic_name__ : List[Any] , **__magic_name__ : Any ):
"""simple docstring"""
lowerCAmelCase__ = kwargs.get("is_split_into_words" , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*__magic_name__ , **__magic_name__ )
def __SCREAMING_SNAKE_CASE ( self : str , *__magic_name__ : Any , **__magic_name__ : str ):
"""simple docstring"""
lowerCAmelCase__ = kwargs.get("is_split_into_words" , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs." )
return super()._encode_plus(*__magic_name__ , **__magic_name__ )
def __SCREAMING_SNAKE_CASE ( self : int , __magic_name__ : str , __magic_name__ : Optional[str] = None ):
"""simple docstring"""
lowerCAmelCase__ = self._tokenizer.model.save(__magic_name__ , name=__magic_name__ )
return tuple(__magic_name__ )
def __SCREAMING_SNAKE_CASE ( self : Tuple , __magic_name__ : Any , __magic_name__ : int=None ):
"""simple docstring"""
lowerCAmelCase__ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __SCREAMING_SNAKE_CASE ( self : int , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ):
"""simple docstring"""
lowerCAmelCase__ = [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 48 |
__lowerCAmelCase = 2_5_6
# Modulus to hash a string
__lowerCAmelCase = 1_0_0_0_0_0_3
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
_UpperCAmelCase = len(_lowerCAmelCase )
_UpperCAmelCase = len(_lowerCAmelCase )
if p_len > t_len:
return False
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 1
# Calculating the hash of pattern and substring of text
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_UpperCAmelCase = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_UpperCAmelCase = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_UpperCAmelCase = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def __lowerCamelCase ( ) -> None:
_UpperCAmelCase = "abc1abc12"
_UpperCAmelCase = "alskfjaldsabc1abc1abc12k23adsfabcabc"
_UpperCAmelCase = "alskfjaldsk23adsfabcabc"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase ) and not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 2)
_UpperCAmelCase = "ABABX"
_UpperCAmelCase = "ABABZABABYABABX"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 3)
_UpperCAmelCase = "AAAB"
_UpperCAmelCase = "ABAAAAAB"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 4)
_UpperCAmelCase = "abcdabcy"
_UpperCAmelCase = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 5)
_UpperCAmelCase = "Lü"
_UpperCAmelCase = "Lüsai"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
_UpperCAmelCase = "Lue"
assert not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 684 | 0 |
"""simple docstring"""
_lowercase : Dict = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def lowercase__ ( snake_case_ :int ):
__UpperCAmelCase = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 100_000]
number //= 100_000
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
_lowercase : list[bool | None] = [None] * 10_00_00_00
_lowercase : List[Any] = True
_lowercase : Any = False
def lowercase__ ( snake_case_ :int ):
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__UpperCAmelCase = chain(next_number(snake_case_ ) )
__UpperCAmelCase = number_chain
while number < 10_000_000:
__UpperCAmelCase = number_chain
number *= 10
return number_chain
def lowercase__ ( snake_case_ :int = 10_000_000 ):
for i in range(1 , snake_case_ ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(snake_case_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"""{solution() = }""")
| 49 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__lowerCAmelCase = random.Random()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=1.0 , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> List[str]:
if rng is None:
_UpperCAmelCase = global_rng
_UpperCAmelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def __init__( self : Optional[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any]=7 , __UpperCamelCase : Union[str, Any]=400 , __UpperCamelCase : List[Any]=2_000 , __UpperCamelCase : Optional[Any]=10 , __UpperCamelCase : Optional[int]=160 , __UpperCamelCase : Any=8 , __UpperCamelCase : List[Any]=0.0 , __UpperCamelCase : Dict=4_000 , __UpperCamelCase : Optional[int]=False , __UpperCamelCase : Tuple=True , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = min_seq_length
_UpperCAmelCase = max_seq_length
_UpperCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_UpperCAmelCase = padding_value
_UpperCAmelCase = sampling_rate
_UpperCAmelCase = return_attention_mask
_UpperCAmelCase = do_normalize
_UpperCAmelCase = feature_size
_UpperCAmelCase = chunk_length
_UpperCAmelCase = hop_length
def UpperCAmelCase__ ( self : Optional[Any] ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : Tuple=False , __UpperCamelCase : Dict=False ):
def _flatten(__UpperCamelCase : Any ):
return list(itertools.chain(*__UpperCamelCase ) )
if equal_length:
_UpperCAmelCase = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_UpperCAmelCase = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : str = WhisperFeatureExtractor if is_speech_available() else None
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = WhisperFeatureExtractionTester(self )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = feat_extract_first.save_pretrained(__UpperCamelCase )[0]
check_json_file_has_correct_format(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = os.path.join(__UpperCamelCase , "feat_extract.json" )
feat_extract_first.to_json_file(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_json_file(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
# Test feature size
_UpperCAmelCase = feature_extractor(__UpperCamelCase , padding="max_length" , return_tensors="np" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
_UpperCAmelCase = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_features
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test batched
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
_UpperCAmelCase = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_UpperCAmelCase = np.asarray(__UpperCamelCase )
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test truncation required
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
_UpperCAmelCase = [x[: feature_extractor.n_samples] for x in speech_inputs]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs_truncated]
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
import torch
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = np.random.rand(100 , 32 ).astype(np.floataa )
_UpperCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Tuple ):
_UpperCAmelCase = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
_UpperCAmelCase = ds.sort("id" ).select(range(__UpperCamelCase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def UpperCAmelCase__ ( self : Tuple ):
# fmt: off
_UpperCAmelCase = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
_UpperCAmelCase = self._load_datasamples(1 )
_UpperCAmelCase = WhisperFeatureExtractor()
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="pt" ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __UpperCamelCase , atol=1e-4 ) )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = self._load_datasamples(1 )[0]
_UpperCAmelCase = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
_UpperCAmelCase = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__UpperCamelCase )[0]
self.assertTrue(np.all(np.mean(__UpperCamelCase ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase ) - 1 ) < 1e-3 ) )
| 684 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = ['input_features', 'attention_mask']
def __init__( self ,_lowerCAmelCase=80 ,_lowerCAmelCase=1_60_00 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=10 ,_lowerCAmelCase=25 ,_lowerCAmelCase="hamming_window" ,_lowerCAmelCase=3_2768.0 ,_lowerCAmelCase=0.97 ,_lowerCAmelCase=1.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,**_lowerCAmelCase ,):
super().__init__(feature_size=_lowerCAmelCase ,sampling_rate=_lowerCAmelCase ,padding_value=_lowerCAmelCase ,**_lowerCAmelCase )
lowerCamelCase__ = feature_size
lowerCamelCase__ = sampling_rate
lowerCamelCase__ = padding_value
lowerCamelCase__ = hop_length
lowerCamelCase__ = win_length
lowerCamelCase__ = frame_signal_scale
lowerCamelCase__ = preemphasis_coeff
lowerCamelCase__ = mel_floor
lowerCamelCase__ = normalize_means
lowerCamelCase__ = normalize_vars
lowerCamelCase__ = win_function
lowerCamelCase__ = return_attention_mask
lowerCamelCase__ = win_length * sampling_rate // 10_00
lowerCamelCase__ = hop_length * sampling_rate // 10_00
lowerCamelCase__ = optimal_fft_length(self.sample_size )
lowerCamelCase__ = (self.n_fft // 2) + 1
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if self.win_function == "hamming_window":
lowerCamelCase__ = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=_lowerCAmelCase )
else:
lowerCamelCase__ = window_function(window_length=self.sample_size ,name=self.win_function )
lowerCamelCase__ = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
lowerCamelCase__ = spectrogram(
one_waveform * self.frame_signal_scale ,window=_lowerCAmelCase ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=_lowerCAmelCase ,preemphasis=self.preemphasis_coeff ,mel_filters=_lowerCAmelCase ,mel_floor=self.mel_floor ,log_mel="""log""" ,)
return msfc_features.T
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
# make sure we normalize float32 arrays
if self.normalize_means:
lowerCamelCase__ = x[:input_length].mean(axis=0 )
lowerCamelCase__ = np.subtract(_lowerCAmelCase ,_lowerCAmelCase )
if self.normalize_vars:
lowerCamelCase__ = x[:input_length].std(axis=0 )
lowerCamelCase__ = np.divide(_lowerCAmelCase ,_lowerCAmelCase )
if input_length < x.shape[0]:
lowerCamelCase__ = padding_value
# make sure array is in float32
lowerCamelCase__ = x.astype(np.floataa )
return x
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(_lowerCAmelCase ,_lowerCAmelCase ,self.padding_value ) for x, n in zip(_lowerCAmelCase ,_lowerCAmelCase )]
def __call__( self ,_lowerCAmelCase ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,**_lowerCAmelCase ,):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
"""It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
lowerCamelCase__ = isinstance(_lowerCAmelCase ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
lowerCamelCase__ = is_batched_numpy or (
isinstance(_lowerCAmelCase ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowerCamelCase__ = [np.asarray(_lowerCAmelCase ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_lowerCAmelCase ,np.ndarray ):
lowerCamelCase__ = np.asarray(_lowerCAmelCase ,dtype=np.floataa )
elif isinstance(_lowerCAmelCase ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCamelCase__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCamelCase__ = [raw_speech]
# extract fbank features
lowerCamelCase__ = [self._extract_mfsc_features(_lowerCAmelCase ) for one_waveform in raw_speech]
# convert into correct format for padding
lowerCamelCase__ = BatchFeature({"""input_features""": features} )
lowerCamelCase__ = self.pad(
_lowerCAmelCase ,padding=_lowerCAmelCase ,max_length=_lowerCAmelCase ,truncation=_lowerCAmelCase ,pad_to_multiple_of=_lowerCAmelCase ,return_attention_mask=_lowerCAmelCase ,**_lowerCAmelCase ,)
# make sure list is in array format
lowerCamelCase__ = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] ,_lowerCAmelCase ):
lowerCamelCase__ = [np.asarray(_lowerCAmelCase ,dtype=np.floataa ) for feature in input_features]
lowerCamelCase__ = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
lowerCamelCase__ = [np.asarray(_lowerCAmelCase ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
lowerCamelCase__ = (
np.array(_lowerCAmelCase ,dtype=np.intaa )
if self._get_padding_strategies(_lowerCAmelCase ,max_length=_lowerCAmelCase ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
lowerCamelCase__ = self.normalize(
padded_inputs["""input_features"""] ,attention_mask=_lowerCAmelCase )
if return_tensors is not None:
lowerCamelCase__ = padded_inputs.convert_to_tensors(_lowerCAmelCase )
return padded_inputs
| 50 |
# Copyright 2023 The HuggingFace Inc. 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.
import re
from ..utils import cached_file
# docstyle-ignore
__lowerCAmelCase = "\nHuman: <<task>>\n\nAssistant: "
__lowerCAmelCase = "huggingface-tools/default-prompts"
__lowerCAmelCase = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase="run" ) -> Union[str, Any]:
if prompt_or_repo_id is None:
_UpperCAmelCase = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , _lowerCAmelCase ) is not None:
return prompt_or_repo_id
_UpperCAmelCase = cached_file(
_lowerCAmelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(_lowerCAmelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 684 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
a__ : Optional[Any] = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[str] = ['HerbertTokenizerFast']
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
a__ : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 51 |
from itertools import permutations
def __lowerCamelCase ( _lowerCAmelCase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(_lowerCAmelCase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def __lowerCamelCase ( _lowerCAmelCase = 10 ) -> int:
return sum(
int("".join(map(_lowerCAmelCase , _lowerCAmelCase ) ) )
for num in permutations(range(_lowerCAmelCase ) )
if is_substring_divisible(_lowerCAmelCase ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 684 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}
class __lowercase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = '''convnextv2'''
def __init__( self , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=4 , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.0_2 , _UpperCAmelCase=1e-1_2 , _UpperCAmelCase=0.0 , _UpperCAmelCase=224 , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase , ):
super().__init__(**_UpperCAmelCase )
__a : List[str] = num_channels
__a : str = patch_size
__a : Dict = num_stages
__a : List[str] = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
__a : List[str] = [3, 3, 9, 3] if depths is None else depths
__a : List[Any] = hidden_act
__a : Any = initializer_range
__a : Optional[int] = layer_norm_eps
__a : List[Any] = drop_path_rate
__a : Any = image_size
__a : str = ['''stem'''] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
__a , __a : Optional[int] = get_aligned_output_features_output_indices(
out_features=_UpperCAmelCase , out_indices=_UpperCAmelCase , stage_names=self.stage_names ) | 52 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
__lowerCAmelCase = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
__lowerCAmelCase = {"facebook/blenderbot-3B": 1_2_8}
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : Optional[int] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : str = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : List[Any] = ["""input_ids""", """attention_mask"""]
__SCREAMING_SNAKE_CASE : List[str] = BlenderbotTokenizer
def __init__( self : Tuple , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]="replace" , __UpperCamelCase : Tuple="<s>" , __UpperCamelCase : str="</s>" , __UpperCamelCase : Dict="</s>" , __UpperCamelCase : Union[str, Any]="<s>" , __UpperCamelCase : Union[str, Any]="<unk>" , __UpperCamelCase : Tuple="<pad>" , __UpperCamelCase : Optional[int]="<mask>" , __UpperCamelCase : Union[str, Any]=False , __UpperCamelCase : List[str]=True , **__UpperCamelCase : int , ):
super().__init__(
__UpperCamelCase , __UpperCamelCase , tokenizer_file=__UpperCamelCase , errors=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = getattr(__UpperCamelCase , pre_tok_state.pop("type" ) )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = pre_tok_class(**__UpperCamelCase )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = "post_processor"
_UpperCAmelCase = getattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
if tokenizer_component_instance:
_UpperCAmelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
_UpperCAmelCase = tuple(state["sep"] )
if "cls" in state:
_UpperCAmelCase = tuple(state["cls"] )
_UpperCAmelCase = False
if state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = True
if state.get("trim_offsets" , __UpperCamelCase ) != trim_offsets:
_UpperCAmelCase = trim_offsets
_UpperCAmelCase = True
if changes_to_apply:
_UpperCAmelCase = getattr(__UpperCamelCase , state.pop("type" ) )
_UpperCAmelCase = component_class(**__UpperCamelCase )
setattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def UpperCAmelCase__ ( self : Union[str, Any] ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else value
_UpperCAmelCase = value
def UpperCAmelCase__ ( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : List[Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : Tuple , *__UpperCamelCase : int , **__UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : str , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None ):
_UpperCAmelCase = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : "Conversation" ):
_UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text )
else:
# Generated responses should contain them already.
inputs.append(__UpperCamelCase )
_UpperCAmelCase = " ".join(__UpperCamelCase )
_UpperCAmelCase = self.encode(__UpperCamelCase )
if len(__UpperCamelCase ) > self.model_max_length:
_UpperCAmelCase = input_ids[-self.model_max_length :]
logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids
| 684 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_snake_case : str = logging.get_logger(__name__)
_snake_case : Union[str, Any] = {
'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = """instructblip_vision_model"""
def __init__( self : Union[str, Any] , lowerCAmelCase_ : str=1_4_0_8 , lowerCAmelCase_ : List[str]=6_1_4_4 , lowerCAmelCase_ : Any=3_9 , lowerCAmelCase_ : int=1_6 , lowerCAmelCase_ : Optional[int]=2_2_4 , lowerCAmelCase_ : Union[str, Any]=1_4 , lowerCAmelCase_ : str="gelu" , lowerCAmelCase_ : Tuple=1e-6 , lowerCAmelCase_ : List[str]=0.0 , lowerCAmelCase_ : Tuple=1e-10 , lowerCAmelCase_ : List[Any]=True , **lowerCAmelCase_ : Dict , ) -> str:
super().__init__(**lowerCAmelCase_ )
__lowerCAmelCase = hidden_size
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = patch_size
__lowerCAmelCase = image_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = attention_dropout
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = hidden_act
__lowerCAmelCase = qkv_bias
@classmethod
def lowercase ( cls : List[str] , lowerCAmelCase_ : Union[str, os.PathLike] , **lowerCAmelCase_ : str ) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowerCAmelCase_ )
__lowerCAmelCase , __lowerCAmelCase = cls.get_config_dict(lowerCAmelCase_ , **lowerCAmelCase_ )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__lowerCAmelCase = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCAmelCase_ , **lowerCAmelCase_ )
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = """instructblip_qformer"""
def __init__( self : Tuple , lowerCAmelCase_ : Optional[Any]=3_0_5_2_2 , lowerCAmelCase_ : Any=7_6_8 , lowerCAmelCase_ : Optional[int]=1_2 , lowerCAmelCase_ : Tuple=1_2 , lowerCAmelCase_ : Tuple=3_0_7_2 , lowerCAmelCase_ : List[str]="gelu" , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : List[str]=5_1_2 , lowerCAmelCase_ : Optional[int]=0.02 , lowerCAmelCase_ : Optional[Any]=1e-12 , lowerCAmelCase_ : Any=0 , lowerCAmelCase_ : Any="absolute" , lowerCAmelCase_ : Any=2 , lowerCAmelCase_ : List[Any]=1_4_0_8 , **lowerCAmelCase_ : Optional[Any] , ) -> int:
super().__init__(pad_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = hidden_act
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = position_embedding_type
__lowerCAmelCase = cross_attention_frequency
__lowerCAmelCase = encoder_hidden_size
@classmethod
def lowercase ( cls : int , lowerCAmelCase_ : Union[str, os.PathLike] , **lowerCAmelCase_ : Tuple ) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowerCAmelCase_ )
__lowerCAmelCase , __lowerCAmelCase = cls.get_config_dict(lowerCAmelCase_ , **lowerCAmelCase_ )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__lowerCAmelCase = config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCAmelCase_ , **lowerCAmelCase_ )
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = """instructblip"""
a_ = True
def __init__( self : Dict , lowerCAmelCase_ : str=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : Optional[Any]=3_2 , **lowerCAmelCase_ : Tuple ) -> int:
super().__init__(**lowerCAmelCase_ )
if vision_config is None:
__lowerCAmelCase = {}
logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.' )
if qformer_config is None:
__lowerCAmelCase = {}
logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.' )
if text_config is None:
__lowerCAmelCase = {}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
__lowerCAmelCase = InstructBlipVisionConfig(**lowerCAmelCase_ )
__lowerCAmelCase = InstructBlipQFormerConfig(**lowerCAmelCase_ )
__lowerCAmelCase = text_config['model_type'] if 'model_type' in text_config else 'opt'
__lowerCAmelCase = CONFIG_MAPPING[text_model_type](**lowerCAmelCase_ )
__lowerCAmelCase = self.text_config.tie_word_embeddings
__lowerCAmelCase = self.text_config.is_encoder_decoder
__lowerCAmelCase = num_query_tokens
__lowerCAmelCase = self.vision_config.hidden_size
__lowerCAmelCase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__lowerCAmelCase = 1.0
__lowerCAmelCase = 0.02
@classmethod
def lowercase ( cls : Dict , lowerCAmelCase_ : InstructBlipVisionConfig , lowerCAmelCase_ : InstructBlipQFormerConfig , lowerCAmelCase_ : PretrainedConfig , **lowerCAmelCase_ : Tuple , ) -> Union[str, Any]:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **lowerCAmelCase_ , )
def lowercase ( self : Dict ) -> Dict:
__lowerCAmelCase = copy.deepcopy(self.__dict__ )
__lowerCAmelCase = self.vision_config.to_dict()
__lowerCAmelCase = self.qformer_config.to_dict()
__lowerCAmelCase = self.text_config.to_dict()
__lowerCAmelCase = self.__class__.model_type
return output
| 53 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
_UpperCAmelCase = WavaVecaForSequenceClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["projector.weight"]
_UpperCAmelCase = downstream_dict["projector.bias"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.weight"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
_UpperCAmelCase = WavaVecaForAudioFrameClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["model.linear.weight"]
_UpperCAmelCase = downstream_dict["model.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = WavaVecaForXVector.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["connector.weight"]
_UpperCAmelCase = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_UpperCAmelCase = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
_UpperCAmelCase = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
_UpperCAmelCase = downstream_dict["objective.W"]
return model
@torch.no_grad()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = torch.load(_lowerCAmelCase , map_location="cpu" )
_UpperCAmelCase = checkpoint["Downstream"]
_UpperCAmelCase = WavaVecaConfig.from_pretrained(_lowerCAmelCase )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , do_normalize=_lowerCAmelCase )
_UpperCAmelCase = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
_UpperCAmelCase = convert_classification(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForAudioFrameClassification" ):
_UpperCAmelCase = convert_diarization(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForXVector" ):
_UpperCAmelCase = convert_xvector(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
_UpperCAmelCase = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(_lowerCAmelCase )
hf_model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
__lowerCAmelCase = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 684 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
__lowercase : Optional[int] =logging.get_logger(__name__)
# General docstring
__lowercase : Tuple ="""PoolFormerConfig"""
# Base docstring
__lowercase : Union[str, Any] ="""sail/poolformer_s12"""
__lowercase : int =[1, 512, 7, 7]
# Image classification docstring
__lowercase : Tuple ="""sail/poolformer_s12"""
__lowercase : Optional[Any] ="""tabby, tabby cat"""
__lowercase : List[str] =[
"""sail/poolformer_s12""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def a__ ( lowercase__ , lowercase__ = 0.0 , lowercase__ = False ):
'''simple docstring'''
if drop_prob == 0.0 or not training:
return input
UpperCAmelCase_ =1 - drop_prob
UpperCAmelCase_ =(input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
UpperCAmelCase_ =keep_prob + torch.rand(lowercase__ , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
UpperCAmelCase_ =input.div(lowercase__ ) * random_tensor
return output
class A ( nn.Module ):
def __init__( self: Optional[Any] , _lowerCAmelCase: Optional[float] = None ) -> None:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =drop_prob
def lowerCAmelCase__ ( self: Any , _lowerCAmelCase: torch.Tensor ) -> torch.Tensor:
'''simple docstring'''
return drop_path(_lowerCAmelCase , self.drop_prob , self.training )
def lowerCAmelCase__ ( self: Tuple ) -> str:
'''simple docstring'''
return "p={}".format(self.drop_prob )
class A ( nn.Module ):
def __init__( self: str , _lowerCAmelCase: Dict , _lowerCAmelCase: str , _lowerCAmelCase: Tuple , _lowerCAmelCase: str , _lowerCAmelCase: List[Any] , _lowerCAmelCase: Optional[Any]=None ) -> List[str]:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =patch_size if isinstance(_lowerCAmelCase , collections.abc.Iterable ) else (patch_size, patch_size)
UpperCAmelCase_ =stride if isinstance(_lowerCAmelCase , collections.abc.Iterable ) else (stride, stride)
UpperCAmelCase_ =padding if isinstance(_lowerCAmelCase , collections.abc.Iterable ) else (padding, padding)
UpperCAmelCase_ =nn.Convad(_lowerCAmelCase , _lowerCAmelCase , kernel_size=_lowerCAmelCase , stride=_lowerCAmelCase , padding=_lowerCAmelCase )
UpperCAmelCase_ =norm_layer(_lowerCAmelCase ) if norm_layer else nn.Identity()
def lowerCAmelCase__ ( self: Any , _lowerCAmelCase: str ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_ =self.projection(_lowerCAmelCase )
UpperCAmelCase_ =self.norm(_lowerCAmelCase )
return embeddings
class A ( nn.GroupNorm ):
def __init__( self: int , _lowerCAmelCase: int , **_lowerCAmelCase: Any ) -> Tuple:
'''simple docstring'''
super().__init__(1 , _lowerCAmelCase , **_lowerCAmelCase )
class A ( nn.Module ):
def __init__( self: Tuple , _lowerCAmelCase: int ) -> List[Any]:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =nn.AvgPoolad(_lowerCAmelCase , stride=1 , padding=pool_size // 2 , count_include_pad=_lowerCAmelCase )
def lowerCAmelCase__ ( self: List[Any] , _lowerCAmelCase: Optional[int] ) -> Optional[int]:
'''simple docstring'''
return self.pool(_lowerCAmelCase ) - hidden_states
class A ( nn.Module ):
def __init__( self: Any , _lowerCAmelCase: int , _lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: List[str] , _lowerCAmelCase: List[str] ) -> Any:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =nn.Convad(_lowerCAmelCase , _lowerCAmelCase , 1 )
UpperCAmelCase_ =nn.Convad(_lowerCAmelCase , _lowerCAmelCase , 1 )
UpperCAmelCase_ =PoolFormerDropPath(_lowerCAmelCase )
if isinstance(config.hidden_act , _lowerCAmelCase ):
UpperCAmelCase_ =ACTaFN[config.hidden_act]
else:
UpperCAmelCase_ =config.hidden_act
def lowerCAmelCase__ ( self: str , _lowerCAmelCase: int ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ =self.conva(_lowerCAmelCase )
UpperCAmelCase_ =self.act_fn(_lowerCAmelCase )
UpperCAmelCase_ =self.drop(_lowerCAmelCase )
UpperCAmelCase_ =self.conva(_lowerCAmelCase )
UpperCAmelCase_ =self.drop(_lowerCAmelCase )
return hidden_states
class A ( nn.Module ):
def __init__( self: str , _lowerCAmelCase: str , _lowerCAmelCase: Any , _lowerCAmelCase: Optional[Any] , _lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: List[Any] , _lowerCAmelCase: Any ) -> List[Any]:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =PoolFormerPooling(_lowerCAmelCase )
UpperCAmelCase_ =PoolFormerOutput(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase_ =PoolFormerGroupNorm(_lowerCAmelCase )
UpperCAmelCase_ =PoolFormerGroupNorm(_lowerCAmelCase )
# Useful for training neural nets
UpperCAmelCase_ =PoolFormerDropPath(_lowerCAmelCase ) if drop_path > 0.0 else nn.Identity()
UpperCAmelCase_ =config.use_layer_scale
if config.use_layer_scale:
UpperCAmelCase_ =nn.Parameter(
config.layer_scale_init_value * torch.ones((_lowerCAmelCase) ) , requires_grad=_lowerCAmelCase )
UpperCAmelCase_ =nn.Parameter(
config.layer_scale_init_value * torch.ones((_lowerCAmelCase) ) , requires_grad=_lowerCAmelCase )
def lowerCAmelCase__ ( self: Tuple , _lowerCAmelCase: List[str] ) -> List[Any]:
'''simple docstring'''
if self.use_layer_scale:
UpperCAmelCase_ =self.pooling(self.before_norm(_lowerCAmelCase ) )
UpperCAmelCase_ =self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
UpperCAmelCase_ =hidden_states + self.drop_path(_lowerCAmelCase )
UpperCAmelCase_ =()
UpperCAmelCase_ =self.output(self.after_norm(_lowerCAmelCase ) )
UpperCAmelCase_ =self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
UpperCAmelCase_ =hidden_states + self.drop_path(_lowerCAmelCase )
UpperCAmelCase_ =(output,) + outputs
return outputs
else:
UpperCAmelCase_ =self.drop_path(self.pooling(self.before_norm(_lowerCAmelCase ) ) )
# First residual connection
UpperCAmelCase_ =pooling_output + hidden_states
UpperCAmelCase_ =()
# Second residual connection inside the PoolFormerOutput block
UpperCAmelCase_ =self.drop_path(self.output(self.after_norm(_lowerCAmelCase ) ) )
UpperCAmelCase_ =hidden_states + layer_output
UpperCAmelCase_ =(output,) + outputs
return outputs
class A ( nn.Module ):
def __init__( self: Union[str, Any] , _lowerCAmelCase: Dict ) -> str:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =config
# stochastic depth decay rule
UpperCAmelCase_ =[x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
UpperCAmelCase_ =[]
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
UpperCAmelCase_ =nn.ModuleList(_lowerCAmelCase )
# Transformer blocks
UpperCAmelCase_ =[]
UpperCAmelCase_ =0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
UpperCAmelCase_ =[]
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
_lowerCAmelCase , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(_lowerCAmelCase ) )
UpperCAmelCase_ =nn.ModuleList(_lowerCAmelCase )
def lowerCAmelCase__ ( self: Optional[Any] , _lowerCAmelCase: Optional[int] , _lowerCAmelCase: Optional[Any]=False , _lowerCAmelCase: Dict=True ) -> str:
'''simple docstring'''
UpperCAmelCase_ =() if output_hidden_states else None
UpperCAmelCase_ =pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
UpperCAmelCase_ , UpperCAmelCase_ =layers
# Get patch embeddings from hidden_states
UpperCAmelCase_ =embedding_layer(_lowerCAmelCase )
# Send the embeddings through the blocks
for _, blk in enumerate(_lowerCAmelCase ):
UpperCAmelCase_ =blk(_lowerCAmelCase )
UpperCAmelCase_ =layer_outputs[0]
if output_hidden_states:
UpperCAmelCase_ =all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_lowerCAmelCase , hidden_states=_lowerCAmelCase )
class A ( __lowercase ):
_snake_case =PoolFormerConfig
_snake_case ='''poolformer'''
_snake_case ='''pixel_values'''
_snake_case =True
def lowerCAmelCase__ ( self: Tuple , _lowerCAmelCase: str ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_lowerCAmelCase , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(_lowerCAmelCase , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def lowerCAmelCase__ ( self: Tuple , _lowerCAmelCase: Optional[int] , _lowerCAmelCase: Tuple=False ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase_ =value
__lowercase : Union[str, Any] =R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
__lowercase : Union[str, Any] =R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
"""
@add_start_docstrings(
'''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.''' , __lowercase , )
class A ( __lowercase ):
def __init__( self: Any , _lowerCAmelCase: List[Any] ) -> str:
'''simple docstring'''
super().__init__(_lowerCAmelCase )
UpperCAmelCase_ =config
UpperCAmelCase_ =PoolFormerEncoder(_lowerCAmelCase )
# Initialize weights and apply final processing
self.post_init()
def lowerCAmelCase__ ( self: str ) -> int:
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCAmelCase__ ( self: Any , _lowerCAmelCase: Optional[torch.FloatTensor] = None , _lowerCAmelCase: Optional[bool] = None , _lowerCAmelCase: Optional[bool] = None , ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
'''simple docstring'''
UpperCAmelCase_ =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase_ =return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values" )
UpperCAmelCase_ =self.encoder(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , )
UpperCAmelCase_ =encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=_lowerCAmelCase , hidden_states=encoder_outputs.hidden_states , )
class A ( nn.Module ):
def __init__( self: str , _lowerCAmelCase: Dict ) -> Dict:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ =nn.Linear(config.hidden_size , config.hidden_size )
def lowerCAmelCase__ ( self: Dict , _lowerCAmelCase: Optional[Any] ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ =self.dense(_lowerCAmelCase )
return output
@add_start_docstrings(
'''
PoolFormer Model transformer with an image classification head on top
''' , __lowercase , )
class A ( __lowercase ):
def __init__( self: Union[str, Any] , _lowerCAmelCase: Optional[int] ) -> Dict:
'''simple docstring'''
super().__init__(_lowerCAmelCase )
UpperCAmelCase_ =config.num_labels
UpperCAmelCase_ =PoolFormerModel(_lowerCAmelCase )
# Final norm
UpperCAmelCase_ =PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
UpperCAmelCase_ =(
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_lowerCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_lowerCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCAmelCase__ ( self: Dict , _lowerCAmelCase: Optional[torch.FloatTensor] = None , _lowerCAmelCase: Optional[torch.LongTensor] = None , _lowerCAmelCase: Optional[bool] = None , _lowerCAmelCase: Optional[bool] = None , ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
'''simple docstring'''
UpperCAmelCase_ =return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase_ =self.poolformer(
_lowerCAmelCase , output_hidden_states=_lowerCAmelCase , return_dict=_lowerCAmelCase , )
UpperCAmelCase_ =outputs[0]
UpperCAmelCase_ =self.classifier(self.norm(_lowerCAmelCase ).mean([-2, -1] ) )
UpperCAmelCase_ =None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCAmelCase_ ="regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCAmelCase_ ="single_label_classification"
else:
UpperCAmelCase_ ="multi_label_classification"
if self.config.problem_type == "regression":
UpperCAmelCase_ =MSELoss()
if self.num_labels == 1:
UpperCAmelCase_ =loss_fct(logits.squeeze() , labels.squeeze() )
else:
UpperCAmelCase_ =loss_fct(_lowerCAmelCase , _lowerCAmelCase )
elif self.config.problem_type == "single_label_classification":
UpperCAmelCase_ =CrossEntropyLoss()
UpperCAmelCase_ =loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCAmelCase_ =BCEWithLogitsLoss()
UpperCAmelCase_ =loss_fct(_lowerCAmelCase , _lowerCAmelCase )
if not return_dict:
UpperCAmelCase_ =(logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_lowerCAmelCase , logits=_lowerCAmelCase , hidden_states=outputs.hidden_states )
| 54 |
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
_UpperCAmelCase = []
_UpperCAmelCase = set({"(", "[", "{"} )
_UpperCAmelCase = set({")", "]", "}"} )
_UpperCAmelCase = {"{": "}", "[": "]", "(": ")"}
for i in range(len(_lowerCAmelCase ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(_lowerCAmelCase ) == 0 or (len(_lowerCAmelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(_lowerCAmelCase ) == 0
def __lowerCamelCase ( ) -> str:
_UpperCAmelCase = input("Enter sequence of brackets: " )
if is_balanced(_lowerCAmelCase ):
print(_lowerCAmelCase , "is balanced" )
else:
print(_lowerCAmelCase , "is not balanced" )
if __name__ == "__main__":
main()
| 684 | 0 |
from math import factorial
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[str] ,A : Optional[int] ,A : int ):
__A = real
if isinstance(A ,A ):
__A = [1] * rank
else:
__A = rank
def __repr__( self : Tuple ):
return (
f'''{self.real}+'''
f'''{'+'.join(str(A )+'E'+str(n+1 )for n,dual in enumerate(self.duals ) )}'''
)
def UpperCamelCase_ ( self : List[Any] ):
__A = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real ,A )
def __add__( self : Optional[Any] ,A : List[Any] ):
if not isinstance(A ,A ):
return Dual(self.real + other ,self.duals )
__A = self.duals.copy()
__A = other.duals.copy()
if len(A ) > len(A ):
o_dual.extend([1] * (len(A ) - len(A )) )
elif len(A ) < len(A ):
s_dual.extend([1] * (len(A ) - len(A )) )
__A = []
for i in range(len(A ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real ,A )
snake_case_ = __add__
def __sub__( self : int ,A : Dict ):
return self + other * -1
def __mul__( self : List[Any] ,A : List[str] ):
if not isinstance(A ,A ):
__A = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other ,A )
__A = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real ,A )
snake_case_ = __mul__
def __truediv__( self : Tuple ,A : Dict ):
if not isinstance(A ,A ):
__A = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other ,A )
raise ValueError
def __floordiv__( self : List[str] ,A : Tuple ):
if not isinstance(A ,A ):
__A = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other ,A )
raise ValueError
def __pow__( self : Optional[Any] ,A : List[str] ):
if n < 0 or isinstance(A ,A ):
raise ValueError("power must be a positive integer" )
if n == 0:
return 1
if n == 1:
return self
__A = self
for _ in range(n - 1 ):
x *= self
return x
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
if not callable(a_ ):
raise ValueError("differentiate() requires a function as input for func" )
if not isinstance(a_ , (float, int) ):
raise ValueError("differentiate() requires a float as input for position" )
if not isinstance(a_ , a_ ):
raise ValueError("differentiate() requires an int as input for order" )
__A = Dual(a_ , 1 )
__A = func(a_ )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
return y**2 * y**4
print(differentiate(f, 9, 2))
| 55 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = equationa
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = equationa
# Calculate the determinants of the matrices
_UpperCAmelCase = aa * ba - aa * ba
_UpperCAmelCase = ca * ba - ca * ba
_UpperCAmelCase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
_UpperCAmelCase = determinant_x / determinant
_UpperCAmelCase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 684 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_a : int = (3, 9, -11, 0, 7, 5, 1, -1)
_a : Tuple = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_SCREAMING_SNAKE_CASE : int
_SCREAMING_SNAKE_CASE : Node | None
class _lowercase :
def __init__( self : int , SCREAMING_SNAKE_CASE_ : Iterable[int] ) -> None:
__snake_case = None
for i in sorted(SCREAMING_SNAKE_CASE_ , reverse=SCREAMING_SNAKE_CASE_ ):
__snake_case = Node(SCREAMING_SNAKE_CASE_ , self.head )
def __iter__( self : str ) -> Iterator[int]:
__snake_case = self.head
while node:
yield node.data
__snake_case = node.next_node
def __len__( self : Tuple ) -> int:
return sum(1 for _ in self )
def __str__( self : List[Any] ) -> str:
return " -> ".join([str(SCREAMING_SNAKE_CASE_ ) for node in self] )
def _a (lowercase__ : SortedLinkedList , lowercase__ : SortedLinkedList ) -> SortedLinkedList:
"""simple docstring"""
return SortedLinkedList(list(lowercase__ ) + list(lowercase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
_a : Tuple = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 56 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
# Initialise PyTorch model
_UpperCAmelCase = RemBertConfig.from_json_file(_lowerCAmelCase )
print("Building PyTorch model from configuration: {}".format(str(_lowerCAmelCase ) ) )
_UpperCAmelCase = RemBertModel(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Save pytorch-model
print("Save PyTorch model to {}".format(_lowerCAmelCase ) )
torch.save(model.state_dict() , _lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--rembert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained RemBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 684 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase=7 , _lowerCamelCase=3 , _lowerCamelCase=1_8 , _lowerCamelCase=3_0 , _lowerCamelCase=4_0_0 , _lowerCamelCase=True , _lowerCamelCase=None , _lowerCamelCase=True , ):
UpperCamelCase_: Dict = size if size is not None else {'height': 1_8, 'width': 1_8}
UpperCamelCase_: Union[str, Any] = parent
UpperCamelCase_: Any = batch_size
UpperCamelCase_: Tuple = num_channels
UpperCamelCase_: Tuple = image_size
UpperCamelCase_: List[Any] = min_resolution
UpperCamelCase_: Union[str, Any] = max_resolution
UpperCamelCase_: Dict = do_resize
UpperCamelCase_: Any = size
UpperCamelCase_: str = apply_ocr
def _a ( self ):
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class _lowerCAmelCase( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
a : Dict =LayoutLMvaImageProcessor if is_pytesseract_available() else None
def _a ( self ):
UpperCamelCase_: int = LayoutLMvaImageProcessingTester(self )
@property
def _a ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self ):
UpperCamelCase_: Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , 'do_resize' ) )
self.assertTrue(hasattr(_lowerCamelCase , 'size' ) )
self.assertTrue(hasattr(_lowerCamelCase , 'apply_ocr' ) )
def _a ( self ):
UpperCamelCase_: List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 1_8, 'width': 1_8} )
UpperCamelCase_: Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 )
self.assertEqual(image_processor.size , {'height': 4_2, 'width': 4_2} )
def _a ( self ):
pass
def _a ( self ):
# Initialize image_processing
UpperCamelCase_: List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase_: str = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
UpperCamelCase_: Tuple = image_processing(image_inputs[0] , return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
self.assertIsInstance(encoding.words , _lowerCamelCase )
self.assertIsInstance(encoding.boxes , _lowerCamelCase )
# Test batched
UpperCamelCase_: List[str] = image_processing(_lowerCamelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def _a ( self ):
# Initialize image_processing
UpperCamelCase_: List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase_: List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
UpperCamelCase_: Optional[int] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
UpperCamelCase_: int = image_processing(_lowerCamelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def _a ( self ):
# Initialize image_processing
UpperCamelCase_: List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase_: Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
UpperCamelCase_: Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
UpperCamelCase_: int = image_processing(_lowerCamelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def _a ( self ):
# with apply_OCR = True
UpperCamelCase_: str = LayoutLMvaImageProcessor()
from datasets import load_dataset
UpperCamelCase_: List[Any] = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' )
UpperCamelCase_: Dict = Image.open(ds[0]['file'] ).convert('RGB' )
UpperCamelCase_: Any = image_processing(_lowerCamelCase , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
UpperCamelCase_: int = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
UpperCamelCase_: Optional[Any] = [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , _lowerCamelCase )
self.assertListEqual(encoding.boxes , _lowerCamelCase )
# with apply_OCR = False
UpperCamelCase_: Union[str, Any] = LayoutLMvaImageProcessor(apply_ocr=_lowerCamelCase )
UpperCamelCase_: Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) ) | 57 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def UpperCAmelCase__ ( *__UpperCamelCase : Dict , **__UpperCamelCase : Optional[int] ):
pass
@is_pipeline_test
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
__SCREAMING_SNAKE_CASE : List[Any] = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
_UpperCAmelCase = [
{
"image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"question": "How many cats are there?",
},
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"question": "How many cats are there?",
},
]
return vqa_pipeline, examples
def UpperCAmelCase__ ( self : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[int] ):
_UpperCAmelCase = vqa_pipeline(__UpperCamelCase , top_k=1 )
self.assertEqual(
__UpperCamelCase , [
[{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}],
[{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}],
] , )
@require_torch
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
_UpperCAmelCase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_UpperCAmelCase = "How many cats are there?"
