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'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['image_processor', 'tokenizer']
lowerCAmelCase_ = 'ViltImageProcessor'
lowerCAmelCase_ = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self : str,__A : int=None,__A : Optional[int]=None,**__A : Union[str, Any] ):
_lowerCamelCase : int = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead.",__A,)
_lowerCamelCase : Tuple = kwargs.pop("feature_extractor" )
_lowerCamelCase : Any = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__A,__A )
_lowerCamelCase : Tuple = self.image_processor
def __call__( self : Optional[int],__A : int,__A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,__A : bool = True,__A : Union[bool, str, PaddingStrategy] = False,__A : Union[bool, str, TruncationStrategy] = None,__A : Optional[int] = None,__A : int = 0,__A : Optional[int] = None,__A : Optional[bool] = None,__A : Optional[bool] = None,__A : bool = False,__A : bool = False,__A : bool = False,__A : bool = False,__A : bool = True,__A : Optional[Union[str, TensorType]] = None,**__A : Union[str, Any],):
_lowerCamelCase : Optional[int] = self.tokenizer(
text=__A,add_special_tokens=__A,padding=__A,truncation=__A,max_length=__A,stride=__A,pad_to_multiple_of=__A,return_token_type_ids=__A,return_attention_mask=__A,return_overflowing_tokens=__A,return_special_tokens_mask=__A,return_offsets_mapping=__A,return_length=__A,verbose=__A,return_tensors=__A,**__A,)
# add pixel_values + pixel_mask
_lowerCamelCase : Dict = self.image_processor(__A,return_tensors=__A )
encoding.update(__A )
return encoding
def lowerCamelCase_ ( self : List[Any],*__A : List[str],**__A : List[str] ):
return self.tokenizer.batch_decode(*__A,**__A )
def lowerCamelCase_ ( self : Optional[int],*__A : Union[str, Any],**__A : str ):
return self.tokenizer.decode(*__A,**__A )
@property
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : Tuple = self.tokenizer.model_input_names
_lowerCamelCase : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def lowerCamelCase_ ( self : Dict ):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",__A,)
return self.image_processor_class
@property
def lowerCamelCase_ ( self : str ):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",__A,)
return self.image_processor
| 44 |
'''simple docstring'''
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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int]=False ):
"""simple docstring"""
_lowerCamelCase : List[Any] = []
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') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
] )
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"
_lowerCamelCase : Optional[int] = [(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"),
] )
return rename_keys
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any]=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
_lowerCamelCase : int = ""
else:
_lowerCamelCase : int = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase : Any = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_lowerCamelCase : Tuple = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : List[str] = in_proj_weight[
: config.hidden_size, :
]
_lowerCamelCase : List[str] = in_proj_bias[: config.hidden_size]
_lowerCamelCase : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
_lowerCamelCase : List[str] = in_proj_bias[-config.hidden_size :]
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = dct.pop(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = val
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : str = ViTConfig()
_lowerCamelCase : List[str] = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Optional[Any] = int(vit_name[-12:-10] )
_lowerCamelCase : str = int(vit_name[-9:-6] )
else:
_lowerCamelCase : List[Any] = 1000
_lowerCamelCase : str = "huggingface/label-files"
_lowerCamelCase : Any = "imagenet-1k-id2label.json"
_lowerCamelCase : int = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : str = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : Optional[Any] = idalabel
_lowerCamelCase : List[str] = {v: k for k, v in idalabel.items()}
_lowerCamelCase : List[str] = int(vit_name[-6:-4] )
_lowerCamelCase : str = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("tiny" ):
_lowerCamelCase : List[Any] = 192
_lowerCamelCase : Optional[int] = 768
_lowerCamelCase : Union[str, Any] = 12
_lowerCamelCase : Optional[Any] = 3
elif vit_name[9:].startswith("small" ):
_lowerCamelCase : Optional[Any] = 384
_lowerCamelCase : Optional[Any] = 1536
_lowerCamelCase : int = 12
_lowerCamelCase : List[str] = 6
else:
pass
else:
if vit_name[4:].startswith("small" ):
_lowerCamelCase : List[str] = 768
_lowerCamelCase : Optional[Any] = 2304
_lowerCamelCase : List[Any] = 8
_lowerCamelCase : List[Any] = 8
elif vit_name[4:].startswith("base" ):
pass
elif vit_name[4:].startswith("large" ):
_lowerCamelCase : List[Any] = 1024
_lowerCamelCase : Optional[Any] = 4096
_lowerCamelCase : List[Any] = 24
_lowerCamelCase : Union[str, Any] = 16
elif vit_name[4:].startswith("huge" ):
_lowerCamelCase : str = 1280
_lowerCamelCase : List[Any] = 5120
_lowerCamelCase : List[str] = 32
_lowerCamelCase : List[str] = 16
# load original model from timm
_lowerCamelCase : int = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
_lowerCamelCase : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = create_rename_keys(_lowerCAmelCase , _lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# load HuggingFace model
if vit_name[-5:] == "in21k":
_lowerCamelCase : int = ViTModel(_lowerCAmelCase ).eval()
else:
_lowerCamelCase : List[str] = ViTForImageClassification(_lowerCAmelCase ).eval()
model.load_state_dict(_lowerCAmelCase )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
_lowerCamelCase : Union[str, Any] = DeiTImageProcessor(size=config.image_size )
else:
_lowerCamelCase : Union[str, Any] = ViTImageProcessor(size=config.image_size )
_lowerCamelCase : Optional[int] = image_processor(images=prepare_img() , return_tensors="pt" )
_lowerCamelCase : Optional[int] = encoding["pixel_values"]
_lowerCamelCase : Union[str, Any] = model(_lowerCAmelCase )
if base_model:
_lowerCamelCase : int = timm_model.forward_features(_lowerCAmelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_lowerCAmelCase , outputs.pooler_output , atol=1E-3 )
else:
_lowerCamelCase : Union[str, Any] = timm_model(_lowerCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCAmelCase , outputs.logits , atol=1E-3 )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_lowerCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the 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.'
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def A_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any]=() , _lowerCAmelCase : Optional[Any]=None , _lowerCAmelCase : Tuple="no" , _lowerCAmelCase : Tuple="29500" ):
"""simple docstring"""
_lowerCamelCase : Any = False
_lowerCamelCase : List[str] = False
if any(key.startswith("KAGGLE" ) for key in os.environ.keys() ):
_lowerCamelCase : Any = True
elif "IPython" in sys.modules:
_lowerCamelCase : Optional[Any] = "google.colab" in str(sys.modules["IPython"].get_ipython() )
try:
_lowerCamelCase : Union[str, Any] = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.' )
if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME" , _lowerCAmelCase ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
"your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`." )
if num_processes is None:
_lowerCamelCase : Optional[int] = 8
_lowerCamelCase : List[Any] = PrepareForLaunch(_lowerCAmelCase , distributed_type="TPU" )
print(F'Launching a training on {num_processes} TPU cores.' )
xmp.spawn(_lowerCAmelCase , args=_lowerCAmelCase , nprocs=_lowerCAmelCase , start_method="fork" )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print("Launching training on one GPU." )
else:
print("Launching training on one CPU." )
function(*_lowerCAmelCase )
else:
if num_processes is None:
raise ValueError(
"You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call." )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
"inside your training function. Restart your notebook and make sure no cells initializes an "
"`Accelerator`." )
if torch.cuda.is_initialized():
raise ValueError(
"To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
"using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
"function." )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=_lowerCAmelCase , master_addr="127.0.01" , master_port=_lowerCAmelCase , mixed_precision=_lowerCAmelCase ):
_lowerCamelCase : Tuple = PrepareForLaunch(_lowerCAmelCase , distributed_type="MULTI_GPU" )
print(F'Launching training on {num_processes} GPUs.' )
try:
start_processes(_lowerCAmelCase , args=_lowerCAmelCase , nprocs=_lowerCAmelCase , start_method="fork" )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
"CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
"This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
"Please review your imports and test them when running the `notebook_launcher()` to identify "
"which one is problematic." ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
_lowerCamelCase : Union[str, Any] = "1"
print("Launching training on MPS." )
elif torch.cuda.is_available():
print("Launching training on one GPU." )
else:
print("Launching training on CPU." )
function(*_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Dict=() , _lowerCAmelCase : str=2 ):
"""simple docstring"""
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=_lowerCAmelCase , master_addr="127.0.01" , master_port="29500" , accelerate_mixed_precision="no" , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu="yes" , ):
_lowerCamelCase : int = PrepareForLaunch(_lowerCAmelCase , debug=_lowerCAmelCase )
start_processes(_lowerCAmelCase , args=_lowerCAmelCase , nprocs=_lowerCAmelCase , start_method="fork" )
| 44 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( _lowerCAmelCase : int = 5000 ):
"""simple docstring"""
_lowerCamelCase : Dict = [(i * (3 * i - 1)) // 2 for i in range(1 , _lowerCAmelCase )]
for i, pentagonal_i in enumerate(_lowerCAmelCase ):
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : List[Any] = pentagonal_nums[j]
_lowerCamelCase : Any = pentagonal_i + pentagonal_j
_lowerCamelCase : Union[str, Any] = pentagonal_j - pentagonal_i
if is_pentagonal(_lowerCAmelCase ) and is_pentagonal(_lowerCAmelCase ):
return b
return -1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 | 1 |
'''simple docstring'''
import argparse
import copy
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = {}
with open(_lowerCAmelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_lowerCamelCase : Dict = []
_list.append([line.split()[1], line.split()[2]] )
_lowerCamelCase : List[Any] = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_lowerCamelCase : List[Any] = []
_list.append([line.split()[0], line.split()[2]] )
_lowerCamelCase : Dict = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def A_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Dict ):
"""simple docstring"""
with open(_lowerCAmelCase ) as f:
_lowerCamelCase : Optional[Any] = f.read(1 )
_lowerCamelCase : Optional[int] = start_node
_lowerCamelCase : List[Any] = []
_lowerCamelCase : List[str] = start_node
_lowerCamelCase : Optional[Any] = 0
while visiting not in first_solution:
_lowerCamelCase : Any = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_lowerCAmelCase ) and k[0] not in first_solution:
_lowerCamelCase : Optional[int] = k[1]
_lowerCamelCase : Tuple = k[0]
first_solution.append(_lowerCAmelCase )
_lowerCamelCase : Any = distance_of_first_solution + int(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = best_node
first_solution.append(_lowerCAmelCase )
_lowerCamelCase : Any = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_lowerCamelCase : Optional[Any] = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = []
for n in solution[1:-1]:
_lowerCamelCase : int = solution.index(_lowerCAmelCase )
for kn in solution[1:-1]:
_lowerCamelCase : List[str] = solution.index(_lowerCAmelCase )
if n == kn:
continue
_lowerCamelCase : Dict = copy.deepcopy(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = kn
_lowerCamelCase : List[Any] = n
_lowerCamelCase : Any = 0
for k in _tmp[:-1]:
_lowerCamelCase : Union[str, Any] = _tmp[_tmp.index(_lowerCAmelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_lowerCamelCase : int = distance + int(i[1] )
_tmp.append(_lowerCAmelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_lowerCamelCase : List[Any] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _lowerCAmelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = 1
_lowerCamelCase : Union[str, Any] = first_solution
_lowerCamelCase : Optional[Any] = []
_lowerCamelCase : Optional[int] = distance_of_first_solution
_lowerCamelCase : List[str] = solution
while count <= iters:
_lowerCamelCase : List[str] = find_neighborhood(_lowerCAmelCase , _lowerCAmelCase )
_lowerCamelCase : List[Any] = 0
_lowerCamelCase : List[Any] = neighborhood[index_of_best_solution]
_lowerCamelCase : Optional[Any] = len(_lowerCAmelCase ) - 1
_lowerCamelCase : List[str] = False
while not found:
_lowerCamelCase : str = 0
while i < len(_lowerCAmelCase ):
if best_solution[i] != solution[i]:
_lowerCamelCase : str = best_solution[i]
_lowerCamelCase : Any = solution[i]
break
_lowerCamelCase : List[Any] = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_lowerCamelCase : Optional[int] = True
_lowerCamelCase : Union[str, Any] = best_solution[:-1]
_lowerCamelCase : Union[str, Any] = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_lowerCamelCase : Tuple = cost
_lowerCamelCase : List[str] = solution
else:
_lowerCamelCase : Dict = index_of_best_solution + 1
_lowerCamelCase : Any = neighborhood[index_of_best_solution]
if len(_lowerCAmelCase ) >= size:
tabu_list.pop(0 )
_lowerCamelCase : Union[str, Any] = count + 1
return best_solution_ever, best_cost
def A_ ( _lowerCAmelCase : List[str]=None ):
"""simple docstring"""
_lowerCamelCase : Tuple = generate_neighbours(args.File )
_lowerCamelCase , _lowerCamelCase : Tuple = generate_first_solution(
args.File , _lowerCAmelCase )
_lowerCamelCase , _lowerCamelCase : int = tabu_search(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.Iterations , args.Size , )
print(F'Best solution: {best_sol}, with total distance: {best_cost}.' )
if __name__ == "__main__":
UpperCAmelCase_ : str = argparse.ArgumentParser(description='Tabu Search')
parser.add_argument(
'-f',
'--File',
type=str,
help='Path to the file containing the data',
required=True,
)
parser.add_argument(
'-i',
'--Iterations',
type=int,
help='How many iterations the algorithm should perform',
required=True,
)
parser.add_argument(
'-s', '--Size', type=int, help='Size of the tabu list', required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileBertForMaskedLM',
'MobileBertForMultipleChoice',
'MobileBertForNextSentencePrediction',
'MobileBertForPreTraining',
'MobileBertForQuestionAnswering',
'MobileBertForSequenceClassification',
'MobileBertForTokenClassification',
'MobileBertLayer',
'MobileBertModel',
'MobileBertPreTrainedModel',
'load_tf_weights_in_mobilebert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileBertForMaskedLM',
'TFMobileBertForMultipleChoice',
'TFMobileBertForNextSentencePrediction',
'TFMobileBertForPreTraining',
'TFMobileBertForQuestionAnswering',
'TFMobileBertForSequenceClassification',
'TFMobileBertForTokenClassification',
'TFMobileBertMainLayer',
'TFMobileBertModel',
'TFMobileBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Tuple = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
'PegasusXForConditionalGeneration',
'PegasusXModel',
'PegasusXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase__ :
def __init__( self : Optional[Any],__A : list[tuple[float, float]] ):
_lowerCamelCase : Tuple = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
_lowerCamelCase : int = len(__A ) - 1
def lowerCamelCase_ ( self : Optional[int],__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree,__A ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__A ),5 ) == 1
return output_values
def lowerCamelCase_ ( self : int,__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : List[Any] = self.basis_function(__A )
_lowerCamelCase : str = 0.0
_lowerCamelCase : str = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowerCamelCase_ ( self : Optional[Any],__A : float = 0.01 ):
from matplotlib import pyplot as plt # type: ignore
_lowerCamelCase : list[float] = [] # x coordinates of points to plot
_lowerCamelCase : list[float] = [] # y coordinates of points to plot
_lowerCamelCase : Tuple = 0.0
while t <= 1:
_lowerCamelCase : str = self.bezier_curve_function(__A )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
_lowerCamelCase : List[str] = [i[0] for i in self.list_of_points]
_lowerCamelCase : Union[str, Any] = [i[1] for i in self.list_of_points]
plt.plot(
__A,__A,color="blue",label="Curve of Degree " + str(self.degree ),)
plt.scatter(__A,__A,color="red",label="Control Points" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 44 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : List[str] = logging.get_logger()
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : LevitConfig , _lowerCAmelCase : Path , _lowerCAmelCase : bool = True ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
_lowerCamelCase : Any = timm.create_model("levit_128s" , pretrained=_lowerCAmelCase )
else:
_lowerCamelCase : Tuple = timm.create_model("levit_128" , pretrained=_lowerCAmelCase )
if hidden_sizes == 192:
_lowerCamelCase : str = timm.create_model("levit_192" , pretrained=_lowerCAmelCase )
if hidden_sizes == 256:
_lowerCamelCase : str = timm.create_model("levit_256" , pretrained=_lowerCAmelCase )
if hidden_sizes == 384:
_lowerCamelCase : Tuple = timm.create_model("levit_384" , pretrained=_lowerCAmelCase )
from_model.eval()
_lowerCamelCase : Any = LevitForImageClassificationWithTeacher(_lowerCAmelCase ).eval()
_lowerCamelCase : Union[str, Any] = OrderedDict()
_lowerCamelCase : Tuple = from_model.state_dict()
_lowerCamelCase : Union[str, Any] = list(from_model.state_dict().keys() )
_lowerCamelCase : List[Any] = list(our_model.state_dict().keys() )
print(len(_lowerCAmelCase ) , len(_lowerCAmelCase ) )
for i in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : str = weights[og_keys[i]]
our_model.load_state_dict(_lowerCAmelCase )
_lowerCamelCase : str = torch.randn((2, 3, 224, 224) )
_lowerCamelCase : Any = from_model(_lowerCAmelCase )
_lowerCamelCase : List[Any] = our_model(_lowerCAmelCase ).logits
assert torch.allclose(_lowerCAmelCase , _lowerCAmelCase ), "The model logits don't match the original one."
_lowerCamelCase : Dict = name
print(_lowerCAmelCase )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
_lowerCamelCase : List[str] = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(F'Pushed {checkpoint_name}' )
def A_ ( _lowerCAmelCase : Path , _lowerCAmelCase : str = None , _lowerCAmelCase : bool = True ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = "imagenet-1k-id2label.json"
_lowerCamelCase : Union[str, Any] = 1000
_lowerCamelCase : str = (1, num_labels)
_lowerCamelCase : List[str] = "huggingface/label-files"
_lowerCamelCase : Optional[int] = num_labels
_lowerCamelCase : List[Any] = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : Union[str, Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : str = idalabel
_lowerCamelCase : List[str] = {v: k for k, v in idalabel.items()}
_lowerCamelCase : Any = partial(_lowerCAmelCase , num_labels=_lowerCAmelCase , idalabel=_lowerCAmelCase , labelaid=_lowerCAmelCase )
_lowerCamelCase : Any = {
"levit-128S": 128,
"levit-128": 128,
"levit-192": 192,
"levit-256": 256,
"levit-384": 384,
}
_lowerCamelCase : str = {
"levit-128S": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
"levit-128": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
"levit-192": ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
"levit-256": ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
"levit-384": ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , _lowerCAmelCase , names_to_config[model_name] , _lowerCAmelCase , _lowerCAmelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return config, expected_shape
if __name__ == "__main__":
UpperCAmelCase_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='levit-dump-folder/',
type=Path,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
UpperCAmelCase_ : Any = parser.parse_args()
UpperCAmelCase_ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 44 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=A ):
lowerCAmelCase_ = ['transformers', 'torch', 'note_seq']
def __init__( self : str,*__A : List[str],**__A : List[Any] ):
requires_backends(self,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Optional[Any],*__A : str,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Dict,*__A : Dict,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
| 44 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : Union[str, Any],*__A : Union[str, Any],**__A : Any ):
warnings.warn(
"The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use DonutImageProcessor instead.",__A,)
super().__init__(*__A,**__A )
| 44 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = CodeGenTokenizer
lowerCAmelCase_ = CodeGenTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = {'add_prefix_space': True}
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : List[str] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_lowerCamelCase : Dict = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
_lowerCamelCase : Any = dict(zip(__A,range(len(__A ) ) ) )
_lowerCamelCase : Optional[int] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_lowerCamelCase : Tuple = {"unk_token": "<unk>"}
_lowerCamelCase : Optional[Any] = 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(__A ) + "\n" )
with open(self.merges_file,"w",encoding="utf-8" ) as fp:
fp.write("\n".join(__A ) )
def lowerCamelCase_ ( self : Dict,**__A : Tuple ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : Union[str, Any],**__A : int ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : str,__A : Dict ):
_lowerCamelCase : Optional[Any] = "lower newer"
_lowerCamelCase : Union[str, Any] = "lower newer"
return input_text, output_text
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : int = CodeGenTokenizer(self.vocab_file,self.merges_file,**self.special_tokens_map )
_lowerCamelCase : Any = "lower newer"
_lowerCamelCase : Optional[Any] = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
self.assertListEqual(__A,__A )
_lowerCamelCase : Union[str, Any] = tokens + [tokenizer.unk_token]
_lowerCamelCase : Dict = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Any ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase : str = self.get_tokenizer()
_lowerCamelCase : Optional[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = "lower newer"
# Testing tokenization
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
_lowerCamelCase : str = rust_tokenizer.tokenize(__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids without special tokens
_lowerCamelCase : str = tokenizer.encode(__A,add_special_tokens=__A,add_prefix_space=__A )
_lowerCamelCase : List[str] = rust_tokenizer.encode(__A,add_special_tokens=__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids with special tokens
_lowerCamelCase : List[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = tokenizer.encode(__A,add_prefix_space=__A )
_lowerCamelCase : Optional[int] = rust_tokenizer.encode(__A )
self.assertListEqual(__A,__A )
# Testing the unknown token
_lowerCamelCase : Optional[int] = tokens + [rust_tokenizer.unk_token]
_lowerCamelCase : Optional[Any] = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Tuple,*__A : Any,**__A : Any ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def lowerCamelCase_ ( self : int,__A : Optional[int]=1_5 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_lowerCamelCase : Tuple = self.rust_tokenizer_class.from_pretrained(__A,**__A )
# Simple input
_lowerCamelCase : Dict = "This is a simple input"
_lowerCamelCase : Any = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Tuple = ("This is a simple input", "This is a pair")
_lowerCamelCase : Tuple = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
# Pair input
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : str = CodeGenTokenizer.from_pretrained(self.tmpdirname,pad_token="<pad>" )
# Simple input
_lowerCamelCase : Tuple = "This is a simple input"
_lowerCamelCase : Dict = ["This is a simple input looooooooong", "This is a simple input"]
_lowerCamelCase : Dict = ("This is a simple input", "This is a pair")
_lowerCamelCase : Dict = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
_lowerCamelCase : Dict = tokenizer.pad_token_id
_lowerCamelCase : Dict = tokenizer(__A,padding="max_length",max_length=3_0,return_tensors="np" )
_lowerCamelCase : int = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
_lowerCamelCase : List[Any] = tokenizer(*__A,padding="max_length",max_length=6_0,return_tensors="np" )
_lowerCamelCase : Tuple = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1],3_0 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1],3_3 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1],6_0 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1],5_2 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : List[Any] = "$$$"
_lowerCamelCase : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname,bos_token=__A,add_bos_token=__A )
_lowerCamelCase : List[str] = "This is a simple input"
_lowerCamelCase : Optional[Any] = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Union[str, Any] = tokenizer.bos_token_id
_lowerCamelCase : Any = tokenizer(__A )
_lowerCamelCase : List[str] = tokenizer(__A )
self.assertEqual(out_s.input_ids[0],__A )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_lowerCamelCase : int = tokenizer.decode(out_s.input_ids )
_lowerCamelCase : str = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0],__A )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : int = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
_lowerCamelCase : Optional[Any] = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
_lowerCamelCase : Dict = "\nif len_a > len_b: result = a\nelse: result = b"
_lowerCamelCase : Any = tokenizer.encode(__A )
_lowerCamelCase : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
_lowerCamelCase : List[Any] = tokenizer.decode(__A,truncate_before_pattern=__A )
self.assertEqual(__A,__A )
def lowerCamelCase_ ( self : Any ):
pass
| 44 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCAmelCase__ ( A ):
def __init__( self : Any ):
# test for the above condition
self.test()
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : Any = 0
_lowerCamelCase : List[str] = False
while not completed:
if counter == 1:
self.reset()
_lowerCamelCase : Any = self.advance()
if not self.does_advance(__A ):
raise Exception(
"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true." )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Dict = self.update(__A )
counter += 1
if counter > 1_0_0_0_0:
raise Exception("update() does not fulfill the constraint." )
if self.remaining() != 0:
raise Exception("Custom Constraint is not defined correctly." )
@abstractmethod
def lowerCamelCase_ ( self : Optional[int] ):
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self : str,__A : int ):
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self : Optional[Any],__A : int ):
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self : str ):
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self : Union[str, Any] ):
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCamelCase_ ( self : int,__A : List[Any]=False ):
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class UpperCAmelCase__ ( A ):
def __init__( self : Dict,__A : List[int] ):
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}.' )
_lowerCamelCase : Tuple = token_ids
_lowerCamelCase : Dict = len(self.token_ids )
_lowerCamelCase : Tuple = -1 # the index of the currently fulfilled step
_lowerCamelCase : Optional[int] = False
def lowerCamelCase_ ( self : List[str] ):
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def lowerCamelCase_ ( self : List[Any],__A : int ):
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 lowerCamelCase_ ( self : List[str],__A : int ):
if not isinstance(__A,__A ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(__A )}' )
_lowerCamelCase : Optional[int] = False
_lowerCamelCase : Dict = False
_lowerCamelCase : Optional[int] = False
if self.does_advance(__A ):
self.fulfilled_idx += 1
_lowerCamelCase : Dict = True
if self.fulfilled_idx == (self.seqlen - 1):
_lowerCamelCase : Dict = True
_lowerCamelCase : int = completed
else:
# failed to make progress.
_lowerCamelCase : Optional[int] = True
self.reset()
return stepped, completed, reset
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : Optional[int] = False
_lowerCamelCase : Dict = 0
def lowerCamelCase_ ( self : Optional[int] ):
return self.seqlen - (self.fulfilled_idx + 1)
def lowerCamelCase_ ( self : str,__A : Any=False ):
_lowerCamelCase : List[Any] = PhrasalConstraint(self.token_ids )
if stateful:
_lowerCamelCase : List[Any] = self.seqlen
_lowerCamelCase : List[str] = self.fulfilled_idx
_lowerCamelCase : int = self.completed
return new_constraint
class UpperCAmelCase__ :
def __init__( self : Union[str, Any],__A : List[List[int]],__A : Any=True ):
_lowerCamelCase : List[str] = max([len(__A ) for one in nested_token_ids] )
_lowerCamelCase : Tuple = {}
for token_ids in nested_token_ids:
_lowerCamelCase : Optional[int] = root
for tidx, token_id in enumerate(__A ):
if token_id not in level:
_lowerCamelCase : Any = {}
_lowerCamelCase : Union[str, 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}.' )
_lowerCamelCase : str = root
def lowerCamelCase_ ( self : Dict,__A : Any ):
_lowerCamelCase : str = self.trie
for current_token in current_seq:
_lowerCamelCase : str = start[current_token]
_lowerCamelCase : Optional[Any] = list(start.keys() )
return next_tokens
def lowerCamelCase_ ( self : Any,__A : int ):
_lowerCamelCase : Optional[Any] = self.next_tokens(__A )
return len(__A ) == 0
def lowerCamelCase_ ( self : List[Any],__A : Any ):
_lowerCamelCase : Any = list(root.values() )
if len(__A ) == 0:
return 1
else:
return sum([self.count_leaves(__A ) for nn in next_nodes] )
def lowerCamelCase_ ( self : int,__A : Optional[Any],__A : Union[str, Any] ):
_lowerCamelCase : Tuple = self.count_leaves(__A )
return len(__A ) != leaf_count
class UpperCAmelCase__ ( A ):
def __init__( self : str,__A : List[List[int]] ):
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}.' )
_lowerCamelCase : Optional[Any] = DisjunctiveTrie(__A )
_lowerCamelCase : Dict = nested_token_ids
_lowerCamelCase : Tuple = self.trie.max_height
_lowerCamelCase : Optional[int] = []
_lowerCamelCase : str = False
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : List[str] = self.trie.next_tokens(self.current_seq )
if len(__A ) == 0:
return None
else:
return token_list
def lowerCamelCase_ ( self : Optional[Any],__A : int ):
if not isinstance(__A,__A ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(__A )}' )
_lowerCamelCase : Any = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def lowerCamelCase_ ( self : List[Any],__A : int ):
if not isinstance(__A,__A ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(__A )}' )
_lowerCamelCase : Any = False
_lowerCamelCase : Any = False
_lowerCamelCase : Any = False
if self.does_advance(__A ):
self.current_seq.append(__A )
_lowerCamelCase : Any = True
else:
_lowerCamelCase : Optional[int] = True
self.reset()
_lowerCamelCase : Union[str, Any] = self.trie.reached_leaf(self.current_seq )
_lowerCamelCase : Dict = completed
return stepped, completed, reset
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : int = []
def lowerCamelCase_ ( self : Tuple ):
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def lowerCamelCase_ ( self : Optional[int],__A : List[Any]=False ):
_lowerCamelCase : str = DisjunctiveConstraint(self.token_ids )
if stateful:
_lowerCamelCase : Union[str, Any] = self.seqlen
_lowerCamelCase : Any = self.current_seq
_lowerCamelCase : str = self.completed
return new_constraint
class UpperCAmelCase__ :
def __init__( self : Optional[int],__A : List[Constraint] ):
_lowerCamelCase : Dict = constraints
# max # of steps required to fulfill a given constraint
_lowerCamelCase : Optional[Any] = max([c.seqlen for c in constraints] )
_lowerCamelCase : str = len(__A )
_lowerCamelCase : Any = False
self.init_state()
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : Union[str, Any] = []
_lowerCamelCase : Optional[Any] = None
_lowerCamelCase : Optional[Any] = [constraint.copy(stateful=__A ) for constraint in self.constraints]
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : 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 lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : List[Any] = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
_lowerCamelCase : Union[str, Any] = constraint.advance()
if isinstance(__A,__A ):
token_list.append(__A )
elif isinstance(__A,__A ):
token_list.extend(__A )
else:
_lowerCamelCase : 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 lowerCamelCase_ ( self : Optional[Any],__A : Optional[List[int]] ):
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
_lowerCamelCase , _lowerCamelCase : Tuple = self.add(__A )
# the entire list of constraints are fulfilled
if self.completed:
break
def lowerCamelCase_ ( self : int,__A : int ):
if not isinstance(__A,__A ):
raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.' )
_lowerCamelCase , _lowerCamelCase : Optional[int] = False, False
if self.completed:
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Tuple = 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
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : List[Any] = 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 ) )
_lowerCamelCase : List[str] = 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 )
_lowerCamelCase : Tuple = None
if len(self.pending_constraints ) == 0:
# we're done!
_lowerCamelCase : 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 ):
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : List[str] = 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 )
_lowerCamelCase : Optional[Any] = None
if not complete and stepped:
_lowerCamelCase : 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".
_lowerCamelCase : List[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.
_lowerCamelCase : Dict = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def lowerCamelCase_ ( self : Any,__A : Union[str, Any]=True ):
_lowerCamelCase : List[str] = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
_lowerCamelCase : List[Any] = [
constraint.copy(stateful=__A ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
_lowerCamelCase : Any = self.inprogress_constraint.copy(stateful=__A )
_lowerCamelCase : Tuple = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 44 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class UpperCAmelCase__ :
def __init__( self : Any,__A : int=2,__A : Any=3,__A : Optional[int]=6_4,__A : Tuple=None ):
_lowerCamelCase : int = np.random.default_rng(__A )
_lowerCamelCase : List[str] = length
_lowerCamelCase : Optional[Any] = rng.normal(size=(length,) ).astype(np.floataa )
_lowerCamelCase : Optional[int] = a * self.x + b + rng.normal(scale=0.1,size=(length,) ).astype(np.floataa )
def __len__( self : Dict ):
return self.length
def __getitem__( self : str,__A : List[str] ):
return {"x": self.x[i], "y": self.y[i]}
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : Optional[Any]=0,__A : Optional[int]=0,__A : Dict=False ):
super().__init__()
_lowerCamelCase : Tuple = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : List[str] = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : Optional[int] = True
def lowerCamelCase_ ( self : List[str],__A : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a[0] + self.b[0]
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : List[str]=0,__A : List[str]=0,__A : int=False ):
super().__init__()
_lowerCamelCase : Optional[int] = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Dict = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Tuple = True
def lowerCamelCase_ ( self : str,__A : List[Any]=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a + self.b
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
_lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("bert-base-cased" )
_lowerCamelCase : List[Any] = {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
_lowerCamelCase : int = load_dataset("csv" , data_files=_lowerCAmelCase )
_lowerCamelCase : Dict = datasets["train"].unique("label" )
_lowerCamelCase : Optional[Any] = {v: i for i, v in enumerate(_lowerCAmelCase )}
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Optional[int] = tokenizer(
examples["sentence1"] , examples["sentence2"] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase , padding="max_length" )
if "label" in examples:
_lowerCamelCase : str = [label_to_id[l] for l in examples["label"]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_lowerCamelCase : Optional[Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["sentence1", "sentence2", "label"] , )
def collate_fn(_lowerCAmelCase : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return tokenizer.pad(_lowerCAmelCase , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
_lowerCamelCase : str = DataLoader(tokenized_datasets["train"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=2 )
_lowerCamelCase : Optional[int] = DataLoader(tokenized_datasets["validation"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=1 )
return train_dataloader, eval_dataloader
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Any = 0
while number > 0:
_lowerCamelCase : Optional[Any] = number % 10
sum_of_digits += last_digit
_lowerCamelCase : Dict = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def A_ ( _lowerCAmelCase : int = 100 ):
"""simple docstring"""
_lowerCamelCase : List[str] = factorial(_lowerCAmelCase )
_lowerCamelCase : List[Any] = split_and_add(_lowerCAmelCase )
return result
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 44 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Optional[Any] = False, False, False
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = None
lowerCAmelCase_ = True
lowerCAmelCase_ = True
lowerCAmelCase_ = None
# Automatically constructed
lowerCAmelCase_ = "dict"
lowerCAmelCase_ = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
lowerCAmelCase_ = field(default='Audio' , init=A , repr=A )
def __call__( self : Tuple ):
return self.pa_type
def lowerCamelCase_ ( self : Any,__A : Union[str, bytes, dict] ):
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
if isinstance(__A,__A ):
return {"bytes": None, "path": value}
elif isinstance(__A,__A ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
_lowerCamelCase : List[Any] = BytesIO()
sf.write(__A,value["array"],value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm" ):
# "PCM" only has raw audio bytes
if value.get("sampling_rate" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
if value.get("bytes" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
_lowerCamelCase : Dict = np.frombuffer(value["bytes"],dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
else:
_lowerCamelCase : str = np.memmap(value["path"],dtype="h",mode="r" ).astype(np.floataa ) / 3_2_7_6_7
_lowerCamelCase : Optional[int] = BytesIO(bytes() )
sf.write(__A,__A,value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' )
def lowerCamelCase_ ( self : Optional[Any],__A : dict,__A : Optional[Dict[str, Union[str, bool, None]]] = None ):
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )
_lowerCamelCase , _lowerCamelCase : Optional[Any] = (value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err
_lowerCamelCase : Tuple = xsplitext(__A )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
if file is None:
_lowerCamelCase : Tuple = token_per_repo_id or {}
_lowerCamelCase : Union[str, Any] = path.split("::" )[-1]
try:
_lowerCamelCase : str = string_to_dict(__A,config.HUB_DATASETS_URL )["repo_id"]
_lowerCamelCase : str = token_per_repo_id[repo_id]
except (ValueError, KeyError):
_lowerCamelCase : Any = None
with xopen(__A,"rb",use_auth_token=__A ) as f:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = sf.read(__A )
else:
_lowerCamelCase , _lowerCamelCase : str = sf.read(__A )
_lowerCamelCase : List[str] = array.T
if self.mono:
_lowerCamelCase : List[str] = librosa.to_mono(__A )
if self.sampling_rate and self.sampling_rate != sampling_rate:
_lowerCamelCase : List[str] = librosa.resample(__A,orig_sr=__A,target_sr=self.sampling_rate )
_lowerCamelCase : Optional[Any] = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def lowerCamelCase_ ( self : Any ):
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature." )
return {
"bytes": Value("binary" ),
"path": Value("string" ),
}
def lowerCamelCase_ ( self : List[str],__A : Union[pa.StringArray, pa.StructArray] ):
if pa.types.is_string(storage.type ):
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
_lowerCamelCase : int = pa.StructArray.from_arrays([bytes_array, storage],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_lowerCamelCase : Dict = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Any = pa.StructArray.from_arrays([storage, path_array],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
_lowerCamelCase : Tuple = pa.array([Audio().encode_example(__A ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
_lowerCamelCase : Tuple = storage.field("bytes" )
else:
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
_lowerCamelCase : List[str] = storage.field("path" )
else:
_lowerCamelCase : Tuple = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Tuple = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=storage.is_null() )
return array_cast(__A,self.pa_type )
def lowerCamelCase_ ( self : str,__A : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(__A : Dict ):
with xopen(__A,"rb" ) as f:
_lowerCamelCase : Any = f.read()
return bytes_
_lowerCamelCase : int = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
],type=pa.binary(),)
_lowerCamelCase : str = pa.array(
[os.path.basename(__A ) if path is not None else None for path in storage.field("path" ).to_pylist()],type=pa.string(),)
_lowerCamelCase : Dict = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=bytes_array.is_null() )
return array_cast(__A,self.pa_type )
| 44 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase__ ( A ):
@staticmethod
@abstractmethod
def lowerCamelCase_ ( __A : ArgumentParser ):
raise NotImplementedError()
@abstractmethod
def lowerCamelCase_ ( self : int ):
raise NotImplementedError()
| 44 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'glpn'
def __init__( self : Tuple,__A : Optional[int]=3,__A : Optional[int]=4,__A : str=[2, 2, 2, 2],__A : Union[str, Any]=[8, 4, 2, 1],__A : Tuple=[3_2, 6_4, 1_6_0, 2_5_6],__A : int=[7, 3, 3, 3],__A : str=[4, 2, 2, 2],__A : int=[1, 2, 5, 8],__A : List[Any]=[4, 4, 4, 4],__A : Optional[int]="gelu",__A : int=0.0,__A : Tuple=0.0,__A : Tuple=0.02,__A : Optional[int]=0.1,__A : Optional[int]=1e-6,__A : Optional[int]=6_4,__A : Optional[Any]=1_0,__A : Tuple=-1,**__A : List[str],):
super().__init__(**__A )
_lowerCamelCase : Tuple = num_channels
_lowerCamelCase : Union[str, Any] = num_encoder_blocks
_lowerCamelCase : Dict = depths
_lowerCamelCase : List[Any] = sr_ratios
_lowerCamelCase : str = hidden_sizes
_lowerCamelCase : Any = patch_sizes
_lowerCamelCase : Any = strides
_lowerCamelCase : Dict = mlp_ratios
_lowerCamelCase : int = num_attention_heads
_lowerCamelCase : List[Any] = hidden_act
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : Optional[int] = initializer_range
_lowerCamelCase : Union[str, Any] = drop_path_rate
_lowerCamelCase : str = layer_norm_eps
_lowerCamelCase : Tuple = decoder_hidden_size
_lowerCamelCase : int = max_depth
_lowerCamelCase : Dict = head_in_index
| 44 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase , _lowerCamelCase : Tuple = emb.weight.shape
_lowerCamelCase : List[str] = nn.Linear(_lowerCAmelCase , _lowerCAmelCase , bias=_lowerCAmelCase )
_lowerCamelCase : int = emb.weight.data
return lin_layer
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = torch.load(_lowerCAmelCase , map_location="cpu" )
_lowerCamelCase : List[Any] = mam_aaa["args"] or mam_aaa["cfg"]["model"]
_lowerCamelCase : Optional[Any] = mam_aaa["model"]
remove_ignore_keys_(_lowerCAmelCase )
_lowerCamelCase : int = state_dict["encoder.embed_tokens.weight"].shape[0]
_lowerCamelCase : str = MaMaaaConfig(
vocab_size=_lowerCAmelCase , max_position_embeddings=1024 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="relu" , )
_lowerCamelCase : Optional[int] = state_dict["decoder.embed_tokens.weight"]
_lowerCamelCase : List[Any] = MaMaaaForConditionalGeneration(_lowerCAmelCase )
model.model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
_lowerCamelCase : Optional[int] = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
UpperCAmelCase_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
UpperCAmelCase_ : Optional[Any] = parser.parse_args()
UpperCAmelCase_ : List[str] = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 44 |
'''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_ : str = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['input_features', 'attention_mask']
def __init__( self : Any,__A : List[Any]=8_0,__A : Dict=1_6_0_0_0,__A : Tuple=0.0,__A : Dict=1_0,__A : int=2_5,__A : Union[str, Any]="hamming_window",__A : List[str]=32768.0,__A : Union[str, Any]=0.97,__A : str=1.0,__A : Union[str, Any]=True,__A : Tuple=True,__A : Optional[Any]=False,**__A : Optional[Any],):
super().__init__(feature_size=__A,sampling_rate=__A,padding_value=__A,**__A )
_lowerCamelCase : Dict = feature_size
_lowerCamelCase : List[str] = sampling_rate
_lowerCamelCase : Any = padding_value
_lowerCamelCase : Dict = hop_length
_lowerCamelCase : Tuple = win_length
_lowerCamelCase : str = frame_signal_scale
_lowerCamelCase : List[str] = preemphasis_coeff
_lowerCamelCase : List[str] = mel_floor
_lowerCamelCase : str = normalize_means
_lowerCamelCase : Any = normalize_vars
_lowerCamelCase : List[str] = win_function
_lowerCamelCase : Tuple = return_attention_mask
_lowerCamelCase : List[Any] = win_length * sampling_rate // 1_0_0_0
_lowerCamelCase : List[Any] = hop_length * sampling_rate // 1_0_0_0
_lowerCamelCase : Any = optimal_fft_length(self.sample_size )
_lowerCamelCase : Dict = (self.n_fft // 2) + 1
def lowerCamelCase_ ( self : Any,__A : np.array ):
if self.win_function == "hamming_window":
_lowerCamelCase : Any = window_function(window_length=self.sample_size,name=self.win_function,periodic=__A )
else:
_lowerCamelCase : Optional[int] = window_function(window_length=self.sample_size,name=self.win_function )
_lowerCamelCase : int = 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 : List[str] = spectrogram(
one_waveform * self.frame_signal_scale,window=__A,frame_length=self.sample_size,hop_length=self.sample_stride,fft_length=self.n_fft,center=__A,preemphasis=self.preemphasis_coeff,mel_filters=__A,mel_floor=self.mel_floor,log_mel="log",)
return msfc_features.T
def lowerCamelCase_ ( self : Optional[int],__A : List[str],__A : Dict,__A : int ):
# make sure we normalize float32 arrays
if self.normalize_means:
_lowerCamelCase : Optional[Any] = x[:input_length].mean(axis=0 )
_lowerCamelCase : Optional[int] = np.subtract(__A,__A )
if self.normalize_vars:
_lowerCamelCase : int = x[:input_length].std(axis=0 )
_lowerCamelCase : Any = np.divide(__A,__A )
if input_length < x.shape[0]:
_lowerCamelCase : Tuple = padding_value
# make sure array is in float32
_lowerCamelCase : Optional[int] = x.astype(np.floataa )
return x
def lowerCamelCase_ ( self : Any,__A : List[np.ndarray],__A : Optional[np.ndarray] = None ):
_lowerCamelCase : Optional[int] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(__A,__A,self.padding_value ) for x, n in zip(__A,__A )]
def __call__( self : Optional[Any],__A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],__A : Union[bool, str, PaddingStrategy] = False,__A : Optional[int] = None,__A : bool = False,__A : Optional[int] = None,__A : Optional[bool] = None,__A : Optional[Union[str, TensorType]] = None,__A : Optional[int] = None,**__A : Optional[Any],):
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 : List[str] = isinstance(__A,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 : List[str] = is_batched_numpy or (
isinstance(__A,(list, tuple) ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list) ))
)
if is_batched:
_lowerCamelCase : List[Any] = [np.asarray(__A,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__A,np.ndarray ):
_lowerCamelCase : Dict = np.asarray(__A,dtype=np.floataa )
elif isinstance(__A,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowerCamelCase : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCamelCase : Tuple = [raw_speech]
# extract fbank features
_lowerCamelCase : str = [self._extract_mfsc_features(__A ) for one_waveform in raw_speech]
# convert into correct format for padding
_lowerCamelCase : Union[str, Any] = BatchFeature({"input_features": features} )
_lowerCamelCase : List[Any] = self.pad(
__A,padding=__A,max_length=__A,truncation=__A,pad_to_multiple_of=__A,return_attention_mask=__A,**__A,)
# make sure list is in array format
_lowerCamelCase : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0],__A ):
_lowerCamelCase : int = [np.asarray(__A,dtype=np.floataa ) for feature in input_features]
_lowerCamelCase : Dict = padded_inputs.get("attention_mask" )
if attention_mask is not None:
_lowerCamelCase : Dict = [np.asarray(__A,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
_lowerCamelCase : Dict = (
np.array(__A,dtype=np.intaa )
if self._get_padding_strategies(__A,max_length=__A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
_lowerCamelCase : Tuple = self.normalize(
padded_inputs["input_features"],attention_mask=__A )
if return_tensors is not None:
_lowerCamelCase : Dict = padded_inputs.convert_to_tensors(__A )
return padded_inputs
| 44 | 1 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCAmelCase_ : List[str] = logging.getLogger(__name__)
class UpperCAmelCase__ :
def __init__( self : int ):
_lowerCamelCase : Any = False
def lowerCamelCase_ ( self : str,__A : Dict,__A : List[str],__A : Optional[int],__A : int ):
if not self.initialized:
_lowerCamelCase : str = RagRetriever(
__A,question_encoder_tokenizer=__A,generator_tokenizer=__A,index=__A,init_retrieval=__A,)
_lowerCamelCase : Optional[int] = True
def lowerCamelCase_ ( self : Optional[int] ):
self.retriever.index.init_index()
def lowerCamelCase_ ( self : str,__A : int,__A : Optional[int] ):
_lowerCamelCase , _lowerCamelCase : Dict = self.retriever._main_retrieve(__A,__A )
return doc_ids, retrieved_doc_embeds
class UpperCAmelCase__ ( A ):
def __init__( self : Any,__A : Optional[int],__A : Tuple,__A : List[str],__A : List[str],__A : Tuple=None ):
if index is not None and index.is_initialized() and len(__A ) > 0:
raise ValueError(
"When using Ray for distributed fine-tuning, "
"you'll need to provide the paths instead, "
"as the dataset and the index are loaded "
"separately. More info in examples/rag/use_own_knowledge_dataset.py " )
super().__init__(
__A,question_encoder_tokenizer=__A,generator_tokenizer=__A,index=__A,init_retrieval=__A,)
_lowerCamelCase : Optional[int] = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(__A,__A,__A,__A )
for worker in self.retrieval_workers
] )
def lowerCamelCase_ ( self : Optional[int] ):
logger.info("initializing retrieval" )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def lowerCamelCase_ ( self : Dict,__A : Any,__A : Optional[int] ):
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
_lowerCamelCase : str = self.retrieval_workers[random.randint(0,len(self.retrieval_workers ) - 1 )]
_lowerCamelCase , _lowerCamelCase : str = ray.get(random_worker.retrieve.remote(__A,__A ) )
else:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self._main_retrieve(__A,__A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__A )