_UpperCAmelCase = vqa_pipeline(image=__UpperCamelCase , question="How many cats are there?" , top_k=2 )
self.assertEqual(
__UpperCamelCase , [{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}, {"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}] )
_UpperCAmelCase = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
__UpperCamelCase , [{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}, {"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}] )
@slow
@require_torch
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="dandelin/vilt-b32-finetuned-vqa" )
_UpperCAmelCase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_UpperCAmelCase = "How many cats are there?"
_UpperCAmelCase = vqa_pipeline(image=__UpperCamelCase , question=__UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
_UpperCAmelCase = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
_UpperCAmelCase = vqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2 , )
@require_tf
@unittest.skip("Visual question answering not implemented in TF" )
def UpperCAmelCase__ ( self : Optional[int] ):
pass
| 684 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=3 , _lowercase=3_2 , _lowercase=3 , _lowercase=1_0 , _lowercase=[1_0, 2_0, 3_0, 4_0] , _lowercase=[1, 1, 2, 1] , _lowercase=True , _lowercase=True , _lowercase="relu" , _lowercase=3 , _lowercase=None , ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = parent
snake_case_ : str = batch_size
snake_case_ : Union[str, Any] = image_size
snake_case_ : str = num_channels
snake_case_ : Union[str, Any] = embeddings_size
snake_case_ : Tuple = hidden_sizes
snake_case_ : int = depths
snake_case_ : int = is_training
snake_case_ : Dict = use_labels
snake_case_ : List[str] = hidden_act
snake_case_ : List[str] = num_labels
snake_case_ : Optional[Any] = scope
snake_case_ : Tuple = len(_lowercase )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Tuple = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = TFResNetModel(config=_lowercase )
snake_case_ : Tuple = model(_lowercase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.num_labels
snake_case_ : Optional[Any] = TFResNetForImageClassification(_lowercase )
snake_case_ : Optional[int] = model(_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Dict = config_and_inputs
snake_case_ : List[str] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
_lowerCamelCase = (
{'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
if is_tf_available()
else {}
)
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = TFResNetModelTester(self )
snake_case_ : List[Any] = ConfigTester(self , config_class=_lowercase , has_text_modality=_lowercase )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return
@unittest.skip(reason="""ResNet does not use inputs_embeds""" )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""ResNet does not support input and output embeddings""" )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ , snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Optional[Any] = model_class(_lowercase )
snake_case_ : List[Any] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Any = [*signature.parameters.keys()]
snake_case_ : str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowercase )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
def check_hidden_states_output(_lowercase , _lowercase , _lowercase ):
snake_case_ : str = model_class(_lowercase )
snake_case_ : Tuple = model(**self._prepare_for_class(_lowercase , _lowercase ) )
snake_case_ : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case_ : Any = self.model_tester.num_stages
self.assertEqual(len(_lowercase ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
snake_case_ , snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Optional[Any] = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
snake_case_ : List[str] = layer_type
snake_case_ : Any = True
check_hidden_states_output(_lowercase , _lowercase , _lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ : Tuple = True
check_hidden_states_output(_lowercase , _lowercase , _lowercase )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowercase )
@slow
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = TFResNetModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : str = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
snake_case_ : str = self.default_image_processor
snake_case_ : Optional[Any] = prepare_img()
snake_case_ : Dict = image_processor(images=_lowercase , return_tensors="""tf""" )
# forward pass
snake_case_ : Dict = model(**_lowercase )
# verify the logits
snake_case_ : str = tf.TensorShape((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _lowercase )
snake_case_ : Optional[Any] = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _lowercase , atol=1E-4 ) )
| 58 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 684 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = CustomTokenizer
pass
| 59 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : str = (UniPCMultistepScheduler,)
__SCREAMING_SNAKE_CASE : Dict = (("""num_inference_steps""", 25),)
def UpperCAmelCase__ ( self : str , **__UpperCamelCase : Any ):
_UpperCAmelCase = {
"num_train_timesteps": 1_000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__UpperCamelCase )
return config
def UpperCAmelCase__ ( self : int , __UpperCamelCase : Any=0 , **__UpperCamelCase : Any ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any]=0 , **__UpperCamelCase : List[Any] ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Dict=None , **__UpperCamelCase : Optional[Any] ):
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 10
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(__UpperCamelCase , "set_timesteps" ):
scheduler.set_timesteps(__UpperCamelCase )
elif num_inference_steps is not None and not hasattr(__UpperCamelCase , "set_timesteps" ):
_UpperCAmelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
_UpperCAmelCase = scheduler.timesteps[5]
_UpperCAmelCase = scheduler.timesteps[6]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCAmelCase__ ( self : Union[str, Any] ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = UniPCMultistepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def UpperCAmelCase__ ( self : str ):
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def UpperCAmelCase__ ( self : int ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase__ ( self : Optional[int] ):
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = self.full_loop(prediction_type="v_prediction" )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1014 ) < 1e-3
def UpperCAmelCase__ ( self : Tuple ):
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 10
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
def UpperCAmelCase__ ( self : str , **__UpperCamelCase : Optional[Any] ):
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 684 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __UpperCamelCase : List[Any]=0 ): # a graph with Node 0,1,...,N-1
_UpperCAmelCase = n
_UpperCAmelCase = [
[math.inf for j in range(0 , __UpperCamelCase )] for i in range(0 , __UpperCamelCase )
] # adjacency matrix for weight
_UpperCAmelCase = [
[math.inf for j in range(0 , __UpperCamelCase )] for i in range(0 , __UpperCamelCase )
] # dp[i][j] stores minimum distance from i to j
def UpperCAmelCase__ ( self : str , __UpperCamelCase : Tuple , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] ):
_UpperCAmelCase = w
def UpperCAmelCase__ ( self : Dict ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCAmelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCAmelCase__ ( self : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Any ):
return self.dp[u][v]
if __name__ == "__main__":
__lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 684 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ['GLPNFeatureExtractor']
UpperCamelCase = ['GLPNImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
'GLPNForDepthEstimation',
'GLPNLayer',
'GLPNModel',
'GLPNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 61 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowercase , lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : Dict = VQModel
__SCREAMING_SNAKE_CASE : Optional[int] = """sample"""
@property
def UpperCAmelCase__ ( self : List[str] , __UpperCamelCase : Optional[int]=(32, 32) ):
_UpperCAmelCase = 4
_UpperCAmelCase = 3
_UpperCAmelCase = floats_tensor((batch_size, num_channels) + sizes ).to(__UpperCamelCase )
return {"sample": image}
@property
def UpperCAmelCase__ ( self : Tuple ):
return (3, 32, 32)
@property
def UpperCAmelCase__ ( self : str ):
return (3, 32, 32)
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 3,
}
_UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Dict ):
pass
def UpperCAmelCase__ ( self : str ):
pass
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = VQModel.from_pretrained("fusing/vqgan-dummy" , output_loading_info=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(__UpperCamelCase )
_UpperCAmelCase = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = VQModel.from_pretrained("fusing/vqgan-dummy" )
model.to(__UpperCamelCase ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
_UpperCAmelCase = torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size )
_UpperCAmelCase = image.to(__UpperCamelCase )
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase ).sample
_UpperCAmelCase = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
_UpperCAmelCase = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] )
# fmt: on
self.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
| 684 | 0 |
import operator as op
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Any = lambda lowercase , lowercase : int(x / y ) # noqa: E731 integer division operation
SCREAMING_SNAKE_CASE : str = {
"^": op.pow,
"*": op.mul,
"/": div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8 ) , "Action".center(12 ) , "Stack" , sep=" | " )
print("-" * (30 + len(lowercase )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowercase ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ("push(" + x + ")").ljust(12 ) , ",".join(lowercase ) , sep=" | " )
else:
SCREAMING_SNAKE_CASE : Any = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + b + ")").ljust(12 ) , ",".join(lowercase ) , sep=" | " )
SCREAMING_SNAKE_CASE : int = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + a + ")").ljust(12 ) , ",".join(lowercase ) , sep=" | " )
stack.append(
str(opr[x](int(lowercase ) , int(lowercase ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ("push(" + a + x + b + ")").ljust(12 ) , ",".join(lowercase ) , sep=" | " , )
return int(stack[0] )
if __name__ == "__main__":
snake_case = input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
print("""\n\tResult = """, solve(Postfix))
| 62 |
import requests
__lowerCAmelCase = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def __lowerCamelCase ( _lowerCAmelCase ) -> None:
# fetching a list of articles in json format
_UpperCAmelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"] , 1 ):
print(F'''{i}.) {article["title"]}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 684 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowerCamelCase__ ( __lowerCamelCase : Tuple ):
__UpperCAmelCase : str = [2, 2, 6, 2] if """tiny""" in model_name else [2, 2, 18, 2]
__UpperCAmelCase : Any = True if """large""" in model_name or """huge""" in model_name else False
__UpperCAmelCase : int = True if """large""" in model_name or """huge""" in model_name else False
__UpperCAmelCase : Optional[int] = True if """large""" in model_name or """huge""" in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
__UpperCAmelCase : Union[str, Any] = [3, 3, 3, 3]
__UpperCAmelCase : Union[str, Any] = [5, 5, 5, 5]
elif "fl4" in model_name:
__UpperCAmelCase : str = [4, 4, 4, 4]
__UpperCAmelCase : Optional[Any] = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
__UpperCAmelCase : Dict = [3, 3, 3, 3]
if "lrf" in model_name:
__UpperCAmelCase : Optional[Any] = [3, 3, 3, 3]
else:
__UpperCAmelCase : Optional[int] = [2, 2, 2, 2]
if "tiny" in model_name:
__UpperCAmelCase : List[str] = 96
elif "small" in model_name:
__UpperCAmelCase : Dict = 96
elif "base" in model_name:
__UpperCAmelCase : List[Any] = 128
elif "large" in model_name:
__UpperCAmelCase : Any = 192
elif "xlarge" in model_name:
__UpperCAmelCase : Tuple = 256
elif "huge" in model_name:
__UpperCAmelCase : int = 352
# set label information
__UpperCAmelCase : Tuple = """huggingface/label-files"""
if "large" in model_name or "huge" in model_name:
__UpperCAmelCase : Any = """imagenet-22k-id2label.json"""
else:
__UpperCAmelCase : Dict = """imagenet-1k-id2label.json"""
__UpperCAmelCase : str = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
__UpperCAmelCase : Optional[int] = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
__UpperCAmelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
__UpperCAmelCase : List[str] = FocalNetConfig(
embed_dim=__lowerCamelCase , depths=__lowerCamelCase , focal_levels=__lowerCamelCase , focal_windows=__lowerCamelCase , use_conv_embed=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , use_post_layernorm=__lowerCamelCase , use_layerscale=__lowerCamelCase , )
return config
def lowerCamelCase__ ( __lowerCamelCase : Tuple ):
if "patch_embed.proj" in name:
__UpperCAmelCase : List[Any] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
__UpperCAmelCase : Dict = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
__UpperCAmelCase : int = """encoder.""" + name
if "encoder.layers" in name:
__UpperCAmelCase : Optional[int] = name.replace("""encoder.layers""" , """encoder.stages""" )
if "downsample.proj" in name:
__UpperCAmelCase : Optional[Any] = name.replace("""downsample.proj""" , """downsample.projection""" )
if "blocks" in name:
__UpperCAmelCase : Union[str, Any] = name.replace("""blocks""" , """layers""" )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
__UpperCAmelCase : List[str] = name.replace("""modulation.f""" , """modulation.projection_in""" )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
__UpperCAmelCase : List[Any] = name.replace("""modulation.h""" , """modulation.projection_context""" )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
__UpperCAmelCase : str = name.replace("""modulation.proj""" , """modulation.projection_out""" )
if name == "norm.weight":
__UpperCAmelCase : Optional[Any] = """layernorm.weight"""
if name == "norm.bias":
__UpperCAmelCase : Dict = """layernorm.bias"""
if "head" in name:
__UpperCAmelCase : Tuple = name.replace("""head""" , """classifier""" )
else:
__UpperCAmelCase : int = """focalnet.""" + name
return name
def lowerCamelCase__ ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : List[str]=False ):
# fmt: off
__UpperCAmelCase : Dict = {
"""focalnet-tiny""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth""",
"""focalnet-tiny-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth""",
"""focalnet-small""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth""",
"""focalnet-small-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth""",
"""focalnet-base""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth""",
"""focalnet-base-lrf""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth""",
"""focalnet-large-lrf-fl3""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth""",
"""focalnet-large-lrf-fl4""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth""",
"""focalnet-xlarge-lrf-fl3""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth""",
"""focalnet-xlarge-lrf-fl4""": """https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth""",
}
# fmt: on
__UpperCAmelCase : int = model_name_to_url[model_name]
print("""Checkpoint URL: """ , __lowerCamelCase )
__UpperCAmelCase : Dict = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location="""cpu""" )["""model"""]
# rename keys
for key in state_dict.copy().keys():
__UpperCAmelCase : Tuple = state_dict.pop(__lowerCamelCase )
__UpperCAmelCase : Optional[Any] = val
__UpperCAmelCase : Optional[Any] = get_focalnet_config(__lowerCamelCase )
__UpperCAmelCase : Any = FocalNetForImageClassification(__lowerCamelCase )
model.eval()
# load state dict
model.load_state_dict(__lowerCamelCase )
# verify conversion
__UpperCAmelCase : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__UpperCAmelCase : Union[str, Any] = BitImageProcessor(
do_resize=__lowerCamelCase , size={"""shortest_edge""": 256} , resample=PILImageResampling.BILINEAR , do_center_crop=__lowerCamelCase , crop_size=224 , do_normalize=__lowerCamelCase , image_mean=__lowerCamelCase , image_std=__lowerCamelCase , )
__UpperCAmelCase : Optional[Any] = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw )
__UpperCAmelCase : List[Any] = processor(images=__lowerCamelCase , return_tensors="""pt""" )
__UpperCAmelCase : str = transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
__UpperCAmelCase : List[Any] = image_transforms(__lowerCamelCase ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , __lowerCamelCase , atol=1E-4 )
__UpperCAmelCase : Dict = model(**__lowerCamelCase )
__UpperCAmelCase : Any = outputs.logits.argmax(-1 ).item()
print("""Predicted class:""" , model.config.idalabel[predicted_class_idx] )
print("""First values of logits:""" , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
__UpperCAmelCase : Union[str, Any] = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] )
elif model_name == "focalnet-tiny-lrf":
__UpperCAmelCase : Dict = torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5] )
elif model_name == "focalnet-small":
__UpperCAmelCase : int = torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1] )
elif model_name == "focalnet-small-lrf":
__UpperCAmelCase : int = torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1] )
elif model_name == "focalnet-base":
__UpperCAmelCase : Optional[int] = torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0] )
elif model_name == "focalnet-base-lrf":
__UpperCAmelCase : Optional[Any] = torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8] )
assert torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
a : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="focalnet-tiny",
type=str,
help="Name of the FocalNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub.",
)
a : Any = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 63 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Any ):
_UpperCAmelCase = 10
def UpperCAmelCase__ ( self : Optional[int] ):
_UpperCAmelCase = [1, 2, 3, 4]
_UpperCAmelCase = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = "It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this."
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , [] )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = ""
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , [] )
self.assertEqual(__UpperCamelCase , [] )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = (
"It was the year of Our Lord one thousand seven hundred and "
"seventy-five\n\nSpiritual revelations were conceded to England "
"at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
)
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
_UpperCAmelCase = [
"It was the year of Our Lord one thousand seven hundred and seventy-five.",
"Spiritual revelations were conceded to England at that favoured period, as at this.",
]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ["It was the best of times."]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = torch.tensor([1, 2, 3, 4] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 0 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 23 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Optional[int] ):
_UpperCAmelCase = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 1 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = 101
_UpperCAmelCase = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
_UpperCAmelCase = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
_UpperCAmelCase = compute_token_type_ids(__UpperCamelCase , __UpperCamelCase )
np.testing.assert_array_equal(__UpperCamelCase , __UpperCamelCase )
| 684 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
lowercase_ : Tuple = logging.getLogger(__name__)
@dataclass
class _lowerCamelCase :
__a = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__a = field(
default=UpperCamelCase_ , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__a = field(
default=UpperCamelCase_ , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__a = field(
default=UpperCamelCase_ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__a = field(
default=UpperCamelCase_ , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
__a = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__a = field(
default=UpperCamelCase_ , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
@dataclass
class _lowerCamelCase :
__a = field(default=UpperCamelCase_ , metadata={"help": "The input training data file (a text file)."} )
__a = field(
default=UpperCamelCase_ , metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."} , )
__a = field(
default=UpperCamelCase_ , metadata={"help": "Overwrite the cached training and evaluation sets"} )
__a = field(
default=UpperCamelCase_ , metadata={"help": "The number of processes to use for the preprocessing."} , )
__a = field(
default=UpperCamelCase_ , metadata={
"help": (
"The maximum total input sequence length after tokenization. If passed, sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
__a = field(
default=UpperCamelCase_ , metadata={
"help": (
"Whether to pad all samples to the maximum sentence length. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
"efficient on GPU but very bad for TPU."
)
} , )
__a = field(
default=UpperCamelCase_ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
__a = field(
default=UpperCamelCase_ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
def UpperCamelCase_ ( self ) -> List[Any]:
if self.train_file is not None:
SCREAMING_SNAKE_CASE__: Union[str, Any]= self.train_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
SCREAMING_SNAKE_CASE__: Union[str, Any]= self.validation_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class _lowerCamelCase :
__a = 42
__a = True
__a = None
__a = None
def __call__( self , lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__: Tuple= '''label''' if '''label''' in features[0].keys() else '''labels'''
SCREAMING_SNAKE_CASE__: Optional[Any]= [feature.pop(lowerCAmelCase ) for feature in features]
SCREAMING_SNAKE_CASE__: Dict= len(lowerCAmelCase )
SCREAMING_SNAKE_CASE__: int= len(features[0]['''input_ids'''] )
SCREAMING_SNAKE_CASE__: Union[str, Any]= [
[{k: v[i] for k, v in feature.items()} for i in range(lowerCAmelCase )] for feature in features
]
SCREAMING_SNAKE_CASE__: List[Any]= list(chain(*lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__: Any= self.tokenizer.pad(
lowerCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
# Un-flatten
SCREAMING_SNAKE_CASE__: Union[str, Any]= {k: v.view(lowerCAmelCase , lowerCAmelCase , -1 ) for k, v in batch.items()}
# Add back labels
SCREAMING_SNAKE_CASE__: List[str]= torch.tensor(lowerCAmelCase , dtype=torch.intaa )
return batch
def A__ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__: Any= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: str= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Optional[Any]= parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_swag''' , snake_case_ , snake_case_ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__: Optional[Any]= training_args.get_process_log_level()
logger.setLevel(snake_case_ )
datasets.utils.logging.set_verbosity(snake_case_ )
transformers.utils.logging.set_verbosity(snake_case_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(F'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__: Union[str, Any]= None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__: List[str]= get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
SCREAMING_SNAKE_CASE__: Union[str, Any]= {}
if data_args.train_file is not None:
SCREAMING_SNAKE_CASE__: List[Any]= data_args.train_file
if data_args.validation_file is not None:
SCREAMING_SNAKE_CASE__: Dict= data_args.validation_file
SCREAMING_SNAKE_CASE__: Tuple= data_args.train_file.split('''.''' )[-1]
SCREAMING_SNAKE_CASE__: int= load_dataset(
snake_case_ , data_files=snake_case_ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
SCREAMING_SNAKE_CASE__: Optional[Any]= load_dataset(
'''swag''' , '''regular''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__: str= AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__: List[str]= AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__: List[str]= AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=snake_case_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
SCREAMING_SNAKE_CASE__: Tuple= [F'ending{i}' for i in range(4 )]
SCREAMING_SNAKE_CASE__: List[str]= '''sent1'''
SCREAMING_SNAKE_CASE__: str= '''sent2'''
if data_args.max_seq_length is None:
SCREAMING_SNAKE_CASE__: str= tokenizer.model_max_length
if max_seq_length > 1_024:
logger.warning(
'''The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'''
''' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'''
''' override this default with `--block_size xxx`.''' )
SCREAMING_SNAKE_CASE__: List[str]= 1_024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
F'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
SCREAMING_SNAKE_CASE__: Any= min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(snake_case_ : int ):
SCREAMING_SNAKE_CASE__: Any= [[context] * 4 for context in examples[context_name]]
SCREAMING_SNAKE_CASE__: List[Any]= examples[question_header_name]
SCREAMING_SNAKE_CASE__: Union[str, Any]= [
[F'{header} {examples[end][i]}' for end in ending_names] for i, header in enumerate(snake_case_ )
]
# Flatten out
SCREAMING_SNAKE_CASE__: Dict= list(chain(*snake_case_ ) )
SCREAMING_SNAKE_CASE__: Union[str, Any]= list(chain(*snake_case_ ) )
# Tokenize
SCREAMING_SNAKE_CASE__: Tuple= tokenizer(
snake_case_ , snake_case_ , truncation=snake_case_ , max_length=snake_case_ , padding='''max_length''' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(snake_case_ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''' )
SCREAMING_SNAKE_CASE__: Optional[int]= raw_datasets['''train''']
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__: Optional[int]= min(len(snake_case_ ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__: int= train_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
SCREAMING_SNAKE_CASE__: Any= train_dataset.map(
snake_case_ , batched=snake_case_ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''' )
SCREAMING_SNAKE_CASE__: List[str]= raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__: int= min(len(snake_case_ ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__: Optional[Any]= eval_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
SCREAMING_SNAKE_CASE__: Dict= eval_dataset.map(
snake_case_ , batched=snake_case_ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
SCREAMING_SNAKE_CASE__: str= (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=snake_case_ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(snake_case_ : Optional[Any] ):
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Tuple= eval_predictions
SCREAMING_SNAKE_CASE__: Union[str, Any]= np.argmax(snake_case_ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
SCREAMING_SNAKE_CASE__: str= Trainer(
model=snake_case_ , args=snake_case_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=snake_case_ , data_collator=snake_case_ , compute_metrics=snake_case_ , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__: Tuple= None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__: List[Any]= training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__: Optional[int]= last_checkpoint
SCREAMING_SNAKE_CASE__: int= trainer.train(resume_from_checkpoint=snake_case_ )
trainer.save_model() # Saves the tokenizer too for easy upload
SCREAMING_SNAKE_CASE__: Optional[Any]= train_result.metrics
SCREAMING_SNAKE_CASE__: Dict= (
data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case_ )
)
SCREAMING_SNAKE_CASE__: Any= min(snake_case_ , len(snake_case_ ) )
trainer.log_metrics('''train''' , snake_case_ )
trainer.save_metrics('''train''' , snake_case_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
SCREAMING_SNAKE_CASE__: List[Any]= trainer.evaluate()
SCREAMING_SNAKE_CASE__: str= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(snake_case_ )
SCREAMING_SNAKE_CASE__: str= min(snake_case_ , len(snake_case_ ) )
trainer.log_metrics('''eval''' , snake_case_ )
trainer.save_metrics('''eval''' , snake_case_ )
SCREAMING_SNAKE_CASE__: List[Any]= {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''multiple-choice''',
'''dataset_tags''': '''swag''',
'''dataset_args''': '''regular''',
'''dataset''': '''SWAG''',
'''language''': '''en''',
}
if training_args.push_to_hub:
trainer.push_to_hub(**snake_case_ )
else:
trainer.create_model_card(**snake_case_ )
def A__ ( snake_case_ : List[str] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 64 |
from __future__ import annotations
from collections import namedtuple
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> tuple:
_UpperCAmelCase = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 0 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
if isinstance(__UpperCamelCase , torch.Tensor ):
return image
elif isinstance(__UpperCamelCase , PIL.Image.Image ):
UpperCAmelCase__ : List[Any] = [image]
if isinstance(image[0] , PIL.Image.Image ):
UpperCAmelCase__ : Optional[int] = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
UpperCAmelCase__ : Union[str, Any] = np.concatenate(__UpperCamelCase , axis=0 )
UpperCAmelCase__ : str = np.array(__UpperCamelCase ).astype(np.floataa ) / 255.0
UpperCAmelCase__ : List[str] = image.transpose(0 , 3 , 1 , 2 )
UpperCAmelCase__ : Optional[int] = 2.0 * image - 1.0
UpperCAmelCase__ : List[Any] = torch.from_numpy(__UpperCamelCase )
elif isinstance(image[0] , torch.Tensor ):
UpperCAmelCase__ : List[Any] = torch.cat(__UpperCamelCase , dim=0 )
return image
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=0.9995 ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , np.ndarray ):
UpperCAmelCase__ : Any = True
UpperCAmelCase__ : Optional[int] = va.device
UpperCAmelCase__ : Tuple = va.cpu().numpy()
UpperCAmelCase__ : Optional[int] = va.cpu().numpy()
UpperCAmelCase__ : Dict = np.sum(va * va / (np.linalg.norm(__UpperCamelCase ) * np.linalg.norm(__UpperCamelCase )) )
if np.abs(__UpperCamelCase ) > DOT_THRESHOLD:
UpperCAmelCase__ : Tuple = (1 - t) * va + t * va
else:
UpperCAmelCase__ : str = np.arccos(__UpperCamelCase )
UpperCAmelCase__ : int = np.sin(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = theta_a * t
UpperCAmelCase__ : int = np.sin(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = np.sin(theta_a - theta_t ) / sin_theta_a
UpperCAmelCase__ : Tuple = sin_theta_t / sin_theta_a
UpperCAmelCase__ : List[Any] = sa * va + sa * va
if inputs_are_torch:
UpperCAmelCase__ : Dict = torch.from_numpy(__UpperCamelCase ).to(__UpperCamelCase )
return va
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : str = F.normalize(__UpperCamelCase , dim=-1 )
UpperCAmelCase__ : Union[str, Any] = F.normalize(__UpperCamelCase , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
for param in model.parameters():
UpperCAmelCase__ : Any = value
class __lowercase ( __lowerCamelCase ):
def __init__( self : Tuple ,A : AutoencoderKL ,A : CLIPTextModel ,A : CLIPModel ,A : CLIPTokenizer ,A : UNetaDConditionModel ,A : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] ,A : CLIPFeatureExtractor ,A : Optional[Any]=None ,A : Union[str, Any]=None ,A : str=None ,):
'''simple docstring'''
super().__init__()
self.register_modules(
vae=A ,text_encoder=A ,clip_model=A ,tokenizer=A ,unet=A ,scheduler=A ,feature_extractor=A ,coca_model=A ,coca_tokenizer=A ,coca_transform=A ,)
UpperCAmelCase__ : List[Any] = (
feature_extractor.size
if isinstance(feature_extractor.size ,A )
else feature_extractor.size["""shortest_edge"""]
)
UpperCAmelCase__ : str = transforms.Normalize(mean=feature_extractor.image_mean ,std=feature_extractor.image_std )
set_requires_grad(self.text_encoder ,A )
set_requires_grad(self.clip_model ,A )
def __lowercase ( self : Optional[int] ,A : Optional[Union[str, int]] = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase__ : int = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def __lowercase ( self : int ):
'''simple docstring'''
self.enable_attention_slicing(A )
def __lowercase ( self : List[str] ):
'''simple docstring'''
set_requires_grad(self.vae ,A )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
set_requires_grad(self.vae ,A )
def __lowercase ( self : List[str] ):
'''simple docstring'''
set_requires_grad(self.unet ,A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
set_requires_grad(self.unet ,A )
def __lowercase ( self : Dict ,A : str ,A : List[Any] ,A : int ):
'''simple docstring'''
# get the original timestep using init_timestep
UpperCAmelCase__ : Any = min(int(num_inference_steps * strength ) ,A )
UpperCAmelCase__ : List[Any] = max(num_inference_steps - init_timestep ,0 )
UpperCAmelCase__ : Any = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def __lowercase ( self : str ,A : Optional[int] ,A : Dict ,A : int ,A : Optional[int] ,A : Optional[Any] ,A : int=None ):
'''simple docstring'''
if not isinstance(A ,torch.Tensor ):
raise ValueError(f"`image` has to be of type `torch.Tensor` but is {type(A )}" )
UpperCAmelCase__ : int = image.to(device=A ,dtype=A )
if isinstance(A ,A ):
UpperCAmelCase__ : List[Any] = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
UpperCAmelCase__ : Union[str, Any] = torch.cat(A ,dim=0 )
else:
UpperCAmelCase__ : List[Any] = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase__ : Any = 0.1_8_2_1_5 * init_latents
UpperCAmelCase__ : Tuple = init_latents.repeat_interleave(A ,dim=0 )
UpperCAmelCase__ : Any = randn_tensor(init_latents.shape ,generator=A ,device=A ,dtype=A )
# get latents
UpperCAmelCase__ : Optional[Any] = self.scheduler.add_noise(A ,A ,A )
UpperCAmelCase__ : Union[str, Any] = init_latents
return latents
def __lowercase ( self : List[Any] ,A : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
UpperCAmelCase__ : Optional[Any] = self.coca_model.generate(transformed_image.to(device=self.device ,dtype=self.coca_model.dtype ) )
UpperCAmelCase__ : str = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>""" ,"""""" ).rstrip(""" .,""" )
def __lowercase ( self : str ,A : List[str] ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.feature_extractor.preprocess(A )
UpperCAmelCase__ : List[Any] = torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
UpperCAmelCase__ : Optional[Any] = self.clip_model.get_image_features(A )
UpperCAmelCase__ : Dict = image_embeddings_clip / image_embeddings_clip.norm(p=2 ,dim=-1 ,keepdim=A )
UpperCAmelCase__ : Tuple = image_embeddings_clip.repeat_interleave(A ,dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def __lowercase ( self : Any ,A : List[Any] ,A : List[Any] ,A : int ,A : int ,A : int ,A : List[str] ,A : Optional[int] ,):
'''simple docstring'''
UpperCAmelCase__ : Tuple = latents.detach().requires_grad_()
UpperCAmelCase__ : Tuple = self.scheduler.scale_model_input(A ,A )
# predict the noise residual
UpperCAmelCase__ : List[Any] = self.unet(A ,A ,encoder_hidden_states=A ).sample
if isinstance(self.scheduler ,(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
UpperCAmelCase__ : str = self.scheduler.alphas_cumprod[timestep]
UpperCAmelCase__ : Any = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase__ : Dict = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
UpperCAmelCase__ : int = torch.sqrt(A )
UpperCAmelCase__ : List[Any] = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler ,A ):
UpperCAmelCase__ : List[Any] = self.scheduler.sigmas[index]
UpperCAmelCase__ : Any = latents - sigma * noise_pred
else:
raise ValueError(f"scheduler type {type(self.scheduler )} not supported" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase__ : List[Any] = 1 / 0.1_8_2_1_5 * sample
UpperCAmelCase__ : Union[str, Any] = self.vae.decode(A ).sample
UpperCAmelCase__ : Optional[int] = (image / 2 + 0.5).clamp(0 ,1 )
UpperCAmelCase__ : Tuple = transforms.Resize(self.feature_extractor_size )(A )
UpperCAmelCase__ : List[Any] = self.normalize(A ).to(latents.dtype )
UpperCAmelCase__ : Union[str, Any] = self.clip_model.get_image_features(A )
UpperCAmelCase__ : Optional[int] = image_embeddings_clip / image_embeddings_clip.norm(p=2 ,dim=-1 ,keepdim=A )
UpperCAmelCase__ : Union[str, Any] = spherical_dist_loss(A ,A ).mean() * clip_guidance_scale
UpperCAmelCase__ : List[Any] = -torch.autograd.grad(A ,A )[0]
if isinstance(self.scheduler ,A ):
UpperCAmelCase__ : List[str] = latents.detach() + grads * (sigma**2)
UpperCAmelCase__ : Optional[Any] = noise_pred_original
else:
UpperCAmelCase__ : Tuple = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Dict ,A : Union[torch.FloatTensor, PIL.Image.Image] ,A : Union[torch.FloatTensor, PIL.Image.Image] ,A : Optional[str] = None ,A : Optional[str] = None ,A : Optional[int] = 512 ,A : Optional[int] = 512 ,A : float = 0.6 ,A : Optional[int] = 50 ,A : Optional[float] = 7.5 ,A : Optional[int] = 1 ,A : float = 0.0 ,A : Optional[float] = 100 ,A : Optional[torch.Generator] = None ,A : Optional[str] = "pil" ,A : bool = True ,A : float = 0.8 ,A : float = 0.1 ,A : float = 0.1 ,):
'''simple docstring'''
if isinstance(A ,A ) and len(A ) != batch_size:
raise ValueError(f"You have passed {batch_size} batch_size, but only {len(A )} generators." )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}." )
if isinstance(A ,torch.Generator ) and batch_size > 1:
UpperCAmelCase__ : int = [generator] + [None] * (batch_size - 1)
UpperCAmelCase__ : Union[str, Any] = [
("""model""", self.coca_model is None),
("""tokenizer""", self.coca_tokenizer is None),
("""transform""", self.coca_transform is None),
]
UpperCAmelCase__ : str = [x[0] for x in coca_is_none if x[1]]
UpperCAmelCase__ : Optional[Any] = """, """.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f"Content prompt is None and CoCa [{coca_is_none_str}] is None."
f"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
UpperCAmelCase__ : Union[str, Any] = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f"Style prompt is None and CoCa [{coca_is_none_str}] is None."
f" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
UpperCAmelCase__ : Optional[Any] = self.get_image_description(A )
# get prompt text embeddings for content and style
UpperCAmelCase__ : Any = self.tokenizer(
A ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,truncation=A ,return_tensors="""pt""" ,)
UpperCAmelCase__ : List[Any] = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
UpperCAmelCase__ : List[str] = self.tokenizer(
A ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,truncation=A ,return_tensors="""pt""" ,)
UpperCAmelCase__ : List[str] = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
UpperCAmelCase__ : Tuple = slerp(A ,A ,A )
# duplicate text embeddings for each generation per prompt
UpperCAmelCase__ : Any = text_embeddings.repeat_interleave(A ,dim=0 )
# set timesteps
UpperCAmelCase__ : List[Any] = """offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
UpperCAmelCase__ : Any = {}
if accepts_offset:
UpperCAmelCase__ : List[Any] = 1
self.scheduler.set_timesteps(A ,**A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.get_timesteps(A ,A ,self.device )
UpperCAmelCase__ : List[str] = timesteps[:1].repeat(A )
# Preprocess image
UpperCAmelCase__ : Tuple = preprocess(A ,A ,A )
UpperCAmelCase__ : str = self.prepare_latents(
A ,A ,A ,text_embeddings.dtype ,self.device ,A )
UpperCAmelCase__ : Tuple = preprocess(A ,A ,A )
UpperCAmelCase__ : Dict = self.prepare_latents(
A ,A ,A ,text_embeddings.dtype ,self.device ,A )
UpperCAmelCase__ : int = slerp(A ,A ,A )
if clip_guidance_scale > 0:
UpperCAmelCase__ : List[Any] = self.get_clip_image_embeddings(A ,A )
UpperCAmelCase__ : Any = self.get_clip_image_embeddings(A ,A )
UpperCAmelCase__ : Optional[Any] = slerp(
A ,A ,A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
UpperCAmelCase__ : Dict = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase__ : Dict = content_text_input.input_ids.shape[-1]
UpperCAmelCase__ : List[Any] = self.tokenizer([""""""] ,padding="""max_length""" ,max_length=A ,return_tensors="""pt""" )
UpperCAmelCase__ : Union[str, Any] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
UpperCAmelCase__ : Optional[int] = uncond_embeddings.repeat_interleave(A ,dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase__ : Optional[int] = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
UpperCAmelCase__ : Dict = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
UpperCAmelCase__ : List[Any] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
UpperCAmelCase__ : Union[str, Any] = torch.randn(A ,generator=A ,device="""cpu""" ,dtype=A ).to(
self.device )
else:
UpperCAmelCase__ : Optional[int] = torch.randn(A ,generator=A ,device=self.device ,dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
UpperCAmelCase__ : List[Any] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase__ : Optional[int] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
UpperCAmelCase__ : Union[str, Any] = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase__ : Optional[int] = {}
if accepts_eta:
UpperCAmelCase__ : Union[str, Any] = eta
# check if the scheduler accepts generator
UpperCAmelCase__ : str = """generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
UpperCAmelCase__ : Optional[int] = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase__ : List[str] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase__ : int = self.scheduler.scale_model_input(A ,A )
# predict the noise residual
UpperCAmelCase__ : Tuple = self.unet(A ,A ,encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = noise_pred.chunk(2 )
UpperCAmelCase__ : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
UpperCAmelCase__ : Optional[int] = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = self.cond_fn(
A ,A ,A ,A ,A ,A ,A ,)
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase__ : List[Any] = self.scheduler.step(A ,A ,A ,**A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
UpperCAmelCase__ : Any = 1 / 0.1_8_2_1_5 * latents
UpperCAmelCase__ : int = self.vae.decode(A ).sample
UpperCAmelCase__ : Any = (image / 2 + 0.5).clamp(0 ,1 )
UpperCAmelCase__ : Optional[Any] = image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
UpperCAmelCase__ : int = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A ,nsfw_content_detected=A )
| 65 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __lowerCamelCase ( _lowerCAmelCase ) -> Any:
_UpperCAmelCase = {}
_UpperCAmelCase = job["started_at"]
_UpperCAmelCase = job["completed_at"]
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
_UpperCAmelCase = start
_UpperCAmelCase = end
_UpperCAmelCase = duration_in_min
return job_info
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=None ) -> str:
_UpperCAmelCase = None
if token is not None:
_UpperCAmelCase = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
_UpperCAmelCase = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
_UpperCAmelCase = requests.get(_lowerCAmelCase , headers=_lowerCAmelCase ).json()
_UpperCAmelCase = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
_UpperCAmelCase = math.ceil((result["total_count"] - 100) / 100 )
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = requests.get(url + F'''&page={i + 2}''' , headers=_lowerCAmelCase ).json()
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = get_job_time(args.workflow_run_id)
__lowerCAmelCase = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v["duration"]}''')
| 684 | 0 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class lowerCAmelCase_ ( __snake_case ):
_UpperCamelCase : torch.FloatTensor
_UpperCamelCase : Optional[torch.FloatTensor] = None
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=0.999 , SCREAMING_SNAKE_CASE="cosine" , ) -> Tuple:
if alpha_transform_type == "cosine":
def alpha_bar_fn(SCREAMING_SNAKE_CASE ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(SCREAMING_SNAKE_CASE ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_lowercase : Dict = []
for i in range(SCREAMING_SNAKE_CASE ):
_lowercase : str = i / num_diffusion_timesteps
_lowercase : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE ) / alpha_bar_fn(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE ) )
return torch.tensor(SCREAMING_SNAKE_CASE , dtype=torch.floataa )
class lowerCAmelCase_ ( __snake_case , __snake_case ):
_UpperCamelCase : Optional[int] = 1
@register_to_config
def __init__( self , _lowerCAmelCase = 1_0_0_0 , _lowerCAmelCase = 0.00_01 , _lowerCAmelCase = 0.02 , _lowerCAmelCase = "linear" , _lowerCAmelCase = None , _lowerCAmelCase = True , _lowerCAmelCase = True , _lowerCAmelCase = 0 , _lowerCAmelCase = "epsilon" , _lowerCAmelCase = 1.0 , **_lowerCAmelCase , ):
if kwargs.get('set_alpha_to_one' , _lowerCAmelCase ) is not None:
_lowercase : Optional[int] = (
'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
)
deprecate('set_alpha_to_one' , '1.0.0' , _lowerCAmelCase , standard_warn=_lowerCAmelCase )
_lowercase : str = kwargs['set_alpha_to_one']
if trained_betas is not None:
_lowercase : List[str] = torch.tensor(_lowerCAmelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
_lowercase : str = torch.linspace(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_lowercase : Optional[int] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , _lowerCAmelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_lowercase : str = betas_for_alpha_bar(_lowerCAmelCase )
else:
raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )
_lowercase : Dict = 1.0 - self.betas
_lowercase : Tuple = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
_lowercase : Union[str, Any] = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
_lowercase : Tuple = 1.0
# setable values
_lowercase : List[str] = None
_lowercase : int = torch.from_numpy(np.arange(0 , _lowerCAmelCase ).copy().astype(np.intaa ) )
def __a ( self , _lowerCAmelCase , _lowerCAmelCase = None ):
return sample
def __a ( self , _lowerCAmelCase , _lowerCAmelCase = None ):
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
F""" maximal {self.config.num_train_timesteps} timesteps.""" )
_lowercase : int = num_inference_steps
_lowercase : int = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : Optional[int] = (np.arange(0 , _lowerCAmelCase ) * step_ratio).round().copy().astype(np.intaa )
_lowercase : Optional[int] = torch.from_numpy(_lowerCAmelCase ).to(_lowerCAmelCase )
self.timesteps += self.config.steps_offset
def __a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0.0 , _lowerCAmelCase = False , _lowerCAmelCase = None , _lowerCAmelCase = True , ):
# 1. get previous step value (=t+1)
_lowercase : str = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
_lowercase : List[str] = self.alphas_cumprod[timestep]
_lowercase : Tuple = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
_lowercase : Any = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
_lowercase : Optional[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
_lowercase : List[Any] = model_output
elif self.config.prediction_type == "sample":
_lowercase : Optional[int] = model_output
_lowercase : Any = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
_lowercase : Any = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
_lowercase : List[Any] = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
' `v_prediction`' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
_lowercase : Optional[Any] = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_lowercase : str = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_lowercase : Optional[int] = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase , pred_original_sample=_lowerCAmelCase )
def __len__( self ):
return self.config.num_train_timesteps
| 66 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__lowerCAmelCase = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 1_3_1_0_7_2,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
return torch.atana(_lowerCAmelCase , _lowerCAmelCase ) / math.pi * 2
def __lowerCamelCase ( _lowerCAmelCase ) -> Union[str, Any]:
_UpperCAmelCase = torch.sin(t * math.pi / 2 ) ** 2
_UpperCAmelCase = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(_lowerCAmelCase , _lowerCAmelCase )
class __SCREAMING_SNAKE_CASE ( lowercase):
pass
class __SCREAMING_SNAKE_CASE ( nn.Module):
def __init__( self : str , __UpperCamelCase : Optional[int] ):
super().__init__()
_UpperCAmelCase = DiffusionAttnUnetaD(__UpperCamelCase , n_attn_layers=4 )
_UpperCAmelCase = deepcopy(self.diffusion )
_UpperCAmelCase = torch.quasirandom.SobolEngine(1 , scramble=__UpperCamelCase )
def __lowerCamelCase ( _lowerCAmelCase ) -> int:
_UpperCAmelCase = MODELS_MAP[model_name]["url"]
os.system(F'''wget {url} ./''' )
return F'''./{model_name}.ckpt'''
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
__lowerCAmelCase = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
__lowerCAmelCase = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
__lowerCAmelCase = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
__lowerCAmelCase = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
if name.startswith("skip" ):
return name.replace("skip" , RES_CONV_MAP["skip"] )
# name has to be of format main.{digit}
if not name.startswith("main." ):
raise ValueError(F'''ResConvBlock error with {name}''' )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[Any]:
for key, value in ATTN_MAP.items():
if name.startswith(_lowerCAmelCase ) and not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return name.replace(_lowerCAmelCase , _lowerCAmelCase )
elif name.startswith(_lowerCAmelCase ):
return [name.replace(_lowerCAmelCase , _lowerCAmelCase ) for v in value]
raise ValueError(F'''Attn error with {name}''' )
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=13 ) -> List[Any]:
_UpperCAmelCase = input_string
if string.split("." )[0] == "timestep_embed":
return string.replace("timestep_embed" , "time_proj" )
_UpperCAmelCase = 0
if string.startswith("net.3." ):
depth += 1
_UpperCAmelCase = string[6:]
elif string.startswith("net." ):
_UpperCAmelCase = string[4:]
while string.startswith("main.7." ):
depth += 1
_UpperCAmelCase = string[7:]
if string.startswith("main." ):
_UpperCAmelCase = string[5:]
# mid block
if string[:2].isdigit():
_UpperCAmelCase = string[:2]
_UpperCAmelCase = string[2:]
else:
_UpperCAmelCase = string[0]
_UpperCAmelCase = string[1:]
if depth == max_depth:
_UpperCAmelCase = MID_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = "mid_block"
elif depth > 0 and int(_lowerCAmelCase ) < 7:
_UpperCAmelCase = DOWN_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''down_blocks.{depth}'''
elif depth > 0 and int(_lowerCAmelCase ) > 7:
_UpperCAmelCase = UP_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - depth - 1}'''
elif depth == 0:
_UpperCAmelCase = DEPTH_0_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - 1}''' if int(_lowerCAmelCase ) > 3 else "down_blocks.0"
if not string_left.startswith("." ):
raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.''' )
_UpperCAmelCase = string_left[1:]
if "resnets" in new_layer:
_UpperCAmelCase = convert_resconv_naming(_lowerCAmelCase )
elif "attentions" in new_layer:
_UpperCAmelCase = convert_attn_naming(_lowerCAmelCase )
_UpperCAmelCase = new_string_left
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = prefix + "." + new_layer + "." + string_left
else:
_UpperCAmelCase = [prefix + "." + new_layer + "." + s for s in string_left]
return new_string
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[int]:
_UpperCAmelCase = {}
for k, v in state_dict.items():
if k.endswith("kernel" ):
# up- and downsample layers, don't have trainable weights
continue
_UpperCAmelCase = rename(_lowerCAmelCase )
# check if we need to transform from Conv => Linear for attention
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = transform_conv_attns(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_UpperCAmelCase = v
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
if len(_lowerCAmelCase ) == 1:
if len(v.shape ) == 3:
# weight
_UpperCAmelCase = v[:, :, 0]
else:
# bias
_UpperCAmelCase = v
else:
# qkv matrices
_UpperCAmelCase = v.shape[0]
_UpperCAmelCase = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase ) -> Tuple:
_UpperCAmelCase = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
_UpperCAmelCase = args.model_path.split("/" )[-1].split("." )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
_UpperCAmelCase = download(_lowerCAmelCase )
_UpperCAmelCase = MODELS_MAP[model_name]["sample_rate"]
_UpperCAmelCase = MODELS_MAP[model_name]["sample_size"]
_UpperCAmelCase = Object()
_UpperCAmelCase = sample_size
_UpperCAmelCase = sample_rate
_UpperCAmelCase = 0
_UpperCAmelCase = UNetaDModel(sample_size=_lowerCAmelCase , sample_rate=_lowerCAmelCase )
_UpperCAmelCase = diffusers_model.state_dict()
_UpperCAmelCase = DiffusionUncond(_lowerCAmelCase )
orig_model.load_state_dict(torch.load(args.model_path , map_location=_lowerCAmelCase )["state_dict"] )
_UpperCAmelCase = orig_model.diffusion_ema.eval()
_UpperCAmelCase = orig_model.state_dict()
_UpperCAmelCase = rename_orig_weights(_lowerCAmelCase )
_UpperCAmelCase = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
_UpperCAmelCase = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(_lowerCAmelCase ) == 0, F'''Problem with {renamed_minus_diffusers}'''
assert all(k.endswith("kernel" ) for k in list(_lowerCAmelCase ) ), F'''Problem with {diffusers_minus_renamed}'''
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
if key == "time_proj.weight":
_UpperCAmelCase = value.squeeze()
_UpperCAmelCase = value
diffusers_model.load_state_dict(_lowerCAmelCase )
_UpperCAmelCase = 100
_UpperCAmelCase = 33
_UpperCAmelCase = IPNDMScheduler(num_train_timesteps=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(_lowerCAmelCase )
_UpperCAmelCase = torch.randn([1, 2, config.sample_size] , generator=_lowerCAmelCase ).to(_lowerCAmelCase )
_UpperCAmelCase = torch.linspace(1 , 0 , steps + 1 , device=_lowerCAmelCase )[:-1]
_UpperCAmelCase = get_crash_schedule(_lowerCAmelCase )
_UpperCAmelCase = DanceDiffusionPipeline(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(33 )
_UpperCAmelCase = pipe(num_inference_steps=_lowerCAmelCase , generator=_lowerCAmelCase ).audios
_UpperCAmelCase = sampling.iplms_sample(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {} )
_UpperCAmelCase = generated.clamp(-1 , 1 )
_UpperCAmelCase = (generated - audio).abs().sum()
_UpperCAmelCase = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("Diff sum" , _lowerCAmelCase )
print("Diff max" , _lowerCAmelCase )
assert diff_max < 1E-3, F'''Diff max: {diff_max} is too much :-/'''
print(F'''Conversion for {model_name} successful!''' )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
__lowerCAmelCase = parser.parse_args()
main(args)
| 684 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A_ ( UpperCAmelCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Tuple ) -> Optional[Any]:
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def __UpperCAmelCase ( self : List[str] ) -> Tuple:
_lowercase = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
return Dataset.from_dict(__A )
def __UpperCAmelCase ( self : Optional[Any] ) -> Any:
_lowercase = self._create_example_records()
_lowercase = Dataset.from_list(__A )
self.assertListEqual(dset.column_names ,['col_1', 'col_2'] )
for i, r in enumerate(__A ):
self.assertDictEqual(__A ,example_records[i] )
def __UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
_lowercase = self._create_example_records()
_lowercase = Dataset.from_list(__A )
_lowercase = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info ,dset_from_dict.info )
def __UpperCAmelCase ( self : int ) -> Tuple: # checks what happens with missing columns
_lowercase = [{'col_1': 1}, {'col_2': 'x'}]
_lowercase = Dataset.from_list(__A )
self.assertDictEqual(dset[0] ,{'col_1': 1} )
self.assertDictEqual(dset[1] ,{'col_1': None} ) # NB: first record is used for columns
def __UpperCAmelCase ( self : List[Any] ) -> List[Any]: # checks if the type can be inferred from the second record
_lowercase = [{'col_1': []}, {'col_1': [1, 2]}]
_lowercase = Dataset.from_list(__A )
self.assertEqual(dset.info.features['col_1'] ,Sequence(Value('int64' ) ) )
def __UpperCAmelCase ( self : List[str] ) -> str:
_lowercase = Dataset.from_list([] )
self.assertEqual(len(__A ) ,0 )
self.assertListEqual(dset.column_names ,[] ) | 67 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
__lowerCAmelCase = get_tests_dir("fixtures")
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 500
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=__UpperCamelCase ) as mock_head:
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase__ ( self : List[Any] ):
# This test is for deprecated behavior and can be removed in v5
_UpperCAmelCase = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json" )
def UpperCAmelCase__ ( self : Dict ):
with self.assertRaises(__UpperCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
_UpperCAmelCase = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants" )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants" , subfolder="feature_extractor" )
self.assertIsNotNone(__UpperCamelCase )
@is_staging_test
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
@classmethod
def UpperCAmelCase__ ( cls : str ):
_UpperCAmelCase = TOKEN
HfFolder.save_token(__UpperCamelCase )
@classmethod
def UpperCAmelCase__ ( cls : Optional[Any] ):
try:
delete_repo(token=cls._token , repo_id="test-image-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-image-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-image-processor" )
except HTTPError:
pass
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="test-image-processor" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("valid_org/test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="valid_org/test-image-processor-org" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : int ):
CustomImageProcessor.register_for_auto_class()
_UpperCAmelCase = CustomImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-dynamic-image-processor" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"} , )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
F'''{USER}/test-dynamic-image-processor''' , trust_remote_code=__UpperCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , "CustomImageProcessor" )
| 684 | 0 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def lowercase__ ( A_: Dict ) -> str:
"""simple docstring"""
if isinstance(A_ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class _A :
"""simple docstring"""
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int ) -> List[str]:
pass
def _a ( self : Optional[int] ) -> Tuple:
pass
def _a ( self : int ) -> str:
pass
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str=None , **__SCREAMING_SNAKE_CASE : List[Any] ) -> List[Any]:
__UpperCAmelCase =VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__UpperCAmelCase =TFVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =model(input_ids=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str=None , **__SCREAMING_SNAKE_CASE : List[Any] ) -> List[str]:
__UpperCAmelCase , __UpperCAmelCase =self.get_vision_text_model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__UpperCAmelCase =TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE , text_model=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =model(input_ids=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : Any , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str]=None , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
__UpperCAmelCase , __UpperCAmelCase =self.get_vision_text_model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__UpperCAmelCase ={"""vision_model""": vision_model, """text_model""": text_model}
__UpperCAmelCase =TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =model(input_ids=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int]=None , **__SCREAMING_SNAKE_CASE : int ) -> Tuple:
__UpperCAmelCase , __UpperCAmelCase =self.get_vision_text_model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__UpperCAmelCase =TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE , text_model=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =model(input_ids=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =TFVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =model(input_ids=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =after_output[0].numpy()
__UpperCAmelCase =np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__SCREAMING_SNAKE_CASE , 1e-5 )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , **__SCREAMING_SNAKE_CASE : Optional[Any] ) -> Optional[int]:
__UpperCAmelCase , __UpperCAmelCase =self.get_vision_text_model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__UpperCAmelCase =TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE , text_model=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =model(
input_ids=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , output_attentions=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =output.vision_model_output.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__UpperCAmelCase =to_atuple(vision_model.config.image_size )
__UpperCAmelCase =to_atuple(vision_model.config.patch_size )
__UpperCAmelCase =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__UpperCAmelCase =num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__UpperCAmelCase =output.text_model_output.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CASE : float ) -> Tuple:
__UpperCAmelCase =np.abs((a - b) ).max()
self.assertLessEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def _a ( self : List[Any] ) -> Optional[int]:
__UpperCAmelCase =self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> int:
__UpperCAmelCase =self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> Any:
__UpperCAmelCase =self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**__SCREAMING_SNAKE_CASE )
def _a ( self : List[Any] ) -> Dict:
__UpperCAmelCase =self.prepare_config_and_inputs()
self.check_save_load(**__SCREAMING_SNAKE_CASE )
def _a ( self : Any ) -> Dict:
__UpperCAmelCase =self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Any ) -> Optional[Any]:
__UpperCAmelCase , __UpperCAmelCase =self.get_pretrained_model_and_inputs()
__UpperCAmelCase =model_a(**__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =TFVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =model_a(**__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =after_outputs[0].numpy()
__UpperCAmelCase =np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__SCREAMING_SNAKE_CASE , 1e-5 )
@require_tf
class _A ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
def _a ( self : str ) -> List[Any]:
__UpperCAmelCase =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
__UpperCAmelCase =13
__UpperCAmelCase =floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__UpperCAmelCase =ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__UpperCAmelCase =random_attention_mask([batch_size, 4] )
__UpperCAmelCase ={"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any ) -> Optional[Any]:
__UpperCAmelCase =TFViTModel(__SCREAMING_SNAKE_CASE , name="""vision_model""" )
__UpperCAmelCase =TFBertModel(__SCREAMING_SNAKE_CASE , name="""text_model""" )
return vision_model, text_model
def _a ( self : Union[str, Any] ) -> Tuple:
__UpperCAmelCase =TFViTModelTester(self )
__UpperCAmelCase =TFBertModelTester(self )
__UpperCAmelCase =vit_model_tester.prepare_config_and_inputs()
__UpperCAmelCase =bert_model_tester.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase =vision_config_and_inputs
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) =text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class _A ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
def _a ( self : Optional[Any] ) -> int:
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
__UpperCAmelCase =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
__UpperCAmelCase =13
__UpperCAmelCase =floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__UpperCAmelCase =ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__UpperCAmelCase =random_attention_mask([batch_size, 4] )
__UpperCAmelCase ={"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict=None , **__SCREAMING_SNAKE_CASE : Any ) -> Dict:
__UpperCAmelCase , __UpperCAmelCase =self.get_vision_text_model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__UpperCAmelCase =TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE , text_model=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =model(
input_ids=__SCREAMING_SNAKE_CASE , pixel_values=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , output_attentions=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =output.vision_model_output.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__UpperCAmelCase =to_atuple(vision_model.config.image_size )
__UpperCAmelCase =to_atuple(vision_model.config.patch_size )
__UpperCAmelCase =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__UpperCAmelCase =num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
__UpperCAmelCase =output.text_model_output.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Any:
__UpperCAmelCase =TFDeiTModel(__SCREAMING_SNAKE_CASE , name="""vision_model""" )
__UpperCAmelCase =TFRobertaModel(__SCREAMING_SNAKE_CASE , name="""text_model""" )
return vision_model, text_model
def _a ( self : Any ) -> Union[str, Any]:
__UpperCAmelCase =TFDeiTModelTester(self )
__UpperCAmelCase =TFRobertaModelTester(self )
__UpperCAmelCase =vit_model_tester.prepare_config_and_inputs()
__UpperCAmelCase =bert_model_tester.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase =vision_config_and_inputs
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) =text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class _A ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
def _a ( self : Any ) -> Optional[Any]:
__UpperCAmelCase =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
__UpperCAmelCase =13
__UpperCAmelCase =floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__UpperCAmelCase =ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
__UpperCAmelCase =random_attention_mask([batch_size, 4] )
__UpperCAmelCase ={"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _a ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int ) -> Tuple:
__UpperCAmelCase =TFCLIPVisionModel(__SCREAMING_SNAKE_CASE , name="""vision_model""" )
__UpperCAmelCase =TFBertModel(__SCREAMING_SNAKE_CASE , name="""text_model""" )
return vision_model, text_model
def _a ( self : int ) -> Tuple:
__UpperCAmelCase =TFCLIPVisionModelTester(self )
__UpperCAmelCase =TFBertModelTester(self )
__UpperCAmelCase =clip_model_tester.prepare_config_and_inputs()
__UpperCAmelCase =bert_model_tester.prepare_config_and_inputs()
__UpperCAmelCase , __UpperCAmelCase =vision_config_and_inputs
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) =text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class _A ( unittest.TestCase ):
"""simple docstring"""
@slow
def _a ( self : Optional[Any] ) -> Optional[int]:
__UpperCAmelCase =TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
__UpperCAmelCase =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
__UpperCAmelCase =processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE , return_tensors="""np""" )
__UpperCAmelCase =model(**__SCREAMING_SNAKE_CASE )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
__UpperCAmelCase =np.array([[1.2_284_727, 0.3_104_122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , __SCREAMING_SNAKE_CASE , atol=1e-3 ) )
| 68 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
return getitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
return setitem, k, v
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
return delitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase ) -> Optional[int]:
try:
return fun(_lowerCAmelCase , *_lowerCAmelCase ), None
except Exception as e:
return None, e
__lowerCAmelCase = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__lowerCAmelCase = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items" ),
pytest.param(_overwrite_items , id="overwrite items" ),
pytest.param(_delete_items , id="delete items" ),
pytest.param(_access_absent_items , id="access absent items" ),
pytest.param(_add_with_resize_up , id="add with resize up" ),
pytest.param(_add_with_resize_down , id="add with resize down" ),
) , )
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
_UpperCAmelCase = HashMap(initial_block_size=4 )
_UpperCAmelCase = {}
for _, (fun, *args) in enumerate(_lowerCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
assert my_res == py_res
assert str(_lowerCAmelCase ) == str(_lowerCAmelCase )
assert set(_lowerCAmelCase ) == set(_lowerCAmelCase )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
assert set(my.items() ) == set(py.items() )
def __lowerCamelCase ( ) -> List[Any]:
def is_public(_lowerCAmelCase ) -> bool:
return not name.startswith("_" )
_UpperCAmelCase = {name for name in dir({} ) if is_public(_lowerCAmelCase )}
_UpperCAmelCase = {name for name in dir(HashMap() ) if is_public(_lowerCAmelCase )}
assert dict_public_names > hash_public_names
| 684 | 0 |
'''simple docstring'''
import numpy
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , a_ : numpy.ndarray , a_ : numpy.ndarray ):
"""simple docstring"""
__snake_case = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
__snake_case = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
__snake_case = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
__snake_case = numpy.random.rand(3 , 1 )
# Real output values provided.
__snake_case = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
__snake_case = numpy.zeros(output_array.shape )
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
__snake_case = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
__snake_case = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def A ( self : Optional[Any] ):
"""simple docstring"""
__snake_case = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
__snake_case = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
__snake_case = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def A ( self : Union[str, Any] , a_ : numpy.ndarray , a_ : int , a_ : bool ):
"""simple docstring"""
for iteration in range(1 , iterations + 1 ):
__snake_case = self.feedforward()
self.back_propagation()
if give_loss:
__snake_case = numpy.mean(numpy.square(output - self.feedforward() ) )
print(f'''Iteration {iteration} Loss: {loss}''' )
def A ( self : Optional[Any] , a_ : numpy.ndarray ):
"""simple docstring"""
__snake_case = input_arr
__snake_case = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
__snake_case = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
__snake_case = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def __UpperCAmelCase ( _UpperCAmelCase : numpy.ndarray ) -> numpy.ndarray:
return 1 / (1 + numpy.exp(-value ))
def __UpperCAmelCase ( _UpperCAmelCase : numpy.ndarray ) -> numpy.ndarray:
return (value) * (1 - (value))
def __UpperCAmelCase ( ) -> int:
__snake_case = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
__snake_case = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
__snake_case = TwoHiddenLayerNeuralNetwork(
input_array=_UpperCAmelCase , output_array=_UpperCAmelCase )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_UpperCAmelCase , iterations=10 , give_loss=_UpperCAmelCase )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 69 |
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
_UpperCAmelCase = len(_lowerCAmelCase )
for i in range(1 , _lowerCAmelCase ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase = 0
_UpperCAmelCase = i - 1
while low <= high:
_UpperCAmelCase = (low + high) // 2
if val < collection[mid]:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
for j in range(_lowerCAmelCase , _lowerCAmelCase , -1 ):
_UpperCAmelCase = collection[j - 1]
_UpperCAmelCase = val
return collection
if __name__ == "__main__":
__lowerCAmelCase = input("Enter numbers separated by a comma:\n").strip()
__lowerCAmelCase = [int(item) for item in user_input.split(",")]
print(binary_insertion_sort(unsorted))
| 684 | 0 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase : Dict = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowerCamelCase_ = argparse.ArgumentParser(
description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).' )
parser.add_argument('--file_path' , type=lowercase , default='data/dump.txt' , help='The path to the data.' )
parser.add_argument('--tokenizer_type' , type=lowercase , default='bert' , choices=['bert', 'roberta', 'gpt2'] )
parser.add_argument('--tokenizer_name' , type=lowercase , default='bert-base-uncased' , help='The tokenizer to use.' )
parser.add_argument('--dump_file' , type=lowercase , default='data/dump' , help='The dump file prefix.' )
lowerCamelCase_ = parser.parse_args()
logger.info(f"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
lowerCamelCase_ = BertTokenizer.from_pretrained(args.tokenizer_name )
lowerCamelCase_ = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
lowerCamelCase_ = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
elif args.tokenizer_type == "roberta":
lowerCamelCase_ = RobertaTokenizer.from_pretrained(args.tokenizer_name )
lowerCamelCase_ = tokenizer.special_tokens_map['cls_token'] # `<s>`
lowerCamelCase_ = tokenizer.special_tokens_map['sep_token'] # `</s>`
elif args.tokenizer_type == "gpt2":
lowerCamelCase_ = GPTaTokenizer.from_pretrained(args.tokenizer_name )
lowerCamelCase_ = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
lowerCamelCase_ = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`
logger.info(f"""Loading text from {args.file_path}""" )
with open(args.file_path , 'r' , encoding='utf8' ) as fp:
lowerCamelCase_ = fp.readlines()
logger.info('Start encoding' )
logger.info(f"""{len(lowercase )} examples to process.""" )
lowerCamelCase_ = []
lowerCamelCase_ = 0
lowerCamelCase_ = 1_00_00
lowerCamelCase_ = time.time()
for text in data:
lowerCamelCase_ = f"""{bos} {text.strip()} {sep}"""
lowerCamelCase_ = tokenizer.encode(lowercase , add_special_tokens=lowercase )
rslt.append(lowercase )
iter += 1
if iter % interval == 0:
lowerCamelCase_ = time.time()
logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
lowerCamelCase_ = time.time()
logger.info('Finished binarization' )
logger.info(f"""{len(lowercase )} examples processed.""" )
lowerCamelCase_ = f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
lowerCamelCase_ = tokenizer.vocab_size
if vocab_size < (1 << 16):
lowerCamelCase_ = [np.uintaa(lowercase ) for d in rslt]
else:
lowerCamelCase_ = [np.intaa(lowercase ) for d in rslt]
random.shuffle(rslt_ )
logger.info(f"""Dump to {dp_file}""" )
with open(lowercase , 'wb' ) as handle:
pickle.dump(rslt_ , lowercase , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 70 |
__lowerCAmelCase = 2_5_6
# Modulus to hash a string
__lowerCAmelCase = 1_0_0_0_0_0_3
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
_UpperCAmelCase = len(_lowerCAmelCase )
_UpperCAmelCase = len(_lowerCAmelCase )
if p_len > t_len:
return False
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 1
# Calculating the hash of pattern and substring of text
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_UpperCAmelCase = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_UpperCAmelCase = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_UpperCAmelCase = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def __lowerCamelCase ( ) -> None:
_UpperCAmelCase = "abc1abc12"
_UpperCAmelCase = "alskfjaldsabc1abc1abc12k23adsfabcabc"
_UpperCAmelCase = "alskfjaldsk23adsfabcabc"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase ) and not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 2)
_UpperCAmelCase = "ABABX"
_UpperCAmelCase = "ABABZABABYABABX"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 3)
_UpperCAmelCase = "AAAB"
_UpperCAmelCase = "ABAAAAAB"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 4)
_UpperCAmelCase = "abcdabcy"
_UpperCAmelCase = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 5)
_UpperCAmelCase = "Lü"
_UpperCAmelCase = "Lüsai"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
_UpperCAmelCase = "Lue"
assert not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 684 | 0 |
'''simple docstring'''
_lowerCamelCase = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 71 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__lowerCAmelCase = random.Random()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=1.0 , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> List[str]:
if rng is None:
_UpperCAmelCase = global_rng
_UpperCAmelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def __init__( self : Optional[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any]=7 , __UpperCamelCase : Union[str, Any]=400 , __UpperCamelCase : List[Any]=2_000 , __UpperCamelCase : Optional[Any]=10 , __UpperCamelCase : Optional[int]=160 , __UpperCamelCase : Any=8 , __UpperCamelCase : List[Any]=0.0 , __UpperCamelCase : Dict=4_000 , __UpperCamelCase : Optional[int]=False , __UpperCamelCase : Tuple=True , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = min_seq_length
_UpperCAmelCase = max_seq_length
_UpperCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_UpperCAmelCase = padding_value
_UpperCAmelCase = sampling_rate
_UpperCAmelCase = return_attention_mask
_UpperCAmelCase = do_normalize
_UpperCAmelCase = feature_size
_UpperCAmelCase = chunk_length
_UpperCAmelCase = hop_length
def UpperCAmelCase__ ( self : Optional[Any] ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : Tuple=False , __UpperCamelCase : Dict=False ):
def _flatten(__UpperCamelCase : Any ):
return list(itertools.chain(*__UpperCamelCase ) )
if equal_length:
_UpperCAmelCase = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_UpperCAmelCase = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : str = WhisperFeatureExtractor if is_speech_available() else None
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = WhisperFeatureExtractionTester(self )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = feat_extract_first.save_pretrained(__UpperCamelCase )[0]
check_json_file_has_correct_format(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = os.path.join(__UpperCamelCase , "feat_extract.json" )
feat_extract_first.to_json_file(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_json_file(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
# Test feature size
_UpperCAmelCase = feature_extractor(__UpperCamelCase , padding="max_length" , return_tensors="np" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
_UpperCAmelCase = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_features
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test batched
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
_UpperCAmelCase = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_UpperCAmelCase = np.asarray(__UpperCamelCase )
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test truncation required
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
_UpperCAmelCase = [x[: feature_extractor.n_samples] for x in speech_inputs]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs_truncated]
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
import torch
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = np.random.rand(100 , 32 ).astype(np.floataa )
_UpperCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Tuple ):
_UpperCAmelCase = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
_UpperCAmelCase = ds.sort("id" ).select(range(__UpperCamelCase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def UpperCAmelCase__ ( self : Tuple ):
# fmt: off
_UpperCAmelCase = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
_UpperCAmelCase = self._load_datasamples(1 )
_UpperCAmelCase = WhisperFeatureExtractor()
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="pt" ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __UpperCamelCase , atol=1e-4 ) )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = self._load_datasamples(1 )[0]
_UpperCAmelCase = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
_UpperCAmelCase = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__UpperCamelCase )[0]
self.assertTrue(np.all(np.mean(__UpperCamelCase ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase ) - 1 ) < 1e-3 ) )
| 684 | 0 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
_UpperCAmelCase : Dict = logging.get_logger(__name__)
enable_full_determinism()
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = UNetaDModel
UpperCamelCase__ = 'sample'
@property
def _A( self ):
lowercase =4
lowercase =3
lowercase =(32, 32)
lowercase =floats_tensor((batch_size, num_channels) + sizes ).to(snake_case_ )
lowercase =torch.tensor([10] ).to(snake_case_ )
return {"sample": noise, "timestep": time_step}
@property
def _A( self ):
return (3, 32, 32)
@property
def _A( self ):
return (3, 32, 32)
def _A( self ):
lowercase ={
'''block_out_channels''': (32, 64),
'''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
'''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
'''attention_head_dim''': 3,
'''out_channels''': 3,
'''in_channels''': 3,
'''layers_per_block''': 2,
'''sample_size''': 32,
}
lowercase =self.dummy_input
return init_dict, inputs_dict
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = UNetaDModel
UpperCamelCase__ = 'sample'
@property
def _A( self ):
lowercase =4
lowercase =4
lowercase =(32, 32)
lowercase =floats_tensor((batch_size, num_channels) + sizes ).to(snake_case_ )
lowercase =torch.tensor([10] ).to(snake_case_ )
return {"sample": noise, "timestep": time_step}
@property
def _A( self ):
return (4, 32, 32)
@property
def _A( self ):
return (4, 32, 32)
def _A( self ):
lowercase ={
'''sample_size''': 32,
'''in_channels''': 4,
'''out_channels''': 4,
'''layers_per_block''': 2,
'''block_out_channels''': (32, 64),
'''attention_head_dim''': 32,
'''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
'''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
}
lowercase =self.dummy_input
return init_dict, inputs_dict
def _A( self ):
lowercase , lowercase =UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' , output_loading_info=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(snake_case_ )
lowercase =model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != '''cuda''' , '''This test is supposed to run on GPU''' )
def _A( self ):
lowercase , lowercase =UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' , output_loading_info=snake_case_ )
model.to(snake_case_ )
lowercase =model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != '''cuda''' , '''This test is supposed to run on GPU''' )
def _A( self ):
# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
lowercase , lowercase =UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' , output_loading_info=snake_case_ )
model_accelerate.to(snake_case_ )
model_accelerate.eval()
lowercase =torch.randn(
1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0 ) , )
lowercase =noise.to(snake_case_ )
lowercase =torch.tensor([10] * noise.shape[0] ).to(snake_case_ )
lowercase =model_accelerate(snake_case_ , snake_case_ )['''sample''']
# two models don't need to stay in the device at the same time
del model_accelerate
torch.cuda.empty_cache()
gc.collect()
lowercase , lowercase =UNetaDModel.from_pretrained(
'''fusing/unet-ldm-dummy-update''' , output_loading_info=snake_case_ , low_cpu_mem_usage=snake_case_ )
model_normal_load.to(snake_case_ )
model_normal_load.eval()
lowercase =model_normal_load(snake_case_ , snake_case_ )['''sample''']
assert torch_all_close(snake_case_ , snake_case_ , rtol=1E-3 )
def _A( self ):
lowercase =UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' )
model.eval()
model.to(snake_case_ )
lowercase =torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
lowercase =noise.to(snake_case_ )
lowercase =torch.tensor([10] * noise.shape[0] ).to(snake_case_ )
with torch.no_grad():
lowercase =model(snake_case_ , snake_case_ ).sample
lowercase =output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
lowercase =torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00] )
# fmt: on
self.assertTrue(torch_all_close(snake_case_ , snake_case_ , rtol=1E-3 ) )
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = UNetaDModel
UpperCamelCase__ = 'sample'
@property
def _A( self , snake_case_=(32, 32) ):
lowercase =4
lowercase =3
lowercase =floats_tensor((batch_size, num_channels) + sizes ).to(snake_case_ )
lowercase =torch.tensor(batch_size * [10] ).to(dtype=torch.intaa , device=snake_case_ )
return {"sample": noise, "timestep": time_step}
@property
def _A( self ):
return (3, 32, 32)
@property
def _A( self ):
return (3, 32, 32)
def _A( self ):
lowercase ={
'''block_out_channels''': [32, 64, 64, 64],
'''in_channels''': 3,
'''layers_per_block''': 1,
'''out_channels''': 3,
'''time_embedding_type''': '''fourier''',
'''norm_eps''': 1E-6,
'''mid_block_scale_factor''': math.sqrt(2.0 ),
'''norm_num_groups''': None,
'''down_block_types''': [
'''SkipDownBlock2D''',
'''AttnSkipDownBlock2D''',
'''SkipDownBlock2D''',
'''SkipDownBlock2D''',
],
'''up_block_types''': [
'''SkipUpBlock2D''',
'''SkipUpBlock2D''',
'''AttnSkipUpBlock2D''',
'''SkipUpBlock2D''',
],
}
lowercase =self.dummy_input
return init_dict, inputs_dict
@slow
def _A( self ):
lowercase , lowercase =UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256''' , output_loading_info=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(snake_case_ )
lowercase =self.dummy_input
lowercase =floats_tensor((4, 3) + (2_56, 2_56) ).to(snake_case_ )
lowercase =noise
lowercase =model(**snake_case_ )
assert image is not None, "Make sure output is not None"
@slow
def _A( self ):
lowercase =UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256''' )
model.to(snake_case_ )
lowercase =4
lowercase =3
lowercase =(2_56, 2_56)
lowercase =torch.ones((batch_size, num_channels) + sizes ).to(snake_case_ )
lowercase =torch.tensor(batch_size * [1E-4] ).to(snake_case_ )
with torch.no_grad():
lowercase =model(snake_case_ , snake_case_ ).sample
lowercase =output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
lowercase =torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08] )
# fmt: on
self.assertTrue(torch_all_close(snake_case_ , snake_case_ , rtol=1E-2 ) )
def _A( self ):
lowercase =UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update''' )
model.to(snake_case_ )
lowercase =4
lowercase =3
lowercase =(32, 32)
lowercase =torch.ones((batch_size, num_channels) + sizes ).to(snake_case_ )
lowercase =torch.tensor(batch_size * [1E-4] ).to(snake_case_ )
with torch.no_grad():
lowercase =model(snake_case_ , snake_case_ ).sample
lowercase =output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
lowercase =torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56] )
# fmt: on
self.assertTrue(torch_all_close(snake_case_ , snake_case_ , rtol=1E-2 ) )
def _A( self ):