@classmethod
def lowerCamelCase_ ( cls : List[str],__A : Optional[int],__A : Any=None,**__A : Dict ):
return super(__A,cls ).get_tokenizers(__A,__A,**__A )
@classmethod
def lowerCamelCase_ ( cls : int,__A : Dict,__A : int,__A : Any=None,**__A : List[Any] ):
_lowerCamelCase : Any = kwargs.pop("config",__A ) or RagConfig.from_pretrained(__A,**__A )
_lowerCamelCase : Optional[int] = RagTokenizer.from_pretrained(__A,config=__A )
_lowerCamelCase : Optional[int] = rag_tokenizer.question_encoder
_lowerCamelCase : Optional[Any] = rag_tokenizer.generator
if indexed_dataset is not None:
_lowerCamelCase : Any = "custom"
_lowerCamelCase : Union[str, Any] = CustomHFIndex(config.retrieval_vector_size,__A )
else:
_lowerCamelCase : Optional[int] = cls._build_index(__A )
return cls(
__A,question_encoder_tokenizer=__A,generator_tokenizer=__A,retrieval_workers=__A,index=__A,)
| 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
UpperCAmelCase_ : int = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = torch.load(_lowerCAmelCase , map_location="cpu" )
return sd
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple=rename_keys_prefix ):
"""simple docstring"""
_lowerCamelCase : Any = OrderedDict()
_lowerCamelCase : str = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
_lowerCamelCase : Any = key
for name_pair in rename_keys_prefix:
_lowerCamelCase : Dict = new_key.replace(name_pair[0] , name_pair[1] )
_lowerCamelCase : Any = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
_lowerCamelCase : List[str] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Dict ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
_lowerCamelCase : Optional[int] = "pretraining"
if "vcr" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
_lowerCamelCase : int = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
_lowerCamelCase : Any = {"visual_embedding_dim": 512}
_lowerCamelCase : List[Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : Tuple = {"visual_embedding_dim": 2048}
_lowerCamelCase : Dict = "vqa_advanced"
elif "vqa" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 2048, "num_labels": 3129}
_lowerCamelCase : Optional[int] = "vqa"
elif "nlvr" in checkpoint_path:
_lowerCamelCase : Tuple = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
_lowerCamelCase : Optional[Any] = "nlvr"
_lowerCamelCase : str = VisualBertConfig(**_lowerCAmelCase )
# Load State Dict
_lowerCamelCase : str = load_state_dict(_lowerCAmelCase )
_lowerCamelCase : List[str] = get_new_dict(_lowerCAmelCase , _lowerCAmelCase )
if model_type == "pretraining":
_lowerCamelCase : List[Any] = VisualBertForPreTraining(_lowerCAmelCase )
elif model_type == "vqa":
_lowerCamelCase : Dict = VisualBertForQuestionAnswering(_lowerCAmelCase )
elif model_type == "nlvr":
_lowerCamelCase : Tuple = VisualBertForVisualReasoning(_lowerCAmelCase )
elif model_type == "multichoice":
_lowerCamelCase : str = VisualBertForMultipleChoice(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
# Save Checkpoints
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class UpperCAmelCase__ :
def __init__( self : int,__A : List[str],__A : Tuple,__A : str,__A : str,__A : List[str],__A : int=0.2,__A : List[str]=0.2 ):
_lowerCamelCase : int = bp_numa
_lowerCamelCase : Optional[int] = bp_numa
_lowerCamelCase : Optional[int] = bp_numa
_lowerCamelCase : Union[str, Any] = conva_get[:2]
_lowerCamelCase : Any = conva_get[2]
_lowerCamelCase : int = size_pa
_lowerCamelCase : Any = rate_w
_lowerCamelCase : Any = rate_t
_lowerCamelCase : str = [
np.mat(-1 * np.random.rand(self.conva[0],self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
_lowerCamelCase : List[Any] = np.mat(-1 * np.random.rand(self.num_bpa,self.num_bpa ) + 0.5 )
_lowerCamelCase : Union[str, Any] = np.mat(-1 * np.random.rand(self.num_bpa,self.num_bpa ) + 0.5 )
_lowerCamelCase : Any = -2 * np.random.rand(self.conva[1] ) + 1
_lowerCamelCase : Tuple = -2 * np.random.rand(self.num_bpa ) + 1
_lowerCamelCase : List[str] = -2 * np.random.rand(self.num_bpa ) + 1
def lowerCamelCase_ ( self : Tuple,__A : int ):
# save model dict with pickle
_lowerCamelCase : Any = {
"num_bp1": self.num_bpa,
"num_bp2": self.num_bpa,
"num_bp3": self.num_bpa,
"conv1": self.conva,
"step_conv1": self.step_conva,
"size_pooling1": self.size_poolinga,
"rate_weight": self.rate_weight,
"rate_thre": self.rate_thre,
"w_conv1": self.w_conva,
"wkj": self.wkj,
"vji": self.vji,
"thre_conv1": self.thre_conva,
"thre_bp2": self.thre_bpa,
"thre_bp3": self.thre_bpa,
}
with open(__A,"wb" ) as f:
pickle.dump(__A,__A )
print(f'Model saved： {save_path}' )
@classmethod
def lowerCamelCase_ ( cls : Any,__A : Dict ):
# read saved model
with open(__A,"rb" ) as f:
_lowerCamelCase : List[str] = pickle.load(__A ) # noqa: S301
_lowerCamelCase : Tuple = model_dic.get("conv1" )
conv_get.append(model_dic.get("step_conv1" ) )
_lowerCamelCase : List[str] = model_dic.get("size_pooling1" )
_lowerCamelCase : Dict = model_dic.get("num_bp1" )
_lowerCamelCase : List[str] = model_dic.get("num_bp2" )
_lowerCamelCase : Optional[Any] = model_dic.get("num_bp3" )
_lowerCamelCase : str = model_dic.get("rate_weight" )
_lowerCamelCase : Any = model_dic.get("rate_thre" )
# create model instance
_lowerCamelCase : Union[str, Any] = CNN(__A,__A,__A,__A,__A,__A,__A )
# modify model parameter
_lowerCamelCase : Dict = model_dic.get("w_conv1" )
_lowerCamelCase : Optional[int] = model_dic.get("wkj" )
_lowerCamelCase : Optional[Any] = model_dic.get("vji" )
_lowerCamelCase : Dict = model_dic.get("thre_conv1" )
_lowerCamelCase : Tuple = model_dic.get("thre_bp2" )
_lowerCamelCase : Optional[int] = model_dic.get("thre_bp3" )
return conv_ins
def lowerCamelCase_ ( self : Optional[Any],__A : Optional[Any] ):
return 1 / (1 + np.exp(-1 * x ))
def lowerCamelCase_ ( self : Dict,__A : str ):
return round(__A,3 )
def lowerCamelCase_ ( self : str,__A : int,__A : Any,__A : Union[str, Any],__A : Dict,__A : List[Any] ):
# convolution process
_lowerCamelCase : Optional[Any] = convs[0]
_lowerCamelCase : List[Any] = convs[1]
_lowerCamelCase : int = np.shape(__A )[0]
# get the data slice of original image data, data_focus
_lowerCamelCase : Tuple = []
for i_focus in range(0,size_data - size_conv + 1,__A ):
for j_focus in range(0,size_data - size_conv + 1,__A ):
_lowerCamelCase : List[Any] = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(__A )
# calculate the feature map of every single kernel, and saved as list of matrix
_lowerCamelCase : Dict = []
_lowerCamelCase : Dict = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(__A ):
_lowerCamelCase : Optional[int] = []
for i_focus in range(len(__A ) ):
_lowerCamelCase : List[Any] = (
np.sum(np.multiply(data_focus[i_focus],w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(__A ) )
_lowerCamelCase : Union[str, Any] = np.asmatrix(__A ).reshape(
__A,__A )
data_featuremap.append(__A )
# expanding the data slice to One dimenssion
_lowerCamelCase : int = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(__A ) )
_lowerCamelCase : Dict = np.asarray(__A )
return focus_list, data_featuremap
def lowerCamelCase_ ( self : Any,__A : Optional[Any],__A : Optional[Any],__A : int="average_pool" ):
# pooling process
_lowerCamelCase : Tuple = len(featuremaps[0] )
_lowerCamelCase : Tuple = int(size_map / size_pooling )
_lowerCamelCase : int = []
for i_map in range(len(__A ) ):
_lowerCamelCase : Optional[Any] = featuremaps[i_map]
_lowerCamelCase : int = []
for i_focus in range(0,__A,__A ):
for j_focus in range(0,__A,__A ):
_lowerCamelCase : int = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(__A ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(__A ) )
_lowerCamelCase : Optional[Any] = np.asmatrix(__A ).reshape(__A,__A )
featuremap_pooled.append(__A )
return featuremap_pooled
def lowerCamelCase_ ( self : Optional[Any],__A : List[str] ):
# expanding three dimension data to one dimension list
_lowerCamelCase : Union[str, Any] = []
for i in range(len(__A ) ):
_lowerCamelCase : int = np.shape(data[i] )
_lowerCamelCase : List[str] = data[i].reshape(1,shapes[0] * shapes[1] )
_lowerCamelCase : List[str] = data_listed.getA().tolist()[0]
data_expanded.extend(__A )
_lowerCamelCase : Tuple = np.asarray(__A )
return data_expanded
def lowerCamelCase_ ( self : Tuple,__A : Optional[int] ):
# expanding matrix to one dimension list
_lowerCamelCase : int = np.asarray(__A )
_lowerCamelCase : List[str] = np.shape(__A )
_lowerCamelCase : Tuple = data_mat.reshape(1,shapes[0] * shapes[1] )
return data_expanded
def lowerCamelCase_ ( self : List[str],__A : Any,__A : List[str],__A : List[Any],__A : Any,__A : Tuple ):
_lowerCamelCase : Tuple = []
_lowerCamelCase : List[Any] = 0
for i_map in range(__A ):
_lowerCamelCase : List[Any] = np.ones((size_map, size_map) )
for i in range(0,__A,__A ):
for j in range(0,__A,__A ):
_lowerCamelCase : int = pd_pool[
i_pool
]
_lowerCamelCase : Dict = i_pool + 1
_lowerCamelCase : Any = np.multiply(
__A,np.multiply(out_map[i_map],(1 - out_map[i_map]) ) )
pd_all.append(__A )
return pd_all
def lowerCamelCase_ ( self : Union[str, Any],__A : Dict,__A : Optional[Any],__A : Union[str, Any],__A : Optional[Any],__A : Union[str, Any],__A : Tuple=bool ):
# model traning
print("----------------------Start Training-------------------------" )
print((" - - Shape: Train_Data ", np.shape(__A )) )
print((" - - Shape: Teach_Data ", np.shape(__A )) )
_lowerCamelCase : Tuple = 0
_lowerCamelCase : Optional[Any] = []
_lowerCamelCase : List[str] = 1_0_0_0_0
while rp < n_repeat and mse >= error_accuracy:
_lowerCamelCase : List[Any] = 0
print(f'-------------Learning Time {rp}--------------' )
for p in range(len(__A ) ):
# print('------------Learning Image: %d--------------'%p)
_lowerCamelCase : List[str] = np.asmatrix(datas_train[p] )
_lowerCamelCase : Dict = np.asarray(datas_teach[p] )
_lowerCamelCase , _lowerCamelCase : int = self.convolute(
__A,self.conva,self.w_conva,self.thre_conva,conv_step=self.step_conva,)
_lowerCamelCase : int = self.pooling(__A,self.size_poolinga )
_lowerCamelCase : List[str] = np.shape(__A )
_lowerCamelCase : Optional[Any] = self._expand(__A )
_lowerCamelCase : List[str] = data_bp_input
_lowerCamelCase : Union[str, Any] = np.dot(__A,self.vji.T ) - self.thre_bpa
_lowerCamelCase : Optional[int] = self.sig(__A )
_lowerCamelCase : Union[str, Any] = np.dot(__A,self.wkj.T ) - self.thre_bpa
_lowerCamelCase : List[Any] = self.sig(__A )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
_lowerCamelCase : Tuple = np.multiply(
(data_teach - bp_outa),np.multiply(__A,(1 - bp_outa) ) )
_lowerCamelCase : List[str] = np.multiply(
np.dot(__A,self.wkj ),np.multiply(__A,(1 - bp_outa) ) )
_lowerCamelCase : List[Any] = np.dot(__A,self.vji )
_lowerCamelCase : Optional[int] = pd_i_all / (self.size_poolinga * self.size_poolinga)
_lowerCamelCase : int = pd_conva_pooled.T.getA().tolist()
_lowerCamelCase : Optional[int] = self._calculate_gradient_from_pool(
__A,__A,shape_featuremapa[0],shape_featuremapa[1],self.size_poolinga,)
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
_lowerCamelCase : str = self._expand_mat(pd_conva_all[k_conv] )
_lowerCamelCase : Optional[int] = self.rate_weight * np.dot(__A,__A )
_lowerCamelCase : Tuple = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
_lowerCamelCase : Optional[Any] = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
_lowerCamelCase : List[str] = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
_lowerCamelCase : Dict = self.vji + pd_j_all.T * bp_outa * self.rate_weight
_lowerCamelCase : List[Any] = self.thre_bpa - pd_k_all * self.rate_thre
_lowerCamelCase : Optional[int] = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
_lowerCamelCase : List[str] = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
_lowerCamelCase : List[Any] = rp + 1
_lowerCamelCase : str = error_count / patterns
all_mse.append(__A )
def draw_error():
_lowerCamelCase : List[str] = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(__A,"+-" )
plt.plot(__A,"r--" )
plt.xlabel("Learning Times" )
plt.ylabel("All_mse" )
plt.grid(__A,alpha=0.5 )
plt.show()
print("------------------Training Complished---------------------" )
print((" - - Training epoch: ", rp, f' - - Mse: {mse:.6f}') )
if draw_e:
draw_error()
return mse
def lowerCamelCase_ ( self : int,__A : List[Any] ):
# model predict
_lowerCamelCase : Any = []
print("-------------------Start Testing-------------------------" )
print((" - - Shape: Test_Data ", np.shape(__A )) )
for p in range(len(__A ) ):
_lowerCamelCase : Optional[int] = np.asmatrix(datas_test[p] )
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.convolute(
__A,self.conva,self.w_conva,self.thre_conva,conv_step=self.step_conva,)
_lowerCamelCase : Optional[int] = self.pooling(__A,self.size_poolinga )
_lowerCamelCase : int = self._expand(__A )
_lowerCamelCase : Any = data_bp_input
_lowerCamelCase : Union[str, Any] = bp_outa * self.vji.T - self.thre_bpa
_lowerCamelCase : Tuple = self.sig(__A )
_lowerCamelCase : Union[str, Any] = bp_outa * self.wkj.T - self.thre_bpa
_lowerCamelCase : str = self.sig(__A )
produce_out.extend(bp_outa.getA().tolist() )
_lowerCamelCase : Union[str, Any] = [list(map(self.do_round,__A ) ) for each in produce_out]
return np.asarray(__A )
def lowerCamelCase_ ( self : Any,__A : str ):
# return the data of image after convoluting process so we can check it out
_lowerCamelCase : Any = np.asmatrix(__A )
_lowerCamelCase , _lowerCamelCase : Optional[int] = self.convolute(
__A,self.conva,self.w_conva,self.thre_conva,conv_step=self.step_conva,)
_lowerCamelCase : Optional[int] = self.pooling(__A,self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 44 |
'''simple docstring'''
import functools
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(_lowerCAmelCase ) != 3 or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(_lowerCAmelCase ) == 0:
return 0
if min(_lowerCAmelCase ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(_lowerCAmelCase ) >= 366:
raise ValueError("All days elements should be less than 366" )
_lowerCamelCase : Union[str, Any] = set(_lowerCAmelCase )
@functools.cache
def dynamic_programming(_lowerCAmelCase : int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 | 1 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Optional[Any] = False, False, False
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = None
lowerCAmelCase_ = True
lowerCAmelCase_ = True
lowerCAmelCase_ = None
# Automatically constructed
lowerCAmelCase_ = "dict"
lowerCAmelCase_ = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
lowerCAmelCase_ = field(default='Audio' , init=A , repr=A )
def __call__( self : Tuple ):
return self.pa_type
def lowerCamelCase_ ( self : Any,__A : Union[str, bytes, dict] ):
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
if isinstance(__A,__A ):
return {"bytes": None, "path": value}
elif isinstance(__A,__A ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
_lowerCamelCase : List[Any] = BytesIO()
sf.write(__A,value["array"],value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm" ):
# "PCM" only has raw audio bytes
if value.get("sampling_rate" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
if value.get("bytes" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
_lowerCamelCase : Dict = np.frombuffer(value["bytes"],dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
else:
_lowerCamelCase : str = np.memmap(value["path"],dtype="h",mode="r" ).astype(np.floataa ) / 3_2_7_6_7
_lowerCamelCase : Optional[int] = BytesIO(bytes() )
sf.write(__A,__A,value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' )
def lowerCamelCase_ ( self : Optional[Any],__A : dict,__A : Optional[Dict[str, Union[str, bool, None]]] = None ):
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )
_lowerCamelCase , _lowerCamelCase : Optional[Any] = (value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err
_lowerCamelCase : Tuple = xsplitext(__A )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
if file is None:
_lowerCamelCase : Tuple = token_per_repo_id or {}
_lowerCamelCase : Union[str, Any] = path.split("::" )[-1]
try:
_lowerCamelCase : str = string_to_dict(__A,config.HUB_DATASETS_URL )["repo_id"]
_lowerCamelCase : str = token_per_repo_id[repo_id]
except (ValueError, KeyError):
_lowerCamelCase : Any = None
with xopen(__A,"rb",use_auth_token=__A ) as f:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = sf.read(__A )
else:
_lowerCamelCase , _lowerCamelCase : str = sf.read(__A )
_lowerCamelCase : List[str] = array.T
if self.mono:
_lowerCamelCase : List[str] = librosa.to_mono(__A )
if self.sampling_rate and self.sampling_rate != sampling_rate:
_lowerCamelCase : List[str] = librosa.resample(__A,orig_sr=__A,target_sr=self.sampling_rate )
_lowerCamelCase : Optional[Any] = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def lowerCamelCase_ ( self : Any ):
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature." )
return {
"bytes": Value("binary" ),
"path": Value("string" ),
}
def lowerCamelCase_ ( self : List[str],__A : Union[pa.StringArray, pa.StructArray] ):
if pa.types.is_string(storage.type ):
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
_lowerCamelCase : int = pa.StructArray.from_arrays([bytes_array, storage],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_lowerCamelCase : Dict = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Any = pa.StructArray.from_arrays([storage, path_array],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
_lowerCamelCase : Tuple = pa.array([Audio().encode_example(__A ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
_lowerCamelCase : Tuple = storage.field("bytes" )
else:
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
_lowerCamelCase : List[str] = storage.field("path" )
else:
_lowerCamelCase : Tuple = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Tuple = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=storage.is_null() )
return array_cast(__A,self.pa_type )
def lowerCamelCase_ ( self : str,__A : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(__A : Dict ):
with xopen(__A,"rb" ) as f:
_lowerCamelCase : Any = f.read()
return bytes_
_lowerCamelCase : int = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
],type=pa.binary(),)
_lowerCamelCase : str = pa.array(
[os.path.basename(__A ) if path is not None else None for path in storage.field("path" ).to_pylist()],type=pa.string(),)
_lowerCamelCase : Dict = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=bytes_array.is_null() )
return array_cast(__A,self.pa_type )
| 44 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_lowerCamelCase : Dict = MaskFormerConfig(backbone_config=_lowerCAmelCase )
_lowerCamelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 847
_lowerCamelCase : str = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_lowerCamelCase : Optional[int] = 150
_lowerCamelCase : Union[str, Any] = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_lowerCamelCase : Union[str, Any] = 171
_lowerCamelCase : str = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_lowerCamelCase : Optional[int] = 133
_lowerCamelCase : Any = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_lowerCamelCase : str = 19
_lowerCamelCase : Tuple = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 65
_lowerCamelCase : Optional[int] = "mapillary-vistas-id2label.json"
_lowerCamelCase : Any = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : Optional[int] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
return config
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : Any = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.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.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight') )
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Tuple = dct.pop(_lowerCAmelCase )
_lowerCamelCase : str = val
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_lowerCamelCase : int = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_lowerCamelCase : Union[str, Any] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
_lowerCamelCase : List[str] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[int] = in_proj_weight[:dim, :]
_lowerCamelCase : Optional[int] = in_proj_bias[: dim]
_lowerCamelCase : List[str] = in_proj_weight[
dim : dim * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
dim : dim * 2
]
_lowerCamelCase : List[Any] = in_proj_weight[
-dim :, :
]
_lowerCamelCase : Union[str, Any] = in_proj_bias[-dim :]
# fmt: on
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : int = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Tuple = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[Any] = in_proj_weight[: hidden_size, :]
_lowerCamelCase : Optional[int] = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : Any = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Any = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
_lowerCamelCase : List[Any] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Tuple = in_proj_weight[: hidden_size, :]
_lowerCamelCase : str = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Optional[int] = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : int = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# fmt: on
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : List[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[Any] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : bool = False ):
"""simple docstring"""
_lowerCamelCase : Tuple = get_maskformer_config(_lowerCAmelCase )
# load original state_dict
with open(_lowerCAmelCase , "rb" ) as f:
_lowerCamelCase : List[Any] = pickle.load(_lowerCAmelCase )
_lowerCamelCase : Optional[Any] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_lowerCamelCase : List[Any] = create_rename_keys(_lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_swin_q_k_v(_lowerCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_lowerCAmelCase , _lowerCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_lowerCamelCase : Dict = torch.from_numpy(_lowerCAmelCase )
# load 🤗 model
_lowerCamelCase : int = MaskFormerForInstanceSegmentation(_lowerCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_lowerCAmelCase , param.shape )
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_lowerCAmelCase ) == 0, F'Unexpected keys: {unexpected_keys}'
# verify results
_lowerCamelCase : Any = prepare_img()
if "vistas" in model_name:
_lowerCamelCase : Any = 65
elif "cityscapes" in model_name:
_lowerCamelCase : Optional[Any] = 65535
else:
_lowerCamelCase : str = 255
_lowerCamelCase : List[str] = True if "ade" in model_name else False
_lowerCamelCase : Union[str, Any] = MaskFormerImageProcessor(ignore_index=_lowerCAmelCase , reduce_labels=_lowerCAmelCase )
_lowerCamelCase : int = image_processor(_lowerCAmelCase , return_tensors="pt" )
_lowerCamelCase : Tuple = model(**_lowerCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_lowerCamelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
image_processor.save_pretrained(_lowerCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F'nielsr/{model_name}' )
image_processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
UpperCAmelCase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='maskformer-swin-tiny-ade',
type=str,
help=('Name of the MaskFormer model you\'d like to convert',),
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
type=str,
help='Path to the original state dict (.pth file).',
)
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.'