# not required for this model
pass
| 72 |
# Copyright 2023 The HuggingFace Inc. 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.
import re
from ..utils import cached_file
# docstyle-ignore
__lowerCAmelCase = "\nHuman: <<task>>\n\nAssistant: "
__lowerCAmelCase = "huggingface-tools/default-prompts"
__lowerCAmelCase = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase="run" ) -> Union[str, Any]:
if prompt_or_repo_id is None:
_UpperCAmelCase = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , _lowerCAmelCase ) is not None:
return prompt_or_repo_id
_UpperCAmelCase = cached_file(
_lowerCAmelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(_lowerCAmelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 684 | 0 |
def lowerCamelCase__ (_UpperCAmelCase):
return "".join(chr(ord(_UpperCAmelCase) - 32) if 'a' <= char <= 'z' else char for char in word)
if __name__ == "__main__":
from doctest import testmod
testmod()
| 73 |
from itertools import permutations
def __lowerCamelCase ( _lowerCAmelCase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(_lowerCAmelCase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def __lowerCamelCase ( _lowerCAmelCase = 10 ) -> int:
return sum(
int("".join(map(_lowerCAmelCase , _lowerCAmelCase ) ) )
for num in permutations(range(_lowerCAmelCase ) )
if is_substring_divisible(_lowerCAmelCase ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 684 | 0 |
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Any , *_A : List[Any] , **_A : Optional[int] ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Optional[Any] , *_A : List[str] , **_A : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : List[str] , *_A : Any , **_A : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : List[Any] , *_A : str , **_A : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Union[str, Any] , *_A : List[Any] , **_A : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Union[str, Any] , *_A : Dict , **_A : int ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : List[str] , *_A : Union[str, Any] , **_A : Optional[int] ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : str , *_A : Tuple , **_A : Any ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Dict , *_A : int , **_A : Optional[Any] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Optional[Any] , *_A : int , **_A : List[Any] ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : List[str] , *_A : Union[str, Any] , **_A : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Union[str, Any] , *_A : Any , **_A : List[Any] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Dict , *_A : Any , **_A : Any ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Optional[int] , *_A : List[Any] , **_A : Any ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Optional[int] , *_A : str , **_A : str ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __UpperCamelCase ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : List[str] , *_A : Union[str, Any] , **_A : int ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Tuple , *_A : int , **_A : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def UpperCAmelCase__ ( cls : Any , *_A : int , **_A : List[Any] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
| 74 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
__lowerCAmelCase = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
__lowerCAmelCase = {"facebook/blenderbot-3B": 1_2_8}
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : Optional[int] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : str = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : List[Any] = ["""input_ids""", """attention_mask"""]
__SCREAMING_SNAKE_CASE : List[str] = BlenderbotTokenizer
def __init__( self : Tuple , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]="replace" , __UpperCamelCase : Tuple="<s>" , __UpperCamelCase : str="</s>" , __UpperCamelCase : Dict="</s>" , __UpperCamelCase : Union[str, Any]="<s>" , __UpperCamelCase : Union[str, Any]="<unk>" , __UpperCamelCase : Tuple="<pad>" , __UpperCamelCase : Optional[int]="<mask>" , __UpperCamelCase : Union[str, Any]=False , __UpperCamelCase : List[str]=True , **__UpperCamelCase : int , ):
super().__init__(
__UpperCamelCase , __UpperCamelCase , tokenizer_file=__UpperCamelCase , errors=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = getattr(__UpperCamelCase , pre_tok_state.pop("type" ) )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = pre_tok_class(**__UpperCamelCase )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = "post_processor"
_UpperCAmelCase = getattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
if tokenizer_component_instance:
_UpperCAmelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
_UpperCAmelCase = tuple(state["sep"] )
if "cls" in state:
_UpperCAmelCase = tuple(state["cls"] )
_UpperCAmelCase = False
if state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = True
if state.get("trim_offsets" , __UpperCamelCase ) != trim_offsets:
_UpperCAmelCase = trim_offsets
_UpperCAmelCase = True
if changes_to_apply:
_UpperCAmelCase = getattr(__UpperCamelCase , state.pop("type" ) )
_UpperCAmelCase = component_class(**__UpperCamelCase )
setattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def UpperCAmelCase__ ( self : Union[str, Any] ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else value
_UpperCAmelCase = value
def UpperCAmelCase__ ( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : List[Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : Tuple , *__UpperCamelCase : int , **__UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : str , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None ):
_UpperCAmelCase = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : "Conversation" ):
_UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text )
else:
# Generated responses should contain them already.
inputs.append(__UpperCamelCase )
_UpperCAmelCase = " ".join(__UpperCamelCase )
_UpperCAmelCase = self.encode(__UpperCamelCase )
if len(__UpperCamelCase ) > self.model_max_length:
_UpperCAmelCase = input_ids[-self.model_max_length :]
logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids
| 684 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase_ ( unittest.TestCase ):
@property
def lowercase_ ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : Tuple = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def lowercase_ ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.dummy_uncond_unet
UpperCAmelCase__ : str = ScoreSdeVeScheduler()
UpperCAmelCase__ : Tuple = ScoreSdeVePipeline(unet=_A , scheduler=_A )
sde_ve.to(_A )
sde_ve.set_progress_bar_config(disable=_A )
UpperCAmelCase__ : int = torch.manual_seed(0 )
UpperCAmelCase__ : Any = sde_ve(num_inference_steps=2 , output_type='''numpy''' , generator=_A ).images
UpperCAmelCase__ : Tuple = torch.manual_seed(0 )
UpperCAmelCase__ : Dict = sde_ve(num_inference_steps=2 , output_type='''numpy''' , generator=_A , return_dict=_A )[
0
]
UpperCAmelCase__ : Optional[Any] = image[0, -3:, -3:, -1]
UpperCAmelCase__ : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase__ : Union[str, Any] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class lowerCamelCase_ ( unittest.TestCase ):
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = '''google/ncsnpp-church-256'''
UpperCAmelCase__ : Dict = UNetaDModel.from_pretrained(_A )
UpperCAmelCase__ : Optional[Any] = ScoreSdeVeScheduler.from_pretrained(_A )
UpperCAmelCase__ : int = ScoreSdeVePipeline(unet=_A , scheduler=_A )
sde_ve.to(_A )
sde_ve.set_progress_bar_config(disable=_A )
UpperCAmelCase__ : Any = torch.manual_seed(0 )
UpperCAmelCase__ : int = sde_ve(num_inference_steps=10 , output_type='''numpy''' , generator=_A ).images
UpperCAmelCase__ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase__ : List[str] = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 75 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
_UpperCAmelCase = WavaVecaForSequenceClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["projector.weight"]
_UpperCAmelCase = downstream_dict["projector.bias"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.weight"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
_UpperCAmelCase = WavaVecaForAudioFrameClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["model.linear.weight"]
_UpperCAmelCase = downstream_dict["model.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = WavaVecaForXVector.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["connector.weight"]
_UpperCAmelCase = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_UpperCAmelCase = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
_UpperCAmelCase = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
_UpperCAmelCase = downstream_dict["objective.W"]
return model
@torch.no_grad()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = torch.load(_lowerCAmelCase , map_location="cpu" )
_UpperCAmelCase = checkpoint["Downstream"]
_UpperCAmelCase = WavaVecaConfig.from_pretrained(_lowerCAmelCase )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , do_normalize=_lowerCAmelCase )
_UpperCAmelCase = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
_UpperCAmelCase = convert_classification(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForAudioFrameClassification" ):
_UpperCAmelCase = convert_diarization(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForXVector" ):
_UpperCAmelCase = convert_xvector(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
_UpperCAmelCase = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(_lowerCAmelCase )
hf_model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
__lowerCAmelCase = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 684 | 0 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
a_ = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 76 |
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
_UpperCAmelCase = []
_UpperCAmelCase = set({"(", "[", "{"} )
_UpperCAmelCase = set({")", "]", "}"} )
_UpperCAmelCase = {"{": "}", "[": "]", "(": ")"}
for i in range(len(_lowerCAmelCase ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(_lowerCAmelCase ) == 0 or (len(_lowerCAmelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(_lowerCAmelCase ) == 0
def __lowerCamelCase ( ) -> str:
_UpperCAmelCase = input("Enter sequence of brackets: " )
if is_balanced(_lowerCAmelCase ):
print(_lowerCAmelCase , "is balanced" )
else:
print(_lowerCAmelCase , "is not balanced" )
if __name__ == "__main__":
main()
| 684 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AutoformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AutoformerForPrediction""",
"""AutoformerModel""",
"""AutoformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = equationa
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = equationa
# Calculate the determinants of the matrices
_UpperCAmelCase = aa * ba - aa * ba
_UpperCAmelCase = ca * ba - ca * ba
_UpperCAmelCase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
_UpperCAmelCase = determinant_x / determinant
_UpperCAmelCase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 684 | 0 |
'''simple docstring'''
from torch import nn
class __A ( nn.Module ):
def __init__(self : Optional[int] , __a : List[Any] , __a : int ):
super().__init__()
UpperCAmelCase_ = class_size
UpperCAmelCase_ = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
UpperCAmelCase_ = nn.Linear(__a , __a )
def _lowercase (self : Optional[Any] , __a : Optional[Any] ):
# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
# hidden_state = self.mlp2(hidden_state)
UpperCAmelCase_ = self.mlp(__a )
return logits
| 78 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
# Initialise PyTorch model
_UpperCAmelCase = RemBertConfig.from_json_file(_lowerCAmelCase )
print("Building PyTorch model from configuration: {}".format(str(_lowerCAmelCase ) ) )
_UpperCAmelCase = RemBertModel(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Save pytorch-model
print("Save PyTorch model to {}".format(_lowerCAmelCase ) )
torch.save(model.state_dict() , _lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--rembert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained RemBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 684 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _lowerCamelCase ( __lowerCamelCase ) -> List[Any]:
'''simple docstring'''
def wrapper(*__lowerCamelCase , **__lowerCamelCase ):
UpperCAmelCase__ : str = timeit.default_timer()
UpperCAmelCase__ : Union[str, Any] = func(*__lowerCamelCase , **__lowerCamelCase )
UpperCAmelCase__ : int = timeit.default_timer() - starttime
return delta
UpperCAmelCase__ : Dict = func.__name__
return wrapper
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase=100 , __lowerCamelCase=None ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = []
UpperCAmelCase__ : Dict = seq_shapes or {}
for i in range(__lowerCamelCase ):
UpperCAmelCase__ : Optional[int] = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(__lowerCamelCase , _ArrayXD ):
UpperCAmelCase__ : Tuple = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(__lowerCamelCase , datasets.Value ):
if v.dtype == "string":
UpperCAmelCase__ : Optional[int] = """The small grey turtle was surprisingly fast when challenged."""
else:
UpperCAmelCase__ : Union[str, Any] = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(__lowerCamelCase , datasets.Sequence ):
while isinstance(__lowerCamelCase , datasets.Sequence ):
UpperCAmelCase__ : str = v.feature
UpperCAmelCase__ : str = seq_shapes[k]
UpperCAmelCase__ : Any = np.random.rand(*__lowerCamelCase ).astype(v.dtype )
UpperCAmelCase__ : Any = data
dummy_data.append((i, example) )
return dummy_data
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=100 , __lowerCamelCase=None ) -> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = generate_examples(__lowerCamelCase , num_examples=__lowerCamelCase , seq_shapes=__lowerCamelCase )
with ArrowWriter(features=__lowerCamelCase , path=__lowerCamelCase ) as writer:
for key, record in dummy_data:
UpperCAmelCase__ : List[Any] = features.encode_example(__lowerCamelCase )
writer.write(__lowerCamelCase )
UpperCAmelCase__ , UpperCAmelCase__ : str = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
UpperCAmelCase__ : Any = datasets.Dataset.from_file(filename=__lowerCamelCase , info=datasets.DatasetInfo(features=__lowerCamelCase ) )
return dataset
| 79 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def UpperCAmelCase__ ( *__UpperCamelCase : Dict , **__UpperCamelCase : Optional[int] ):
pass
@is_pipeline_test
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
__SCREAMING_SNAKE_CASE : List[Any] = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
_UpperCAmelCase = [
{
"image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"question": "How many cats are there?",
},
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"question": "How many cats are there?",
},
]
return vqa_pipeline, examples
def UpperCAmelCase__ ( self : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[int] ):
_UpperCAmelCase = vqa_pipeline(__UpperCamelCase , top_k=1 )
self.assertEqual(
__UpperCamelCase , [
[{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}],
[{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}],
] , )
@require_torch
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
_UpperCAmelCase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_UpperCAmelCase = "How many cats are there?"
_UpperCAmelCase = vqa_pipeline(image=__UpperCamelCase , question="How many cats are there?" , top_k=2 )
self.assertEqual(
__UpperCamelCase , [{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}, {"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}] )
_UpperCAmelCase = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
__UpperCamelCase , [{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}, {"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}] )
@slow
@require_torch
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="dandelin/vilt-b32-finetuned-vqa" )
_UpperCAmelCase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_UpperCAmelCase = "How many cats are there?"
_UpperCAmelCase = vqa_pipeline(image=__UpperCamelCase , question=__UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
_UpperCAmelCase = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
_UpperCAmelCase = vqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2 , )
@require_tf
@unittest.skip("Visual question answering not implemented in TF" )
def UpperCAmelCase__ ( self : Optional[int] ):
pass
| 684 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
raise Exception("""requires fairseq >= 1.0.0a""")
logging.set_verbosity_info()
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : str = """Hello world! cécé herlolip"""
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = FairseqRobertaModel.from_pretrained(lowerCamelCase )
roberta.eval() # disable dropout
__lowercase = roberta.model.encoder.sentence_encoder
__lowercase = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , )
if classification_head:
__lowercase = roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our RoBERTa config:""" , lowerCamelCase )
__lowercase = XLMRobertaXLForSequenceClassification(lowerCamelCase ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase )
model.eval()
# Now let's copy all the weights.
# Embeddings
__lowercase = roberta_sent_encoder.embed_tokens.weight
__lowercase = roberta_sent_encoder.embed_positions.weight
__lowercase = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
__lowercase = roberta_sent_encoder.layer_norm.weight
__lowercase = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
__lowercase = model.roberta.encoder.layer[i]
__lowercase = roberta_sent_encoder.layers[i]
__lowercase = layer.attention
__lowercase = roberta_layer.self_attn_layer_norm.weight
__lowercase = roberta_layer.self_attn_layer_norm.bias
# self attention
__lowercase = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
__lowercase = roberta_layer.self_attn.q_proj.weight
__lowercase = roberta_layer.self_attn.q_proj.bias
__lowercase = roberta_layer.self_attn.k_proj.weight
__lowercase = roberta_layer.self_attn.k_proj.bias
__lowercase = roberta_layer.self_attn.v_proj.weight
__lowercase = roberta_layer.self_attn.v_proj.bias
# self-attention output
__lowercase = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
__lowercase = roberta_layer.self_attn.out_proj.weight
__lowercase = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
__lowercase = roberta_layer.final_layer_norm.weight
__lowercase = roberta_layer.final_layer_norm.bias
# intermediate
__lowercase = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
__lowercase = roberta_layer.fca.weight
__lowercase = roberta_layer.fca.bias
# output
__lowercase = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
__lowercase = roberta_layer.fca.weight
__lowercase = roberta_layer.fca.bias
# end of layer
if classification_head:
__lowercase = roberta.model.classification_heads["""mnli"""].dense.weight
__lowercase = roberta.model.classification_heads["""mnli"""].dense.bias
__lowercase = roberta.model.classification_heads["""mnli"""].out_proj.weight
__lowercase = roberta.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
__lowercase = roberta.model.encoder.lm_head.dense.weight
__lowercase = roberta.model.encoder.lm_head.dense.bias
__lowercase = roberta.model.encoder.lm_head.layer_norm.weight
__lowercase = roberta.model.encoder.lm_head.layer_norm.bias
__lowercase = roberta.model.encoder.lm_head.weight
__lowercase = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
__lowercase = roberta.encode(lowerCamelCase ).unsqueeze(0 ) # batch of size 1
__lowercase = model(lowerCamelCase )[0]
if classification_head:
__lowercase = roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase ) )
else:
__lowercase = roberta.model(lowerCamelCase )[0]
print(our_output.shape , their_output.shape )
__lowercase = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'max_absolute_diff = {max_absolute_diff}' ) # ~ 1e-7
__lowercase = torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
pathlib.Path(lowerCamelCase ).mkdir(parents=lowerCamelCase , exist_ok=lowerCamelCase )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
__UpperCamelCase : List[Any] = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 80 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 684 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
_snake_case : List[str] = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_snake_case : Optional[Any] = 128_022
_snake_case : Optional[Any] = 128_028
@require_sentencepiece
class a (_lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Dict = MaMaaaTokenizer
__UpperCAmelCase : Tuple = False
__UpperCAmelCase : Optional[Any] = False
__UpperCAmelCase : Dict = True
def __snake_case ( self : Union[str, Any] ) -> int:
super().setUp()
__snake_case : Optional[Any] = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
__snake_case : List[Any] = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
__snake_case : Dict = Path(self.tmpdirname )
save_json(lowerCamelCase , save_dir / VOCAB_FILES_NAMES["vocab_file"] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCamelCase , save_dir / VOCAB_FILES_NAMES["spm_file"] )
__snake_case : Any = MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __snake_case ( self : Optional[Any] , **lowerCamelCase : Dict ) -> Tuple:
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase )
def __snake_case ( self : Any , lowerCamelCase : str ) -> List[Any]:
return (
"This is a test",
"This is a test",
)
def __snake_case ( self : Tuple ) -> Optional[Any]:
__snake_case : Dict = "</s>"
__snake_case : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase ) , lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase ) , lowerCamelCase )
def __snake_case ( self : str ) -> str:
__snake_case : Tuple = self.get_tokenizer()
__snake_case : str = list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "</s>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "<s>" )
self.assertEqual(len(lowerCamelCase ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip("Skip this test while all models are still to be uploaded." )
def __snake_case ( self : Dict ) -> str:
pass
def __snake_case ( self : Any ) -> Any:
__snake_case : Any = self.get_tokenizer()
__snake_case : List[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(lowerCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase ) , [2, 3, 4, 5, 6] , )
__snake_case : Dict = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(lowerCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
__snake_case : Optional[Any] = tokenizer.convert_tokens_to_string(lowerCamelCase )
self.assertEqual(lowerCamelCase , "This is a test" )
@slow
def __snake_case ( self : Optional[int] ) -> Optional[Any]:
# fmt: off
__snake_case : List[str] = {"input_ids": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase , model_name="facebook/m2m100_418M" , revision="c168bae485c864188cf9aa0e4108b0b6934dc91e" , )
@require_torch
@require_sentencepiece
@require_tokenizers
class a (unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = "facebook/m2m100_418M"
__UpperCAmelCase : Optional[Any] = [
"In my opinion, there are two levels of response from the French government.",
"NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
]
__UpperCAmelCase : Dict = [
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
]
# fmt: off
__UpperCAmelCase : Optional[Any] = [EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]
@classmethod
def __snake_case ( cls : Tuple ) -> int:
__snake_case : MaMaaaTokenizer = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en" , tgt_lang="fr" )
__snake_case : Optional[Any] = 1
return cls
def __snake_case ( self : Optional[Any] ) -> Dict:
self.assertEqual(self.tokenizer.get_lang_id("ar" ) , 128006 )
self.assertEqual(self.tokenizer.get_lang_id("en" ) , 128022 )
self.assertEqual(self.tokenizer.get_lang_id("ro" ) , 128076 )
self.assertEqual(self.tokenizer.get_lang_id("mr" ) , 128063 )
def __snake_case ( self : Tuple ) -> List[str]:
__snake_case : List[str] = self.tokenizer.get_vocab()
self.assertEqual(len(lowerCamelCase ) , self.tokenizer.vocab_size )
self.assertEqual(vocab["<unk>"] , 3 )
self.assertIn(self.tokenizer.get_lang_token("en" ) , lowerCamelCase )
def __snake_case ( self : Tuple ) -> int:
__snake_case : str = "en"
__snake_case : Any = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowerCamelCase )
def __snake_case ( self : Dict ) -> str:
self.assertIn(lowerCamelCase , self.tokenizer.all_special_ids )
# fmt: off
__snake_case : Any = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
__snake_case : Any = self.tokenizer.decode(lowerCamelCase , skip_special_tokens=lowerCamelCase )
__snake_case : Optional[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowerCamelCase )
self.assertEqual(lowerCamelCase , lowerCamelCase )
self.assertNotIn(self.tokenizer.eos_token , lowerCamelCase )
def __snake_case ( self : int ) -> int:
__snake_case : Dict = tempfile.mkdtemp()
__snake_case : Optional[Any] = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(lowerCamelCase )
__snake_case : Optional[Any] = MaMaaaTokenizer.from_pretrained(lowerCamelCase )
self.assertDictEqual(new_tok.lang_token_to_id , lowerCamelCase )
@require_torch
def __snake_case ( self : List[str] ) -> str:
__snake_case : Tuple = "en"
__snake_case : List[str] = "fr"
__snake_case : Any = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowerCamelCase , return_tensors="pt" )
__snake_case : Union[str, Any] = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
__snake_case : str = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def __snake_case ( self : str ) -> List[str]:
__snake_case : Any = "mr"
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
__snake_case : Tuple = "zh"
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def __snake_case ( self : Optional[Any] ) -> int:
__snake_case : Optional[Any] = "mr"
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
__snake_case : Dict = "zh"
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def __snake_case ( self : List[str] ) -> List[Any]:
__snake_case : List[Any] = self.tokenizer._build_translation_inputs("A test" , return_tensors="pt" , src_lang="en" , tgt_lang="ar" )
self.assertEqual(
nested_simplify(lowerCamelCase ) , {
# en_XX, A, test, EOS
"input_ids": [[128022, 58, 4183, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 128006,
} , )
| 81 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : str = (UniPCMultistepScheduler,)
__SCREAMING_SNAKE_CASE : Dict = (("""num_inference_steps""", 25),)
def UpperCAmelCase__ ( self : str , **__UpperCamelCase : Any ):
_UpperCAmelCase = {
"num_train_timesteps": 1_000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__UpperCamelCase )
return config
def UpperCAmelCase__ ( self : int , __UpperCamelCase : Any=0 , **__UpperCamelCase : Any ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any]=0 , **__UpperCamelCase : List[Any] ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Dict=None , **__UpperCamelCase : Optional[Any] ):
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 10
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(__UpperCamelCase , "set_timesteps" ):
scheduler.set_timesteps(__UpperCamelCase )
elif num_inference_steps is not None and not hasattr(__UpperCamelCase , "set_timesteps" ):
_UpperCAmelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
_UpperCAmelCase = scheduler.timesteps[5]
_UpperCAmelCase = scheduler.timesteps[6]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCAmelCase__ ( self : Union[str, Any] ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = UniPCMultistepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def UpperCAmelCase__ ( self : str ):
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def UpperCAmelCase__ ( self : int ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase__ ( self : Optional[int] ):
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = self.full_loop(prediction_type="v_prediction" )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1014 ) < 1e-3
def UpperCAmelCase__ ( self : Tuple ):
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 10
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
def UpperCAmelCase__ ( self : str , **__UpperCamelCase : Optional[Any] ):
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 684 | 0 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[str] , _UpperCAmelCase : Distribution , _UpperCAmelCase : Any=None , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Dict=0 ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ = 1.0 if scale is None else scale
UpperCAmelCase_ = 0.0 if loc is None else loc
super().__init__(_UpperCAmelCase , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=_UpperCAmelCase )] )
@property
def lowercase__ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
return self.base_dist.mean * self.scale + self.loc
@property
def lowercase__ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
return self.base_dist.variance * self.scale**2
@property
def lowercase__ ( self : Tuple ) -> Dict:
'''simple docstring'''
return self.variance.sqrt()
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : Callable[..., Tuple[torch.Tensor]] , **_UpperCAmelCase : List[Any] ) -> None:
'''simple docstring'''
super().__init__(**_UpperCAmelCase )
UpperCAmelCase_ = args_dim
UpperCAmelCase_ = nn.ModuleList([nn.Linear(_UpperCAmelCase , _UpperCAmelCase ) for dim in args_dim.values()] )
UpperCAmelCase_ = domain_map
def lowercase__ ( self : Optional[int] , _UpperCAmelCase : torch.Tensor ) -> Tuple[torch.Tensor]:
'''simple docstring'''
UpperCAmelCase_ = [proj(_UpperCAmelCase ) for proj in self.proj]
return self.domain_map(*_UpperCAmelCase )
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
UpperCAmelCase_ = function
def lowercase__ ( self : str , _UpperCAmelCase : str , *_UpperCAmelCase : Any ) -> List[Any]:
'''simple docstring'''
return self.function(_UpperCAmelCase , *_UpperCAmelCase )
class lowercase__ :
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
def __init__( self : List[Any] , _UpperCAmelCase : int = 1 ) -> None:
'''simple docstring'''
UpperCAmelCase_ = dim
UpperCAmelCase_ = {k: dim * self.args_dim[k] for k in self.args_dim}
def lowercase__ ( self : int , _UpperCAmelCase : Dict ) -> int:
'''simple docstring'''
if self.dim == 1:
return self.distribution_class(*_UpperCAmelCase )
else:
return Independent(self.distribution_class(*_UpperCAmelCase ) , 1 )
def lowercase__ ( self : List[str] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[torch.Tensor] = None , _UpperCAmelCase : Optional[torch.Tensor] = None , ) -> Distribution:
'''simple docstring'''
UpperCAmelCase_ = self._base_distribution(_UpperCAmelCase )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(_UpperCAmelCase , loc=_UpperCAmelCase , scale=_UpperCAmelCase , event_dim=self.event_dim )
@property
def lowercase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
return () if self.dim == 1 else (self.dim,)
@property
def lowercase__ ( self : Dict ) -> int:
'''simple docstring'''
return len(self.event_shape )
@property
def lowercase__ ( self : List[Any] ) -> float:
'''simple docstring'''
return 0.0
def lowercase__ ( self : Tuple , _UpperCAmelCase : int ) -> nn.Module:
'''simple docstring'''
return ParameterProjection(
in_features=_UpperCAmelCase , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def lowercase__ ( self : List[str] , *_UpperCAmelCase : torch.Tensor ) -> Tuple:
'''simple docstring'''
raise NotImplementedError()
@staticmethod
def lowercase__ ( _UpperCAmelCase : torch.Tensor ) -> torch.Tensor:
'''simple docstring'''
return (x + torch.sqrt(torch.square(_UpperCAmelCase ) + 4.0 )) / 2.0
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = {"df": 1, "loc": 1, "scale": 1}
UpperCamelCase = StudentT
@classmethod
def lowercase__ ( cls : Dict , _UpperCAmelCase : torch.Tensor , _UpperCAmelCase : torch.Tensor , _UpperCAmelCase : torch.Tensor ) -> int:
'''simple docstring'''
UpperCAmelCase_ = cls.squareplus(_UpperCAmelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
UpperCAmelCase_ = 2.0 + cls.squareplus(_UpperCAmelCase )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = {"loc": 1, "scale": 1}
UpperCamelCase = Normal
@classmethod
def lowercase__ ( cls : int , _UpperCAmelCase : torch.Tensor , _UpperCAmelCase : torch.Tensor ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ = cls.squareplus(_UpperCAmelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = {"total_count": 1, "logits": 1}
UpperCamelCase = NegativeBinomial
@classmethod
def lowercase__ ( cls : Optional[Any] , _UpperCAmelCase : torch.Tensor , _UpperCAmelCase : torch.Tensor ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ = cls.squareplus(_UpperCAmelCase )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def lowercase__ ( self : Tuple , _UpperCAmelCase : List[str] ) -> Distribution:
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ = distr_args
if self.dim == 1:
return self.distribution_class(total_count=_UpperCAmelCase , logits=_UpperCAmelCase )
else:
return Independent(self.distribution_class(total_count=_UpperCAmelCase , logits=_UpperCAmelCase ) , 1 )
def lowercase__ ( self : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[torch.Tensor] = None , _UpperCAmelCase : Optional[torch.Tensor] = None ) -> Distribution:
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 82 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __UpperCamelCase : List[Any]=0 ): # a graph with Node 0,1,...,N-1
_UpperCAmelCase = n
_UpperCAmelCase = [
[math.inf for j in range(0 , __UpperCamelCase )] for i in range(0 , __UpperCamelCase )
] # adjacency matrix for weight
_UpperCAmelCase = [
[math.inf for j in range(0 , __UpperCamelCase )] for i in range(0 , __UpperCamelCase )
] # dp[i][j] stores minimum distance from i to j
def UpperCAmelCase__ ( self : str , __UpperCamelCase : Tuple , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] ):
_UpperCAmelCase = w
def UpperCAmelCase__ ( self : Dict ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCAmelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCAmelCase__ ( self : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Any ):
return self.dp[u][v]
if __name__ == "__main__":
__lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 684 | 0 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase__ = direct_transformers_import(PATH_TO_TRANSFORMERS)
lowerCAmelCase__ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
lowerCAmelCase__ = {
# used to compute the property `self.chunk_length`
'''EncodecConfig''': ['''overlap'''],
# used as `self.bert_model = BertModel(config, ...)`
'''DPRConfig''': True,
# not used in modeling files, but it's an important information
'''FSMTConfig''': ['''langs'''],
# used internally in the configuration class file
'''GPTNeoConfig''': ['''attention_types'''],
# used internally in the configuration class file
'''EsmConfig''': ['''is_folding_model'''],
# used during training (despite we don't have training script for these models yet)
'''Mask2FormerConfig''': ['''ignore_value'''],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
'''OneFormerConfig''': ['''ignore_value''', '''norm'''],
# used during preprocessing and collation, see `collating_graphormer.py`
'''GraphormerConfig''': ['''spatial_pos_max'''],
# used internally in the configuration class file
'''T5Config''': ['''feed_forward_proj'''],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
'''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
'''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
# used internally in the configuration class file
'''LongT5Config''': ['''feed_forward_proj'''],
# used internally in the configuration class file
'''SwitchTransformersConfig''': ['''feed_forward_proj'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''BioGptConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''GLPNConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''SegformerConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''CvtConfig''': ['''layer_norm_eps'''],
# having default values other than `1e-5` - we can't fix them without breaking
'''PerceiverConfig''': ['''layer_norm_eps'''],
# used internally to calculate the feature size
'''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate the feature size
'''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate the feature size
'''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
# used internally to calculate `mlp_dim`
'''SamVisionConfig''': ['''mlp_ratio'''],
# For (head) training, but so far not implemented
'''ClapAudioConfig''': ['''num_classes'''],
# Not used, but providing useful information to users
'''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
'''CLIPSegConfig''': True,
'''DeformableDetrConfig''': True,
'''DetaConfig''': True,
'''DinatConfig''': True,
'''DonutSwinConfig''': True,
'''EfficientFormerConfig''': True,
'''FSMTConfig''': True,
'''JukeboxConfig''': True,
'''LayoutLMv2Config''': True,
'''MaskFormerSwinConfig''': True,
'''MT5Config''': True,
'''NatConfig''': True,
'''OneFormerConfig''': True,
'''PerceiverConfig''': True,
'''RagConfig''': True,
'''SpeechT5Config''': True,
'''SwinConfig''': True,
'''Swin2SRConfig''': True,
'''Swinv2Config''': True,
'''SwitchTransformersConfig''': True,
'''TableTransformerConfig''': True,
'''TapasConfig''': True,
'''TransfoXLConfig''': True,
'''UniSpeechConfig''': True,
'''UniSpeechSatConfig''': True,
'''WavLMConfig''': True,
'''WhisperConfig''': True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
'''JukeboxPriorConfig''': True,
# TODO: @Younes (for `is_decoder`)
'''Pix2StructTextConfig''': True,
}
)
def snake_case_ ( A_ : Optional[int], A_ : Any, A_ : int, A_ : int ):
'''simple docstring'''
_lowerCamelCase : Tuple = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
F'''config.{attribute}''' in modeling_source
or F'''getattr(config, "{attribute}"''' in modeling_source
or F'''getattr(self.config, "{attribute}"''' in modeling_source
):
_lowerCamelCase : Union[str, Any] = True
# Deal with multi-line cases
elif (
re.search(
RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"''', A_, )
is not None
):
_lowerCamelCase : str = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
_lowerCamelCase : Optional[Any] = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
_lowerCamelCase : str = [
'''bos_index''',
'''eos_index''',
'''pad_index''',
'''unk_index''',
'''mask_index''',
'''image_size''',
'''use_cache''',
'''out_features''',
'''out_indices''',
]
_lowerCamelCase : Optional[Any] = ['''encoder_no_repeat_ngram_size''']
# Special cases to be allowed
_lowerCamelCase : Any = True
if not attribute_used:
_lowerCamelCase : Tuple = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
_lowerCamelCase : str = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
_lowerCamelCase : Dict = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
_lowerCamelCase : List[str] = True
elif attribute.endswith('''_token_id''' ):
_lowerCamelCase : Optional[int] = True
# configuration class specific cases
if not case_allowed:
_lowerCamelCase : Optional[int] = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__, [] )
_lowerCamelCase : List[str] = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def snake_case_ ( A_ : Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = dict(inspect.signature(config_class.__init__ ).parameters )
_lowerCamelCase : List[Any] = [x for x in list(signature.keys() ) if x not in ['''self''', '''kwargs''']]
_lowerCamelCase : Union[str, Any] = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
_lowerCamelCase : Union[str, Any] = {}
if len(config_class.attribute_map ) > 0:
_lowerCamelCase : List[Any] = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
_lowerCamelCase : Dict = inspect.getsourcefile(A_ )
_lowerCamelCase : Dict = os.path.dirname(A_ )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
_lowerCamelCase : List[Any] = [os.path.join(A_, A_ ) for fn in os.listdir(A_ ) if fn.startswith('''modeling_''' )]
# Get the source code strings
_lowerCamelCase : Dict = []
for path in modeling_paths:
if os.path.isfile(A_ ):
with open(A_ ) as fp:
modeling_sources.append(fp.read() )
_lowerCamelCase : Dict = []
for config_param, default_value in zip(A_, A_ ):
# `attributes` here is all the variant names for `config_param`
_lowerCamelCase : Tuple = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(A_, A_, A_, A_ ):
unused_attributes.append(attributes[0] )
return sorted(A_ )
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : List[str] = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
_lowerCamelCase : Dict = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ), lambda A_ : inspect.isclass(A_ )
and issubclass(A_, A_ )
and inspect.getmodule(A_ ) == inspect.getmodule(_config_class ), )
]
for config_class in config_classes_in_module:
_lowerCamelCase : Union[str, Any] = check_config_attributes_being_used(A_ )
if len(A_ ) > 0:
_lowerCamelCase : Optional[int] = unused_attributes
if len(A_ ) > 0:
_lowerCamelCase : Union[str, Any] = '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
for name, attributes in configs_with_unused_attributes.items():
error += F'''{name}: {attributes}\n'''
raise ValueError(A_ )
if __name__ == "__main__":
check_config_attributes()
| 83 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowercase , lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : Dict = VQModel
__SCREAMING_SNAKE_CASE : Optional[int] = """sample"""
@property
def UpperCAmelCase__ ( self : List[str] , __UpperCamelCase : Optional[int]=(32, 32) ):
_UpperCAmelCase = 4
_UpperCAmelCase = 3
_UpperCAmelCase = floats_tensor((batch_size, num_channels) + sizes ).to(__UpperCamelCase )
return {"sample": image}
@property
def UpperCAmelCase__ ( self : Tuple ):
return (3, 32, 32)
@property
def UpperCAmelCase__ ( self : str ):
return (3, 32, 32)
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 3,
}
_UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Dict ):
pass
def UpperCAmelCase__ ( self : str ):
pass
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = VQModel.from_pretrained("fusing/vqgan-dummy" , output_loading_info=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(__UpperCamelCase )
_UpperCAmelCase = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = VQModel.from_pretrained("fusing/vqgan-dummy" )
model.to(__UpperCamelCase ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
_UpperCAmelCase = torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size )
_UpperCAmelCase = image.to(__UpperCamelCase )
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase ).sample
_UpperCAmelCase = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
_UpperCAmelCase = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] )
# fmt: on
self.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
| 684 | 0 |
import heapq as hq
import math
from collections.abc import Iterator
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = str(id_ )
lowercase = None
lowercase = None
lowercase = []
lowercase = {} # {vertex:distance}
def __lt__( self , snake_case ):
return self.key < other.key
def __repr__( self ):
return self.id
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
self.neighbors.append(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = weight
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __SCREAMING_SNAKE_CASE )
graph[b - 1].add_edge(graph[a - 1] , __SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
for u in graph:
lowercase = math.inf
lowercase = None
lowercase = 0
lowercase = graph[:]
while q:
lowercase = min(__SCREAMING_SNAKE_CASE )
q.remove(__SCREAMING_SNAKE_CASE )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
lowercase = u
lowercase = u.edges[v.id]
for i in range(1 , len(__SCREAMING_SNAKE_CASE ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for u in graph:
lowercase = math.inf
lowercase = None
lowercase = 0
lowercase = list(__SCREAMING_SNAKE_CASE )
hq.heapify(__SCREAMING_SNAKE_CASE )
while h:
lowercase = hq.heappop(__SCREAMING_SNAKE_CASE )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
lowercase = u
lowercase = u.edges[v.id]
hq.heapify(__SCREAMING_SNAKE_CASE )
for i in range(1 , len(__SCREAMING_SNAKE_CASE ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCAmelCase_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
import requests
__lowerCAmelCase = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def __lowerCamelCase ( _lowerCAmelCase ) -> None:
# fetching a list of articles in json format
_UpperCAmelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"] , 1 ):
print(F'''{i}.) {article["title"]}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 684 | 0 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : List[Any] = 8
# DPR tok
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , 'dpr_tokenizer' )
os.makedirs(a_ , exist_ok=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(a_ , DPR_VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
# BART tok
SCREAMING_SNAKE_CASE__ : List[str] = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
SCREAMING_SNAKE_CASE__ : Any = dict(zip(a_ , range(len(a_ ) ) ) )
SCREAMING_SNAKE_CASE__ : Tuple = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'unk_token': '<unk>'}
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(self.tmpdirname , 'bart_tokenizer' )
os.makedirs(a_ , exist_ok=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(a_ , BART_VOCAB_FILES_NAMES['vocab_file'] )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(a_ , BART_VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(a_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(a_ ) )
def __lowercase( self : Union[str, Any] )-> DPRQuestionEncoderTokenizer:
"""simple docstring"""
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'dpr_tokenizer' ) )
def __lowercase( self : Union[str, Any] )-> DPRContextEncoderTokenizer:
"""simple docstring"""
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'dpr_tokenizer' ) )
def __lowercase( self : List[Any] )-> BartTokenizer:
"""simple docstring"""
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'bart_tokenizer' ) )
def __lowercase( self : str )-> int:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowercase( self : str )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = Dataset.from_dict(
{
'id': ['0', '1'],
'text': ['foo', 'bar'],
'title': ['Foo', 'Bar'],
'embeddings': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('embeddings' , string_factory='Flat' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def __lowercase( self : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_dummy_dataset()
SCREAMING_SNAKE_CASE__ : Optional[int] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('transformers.models.rag.retrieval_rag.load_dataset' ) as mock_load_dataset:
SCREAMING_SNAKE_CASE__ : Any = dataset
SCREAMING_SNAKE_CASE__ : int = RagRetriever(
a_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def __lowercase( self : Tuple , a_ : bool )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_dataset()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='custom' , )
if from_disk:
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(self.tmpdirname , 'dataset' )
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.join(self.tmpdirname , 'index.faiss' )
dataset.get_index('embeddings' ).save(os.path.join(self.tmpdirname , 'index.faiss' ) )
dataset.drop_index('embeddings' )
dataset.save_to_disk(os.path.join(self.tmpdirname , 'dataset' ) )
del dataset
SCREAMING_SNAKE_CASE__ : Union[str, Any] = RagRetriever(
a_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = RagRetriever(
a_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , a_ ) , )
return retriever
def __lowercase( self : Any )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = Dataset.from_dict(
{
'id': ['0', '1'],
'text': ['foo', 'bar'],
'title': ['Foo', 'Bar'],
'embeddings': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('embeddings' , string_factory='Flat' , metric_type=faiss.METRIC_INNER_PRODUCT )
SCREAMING_SNAKE_CASE__ : str = os.path.join(self.tmpdirname , 'hf_bert_base.hnswSQ8_correct_phi_128.c_index' )
dataset.save_faiss_index('embeddings' , index_file_name + '.index.dpr' )
pickle.dump(dataset['id'] , open(index_file_name + '.index_meta.dpr' , 'wb' ) )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(self.tmpdirname , 'psgs_w100.tsv.pkl' )
SCREAMING_SNAKE_CASE__ : List[str] = {sample['id']: [sample['text'], sample['title']] for sample in dataset}
pickle.dump(a_ , open(a_ , 'wb' ) )
SCREAMING_SNAKE_CASE__ : Any = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='legacy' , index_path=self.tmpdirname , )
SCREAMING_SNAKE_CASE__ : int = RagRetriever(
a_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def __lowercase( self : Tuple )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = 1
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_dummy_canonical_hf_index_retriever()
SCREAMING_SNAKE_CASE__ : Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = retriever.retrieve(a_ , n_docs=a_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(a_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['embeddings', 'id', 'text', 'title'] )
self.assertEqual(len(doc_dicts[0]['id'] ) , a_ )
self.assertEqual(doc_dicts[0]['id'][0] , '1' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['id'][0] , '0' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __lowercase( self : List[str] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('transformers.models.rag.retrieval_rag.load_dataset' ) as mock_load_dataset:
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_dataset()
retriever.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = RagRetriever.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : List[Any] = retriever.retrieve(a_ , n_docs=1 )
self.assertTrue(out is not None )
def __lowercase( self : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = retriever.retrieve(a_ , n_docs=a_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(a_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['embeddings', 'id', 'text', 'title'] )
self.assertEqual(len(doc_dicts[0]['id'] ) , a_ )
self.assertEqual(doc_dicts[0]['id'][0] , '1' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['id'][0] , '0' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __lowercase( self : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = RagRetriever.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Tuple = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : Optional[Any] = retriever.retrieve(a_ , n_docs=1 )
self.assertTrue(out is not None )
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = retriever.retrieve(a_ , n_docs=a_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(a_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['embeddings', 'id', 'text', 'title'] )
self.assertEqual(len(doc_dicts[0]['id'] ) , a_ )
self.assertEqual(doc_dicts[0]['id'][0] , '1' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['id'][0] , '0' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = RagRetriever.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : Any = retriever.retrieve(a_ , n_docs=1 )
self.assertTrue(out is not None )
def __lowercase( self : str )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_legacy_index_retriever()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = retriever.retrieve(a_ , n_docs=a_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(a_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['text', 'title'] )
self.assertEqual(len(doc_dicts[0]['text'] ) , a_ )
self.assertEqual(doc_dicts[0]['text'][0] , 'bar' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['text'][0] , 'foo' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __lowercase( self : Dict )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = RagRetriever.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = retriever.retrieve(a_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def __lowercase( self : int )-> Tuple:
"""simple docstring"""
import torch
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : Any = self.get_dummy_canonical_hf_index_retriever()
SCREAMING_SNAKE_CASE__ : Optional[Any] = [[5, 7], [10, 11]]
SCREAMING_SNAKE_CASE__ : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = retriever(a_ , a_ , prefix=retriever.config.generator.prefix , n_docs=a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = (
out['context_input_ids'],
out['context_attention_mask'],
out['retrieved_doc_embeds'],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(a_ , a_ )
self.assertIsInstance(a_ , a_ )
self.assertIsInstance(a_ , np.ndarray )
SCREAMING_SNAKE_CASE__ : int = retriever(
a_ , a_ , prefix=retriever.config.generator.prefix , n_docs=a_ , return_tensors='pt' , )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = ( # noqa: F841
out['context_input_ids'],
out['context_attention_mask'],
out['retrieved_doc_embeds'],
out['doc_ids'],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(a_ , torch.Tensor )
self.assertIsInstance(a_ , torch.Tensor )
self.assertIsInstance(a_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dpr_ctx_encoder_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = 1
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
retriever.set_ctx_encoder_tokenizer(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [[5, 7], [10, 11]]
SCREAMING_SNAKE_CASE__ : List[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : int = retriever(a_ , a_ , prefix=retriever.config.generator.prefix , n_docs=a_ )
self.assertEqual(
len(a_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask') ) , a_ ) # check for doc token related keys in dictionary.
| 85 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Any ):
_UpperCAmelCase = 10
def UpperCAmelCase__ ( self : Optional[int] ):
_UpperCAmelCase = [1, 2, 3, 4]
_UpperCAmelCase = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = "It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this."
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , [] )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = ""
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , [] )
self.assertEqual(__UpperCamelCase , [] )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = (
"It was the year of Our Lord one thousand seven hundred and "
"seventy-five\n\nSpiritual revelations were conceded to England "
"at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
)
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
_UpperCAmelCase = [
"It was the year of Our Lord one thousand seven hundred and seventy-five.",
"Spiritual revelations were conceded to England at that favoured period, as at this.",
]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ["It was the best of times."]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = torch.tensor([1, 2, 3, 4] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 0 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 23 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Optional[int] ):
_UpperCAmelCase = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 1 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = 101
_UpperCAmelCase = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
_UpperCAmelCase = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
_UpperCAmelCase = compute_token_type_ids(__UpperCamelCase , __UpperCamelCase )
np.testing.assert_array_equal(__UpperCamelCase , __UpperCamelCase )
| 684 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a :int = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :Optional[Any] = [
'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
'SwinForImageClassification',
'SwinForMaskedImageModeling',
'SwinModel',
'SwinPreTrainedModel',
'SwinBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a :int = [
'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFSwinForImageClassification',
'TFSwinForMaskedImageModeling',
'TFSwinModel',
'TFSwinPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
__a :Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 86 |
from __future__ import annotations
from collections import namedtuple
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> tuple:
_UpperCAmelCase = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_lowerCamelCase : Any = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class UpperCamelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : List[str]) ->Any:
'''simple docstring'''
super().__init__()
A__ = torchvision.models.resnetaaa(pretrained=UpperCAmelCase__)
A__ = list(model.children())[:-2]
A__ = nn.Sequential(*UpperCAmelCase__)
A__ = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , UpperCAmelCase__ : Optional[int]) ->int:
'''simple docstring'''
A__ = self.pool(self.model(UpperCAmelCase__))
A__ = torch.flatten(UpperCAmelCase__ , start_dim=2)
A__ = out.transpose(1 , 2).contiguous()
return out # BxNx2048
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Any , UpperCAmelCase__ : Any , UpperCAmelCase__ : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int) ->Tuple:
'''simple docstring'''
A__ = [json.loads(UpperCAmelCase__) for l in open(UpperCAmelCase__)]
A__ = os.path.dirname(UpperCAmelCase__)
A__ = tokenizer
A__ = labels
A__ = len(UpperCAmelCase__)
A__ = max_seq_length
A__ = transforms
def __len__( self : Optional[int]) ->Any:
'''simple docstring'''
return len(self.data)
def __getitem__( self : int , UpperCAmelCase__ : Tuple) ->str:
'''simple docstring'''
A__ = torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=UpperCAmelCase__))
A__ , A__ , A__ = sentence[0], sentence[1:-1], sentence[-1]
A__ = sentence[: self.max_seq_length]
A__ = torch.zeros(self.n_classes)
A__ = 1
A__ = Image.open(os.path.join(self.data_dir , self.data[index]['''img'''])).convert('''RGB''')
A__ = self.transforms(UpperCAmelCase__)
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def SCREAMING_SNAKE_CASE ( self : List[str]) ->List[str]:
'''simple docstring'''
A__ = Counter()
for row in self.data:
label_freqs.update(row['''label'''])
return label_freqs
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[Any]:
"""simple docstring"""
A__ = [len(row['''sentence'''] ) for row in batch]
A__ , A__ = len(lowercase_ ), max(lowercase_ )
A__ = torch.zeros(lowercase_ , lowercase_ , dtype=torch.long )
A__ = torch.zeros(lowercase_ , lowercase_ , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(lowercase_ , lowercase_ ) ):
A__ = input_row['''sentence''']
A__ = 1
A__ = torch.stack([row['''image'''] for row in batch] )
A__ = torch.stack([row['''label'''] for row in batch] )
A__ = torch.stack([row['''image_start_token'''] for row in batch] )
A__ = torch.stack([row['''image_end_token'''] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def SCREAMING_SNAKE_CASE ( ) -> str:
"""simple docstring"""
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
"""simple docstring"""
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17] , std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69] , ),
] )
| 87 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __lowerCamelCase ( _lowerCAmelCase ) -> Any:
_UpperCAmelCase = {}
_UpperCAmelCase = job["started_at"]
_UpperCAmelCase = job["completed_at"]
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
_UpperCAmelCase = start
_UpperCAmelCase = end
_UpperCAmelCase = duration_in_min
return job_info
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=None ) -> str:
_UpperCAmelCase = None
if token is not None:
_UpperCAmelCase = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
_UpperCAmelCase = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
_UpperCAmelCase = requests.get(_lowerCAmelCase , headers=_lowerCAmelCase ).json()
_UpperCAmelCase = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
_UpperCAmelCase = math.ceil((result["total_count"] - 100) / 100 )
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = requests.get(url + F'''&page={i + 2}''' , headers=_lowerCAmelCase ).json()
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = get_job_time(args.workflow_run_id)
__lowerCAmelCase = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v["duration"]}''')
| 684 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class lowercase__ ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=13 , SCREAMING_SNAKE_CASE=7 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=99 , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=5 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=37 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=512 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=4 , ) -> str:
_lowerCamelCase : str = parent
_lowerCamelCase : Any = batch_size
_lowerCamelCase : List[str] = seq_length
_lowerCamelCase : Union[str, Any] = is_training
_lowerCamelCase : Any = use_attention_mask
_lowerCamelCase : Optional[int] = use_token_type_ids
_lowerCamelCase : Any = use_labels
_lowerCamelCase : Optional[int] = vocab_size
_lowerCamelCase : Dict = hidden_size
_lowerCamelCase : int = num_hidden_layers
_lowerCamelCase : Dict = num_attention_heads
_lowerCamelCase : Optional[Any] = intermediate_size
_lowerCamelCase : Any = hidden_act
_lowerCamelCase : Optional[int] = hidden_dropout_prob
_lowerCamelCase : int = attention_probs_dropout_prob
_lowerCamelCase : List[str] = max_position_embeddings
_lowerCamelCase : List[Any] = type_vocab_size
_lowerCamelCase : Any = type_sequence_label_size
_lowerCamelCase : Dict = initializer_range
_lowerCamelCase : List[str] = num_choices
def UpperCamelCase_ ( self) -> Union[str, Any]:
_lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_lowerCamelCase : Dict = None
if self.use_attention_mask:
_lowerCamelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length])
_lowerCamelCase : int = None
if self.use_token_type_ids:
_lowerCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_lowerCamelCase : List[str] = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCamelCase_ ( self) -> Optional[Any]:
_lowerCamelCase : Any = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = config_and_inputs
_lowerCamelCase : List[str] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def UpperCamelCase_ ( self) -> Optional[Any]:
_lowerCamelCase : List[Any] = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : str = config_and_inputs
_lowerCamelCase : List[str] = True
_lowerCamelCase : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
_lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class lowercase__ ( A_ ,unittest.TestCase ):
__UpperCAmelCase = True
__UpperCAmelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCamelCase_ ( self) -> str:
_lowerCamelCase : Tuple = FlaxRobertaModelTester(self)
@slow
def UpperCamelCase_ ( self) -> Dict:
for model_class_name in self.all_model_classes:
_lowerCamelCase : Any = model_class_name.from_pretrained("""roberta-base""" , from_pt=SCREAMING_SNAKE_CASE)
_lowerCamelCase : int = model(np.ones((1, 1)))
self.assertIsNotNone(SCREAMING_SNAKE_CASE)
| 88 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__lowerCAmelCase = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 1_3_1_0_7_2,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
return torch.atana(_lowerCAmelCase , _lowerCAmelCase ) / math.pi * 2
def __lowerCamelCase ( _lowerCAmelCase ) -> Union[str, Any]:
_UpperCAmelCase = torch.sin(t * math.pi / 2 ) ** 2
_UpperCAmelCase = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(_lowerCAmelCase , _lowerCAmelCase )
class __SCREAMING_SNAKE_CASE ( lowercase):
pass
class __SCREAMING_SNAKE_CASE ( nn.Module):
def __init__( self : str , __UpperCamelCase : Optional[int] ):
super().__init__()
_UpperCAmelCase = DiffusionAttnUnetaD(__UpperCamelCase , n_attn_layers=4 )
_UpperCAmelCase = deepcopy(self.diffusion )
_UpperCAmelCase = torch.quasirandom.SobolEngine(1 , scramble=__UpperCamelCase )
def __lowerCamelCase ( _lowerCAmelCase ) -> int:
_UpperCAmelCase = MODELS_MAP[model_name]["url"]
os.system(F'''wget {url} ./''' )
return F'''./{model_name}.ckpt'''
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
__lowerCAmelCase = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
__lowerCAmelCase = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
__lowerCAmelCase = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
__lowerCAmelCase = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
if name.startswith("skip" ):
return name.replace("skip" , RES_CONV_MAP["skip"] )
# name has to be of format main.{digit}
if not name.startswith("main." ):
raise ValueError(F'''ResConvBlock error with {name}''' )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[Any]:
for key, value in ATTN_MAP.items():
if name.startswith(_lowerCAmelCase ) and not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return name.replace(_lowerCAmelCase , _lowerCAmelCase )
elif name.startswith(_lowerCAmelCase ):
return [name.replace(_lowerCAmelCase , _lowerCAmelCase ) for v in value]
raise ValueError(F'''Attn error with {name}''' )
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=13 ) -> List[Any]:
_UpperCAmelCase = input_string
if string.split("." )[0] == "timestep_embed":
return string.replace("timestep_embed" , "time_proj" )
_UpperCAmelCase = 0
if string.startswith("net.3." ):
depth += 1
_UpperCAmelCase = string[6:]
elif string.startswith("net." ):
_UpperCAmelCase = string[4:]
while string.startswith("main.7." ):
depth += 1
_UpperCAmelCase = string[7:]
if string.startswith("main." ):
_UpperCAmelCase = string[5:]
# mid block
if string[:2].isdigit():
_UpperCAmelCase = string[:2]
_UpperCAmelCase = string[2:]
else:
_UpperCAmelCase = string[0]
_UpperCAmelCase = string[1:]
if depth == max_depth:
_UpperCAmelCase = MID_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = "mid_block"
elif depth > 0 and int(_lowerCAmelCase ) < 7:
_UpperCAmelCase = DOWN_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''down_blocks.{depth}'''
elif depth > 0 and int(_lowerCAmelCase ) > 7:
_UpperCAmelCase = UP_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - depth - 1}'''
elif depth == 0:
_UpperCAmelCase = DEPTH_0_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - 1}''' if int(_lowerCAmelCase ) > 3 else "down_blocks.0"
if not string_left.startswith("." ):
raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.''' )
_UpperCAmelCase = string_left[1:]
if "resnets" in new_layer:
_UpperCAmelCase = convert_resconv_naming(_lowerCAmelCase )
elif "attentions" in new_layer:
_UpperCAmelCase = convert_attn_naming(_lowerCAmelCase )
_UpperCAmelCase = new_string_left
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = prefix + "." + new_layer + "." + string_left
else:
_UpperCAmelCase = [prefix + "." + new_layer + "." + s for s in string_left]
return new_string
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[int]:
_UpperCAmelCase = {}
for k, v in state_dict.items():
if k.endswith("kernel" ):
# up- and downsample layers, don't have trainable weights
continue
_UpperCAmelCase = rename(_lowerCAmelCase )
# check if we need to transform from Conv => Linear for attention
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = transform_conv_attns(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_UpperCAmelCase = v
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
if len(_lowerCAmelCase ) == 1:
if len(v.shape ) == 3:
# weight
_UpperCAmelCase = v[:, :, 0]
else:
# bias
_UpperCAmelCase = v
else:
# qkv matrices
_UpperCAmelCase = v.shape[0]
_UpperCAmelCase = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase ) -> Tuple:
_UpperCAmelCase = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
_UpperCAmelCase = args.model_path.split("/" )[-1].split("." )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
_UpperCAmelCase = download(_lowerCAmelCase )
_UpperCAmelCase = MODELS_MAP[model_name]["sample_rate"]
_UpperCAmelCase = MODELS_MAP[model_name]["sample_size"]
_UpperCAmelCase = Object()
_UpperCAmelCase = sample_size
_UpperCAmelCase = sample_rate
_UpperCAmelCase = 0
_UpperCAmelCase = UNetaDModel(sample_size=_lowerCAmelCase , sample_rate=_lowerCAmelCase )
_UpperCAmelCase = diffusers_model.state_dict()
_UpperCAmelCase = DiffusionUncond(_lowerCAmelCase )
orig_model.load_state_dict(torch.load(args.model_path , map_location=_lowerCAmelCase )["state_dict"] )
_UpperCAmelCase = orig_model.diffusion_ema.eval()
_UpperCAmelCase = orig_model.state_dict()
_UpperCAmelCase = rename_orig_weights(_lowerCAmelCase )
_UpperCAmelCase = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
_UpperCAmelCase = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(_lowerCAmelCase ) == 0, F'''Problem with {renamed_minus_diffusers}'''
assert all(k.endswith("kernel" ) for k in list(_lowerCAmelCase ) ), F'''Problem with {diffusers_minus_renamed}'''
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
if key == "time_proj.weight":
_UpperCAmelCase = value.squeeze()
_UpperCAmelCase = value
diffusers_model.load_state_dict(_lowerCAmelCase )
_UpperCAmelCase = 100
_UpperCAmelCase = 33
_UpperCAmelCase = IPNDMScheduler(num_train_timesteps=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(_lowerCAmelCase )
_UpperCAmelCase = torch.randn([1, 2, config.sample_size] , generator=_lowerCAmelCase ).to(_lowerCAmelCase )
_UpperCAmelCase = torch.linspace(1 , 0 , steps + 1 , device=_lowerCAmelCase )[:-1]
_UpperCAmelCase = get_crash_schedule(_lowerCAmelCase )
_UpperCAmelCase = DanceDiffusionPipeline(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(33 )
_UpperCAmelCase = pipe(num_inference_steps=_lowerCAmelCase , generator=_lowerCAmelCase ).audios
_UpperCAmelCase = sampling.iplms_sample(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {} )
_UpperCAmelCase = generated.clamp(-1 , 1 )
_UpperCAmelCase = (generated - audio).abs().sum()
_UpperCAmelCase = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("Diff sum" , _lowerCAmelCase )
print("Diff max" , _lowerCAmelCase )
assert diff_max < 1E-3, F'''Diff max: {diff_max} is too much :-/'''
print(F'''Conversion for {model_name} successful!''' )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
__lowerCAmelCase = parser.parse_args()
main(args)
| 684 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class _lowerCamelCase( _a ):
lowercase_ : Any = """deta"""
lowercase_ : Union[str, Any] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self, lowerCamelCase=None, lowerCamelCase=9_00, lowerCamelCase=20_48, lowerCamelCase=6, lowerCamelCase=20_48, lowerCamelCase=8, lowerCamelCase=6, lowerCamelCase=10_24, lowerCamelCase=8, lowerCamelCase=0.0, lowerCamelCase=True, lowerCamelCase="relu", lowerCamelCase=2_56, lowerCamelCase=0.1, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.0_2, lowerCamelCase=1.0, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase="sine", lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=True, lowerCamelCase=3_00, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=0.1, lowerCamelCase=0.2_5, **lowerCamelCase, ) -> Any:
"""simple docstring"""
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
_lowercase : List[Any] = CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'])
else:
if isinstance(lowerCamelCase, lowerCamelCase):
_lowercase : Dict = backbone_config.pop('model_type')
_lowercase : int = CONFIG_MAPPING[backbone_model_type]
_lowercase : Union[str, Any] = config_class.from_dict(lowerCamelCase)
_lowercase : Union[str, Any] = backbone_config
_lowercase : Any = num_queries
_lowercase : Union[str, Any] = max_position_embeddings
_lowercase : Union[str, Any] = d_model
_lowercase : Optional[int] = encoder_ffn_dim
_lowercase : Optional[int] = encoder_layers
_lowercase : Optional[Any] = encoder_attention_heads
_lowercase : Optional[Any] = decoder_ffn_dim
_lowercase : Dict = decoder_layers
_lowercase : Tuple = decoder_attention_heads
_lowercase : Union[str, Any] = dropout
_lowercase : Optional[Any] = attention_dropout
_lowercase : int = activation_dropout
_lowercase : Tuple = activation_function
_lowercase : List[Any] = init_std
_lowercase : Union[str, Any] = init_xavier_std
_lowercase : int = encoder_layerdrop
_lowercase : Optional[int] = auxiliary_loss
_lowercase : Dict = position_embedding_type
# deformable attributes
_lowercase : Any = num_feature_levels
_lowercase : str = encoder_n_points
_lowercase : Any = decoder_n_points
_lowercase : List[str] = two_stage
_lowercase : Dict = two_stage_num_proposals
_lowercase : Any = with_box_refine
_lowercase : List[Any] = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('If two_stage is True, with_box_refine must be True.')
# Hungarian matcher
_lowercase : List[Any] = class_cost
_lowercase : Optional[int] = bbox_cost
_lowercase : str = giou_cost
# Loss coefficients
_lowercase : Optional[int] = mask_loss_coefficient
_lowercase : int = dice_loss_coefficient
_lowercase : List[Any] = bbox_loss_coefficient
_lowercase : Optional[Any] = giou_loss_coefficient
_lowercase : str = eos_coefficient
_lowercase : int = focal_alpha
super().__init__(is_encoder_decoder=lowerCamelCase, **lowerCamelCase)
@property
def UpperCamelCase ( self) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def UpperCamelCase ( self) -> int:
"""simple docstring"""
return self.d_model
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : int = copy.deepcopy(self.__dict__)
_lowercase : Optional[int] = self.backbone_config.to_dict()
_lowercase : Optional[Any] = self.__class__.model_type
return output
| 89 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
__lowerCAmelCase = get_tests_dir("fixtures")
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 500
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=__UpperCamelCase ) as mock_head:
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase__ ( self : List[Any] ):
# This test is for deprecated behavior and can be removed in v5
_UpperCAmelCase = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json" )
def UpperCAmelCase__ ( self : Dict ):
with self.assertRaises(__UpperCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
_UpperCAmelCase = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants" )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants" , subfolder="feature_extractor" )
self.assertIsNotNone(__UpperCamelCase )
@is_staging_test
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
@classmethod
def UpperCAmelCase__ ( cls : str ):
_UpperCAmelCase = TOKEN
HfFolder.save_token(__UpperCamelCase )
@classmethod
def UpperCAmelCase__ ( cls : Optional[Any] ):
try:
delete_repo(token=cls._token , repo_id="test-image-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-image-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-image-processor" )
except HTTPError:
pass
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="test-image-processor" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("valid_org/test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="valid_org/test-image-processor-org" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : int ):
CustomImageProcessor.register_for_auto_class()
_UpperCAmelCase = CustomImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-dynamic-image-processor" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"} , )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
F'''{USER}/test-dynamic-image-processor''' , trust_remote_code=__UpperCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , "CustomImageProcessor" )
| 684 | 0 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__UpperCAmelCase = '''\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
'''
__UpperCAmelCase = '''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''
__UpperCAmelCase = '''
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of candidates to evaluate. Each candidates should be a list
of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the
correctness of a code candidate.
k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
num_workers: number of workers used to evaluate the canidate programs (Default: 4).
timeout:
Returns:
pass_at_k: dict with pass rates for each k
results: dict with granular results of each unittest
Examples:
>>> code_eval = datasets.load_metric("code_eval")
>>> test_cases = ["assert add(2,3)==5"]
>>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
>>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
>>> print(pass_at_k)
{\'pass@1\': 0.5, \'pass@2\': 1.0}
'''
__UpperCAmelCase = '''
################################################################################
!!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).
Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:
>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"
################################################################################\
'''
__UpperCAmelCase = '''The MIT License
Copyright (c) OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Value('''string''' ),
} ) , homepage='''https://github.com/openai/human-eval''' , codebase_urls=['''https://github.com/openai/human-eval'''] , reference_urls=['''https://github.com/openai/human-eval'''] , license=_LICENSE , )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=[1, 10, 1_00] , lowerCamelCase_=4 , lowerCamelCase_=3.0 ) -> Any:
if os.getenv('''HF_ALLOW_CODE_EVAL''' , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError('''This metric is currently not supported on Windows.''' )
with ThreadPoolExecutor(max_workers=lowerCamelCase_ ) as executor:
lowerCAmelCase__ = []
lowerCAmelCase__ = Counter()
lowerCAmelCase__ = 0
lowerCAmelCase__ = defaultdict(lowerCamelCase_ )
for task_id, (candidates, test_case) in enumerate(zip(lowerCamelCase_ , lowerCamelCase_ ) ):
for candidate in candidates:
lowerCAmelCase__ = candidate + '''\n''' + test_case
lowerCAmelCase__ = (test_program, timeout, task_id, completion_id[task_id])
lowerCAmelCase__ = executor.submit(lowerCamelCase_ , *lowerCamelCase_ )
futures.append(lowerCamelCase_ )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(lowerCamelCase_ ):
lowerCAmelCase__ = future.result()
results[result["task_id"]].append((result['''completion_id'''], result) )
lowerCAmelCase__ , lowerCAmelCase__ = [], []
for result in results.values():
result.sort()
lowerCAmelCase__ = [r[1]['''passed'''] for r in result]
total.append(len(lowerCamelCase_ ) )
correct.append(sum(lowerCamelCase_ ) )
lowerCAmelCase__ = np.array(lowerCamelCase_ )
lowerCAmelCase__ = np.array(lowerCamelCase_ )
lowerCAmelCase__ = k
lowerCAmelCase__ = {F"""pass@{k}""": estimate_pass_at_k(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def _snake_case ( A , A , A ) -> List[str]:
def estimator(A , A , A ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(A , A ):
lowerCAmelCase__ = itertools.repeat(A , len(A ) )
else:
assert len(A ) == len(A )
lowerCAmelCase__ = iter(A )
return np.array([estimator(int(A ) , int(A ) , A ) for n, c in zip(A , A )] ) | 90 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
return getitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
return setitem, k, v
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
return delitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase ) -> Optional[int]:
try:
return fun(_lowerCAmelCase , *_lowerCAmelCase ), None
except Exception as e:
return None, e
__lowerCAmelCase = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__lowerCAmelCase = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items" ),
pytest.param(_overwrite_items , id="overwrite items" ),
pytest.param(_delete_items , id="delete items" ),
pytest.param(_access_absent_items , id="access absent items" ),
pytest.param(_add_with_resize_up , id="add with resize up" ),
pytest.param(_add_with_resize_down , id="add with resize down" ),
) , )
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
_UpperCAmelCase = HashMap(initial_block_size=4 )
_UpperCAmelCase = {}
for _, (fun, *args) in enumerate(_lowerCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
assert my_res == py_res
assert str(_lowerCAmelCase ) == str(_lowerCAmelCase )
assert set(_lowerCAmelCase ) == set(_lowerCAmelCase )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
assert set(my.items() ) == set(py.items() )
def __lowerCamelCase ( ) -> List[Any]:
def is_public(_lowerCAmelCase ) -> bool:
return not name.startswith("_" )
_UpperCAmelCase = {name for name in dir({} ) if is_public(_lowerCAmelCase )}
_UpperCAmelCase = {name for name in dir(HashMap() ) if is_public(_lowerCAmelCase )}
assert dict_public_names > hash_public_names
| 684 | 0 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
_lowercase = float('''nan''')
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : List[str] ,A_ : Tuple ) -> Any:
A = sys.stdout
A = open(A_ ,'a' )
def __getattr__( self : int ,A_ : Optional[Any] ) -> Tuple:
return getattr(self.stdout ,A_ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,A_ : Optional[int] ) -> str:
self.stdout.write(A_ )
# strip tqdm codes
self.file.write(re.sub(R'^.*\r' ,'' ,A_ ,0 ,re.M ) )
def _snake_case ( snake_case__ : Optional[Any]=80 , snake_case__ : List[str]=False ):
A = []
# deal with critical env vars
A = ['CUDA_VISIBLE_DEVICES']
for key in env_keys:
A = os.environ.get(snake_case__ , snake_case__ )
if val is not None:
cmd.append(F'{key}={val}' )
# python executable (not always needed if the script is executable)
A = sys.executable if full_python_path else sys.executable.split('/' )[-1]
cmd.append(snake_case__ )
# now the normal args
cmd += list(map(shlex.quote , sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
A = []
A = ''
while len(snake_case__ ) > 0:
current_line += F'{cmd.pop(0 )} '
if len(snake_case__ ) == 0 or len(snake_case__ ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(snake_case__ )
A = ''
return "\\\n".join(snake_case__ )
def _snake_case ( snake_case__ : str , snake_case__ : str ):
# unwrap multi-line input
A = re.sub(r'[\\\n]+' , ' ' , args.base_cmd )
# remove --output_dir if any and set our own
A = re.sub('--output_dir\s+[^\s]+' , '' , args.base_cmd )
args.base_cmd += F' --output_dir {output_dir}'
# ensure we have --overwrite_output_dir
A = re.sub('--overwrite_output_dir\s+' , '' , args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def _snake_case ( snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : str , snake_case__ : int , snake_case__ : Optional[Any] , snake_case__ : Any , snake_case__ : List[Any] ):