)
UpperCAmelCase_ : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : int = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Any = ['LlamaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = ['LlamaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : str = [
'LlamaForCausalLM',
'LlamaModel',
'LlamaPreTrainedModel',
'LlamaForSequenceClassification',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
UpperCAmelCase_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 |
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = range(2, 20 + 1)
UpperCAmelCase_ : str = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = sum(a_i[j] for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ) )
_lowerCamelCase : List[str] = sum(a_i[j] * base[j] for j in range(min(len(_lowerCAmelCase ) , _lowerCAmelCase ) ) )
_lowerCamelCase , _lowerCamelCase : int = 0, 0
_lowerCamelCase : Dict = n - i
_lowerCamelCase : int = memo.get(_lowerCAmelCase )
if sub_memo is not None:
_lowerCamelCase : List[str] = sub_memo.get(_lowerCAmelCase )
if jumps is not None and len(_lowerCAmelCase ) > 0:
# find and make the largest jump without going over
_lowerCamelCase : List[Any] = -1
for _k in range(len(_lowerCAmelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
_lowerCamelCase : Any = _k
break
if max_jump >= 0:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : str = jumps[max_jump]
# since the difference between jumps is cached, add c
_lowerCamelCase : str = diff + c
for j in range(min(_lowerCAmelCase , len(_lowerCAmelCase ) ) ):
_lowerCamelCase , _lowerCamelCase : List[Any] = divmod(_lowerCAmelCase , 10 )
if new_c > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_lowerCamelCase : int = []
else:
_lowerCamelCase : Tuple = {c: []}
_lowerCamelCase : Any = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_lowerCamelCase , _lowerCamelCase : Optional[int] = next_term(_lowerCAmelCase , k - 1 , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_lowerCamelCase , _lowerCamelCase : List[str] = compute(_lowerCAmelCase , _lowerCAmelCase , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
_lowerCamelCase : List[str] = sub_memo[c]
# keep jumps sorted by # of terms skipped
_lowerCamelCase : int = 0
while j < len(_lowerCAmelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_lowerCAmelCase , (diff, dn, k) )
return (diff, dn)
def A_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
if i >= n:
return 0, i
if k > len(_lowerCAmelCase ):
a_i.extend([0 for _ in range(k - len(_lowerCAmelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
_lowerCamelCase : List[str] = i
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Any = 0, 0, 0
for j in range(len(_lowerCAmelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
_lowerCamelCase : int = ds_c + ds_b
diff += addend
_lowerCamelCase : List[str] = 0
for j in range(_lowerCAmelCase ):
_lowerCamelCase : List[Any] = a_i[j] + addend
_lowerCamelCase , _lowerCamelCase : Any = divmod(_lowerCAmelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return diff, i - start_i
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : Tuple = digits[j] + addend
if s >= 10:
_lowerCamelCase , _lowerCamelCase : Optional[int] = divmod(_lowerCAmelCase , 10 )
_lowerCamelCase : Any = addend // 10 + quotient
else:
_lowerCamelCase : Tuple = s
_lowerCamelCase : List[Any] = addend // 10
if addend == 0:
break
while addend > 0:
_lowerCamelCase , _lowerCamelCase : str = divmod(_lowerCAmelCase , 10 )
digits.append(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : int = 10**15 ):
"""simple docstring"""
_lowerCamelCase : Tuple = [1]
_lowerCamelCase : List[Any] = 1
_lowerCamelCase : List[str] = 0
while True:
_lowerCamelCase , _lowerCamelCase : Dict = next_term(_lowerCAmelCase , 20 , i + dn , _lowerCAmelCase )
dn += terms_jumped
if dn == n - i:
break
_lowerCamelCase : Optional[Any] = 0
for j in range(len(_lowerCAmelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 | 1 |
'''simple docstring'''
import math
import unittest
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCase_ ( self : Union[str, Any] ):
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(1_1 ) )
self.assertTrue(is_prime(1_3 ) )
self.assertTrue(is_prime(1_7 ) )
self.assertTrue(is_prime(1_9 ) )
self.assertTrue(is_prime(2_3 ) )
self.assertTrue(is_prime(2_9 ) )
def lowerCamelCase_ ( self : Tuple ):
with self.assertRaises(__A ):
is_prime(-1_9 )
self.assertFalse(
is_prime(0 ),"Zero doesn't have any positive factors, primes must have exactly two.",)
self.assertFalse(
is_prime(1 ),"One only has 1 positive factor, primes must have exactly two.",)
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 44 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase_ : Any = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether tp freeze the encoder.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
lowerCAmelCase_ = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
lowerCAmelCase_ = field(
default=1024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Source language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Target language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': '# num_beams to use for evaluation.'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any ):
"""simple docstring"""
logger.info(F'***** {split} metrics *****' )
for key in sorted(metrics.keys() ):
logger.info(F' {key} = {metrics[key]}' )
save_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , F'{split}_results.json' ) )
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : str = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Optional[Any] = parser.parse_args_into_dataclasses()
check_output_dir(_lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , _lowerCAmelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCamelCase : Optional[int] = 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 , )
_lowerCamelCase : Tuple = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
assert hasattr(_lowerCAmelCase , _lowerCAmelCase ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
setattr(_lowerCAmelCase , _lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
_lowerCamelCase : List[Any] = 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 , )
_lowerCamelCase : int = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=_lowerCAmelCase , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(_lowerCAmelCase , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCamelCase : List[Any] = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(_lowerCAmelCase , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Any = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCamelCase : int = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(_lowerCAmelCase )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCamelCase : int = SeqaSeqDataset
# Get datasets
_lowerCamelCase : Tuple = (
dataset_class(
_lowerCAmelCase , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
_lowerCamelCase : List[Any] = (
dataset_class(
_lowerCAmelCase , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCamelCase : Optional[int] = (
dataset_class(
_lowerCAmelCase , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCamelCase : int = (
build_compute_metrics_fn(data_args.task , _lowerCAmelCase ) if training_args.predict_with_generate else None
)
_lowerCamelCase : List[Any] = SeqaSeqTrainer(
model=_lowerCAmelCase , args=_lowerCAmelCase , data_args=_lowerCAmelCase , train_dataset=_lowerCAmelCase , eval_dataset=_lowerCAmelCase , data_collator=SeqaSeqDataCollator(
_lowerCAmelCase , _lowerCAmelCase , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=_lowerCAmelCase , tokenizer=_lowerCAmelCase , )
_lowerCamelCase : Optional[Any] = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
_lowerCamelCase : Optional[Any] = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCamelCase : int = train_result.metrics
_lowerCamelCase : Optional[int] = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_lowerCamelCase : Optional[Any] = trainer.evaluate(metric_key_prefix="val" )
_lowerCamelCase : Dict = data_args.n_val
_lowerCamelCase : List[Any] = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.do_predict:
logger.info("*** Predict ***" )
_lowerCamelCase : Any = trainer.predict(test_dataset=_lowerCAmelCase , metric_key_prefix="test" )
_lowerCamelCase : Dict = test_output.metrics
_lowerCamelCase : Optional[int] = data_args.n_test
if trainer.is_world_process_zero():
_lowerCamelCase : int = round(metrics["test_loss"] , 4 )
handle_metrics("test" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.predict_with_generate:
_lowerCamelCase : List[str] = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
_lowerCamelCase : Any = lmap(str.strip , _lowerCAmelCase )
write_txt_file(_lowerCAmelCase , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(_lowerCAmelCase , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int = 50 ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase__ :
def __init__( self : List[Any],__A : str,__A : List[str]=1_3,__A : str=3_2,__A : Tuple=2,__A : Any=3,__A : Dict=1_6,__A : Dict=[3_2, 6_4, 1_2_8],__A : List[str]=[1, 2, 1],__A : str=[2, 2, 4],__A : Optional[int]=2,__A : Dict=2.0,__A : str=True,__A : Tuple=0.0,__A : int=0.0,__A : List[str]=0.1,__A : Any="gelu",__A : List[Any]=False,__A : Optional[Any]=True,__A : List[str]=0.02,__A : Tuple=1e-5,__A : Any=True,__A : Tuple=None,__A : Tuple=True,__A : Tuple=1_0,__A : List[Any]=8,__A : Optional[int]=["stage1", "stage2"],__A : int=[1, 2],):
_lowerCamelCase : List[Any] = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : Optional[int] = image_size
_lowerCamelCase : int = patch_size
_lowerCamelCase : Optional[Any] = num_channels
_lowerCamelCase : int = embed_dim
_lowerCamelCase : int = hidden_sizes
_lowerCamelCase : List[Any] = depths
_lowerCamelCase : Any = num_heads
_lowerCamelCase : List[str] = window_size
_lowerCamelCase : str = mlp_ratio
_lowerCamelCase : Any = qkv_bias
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : List[str] = drop_path_rate
_lowerCamelCase : str = hidden_act
_lowerCamelCase : Union[str, Any] = use_absolute_embeddings
_lowerCamelCase : List[Any] = patch_norm
_lowerCamelCase : Tuple = layer_norm_eps
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Optional[int] = is_training
_lowerCamelCase : Tuple = scope
_lowerCamelCase : List[Any] = use_labels
_lowerCamelCase : int = type_sequence_label_size
_lowerCamelCase : Tuple = encoder_stride
_lowerCamelCase : Any = out_features
_lowerCamelCase : Any = out_indices
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = None
if self.use_labels:
_lowerCamelCase : str = ids_tensor([self.batch_size],self.type_sequence_label_size )
_lowerCamelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Union[str, Any] ):
return FocalNetConfig(
image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,embed_dim=self.embed_dim,hidden_sizes=self.hidden_sizes,depths=self.depths,num_heads=self.num_heads,window_size=self.window_size,mlp_ratio=self.mlp_ratio,qkv_bias=self.qkv_bias,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,drop_path_rate=self.drop_path_rate,hidden_act=self.hidden_act,use_absolute_embeddings=self.use_absolute_embeddings,path_norm=self.patch_norm,layer_norm_eps=self.layer_norm_eps,initializer_range=self.initializer_range,encoder_stride=self.encoder_stride,out_features=self.out_features,out_indices=self.out_indices,)
def lowerCamelCase_ ( self : int,__A : Union[str, Any],__A : Tuple,__A : List[Any] ):
_lowerCamelCase : Optional[Any] = FocalNetModel(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[Any] = model(__A )
_lowerCamelCase : Optional[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_lowerCamelCase : Union[str, Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, expected_seq_len, expected_dim) )
def lowerCamelCase_ ( self : int,__A : Optional[int],__A : int,__A : Optional[int] ):
_lowerCamelCase : Any = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ),len(config.out_features ) )
self.parent.assertListEqual(model.channels,config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
_lowerCamelCase : List[str] = None
_lowerCamelCase : List[str] = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ),1 )
self.parent.assertListEqual(model.channels,[config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : Optional[int],__A : Optional[int],__A : Dict,__A : Dict ):
_lowerCamelCase : List[Any] = FocalNetForMaskedImageModeling(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
self.parent.assertEqual(
result.reconstruction.shape,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_lowerCamelCase : Dict = 1
_lowerCamelCase : Any = FocalNetForMaskedImageModeling(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : Optional[int] = model(__A )
self.parent.assertEqual(result.reconstruction.shape,(self.batch_size, 1, self.image_size, self.image_size) )
def lowerCamelCase_ ( self : List[Any],__A : Union[str, Any],__A : List[Any],__A : Optional[Any] ):
_lowerCamelCase : Union[str, Any] = self.type_sequence_label_size
_lowerCamelCase : Optional[Any] = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[int] = model(__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_lowerCamelCase : str = 1
_lowerCamelCase : str = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = model(__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : int = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Union[str, Any] = config_and_inputs
_lowerCamelCase : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[int] = FocalNetModelTester(self )
_lowerCamelCase : int = ConfigTester(self,config_class=__A,embed_dim=3_7,has_text_modality=__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
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 lowerCamelCase_ ( self : List[str] ):
return
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
@unittest.skip(reason="FocalNet does not use inputs_embeds" )
def lowerCamelCase_ ( self : Optional[int] ):
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking" )
def lowerCamelCase_ ( self : List[str] ):
pass
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase , _lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : str = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(),(nn.Module) )
_lowerCamelCase : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A,nn.Linear ) )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : Union[str, Any] = model_class(__A )
_lowerCamelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase : int = [*signature.parameters.keys()]
_lowerCamelCase : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1],__A )
def lowerCamelCase_ ( self : Tuple,__A : Any,__A : List[Any],__A : str,__A : Any ):
_lowerCamelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
_lowerCamelCase : Optional[int] = model(**self._prepare_for_class(__A,__A ) )
_lowerCamelCase : Optional[int] = outputs.hidden_states
_lowerCamelCase : int = getattr(
self.model_tester,"expected_num_hidden_layers",len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__A ),__A )
# FocalNet has a different seq_length
_lowerCamelCase : Optional[Any] = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : List[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
_lowerCamelCase : Any = outputs.reshaped_hidden_states
self.assertEqual(len(__A ),__A )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Tuple = reshaped_hidden_states[0].shape
_lowerCamelCase : List[str] = (
reshaped_hidden_states[0].view(__A,__A,height * width ).permute(0,2,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase , _lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase , _lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Tuple = 3
_lowerCamelCase : Optional[int] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
_lowerCamelCase : Tuple = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : Any = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_lowerCamelCase : int = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : Optional[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
@slow
def lowerCamelCase_ ( self : Tuple ):
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = FocalNetModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase , _lowerCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = _config_zero_init(__A )
for model_class in self.all_model_classes:
_lowerCamelCase : Any = model_class(config=__A )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),[0.0, 1.0],msg=f'Parameter {name} of model {model_class} seems not properly initialized',)
@require_vision
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : Union[str, Any] ):
# TODO update organization
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny" ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny" ).to(__A )
_lowerCamelCase : int = self.default_image_processor
_lowerCamelCase : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_lowerCamelCase : Dict = image_processor(images=__A,return_tensors="pt" ).to(__A )
# forward pass
with torch.no_grad():
_lowerCamelCase : Dict = model(**__A )
# verify the logits
_lowerCamelCase : List[Any] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape,__A )
_lowerCamelCase : List[str] = torch.tensor([0.2166, -0.4368, 0.2191] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3],__A,atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item(),2_8_1 )
@require_torch
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = (FocalNetBackbone,) if is_torch_available() else ()
lowerCAmelCase_ = FocalNetConfig
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : int = FocalNetModelTester(self )
| 44 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase__ :
def __init__( self : List[Any],__A : str,__A : List[str]=1_3,__A : str=3_2,__A : Tuple=2,__A : Any=3,__A : Dict=1_6,__A : Dict=[3_2, 6_4, 1_2_8],__A : List[str]=[1, 2, 1],__A : str=[2, 2, 4],__A : Optional[int]=2,__A : Dict=2.0,__A : str=True,__A : Tuple=0.0,__A : int=0.0,__A : List[str]=0.1,__A : Any="gelu",__A : List[Any]=False,__A : Optional[Any]=True,__A : List[str]=0.02,__A : Tuple=1e-5,__A : Any=True,__A : Tuple=None,__A : Tuple=True,__A : Tuple=1_0,__A : List[Any]=8,__A : Optional[int]=["stage1", "stage2"],__A : int=[1, 2],):
_lowerCamelCase : List[Any] = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : Optional[int] = image_size
_lowerCamelCase : int = patch_size
_lowerCamelCase : Optional[Any] = num_channels
_lowerCamelCase : int = embed_dim
_lowerCamelCase : int = hidden_sizes
_lowerCamelCase : List[Any] = depths
_lowerCamelCase : Any = num_heads
_lowerCamelCase : List[str] = window_size
_lowerCamelCase : str = mlp_ratio
_lowerCamelCase : Any = qkv_bias
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : List[str] = drop_path_rate
_lowerCamelCase : str = hidden_act
_lowerCamelCase : Union[str, Any] = use_absolute_embeddings
_lowerCamelCase : List[Any] = patch_norm
_lowerCamelCase : Tuple = layer_norm_eps
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Optional[int] = is_training
_lowerCamelCase : Tuple = scope
_lowerCamelCase : List[Any] = use_labels
_lowerCamelCase : int = type_sequence_label_size
_lowerCamelCase : Tuple = encoder_stride
_lowerCamelCase : Any = out_features
_lowerCamelCase : Any = out_indices
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = None
if self.use_labels:
_lowerCamelCase : str = ids_tensor([self.batch_size],self.type_sequence_label_size )
_lowerCamelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Union[str, Any] ):
return FocalNetConfig(
image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,embed_dim=self.embed_dim,hidden_sizes=self.hidden_sizes,depths=self.depths,num_heads=self.num_heads,window_size=self.window_size,mlp_ratio=self.mlp_ratio,qkv_bias=self.qkv_bias,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,drop_path_rate=self.drop_path_rate,hidden_act=self.hidden_act,use_absolute_embeddings=self.use_absolute_embeddings,path_norm=self.patch_norm,layer_norm_eps=self.layer_norm_eps,initializer_range=self.initializer_range,encoder_stride=self.encoder_stride,out_features=self.out_features,out_indices=self.out_indices,)
def lowerCamelCase_ ( self : int,__A : Union[str, Any],__A : Tuple,__A : List[Any] ):
_lowerCamelCase : Optional[Any] = FocalNetModel(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[Any] = model(__A )
_lowerCamelCase : Optional[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_lowerCamelCase : Union[str, Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, expected_seq_len, expected_dim) )
def lowerCamelCase_ ( self : int,__A : Optional[int],__A : int,__A : Optional[int] ):
_lowerCamelCase : Any = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ),len(config.out_features ) )
self.parent.assertListEqual(model.channels,config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
_lowerCamelCase : List[str] = None
_lowerCamelCase : List[str] = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ),1 )
self.parent.assertListEqual(model.channels,[config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : Optional[int],__A : Optional[int],__A : Dict,__A : Dict ):
_lowerCamelCase : List[Any] = FocalNetForMaskedImageModeling(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
self.parent.assertEqual(
result.reconstruction.shape,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_lowerCamelCase : Dict = 1
_lowerCamelCase : Any = FocalNetForMaskedImageModeling(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : Optional[int] = model(__A )
self.parent.assertEqual(result.reconstruction.shape,(self.batch_size, 1, self.image_size, self.image_size) )
def lowerCamelCase_ ( self : List[Any],__A : Union[str, Any],__A : List[Any],__A : Optional[Any] ):
_lowerCamelCase : Union[str, Any] = self.type_sequence_label_size
_lowerCamelCase : Optional[Any] = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[int] = model(__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_lowerCamelCase : str = 1
_lowerCamelCase : str = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = model(__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : int = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Union[str, Any] = config_and_inputs
_lowerCamelCase : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[int] = FocalNetModelTester(self )
_lowerCamelCase : int = ConfigTester(self,config_class=__A,embed_dim=3_7,has_text_modality=__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
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 lowerCamelCase_ ( self : List[str] ):
return
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
@unittest.skip(reason="FocalNet does not use inputs_embeds" )
def lowerCamelCase_ ( self : Optional[int] ):
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking" )
def lowerCamelCase_ ( self : List[str] ):
pass
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase , _lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : str = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(),(nn.Module) )
_lowerCamelCase : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A,nn.Linear ) )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : Union[str, Any] = model_class(__A )
_lowerCamelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase : int = [*signature.parameters.keys()]
_lowerCamelCase : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1],__A )
def lowerCamelCase_ ( self : Tuple,__A : Any,__A : List[Any],__A : str,__A : Any ):
_lowerCamelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
_lowerCamelCase : Optional[int] = model(**self._prepare_for_class(__A,__A ) )
_lowerCamelCase : Optional[int] = outputs.hidden_states
_lowerCamelCase : int = getattr(
self.model_tester,"expected_num_hidden_layers",len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__A ),__A )
# FocalNet has a different seq_length
_lowerCamelCase : Optional[Any] = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : List[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
_lowerCamelCase : Any = outputs.reshaped_hidden_states
self.assertEqual(len(__A ),__A )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Tuple = reshaped_hidden_states[0].shape
_lowerCamelCase : List[str] = (
reshaped_hidden_states[0].view(__A,__A,height * width ).permute(0,2,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase , _lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase , _lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Tuple = 3
_lowerCamelCase : Optional[int] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
_lowerCamelCase : Tuple = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : Any = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_lowerCamelCase : int = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : Optional[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
@slow
def lowerCamelCase_ ( self : Tuple ):
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = FocalNetModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase , _lowerCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = _config_zero_init(__A )
for model_class in self.all_model_classes:
_lowerCamelCase : Any = model_class(config=__A )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),[0.0, 1.0],msg=f'Parameter {name} of model {model_class} seems not properly initialized',)
@require_vision
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : Union[str, Any] ):
# TODO update organization
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny" ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny" ).to(__A )
_lowerCamelCase : int = self.default_image_processor
_lowerCamelCase : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_lowerCamelCase : Dict = image_processor(images=__A,return_tensors="pt" ).to(__A )
# forward pass
with torch.no_grad():
_lowerCamelCase : Dict = model(**__A )
# verify the logits
_lowerCamelCase : List[Any] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape,__A )
_lowerCamelCase : List[str] = torch.tensor([0.2166, -0.4368, 0.2191] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3],__A,atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item(),2_8_1 )
@require_torch
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = (FocalNetBackbone,) if is_torch_available() else ()
lowerCAmelCase_ = FocalNetConfig
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : int = FocalNetModelTester(self )
| 44 |
'''simple docstring'''
class UpperCAmelCase__ :
def __init__( self : Any,__A : Any,__A : Any,__A : Any ):
_lowerCamelCase : List[Any] = name
_lowerCamelCase : Union[str, Any] = value
_lowerCamelCase : str = weight
def __repr__( self : Any ):
return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def lowerCamelCase_ ( self : Optional[int] ):
return self.value
def lowerCamelCase_ ( self : Any ):
return self.name
def lowerCamelCase_ ( self : List[Any] ):
return self.weight
def lowerCamelCase_ ( self : str ):
return self.value / self.weight
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = []
for i in range(len(_lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = sorted(_lowerCAmelCase , key=_lowerCAmelCase , reverse=_lowerCAmelCase )
_lowerCamelCase : Optional[int] = []
_lowerCamelCase , _lowerCamelCase : Optional[int] = 0.0, 0.0
for i in range(len(_lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def A_ ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'WhisperFeatureExtractor'
lowerCAmelCase_ = 'WhisperTokenizer'
def __init__( self : List[str],__A : Tuple,__A : Any ):
super().__init__(__A,__A )
_lowerCamelCase : List[str] = self.feature_extractor
_lowerCamelCase : List[str] = False
def lowerCamelCase_ ( self : List[Any],__A : Optional[Any]=None,__A : str=None,__A : Tuple=True ):
return self.tokenizer.get_decoder_prompt_ids(task=__A,language=__A,no_timestamps=__A )
def __call__( self : Union[str, Any],*__A : List[str],**__A : Any ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__A,**__A )
_lowerCamelCase : Dict = kwargs.pop("audio",__A )
_lowerCamelCase : List[Any] = kwargs.pop("sampling_rate",__A )
_lowerCamelCase : Optional[Any] = kwargs.pop("text",__A )
if len(__A ) > 0:
_lowerCamelCase : Dict = args[0]
_lowerCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
_lowerCamelCase : Any = self.feature_extractor(__A,*__A,sampling_rate=__A,**__A )
if text is not None:
_lowerCamelCase : Any = self.tokenizer(__A,**__A )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_lowerCamelCase : int = encodings["input_ids"]
return inputs
def lowerCamelCase_ ( self : Any,*__A : List[Any],**__A : Tuple ):
return self.tokenizer.batch_decode(*__A,**__A )
def lowerCamelCase_ ( self : List[str],*__A : Optional[Any],**__A : Optional[int] ):
return self.tokenizer.decode(*__A,**__A )
def lowerCamelCase_ ( self : Optional[Any],__A : str,__A : List[str]="np" ):
return self.tokenizer.get_prompt_ids(__A,return_tensors=__A )
| 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : List[Any] = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = ['ConditionalDetrFeatureExtractor']
UpperCAmelCase_ : str = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : str = [
'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConditionalDetrForObjectDetection',
'ConditionalDetrForSegmentation',
'ConditionalDetrModel',
'ConditionalDetrPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
_lowerCamelCase : str = len(bin(_lowerCAmelCase )[3:] )
_lowerCamelCase : List[str] = bin(abs(_lowerCAmelCase ) - (1 << binary_number_length) )[3:]
_lowerCamelCase : List[str] = (
(
"1"
+ "0" * (binary_number_length - len(_lowerCAmelCase ))
+ twos_complement_number
)
if number < 0
else "0"
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Tuple = tmp_path / "file.csv"
_lowerCamelCase : Optional[int] = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Any = tmp_path / "malformed_file.csv"
_lowerCamelCase : Any = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20,\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : int = tmp_path / "csv_with_image.csv"
_lowerCamelCase : int = textwrap.dedent(
F'\\n image\n {image_file}\n ' )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Dict = tmp_path / "csv_with_label.csv"
_lowerCamelCase : int = textwrap.dedent(
"\\n label\n good\n bad\n good\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Dict = tmp_path / "csv_with_int_list.csv"
_lowerCamelCase : Any = textwrap.dedent(
"\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : List[Any] = Csv()
_lowerCamelCase : Any = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(_lowerCAmelCase , match="Error tokenizing data" ):
for _ in generator:
pass
assert any(
record.levelname == "ERROR"
and "Failed to read file" in record.message
and os.path.basename(_lowerCAmelCase ) in record.message
for record in caplog.records )
@require_pil
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
with open(_lowerCAmelCase , encoding="utf-8" ) as f:
_lowerCamelCase : Any = f.read().splitlines()[1]
_lowerCamelCase : Optional[Any] = Csv(encoding="utf-8" , features=Features({"image": Image()} ) )
_lowerCamelCase : Union[str, Any] = csv._generate_tables([[csv_file_with_image]] )
_lowerCamelCase : List[str] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("image" ).type == Image()()
_lowerCamelCase : int = pa_table.to_pydict()["image"]
assert generated_content == [{"path": image_file, "bytes": None}]
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
with open(_lowerCAmelCase , encoding="utf-8" ) as f:
_lowerCamelCase : List[Any] = f.read().splitlines()[1:]
_lowerCamelCase : int = Csv(encoding="utf-8" , features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
_lowerCamelCase : Tuple = csv._generate_tables([[csv_file_with_label]] )
_lowerCamelCase : int = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
_lowerCamelCase : Union[str, Any] = pa_table.to_pydict()["label"]
assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(_lowerCAmelCase ) for label in labels]
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Dict = Csv(encoding="utf-8" , sep="," , converters={"int_list": lambda _lowerCAmelCase : [int(_lowerCAmelCase ) for i in x.split()]} )
_lowerCamelCase : List[Any] = csv._generate_tables([[csv_file_with_int_list]] )
_lowerCamelCase : Optional[int] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
_lowerCamelCase : Optional[Any] = pa_table.to_pydict()["int_list"]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 44 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = (PNDMScheduler,)
lowerCAmelCase_ = (('num_inference_steps', 50),)
def lowerCamelCase_ ( self : Tuple,**__A : Any ):
_lowerCamelCase : Optional[Any] = {
"num_train_timesteps": 1_0_0_0,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**__A )
return config
def lowerCamelCase_ ( self : Dict,__A : Tuple=0,**__A : int ):
_lowerCamelCase : Union[str, Any] = dict(self.forward_default_kwargs )
_lowerCamelCase : Tuple = kwargs.pop("num_inference_steps",__A )
_lowerCamelCase : Tuple = self.dummy_sample
_lowerCamelCase : str = 0.1 * sample
_lowerCamelCase : str = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
_lowerCamelCase : List[str] = self.get_scheduler_config(**__A )
_lowerCamelCase : int = scheduler_class(**__A )
scheduler.set_timesteps(__A )
# copy over dummy past residuals
_lowerCamelCase : Optional[int] = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__A )
_lowerCamelCase : Optional[Any] = scheduler_class.from_pretrained(__A )
new_scheduler.set_timesteps(__A )
# copy over dummy past residuals
_lowerCamelCase : Union[str, Any] = dummy_past_residuals[:]
_lowerCamelCase : Union[str, Any] = scheduler.step_prk(__A,__A,__A,**__A ).prev_sample
_lowerCamelCase : int = new_scheduler.step_prk(__A,__A,__A,**__A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
_lowerCamelCase : Tuple = scheduler.step_plms(__A,__A,__A,**__A ).prev_sample
_lowerCamelCase : int = new_scheduler.step_plms(__A,__A,__A,**__A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowerCamelCase_ ( self : str ):
pass
def lowerCamelCase_ ( self : List[Any],__A : List[Any]=0,**__A : Union[str, Any] ):
_lowerCamelCase : Any = dict(self.forward_default_kwargs )
_lowerCamelCase : Optional[int] = kwargs.pop("num_inference_steps",__A )
_lowerCamelCase : Tuple = self.dummy_sample
_lowerCamelCase : Union[str, Any] = 0.1 * sample
_lowerCamelCase : List[str] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
_lowerCamelCase : str = self.get_scheduler_config()
_lowerCamelCase : Dict = scheduler_class(**__A )
scheduler.set_timesteps(__A )
# copy over dummy past residuals (must be after setting timesteps)
_lowerCamelCase : str = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__A )
_lowerCamelCase : Optional[Any] = scheduler_class.from_pretrained(__A )
# copy over dummy past residuals
new_scheduler.set_timesteps(__A )
# copy over dummy past residual (must be after setting timesteps)
_lowerCamelCase : Any = dummy_past_residuals[:]
_lowerCamelCase : List[Any] = scheduler.step_prk(__A,__A,__A,**__A ).prev_sample
_lowerCamelCase : Union[str, Any] = new_scheduler.step_prk(__A,__A,__A,**__A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
_lowerCamelCase : Any = scheduler.step_plms(__A,__A,__A,**__A ).prev_sample
_lowerCamelCase : Optional[Any] = new_scheduler.step_plms(__A,__A,__A,**__A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowerCamelCase_ ( self : int,**__A : Optional[Any] ):
_lowerCamelCase : str = self.scheduler_classes[0]
_lowerCamelCase : str = self.get_scheduler_config(**__A )
_lowerCamelCase : str = scheduler_class(**__A )
_lowerCamelCase : Any = 1_0
_lowerCamelCase : Optional[Any] = self.dummy_model()
_lowerCamelCase : Optional[int] = self.dummy_sample_deter
scheduler.set_timesteps(__A )
for i, t in enumerate(scheduler.prk_timesteps ):
_lowerCamelCase : Dict = model(__A,__A )
_lowerCamelCase : Optional[int] = scheduler.step_prk(__A,__A,__A ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
_lowerCamelCase : List[Any] = model(__A,__A )
_lowerCamelCase : Dict = scheduler.step_plms(__A,__A,__A ).prev_sample
return sample
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : int = dict(self.forward_default_kwargs )
_lowerCamelCase : Optional[Any] = kwargs.pop("num_inference_steps",__A )
for scheduler_class in self.scheduler_classes:
_lowerCamelCase : Optional[Any] = self.get_scheduler_config()
_lowerCamelCase : List[Any] = scheduler_class(**__A )
_lowerCamelCase : Any = self.dummy_sample
_lowerCamelCase : Union[str, Any] = 0.1 * sample
if num_inference_steps is not None and hasattr(__A,"set_timesteps" ):
scheduler.set_timesteps(__A )
elif num_inference_steps is not None and not hasattr(__A,"set_timesteps" ):
_lowerCamelCase : Tuple = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_lowerCamelCase : str = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
_lowerCamelCase : List[Any] = dummy_past_residuals[:]
_lowerCamelCase : List[Any] = scheduler.step_prk(__A,0,__A,**__A ).prev_sample
_lowerCamelCase : Dict = scheduler.step_prk(__A,1,__A,**__A ).prev_sample
self.assertEqual(output_a.shape,sample.shape )
self.assertEqual(output_a.shape,output_a.shape )
_lowerCamelCase : Dict = scheduler.step_plms(__A,0,__A,**__A ).prev_sample
_lowerCamelCase : List[Any] = scheduler.step_plms(__A,1,__A,**__A ).prev_sample
self.assertEqual(output_a.shape,sample.shape )
self.assertEqual(output_a.shape,output_a.shape )
def lowerCamelCase_ ( self : Optional[Any] ):
for timesteps in [1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__A )
def lowerCamelCase_ ( self : Optional[int] ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=__A )
_lowerCamelCase : Tuple = self.scheduler_classes[0]
_lowerCamelCase : List[str] = self.get_scheduler_config(steps_offset=1 )
_lowerCamelCase : Optional[int] = scheduler_class(**__A )
scheduler.set_timesteps(1_0 )
assert torch.equal(
scheduler.timesteps,torch.LongTensor(
[9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1] ),)
def lowerCamelCase_ ( self : str ):
for beta_start, beta_end in zip([0.0001, 0.001],[0.002, 0.02] ):
self.check_over_configs(beta_start=__A,beta_end=__A )
def lowerCamelCase_ ( self : List[Any] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__A )
def lowerCamelCase_ ( self : str ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__A )
def lowerCamelCase_ ( self : List[Any] ):
for t in [1, 5, 1_0]:
self.check_over_forward(time_step=__A )
def lowerCamelCase_ ( self : Optional[int] ):
for t, num_inference_steps in zip([1, 5, 1_0],[1_0, 5_0, 1_0_0] ):
self.check_over_forward(num_inference_steps=__A )
def lowerCamelCase_ ( self : Dict ):
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
_lowerCamelCase : List[str] = 2_7
for scheduler_class in self.scheduler_classes:
_lowerCamelCase : List[Any] = self.dummy_sample
_lowerCamelCase : str = 0.1 * sample
_lowerCamelCase : Dict = self.get_scheduler_config()
_lowerCamelCase : Any = scheduler_class(**__A )
scheduler.set_timesteps(__A )
# 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] ):
_lowerCamelCase : List[str] = scheduler.step_prk(__A,__A,__A ).prev_sample
def lowerCamelCase_ ( self : Union[str, Any] ):
with self.assertRaises(__A ):
_lowerCamelCase : Optional[int] = self.scheduler_classes[0]
_lowerCamelCase : Union[str, Any] = self.get_scheduler_config()
_lowerCamelCase : str = scheduler_class(**__A )
scheduler.step_plms(self.dummy_sample,1,self.dummy_sample ).prev_sample
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase : str = self.full_loop()
_lowerCamelCase : str = torch.sum(torch.abs(__A ) )
_lowerCamelCase : Any = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 198.1318 ) < 1e-2
assert abs(result_mean.item() - 0.2580 ) < 1e-3
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : Dict = self.full_loop(prediction_type="v_prediction" )
_lowerCamelCase : int = torch.sum(torch.abs(__A ) )
_lowerCamelCase : int = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 67.3986 ) < 1e-2
assert abs(result_mean.item() - 0.0878 ) < 1e-3
def lowerCamelCase_ ( self : Any ):
# We specify different beta, so that the first alpha is 0.99
_lowerCamelCase : Optional[Any] = self.full_loop(set_alpha_to_one=__A,beta_start=0.01 )
_lowerCamelCase : str = torch.sum(torch.abs(__A ) )
_lowerCamelCase : Dict = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 230.0399 ) < 1e-2
assert abs(result_mean.item() - 0.2995 ) < 1e-3
def lowerCamelCase_ ( self : Tuple ):
# We specify different beta, so that the first alpha is 0.99
_lowerCamelCase : Union[str, Any] = self.full_loop(set_alpha_to_one=__A,beta_start=0.01 )
_lowerCamelCase : Optional[Any] = torch.sum(torch.abs(__A ) )
_lowerCamelCase : List[Any] = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 186.9482 ) < 1e-2
assert abs(result_mean.item() - 0.2434 ) < 1e-3
| 44 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = IFInpaintingSuperResolutionPipeline
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
lowerCAmelCase_ = PipelineTesterMixin.required_optional_params - {'latents'}
def lowerCamelCase_ ( self : List[str] ):
return self._get_superresolution_dummy_components()
def lowerCamelCase_ ( self : str,__A : List[str],__A : List[str]=0 ):
if str(__A ).startswith("mps" ):
_lowerCamelCase : List[str] = torch.manual_seed(__A )
else:
_lowerCamelCase : Optional[int] = torch.Generator(device=__A ).manual_seed(__A )
_lowerCamelCase : List[Any] = floats_tensor((1, 3, 1_6, 1_6),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Any = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Tuple = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Dict = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),reason="XFormers attention is only available with CUDA and `xformers` installed",)
def lowerCamelCase_ ( self : Optional[int] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowerCamelCase_ ( self : Dict ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda",reason="float16 requires CUDA" )
def lowerCamelCase_ ( self : Optional[Any] ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowerCamelCase_ ( self : Any ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowerCamelCase_ ( self : Dict ):
self._test_save_load_local()
def lowerCamelCase_ ( self : Any ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,)
| 44 | 1 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
_lowerCamelCase : Tuple = True
# 0 and 1 are none primes.
if number <= 1:
_lowerCamelCase : int = False
for divisor in range(2 , int(round(sqrt(_lowerCAmelCase ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
_lowerCamelCase : Optional[int] = False
break
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'status' must been from type bool"
return status
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
_lowerCamelCase : Dict = list(range(2 , n + 1 ) )
_lowerCamelCase : Dict = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(_lowerCAmelCase ) ):
for j in range(i + 1 , len(_lowerCAmelCase ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
_lowerCamelCase : List[Any] = 0
# filters actual prime numbers.
_lowerCamelCase : Any = [x for x in begin_list if x != 0]
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type list"
return ans
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n > 2), "'N' must been an int and > 2"
_lowerCamelCase : Dict = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(_lowerCAmelCase ):
ans.append(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type list"
return ans
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and number >= 0, "'number' must been an int and >= 0"
_lowerCamelCase : Tuple = [] # this list will be returns of the function.
# potential prime number factors.
_lowerCamelCase : Optional[Any] = 2
_lowerCamelCase : Optional[Any] = number
if number == 0 or number == 1:
ans.append(_lowerCAmelCase )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(_lowerCAmelCase ):
while quotient != 1:
if is_prime(_lowerCAmelCase ) and (quotient % factor == 0):
ans.append(_lowerCAmelCase )
quotient /= factor
else:
factor += 1
else:
ans.append(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type list"
return ans
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' bust been an int and >= 0"
_lowerCamelCase : Union[str, Any] = 0
# prime factorization of 'number'
_lowerCamelCase : Tuple = prime_factorization(_lowerCAmelCase )
_lowerCamelCase : int = max(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type int"
return ans
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' bust been an int and >= 0"
_lowerCamelCase : int = 0
# prime factorization of 'number'
_lowerCamelCase : List[Any] = prime_factorization(_lowerCAmelCase )
_lowerCamelCase : Any = min(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'ans' must been from type int"
return ans
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'number' must been an int"
assert isinstance(number % 2 == 0 , _lowerCAmelCase ), "compare bust been from type bool"
return number % 2 == 0
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), "'number' must been an int"
assert isinstance(number % 2 != 0 , _lowerCAmelCase ), "compare bust been from type bool"
return number % 2 != 0
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (number > 2) and is_even(_lowerCAmelCase )
), "'number' must been an int, even and > 2"
_lowerCamelCase : int = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
_lowerCamelCase : Any = get_prime_numbers(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = len(_lowerCAmelCase )
# run variable for while-loops.
_lowerCamelCase : List[str] = 0
_lowerCamelCase : str = None
# exit variable. for break up the loops
_lowerCamelCase : List[Any] = True
while i < len_pn and loop:
_lowerCamelCase : List[Any] = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
_lowerCamelCase : Optional[Any] = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (len(_lowerCAmelCase ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def A_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple ):
"""simple docstring"""
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
_lowerCamelCase : Optional[int] = 0
while numbera != 0:
_lowerCamelCase : Tuple = numbera % numbera
_lowerCamelCase : List[Any] = numbera
_lowerCamelCase : str = rest
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple ):
"""simple docstring"""
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
_lowerCamelCase : int = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
_lowerCamelCase : Optional[int] = prime_factorization(_lowerCAmelCase )
_lowerCamelCase : Optional[Any] = prime_factorization(_lowerCAmelCase )
elif numbera == 1 or numbera == 1:
_lowerCamelCase : List[str] = []
_lowerCamelCase : Any = []
_lowerCamelCase : Dict = max(_lowerCAmelCase , _lowerCAmelCase )
_lowerCamelCase : Dict = 0
_lowerCamelCase : Optional[Any] = 0
_lowerCamelCase : Optional[Any] = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
_lowerCamelCase : Any = prime_fac_a.count(_lowerCAmelCase )
_lowerCamelCase : int = prime_fac_a.count(_lowerCAmelCase )
for _ in range(max(_lowerCAmelCase , _lowerCAmelCase ) ):
ans *= n
else:
_lowerCamelCase : str = prime_fac_a.count(_lowerCAmelCase )
for _ in range(_lowerCAmelCase ):
ans *= n
done.append(_lowerCAmelCase )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
_lowerCamelCase : Optional[Any] = prime_fac_a.count(_lowerCAmelCase )
for _ in range(_lowerCAmelCase ):
ans *= n
done.append(_lowerCAmelCase )
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 0), "'number' must been a positive int"
_lowerCamelCase : int = 0
_lowerCamelCase : Dict = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(_lowerCAmelCase ):
ans += 1
# precondition
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and is_prime(
_lowerCAmelCase ), "'ans' must been a prime number and from type int"
return ans
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
assert (
is_prime(_lowerCAmelCase ) and is_prime(_lowerCAmelCase ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
_lowerCamelCase : Any = p_number_a + 1 # jump to the next number
_lowerCamelCase : Optional[int] = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(_lowerCAmelCase ):
number += 1
while number < p_number_a:
ans.append(_lowerCAmelCase )