# Enable to debug everything but the run itself, to do it fast and see the progress.
# This is useful for debugging the output formatting quickly - we can remove it later once
# everybody is happy with the output
if 0:
import random
from time import sleep
sleep(0 )
return dict(
{k: random.uniform(0 , 100 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222] )} , )
A = subprocess.run(snake_case__ , capture_output=snake_case__ , text=snake_case__ )
if verbose:
print('STDOUT' , result.stdout )
print('STDERR' , result.stderr )
# save the streams
A = variation.replace(' ' , '-' )
with open(Path(snake_case__ ) / F'log.{prefix}.stdout.txt' , 'w' ) as f:
f.write(result.stdout )
with open(Path(snake_case__ ) / F'log.{prefix}.stderr.txt' , 'w' ) as f:
f.write(result.stderr )
if result.returncode != 0:
if verbose:
print('failed' )
return {target_metric_key: nan}
with io.open(F'{output_dir}/all_results.json' , 'r' , encoding='utf-8' ) as f:
A = json.load(snake_case__ )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def _snake_case ( snake_case__ : str , snake_case__ : str , snake_case__ : str , snake_case__ : List[str] , snake_case__ : Optional[int] , snake_case__ : Tuple , snake_case__ : List[Any] , snake_case__ : Tuple , snake_case__ : Dict , snake_case__ : Optional[Any] , ):
A = []
A = []
A = F'{id}: {variation:<{longest_variation_len}}'
A = F'{preamble}: '
A = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(snake_case__ ) , desc=snake_case__ , leave=snake_case__ ):
A = process_run_single(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
A = single_run_metrics[target_metric_key]
if not math.isnan(snake_case__ ):
metrics.append(snake_case__ )
results.append(snake_case__ )
outcome += "✓"
else:
outcome += "✘"
A = F'\33[2K\r{outcome}'
if len(snake_case__ ) > 0:
A = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
A = round(mean_metrics[target_metric_key] , 2 )
A = F'{outcome} {mean_target}'
if len(snake_case__ ) > 1:
results_str += F' {tuple(round(snake_case__ , 2 ) for x in results )}'
print(snake_case__ )
A = variation
return mean_metrics
else:
print(snake_case__ )
return {variation_key: variation, target_metric_key: nan}
def _snake_case ( ):
A = torch.cuda.get_device_properties(torch.device('cuda' ) )
return F'\nDatetime : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch : {torch.__version__}\ncuda : {torch.version.cuda}\npython : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n'
def _snake_case ( snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : Union[str, Any] ):
A = pd.DataFrame(snake_case__ )
A = 'variation'
A = 'diff_%'
A = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
A = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(snake_case__ ):
# as a fallback, use the minimal value as the sentinel
A = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(snake_case__ ):
A = df.apply(
lambda snake_case__ : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value )
if not math.isnan(r[target_metric_key] )
else 0 , axis='columns' , )
# re-order columns
A = [variation_key, target_metric_key, diff_key, *report_metric_keys]
A = df.reindex(snake_case__ , axis='columns' ) # reorder cols
# capitalize
A = df.rename(str.capitalize , axis='columns' )
# make the cols as narrow as possible
A = df.rename(lambda snake_case__ : c.replace('_' , '<br>' ) , axis='columns' )
A = df.rename(lambda snake_case__ : c.replace('_' , '\n' ) , axis='columns' )
A = ['', 'Copy between the cut-here-lines and paste as is to github or a forum']
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=snake_case__ , floatfmt='.2f' )]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=snake_case__ , floatfmt='.2f' )]
print('\n\n'.join(snake_case__ ) )
def _snake_case ( ):
A = argparse.ArgumentParser()
parser.add_argument(
'--base-cmd' , default=snake_case__ , type=snake_case__ , required=snake_case__ , help='Base cmd' , )
parser.add_argument(
'--variations' , default=snake_case__ , type=snake_case__ , nargs='+' , required=snake_case__ , help='Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\'' , )
parser.add_argument(
'--base-variation' , default=snake_case__ , type=snake_case__ , help='Baseline variation to compare to. if None the minimal target value will be used to compare against' , )
parser.add_argument(
'--target-metric-key' , default=snake_case__ , type=snake_case__ , required=snake_case__ , help='Target metric key in output_dir/all_results.json, e.g., train_samples_per_second' , )
parser.add_argument(
'--report-metric-keys' , default='' , type=snake_case__ , help='Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples' , )
parser.add_argument(
'--repeat-times' , default=1 , type=snake_case__ , help='How many times to re-run each variation - an average will be reported' , )
parser.add_argument(
'--output_dir' , default='output_benchmark' , type=snake_case__ , help='The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked' , )
parser.add_argument(
'--verbose' , default=snake_case__ , action='store_true' , help='Whether to show the outputs of each run or just the benchmark progress' , )
A = parser.parse_args()
A = args.output_dir
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
A = get_base_command(snake_case__ , snake_case__ )
# split each dimension into its --foo variations
A = [list(map(str.strip , re.split(r'\|' , snake_case__ ) ) ) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
A = list(map(str.strip , map(' '.join , itertools.product(*snake_case__ ) ) ) )
A = max(len(snake_case__ ) for x in variations )
# split wanted keys
A = args.report_metric_keys.split()
# capture prints into a log file for convenience
A = F'benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S" )}.txt'
print(F'\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt' )
print(F'and this script\'s output is also piped into {report_fn}' )
A = Tee(snake_case__ )
print(F'\n*** Running {len(snake_case__ )} benchmarks:' )
print(F'Base command: {" ".join(snake_case__ )}' )
A = 'variation'
A = []
for id, variation in enumerate(tqdm(snake_case__ , desc='Total completion: ' , leave=snake_case__ ) ):
A = base_cmd + variation.split()
results.append(
process_run(
id + 1 , snake_case__ , snake_case__ , snake_case__ , snake_case__ , args.target_metric_key , snake_case__ , args.repeat_times , snake_case__ , args.verbose , ) )
process_results(snake_case__ , args.target_metric_key , snake_case__ , args.base_variation , snake_case__ )
if __name__ == "__main__":
main() | 91 |
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
_UpperCAmelCase = len(_lowerCAmelCase )
for i in range(1 , _lowerCAmelCase ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase = 0
_UpperCAmelCase = i - 1
while low <= high:
_UpperCAmelCase = (low + high) // 2
if val < collection[mid]:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
for j in range(_lowerCAmelCase , _lowerCAmelCase , -1 ):
_UpperCAmelCase = collection[j - 1]
_UpperCAmelCase = val
return collection
if __name__ == "__main__":
__lowerCAmelCase = input("Enter numbers separated by a comma:\n").strip()
__lowerCAmelCase = [int(item) for item in user_input.split(",")]
print(binary_insertion_sort(unsorted))
| 684 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int = 600851475143 ) -> int:
try:
lowercase : Any =int(__magic_name__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
lowercase : Optional[Any] =2
lowercase : Dict =0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowercase : Union[str, Any] =i
while n % i == 0:
lowercase : Optional[int] =n // i
i += 1
return int(__magic_name__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 92 |
__lowerCAmelCase = 2_5_6
# Modulus to hash a string
__lowerCAmelCase = 1_0_0_0_0_0_3
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
_UpperCAmelCase = len(_lowerCAmelCase )
_UpperCAmelCase = len(_lowerCAmelCase )
if p_len > t_len:
return False
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 1
# Calculating the hash of pattern and substring of text
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_UpperCAmelCase = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_UpperCAmelCase = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_UpperCAmelCase = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def __lowerCamelCase ( ) -> None:
_UpperCAmelCase = "abc1abc12"
_UpperCAmelCase = "alskfjaldsabc1abc1abc12k23adsfabcabc"
_UpperCAmelCase = "alskfjaldsk23adsfabcabc"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase ) and not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 2)
_UpperCAmelCase = "ABABX"
_UpperCAmelCase = "ABABZABABYABABX"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 3)
_UpperCAmelCase = "AAAB"
_UpperCAmelCase = "ABAAAAAB"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 4)
_UpperCAmelCase = "abcdabcy"
_UpperCAmelCase = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 5)
_UpperCAmelCase = "Lü"
_UpperCAmelCase = "Lüsai"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
_UpperCAmelCase = "Lue"
assert not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 684 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Dict = tempfile.mkdtemp()
# fmt: off
lowerCAmelCase__ :List[str] = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
lowerCAmelCase__ :Tuple = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
lowerCAmelCase__ :int = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
lowerCAmelCase__ :List[Any] = {'unk_token': '<unk>'}
lowerCAmelCase__ :Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCAmelCase__ :Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__UpperCAmelCase ) )
lowerCAmelCase__ :str = {
'do_resize': True,
'size': 2_0,
'do_center_crop': True,
'crop_size': 1_8,
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
lowerCAmelCase__ :Optional[Any] = os.path.join(self.tmpdirname , __UpperCAmelCase )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self , **__UpperCAmelCase ):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def snake_case ( self , **__UpperCAmelCase ):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def snake_case ( self , **__UpperCAmelCase ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
lowerCAmelCase__ :Optional[Any] = [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = self.get_tokenizer()
lowerCAmelCase__ :str = self.get_rust_tokenizer()
lowerCAmelCase__ :Optional[Any] = self.get_image_processor()
lowerCAmelCase__ :Optional[Any] = CLIPSegProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowerCAmelCase__ :Tuple = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=__UpperCAmelCase )
lowerCAmelCase__ :int = CLIPSegProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowerCAmelCase__ :List[Any] = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , __UpperCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , __UpperCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , __UpperCAmelCase )
self.assertIsInstance(processor_fast.image_processor , __UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase__ :str = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
lowerCAmelCase__ :Optional[Any] = self.get_image_processor(do_normalize=__UpperCAmelCase , padding_value=1.0 )
lowerCAmelCase__ :str = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = self.get_image_processor()
lowerCAmelCase__ :Union[str, Any] = self.get_tokenizer()
lowerCAmelCase__ :Tuple = CLIPSegProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
lowerCAmelCase__ :Any = self.prepare_image_inputs()
lowerCAmelCase__ :int = image_processor(__UpperCAmelCase , return_tensors='np' )
lowerCAmelCase__ :Optional[Any] = processor(images=__UpperCAmelCase , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.get_image_processor()
lowerCAmelCase__ :Dict = self.get_tokenizer()
lowerCAmelCase__ :Tuple = CLIPSegProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
lowerCAmelCase__ :Any = 'lower newer'
lowerCAmelCase__ :Any = processor(text=__UpperCAmelCase )
lowerCAmelCase__ :int = tokenizer(__UpperCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.get_image_processor()
lowerCAmelCase__ :int = self.get_tokenizer()
lowerCAmelCase__ :Any = CLIPSegProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
lowerCAmelCase__ :Optional[Any] = 'lower newer'
lowerCAmelCase__ :Tuple = self.prepare_image_inputs()
lowerCAmelCase__ :Any = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(__UpperCAmelCase ):
processor()
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = self.get_image_processor()
lowerCAmelCase__ :Union[str, Any] = self.get_tokenizer()
lowerCAmelCase__ :Any = CLIPSegProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
lowerCAmelCase__ :Optional[Any] = self.prepare_image_inputs()
lowerCAmelCase__ :int = self.prepare_image_inputs()
lowerCAmelCase__ :Dict = processor(images=__UpperCAmelCase , visual_prompt=__UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'conditional_pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(__UpperCAmelCase ):
processor()
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = self.get_image_processor()
lowerCAmelCase__ :Any = self.get_tokenizer()
lowerCAmelCase__ :Dict = CLIPSegProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase )
lowerCAmelCase__ :int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase__ :List[Any] = processor.batch_decode(__UpperCAmelCase )
lowerCAmelCase__ :Optional[Any] = tokenizer.batch_decode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
| 93 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__lowerCAmelCase = random.Random()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=1.0 , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> List[str]:
if rng is None:
_UpperCAmelCase = global_rng
_UpperCAmelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def __init__( self : Optional[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any]=7 , __UpperCamelCase : Union[str, Any]=400 , __UpperCamelCase : List[Any]=2_000 , __UpperCamelCase : Optional[Any]=10 , __UpperCamelCase : Optional[int]=160 , __UpperCamelCase : Any=8 , __UpperCamelCase : List[Any]=0.0 , __UpperCamelCase : Dict=4_000 , __UpperCamelCase : Optional[int]=False , __UpperCamelCase : Tuple=True , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = min_seq_length
_UpperCAmelCase = max_seq_length
_UpperCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_UpperCAmelCase = padding_value
_UpperCAmelCase = sampling_rate
_UpperCAmelCase = return_attention_mask
_UpperCAmelCase = do_normalize
_UpperCAmelCase = feature_size
_UpperCAmelCase = chunk_length
_UpperCAmelCase = hop_length
def UpperCAmelCase__ ( self : Optional[Any] ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : Tuple=False , __UpperCamelCase : Dict=False ):
def _flatten(__UpperCamelCase : Any ):
return list(itertools.chain(*__UpperCamelCase ) )
if equal_length:
_UpperCAmelCase = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_UpperCAmelCase = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : str = WhisperFeatureExtractor if is_speech_available() else None
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = WhisperFeatureExtractionTester(self )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = feat_extract_first.save_pretrained(__UpperCamelCase )[0]
check_json_file_has_correct_format(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = os.path.join(__UpperCamelCase , "feat_extract.json" )
feat_extract_first.to_json_file(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_json_file(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
# Test feature size
_UpperCAmelCase = feature_extractor(__UpperCamelCase , padding="max_length" , return_tensors="np" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
_UpperCAmelCase = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_features
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test batched
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
_UpperCAmelCase = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_UpperCAmelCase = np.asarray(__UpperCamelCase )
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test truncation required
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
_UpperCAmelCase = [x[: feature_extractor.n_samples] for x in speech_inputs]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs_truncated]
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
import torch
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = np.random.rand(100 , 32 ).astype(np.floataa )
_UpperCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Tuple ):
_UpperCAmelCase = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
_UpperCAmelCase = ds.sort("id" ).select(range(__UpperCamelCase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def UpperCAmelCase__ ( self : Tuple ):
# fmt: off
_UpperCAmelCase = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
_UpperCAmelCase = self._load_datasamples(1 )
_UpperCAmelCase = WhisperFeatureExtractor()
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="pt" ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __UpperCamelCase , atol=1e-4 ) )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = self._load_datasamples(1 )[0]
_UpperCAmelCase = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
_UpperCAmelCase = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__UpperCamelCase )[0]
self.assertTrue(np.all(np.mean(__UpperCamelCase ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase ) - 1 ) < 1e-3 ) )
| 684 | 0 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
SCREAMING_SNAKE_CASE = parser.parse_args()
SCREAMING_SNAKE_CASE = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
SCREAMING_SNAKE_CASE = CLIPImageProcessor()
SCREAMING_SNAKE_CASE = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
SCREAMING_SNAKE_CASE = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 94 |
# Copyright 2023 The HuggingFace Inc. 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.
import re
from ..utils import cached_file
# docstyle-ignore
__lowerCAmelCase = "\nHuman: <<task>>\n\nAssistant: "
__lowerCAmelCase = "huggingface-tools/default-prompts"
__lowerCAmelCase = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase="run" ) -> Union[str, Any]:
if prompt_or_repo_id is None:
_UpperCAmelCase = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , _lowerCAmelCase ) is not None:
return prompt_or_repo_id
_UpperCAmelCase = cached_file(
_lowerCAmelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(_lowerCAmelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 684 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def snake_case ( ):
print("Making key files..." )
make_key_files("rsa" ,10_24 )
print("Key files generation successful." )
def snake_case ( A__ ):
print("Generating prime p..." )
UpperCAmelCase_ : int = rabinMiller.generate_large_prime(A__ )
print("Generating prime q..." )
UpperCAmelCase_ : Dict = rabinMiller.generate_large_prime(A__ )
UpperCAmelCase_ : Union[str, Any] = p * q
print("Generating e that is relatively prime to (p - 1) * (q - 1)..." )
while True:
UpperCAmelCase_ : Any = random.randrange(2 ** (key_size - 1) ,2 ** (key_size) )
if cryptoMath.gcd(A__ ,(p - 1) * (q - 1) ) == 1:
break
print("Calculating d that is mod inverse of e..." )
UpperCAmelCase_ : Union[str, Any] = cryptoMath.find_mod_inverse(A__ ,(p - 1) * (q - 1) )
UpperCAmelCase_ : List[str] = (n, e)
UpperCAmelCase_ : List[str] = (n, d)
return (public_key, private_key)
def snake_case ( A__ ,A__ ):
if os.path.exists(F"""{name}_pubkey.txt""" ) or os.path.exists(F"""{name}_privkey.txt""" ):
print("\nWARNING:" )
print(
F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
"Use a different name or delete these files and re-run this program." )
sys.exit()
UpperCAmelCase_ , UpperCAmelCase_ : Any = generate_key(A__ )
print(F"""\nWriting public key to file {name}_pubkey.txt...""" )
with open(F"""{name}_pubkey.txt""" ,"w" ) as out_file:
out_file.write(F"""{key_size},{public_key[0]},{public_key[1]}""" )
print(F"""Writing private key to file {name}_privkey.txt...""" )
with open(F"""{name}_privkey.txt""" ,"w" ) as out_file:
out_file.write(F"""{key_size},{private_key[0]},{private_key[1]}""" )
if __name__ == "__main__":
main()
| 95 |
from itertools import permutations
def __lowerCamelCase ( _lowerCAmelCase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(_lowerCAmelCase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def __lowerCamelCase ( _lowerCAmelCase = 10 ) -> int:
return sum(
int("".join(map(_lowerCAmelCase , _lowerCAmelCase ) ) )
for num in permutations(range(_lowerCAmelCase ) )
if is_substring_divisible(_lowerCAmelCase ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 684 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a ( ) -> Tuple:
__magic_name__: Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__magic_name__: Any = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(__UpperCAmelCase )
# Let's go
__magic_name__: int = parser.parse_args()
if not hasattr(__UpperCAmelCase , """func""" ):
parser.print_help()
exit(1 )
# Run
__magic_name__: Optional[Any] = args.func(__UpperCAmelCase )
service.run()
if __name__ == "__main__":
main()
| 96 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
__lowerCAmelCase = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
__lowerCAmelCase = {"facebook/blenderbot-3B": 1_2_8}
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : Optional[int] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : str = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : List[Any] = ["""input_ids""", """attention_mask"""]
__SCREAMING_SNAKE_CASE : List[str] = BlenderbotTokenizer
def __init__( self : Tuple , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]="replace" , __UpperCamelCase : Tuple="<s>" , __UpperCamelCase : str="</s>" , __UpperCamelCase : Dict="</s>" , __UpperCamelCase : Union[str, Any]="<s>" , __UpperCamelCase : Union[str, Any]="<unk>" , __UpperCamelCase : Tuple="<pad>" , __UpperCamelCase : Optional[int]="<mask>" , __UpperCamelCase : Union[str, Any]=False , __UpperCamelCase : List[str]=True , **__UpperCamelCase : int , ):
super().__init__(
__UpperCamelCase , __UpperCamelCase , tokenizer_file=__UpperCamelCase , errors=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = getattr(__UpperCamelCase , pre_tok_state.pop("type" ) )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = pre_tok_class(**__UpperCamelCase )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = "post_processor"
_UpperCAmelCase = getattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
if tokenizer_component_instance:
_UpperCAmelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
_UpperCAmelCase = tuple(state["sep"] )
if "cls" in state:
_UpperCAmelCase = tuple(state["cls"] )
_UpperCAmelCase = False
if state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = True
if state.get("trim_offsets" , __UpperCamelCase ) != trim_offsets:
_UpperCAmelCase = trim_offsets
_UpperCAmelCase = True
if changes_to_apply:
_UpperCAmelCase = getattr(__UpperCamelCase , state.pop("type" ) )
_UpperCAmelCase = component_class(**__UpperCamelCase )
setattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def UpperCAmelCase__ ( self : Union[str, Any] ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else value
_UpperCAmelCase = value
def UpperCAmelCase__ ( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : List[Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : Tuple , *__UpperCamelCase : int , **__UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : str , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None ):
_UpperCAmelCase = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : "Conversation" ):
_UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text )
else:
# Generated responses should contain them already.
inputs.append(__UpperCamelCase )
_UpperCAmelCase = " ".join(__UpperCamelCase )
_UpperCAmelCase = self.encode(__UpperCamelCase )
if len(__UpperCamelCase ) > self.model_max_length:
_UpperCAmelCase = input_ids[-self.model_max_length :]
logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids
| 684 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class lowercase__:
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple=1_3 , SCREAMING_SNAKE_CASE_ : Optional[Any]=7 , SCREAMING_SNAKE_CASE_ : Dict=6 , SCREAMING_SNAKE_CASE_ : int=1_7 , SCREAMING_SNAKE_CASE_ : str=2_3 , SCREAMING_SNAKE_CASE_ : Dict=1_1 , SCREAMING_SNAKE_CASE_ : List[str]=True , ) -> Any:
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = act_dim
lowercase_ = state_dim
lowercase_ = hidden_size
lowercase_ = max_length
lowercase_ = is_training
def _lowercase ( self : str ) -> Optional[Any]:
lowercase_ = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowercase_ = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowercase_ = floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase_ = floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase_ = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_0_0_0 )
lowercase_ = random_attention_mask((self.batch_size, self.seq_length) )
lowercase_ = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def _lowercase ( self : int ) -> List[Any]:
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def _lowercase ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , ) -> Dict:
lowercase_ = DecisionTransformerModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowercase_ = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def _lowercase ( self : Union[str, Any] ) -> Dict:
lowercase_ = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) = config_and_inputs
lowercase_ = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class lowercase__( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Dict = (DecisionTransformerModel,) if is_torch_available() else ()
a :int = ()
a :Optional[Any] = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
a :List[str] = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
a :Optional[int] = False
a :Optional[int] = False
a :Union[str, Any] = False
a :Optional[Any] = False
a :Optional[int] = False
a :Optional[Any] = False
a :Any = False
a :Union[str, Any] = False
a :Tuple = False
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
lowercase_ = DecisionTransformerModelTester(self )
lowercase_ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=3_7 )
def _lowercase ( self : Any ) -> str:
self.config_tester.run_common_tests()
def _lowercase ( self : Dict ) -> int:
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
@slow
def _lowercase ( self : List[str] ) -> Union[str, Any]:
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ = DecisionTransformerModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[Any] ) -> int:
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(SCREAMING_SNAKE_CASE_ )
lowercase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ = [*signature.parameters.keys()]
lowercase_ = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(SCREAMING_SNAKE_CASE_ )] , SCREAMING_SNAKE_CASE_ )
@require_torch
class lowercase__( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowercase ( self : Optional[Any] ) -> str:
lowercase_ = 2 # number of steps of autoregressive prediction we will perform
lowercase_ = 1_0 # defined by the RL environment, may be normalized
lowercase_ = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
lowercase_ = model.to(SCREAMING_SNAKE_CASE_ )
lowercase_ = model.config
torch.manual_seed(0 )
lowercase_ = torch.randn(1 , 1 , config.state_dim ).to(device=SCREAMING_SNAKE_CASE_ , dtype=torch.floataa ) # env.reset()
lowercase_ = torch.tensor(
[[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]] , device=SCREAMING_SNAKE_CASE_ )
lowercase_ = torch.tensor(SCREAMING_SNAKE_CASE_ , device=SCREAMING_SNAKE_CASE_ , dtype=torch.floataa ).reshape(1 , 1 , 1 )
lowercase_ = state
lowercase_ = torch.zeros(1 , 0 , config.act_dim , device=SCREAMING_SNAKE_CASE_ , dtype=torch.floataa )
lowercase_ = torch.zeros(1 , 0 , device=SCREAMING_SNAKE_CASE_ , dtype=torch.floataa )
lowercase_ = torch.tensor(0 , device=SCREAMING_SNAKE_CASE_ , dtype=torch.long ).reshape(1 , 1 )
for step in range(SCREAMING_SNAKE_CASE_ ):
lowercase_ = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=SCREAMING_SNAKE_CASE_ )] , dim=1 )
lowercase_ = torch.cat([rewards, torch.zeros(1 , 1 , device=SCREAMING_SNAKE_CASE_ )] , dim=1 )
lowercase_ = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
lowercase_ , lowercase_ , lowercase_ = model(
states=SCREAMING_SNAKE_CASE_ , actions=SCREAMING_SNAKE_CASE_ , rewards=SCREAMING_SNAKE_CASE_ , returns_to_go=SCREAMING_SNAKE_CASE_ , timesteps=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1e-4 ) )
lowercase_ , lowercase_ , lowercase_ , lowercase_ = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=SCREAMING_SNAKE_CASE_ , dtype=torch.floataa ),
1.0,
False,
{},
)
lowercase_ = action_pred[0, -1]
lowercase_ = torch.cat([states, state] , dim=1 )
lowercase_ = returns_to_go[0, -1] - reward
lowercase_ = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
lowercase_ = torch.cat(
[timesteps, torch.ones((1, 1) , device=SCREAMING_SNAKE_CASE_ , dtype=torch.long ) * (step + 1)] , dim=1 )
| 97 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
_UpperCAmelCase = WavaVecaForSequenceClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["projector.weight"]
_UpperCAmelCase = downstream_dict["projector.bias"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.weight"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
_UpperCAmelCase = WavaVecaForAudioFrameClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["model.linear.weight"]
_UpperCAmelCase = downstream_dict["model.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = WavaVecaForXVector.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["connector.weight"]
_UpperCAmelCase = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_UpperCAmelCase = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
_UpperCAmelCase = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
_UpperCAmelCase = downstream_dict["objective.W"]
return model
@torch.no_grad()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = torch.load(_lowerCAmelCase , map_location="cpu" )
_UpperCAmelCase = checkpoint["Downstream"]
_UpperCAmelCase = WavaVecaConfig.from_pretrained(_lowerCAmelCase )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , do_normalize=_lowerCAmelCase )
_UpperCAmelCase = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
_UpperCAmelCase = convert_classification(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForAudioFrameClassification" ):
_UpperCAmelCase = convert_diarization(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForXVector" ):
_UpperCAmelCase = convert_xvector(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
_UpperCAmelCase = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(_lowerCAmelCase )
hf_model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
__lowerCAmelCase = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 684 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
lowercase__ : Dict = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : Tuple = PegasusConfig
_snake_case : Any = {}
_snake_case : Dict = 'gelu'
def __init__( self : int , lowerCAmelCase__ : int , lowerCAmelCase__ : Dict=13 , lowerCAmelCase__ : Optional[Any]=7 , lowerCAmelCase__ : int=True , lowerCAmelCase__ : int=False , lowerCAmelCase__ : Tuple=99 , lowerCAmelCase__ : Optional[Any]=32 , lowerCAmelCase__ : Dict=5 , lowerCAmelCase__ : Dict=4 , lowerCAmelCase__ : Union[str, Any]=37 , lowerCAmelCase__ : str=0.1 , lowerCAmelCase__ : Any=0.1 , lowerCAmelCase__ : List[str]=20 , lowerCAmelCase__ : Optional[Any]=2 , lowerCAmelCase__ : str=1 , lowerCAmelCase__ : Tuple=0 , ) -> int:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = eos_token_id
_UpperCamelCase = pad_token_id
_UpperCamelCase = bos_token_id
def snake_case__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
_UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
_UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1 )
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_UpperCamelCase = prepare_pegasus_inputs_dict(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return config, inputs_dict
def snake_case__ ( self : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[int] ) -> int:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(lowerCAmelCase__ )
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''] )
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ , past_key_values=lowerCAmelCase__ , decoder_position_ids=lowerCAmelCase__ , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCAmelCase__ , )
_UpperCamelCase = model.decode(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def snake_case__ ( self : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : Dict ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(lowerCAmelCase__ )
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''] )
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ , past_key_values=lowerCAmelCase__ , decoder_position_ids=lowerCAmelCase__ , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , lowerCAmelCase__ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCAmelCase__ , decoder_position_ids=lowerCAmelCase__ , )
_UpperCamelCase = model.decode(lowerCAmelCase__ , lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ )
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def a__ ( lowercase : Union[str, Any], lowercase : Tuple, lowercase : Optional[Any], lowercase : Union[str, Any]=None, lowercase : Optional[Any]=None, ) -> Any:
"""simple docstring"""
if attention_mask is None:
_UpperCamelCase = np.not_equal(lowercase, config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_UpperCamelCase = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ),
], axis=-1, )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
"""simple docstring"""
_snake_case : Optional[int] = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
_snake_case : Optional[Any] = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
_snake_case : Union[str, Any] = True
_snake_case : Optional[int] = False
_snake_case : Any = False
_snake_case : List[Any] = False
def snake_case__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=lowerCAmelCase__ )
def snake_case__ ( self : Dict ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case__ ( self : str ) -> Any:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_UpperCamelCase = self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase = model_class(lowerCAmelCase__ )
@jax.jit
def encode_jitted(lowerCAmelCase__ : Dict , lowerCAmelCase__ : Dict=None , **lowerCAmelCase__ : Union[str, Any] ):
return model.encode(input_ids=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
with self.subTest('''JIT Enabled''' ):
_UpperCamelCase = encode_jitted(**lowerCAmelCase__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
_UpperCamelCase = encode_jitted(**lowerCAmelCase__ ).to_tuple()
self.assertEqual(len(lowerCAmelCase__ ) , len(lowerCAmelCase__ ) )
for jitted_output, output in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
def snake_case__ ( self : int ) -> List[str]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_UpperCamelCase = model_class(lowerCAmelCase__ )
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
_UpperCamelCase = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Dict ):
return model.decode(
decoder_input_ids=lowerCAmelCase__ , decoder_attention_mask=lowerCAmelCase__ , encoder_outputs=lowerCAmelCase__ , )
with self.subTest('''JIT Enabled''' ):
_UpperCamelCase = decode_jitted(**lowerCAmelCase__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
_UpperCamelCase = decode_jitted(**lowerCAmelCase__ ).to_tuple()
self.assertEqual(len(lowerCAmelCase__ ) , len(lowerCAmelCase__ ) )
for jitted_output, output in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def snake_case__ ( self : Any ) -> int:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=lowerCAmelCase__ )
_UpperCamelCase = np.ones((1, 1) )
_UpperCamelCase = model(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@slow
def snake_case__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''' )
_UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''' )
_UpperCamelCase = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
_UpperCamelCase = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
_UpperCamelCase = tokenizer(lowerCAmelCase__ , return_tensors='''np''' , truncation=lowerCAmelCase__ , max_length=512 , padding=lowerCAmelCase__ )
_UpperCamelCase = model.generate(**lowerCAmelCase__ , num_beams=2 ).sequences
_UpperCamelCase = tokenizer.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
assert tgt_text == decoded
| 98 |
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
_UpperCAmelCase = []
_UpperCAmelCase = set({"(", "[", "{"} )
_UpperCAmelCase = set({")", "]", "}"} )
_UpperCAmelCase = {"{": "}", "[": "]", "(": ")"}
for i in range(len(_lowerCAmelCase ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(_lowerCAmelCase ) == 0 or (len(_lowerCAmelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(_lowerCAmelCase ) == 0
def __lowerCamelCase ( ) -> str:
_UpperCAmelCase = input("Enter sequence of brackets: " )
if is_balanced(_lowerCAmelCase ):
print(_lowerCAmelCase , "is balanced" )
else:
print(_lowerCAmelCase , "is not balanced" )
if __name__ == "__main__":
main()
| 684 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self , __A , __A=13 , __A=7 , __A=True , __A=True , __A=True , __A=99 , __A=32 , __A=5 , __A=4 , __A=37 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=16 , __A=2 , __A=0.02 , __A=3 , __A=4 , __A=None , ):
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_token_type_ids
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = type_sequence_label_size
__a = initializer_range
__a = num_labels
__a = num_choices
__a = scope
__a = self.vocab_size - 1
def snake_case_ ( self ):
__a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a = None
if self.use_token_type_ids:
__a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a = None
__a = None
__a = None
if self.use_labels:
__a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a = ids_tensor([self.batch_size] , self.num_choices )
__a = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
__a = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def snake_case_ ( self , __A , __A , __A , __A , *__A ):
__a = OpenAIGPTModel(config=__A )
model.to(__A )
model.eval()
__a = model(__A , token_type_ids=__A , head_mask=__A )
__a = model(__A , token_type_ids=__A )
__a = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self , __A , __A , __A , __A , *__A ):
__a = OpenAIGPTLMHeadModel(__A )
model.to(__A )
model.eval()
__a = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , __A , __A , __A , __A , *__A ):
__a = OpenAIGPTDoubleHeadsModel(__A )
model.to(__A )
model.eval()
__a = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self , __A , __A , __A , __A , *__A ):
__a = self.num_labels
__a = OpenAIGPTForSequenceClassification(__A )
model.to(__A )
model.eval()
__a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case_ ( self ):
__a = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) = config_and_inputs
__a = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""head_mask""": head_mask,
}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( __A , __A , __A , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_lowerCamelCase = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_lowerCamelCase = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def snake_case_ ( self , __A , __A , __A , __A , __A ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def snake_case_ ( self , __A , __A , __A=False ):
__a = super()._prepare_for_class(__A , __A , return_labels=__A )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
__a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=__A , )
__a = inputs_dict["""labels"""]
__a = inputs_dict["""labels"""]
__a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=__A , )
__a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
return inputs_dict
def snake_case_ ( self ):
__a = OpenAIGPTModelTester(self )
__a = ConfigTester(self , config_class=__A , n_embd=37 )
def snake_case_ ( self ):
self.config_tester.run_common_tests()
def snake_case_ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*__A )
def snake_case_ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__A )
def snake_case_ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*__A )
def snake_case_ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__A )
@slow
def snake_case_ ( self ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = OpenAIGPTModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case_ ( self ):
__a = OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
model.to(__A )
__a = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=__A ) # the president is
__a = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
__a = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].tolist() , __A )
| 99 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = equationa
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = equationa
# Calculate the determinants of the matrices
_UpperCAmelCase = aa * ba - aa * ba
_UpperCAmelCase = ca * ba - ca * ba
_UpperCAmelCase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
_UpperCAmelCase = determinant_x / determinant
_UpperCAmelCase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 684 | 0 |
# Copyright 2022 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.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def __snake_case ( lowerCAmelCase_=None ) -> str:
if subparsers is not None:
SCREAMING_SNAKE_CASE__ = subparsers.add_parser('''env''' )
else:
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser('''Accelerate env command''' )
parser.add_argument(
'''--config_file''' , default=lowerCAmelCase_ , help='''The config file to use for the default values in the launching script.''' )
if subparsers is not None:
parser.set_defaults(func=lowerCAmelCase_ )
return parser
def __snake_case ( lowerCAmelCase_ ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ = torch.__version__
SCREAMING_SNAKE_CASE__ = torch.cuda.is_available()
SCREAMING_SNAKE_CASE__ = is_xpu_available()
SCREAMING_SNAKE_CASE__ = is_npu_available()
SCREAMING_SNAKE_CASE__ = '''Not found'''
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE__ = load_config_from_file(args.config_file ).to_dict()
SCREAMING_SNAKE_CASE__ = {
'''`Accelerate` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Numpy version''': np.__version__,
'''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
'''PyTorch XPU available''': str(lowerCAmelCase_ ),
'''PyTorch NPU available''': str(lowerCAmelCase_ ),
'''System RAM''': f'''{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB''',
}
if pt_cuda_available:
SCREAMING_SNAKE_CASE__ = torch.cuda.get_device_name()
print('''\nCopy-and-paste the text below in your GitHub issue\n''' )
print('''\n'''.join([f'''- {prop}: {val}''' for prop, val in info.items()] ) )
print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:''' )
SCREAMING_SNAKE_CASE__ = (
'''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
else f'''\t{accelerate_config}'''
)
print(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ = accelerate_config
return info
def __snake_case ( ) -> int:
SCREAMING_SNAKE_CASE__ = env_command_parser()
SCREAMING_SNAKE_CASE__ = parser.parse_args()
env_command(lowerCAmelCase_ )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 100 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
# Initialise PyTorch model
_UpperCAmelCase = RemBertConfig.from_json_file(_lowerCAmelCase )
print("Building PyTorch model from configuration: {}".format(str(_lowerCAmelCase ) ) )
_UpperCAmelCase = RemBertModel(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Save pytorch-model
print("Save PyTorch model to {}".format(_lowerCAmelCase ) )
torch.save(model.state_dict() , _lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--rembert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained RemBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 684 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a__ ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(A__ ):
requests.request('GET', 'https://huggingface.co' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('GET', 'https://huggingface.co', timeout=1.0 )
@pytest.mark.integration
def a__ ( ):
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('GET', 'https://huggingface.co' )
def a__ ( ):
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(A__ ):
http_head('https://huggingface.co' )
| 101 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def UpperCAmelCase__ ( *__UpperCamelCase : Dict , **__UpperCamelCase : Optional[int] ):
pass
@is_pipeline_test
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
__SCREAMING_SNAKE_CASE : List[Any] = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
_UpperCAmelCase = [
{
"image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"question": "How many cats are there?",
},
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"question": "How many cats are there?",
},
]
return vqa_pipeline, examples
def UpperCAmelCase__ ( self : List[Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[int] ):
_UpperCAmelCase = vqa_pipeline(__UpperCamelCase , top_k=1 )
self.assertEqual(
__UpperCamelCase , [
[{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}],
[{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}],
] , )
@require_torch
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
_UpperCAmelCase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_UpperCAmelCase = "How many cats are there?"
_UpperCAmelCase = vqa_pipeline(image=__UpperCamelCase , question="How many cats are there?" , top_k=2 )
self.assertEqual(
__UpperCamelCase , [{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}, {"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}] )
_UpperCAmelCase = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
__UpperCamelCase , [{"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}, {"score": ANY(__UpperCamelCase ), "answer": ANY(__UpperCamelCase )}] )
@slow
@require_torch
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = pipeline("visual-question-answering" , model="dandelin/vilt-b32-finetuned-vqa" )
_UpperCAmelCase = "./tests/fixtures/tests_samples/COCO/000000039769.png"
_UpperCAmelCase = "How many cats are there?"