number += 1
# fetch the next prime number.
while not is_prime(_lowerCAmelCase ):
number += 1
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and ans[0] != p_number_a
and ans[len(_lowerCAmelCase ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 1), "'n' must been int and >= 1"
_lowerCamelCase : int = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(_lowerCAmelCase )
# precondition
assert ans[0] == 1 and ans[len(_lowerCAmelCase ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number > 1
), "'number' must been an int and >= 1"
_lowerCamelCase : Union[str, Any] = get_divisors(_lowerCAmelCase )
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (divisors[0] == 1)
and (divisors[len(_lowerCAmelCase ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
_lowerCamelCase : Any = gcd(abs(_lowerCAmelCase ) , abs(_lowerCAmelCase ) )
# precondition
assert (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 0), "'n' must been a int and >= 0"
_lowerCamelCase : Dict = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (n >= 0), "'n' must been an int and >= 0"
_lowerCamelCase : int = 0
_lowerCamelCase : str = 1
_lowerCamelCase : Tuple = 1 # this will be return
for _ in range(n - 1 ):
_lowerCamelCase : str = ans
ans += fiba
_lowerCamelCase : Dict = tmp
return ans
| 44 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class UpperCAmelCase__ ( A ):
def __init__( self : List[Any],__A : Tuple,__A : Optional[int],__A : Optional[int]=1_0_2_4,__A : int=1_0_2_4,__A : Any=3.6 ):
_lowerCamelCase : List[str] = tokenizer
_lowerCamelCase : Dict = tokenizer.bos_token_id
_lowerCamelCase : Tuple = dataset
_lowerCamelCase : Any = seq_length
_lowerCamelCase : List[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self : Tuple ):
_lowerCamelCase : Union[str, Any] = iter(self.dataset )
_lowerCamelCase : str = True
while more_examples:
_lowerCamelCase , _lowerCamelCase : Optional[int] = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__A )["content"] )
buffer_len += len(buffer[-1] )
except StopIteration:
_lowerCamelCase : Tuple = False
break
_lowerCamelCase : int = tokenizer(__A,truncation=__A )["input_ids"]
_lowerCamelCase : int = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0,len(__A ),self.seq_length ):
_lowerCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(__A ) == self.seq_length:
yield torch.tensor(__A )
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = {"streaming": True}
_lowerCamelCase : Optional[Any] = load_dataset(args.dataset_name , split="train" , **_lowerCAmelCase )
_lowerCamelCase : int = ConstantLengthDataset(_lowerCAmelCase , _lowerCAmelCase , seq_length=args.seq_length )
_lowerCamelCase : Dict = DataLoader(_lowerCAmelCase , batch_size=args.batch_size )
return eval_dataloader
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
model.eval()
_lowerCamelCase : Optional[int] = []
for step, batch in enumerate(_lowerCAmelCase ):
with torch.no_grad():
_lowerCamelCase : List[str] = model(_lowerCAmelCase , labels=_lowerCAmelCase )
_lowerCamelCase : List[Any] = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_lowerCAmelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
_lowerCamelCase : Dict = torch.mean(torch.cat(_lowerCAmelCase ) )
try:
_lowerCamelCase : List[Any] = torch.exp(_lowerCAmelCase )
except OverflowError:
_lowerCamelCase : Optional[int] = float("inf" )
return loss.item(), perplexity.item()
# Setup Accelerator
UpperCAmelCase_ : List[str] = Accelerator()
# Parse configuration
UpperCAmelCase_ : Tuple = HfArgumentParser(EvaluationArguments)
UpperCAmelCase_ : Dict = parser.parse_args()
set_seed(args.seed)
# Logging
UpperCAmelCase_ : Optional[int] = logging.getLogger(__name__)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
# Load model and tokenizer
UpperCAmelCase_ : Tuple = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase_ : Dict = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
UpperCAmelCase_ : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
UpperCAmelCase_, UpperCAmelCase_ : Dict = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCAmelCase_, UpperCAmelCase_ : str = evaluate(args)
logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 44 | 1 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase_ : Optional[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
UpperCAmelCase_ : Union[str, Any] = [0, 25, 50]
UpperCAmelCase_ : int = [25, 50, 75]
UpperCAmelCase_ : Dict = fuzz.membership.trimf(X, abca)
UpperCAmelCase_ : Union[str, Any] = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
UpperCAmelCase_ : Optional[int] = np.ones(75)
UpperCAmelCase_ : Optional[int] = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
UpperCAmelCase_ : int = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
UpperCAmelCase_ : List[Any] = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
UpperCAmelCase_ : Optional[int] = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
UpperCAmelCase_ : Any = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
UpperCAmelCase_ : str = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
UpperCAmelCase_ : str = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
UpperCAmelCase_ : int = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
UpperCAmelCase_ : List[Any] = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 44 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCAmelCase_ : Union[str, Any] = {
'vocab_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
},
'merges_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
},
'tokenizer_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
},
}
UpperCAmelCase_ : List[str] = {
'allenai/led-base-16384': 1_6384,
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = LEDTokenizer
lowerCAmelCase_ = ['input_ids', 'attention_mask']
def __init__( self : Union[str, Any],__A : List[Any]=None,__A : str=None,__A : str=None,__A : Optional[int]="replace",__A : Union[str, Any]="<s>",__A : Union[str, Any]="</s>",__A : Any="</s>",__A : Optional[int]="<s>",__A : List[str]="<unk>",__A : str="<pad>",__A : Tuple="<mask>",__A : Union[str, Any]=False,__A : Optional[int]=True,**__A : Optional[int],):
super().__init__(
__A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)
_lowerCamelCase : List[str] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : str = getattr(__A,pre_tok_state.pop("type" ) )
_lowerCamelCase : List[Any] = add_prefix_space
_lowerCamelCase : Tuple = pre_tok_class(**__A )
_lowerCamelCase : Optional[int] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
_lowerCamelCase : List[str] = "post_processor"
_lowerCamelCase : int = getattr(self.backend_tokenizer,__A,__A )
if tokenizer_component_instance:
_lowerCamelCase : Tuple = 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 : str = tuple(state["sep"] )
if "cls" in state:
_lowerCamelCase : List[str] = tuple(state["cls"] )
_lowerCamelCase : Dict = False
if state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : List[str] = add_prefix_space
_lowerCamelCase : List[Any] = True
if state.get("trim_offsets",__A ) != trim_offsets:
_lowerCamelCase : List[str] = trim_offsets
_lowerCamelCase : List[str] = True
if changes_to_apply:
_lowerCamelCase : Tuple = getattr(__A,state.pop("type" ) )
_lowerCamelCase : Any = component_class(**__A )
setattr(self.backend_tokenizer,__A,__A )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def lowerCamelCase_ ( self : str ):
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 lowerCamelCase_ ( self : List[str],__A : str ):
_lowerCamelCase : Optional[Any] = AddedToken(__A,lstrip=__A,rstrip=__A ) if isinstance(__A,__A ) else value
_lowerCamelCase : str = value
def lowerCamelCase_ ( self : List[str],*__A : List[Any],**__A : int ):
_lowerCamelCase : List[str] = kwargs.get("is_split_into_words",__A )
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(*__A,**__A )
def lowerCamelCase_ ( self : Optional[int],*__A : Optional[Any],**__A : Union[str, Any] ):
_lowerCamelCase : List[Any] = kwargs.get("is_split_into_words",__A )
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(*__A,**__A )
def lowerCamelCase_ ( self : Dict,__A : str,__A : Optional[str] = None ):
_lowerCamelCase : List[str] = self._tokenizer.model.save(__A,name=__A )
return tuple(__A )
def lowerCamelCase_ ( self : List[str],__A : Optional[Any],__A : List[str]=None ):
_lowerCamelCase : Optional[Any] = [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 lowerCamelCase_ ( self : Dict,__A : List[int],__A : Optional[List[int]] = None ):
_lowerCamelCase : Tuple = [self.sep_token_id]
_lowerCamelCase : Union[str, Any] = [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 lowerCamelCase_ ( self : Any,__A : Union[Dict[str, EncodedInput], BatchEncoding],__A : Optional[int] = None,__A : PaddingStrategy = PaddingStrategy.DO_NOT_PAD,__A : Optional[int] = None,__A : Optional[bool] = None,):
_lowerCamelCase : List[str] = super()._pad(
encoded_inputs=__A,max_length=__A,padding_strategy=__A,pad_to_multiple_of=__A,return_attention_mask=__A,)
# Load from model defaults
if return_attention_mask is None:
_lowerCamelCase : Any = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_lowerCamelCase : Union[str, Any] = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_lowerCamelCase : Optional[Any] = len(encoded_inputs["global_attention_mask"] ) != len(__A )
if needs_to_be_padded:
_lowerCamelCase : str = len(__A ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_lowerCamelCase : Tuple = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
_lowerCamelCase : int = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 44 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = 42
lowerCAmelCase_ = 42
class UpperCAmelCase__ :
def __init__( self : Optional[Any],__A : int ):
_lowerCamelCase : list[list[Edge]] = [[] for _ in range(__A )]
_lowerCamelCase : str = size
def __getitem__( self : Any,__A : int ):
return iter(self._graph[vertex] )
@property
def lowerCamelCase_ ( self : Optional[int] ):
return self._size
def lowerCamelCase_ ( self : Optional[Any],__A : int,__A : int,__A : int ):
if weight not in (0, 1):
raise ValueError("Edge weight must be either 0 or 1." )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("Vertex indexes must be in [0; size)." )
self._graph[from_vertex].append(Edge(__A,__A ) )
def lowerCamelCase_ ( self : List[Any],__A : int,__A : int ):
_lowerCamelCase : Tuple = deque([start_vertex] )
_lowerCamelCase : list[int | None] = [None] * self.size
_lowerCamelCase : str = 0
while queue:
_lowerCamelCase : Union[str, Any] = queue.popleft()
_lowerCamelCase : Optional[Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_lowerCamelCase : Union[str, Any] = current_distance + edge.weight
_lowerCamelCase : Tuple = distances[edge.destination_vertex]
if (
isinstance(__A,__A )
and new_distance >= dest_vertex_distance
):
continue
_lowerCamelCase : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("No path from start_vertex to finish_vertex." )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
'''simple docstring'''
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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int]=False ):
"""simple docstring"""
_lowerCamelCase : List[Any] = []
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') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
] )
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"
_lowerCamelCase : Optional[int] = [(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"),
] )
return rename_keys
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any]=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
_lowerCamelCase : int = ""
else:
_lowerCamelCase : int = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase : Any = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_lowerCamelCase : Tuple = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : List[str] = in_proj_weight[
: config.hidden_size, :
]
_lowerCamelCase : List[str] = in_proj_bias[: config.hidden_size]
_lowerCamelCase : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
_lowerCamelCase : List[str] = in_proj_bias[-config.hidden_size :]
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = dct.pop(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = val
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : str = ViTConfig()
_lowerCamelCase : List[str] = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Optional[Any] = int(vit_name[-12:-10] )
_lowerCamelCase : str = int(vit_name[-9:-6] )
else:
_lowerCamelCase : List[Any] = 1000
_lowerCamelCase : str = "huggingface/label-files"
_lowerCamelCase : Any = "imagenet-1k-id2label.json"
_lowerCamelCase : int = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : str = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : Optional[Any] = idalabel
_lowerCamelCase : List[str] = {v: k for k, v in idalabel.items()}
_lowerCamelCase : List[str] = int(vit_name[-6:-4] )
_lowerCamelCase : str = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("tiny" ):
_lowerCamelCase : List[Any] = 192
_lowerCamelCase : Optional[int] = 768
_lowerCamelCase : Union[str, Any] = 12
_lowerCamelCase : Optional[Any] = 3
elif vit_name[9:].startswith("small" ):
_lowerCamelCase : Optional[Any] = 384
_lowerCamelCase : Optional[Any] = 1536
_lowerCamelCase : int = 12
_lowerCamelCase : List[str] = 6
else:
pass
else:
if vit_name[4:].startswith("small" ):
_lowerCamelCase : List[str] = 768
_lowerCamelCase : Optional[Any] = 2304
_lowerCamelCase : List[Any] = 8
_lowerCamelCase : List[Any] = 8
elif vit_name[4:].startswith("base" ):
pass
elif vit_name[4:].startswith("large" ):
_lowerCamelCase : List[Any] = 1024
_lowerCamelCase : Optional[Any] = 4096
_lowerCamelCase : List[Any] = 24
_lowerCamelCase : Union[str, Any] = 16
elif vit_name[4:].startswith("huge" ):
_lowerCamelCase : str = 1280
_lowerCamelCase : List[Any] = 5120
_lowerCamelCase : List[str] = 32
_lowerCamelCase : List[str] = 16
# load original model from timm
_lowerCamelCase : int = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
_lowerCamelCase : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = create_rename_keys(_lowerCAmelCase , _lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# load HuggingFace model
if vit_name[-5:] == "in21k":
_lowerCamelCase : int = ViTModel(_lowerCAmelCase ).eval()
else:
_lowerCamelCase : List[str] = ViTForImageClassification(_lowerCAmelCase ).eval()
model.load_state_dict(_lowerCAmelCase )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
_lowerCamelCase : Union[str, Any] = DeiTImageProcessor(size=config.image_size )
else:
_lowerCamelCase : Union[str, Any] = ViTImageProcessor(size=config.image_size )
_lowerCamelCase : Optional[int] = image_processor(images=prepare_img() , return_tensors="pt" )
_lowerCamelCase : Optional[int] = encoding["pixel_values"]
_lowerCamelCase : Union[str, Any] = model(_lowerCAmelCase )
if base_model:
_lowerCamelCase : int = timm_model.forward_features(_lowerCAmelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_lowerCAmelCase , outputs.pooler_output , atol=1E-3 )
else:
_lowerCamelCase : Union[str, Any] = timm_model(_lowerCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCAmelCase , outputs.logits , atol=1E-3 )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_lowerCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the 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.'
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str]=False ):
"""simple docstring"""
try:
_lowerCamelCase : Tuple = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_lowerCamelCase : str = default
else:
# KEY is set, convert it to True or False.
try:
_lowerCamelCase : Optional[int] = 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
UpperCAmelCase_ : Any = parse_flag_from_env('RUN_SLOW', default=False)
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
return unittest.skip("Test was skipped" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
return unittest.skipUnless(_run_slow_tests , "test is slow" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
return unittest.skipUnless(not torch.cuda.is_available() , "test requires only a CPU" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
return unittest.skipUnless(torch.cuda.is_available() , "test requires a GPU" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
return unittest.skipUnless(is_xpu_available() , "test requires a XPU" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
return unittest.skipUnless(is_mps_available() , "test requires a `mps` backend support in `torch`" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , "test requires the Hugging Face suite" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
return unittest.skipUnless(is_bnb_available() , "test requires the bitsandbytes library" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
return unittest.skipUnless(is_tpu_available() , "test requires TPU" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() == 1 , "test requires a GPU" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() == 1 , "test requires a XPU" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() > 1 , "test requires multiple GPUs" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() > 1 , "test requires multiple XPUs" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
return unittest.skipUnless(is_safetensors_available() , "test requires safetensors" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
return unittest.skipUnless(is_deepspeed_available() , "test requires DeepSpeed" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
return unittest.skipUnless(is_torch_version(">=" , "1.12.0" ) , "test requires torch version >= 1.12.0" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : Optional[int]=None ):
"""simple docstring"""
if test_case is None:
return partial(_lowerCAmelCase , version=_lowerCAmelCase )
return unittest.skipUnless(is_torch_version(">=" , _lowerCAmelCase ) , F'test requires torch version >= {version}' )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
return unittest.skipUnless(is_tensorboard_available() , "test requires Tensorboard" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
return unittest.skipUnless(is_wandb_available() , "test requires wandb" )(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
return unittest.skipUnless(is_comet_ml_available() , "test requires comet_ml" )(_lowerCAmelCase )
UpperCAmelCase_ : Optional[Any] = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
return unittest.skipUnless(
_atleast_one_tracker_available , "test requires at least one tracker to be available and for `comet_ml` to not be installed" , )(_lowerCAmelCase )
class UpperCAmelCase__ ( unittest.TestCase ):
lowerCAmelCase_ = True
@classmethod
def lowerCamelCase_ ( cls : Any ):
_lowerCamelCase : List[str] = tempfile.mkdtemp()
@classmethod
def lowerCamelCase_ ( cls : Tuple ):
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def lowerCamelCase_ ( self : Optional[int] ):
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("**/*" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(__A )
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCase_ ( self : Any ):
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCase_ ( self : int,__A : Union[mock.Mock, List[mock.Mock]] ):
_lowerCamelCase : Tuple = mocks if isinstance(__A,(tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : Tuple = AcceleratorState()
_lowerCamelCase : str = tensor[None].clone().to(state.device )
_lowerCamelCase : List[Any] = gather(_lowerCAmelCase ).cpu()
_lowerCamelCase : Optional[Any] = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _lowerCAmelCase ):
return False
return True
class UpperCAmelCase__ :
def __init__( self : int,__A : Any,__A : List[Any],__A : str ):
_lowerCamelCase : Tuple = returncode
_lowerCamelCase : List[str] = stdout
_lowerCamelCase : Any = stderr
async def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : int ):
"""simple docstring"""
while True:
_lowerCamelCase : Optional[Any] = await stream.readline()
if line:
callback(_lowerCAmelCase )
else:
break
async def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any]=None , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : List[Any]=False , _lowerCAmelCase : Optional[int]=False ):
"""simple docstring"""
if echo:
print("\nRunning: " , " ".join(_lowerCAmelCase ) )
_lowerCamelCase : List[str] = 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)
_lowerCamelCase : List[Any] = []
_lowerCamelCase : List[str] = []
def tee(_lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : int , _lowerCAmelCase : int="" ):
_lowerCamelCase : Optional[Any] = 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(
[
asyncio.create_task(_read_stream(p.stdout , lambda _lowerCAmelCase : tee(_lowerCAmelCase , _lowerCAmelCase , sys.stdout , label="stdout:" ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _lowerCAmelCase : tee(_lowerCAmelCase , _lowerCAmelCase , sys.stderr , label="stderr:" ) ) ),
] , timeout=_lowerCAmelCase , )
return _RunOutput(await p.wait() , _lowerCAmelCase , _lowerCAmelCase )
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : int=None , _lowerCAmelCase : int=None , _lowerCAmelCase : List[Any]=180 , _lowerCAmelCase : Tuple=False , _lowerCAmelCase : Optional[Any]=True ):
"""simple docstring"""
_lowerCamelCase : Dict = asyncio.get_event_loop()
_lowerCamelCase : List[Any] = loop.run_until_complete(
_stream_subprocess(_lowerCAmelCase , env=_lowerCAmelCase , stdin=_lowerCAmelCase , timeout=_lowerCAmelCase , quiet=_lowerCAmelCase , echo=_lowerCAmelCase ) )
_lowerCamelCase : List[str] = " ".join(_lowerCAmelCase )
if result.returncode > 0:
_lowerCamelCase : int = "\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}' )
return result
class UpperCAmelCase__ ( A ):
pass
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Tuple=False ):
"""simple docstring"""
try:
_lowerCamelCase : Optional[Any] = subprocess.check_output(_lowerCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_lowerCAmelCase , "decode" ):
_lowerCamelCase : List[str] = output.decode("utf-8" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F'Command `{" ".join(_lowerCAmelCase )}` failed with the following error:\n\n{e.output.decode()}' ) from e
| 44 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( _lowerCAmelCase : int = 5000 ):
"""simple docstring"""
_lowerCamelCase : Dict = [(i * (3 * i - 1)) // 2 for i in range(1 , _lowerCAmelCase )]
for i, pentagonal_i in enumerate(_lowerCAmelCase ):
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : List[Any] = pentagonal_nums[j]
_lowerCamelCase : Any = pentagonal_i + pentagonal_j
_lowerCamelCase : Union[str, Any] = pentagonal_j - pentagonal_i
if is_pentagonal(_lowerCAmelCase ) and is_pentagonal(_lowerCAmelCase ):
return b
return -1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 | 1 |
'''simple docstring'''
import math
import sys
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Dict = ""
try:
with open(_lowerCAmelCase , "rb" ) as binary_file:
_lowerCamelCase : List[Any] = binary_file.read()
for dat in data:
_lowerCamelCase : Optional[Any] = F'{dat:08b}'
result += curr_byte
return result
except OSError:
print("File not accessible" )
sys.exit()
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = {"0": "0", "1": "1"}
_lowerCamelCase , _lowerCamelCase : List[str] = "", ""
_lowerCamelCase : List[Any] = len(_lowerCAmelCase )
for i in range(len(_lowerCAmelCase ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
_lowerCamelCase : Optional[Any] = lexicon[curr_string]
result += last_match_id
_lowerCamelCase : List[str] = last_match_id + "0"
if math.loga(_lowerCAmelCase ).is_integer():
_lowerCamelCase : Dict = {}
for curr_key in list(_lowerCAmelCase ):
_lowerCamelCase : Union[str, Any] = lexicon.pop(_lowerCAmelCase )
_lowerCamelCase : List[str] = new_lex
_lowerCamelCase : Union[str, Any] = last_match_id + "1"
index += 1
_lowerCamelCase : Union[str, Any] = ""
return result
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : str = 8
try:
with open(_lowerCAmelCase , "wb" ) as opened_file:
_lowerCamelCase : int = [
to_write[i : i + byte_length]
for i in range(0 , len(_lowerCAmelCase ) , _lowerCAmelCase )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append("10000000" )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(_lowerCAmelCase , 2 ).to_bytes(1 , byteorder="big" ) )
except OSError:
print("File not accessible" )
sys.exit()
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
_lowerCamelCase : Tuple = data_bits[counter:]
_lowerCamelCase : int = data_bits[counter + 1 :]
return data_bits
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Dict = read_file_binary(_lowerCAmelCase )
_lowerCamelCase : List[str] = remove_prefix(_lowerCAmelCase )
_lowerCamelCase : List[Any] = decompress_data(_lowerCAmelCase )
write_file_binary(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileBertForMaskedLM',
'MobileBertForMultipleChoice',
'MobileBertForNextSentencePrediction',
'MobileBertForPreTraining',
'MobileBertForQuestionAnswering',
'MobileBertForSequenceClassification',
'MobileBertForTokenClassification',
'MobileBertLayer',
'MobileBertModel',
'MobileBertPreTrainedModel',
'load_tf_weights_in_mobilebert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileBertForMaskedLM',
'TFMobileBertForMultipleChoice',
'TFMobileBertForNextSentencePrediction',
'TFMobileBertForPreTraining',
'TFMobileBertForQuestionAnswering',
'TFMobileBertForSequenceClassification',
'TFMobileBertForTokenClassification',
'TFMobileBertMainLayer',
'TFMobileBertModel',
'TFMobileBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : int = {
'configuration_owlvit': [
'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'OwlViTConfig',
'OwlViTOnnxConfig',
'OwlViTTextConfig',
'OwlViTVisionConfig',
],
'processing_owlvit': ['OwlViTProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Any = ['OwlViTFeatureExtractor']
UpperCAmelCase_ : Tuple = ['OwlViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : str = [
'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'OwlViTModel',
'OwlViTPreTrainedModel',
'OwlViTTextModel',
'OwlViTVisionModel',
'OwlViTForObjectDetection',
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
UpperCAmelCase_ : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase__ :
def __init__( self : Optional[Any],__A : list[tuple[float, float]] ):
_lowerCamelCase : Tuple = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
_lowerCamelCase : int = len(__A ) - 1
def lowerCamelCase_ ( self : Optional[int],__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree,__A ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__A ),5 ) == 1
return output_values
def lowerCamelCase_ ( self : int,__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : List[Any] = self.basis_function(__A )
_lowerCamelCase : str = 0.0
_lowerCamelCase : str = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowerCamelCase_ ( self : Optional[Any],__A : float = 0.01 ):
from matplotlib import pyplot as plt # type: ignore
_lowerCamelCase : list[float] = [] # x coordinates of points to plot
_lowerCamelCase : list[float] = [] # y coordinates of points to plot
_lowerCamelCase : Tuple = 0.0
while t <= 1:
_lowerCamelCase : str = self.bezier_curve_function(__A )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
_lowerCamelCase : List[str] = [i[0] for i in self.list_of_points]
_lowerCamelCase : Union[str, Any] = [i[1] for i in self.list_of_points]
plt.plot(
__A,__A,color="blue",label="Curve of Degree " + str(self.degree ),)
plt.scatter(__A,__A,color="red",label="Control Points" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 44 | 1 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'char'
lowerCAmelCase_ = 'bpe'
lowerCAmelCase_ = 'wp'
UpperCAmelCase_ : Optional[int] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['image_processor', 'char_tokenizer']
lowerCAmelCase_ = 'ViTImageProcessor'
lowerCAmelCase_ = 'MgpstrTokenizer'
def __init__( self : List[str],__A : Union[str, Any]=None,__A : Optional[Any]=None,**__A : int ):
_lowerCamelCase : Union[str, Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead.",__A,)
_lowerCamelCase : Dict = kwargs.pop("feature_extractor" )
_lowerCamelCase : str = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
_lowerCamelCase : str = tokenizer
_lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained("gpt2" )
_lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained("bert-base-uncased" )
super().__init__(__A,__A )
def __call__( self : Union[str, Any],__A : List[str]=None,__A : Optional[Any]=None,__A : str=None,**__A : Optional[int] ):
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
_lowerCamelCase : List[Any] = self.image_processor(__A,return_tensors=__A,**__A )
if text is not None:
_lowerCamelCase : int = self.char_tokenizer(__A,return_tensors=__A,**__A )
if text is None:
return inputs
elif images is None:
return encodings
else:
_lowerCamelCase : List[str] = encodings["input_ids"]
return inputs
def lowerCamelCase_ ( self : Tuple,__A : str ):
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Tuple = sequences
_lowerCamelCase : List[str] = char_preds.size(0 )
_lowerCamelCase , _lowerCamelCase : Any = self._decode_helper(__A,"char" )
_lowerCamelCase , _lowerCamelCase : Any = self._decode_helper(__A,"bpe" )
_lowerCamelCase , _lowerCamelCase : Optional[int] = self._decode_helper(__A,"wp" )
_lowerCamelCase : Tuple = []
_lowerCamelCase : str = []
for i in range(__A ):
_lowerCamelCase : str = [char_scores[i], bpe_scores[i], wp_scores[i]]
_lowerCamelCase : Any = [char_strs[i], bpe_strs[i], wp_strs[i]]
_lowerCamelCase : Dict = scores.index(max(__A ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
_lowerCamelCase : str = {}
_lowerCamelCase : str = final_strs
_lowerCamelCase : Any = final_scores
_lowerCamelCase : int = char_strs
_lowerCamelCase : Any = bpe_strs
_lowerCamelCase : Union[str, Any] = wp_strs
return out
def lowerCamelCase_ ( self : int,__A : Tuple,__A : Optional[Any] ):
if format == DecodeType.CHARACTER:
_lowerCamelCase : Tuple = self.char_decode
_lowerCamelCase : Tuple = 1
_lowerCamelCase : Optional[int] = "[s]"
elif format == DecodeType.BPE:
_lowerCamelCase : Dict = self.bpe_decode
_lowerCamelCase : Dict = 2
_lowerCamelCase : int = "#"
elif format == DecodeType.WORDPIECE:
_lowerCamelCase : str = self.wp_decode
_lowerCamelCase : str = 1_0_2
_lowerCamelCase : Optional[int] = "[SEP]"
else:
raise ValueError(f'Format {format} is not supported.' )
_lowerCamelCase , _lowerCamelCase : Dict = [], []
_lowerCamelCase : str = pred_logits.size(0 )
_lowerCamelCase : str = pred_logits.size(1 )
_lowerCamelCase , _lowerCamelCase : int = pred_logits.topk(1,dim=-1,largest=__A,sorted=__A )
_lowerCamelCase : str = preds_index.view(-1,__A )[:, 1:]
_lowerCamelCase : int = decoder(__A )
_lowerCamelCase , _lowerCamelCase : str = torch.nn.functional.softmax(__A,dim=2 ).max(dim=2 )
_lowerCamelCase : Dict = preds_max_prob[:, 1:]
for index in range(__A ):
_lowerCamelCase : List[Any] = preds_str[index].find(__A )
_lowerCamelCase : Union[str, Any] = preds_str[index][:pred_eos]
_lowerCamelCase : Optional[Any] = preds_index[index].cpu().tolist()
_lowerCamelCase : List[str] = pred_index.index(__A ) if eos_token in pred_index else -1
_lowerCamelCase : Tuple = preds_max_prob[index][: pred_eos_index + 1]
_lowerCamelCase : str = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(__A )
conf_scores.append(__A )
return dec_strs, conf_scores
def lowerCamelCase_ ( self : List[str],__A : List[Any] ):
_lowerCamelCase : str = [seq.replace(" ","" ) for seq in self.char_tokenizer.batch_decode(__A )]
return decode_strs
def lowerCamelCase_ ( self : Optional[Any],__A : str ):
return self.bpe_tokenizer.batch_decode(__A )
def lowerCamelCase_ ( self : Dict,__A : List[str] ):
_lowerCamelCase : List[Any] = [seq.replace(" ","" ) for seq in self.wp_tokenizer.batch_decode(__A )]
return decode_strs
| 44 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=A ):
lowerCAmelCase_ = ['transformers', 'torch', 'note_seq']
def __init__( self : str,*__A : List[str],**__A : List[Any] ):
requires_backends(self,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Optional[Any],*__A : str,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Dict,*__A : Dict,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
| 44 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = StableDiffusionPanoramaPipeline
lowerCAmelCase_ = TEXT_TO_IMAGE_PARAMS
lowerCAmelCase_ = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase_ = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase_ = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase_ ( self : Tuple ):
torch.manual_seed(0 )
_lowerCamelCase : Optional[Any] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4),layers_per_block=1,sample_size=3_2,in_channels=4,out_channels=4,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),cross_attention_dim=3_2,)
_lowerCamelCase : List[Any] = DDIMScheduler()
torch.manual_seed(0 )
_lowerCamelCase : Dict = AutoencoderKL(
block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],latent_channels=4,)
torch.manual_seed(0 )
_lowerCamelCase : List[Any] = CLIPTextConfig(
bos_token_id=0,eos_token_id=2,hidden_size=3_2,intermediate_size=3_7,layer_norm_eps=1e-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,)
_lowerCamelCase : Tuple = CLIPTextModel(__A )
_lowerCamelCase : Tuple = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_lowerCamelCase : Tuple = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def lowerCamelCase_ ( self : Optional[int],__A : Any,__A : Optional[int]=0 ):
_lowerCamelCase : Tuple = torch.manual_seed(__A )
_lowerCamelCase : Any = {
"prompt": "a photo of the dolomites",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Tuple = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Optional[int] = self.get_dummy_components()
_lowerCamelCase : str = StableDiffusionPanoramaPipeline(**__A )
_lowerCamelCase : List[str] = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Dict = self.get_dummy_inputs(__A )
_lowerCamelCase : Tuple = sd_pipe(**__A ).images
_lowerCamelCase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_lowerCamelCase : Tuple = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCamelCase_ ( self : int ):
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase_ ( self : Tuple ):
super().test_inference_batch_single_identical(batch_size=2,expected_max_diff=3.25e-3 )
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : Union[str, Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Tuple = self.get_dummy_components()
_lowerCamelCase : int = StableDiffusionPanoramaPipeline(**__A )
_lowerCamelCase : Union[str, Any] = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Any = self.get_dummy_inputs(__A )
_lowerCamelCase : Tuple = "french fries"
_lowerCamelCase : Tuple = sd_pipe(**__A,negative_prompt=__A )
_lowerCamelCase : Dict = output.images
_lowerCamelCase : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_lowerCamelCase : Union[str, Any] = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : Optional[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Optional[Any] = self.get_dummy_components()
_lowerCamelCase : Union[str, Any] = StableDiffusionPanoramaPipeline(**__A )
_lowerCamelCase : int = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Any = self.get_dummy_inputs(__A )
_lowerCamelCase : Any = sd_pipe(**__A,view_batch_size=2 )
_lowerCamelCase : Tuple = output.images
_lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_lowerCamelCase : List[Any] = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : int = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : List[Any] = self.get_dummy_components()
_lowerCamelCase : Optional[int] = EulerAncestralDiscreteScheduler(
beta_start=0.00085,beta_end=0.012,beta_schedule="scaled_linear" )
_lowerCamelCase : Any = StableDiffusionPanoramaPipeline(**__A )
_lowerCamelCase : Any = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Any = self.get_dummy_inputs(__A )
_lowerCamelCase : Union[str, Any] = sd_pipe(**__A ).images
_lowerCamelCase : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_lowerCamelCase : str = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : Union[str, Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Dict = self.get_dummy_components()
_lowerCamelCase : Optional[int] = PNDMScheduler(
beta_start=0.00085,beta_end=0.012,beta_schedule="scaled_linear",skip_prk_steps=__A )
_lowerCamelCase : Union[str, Any] = StableDiffusionPanoramaPipeline(**__A )
_lowerCamelCase : List[Any] = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Tuple = self.get_dummy_inputs(__A )
_lowerCamelCase : Dict = sd_pipe(**__A ).images
_lowerCamelCase : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_lowerCamelCase : List[Any] = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCase_ ( self : Optional[int] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self : List[str],__A : Dict=0 ):
_lowerCamelCase : List[Any] = torch.manual_seed(__A )
_lowerCamelCase : Union[str, Any] = {
"prompt": "a photo of the dolomites",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : List[str] = "stabilityai/stable-diffusion-2-base"
_lowerCamelCase : Any = DDIMScheduler.from_pretrained(__A,subfolder="scheduler" )
_lowerCamelCase : Tuple = StableDiffusionPanoramaPipeline.from_pretrained(__A,scheduler=__A,safety_checker=__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCamelCase : str = self.get_inputs()
_lowerCamelCase : int = pipe(**__A ).images
_lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 2_0_4_8, 3)
_lowerCamelCase : List[Any] = np.array(
[
0.36968392,
0.27025372,
0.32446766,
0.28379387,
0.36363274,
0.30733347,
0.27100027,
0.27054125,
0.25536096,
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-2
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Optional[Any] = StableDiffusionPanoramaPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base",safety_checker=__A )
_lowerCamelCase : Union[str, Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCamelCase : List[Any] = self.get_inputs()
_lowerCamelCase : Tuple = pipe(**__A ).images
_lowerCamelCase : List[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 2_0_4_8, 3)
_lowerCamelCase : List[Any] = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : Any = 0
def callback_fn(__A : int,__A : int,__A : torch.FloatTensor ) -> None:
_lowerCamelCase : str = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
_lowerCamelCase : int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 2_5_6)
_lowerCamelCase : Dict = latents[0, -3:, -3:, -1]
_lowerCamelCase : str = np.array(
[
0.18681869,
0.33907816,
0.5361276,
0.14432865,
-0.02856611,
-0.73941123,
0.23397987,
0.47322682,
-0.37823164,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
_lowerCamelCase : Optional[int] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 2_5_6)
_lowerCamelCase : Optional[int] = latents[0, -3:, -3:, -1]
_lowerCamelCase : Tuple = np.array(
[
0.18539645,
0.33987248,
0.5378559,
0.14437142,
-0.02455261,
-0.7338317,
0.23990755,
0.47356272,
-0.3786505,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
_lowerCamelCase : List[str] = False
_lowerCamelCase : Optional[int] = "stabilityai/stable-diffusion-2-base"
_lowerCamelCase : Optional[Any] = DDIMScheduler.from_pretrained(__A,subfolder="scheduler" )
_lowerCamelCase : Union[str, Any] = StableDiffusionPanoramaPipeline.from_pretrained(__A,scheduler=__A,safety_checker=__A )
_lowerCamelCase : Union[str, Any] = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCamelCase : int = self.get_inputs()
pipe(**__A,callback=__A,callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowerCamelCase_ ( self : Tuple ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowerCamelCase : int = "stabilityai/stable-diffusion-2-base"
_lowerCamelCase : Tuple = DDIMScheduler.from_pretrained(__A,subfolder="scheduler" )
_lowerCamelCase : List[Any] = StableDiffusionPanoramaPipeline.from_pretrained(__A,scheduler=__A,safety_checker=__A )
_lowerCamelCase : Optional[Any] = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_lowerCamelCase : Optional[Any] = self.get_inputs()
_lowerCamelCase : str = pipe(**__A )
_lowerCamelCase : Union[str, Any] = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 1_0**9
| 44 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = CodeGenTokenizer
lowerCAmelCase_ = CodeGenTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = {'add_prefix_space': True}
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : List[str] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_lowerCamelCase : Dict = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
_lowerCamelCase : Any = dict(zip(__A,range(len(__A ) ) ) )
_lowerCamelCase : Optional[int] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_lowerCamelCase : Tuple = {"unk_token": "<unk>"}
_lowerCamelCase : Optional[Any] = 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(__A ) + "\n" )
with open(self.merges_file,"w",encoding="utf-8" ) as fp:
fp.write("\n".join(__A ) )
def lowerCamelCase_ ( self : Dict,**__A : Tuple ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : Union[str, Any],**__A : int ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : str,__A : Dict ):
_lowerCamelCase : Optional[Any] = "lower newer"
_lowerCamelCase : Union[str, Any] = "lower newer"
return input_text, output_text
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : int = CodeGenTokenizer(self.vocab_file,self.merges_file,**self.special_tokens_map )
_lowerCamelCase : Any = "lower newer"
_lowerCamelCase : Optional[Any] = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
self.assertListEqual(__A,__A )
_lowerCamelCase : Union[str, Any] = tokens + [tokenizer.unk_token]
_lowerCamelCase : Dict = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Any ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase : str = self.get_tokenizer()
_lowerCamelCase : Optional[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = "lower newer"
# Testing tokenization
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
_lowerCamelCase : str = rust_tokenizer.tokenize(__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids without special tokens
_lowerCamelCase : str = tokenizer.encode(__A,add_special_tokens=__A,add_prefix_space=__A )
_lowerCamelCase : List[str] = rust_tokenizer.encode(__A,add_special_tokens=__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids with special tokens
_lowerCamelCase : List[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = tokenizer.encode(__A,add_prefix_space=__A )
_lowerCamelCase : Optional[int] = rust_tokenizer.encode(__A )
self.assertListEqual(__A,__A )
# Testing the unknown token
_lowerCamelCase : Optional[int] = tokens + [rust_tokenizer.unk_token]
_lowerCamelCase : Optional[Any] = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Tuple,*__A : Any,**__A : Any ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def lowerCamelCase_ ( self : int,__A : Optional[int]=1_5 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_lowerCamelCase : Tuple = self.rust_tokenizer_class.from_pretrained(__A,**__A )
# Simple input
_lowerCamelCase : Dict = "This is a simple input"
_lowerCamelCase : Any = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Tuple = ("This is a simple input", "This is a pair")
_lowerCamelCase : Tuple = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
# Pair input
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : str = CodeGenTokenizer.from_pretrained(self.tmpdirname,pad_token="<pad>" )
# Simple input
_lowerCamelCase : Tuple = "This is a simple input"
_lowerCamelCase : Dict = ["This is a simple input looooooooong", "This is a simple input"]
_lowerCamelCase : Dict = ("This is a simple input", "This is a pair")
_lowerCamelCase : Dict = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
_lowerCamelCase : Dict = tokenizer.pad_token_id
_lowerCamelCase : Dict = tokenizer(__A,padding="max_length",max_length=3_0,return_tensors="np" )
_lowerCamelCase : int = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
_lowerCamelCase : List[Any] = tokenizer(*__A,padding="max_length",max_length=6_0,return_tensors="np" )
_lowerCamelCase : Tuple = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1],3_0 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1],3_3 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1],6_0 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1],5_2 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : List[Any] = "$$$"
_lowerCamelCase : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname,bos_token=__A,add_bos_token=__A )
_lowerCamelCase : List[str] = "This is a simple input"
_lowerCamelCase : Optional[Any] = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Union[str, Any] = tokenizer.bos_token_id
_lowerCamelCase : Any = tokenizer(__A )
_lowerCamelCase : List[str] = tokenizer(__A )
self.assertEqual(out_s.input_ids[0],__A )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_lowerCamelCase : int = tokenizer.decode(out_s.input_ids )
_lowerCamelCase : str = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0],__A )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : int = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
_lowerCamelCase : Optional[Any] = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
_lowerCamelCase : Dict = "\nif len_a > len_b: result = a\nelse: result = b"
_lowerCamelCase : Any = tokenizer.encode(__A )
_lowerCamelCase : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
_lowerCamelCase : List[Any] = tokenizer.decode(__A,truncate_before_pattern=__A )
self.assertEqual(__A,__A )
def lowerCamelCase_ ( self : Any ):
pass
| 44 | 1 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase__ :
def __init__( self : str,__A : int,__A : Union[str, Any]=1_3,__A : Optional[int]=7,__A : Dict=True,__A : Dict=True,__A : Optional[int]=True,__A : List[str]=True,__A : int=True,__A : int=False,__A : List[Any]=False,__A : Union[str, Any]=False,__A : Union[str, Any]=2,__A : str=9_9,__A : List[str]=0,__A : Any=3_2,__A : Optional[Any]=5,__A : Union[str, Any]=4,__A : List[Any]=0.1,__A : Tuple=0.1,__A : Dict=5_1_2,__A : Optional[int]=2,__A : List[str]=0.02,__A : Tuple=2,__A : Optional[int]=4,__A : List[Any]="last",__A : Optional[Any]=True,__A : Any=None,__A : Optional[Any]=0,):
_lowerCamelCase : Any = parent
_lowerCamelCase : Union[str, Any] = batch_size
_lowerCamelCase : int = seq_length
_lowerCamelCase : Optional[int] = is_training
_lowerCamelCase : Any = use_input_lengths
_lowerCamelCase : Any = use_token_type_ids
_lowerCamelCase : Optional[Any] = use_labels
_lowerCamelCase : Union[str, Any] = gelu_activation
_lowerCamelCase : Optional[Any] = sinusoidal_embeddings
_lowerCamelCase : Optional[int] = causal
_lowerCamelCase : str = asm
_lowerCamelCase : Optional[int] = n_langs
_lowerCamelCase : List[Any] = vocab_size
_lowerCamelCase : Union[str, Any] = n_special
_lowerCamelCase : Any = hidden_size
_lowerCamelCase : Any = num_hidden_layers
_lowerCamelCase : Optional[int] = num_attention_heads
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : Dict = attention_probs_dropout_prob
_lowerCamelCase : Dict = max_position_embeddings
_lowerCamelCase : Tuple = type_sequence_label_size
_lowerCamelCase : Any = initializer_range
_lowerCamelCase : Tuple = num_labels
_lowerCamelCase : Any = num_choices
_lowerCamelCase : List[str] = summary_type
_lowerCamelCase : Union[str, Any] = use_proj
_lowerCamelCase : Dict = scope
_lowerCamelCase : List[Any] = bos_token_id
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length],self.vocab_size )
_lowerCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : Tuple = None
if self.use_input_lengths:
_lowerCamelCase : Optional[Any] = (
ids_tensor([self.batch_size],vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_lowerCamelCase : Union[str, Any] = None
if self.use_token_type_ids:
_lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length],self.n_langs )