_UpperCAmelCase = vqa_pipeline(image=__UpperCamelCase , question=__UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
_UpperCAmelCase = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
_UpperCAmelCase = vqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2 , )
@require_tf
@unittest.skip("Visual question answering not implemented in TF" )
def UpperCAmelCase__ ( self : Optional[int] ):
pass
| 684 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowercase__ :
"""simple docstring"""
@staticmethod
def _a ( *_A , **_A ):
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class lowercase__ ( unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase : Tuple = MODEL_FOR_OBJECT_DETECTION_MAPPING
def _a ( self , _A , _A , _A ):
'''simple docstring'''
UpperCamelCase : Any = ObjectDetectionPipeline(model=_A , image_processor=_A )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def _a ( self , _A , _A ):
'''simple docstring'''
UpperCamelCase : Any = object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png""" , threshold=0.0 )
self.assertGreater(len(_A ) , 0 )
for detected_object in outputs:
self.assertEqual(
_A , {
"""score""": ANY(_A ),
"""label""": ANY(_A ),
"""box""": {"""xmin""": ANY(_A ), """ymin""": ANY(_A ), """xmax""": ANY(_A ), """ymax""": ANY(_A )},
} , )
import datasets
UpperCamelCase : int = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" , """image""" , split="""test""" )
UpperCamelCase : Optional[int] = [
Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
# RGBA
dataset[0]["""file"""],
# LA
dataset[1]["""file"""],
# L
dataset[2]["""file"""],
]
UpperCamelCase : Dict = object_detector(_A , threshold=0.0 )
self.assertEqual(len(_A ) , len(_A ) )
for outputs in batch_outputs:
self.assertGreater(len(_A ) , 0 )
for detected_object in outputs:
self.assertEqual(
_A , {
"""score""": ANY(_A ),
"""label""": ANY(_A ),
"""box""": {"""xmin""": ANY(_A ), """ymin""": ANY(_A ), """xmax""": ANY(_A ), """ymax""": ANY(_A )},
} , )
@require_tf
@unittest.skip("""Object detection not implemented in TF""" )
def _a ( self ):
'''simple docstring'''
pass
@require_torch
def _a ( self ):
'''simple docstring'''
UpperCamelCase : int = """hf-internal-testing/tiny-detr-mobilenetsv3"""
UpperCamelCase : Optional[int] = AutoModelForObjectDetection.from_pretrained(_A )
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(_A )
UpperCamelCase : Tuple = ObjectDetectionPipeline(model=_A , feature_extractor=_A )
UpperCamelCase : Dict = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" , threshold=0.0 )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
] , )
UpperCamelCase : int = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
[
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
],
[
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
],
] , )
@require_torch
@slow
def _a ( self ):
'''simple docstring'''
UpperCamelCase : Any = """facebook/detr-resnet-50"""
UpperCamelCase : int = AutoModelForObjectDetection.from_pretrained(_A )
UpperCamelCase : Union[str, Any] = AutoFeatureExtractor.from_pretrained(_A )
UpperCamelCase : Dict = ObjectDetectionPipeline(model=_A , feature_extractor=_A )
UpperCamelCase : Dict = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
] , )
UpperCamelCase : Optional[int] = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
],
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
],
] , )
@require_torch
@slow
def _a ( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = """facebook/detr-resnet-50"""
UpperCamelCase : Optional[Any] = pipeline("""object-detection""" , model=_A )
UpperCamelCase : Optional[Any] = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
] , )
UpperCamelCase : int = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
],
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
],
] , )
@require_torch
@slow
def _a ( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = 0.99_85
UpperCamelCase : Tuple = """facebook/detr-resnet-50"""
UpperCamelCase : Dict = pipeline("""object-detection""" , model=_A )
UpperCamelCase : str = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" , threshold=_A )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
] , )
@require_torch
@require_pytesseract
@slow
def _a ( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = """Narsil/layoutlmv3-finetuned-funsd"""
UpperCamelCase : Optional[int] = 0.99_93
UpperCamelCase : List[Any] = pipeline("""object-detection""" , model=_A , threshold=_A )
UpperCamelCase : Tuple = object_detector(
"""https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png""" )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
{"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_9_4, """ymin""": 2_5_4, """xmax""": 3_4_3, """ymax""": 2_6_4}},
{"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_9_4, """ymin""": 2_5_4, """xmax""": 3_4_3, """ymax""": 2_6_4}},
] , )
| 102 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 684 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
snake_case = {
'''vocab_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
},
}
snake_case = {
'''allenai/longformer-base-4096''': 4_0_9_6,
'''allenai/longformer-large-4096''': 4_0_9_6,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 4_0_9_6,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 4_0_9_6,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 4_0_9_6,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def snake_case ( ) -> Tuple:
_snake_case = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_snake_case = bs[:]
_snake_case = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCAmelCase_ )
cs.append(2**8 + n )
n += 1
_snake_case = [chr(lowerCAmelCase_ ) for n in cs]
return dict(zip(lowerCAmelCase_ , lowerCAmelCase_ ) )
def snake_case ( lowerCAmelCase_ ) -> Optional[Any]:
_snake_case = set()
_snake_case = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_snake_case = char
return pairs
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : Dict = VOCAB_FILES_NAMES
A__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
A__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : Optional[int] = ['''input_ids''', '''attention_mask''']
def __init__( self : Optional[int] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any]="replace" , __lowerCamelCase : Union[str, Any]="<s>" , __lowerCamelCase : Optional[int]="</s>" , __lowerCamelCase : List[Any]="</s>" , __lowerCamelCase : str="<s>" , __lowerCamelCase : Dict="<unk>" , __lowerCamelCase : Any="<pad>" , __lowerCamelCase : Optional[Any]="<mask>" , __lowerCamelCase : List[str]=False , **__lowerCamelCase : Optional[Any] , ):
"""simple docstring"""
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else bos_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else eos_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else sep_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else cls_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else unk_token
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_snake_case = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
super().__init__(
errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , **__lowerCamelCase , )
with open(__lowerCamelCase , encoding='''utf-8''' ) as vocab_handle:
_snake_case = json.load(__lowerCamelCase )
_snake_case = {v: k for k, v in self.encoder.items()}
_snake_case = errors # how to handle errors in decoding
_snake_case = bytes_to_unicode()
_snake_case = {v: k for k, v in self.byte_encoder.items()}
with open(__lowerCamelCase , encoding='''utf-8''' ) as merges_handle:
_snake_case = merges_handle.read().split('''\n''' )[1:-1]
_snake_case = [tuple(merge.split() ) for merge in bpe_merges]
_snake_case = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
_snake_case = {}
_snake_case = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_snake_case = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def __UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
return len(self.encoder )
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def __UpperCAmelCase ( self : int , __lowerCamelCase : List[Any] ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
_snake_case = tuple(__lowerCamelCase )
_snake_case = get_pairs(__lowerCamelCase )
if not pairs:
return token
while True:
_snake_case = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_snake_case , _snake_case = bigram
_snake_case = []
_snake_case = 0
while i < len(__lowerCamelCase ):
try:
_snake_case = word.index(__lowerCamelCase , __lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_snake_case = j
if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_snake_case = tuple(__lowerCamelCase )
_snake_case = new_word
if len(__lowerCamelCase ) == 1:
break
else:
_snake_case = get_pairs(__lowerCamelCase )
_snake_case = ''' '''.join(__lowerCamelCase )
_snake_case = word
return word
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : int ):
"""simple docstring"""
_snake_case = []
for token in re.findall(self.pat , __lowerCamelCase ):
_snake_case = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase ).split(''' ''' ) )
return bpe_tokens
def __UpperCAmelCase ( self : str , __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) )
def __UpperCAmelCase ( self : int , __lowerCamelCase : Dict ):
"""simple docstring"""
return self.decoder.get(__lowerCamelCase )
def __UpperCAmelCase ( self : Any , __lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
_snake_case = ''''''.join(__lowerCamelCase )
_snake_case = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_snake_case = os.path.join(
__lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_snake_case = os.path.join(
__lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + '''\n''' )
_snake_case = 0
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
_snake_case = token_index
writer.write(''' '''.join(__lowerCamelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
def __UpperCAmelCase ( self : Any , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_snake_case = [self.cls_token_id]
_snake_case = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCamelCase , token_ids_a=__lowerCamelCase , already_has_special_tokens=__lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCamelCase )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def __UpperCAmelCase ( self : str , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
_snake_case = [self.sep_token_id]
_snake_case = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : Tuple , __lowerCamelCase : List[Any]=False , **__lowerCamelCase : Dict ):
"""simple docstring"""
_snake_case = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()):
_snake_case = ''' ''' + text
return (text, kwargs)
| 103 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : str = (UniPCMultistepScheduler,)
__SCREAMING_SNAKE_CASE : Dict = (("""num_inference_steps""", 25),)
def UpperCAmelCase__ ( self : str , **__UpperCamelCase : Any ):
_UpperCAmelCase = {
"num_train_timesteps": 1_000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__UpperCamelCase )
return config
def UpperCAmelCase__ ( self : int , __UpperCamelCase : Any=0 , **__UpperCamelCase : Any ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any]=0 , **__UpperCamelCase : List[Any] ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Dict=None , **__UpperCamelCase : Optional[Any] ):
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 10
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop("num_inference_steps" , __UpperCamelCase )
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(__UpperCamelCase , "set_timesteps" ):
scheduler.set_timesteps(__UpperCamelCase )
elif num_inference_steps is not None and not hasattr(__UpperCamelCase , "set_timesteps" ):
_UpperCAmelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10]
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
_UpperCAmelCase = scheduler.timesteps[5]
_UpperCAmelCase = scheduler.timesteps[6]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCAmelCase__ ( self : Union[str, Any] ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = UniPCMultistepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def UpperCAmelCase__ ( self : str ):
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def UpperCAmelCase__ ( self : int ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase__ ( self : Optional[int] ):
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2464 ) < 1e-3
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = self.full_loop(prediction_type="v_prediction" )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1014 ) < 1e-3
def UpperCAmelCase__ ( self : Tuple ):
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 10
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
def UpperCAmelCase__ ( self : str , **__UpperCamelCase : Optional[Any] ):
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 684 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCamelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
A__ : int = ["pixel_values"]
def __init__( self , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = 1 / 255 , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ) -> None:
super().__init__(**SCREAMING_SNAKE_CASE__ )
A__ = size if size is not None else {"shortest_edge": 256}
A__ = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
A__ = crop_size if crop_size is not None else {"height": 224, "width": 224}
A__ = get_size_dict(SCREAMING_SNAKE_CASE__ )
A__ = do_resize
A__ = size
A__ = resample
A__ = do_center_crop
A__ = crop_size
A__ = do_rescale
A__ = rescale_factor
A__ = do_normalize
A__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ) -> np.ndarray:
A__ = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
A__ = get_resize_output_image_size(SCREAMING_SNAKE_CASE__ , size=size["shortest_edge"] , default_to_square=SCREAMING_SNAKE_CASE__ )
return resize(SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ , resample=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ) -> np.ndarray:
A__ = get_size_dict(SCREAMING_SNAKE_CASE__ )
return center_crop(SCREAMING_SNAKE_CASE__ , size=(size["height"], size["width"]) , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ ) -> np.ndarray:
return rescale(SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ) -> np.ndarray:
return normalize(SCREAMING_SNAKE_CASE__ , mean=SCREAMING_SNAKE_CASE__ , std=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE__ , ) -> Optional[int]:
A__ = do_resize if do_resize is not None else self.do_resize
A__ = size if size is not None else self.size
A__ = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
A__ = resample if resample is not None else self.resample
A__ = do_center_crop if do_center_crop is not None else self.do_center_crop
A__ = crop_size if crop_size is not None else self.crop_size
A__ = get_size_dict(SCREAMING_SNAKE_CASE__ )
A__ = do_rescale if do_rescale is not None else self.do_rescale
A__ = rescale_factor if rescale_factor is not None else self.rescale_factor
A__ = do_normalize if do_normalize is not None else self.do_normalize
A__ = image_mean if image_mean is not None else self.image_mean
A__ = image_std if image_std is not None else self.image_std
A__ = make_list_of_images(SCREAMING_SNAKE_CASE__ )
if not valid_images(SCREAMING_SNAKE_CASE__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
A__ = [to_numpy_array(SCREAMING_SNAKE_CASE__ ) for image in images]
if do_resize:
A__ = [self.resize(image=SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ , resample=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_center_crop:
A__ = [self.center_crop(image=SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_rescale:
A__ = [self.rescale(image=SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_normalize:
A__ = [self.normalize(image=SCREAMING_SNAKE_CASE__ , mean=SCREAMING_SNAKE_CASE__ , std=SCREAMING_SNAKE_CASE__ ) for image in images]
A__ = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for image in images]
A__ = {"pixel_values": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE__ , tensor_type=SCREAMING_SNAKE_CASE__ )
| 104 |
import math
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __UpperCamelCase : List[Any]=0 ): # a graph with Node 0,1,...,N-1
_UpperCAmelCase = n
_UpperCAmelCase = [
[math.inf for j in range(0 , __UpperCamelCase )] for i in range(0 , __UpperCamelCase )
] # adjacency matrix for weight
_UpperCAmelCase = [
[math.inf for j in range(0 , __UpperCamelCase )] for i in range(0 , __UpperCamelCase )
] # dp[i][j] stores minimum distance from i to j
def UpperCAmelCase__ ( self : str , __UpperCamelCase : Tuple , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] ):
_UpperCAmelCase = w
def UpperCAmelCase__ ( self : Dict ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCAmelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCAmelCase__ ( self : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Any ):
return self.dp[u][v]
if __name__ == "__main__":
__lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 684 | 0 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def __UpperCAmelCase ( lowerCamelCase_ : List[str] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = model.config
SCREAMING_SNAKE_CASE_ : Union[str, Any] = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=1_28 , )
SCREAMING_SNAKE_CASE_ : List[str] = MBartConfig(
is_decoder=lowerCamelCase_ , is_encoder_decoder=lowerCamelCase_ , add_cross_attention=lowerCamelCase_ , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=lowerCamelCase_ , add_final_layer_norm=lowerCamelCase_ , )
return encoder_config, decoder_config
def __UpperCAmelCase ( lowerCamelCase_ : Optional[int] ) -> int:
"""simple docstring"""
if "encoder.model" in name:
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
SCREAMING_SNAKE_CASE_ : int = name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE_ : Any = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE_ : Any = name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
SCREAMING_SNAKE_CASE_ : Any = 'encoder.' + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE_ : Dict = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('attn' , 'attention.self' )
if "norm1" in name:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
SCREAMING_SNAKE_CASE_ : List[Any] = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE_ : Tuple = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE_ : Optional[Any] = name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 'encoder.layernorm.weight'
if name == "encoder.norm.bias":
SCREAMING_SNAKE_CASE_ : Tuple = 'encoder.layernorm.bias'
return name
def __UpperCAmelCase ( lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Tuple ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE_ : List[str] = orig_state_dict.pop(lowerCamelCase_ )
if "qkv" in key:
SCREAMING_SNAKE_CASE_ : Dict = key.split('.' )
SCREAMING_SNAKE_CASE_ : Optional[int] = int(key_split[3] )
SCREAMING_SNAKE_CASE_ : Tuple = int(key_split[5] )
SCREAMING_SNAKE_CASE_ : List[Any] = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE_ : str = val[:dim, :]
SCREAMING_SNAKE_CASE_ : Optional[int] = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = val[:dim]
SCREAMING_SNAKE_CASE_ : Dict = val[dim : dim * 2]
SCREAMING_SNAKE_CASE_ : Optional[Any] = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
SCREAMING_SNAKE_CASE_ : List[str] = val
return orig_state_dict
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Dict=None , lowerCamelCase_ : int=False ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = DonutModel.from_pretrained(lowerCamelCase_ ).eval()
# load HuggingFace model
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[int] = get_configs(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : int = DonutSwinModel(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Any = MBartForCausalLM(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : List[str] = VisionEncoderDecoderModel(encoder=lowerCamelCase_ , decoder=lowerCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = original_model.state_dict()
SCREAMING_SNAKE_CASE_ : Any = convert_state_dict(lowerCamelCase_ , lowerCamelCase_ )
model.load_state_dict(lowerCamelCase_ )
# verify results on scanned document
SCREAMING_SNAKE_CASE_ : Dict = load_dataset('hf-internal-testing/example-documents' )
SCREAMING_SNAKE_CASE_ : int = dataset['test'][0]['image'].convert('RGB' )
SCREAMING_SNAKE_CASE_ : List[Any] = XLMRobertaTokenizerFast.from_pretrained(lowerCamelCase_ , from_slow=lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
SCREAMING_SNAKE_CASE_ : List[Any] = DonutProcessor(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tuple = processor(lowerCamelCase_ , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
SCREAMING_SNAKE_CASE_ : Union[str, Any] = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
SCREAMING_SNAKE_CASE_ : List[Any] = 'When is the coffee break?'
SCREAMING_SNAKE_CASE_ : str = task_prompt.replace('{user_input}' , lowerCamelCase_ )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
SCREAMING_SNAKE_CASE_ : Union[str, Any] = '<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
SCREAMING_SNAKE_CASE_ : Any = '<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
SCREAMING_SNAKE_CASE_ : int = 's_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
SCREAMING_SNAKE_CASE_ : str = '<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 'hello world'
else:
raise ValueError('Model name not supported' )
SCREAMING_SNAKE_CASE_ : Optional[int] = original_model.decoder.tokenizer(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , return_tensors='pt' )[
'input_ids'
]
SCREAMING_SNAKE_CASE_ : int = original_model.encoder.model.patch_embed(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = model.encoder.embeddings(lowerCamelCase_ )
assert torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1E-3 )
# verify encoder hidden states
SCREAMING_SNAKE_CASE_ : Any = original_model.encoder(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = model.encoder(lowerCamelCase_ ).last_hidden_state
assert torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1E-2 )
# verify decoder hidden states
SCREAMING_SNAKE_CASE_ : Dict = original_model(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ).logits
SCREAMING_SNAKE_CASE_ : int = model(lowerCamelCase_ , decoder_input_ids=lowerCamelCase_ ).logits
assert torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1E-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F'Saving model and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCamelCase_ )
processor.save_pretrained(lowerCamelCase_ )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''naver-clova-ix/donut-base-finetuned-docvqa''',
required=False,
type=str,
help='''Name of the original model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
required=False,
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether or not to push the converted model and processor to the 🤗 hub.''',
)
UpperCamelCase__ : Optional[int] = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 105 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowercase , lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : Dict = VQModel
__SCREAMING_SNAKE_CASE : Optional[int] = """sample"""
@property
def UpperCAmelCase__ ( self : List[str] , __UpperCamelCase : Optional[int]=(32, 32) ):
_UpperCAmelCase = 4
_UpperCAmelCase = 3
_UpperCAmelCase = floats_tensor((batch_size, num_channels) + sizes ).to(__UpperCamelCase )
return {"sample": image}
@property
def UpperCAmelCase__ ( self : Tuple ):
return (3, 32, 32)
@property
def UpperCAmelCase__ ( self : str ):
return (3, 32, 32)
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 3,
}
_UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def UpperCAmelCase__ ( self : Dict ):
pass
def UpperCAmelCase__ ( self : str ):
pass
def UpperCAmelCase__ ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = VQModel.from_pretrained("fusing/vqgan-dummy" , output_loading_info=__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(__UpperCamelCase )
_UpperCAmelCase = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = VQModel.from_pretrained("fusing/vqgan-dummy" )
model.to(__UpperCamelCase ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
_UpperCAmelCase = torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size )
_UpperCAmelCase = image.to(__UpperCamelCase )
with torch.no_grad():
_UpperCAmelCase = model(__UpperCamelCase ).sample
_UpperCAmelCase = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
_UpperCAmelCase = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] )
# fmt: on
self.assertTrue(torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
| 684 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case :Tuple ={
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
'tokenization_convbert': ['ConvBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case :Dict =['ConvBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case :Optional[int] =[
'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvBertForMaskedLM',
'ConvBertForMultipleChoice',
'ConvBertForQuestionAnswering',
'ConvBertForSequenceClassification',
'ConvBertForTokenClassification',
'ConvBertLayer',
'ConvBertModel',
'ConvBertPreTrainedModel',
'load_tf_weights_in_convbert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case :Optional[Any] =[
'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFConvBertForMaskedLM',
'TFConvBertForMultipleChoice',
'TFConvBertForQuestionAnswering',
'TFConvBertForSequenceClassification',
'TFConvBertForTokenClassification',
'TFConvBertLayer',
'TFConvBertModel',
'TFConvBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
__snake_case :Dict =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 106 |
import requests
__lowerCAmelCase = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def __lowerCamelCase ( _lowerCAmelCase ) -> None:
# fetching a list of articles in json format
_UpperCAmelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"] , 1 ):
print(F'''{i}.) {article["title"]}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 684 | 0 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
_UpperCAmelCase : Dict = '''\
@inproceedings{pillutla-etal:mauve:neurips2021,
title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
booktitle = {NeurIPS},
year = {2021}
}
'''
_UpperCAmelCase : Any = '''\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.
MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.
For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).
This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
'''
_UpperCAmelCase : Dict = '''
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
predictions: list of generated text to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Optional Args:
num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
kmeans_max_iter: maximum number of k-means iterations. Default 500
featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].
device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
max_text_length: maximum number of tokens to consider. Default 1024
divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
mauve_scaling_factor: "c" from the paper. Default 5.
verbose: If True (default), print running time updates
seed: random seed to initialize k-means cluster assignments.
Returns:
mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
q_hist: same as above, but with q_text.
Examples:
>>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
>>> import datasets
>>> mauve = datasets.load_metric(\'mauve\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
>>> print(out.mauve) # doctest: +SKIP
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
"""simple docstring"""
def __UpperCAmelCase ( self : Tuple ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, homepage='https://github.com/krishnap25/mauve', inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'predictions': datasets.Value('string', id='sequence' ),
'references': datasets.Value('string', id='sequence' ),
} ), codebase_urls=['https://github.com/krishnap25/mauve'], reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
], )
def __UpperCAmelCase ( self : int, UpperCamelCase__ : Dict, UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Optional[int]=None, UpperCamelCase__ : Any=None, UpperCamelCase__ : Tuple=None, UpperCamelCase__ : str=None, UpperCamelCase__ : Any="auto", UpperCamelCase__ : Optional[int]=-1, UpperCamelCase__ : Dict=0.9, UpperCamelCase__ : List[str]=5, UpperCamelCase__ : Dict=5_00, UpperCamelCase__ : Optional[int]="gpt2-large", UpperCamelCase__ : Dict=-1, UpperCamelCase__ : Union[str, Any]=10_24, UpperCamelCase__ : str=25, UpperCamelCase__ : Any=5, UpperCamelCase__ : str=True, UpperCamelCase__ : List[Any]=25, ) -> Tuple:
_A = compute_mauve(
p_text=UpperCamelCase__, q_text=UpperCamelCase__, p_features=UpperCamelCase__, q_features=UpperCamelCase__, p_tokens=UpperCamelCase__, q_tokens=UpperCamelCase__, num_buckets=UpperCamelCase__, pca_max_data=UpperCamelCase__, kmeans_explained_var=UpperCamelCase__, kmeans_num_redo=UpperCamelCase__, kmeans_max_iter=UpperCamelCase__, featurize_model_name=UpperCamelCase__, device_id=UpperCamelCase__, max_text_length=UpperCamelCase__, divergence_curve_discretization_size=UpperCamelCase__, mauve_scaling_factor=UpperCamelCase__, verbose=UpperCamelCase__, seed=UpperCamelCase__, )
return out
| 107 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Any ):
_UpperCAmelCase = 10
def UpperCAmelCase__ ( self : Optional[int] ):
_UpperCAmelCase = [1, 2, 3, 4]
_UpperCAmelCase = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : List[Any] ):
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__UpperCamelCase , self.block_size , 0 ) , __UpperCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = "It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this."
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , [] )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = ""
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
self.assertEqual(__UpperCamelCase , [] )
self.assertEqual(__UpperCamelCase , [] )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = (
"It was the year of Our Lord one thousand seven hundred and "
"seventy-five\n\nSpiritual revelations were conceded to England "
"at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
)
_UpperCAmelCase , _UpperCAmelCase = process_story(__UpperCamelCase )
_UpperCAmelCase = [
"It was the year of Our Lord one thousand seven hundred and seventy-five.",
"Spiritual revelations were conceded to England at that favoured period, as at this.",
]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = ["It was the best of times."]
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = torch.tensor([1, 2, 3, 4] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 0 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 23 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Optional[int] ):
_UpperCAmelCase = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
_UpperCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__UpperCamelCase , 1 ).numpy() , expected.numpy() )
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = 101
_UpperCAmelCase = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
_UpperCAmelCase = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
_UpperCAmelCase = compute_token_type_ids(__UpperCamelCase , __UpperCamelCase )
np.testing.assert_array_equal(__UpperCamelCase , __UpperCamelCase )
| 684 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a: Optional[Any] = logging.get_logger(__name__)
__a: Optional[Any] = {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
'''umberto-commoncrawl-cased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
),
'''umberto-wikipedia-uncased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
_lowerCamelCase = '''camembert'''
def __init__( self : Optional[int] , lowerCamelCase : List[str]=3_0522 , lowerCamelCase : Dict=768 , lowerCamelCase : Optional[int]=12 , lowerCamelCase : List[Any]=12 , lowerCamelCase : List[Any]=3072 , lowerCamelCase : List[Any]="gelu" , lowerCamelCase : List[Any]=0.1 , lowerCamelCase : Dict=0.1 , lowerCamelCase : Optional[int]=512 , lowerCamelCase : int=2 , lowerCamelCase : Any=0.02 , lowerCamelCase : List[Any]=1E-12 , lowerCamelCase : int=1 , lowerCamelCase : Dict=0 , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : str="absolute" , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : Optional[Any]=None , **lowerCamelCase : Union[str, Any] , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , **lowerCamelCase )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
@property
def lowerCamelCase ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_UpperCAmelCase = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_UpperCAmelCase = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] ) | 108 |
from __future__ import annotations
from collections import namedtuple
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> tuple:
_UpperCAmelCase = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Only one argument must be 0" )
elif power < 0:
raise ValueError(
"Power cannot be negative in any electrical/electronics system" )
elif voltage == 0:
return result("voltage" , power / current )
elif current == 0:
return result("current" , power / voltage )
elif power == 0:
return result("power" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __a ( _snake_case ):
def __init__( self : Optional[int] ,lowerCamelCase : Optional[NestedDataStructureLike[PathLike]] = None ,lowerCamelCase : Optional[NamedSplit] = None ,lowerCamelCase : Optional[Features] = None ,lowerCamelCase : str = None ,lowerCamelCase : bool = False ,lowerCamelCase : bool = False ,lowerCamelCase : Optional[int] = None ,**lowerCamelCase : Optional[int] ,):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = path_or_paths
__SCREAMING_SNAKE_CASE = split if split or isinstance(lowerCamelCase ,lowerCamelCase ) else """train"""
__SCREAMING_SNAKE_CASE = features
__SCREAMING_SNAKE_CASE = cache_dir
__SCREAMING_SNAKE_CASE = keep_in_memory
__SCREAMING_SNAKE_CASE = streaming
__SCREAMING_SNAKE_CASE = num_proc
__SCREAMING_SNAKE_CASE = kwargs
@abstractmethod
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
pass
class __a ( _snake_case ):
def __init__( self : Tuple ,lowerCamelCase : Optional[Features] = None ,lowerCamelCase : str = None ,lowerCamelCase : bool = False ,lowerCamelCase : bool = False ,lowerCamelCase : Optional[int] = None ,**lowerCamelCase : Optional[int] ,):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = features
__SCREAMING_SNAKE_CASE = cache_dir
__SCREAMING_SNAKE_CASE = keep_in_memory
__SCREAMING_SNAKE_CASE = streaming
__SCREAMING_SNAKE_CASE = num_proc
__SCREAMING_SNAKE_CASE = kwargs
@abstractmethod
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
pass
| 109 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __lowerCamelCase ( _lowerCAmelCase ) -> Any:
_UpperCAmelCase = {}
_UpperCAmelCase = job["started_at"]
_UpperCAmelCase = job["completed_at"]
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = date_parser.parse(_lowerCAmelCase )
_UpperCAmelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
_UpperCAmelCase = start
_UpperCAmelCase = end
_UpperCAmelCase = duration_in_min
return job_info
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=None ) -> str:
_UpperCAmelCase = None
if token is not None:
_UpperCAmelCase = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
_UpperCAmelCase = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
_UpperCAmelCase = requests.get(_lowerCAmelCase , headers=_lowerCAmelCase ).json()
_UpperCAmelCase = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
_UpperCAmelCase = math.ceil((result["total_count"] - 100) / 100 )
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = requests.get(url + F'''&page={i + 2}''' , headers=_lowerCAmelCase ).json()
job_time.update({job["name"]: extract_time_from_single_job(_lowerCAmelCase ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = get_job_time(args.workflow_run_id)
__lowerCAmelCase = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v["duration"]}''')
| 684 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : Union[str, Any] = """M-CLIP"""
def __init__( self : Optional[int] , lowerCAmelCase : Union[str, Any]=1024 , lowerCAmelCase : Dict=768 , **lowerCAmelCase : Union[str, Any]) -> Tuple:
"""simple docstring"""
_snake_case : List[Any] = transformerDimSize
_snake_case : Optional[Any] = imageDimSize
super().__init__(**__UpperCamelCase)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : Dict = MCLIPConfig
def __init__( self : Union[str, Any] , lowerCAmelCase : Dict , *lowerCAmelCase : List[str] , **lowerCAmelCase : int) -> Optional[int]:
"""simple docstring"""
super().__init__(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase)
_snake_case : str = XLMRobertaModel(__UpperCamelCase)
_snake_case : List[str] = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims)
def UpperCamelCase_ ( self : Tuple , lowerCAmelCase : Any , lowerCAmelCase : Optional[int]) -> Tuple:
"""simple docstring"""
_snake_case : List[Any] = self.transformer(input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase)[0]
_snake_case : List[str] = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
return self.LinearTransformation(__UpperCamelCase), embs
| 477 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__lowerCAmelCase = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 6_5_5_3_6,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 4_8_0_0_0,
"sample_size": 1_3_1_0_7_2,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 1_6_0_0_0,
"sample_size": 6_5_5_3_6,
},
}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
return torch.atana(_lowerCAmelCase , _lowerCAmelCase ) / math.pi * 2
def __lowerCamelCase ( _lowerCAmelCase ) -> Union[str, Any]:
_UpperCAmelCase = torch.sin(t * math.pi / 2 ) ** 2
_UpperCAmelCase = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(_lowerCAmelCase , _lowerCAmelCase )
class __SCREAMING_SNAKE_CASE ( lowercase):
pass
class __SCREAMING_SNAKE_CASE ( nn.Module):
def __init__( self : str , __UpperCamelCase : Optional[int] ):
super().__init__()
_UpperCAmelCase = DiffusionAttnUnetaD(__UpperCamelCase , n_attn_layers=4 )
_UpperCAmelCase = deepcopy(self.diffusion )
_UpperCAmelCase = torch.quasirandom.SobolEngine(1 , scramble=__UpperCamelCase )
def __lowerCamelCase ( _lowerCAmelCase ) -> int:
_UpperCAmelCase = MODELS_MAP[model_name]["url"]
os.system(F'''wget {url} ./''' )
return F'''./{model_name}.ckpt'''
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
__lowerCAmelCase = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
__lowerCAmelCase = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
__lowerCAmelCase = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
__lowerCAmelCase = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
__lowerCAmelCase = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
if name.startswith("skip" ):
return name.replace("skip" , RES_CONV_MAP["skip"] )
# name has to be of format main.{digit}
if not name.startswith("main." ):
raise ValueError(F'''ResConvBlock error with {name}''' )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[Any]:
for key, value in ATTN_MAP.items():
if name.startswith(_lowerCAmelCase ) and not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return name.replace(_lowerCAmelCase , _lowerCAmelCase )
elif name.startswith(_lowerCAmelCase ):
return [name.replace(_lowerCAmelCase , _lowerCAmelCase ) for v in value]
raise ValueError(F'''Attn error with {name}''' )
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=13 ) -> List[Any]:
_UpperCAmelCase = input_string
if string.split("." )[0] == "timestep_embed":
return string.replace("timestep_embed" , "time_proj" )
_UpperCAmelCase = 0
if string.startswith("net.3." ):
depth += 1
_UpperCAmelCase = string[6:]
elif string.startswith("net." ):
_UpperCAmelCase = string[4:]
while string.startswith("main.7." ):
depth += 1
_UpperCAmelCase = string[7:]
if string.startswith("main." ):
_UpperCAmelCase = string[5:]
# mid block
if string[:2].isdigit():
_UpperCAmelCase = string[:2]
_UpperCAmelCase = string[2:]
else:
_UpperCAmelCase = string[0]
_UpperCAmelCase = string[1:]
if depth == max_depth:
_UpperCAmelCase = MID_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = "mid_block"
elif depth > 0 and int(_lowerCAmelCase ) < 7:
_UpperCAmelCase = DOWN_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''down_blocks.{depth}'''
elif depth > 0 and int(_lowerCAmelCase ) > 7:
_UpperCAmelCase = UP_NUM_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - depth - 1}'''
elif depth == 0:
_UpperCAmelCase = DEPTH_0_TO_LAYER[layer_num]
_UpperCAmelCase = F'''up_blocks.{max_depth - 1}''' if int(_lowerCAmelCase ) > 3 else "down_blocks.0"
if not string_left.startswith("." ):
raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.''' )
_UpperCAmelCase = string_left[1:]
if "resnets" in new_layer:
_UpperCAmelCase = convert_resconv_naming(_lowerCAmelCase )
elif "attentions" in new_layer:
_UpperCAmelCase = convert_attn_naming(_lowerCAmelCase )
_UpperCAmelCase = new_string_left
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = prefix + "." + new_layer + "." + string_left
else:
_UpperCAmelCase = [prefix + "." + new_layer + "." + s for s in string_left]
return new_string
def __lowerCamelCase ( _lowerCAmelCase ) -> Optional[int]:
_UpperCAmelCase = {}
for k, v in state_dict.items():
if k.endswith("kernel" ):
# up- and downsample layers, don't have trainable weights
continue
_UpperCAmelCase = rename(_lowerCAmelCase )
# check if we need to transform from Conv => Linear for attention
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_UpperCAmelCase = transform_conv_attns(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_UpperCAmelCase = v
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
if len(_lowerCAmelCase ) == 1:
if len(v.shape ) == 3:
# weight
_UpperCAmelCase = v[:, :, 0]
else:
# bias
_UpperCAmelCase = v
else:
# qkv matrices
_UpperCAmelCase = v.shape[0]
_UpperCAmelCase = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
_UpperCAmelCase = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def __lowerCamelCase ( _lowerCAmelCase ) -> Tuple:
_UpperCAmelCase = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
_UpperCAmelCase = args.model_path.split("/" )[-1].split("." )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
_UpperCAmelCase = download(_lowerCAmelCase )
_UpperCAmelCase = MODELS_MAP[model_name]["sample_rate"]
_UpperCAmelCase = MODELS_MAP[model_name]["sample_size"]
_UpperCAmelCase = Object()
_UpperCAmelCase = sample_size
_UpperCAmelCase = sample_rate
_UpperCAmelCase = 0
_UpperCAmelCase = UNetaDModel(sample_size=_lowerCAmelCase , sample_rate=_lowerCAmelCase )
_UpperCAmelCase = diffusers_model.state_dict()
_UpperCAmelCase = DiffusionUncond(_lowerCAmelCase )
orig_model.load_state_dict(torch.load(args.model_path , map_location=_lowerCAmelCase )["state_dict"] )
_UpperCAmelCase = orig_model.diffusion_ema.eval()
_UpperCAmelCase = orig_model.state_dict()
_UpperCAmelCase = rename_orig_weights(_lowerCAmelCase )
_UpperCAmelCase = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
_UpperCAmelCase = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(_lowerCAmelCase ) == 0, F'''Problem with {renamed_minus_diffusers}'''
assert all(k.endswith("kernel" ) for k in list(_lowerCAmelCase ) ), F'''Problem with {diffusers_minus_renamed}'''
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
if key == "time_proj.weight":
_UpperCAmelCase = value.squeeze()
_UpperCAmelCase = value
diffusers_model.load_state_dict(_lowerCAmelCase )
_UpperCAmelCase = 100
_UpperCAmelCase = 33
_UpperCAmelCase = IPNDMScheduler(num_train_timesteps=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(_lowerCAmelCase )
_UpperCAmelCase = torch.randn([1, 2, config.sample_size] , generator=_lowerCAmelCase ).to(_lowerCAmelCase )
_UpperCAmelCase = torch.linspace(1 , 0 , steps + 1 , device=_lowerCAmelCase )[:-1]
_UpperCAmelCase = get_crash_schedule(_lowerCAmelCase )
_UpperCAmelCase = DanceDiffusionPipeline(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
_UpperCAmelCase = torch.manual_seed(33 )
_UpperCAmelCase = pipe(num_inference_steps=_lowerCAmelCase , generator=_lowerCAmelCase ).audios
_UpperCAmelCase = sampling.iplms_sample(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {} )
_UpperCAmelCase = generated.clamp(-1 , 1 )
_UpperCAmelCase = (generated - audio).abs().sum()
_UpperCAmelCase = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("Diff sum" , _lowerCAmelCase )
print("Diff max" , _lowerCAmelCase )
assert diff_max < 1E-3, F'''Diff max: {diff_max} is too much :-/'''
print(F'''Conversion for {model_name} successful!''' )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
__lowerCAmelCase = parser.parse_args()
main(args)
| 684 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A_ = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["DPTFeatureExtractor"]
A_ = ["DPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DPTForDepthEstimation",
"DPTForSemanticSegmentation",
"DPTModel",
"DPTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 391 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
__lowerCAmelCase = get_tests_dir("fixtures")
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 500
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=__UpperCamelCase ) as mock_head:
_UpperCAmelCase = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase__ ( self : List[Any] ):
# This test is for deprecated behavior and can be removed in v5
_UpperCAmelCase = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json" )
def UpperCAmelCase__ ( self : Dict ):
with self.assertRaises(__UpperCamelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
_UpperCAmelCase = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants" )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants" , subfolder="feature_extractor" )
self.assertIsNotNone(__UpperCamelCase )
@is_staging_test
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
@classmethod
def UpperCAmelCase__ ( cls : str ):
_UpperCAmelCase = TOKEN
HfFolder.save_token(__UpperCamelCase )
@classmethod
def UpperCAmelCase__ ( cls : Optional[Any] ):
try:
delete_repo(token=cls._token , repo_id="test-image-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-image-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-image-processor" )
except HTTPError:
pass
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="test-image-processor" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = ViTImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("valid_org/test-image-processor" , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-image-processor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__UpperCamelCase , repo_id="valid_org/test-image-processor-org" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
_UpperCAmelCase = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def UpperCAmelCase__ ( self : int ):
CustomImageProcessor.register_for_auto_class()
_UpperCAmelCase = CustomImageProcessor.from_pretrained(__UpperCamelCase )
image_processor.push_to_hub("test-dynamic-image-processor" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"} , )
_UpperCAmelCase = AutoImageProcessor.from_pretrained(
F'''{USER}/test-dynamic-image-processor''' , trust_remote_code=__UpperCamelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , "CustomImageProcessor" )
| 684 | 0 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __a ( __UpperCAmelCase ):
a__ = []
for line in lines:
a__ = re.sub(R'''#.*''' , '''''' , _lowerCAmelCase ) # remove comments
if line:
filtered_lines.append(_lowerCAmelCase )
a__ = '''\n'''.join(_lowerCAmelCase )
# Make a hash from all this code
a__ = full_str.encode('''utf-8''' )
return shaaaa(_lowerCAmelCase ).hexdigest()
# get importable module names and hash for caching
a_ : List[Any] = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
a_ : int = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
a_ : str = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
a_ : str = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 194 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
return getitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
return setitem, k, v
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
return delitem, k
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase ) -> Optional[int]:
try:
return fun(_lowerCAmelCase , *_lowerCAmelCase ), None
except Exception as e:
return None, e
__lowerCAmelCase = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
__lowerCAmelCase = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
__lowerCAmelCase = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
__lowerCAmelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items" ),
pytest.param(_overwrite_items , id="overwrite items" ),
pytest.param(_delete_items , id="delete items" ),
pytest.param(_access_absent_items , id="access absent items" ),
pytest.param(_add_with_resize_up , id="add with resize up" ),
pytest.param(_add_with_resize_down , id="add with resize down" ),
) , )
def __lowerCamelCase ( _lowerCAmelCase ) -> List[str]:
_UpperCAmelCase = HashMap(initial_block_size=4 )
_UpperCAmelCase = {}
for _, (fun, *args) in enumerate(_lowerCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = _run_operation(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase )
assert my_res == py_res
assert str(_lowerCAmelCase ) == str(_lowerCAmelCase )
assert set(_lowerCAmelCase ) == set(_lowerCAmelCase )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
assert set(my.items() ) == set(py.items() )
def __lowerCamelCase ( ) -> List[Any]:
def is_public(_lowerCAmelCase ) -> bool:
return not name.startswith("_" )
_UpperCAmelCase = {name for name in dir({} ) if is_public(_lowerCAmelCase )}
_UpperCAmelCase = {name for name in dir(HashMap() ) if is_public(_lowerCAmelCase )}
assert dict_public_names > hash_public_names
| 684 | 0 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowerCAmelCase_ ( unittest.TestCase):
@parameterized.expand([(None,), ("foo.json",)] )
def _snake_case ( self : str , __A : Optional[int] ) ->Optional[Any]:
"""simple docstring"""
a__ :int = GenerationConfig(
do_sample=__UpperCamelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__UpperCamelCase , config_name=__UpperCamelCase )
a__ :List[str] = GenerationConfig.from_pretrained(__UpperCamelCase , config_name=__UpperCamelCase )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , __UpperCamelCase )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , __UpperCamelCase )
def _snake_case ( self : Any ) ->int:
"""simple docstring"""
a__ :Optional[int] = AutoConfig.from_pretrained("gpt2" )
a__ :Dict = GenerationConfig.from_model_config(__UpperCamelCase )
a__ :List[str] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(__UpperCamelCase , __UpperCamelCase )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def _snake_case ( self : Optional[int] ) ->Optional[int]:
"""simple docstring"""
a__ :str = GenerationConfig()
a__ :Optional[int] = {
"max_new_tokens": 1024,
"foo": "bar",
}
a__ :Union[str, Any] = copy.deepcopy(__UpperCamelCase )
a__ :Optional[int] = generation_config.update(**__UpperCamelCase )
# update_kwargs was not modified (no side effects)
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(__UpperCamelCase , {"foo": "bar"} )
def _snake_case ( self : str ) ->Any:
"""simple docstring"""
a__ :Any = GenerationConfig()
a__ :List[Any] = "bar"
with tempfile.TemporaryDirectory("test-generation-config" ) as tmp_dir:
generation_config.save_pretrained(__UpperCamelCase )
a__ :Dict = GenerationConfig.from_pretrained(__UpperCamelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , "bar" )
a__ :Optional[Any] = GenerationConfig.from_model_config(__UpperCamelCase )
assert not hasattr(__UpperCamelCase , "foo" ) # no new kwargs should be initialized if from config
def _snake_case ( self : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
a__ :List[Any] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , __UpperCamelCase )
self.assertEqual(default_config.num_beams , 1 )
a__ :Optional[Any] = GenerationConfig(
do_sample=__UpperCamelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , __UpperCamelCase )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__UpperCamelCase )
a__ :Any = GenerationConfig.from_pretrained(__UpperCamelCase , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , __UpperCamelCase )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class lowerCAmelCase_ ( unittest.TestCase):
@classmethod
def _snake_case ( cls : List[Any] ) ->List[Any]:
"""simple docstring"""
a__ :List[str] = TOKEN
HfFolder.save_token(__UpperCamelCase )
@classmethod
def _snake_case ( cls : Dict ) ->str:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="test-generation-config" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-generation-config-org" )
except HTTPError:
pass
def _snake_case ( self : Dict ) ->str:
"""simple docstring"""
a__ :List[Any] = GenerationConfig(
do_sample=__UpperCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("test-generation-config" , use_auth_token=self._token )
a__ :int = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-generation-config" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__UpperCamelCase , repo_id="test-generation-config" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
a__ :Union[str, Any] = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
def _snake_case ( self : List[Any] ) ->Dict:
"""simple docstring"""
a__ :Optional[int] = GenerationConfig(
do_sample=__UpperCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("valid_org/test-generation-config-org" , use_auth_token=self._token )
a__ :List[str] = GenerationConfig.from_pretrained("valid_org/test-generation-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-generation-config-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__UpperCamelCase , repo_id="valid_org/test-generation-config-org" , push_to_hub=__UpperCamelCase , use_auth_token=self._token )
a__ :Optional[int] = GenerationConfig.from_pretrained("valid_org/test-generation-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
| 395 |
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
_UpperCAmelCase = len(_lowerCAmelCase )
for i in range(1 , _lowerCAmelCase ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase = 0
_UpperCAmelCase = i - 1
while low <= high:
_UpperCAmelCase = (low + high) // 2
if val < collection[mid]:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
for j in range(_lowerCAmelCase , _lowerCAmelCase , -1 ):
_UpperCAmelCase = collection[j - 1]
_UpperCAmelCase = val
return collection
if __name__ == "__main__":
__lowerCAmelCase = input("Enter numbers separated by a comma:\n").strip()
__lowerCAmelCase = [int(item) for item in user_input.split(",")]
print(binary_insertion_sort(unsorted))
| 684 | 0 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def A__ ( A_ ) -> Any:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4E_00 and cp <= 0X9F_FF)
or (cp >= 0X34_00 and cp <= 0X4D_BF) #
or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF) #
or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F) #
or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F) #
or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF) #
or (cp >= 0XF9_00 and cp <= 0XFA_FF)
or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F) #
): #
return True
return False
def A__ ( A_ ) -> List[Any]:
# word like '180' or '身高' or '神'
for char in word:
_lowercase = ord(_lowerCAmelCase )
if not _is_chinese_char(_lowerCAmelCase ):
return 0
return 1
def A__ ( A_ ) -> List[str]:
_lowercase = set()
for token in tokens:
_lowercase = len(_lowerCAmelCase ) > 1 and is_chinese(_lowerCAmelCase )
if chinese_word:
word_set.add(_lowerCAmelCase )
_lowercase = list(_lowerCAmelCase )
return word_list
def A__ ( A_ , A_ ) -> str:
if not chinese_word_set:
return bert_tokens
_lowercase = max([len(_lowerCAmelCase ) for w in chinese_word_set] )
_lowercase = bert_tokens
_lowercase , _lowercase = 0, len(_lowerCAmelCase )
while start < end:
_lowercase = True
if is_chinese(bert_word[start] ):
_lowercase = min(end - start , _lowerCAmelCase )
for i in range(_lowerCAmelCase , 1 , -1 ):
_lowercase = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
_lowercase = "##" + bert_word[j]
_lowercase = start + i
_lowercase = False
break
if single_word:
start += 1
return bert_word
def A__ ( A_ , A_ , A_ ) -> List[str]:
_lowercase = []
for i in range(0 , len(_lowerCAmelCase ) , 100 ):
_lowercase = ltp_tokenizer.seg(lines[i : i + 100] )[0]
_lowercase = [get_chinese_word(_lowerCAmelCase ) for r in res]
ltp_res.extend(_lowerCAmelCase )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
_lowercase = []
for i in range(0 , len(_lowerCAmelCase ) , 100 ):
_lowercase = bert_tokenizer(lines[i : i + 100] , add_special_tokens=_lowerCAmelCase , truncation=_lowerCAmelCase , max_length=512 )
bert_res.extend(res["input_ids"] )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
_lowercase = []
for input_ids, chinese_word in zip(_lowerCAmelCase , _lowerCAmelCase ):
_lowercase = []
for id in input_ids:
_lowercase = bert_tokenizer._convert_id_to_token(_lowerCAmelCase )
input_tokens.append(_lowerCAmelCase )
_lowercase = add_sub_symbol(_lowerCAmelCase , _lowerCAmelCase )
_lowercase = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(_lowerCAmelCase ):
if token[:2] == "##":
_lowercase = token[2:]
# save chinese tokens' pos
if len(_lowerCAmelCase ) == 1 and _is_chinese_char(ord(_lowerCAmelCase ) ):
ref_id.append(_lowerCAmelCase )
ref_ids.append(_lowerCAmelCase )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
return ref_ids
def A__ ( A_ ) -> Tuple:
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , "r" , encoding="utf-8" ) as f:
_lowercase = f.readlines()
_lowercase = [line.strip() for line in data if len(_lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_lowercase = LTP(args.ltp ) # faster in GPU device
_lowercase = BertTokenizer.from_pretrained(args.bert )
_lowercase = prepare_ref(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
with open(args.save_path , "w" , encoding="utf-8" ) as f:
_lowercase = [json.dumps(_lowerCAmelCase ) + "\n" for ref in ref_ids]
f.writelines(_lowerCAmelCase )
if __name__ == "__main__":
__magic_name__ : Optional[int] = argparse.ArgumentParser(description='''prepare_chinese_ref''')
parser.add_argument(
'''--file_name''',
type=str,
default='''./resources/chinese-demo.txt''',
help='''file need process, same as training data in lm''',
)
parser.add_argument(
'''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
)
parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')
__magic_name__ : str = parser.parse_args()
main(args)
| 497 |
__lowerCAmelCase = 2_5_6
# Modulus to hash a string
__lowerCAmelCase = 1_0_0_0_0_0_3
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
_UpperCAmelCase = len(_lowerCAmelCase )
_UpperCAmelCase = len(_lowerCAmelCase )
if p_len > t_len:
return False
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 1
# Calculating the hash of pattern and substring of text
for i in range(_lowerCAmelCase ):
_UpperCAmelCase = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_UpperCAmelCase = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_UpperCAmelCase = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_UpperCAmelCase = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def __lowerCamelCase ( ) -> None:
_UpperCAmelCase = "abc1abc12"
_UpperCAmelCase = "alskfjaldsabc1abc1abc12k23adsfabcabc"
_UpperCAmelCase = "alskfjaldsk23adsfabcabc"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase ) and not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 2)
_UpperCAmelCase = "ABABX"
_UpperCAmelCase = "ABABZABABYABABX"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 3)
_UpperCAmelCase = "AAAB"
_UpperCAmelCase = "ABAAAAAB"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 4)
_UpperCAmelCase = "abcdabcy"
_UpperCAmelCase = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
# Test 5)
_UpperCAmelCase = "Lü"
_UpperCAmelCase = "Lüsai"
assert rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
_UpperCAmelCase = "Lue"
assert not rabin_karp(_lowerCAmelCase , _lowerCAmelCase )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 684 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
),
}
class snake_case ( __snake_case ):
"""simple docstring"""
__lowerCAmelCase = """efficientformer"""
def __init__( self , lowerCAmelCase_ = [3, 2, 6, 4] , lowerCAmelCase_ = [48, 96, 224, 448] , lowerCAmelCase_ = [True, True, True, True] , lowerCAmelCase_ = 448 , lowerCAmelCase_ = 32 , lowerCAmelCase_ = 4 , lowerCAmelCase_ = 7 , lowerCAmelCase_ = 5 , lowerCAmelCase_ = 8 , lowerCAmelCase_ = 4 , lowerCAmelCase_ = 0.0 , lowerCAmelCase_ = 16 , lowerCAmelCase_ = 3 , lowerCAmelCase_ = 3 , lowerCAmelCase_ = 3 , lowerCAmelCase_ = 2 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 0.0 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = True , lowerCAmelCase_ = True , lowerCAmelCase_ = 1E-5 , lowerCAmelCase_ = "gelu" , lowerCAmelCase_ = 0.02 , lowerCAmelCase_ = 1E-1_2 , lowerCAmelCase_ = 224 , lowerCAmelCase_ = 1E-0_5 , **lowerCAmelCase_ , ):
super().__init__(**__UpperCamelCase )
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = hidden_sizes
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = patch_size
__lowercase = num_channels
__lowercase = depths
__lowercase = mlp_expansion_ratio
__lowercase = downsamples
__lowercase = dim
__lowercase = key_dim
__lowercase = attention_ratio
__lowercase = resolution
__lowercase = pool_size
__lowercase = downsample_patch_size
__lowercase = downsample_stride
__lowercase = downsample_pad
__lowercase = drop_path_rate
__lowercase = num_metaad_blocks
__lowercase = distillation
__lowercase = use_layer_scale
__lowercase = layer_scale_init_value
__lowercase = image_size
__lowercase = batch_norm_eps
| 321 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__lowerCAmelCase = random.Random()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase=1.0 , _lowerCAmelCase=None , _lowerCAmelCase=None ) -> List[str]:
if rng is None:
_UpperCAmelCase = global_rng
_UpperCAmelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def __init__( self : Optional[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any]=7 , __UpperCamelCase : Union[str, Any]=400 , __UpperCamelCase : List[Any]=2_000 , __UpperCamelCase : Optional[Any]=10 , __UpperCamelCase : Optional[int]=160 , __UpperCamelCase : Any=8 , __UpperCamelCase : List[Any]=0.0 , __UpperCamelCase : Dict=4_000 , __UpperCamelCase : Optional[int]=False , __UpperCamelCase : Tuple=True , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = min_seq_length
_UpperCAmelCase = max_seq_length
_UpperCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_UpperCAmelCase = padding_value
_UpperCAmelCase = sampling_rate
_UpperCAmelCase = return_attention_mask
_UpperCAmelCase = do_normalize
_UpperCAmelCase = feature_size
_UpperCAmelCase = chunk_length
_UpperCAmelCase = hop_length
def UpperCAmelCase__ ( self : Optional[Any] ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : Tuple=False , __UpperCamelCase : Dict=False ):
def _flatten(__UpperCamelCase : Any ):
return list(itertools.chain(*__UpperCamelCase ) )
if equal_length:
_UpperCAmelCase = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_UpperCAmelCase = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( lowercase , unittest.TestCase):
__SCREAMING_SNAKE_CASE : str = WhisperFeatureExtractor if is_speech_available() else None
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = WhisperFeatureExtractionTester(self )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = feat_extract_first.save_pretrained(__UpperCamelCase )[0]
check_json_file_has_correct_format(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = os.path.join(__UpperCamelCase , "feat_extract.json" )
feat_extract_first.to_json_file(__UpperCamelCase )
_UpperCAmelCase = self.feature_extraction_class.from_json_file(__UpperCamelCase )
_UpperCAmelCase = feat_extract_first.to_dict()
_UpperCAmelCase = feat_extract_second.to_dict()
_UpperCAmelCase = feat_extract_first.mel_filters
_UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase ) )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def UpperCAmelCase__ ( self : int ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
# Test feature size
_UpperCAmelCase = feature_extractor(__UpperCamelCase , padding="max_length" , return_tensors="np" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
_UpperCAmelCase = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_features
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test batched
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
_UpperCAmelCase = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_UpperCAmelCase = np.asarray(__UpperCamelCase )
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
# Test truncation required
_UpperCAmelCase = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]
_UpperCAmelCase = [x[: feature_extractor.n_samples] for x in speech_inputs]
_UpperCAmelCase = [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs_truncated]
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__UpperCamelCase , __UpperCamelCase ):
self.assertTrue(np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1e-3 ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
import torch
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = np.random.rand(100 , 32 ).astype(np.floataa )
_UpperCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_UpperCAmelCase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : Tuple ):
_UpperCAmelCase = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
_UpperCAmelCase = ds.sort("id" ).select(range(__UpperCamelCase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def UpperCAmelCase__ ( self : Tuple ):
# fmt: off
_UpperCAmelCase = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
_UpperCAmelCase = self._load_datasamples(1 )
_UpperCAmelCase = WhisperFeatureExtractor()
_UpperCAmelCase = feature_extractor(__UpperCamelCase , return_tensors="pt" ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __UpperCamelCase , atol=1e-4 ) )
def UpperCAmelCase__ ( self : Optional[Any] ):
_UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase = self._load_datasamples(1 )[0]
_UpperCAmelCase = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
_UpperCAmelCase = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__UpperCamelCase )[0]
self.assertTrue(np.all(np.mean(__UpperCamelCase ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase ) - 1 ) < 1e-3 ) )
| 684 | 0 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str=False ) -> Any:
try:
__snake_case = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
__snake_case = default
else:
# KEY is set, convert it to True or False.
try:
__snake_case = strtobool(_lowerCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F'''If set, {key} must be yes or no.''' )
return _value
a : List[Any] = parse_flag_from_env('''RUN_SLOW''', default=False)
a : Optional[Any] = parse_flag_from_env('''RUN_REMOTE''', default=False)
a : int = parse_flag_from_env('''RUN_LOCAL''', default=True)
a : Any = parse_flag_from_env('''RUN_PACKAGED''', default=True)
# Compression
a : Any = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='''test requires lz4''')
a : Tuple = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='''test requires py7zr''')
a : Union[str, Any] = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='''test requires zstandard''')
# Audio
a : Optional[int] = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec('''soundfile''') is None or version.parse(importlib_metadata.version('''soundfile''')) < version.parse('''0.12.0'''),
reason='''test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ''',
)
# Beam
a : List[str] = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('''0.3.2'''),
reason='''test requires apache-beam and a compatible dill version''',
)
# Dill-cloudpickle compatibility
a : Union[str, Any] = pytest.mark.skipif(
config.DILL_VERSION <= version.parse('''0.3.2'''),
reason='''test requires dill>0.3.2 for cloudpickle compatibility''',
)
# Windows
a : Tuple = pytest.mark.skipif(
sys.platform == '''win32''',
reason='''test should not be run on Windows''',
)
def __UpperCAmelCase ( _UpperCAmelCase : List[Any] ) -> Tuple:
try:
import faiss # noqa
except ImportError:
__snake_case = unittest.skip("test requires faiss" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Dict ) -> Union[str, Any]:
try:
import regex # noqa
except ImportError:
__snake_case = unittest.skip("test requires regex" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Any ) -> str:
try:
import elasticsearch # noqa
except ImportError:
__snake_case = unittest.skip("test requires elasticsearch" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Optional[Any] ) -> Dict:
try:
import sqlalchemy # noqa
except ImportError:
__snake_case = unittest.skip("test requires sqlalchemy" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
if not config.TORCH_AVAILABLE:
__snake_case = unittest.skip("test requires PyTorch" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
if not config.TF_AVAILABLE:
__snake_case = unittest.skip("test requires TensorFlow" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Optional[int]:
if not config.JAX_AVAILABLE:
__snake_case = unittest.skip("test requires JAX" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Union[str, Any]:
if not config.PIL_AVAILABLE:
__snake_case = unittest.skip("test requires Pillow" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Tuple ) -> int:
try:
import transformers # noqa F401
except ImportError:
return unittest.skip("test requires transformers" )(_lowerCAmelCase )
else:
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Optional[Any] ) -> Any:
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip("test requires tiktoken" )(_lowerCAmelCase )
else:
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
try:
import spacy # noqa F401
except ImportError:
return unittest.skip("test requires spacy" )(_lowerCAmelCase )
else:
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Dict ) -> Tuple:
def _require_spacy_model(_UpperCAmelCase : Tuple ):
try:
import spacy # noqa F401
spacy.load(_lowerCAmelCase )
except ImportError:
return unittest.skip("test requires spacy" )(_lowerCAmelCase )
except OSError:
return unittest.skip("test requires spacy model '{}'".format(_lowerCAmelCase ) )(_lowerCAmelCase )
else:
return test_case
return _require_spacy_model
def __UpperCAmelCase ( _UpperCAmelCase : Optional[Any] ) -> str:
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip("test requires pyspark" )(_lowerCAmelCase )
else:
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Dict ) -> Any:
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip("test requires joblibspark" )(_lowerCAmelCase )
else:
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Tuple ) -> int:
if not _run_slow_tests or _run_slow_tests == 0:
__snake_case = unittest.skip("test is slow" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Optional[int]:
if not _run_local_tests or _run_local_tests == 0:
__snake_case = unittest.skip("test is local" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Any ) -> List[Any]:
if not _run_packaged_tests or _run_packaged_tests == 0:
__snake_case = unittest.skip("test is packaged" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( _UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
if not _run_remote_tests or _run_remote_tests == 0:
__snake_case = unittest.skip("test requires remote" )(_lowerCAmelCase )
return test_case
def __UpperCAmelCase ( *_UpperCAmelCase : Tuple ) -> Tuple:
def decorate(cls : str ):
for name, fn in cls.__dict__.items():
if callable(_lowerCAmelCase ) and name.startswith("test" ):
for decorator in decorators:
__snake_case = decorator(_lowerCAmelCase )
setattr(cls , _lowerCAmelCase , _lowerCAmelCase )
return cls
return decorate
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
pass
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
@contextmanager
def __UpperCAmelCase ( _UpperCAmelCase : List[str]=OfflineSimulationMode.CONNECTION_FAILS , _UpperCAmelCase : int=1E-1_6 ) -> List[Any]:
__snake_case = requests.Session().request
def timeout_request(_UpperCAmelCase : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[Any] , **_UpperCAmelCase : Optional[Any] ):
# Change the url to an invalid url so that the connection hangs
__snake_case = "https://10.255.255.1"
if kwargs.get("timeout" ) is None:
raise RequestWouldHangIndefinitelyError(
F'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.''' )
__snake_case = timeout
try:
return online_request(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
__snake_case = url
__snake_case = e.args[0]
__snake_case = (max_retry_error.args[0].replace("10.255.255.1" , F'''OfflineMock[{url}]''' ),)
__snake_case = (max_retry_error,)
raise
def raise_connection_error(_UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , **_UpperCAmelCase : Dict ):
raise requests.ConnectionError("Offline mode is enabled." , request=_lowerCAmelCase )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch("requests.Session.send" , _lowerCAmelCase ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch("requests.Session.request" , _lowerCAmelCase ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch("datasets.config.HF_DATASETS_OFFLINE" , _lowerCAmelCase ):
yield
else:
raise ValueError("Please use a value from the OfflineSimulationMode enum." )
@contextmanager
def __UpperCAmelCase ( *_UpperCAmelCase : Tuple , **_UpperCAmelCase : Optional[Any] ) -> Any:
__snake_case = str(Path().resolve() )
with tempfile.TemporaryDirectory(*_lowerCAmelCase , **_lowerCAmelCase ) as tmp_dir:
try:
os.chdir(_lowerCAmelCase )
yield
finally:
os.chdir(_lowerCAmelCase )
@contextmanager
def __UpperCAmelCase ( ) -> str:
import gc
gc.collect()
__snake_case = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def __UpperCAmelCase ( ) -> Optional[Any]:
import gc
gc.collect()
__snake_case = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def __UpperCAmelCase ( _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ) -> List[Any]:
return deepcopy(_lowerCAmelCase ).integers(0 , 1_00 , 10 ).tolist() == deepcopy(_lowerCAmelCase ).integers(0 , 1_00 , 10 ).tolist()
def __UpperCAmelCase ( _UpperCAmelCase : List[str] ) -> str:
import decorator
from requests.exceptions import HTTPError
def _wrapper(_UpperCAmelCase : Optional[int] , *_UpperCAmelCase : List[Any] , **_UpperCAmelCase : Union[str, Any] ):
try:
return func(*_lowerCAmelCase , **_lowerCAmelCase )
except HTTPError as err:
if str(_lowerCAmelCase ).startswith("500" ) or str(_lowerCAmelCase ).startswith("502" ):
pytest.xfail(str(_lowerCAmelCase ) )
raise err
return decorator.decorator(_wrapper , _lowerCAmelCase )
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple , a_ : Optional[Any] , a_ : str , a_ : List[Any] ):
"""simple docstring"""
__snake_case = returncode
__snake_case = stdout
__snake_case = stderr
async def __UpperCAmelCase ( _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> List[str]:
while True:
__snake_case = await stream.readline()
if line:
callback(_lowerCAmelCase )
else:
break
async def __UpperCAmelCase ( _UpperCAmelCase : Any , _UpperCAmelCase : str=None , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : Any=None , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : str=False ) -> _RunOutput:
if echo:
print("\nRunning: " , " ".join(_lowerCAmelCase ) )
__snake_case = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_lowerCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_lowerCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
__snake_case = []
__snake_case = []
def tee(_UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
__snake_case = line.decode("utf-8" ).rstrip()
sink.append(_lowerCAmelCase )
if not quiet:
print(_lowerCAmelCase , _lowerCAmelCase , file=_lowerCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_lowerCAmelCase , _lowerCAmelCase , sys.stdout , label="stdout:" ) ),
_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_lowerCAmelCase , _lowerCAmelCase , sys.stderr , label="stderr:" ) ),
] , timeout=_lowerCAmelCase , )
return _RunOutput(await p.wait() , _lowerCAmelCase , _lowerCAmelCase )
def __UpperCAmelCase ( _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : List[Any]=1_80 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Any=True ) -> _RunOutput:
__snake_case = asyncio.get_event_loop()
__snake_case = loop.run_until_complete(
_stream_subprocess(_lowerCAmelCase , env=_lowerCAmelCase , stdin=_lowerCAmelCase , timeout=_lowerCAmelCase , quiet=_lowerCAmelCase , echo=_lowerCAmelCase ) )
__snake_case = " ".join(_lowerCAmelCase )
if result.returncode > 0:
__snake_case = "\n".join(result.stderr )
raise RuntimeError(
F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
F'''The combined stderr from workers follows:\n{stderr}''' )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(F'''\'{cmd_str}\' produced no output.''' )
return result
def __UpperCAmelCase ( ) -> Union[str, Any]:
__snake_case = os.environ.get("PYTEST_XDIST_WORKER" , "gw0" )
__snake_case = re.sub(R"^gw" , "" , _lowerCAmelCase , 0 , re.M )
return int(_lowerCAmelCase )
def __UpperCAmelCase ( ) -> Optional[Any]:
__snake_case = 2_95_00
__snake_case = pytest_xdist_worker_id()
return port + uniq_delta
| 69 |
# Copyright 2023 The HuggingFace Inc. 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.
import re
from ..utils import cached_file
# docstyle-ignore
__lowerCAmelCase = "\nHuman: <<task>>\n\nAssistant: "
__lowerCAmelCase = "huggingface-tools/default-prompts"
__lowerCAmelCase = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase="run" ) -> Union[str, Any]:
if prompt_or_repo_id is None:
_UpperCAmelCase = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , _lowerCAmelCase ) is not None:
return prompt_or_repo_id
_UpperCAmelCase = cached_file(
_lowerCAmelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(_lowerCAmelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 684 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class __a ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = """gpt_neox_japanese"""
def __init__( self : Optional[Any] , snake_case_ : List[str]=3_20_00 , snake_case_ : Any=25_60 , snake_case_ : List[Any]=32 , snake_case_ : int=32 , snake_case_ : List[str]=4 , snake_case_ : Tuple="gelu" , snake_case_ : Union[str, Any]=1.0_0 , snake_case_ : int=1_00_00 , snake_case_ : Any=20_48 , snake_case_ : str=0.0_2 , snake_case_ : List[str]=1e-5 , snake_case_ : Tuple=True , snake_case_ : Union[str, Any]=3_19_96 , snake_case_ : int=3_19_99 , snake_case_ : Union[str, Any]=0.1 , snake_case_ : str=0.0 , **snake_case_ : Optional[int] , )-> List[Any]:
super().__init__(bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase)
__lowerCAmelCase =vocab_size
__lowerCAmelCase =max_position_embeddings
__lowerCAmelCase =hidden_size
__lowerCAmelCase =num_hidden_layers
__lowerCAmelCase =num_attention_heads
__lowerCAmelCase =intermediate_multiple_size
__lowerCAmelCase =hidden_act
__lowerCAmelCase =rotary_pct
__lowerCAmelCase =rotary_emb_base
__lowerCAmelCase =initializer_range
__lowerCAmelCase =layer_norm_eps
__lowerCAmelCase =use_cache
__lowerCAmelCase =attention_dropout
__lowerCAmelCase =hidden_dropout
| 354 |
from itertools import permutations
def __lowerCamelCase ( _lowerCAmelCase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(_lowerCAmelCase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def __lowerCamelCase ( _lowerCAmelCase = 10 ) -> int:
return sum(
int("".join(map(_lowerCAmelCase , _lowerCAmelCase ) ) )
for num in permutations(range(_lowerCAmelCase ) )
if is_substring_divisible(_lowerCAmelCase ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 684 | 0 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
UpperCamelCase_ = """facebook/wmt19-en-de"""
UpperCamelCase_ = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
UpperCamelCase_ = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
UpperCamelCase_ = FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')
# Test
UpperCamelCase_ = tokenizer(["""Making tiny model"""], return_tensors="""pt""")
UpperCamelCase_ = tiny_model(**batch)
print("""test output:""", len(outputs.logits[0]))
# Save
UpperCamelCase_ = """tiny-wmt19-en-de"""
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 92 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
__lowerCAmelCase = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
__lowerCAmelCase = {"facebook/blenderbot-3B": 1_2_8}
class __SCREAMING_SNAKE_CASE ( lowercase):
__SCREAMING_SNAKE_CASE : Optional[int] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : str = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : List[Any] = ["""input_ids""", """attention_mask"""]
__SCREAMING_SNAKE_CASE : List[str] = BlenderbotTokenizer
def __init__( self : Tuple , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]=None , __UpperCamelCase : List[str]=None , __UpperCamelCase : Union[str, Any]="replace" , __UpperCamelCase : Tuple="<s>" , __UpperCamelCase : str="</s>" , __UpperCamelCase : Dict="</s>" , __UpperCamelCase : Union[str, Any]="<s>" , __UpperCamelCase : Union[str, Any]="<unk>" , __UpperCamelCase : Tuple="<pad>" , __UpperCamelCase : Optional[int]="<mask>" , __UpperCamelCase : Union[str, Any]=False , __UpperCamelCase : List[str]=True , **__UpperCamelCase : int , ):
super().__init__(
__UpperCamelCase , __UpperCamelCase , tokenizer_file=__UpperCamelCase , errors=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , add_prefix_space=__UpperCamelCase , trim_offsets=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = getattr(__UpperCamelCase , pre_tok_state.pop("type" ) )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = pre_tok_class(**__UpperCamelCase )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = "post_processor"
_UpperCAmelCase = getattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
if tokenizer_component_instance:
_UpperCAmelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
_UpperCAmelCase = tuple(state["sep"] )
if "cls" in state:
_UpperCAmelCase = tuple(state["cls"] )
_UpperCAmelCase = False
if state.get("add_prefix_space" , __UpperCamelCase ) != add_prefix_space:
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = True
if state.get("trim_offsets" , __UpperCamelCase ) != trim_offsets:
_UpperCAmelCase = trim_offsets
_UpperCAmelCase = True
if changes_to_apply:
_UpperCAmelCase = getattr(__UpperCamelCase , state.pop("type" ) )
_UpperCAmelCase = component_class(**__UpperCamelCase )
setattr(self.backend_tokenizer , __UpperCamelCase , __UpperCamelCase )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def UpperCAmelCase__ ( self : Union[str, Any] ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase__ ( self : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else value
_UpperCAmelCase = value
def UpperCAmelCase__ ( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : List[Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : Tuple , *__UpperCamelCase : int , **__UpperCamelCase : Union[str, Any] ):
_UpperCAmelCase = kwargs.get("is_split_into_words" , __UpperCamelCase )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__UpperCamelCase , **__UpperCamelCase )
def UpperCAmelCase__ ( self : str , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None ):
_UpperCAmelCase = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase )
return tuple(__UpperCamelCase )
def UpperCAmelCase__ ( self : Optional[Any] , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase__ ( self : Dict , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self : Tuple , __UpperCamelCase : "Conversation" ):
_UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text )
else:
# Generated responses should contain them already.
inputs.append(__UpperCamelCase )
_UpperCAmelCase = " ".join(__UpperCamelCase )
_UpperCAmelCase = self.encode(__UpperCamelCase )
if len(__UpperCamelCase ) > self.model_max_length:
_UpperCAmelCase = input_ids[-self.model_max_length :]
logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids
| 684 | 0 |
import string
import numpy
def UpperCamelCase ( _a , _a ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _lowerCAmelCase )
class UpperCamelCase :
'''simple docstring'''
lowercase : Union[str, Any] =string.ascii_uppercase + string.digits
# This cipher takes alphanumerics into account
# i.e. a total of 36 characters
# take x and return x % len(key_string)
lowercase : Optional[Any] =numpy.vectorize(lambda lowercase__ : x % 36 )
lowercase : Union[str, Any] =numpy.vectorize(lowercase__ )
def __init__( self , UpperCamelCase_ ):
lowercase_ :Dict = self.modulus(__UpperCamelCase ) # mod36 calc's on the encrypt key
self.check_determinant() # validate the determinant of the encryption key
lowercase_ :Tuple = encrypt_key.shape[0]
def UpperCamelCase ( self , UpperCamelCase_ ):
return self.key_string.index(__UpperCamelCase )
def UpperCamelCase ( self , UpperCamelCase_ ):
return self.key_string[round(__UpperCamelCase )]
def UpperCamelCase ( self ):
lowercase_ :List[Any] = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
lowercase_ :Any = det % len(self.key_string )
lowercase_ :int = len(self.key_string )
if greatest_common_divisor(__UpperCamelCase , len(self.key_string ) ) != 1:
lowercase_ :List[str] = (
f"determinant modular {req_l} of encryption key({det}) "
f"is not co prime w.r.t {req_l}.\nTry another key."
)
raise ValueError(__UpperCamelCase )
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :List[str] = [char for char in text.upper() if char in self.key_string]
lowercase_ :int = chars[-1]
while len(__UpperCamelCase ) % self.break_key != 0:
chars.append(__UpperCamelCase )
return "".join(__UpperCamelCase )
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :Union[str, Any] = self.process_text(text.upper() )
lowercase_ :int = ''''''
for i in range(0 , len(__UpperCamelCase ) - self.break_key + 1 , self.break_key ):
lowercase_ :int = text[i : i + self.break_key]
lowercase_ :Any = [self.replace_letters(__UpperCamelCase ) for char in batch]
lowercase_ :int = numpy.array([vec] ).T
lowercase_ :Union[str, Any] = self.modulus(self.encrypt_key.dot(__UpperCamelCase ) ).T.tolist()[
0
]
lowercase_ :Any = ''''''.join(
self.replace_digits(__UpperCamelCase ) for num in batch_encrypted )
encrypted += encrypted_batch
return encrypted
def UpperCamelCase ( self ):
lowercase_ :List[Any] = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
lowercase_ :List[Any] = det % len(self.key_string )
lowercase_ :Optional[int] = None
for i in range(len(self.key_string ) ):
if (det * i) % len(self.key_string ) == 1:
lowercase_ :Optional[Any] = i
break
lowercase_ :str = (
det_inv
* numpy.linalg.det(self.encrypt_key )
* numpy.linalg.inv(self.encrypt_key )
)
return self.to_int(self.modulus(__UpperCamelCase ) )
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :Dict = self.make_decrypt_key()
lowercase_ :List[str] = self.process_text(text.upper() )
lowercase_ :List[Any] = ''''''
for i in range(0 , len(__UpperCamelCase ) - self.break_key + 1 , self.break_key ):
lowercase_ :Optional[Any] = text[i : i + self.break_key]
lowercase_ :List[str] = [self.replace_letters(__UpperCamelCase ) for char in batch]
lowercase_ :Dict = numpy.array([vec] ).T
lowercase_ :Dict = self.modulus(decrypt_key.dot(__UpperCamelCase ) ).T.tolist()[0]
lowercase_ :List[str] = ''''''.join(
self.replace_digits(__UpperCamelCase ) for num in batch_decrypted )
decrypted += decrypted_batch
return decrypted
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase_ :Union[str, Any] = int(input('''Enter the order of the encryption key: ''' ) )
lowercase_ :List[str] = []
print('''Enter each row of the encryption key with space separated integers''' )
for _ in range(_lowerCAmelCase ):
lowercase_ :List[str] = [int(_lowerCAmelCase ) for x in input().split()]
hill_matrix.append(_lowerCAmelCase )
lowercase_ :Tuple = HillCipher(numpy.array(_lowerCAmelCase ) )
print('''Would you like to encrypt or decrypt some text? (1 or 2)''' )
lowercase_ :Union[str, Any] = input('''\n1. Encrypt\n2. Decrypt\n''' )
if option == "1":
lowercase_ :int = input('''What text would you like to encrypt?: ''' )
print('''Your encrypted text is:''' )
print(hc.encrypt(_lowerCAmelCase ) )
elif option == "2":
lowercase_ :Dict = input('''What text would you like to decrypt?: ''' )
print('''Your decrypted text is:''' )
print(hc.decrypt(_lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 257 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
_UpperCAmelCase = WavaVecaForSequenceClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["projector.weight"]
_UpperCAmelCase = downstream_dict["projector.bias"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.weight"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
_UpperCAmelCase = WavaVecaForAudioFrameClassification.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["model.linear.weight"]
_UpperCAmelCase = downstream_dict["model.linear.bias"]
return model
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = WavaVecaForXVector.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
_UpperCAmelCase = downstream_dict["connector.weight"]
_UpperCAmelCase = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_UpperCAmelCase = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
_UpperCAmelCase = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
_UpperCAmelCase = downstream_dict["objective.W"]
return model
@torch.no_grad()
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
_UpperCAmelCase = torch.load(_lowerCAmelCase , map_location="cpu" )
_UpperCAmelCase = checkpoint["Downstream"]
_UpperCAmelCase = WavaVecaConfig.from_pretrained(_lowerCAmelCase )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , do_normalize=_lowerCAmelCase )
_UpperCAmelCase = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
_UpperCAmelCase = convert_classification(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForAudioFrameClassification" ):
_UpperCAmelCase = convert_diarization(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
elif arch.endswith("ForXVector" ):
_UpperCAmelCase = convert_xvector(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
_UpperCAmelCase = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(_lowerCAmelCase )
hf_model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
__lowerCAmelCase = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 684 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.