_lowerCamelCase : Optional[int] = None
_lowerCamelCase : Dict = None
_lowerCamelCase : int = None
if self.use_labels:
_lowerCamelCase : str = ids_tensor([self.batch_size],self.type_sequence_label_size )
_lowerCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length],self.num_labels )
_lowerCamelCase : int = ids_tensor([self.batch_size],2 ).float()
_lowerCamelCase : Dict = ids_tensor([self.batch_size],self.num_choices )
_lowerCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def lowerCamelCase_ ( self : Union[str, Any] ):
return XLMConfig(
vocab_size=self.vocab_size,n_special=self.n_special,emb_dim=self.hidden_size,n_layers=self.num_hidden_layers,n_heads=self.num_attention_heads,dropout=self.hidden_dropout_prob,attention_dropout=self.attention_probs_dropout_prob,gelu_activation=self.gelu_activation,sinusoidal_embeddings=self.sinusoidal_embeddings,asm=self.asm,causal=self.causal,n_langs=self.n_langs,max_position_embeddings=self.max_position_embeddings,initializer_range=self.initializer_range,summary_type=self.summary_type,use_proj=self.use_proj,num_labels=self.num_labels,bos_token_id=self.bos_token_id,)
def lowerCamelCase_ ( self : Union[str, Any],__A : Optional[int],__A : Optional[int],__A : int,__A : List[Any],__A : List[str],__A : Optional[Any],__A : Optional[int],__A : Union[str, Any],__A : List[Any],):
_lowerCamelCase : List[Any] = XLMModel(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : int = model(__A,lengths=__A,langs=__A )
_lowerCamelCase : str = model(__A,langs=__A )
_lowerCamelCase : str = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self : Dict,__A : Optional[int],__A : int,__A : Optional[int],__A : Union[str, Any],__A : Union[str, Any],__A : int,__A : Optional[int],__A : int,__A : List[str],):
_lowerCamelCase : List[str] = XLMWithLMHeadModel(__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[Any] = 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 lowerCamelCase_ ( self : Optional[int],__A : int,__A : List[Any],__A : Optional[int],__A : Union[str, Any],__A : str,__A : Tuple,__A : List[Any],__A : str,__A : List[Any],):
_lowerCamelCase : List[str] = XLMForQuestionAnsweringSimple(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = model(__A )
_lowerCamelCase : str = model(__A,start_positions=__A,end_positions=__A )
_lowerCamelCase : Optional[int] = outputs
self.parent.assertEqual(result.start_logits.shape,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape,(self.batch_size, self.seq_length) )
def lowerCamelCase_ ( self : Tuple,__A : int,__A : Union[str, Any],__A : Optional[Any],__A : Optional[int],__A : Optional[Any],__A : Dict,__A : int,__A : Union[str, Any],__A : List[Any],):
_lowerCamelCase : List[str] = XLMForQuestionAnswering(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = model(__A )
_lowerCamelCase : Dict = model(
__A,start_positions=__A,end_positions=__A,cls_index=__A,is_impossible=__A,p_mask=__A,)
_lowerCamelCase : str = model(
__A,start_positions=__A,end_positions=__A,cls_index=__A,is_impossible=__A,)
((_lowerCamelCase) , ) : int = result_with_labels.to_tuple()
_lowerCamelCase : int = model(__A,start_positions=__A,end_positions=__A )
((_lowerCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape,() )
self.parent.assertEqual(result.start_top_log_probs.shape,(self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape,(self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape,(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape,(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape,(self.batch_size,) )
def lowerCamelCase_ ( self : List[Any],__A : Optional[int],__A : str,__A : int,__A : Any,__A : str,__A : str,__A : Tuple,__A : int,__A : List[str],):
_lowerCamelCase : Union[str, Any] = XLMForSequenceClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[Any] = model(__A )
_lowerCamelCase : Any = model(__A,labels=__A )
self.parent.assertEqual(result.loss.shape,() )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : List[Any],__A : Dict,__A : Union[str, Any],__A : List[str],__A : List[str],__A : Optional[int],__A : Optional[Any],__A : List[str],__A : str,__A : Optional[int],):
_lowerCamelCase : int = self.num_labels
_lowerCamelCase : List[Any] = XLMForTokenClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[int] = model(__A,attention_mask=__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase_ ( self : Optional[Any],__A : Dict,__A : Dict,__A : Union[str, Any],__A : Any,__A : int,__A : Tuple,__A : Any,__A : Union[str, Any],__A : List[Any],):
_lowerCamelCase : Optional[int] = self.num_choices
_lowerCamelCase : List[Any] = XLMForMultipleChoice(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
_lowerCamelCase : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
_lowerCamelCase : List[Any] = input_mask.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
_lowerCamelCase : str = model(
__A,attention_mask=__A,token_type_ids=__A,labels=__A,)
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices) )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : str = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) : Optional[int] = config_and_inputs
_lowerCamelCase : List[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A , A , A , unittest.TestCase ):
lowerCAmelCase_ = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowerCAmelCase_ = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def lowerCamelCase_ ( self : int,__A : Tuple,__A : Union[str, Any],__A : Optional[int],__A : Union[str, Any],__A : Any ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def lowerCamelCase_ ( self : List[Any],__A : List[Any],__A : Dict,__A : Union[str, Any]=False ):
_lowerCamelCase : List[str] = super()._prepare_for_class(__A,__A,return_labels=__A )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
_lowerCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size,dtype=torch.long,device=__A )
_lowerCamelCase : Dict = torch.zeros(
self.model_tester.batch_size,dtype=torch.long,device=__A )
return inputs_dict
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : int = XLMModelTester(self )
_lowerCamelCase : Optional[int] = ConfigTester(self,config_class=__A,emb_dim=3_7 )
def lowerCamelCase_ ( self : str ):
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__A )
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__A )
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__A )
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__A )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__A )
def lowerCamelCase_ ( self : str,__A : Optional[Any],__A : str,__A : List[str],__A : Tuple,__A : Optional[Any],__A : Optional[int]=False,__A : Optional[int]=1 ):
self.assertIsInstance(__A,__A )
self.assertListEqual(
[isinstance(__A,__A ) for iter_attentions in attentions],[True] * len(__A ) )
self.assertEqual(len(__A ),(max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__A ):
# adds PAD dummy token
_lowerCamelCase : Optional[Any] = min_length + idx + 1
_lowerCamelCase : int = min_length + idx + 1
_lowerCamelCase : Optional[int] = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions],[expected_shape] * len(__A ) )
def lowerCamelCase_ ( self : Optional[Any],__A : Union[str, Any],__A : Dict,__A : List[Any],__A : Dict,__A : List[str],__A : Union[str, Any]=False,__A : Dict=1 ):
self.assertIsInstance(__A,__A )
self.assertListEqual(
[isinstance(__A,__A ) for iter_hidden_states in hidden_states],[True] * len(__A ),)
self.assertEqual(len(__A ),(max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__A ):
# adds PAD dummy token
_lowerCamelCase : int = min_length + idx + 1
_lowerCamelCase : Any = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],[expected_shape] * len(__A ),)
pass
@slow
def lowerCamelCase_ ( self : Tuple ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = XLMModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : int = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
model.to(__A )
_lowerCamelCase : int = torch.tensor([[1_4, 4_4_7]],dtype=torch.long,device=__A ) # the president
_lowerCamelCase : Optional[Any] = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
_lowerCamelCase : Optional[Any] = model.generate(__A,do_sample=__A )
self.assertListEqual(output_ids[0].cpu().numpy().tolist(),__A )
| 44 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class UpperCAmelCase__ :
def __init__( self : Any,__A : int=2,__A : Any=3,__A : Optional[int]=6_4,__A : Tuple=None ):
_lowerCamelCase : int = np.random.default_rng(__A )
_lowerCamelCase : List[str] = length
_lowerCamelCase : Optional[Any] = rng.normal(size=(length,) ).astype(np.floataa )
_lowerCamelCase : Optional[int] = a * self.x + b + rng.normal(scale=0.1,size=(length,) ).astype(np.floataa )
def __len__( self : Dict ):
return self.length
def __getitem__( self : str,__A : List[str] ):
return {"x": self.x[i], "y": self.y[i]}
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : Optional[Any]=0,__A : Optional[int]=0,__A : Dict=False ):
super().__init__()
_lowerCamelCase : Tuple = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : List[str] = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : Optional[int] = True
def lowerCamelCase_ ( self : List[str],__A : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a[0] + self.b[0]
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : List[str]=0,__A : List[str]=0,__A : int=False ):
super().__init__()
_lowerCamelCase : Optional[int] = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Dict = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Tuple = True
def lowerCamelCase_ ( self : str,__A : List[Any]=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a + self.b
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
_lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("bert-base-cased" )
_lowerCamelCase : List[Any] = {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
_lowerCamelCase : int = load_dataset("csv" , data_files=_lowerCAmelCase )
_lowerCamelCase : Dict = datasets["train"].unique("label" )
_lowerCamelCase : Optional[Any] = {v: i for i, v in enumerate(_lowerCAmelCase )}
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Optional[int] = tokenizer(
examples["sentence1"] , examples["sentence2"] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase , padding="max_length" )
if "label" in examples:
_lowerCamelCase : str = [label_to_id[l] for l in examples["label"]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_lowerCamelCase : Optional[Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["sentence1", "sentence2", "label"] , )
def collate_fn(_lowerCAmelCase : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return tokenizer.pad(_lowerCAmelCase , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
_lowerCamelCase : str = DataLoader(tokenized_datasets["train"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=2 )
_lowerCamelCase : Optional[int] = DataLoader(tokenized_datasets["validation"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=1 )
return train_dataloader, eval_dataloader
| 44 | 1 |
'''simple docstring'''
# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
UpperCAmelCase_ : Optional[int] = {
'Acehnese Arabic': 'ace_Arab',
'Acehnese Latin': 'ace_Latn',
'Mesopotamian Arabic': 'acm_Arab',
'Ta\'izzi-Adeni Arabic': 'acq_Arab',
'Tunisian Arabic': 'aeb_Arab',
'Afrikaans': 'afr_Latn',
'South Levantine Arabic': 'ajp_Arab',
'Akan': 'aka_Latn',
'Amharic': 'amh_Ethi',
'North Levantine Arabic': 'apc_Arab',
'Modern Standard Arabic': 'arb_Arab',
'Modern Standard Arabic Romanized': 'arb_Latn',
'Najdi Arabic': 'ars_Arab',
'Moroccan Arabic': 'ary_Arab',
'Egyptian Arabic': 'arz_Arab',
'Assamese': 'asm_Beng',
'Asturian': 'ast_Latn',
'Awadhi': 'awa_Deva',
'Central Aymara': 'ayr_Latn',
'South Azerbaijani': 'azb_Arab',
'North Azerbaijani': 'azj_Latn',
'Bashkir': 'bak_Cyrl',
'Bambara': 'bam_Latn',
'Balinese': 'ban_Latn',
'Belarusian': 'bel_Cyrl',
'Bemba': 'bem_Latn',
'Bengali': 'ben_Beng',
'Bhojpuri': 'bho_Deva',
'Banjar Arabic': 'bjn_Arab',
'Banjar Latin': 'bjn_Latn',
'Standard Tibetan': 'bod_Tibt',
'Bosnian': 'bos_Latn',
'Buginese': 'bug_Latn',
'Bulgarian': 'bul_Cyrl',
'Catalan': 'cat_Latn',
'Cebuano': 'ceb_Latn',
'Czech': 'ces_Latn',
'Chokwe': 'cjk_Latn',
'Central Kurdish': 'ckb_Arab',
'Crimean Tatar': 'crh_Latn',
'Welsh': 'cym_Latn',
'Danish': 'dan_Latn',
'German': 'deu_Latn',
'Southwestern Dinka': 'dik_Latn',
'Dyula': 'dyu_Latn',
'Dzongkha': 'dzo_Tibt',
'Greek': 'ell_Grek',
'English': 'eng_Latn',
'Esperanto': 'epo_Latn',
'Estonian': 'est_Latn',
'Basque': 'eus_Latn',
'Ewe': 'ewe_Latn',
'Faroese': 'fao_Latn',
'Fijian': 'fij_Latn',
'Finnish': 'fin_Latn',
'Fon': 'fon_Latn',
'French': 'fra_Latn',
'Friulian': 'fur_Latn',
'Nigerian Fulfulde': 'fuv_Latn',
'Scottish Gaelic': 'gla_Latn',
'Irish': 'gle_Latn',
'Galician': 'glg_Latn',
'Guarani': 'grn_Latn',
'Gujarati': 'guj_Gujr',
'Haitian Creole': 'hat_Latn',
'Hausa': 'hau_Latn',
'Hebrew': 'heb_Hebr',
'Hindi': 'hin_Deva',
'Chhattisgarhi': 'hne_Deva',
'Croatian': 'hrv_Latn',
'Hungarian': 'hun_Latn',
'Armenian': 'hye_Armn',
'Igbo': 'ibo_Latn',
'Ilocano': 'ilo_Latn',
'Indonesian': 'ind_Latn',
'Icelandic': 'isl_Latn',
'Italian': 'ita_Latn',
'Javanese': 'jav_Latn',
'Japanese': 'jpn_Jpan',
'Kabyle': 'kab_Latn',
'Jingpho': 'kac_Latn',
'Kamba': 'kam_Latn',
'Kannada': 'kan_Knda',
'Kashmiri Arabic': 'kas_Arab',
'Kashmiri Devanagari': 'kas_Deva',
'Georgian': 'kat_Geor',
'Central Kanuri Arabic': 'knc_Arab',
'Central Kanuri Latin': 'knc_Latn',
'Kazakh': 'kaz_Cyrl',
'Kabiyè': 'kbp_Latn',
'Kabuverdianu': 'kea_Latn',
'Khmer': 'khm_Khmr',
'Kikuyu': 'kik_Latn',
'Kinyarwanda': 'kin_Latn',
'Kyrgyz': 'kir_Cyrl',
'Kimbundu': 'kmb_Latn',
'Northern Kurdish': 'kmr_Latn',
'Kikongo': 'kon_Latn',
'Korean': 'kor_Hang',
'Lao': 'lao_Laoo',
'Ligurian': 'lij_Latn',
'Limburgish': 'lim_Latn',
'Lingala': 'lin_Latn',
'Lithuanian': 'lit_Latn',
'Lombard': 'lmo_Latn',
'Latgalian': 'ltg_Latn',
'Luxembourgish': 'ltz_Latn',
'Luba-Kasai': 'lua_Latn',
'Ganda': 'lug_Latn',
'Luo': 'luo_Latn',
'Mizo': 'lus_Latn',
'Standard Latvian': 'lvs_Latn',
'Magahi': 'mag_Deva',
'Maithili': 'mai_Deva',
'Malayalam': 'mal_Mlym',
'Marathi': 'mar_Deva',
'Minangkabau Arabic ': 'min_Arab',
'Minangkabau Latin': 'min_Latn',
'Macedonian': 'mkd_Cyrl',
'Plateau Malagasy': 'plt_Latn',
'Maltese': 'mlt_Latn',
'Meitei Bengali': 'mni_Beng',
'Halh Mongolian': 'khk_Cyrl',
'Mossi': 'mos_Latn',
'Maori': 'mri_Latn',
'Burmese': 'mya_Mymr',
'Dutch': 'nld_Latn',
'Norwegian Nynorsk': 'nno_Latn',
'Norwegian Bokmål': 'nob_Latn',
'Nepali': 'npi_Deva',
'Northern Sotho': 'nso_Latn',
'Nuer': 'nus_Latn',
'Nyanja': 'nya_Latn',
'Occitan': 'oci_Latn',
'West Central Oromo': 'gaz_Latn',
'Odia': 'ory_Orya',
'Pangasinan': 'pag_Latn',
'Eastern Panjabi': 'pan_Guru',
'Papiamento': 'pap_Latn',
'Western Persian': 'pes_Arab',
'Polish': 'pol_Latn',
'Portuguese': 'por_Latn',
'Dari': 'prs_Arab',
'Southern Pashto': 'pbt_Arab',
'Ayacucho Quechua': 'quy_Latn',
'Romanian': 'ron_Latn',
'Rundi': 'run_Latn',
'Russian': 'rus_Cyrl',
'Sango': 'sag_Latn',
'Sanskrit': 'san_Deva',
'Santali': 'sat_Olck',
'Sicilian': 'scn_Latn',
'Shan': 'shn_Mymr',
'Sinhala': 'sin_Sinh',
'Slovak': 'slk_Latn',
'Slovenian': 'slv_Latn',
'Samoan': 'smo_Latn',
'Shona': 'sna_Latn',
'Sindhi': 'snd_Arab',
'Somali': 'som_Latn',
'Southern Sotho': 'sot_Latn',
'Spanish': 'spa_Latn',
'Tosk Albanian': 'als_Latn',
'Sardinian': 'srd_Latn',
'Serbian': 'srp_Cyrl',
'Swati': 'ssw_Latn',
'Sundanese': 'sun_Latn',
'Swedish': 'swe_Latn',
'Swahili': 'swh_Latn',
'Silesian': 'szl_Latn',
'Tamil': 'tam_Taml',
'Tatar': 'tat_Cyrl',
'Telugu': 'tel_Telu',
'Tajik': 'tgk_Cyrl',
'Tagalog': 'tgl_Latn',
'Thai': 'tha_Thai',
'Tigrinya': 'tir_Ethi',
'Tamasheq Latin': 'taq_Latn',
'Tamasheq Tifinagh': 'taq_Tfng',
'Tok Pisin': 'tpi_Latn',
'Tswana': 'tsn_Latn',
'Tsonga': 'tso_Latn',
'Turkmen': 'tuk_Latn',
'Tumbuka': 'tum_Latn',
'Turkish': 'tur_Latn',
'Twi': 'twi_Latn',
'Central Atlas Tamazight': 'tzm_Tfng',
'Uyghur': 'uig_Arab',
'Ukrainian': 'ukr_Cyrl',
'Umbundu': 'umb_Latn',
'Urdu': 'urd_Arab',
'Northern Uzbek': 'uzn_Latn',
'Venetian': 'vec_Latn',
'Vietnamese': 'vie_Latn',
'Waray': 'war_Latn',
'Wolof': 'wol_Latn',
'Xhosa': 'xho_Latn',
'Eastern Yiddish': 'ydd_Hebr',
'Yoruba': 'yor_Latn',
'Yue Chinese': 'yue_Hant',
'Chinese Simplified': 'zho_Hans',
'Chinese Traditional': 'zho_Hant',
'Standard Malay': 'zsm_Latn',
'Zulu': 'zul_Latn',
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'facebook/nllb-200-distilled-600M'
lowerCAmelCase_ = (
'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
)
lowerCAmelCase_ = 'translator'
lowerCAmelCase_ = AutoTokenizer
lowerCAmelCase_ = AutoModelForSeqaSeqLM
lowerCAmelCase_ = LANGUAGE_CODES
lowerCAmelCase_ = ['text', 'text', 'text']
lowerCAmelCase_ = ['text']
def lowerCamelCase_ ( self : Optional[int],__A : str,__A : Optional[Any],__A : int ):
if src_lang not in self.lang_to_code:
raise ValueError(f'{src_lang} is not a supported language.' )
if tgt_lang not in self.lang_to_code:
raise ValueError(f'{tgt_lang} is not a supported language.' )
_lowerCamelCase : List[str] = self.lang_to_code[src_lang]
_lowerCamelCase : str = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
__A,return_tensors="pt",src_lang=__A,tgt_lang=__A )
def lowerCamelCase_ ( self : Dict,__A : str ):
return self.model.generate(**__A )
def lowerCamelCase_ ( self : int,__A : Optional[Any] ):
return self.post_processor.decode(outputs[0].tolist(),skip_special_tokens=__A )
| 44 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Optional[Any] = False, False, False
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = None
lowerCAmelCase_ = True
lowerCAmelCase_ = True
lowerCAmelCase_ = None
# Automatically constructed
lowerCAmelCase_ = "dict"
lowerCAmelCase_ = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
lowerCAmelCase_ = field(default='Audio' , init=A , repr=A )
def __call__( self : Tuple ):
return self.pa_type
def lowerCamelCase_ ( self : Any,__A : Union[str, bytes, dict] ):
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
if isinstance(__A,__A ):
return {"bytes": None, "path": value}
elif isinstance(__A,__A ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
_lowerCamelCase : List[Any] = BytesIO()
sf.write(__A,value["array"],value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm" ):
# "PCM" only has raw audio bytes
if value.get("sampling_rate" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
if value.get("bytes" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
_lowerCamelCase : Dict = np.frombuffer(value["bytes"],dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
else:
_lowerCamelCase : str = np.memmap(value["path"],dtype="h",mode="r" ).astype(np.floataa ) / 3_2_7_6_7
_lowerCamelCase : Optional[int] = BytesIO(bytes() )
sf.write(__A,__A,value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' )
def lowerCamelCase_ ( self : Optional[Any],__A : dict,__A : Optional[Dict[str, Union[str, bool, None]]] = None ):
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )
_lowerCamelCase , _lowerCamelCase : Optional[Any] = (value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err
_lowerCamelCase : Tuple = xsplitext(__A )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
if file is None:
_lowerCamelCase : Tuple = token_per_repo_id or {}
_lowerCamelCase : Union[str, Any] = path.split("::" )[-1]
try:
_lowerCamelCase : str = string_to_dict(__A,config.HUB_DATASETS_URL )["repo_id"]
_lowerCamelCase : str = token_per_repo_id[repo_id]
except (ValueError, KeyError):
_lowerCamelCase : Any = None
with xopen(__A,"rb",use_auth_token=__A ) as f:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = sf.read(__A )
else:
_lowerCamelCase , _lowerCamelCase : str = sf.read(__A )
_lowerCamelCase : List[str] = array.T
if self.mono:
_lowerCamelCase : List[str] = librosa.to_mono(__A )
if self.sampling_rate and self.sampling_rate != sampling_rate:
_lowerCamelCase : List[str] = librosa.resample(__A,orig_sr=__A,target_sr=self.sampling_rate )
_lowerCamelCase : Optional[Any] = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def lowerCamelCase_ ( self : Any ):
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature." )
return {
"bytes": Value("binary" ),
"path": Value("string" ),
}
def lowerCamelCase_ ( self : List[str],__A : Union[pa.StringArray, pa.StructArray] ):
if pa.types.is_string(storage.type ):
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
_lowerCamelCase : int = pa.StructArray.from_arrays([bytes_array, storage],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_lowerCamelCase : Dict = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Any = pa.StructArray.from_arrays([storage, path_array],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
_lowerCamelCase : Tuple = pa.array([Audio().encode_example(__A ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
_lowerCamelCase : Tuple = storage.field("bytes" )
else:
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
_lowerCamelCase : List[str] = storage.field("path" )
else:
_lowerCamelCase : Tuple = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Tuple = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=storage.is_null() )
return array_cast(__A,self.pa_type )
def lowerCamelCase_ ( self : str,__A : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(__A : Dict ):
with xopen(__A,"rb" ) as f:
_lowerCamelCase : Any = f.read()
return bytes_
_lowerCamelCase : int = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
],type=pa.binary(),)
_lowerCamelCase : str = pa.array(
[os.path.basename(__A ) if path is not None else None for path in storage.field("path" ).to_pylist()],type=pa.string(),)
_lowerCamelCase : Dict = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=bytes_array.is_null() )
return array_cast(__A,self.pa_type )
| 44 | 1 |
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = range(2, 20 + 1)
UpperCAmelCase_ : str = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = sum(a_i[j] for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ) )
_lowerCamelCase : List[str] = sum(a_i[j] * base[j] for j in range(min(len(_lowerCAmelCase ) , _lowerCAmelCase ) ) )
_lowerCamelCase , _lowerCamelCase : int = 0, 0
_lowerCamelCase : Dict = n - i
_lowerCamelCase : int = memo.get(_lowerCAmelCase )
if sub_memo is not None:
_lowerCamelCase : List[str] = sub_memo.get(_lowerCAmelCase )
if jumps is not None and len(_lowerCAmelCase ) > 0:
# find and make the largest jump without going over
_lowerCamelCase : List[Any] = -1
for _k in range(len(_lowerCAmelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
_lowerCamelCase : Any = _k
break
if max_jump >= 0:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : str = jumps[max_jump]
# since the difference between jumps is cached, add c
_lowerCamelCase : str = diff + c
for j in range(min(_lowerCAmelCase , len(_lowerCAmelCase ) ) ):
_lowerCamelCase , _lowerCamelCase : List[Any] = divmod(_lowerCAmelCase , 10 )
if new_c > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_lowerCamelCase : int = []
else:
_lowerCamelCase : Tuple = {c: []}
_lowerCamelCase : Any = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_lowerCamelCase , _lowerCamelCase : Optional[int] = next_term(_lowerCAmelCase , k - 1 , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_lowerCamelCase , _lowerCamelCase : List[str] = compute(_lowerCAmelCase , _lowerCAmelCase , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
_lowerCamelCase : List[str] = sub_memo[c]
# keep jumps sorted by # of terms skipped
_lowerCamelCase : int = 0
while j < len(_lowerCAmelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_lowerCAmelCase , (diff, dn, k) )
return (diff, dn)
def A_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
if i >= n:
return 0, i
if k > len(_lowerCAmelCase ):
a_i.extend([0 for _ in range(k - len(_lowerCAmelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
_lowerCamelCase : List[str] = i
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Any = 0, 0, 0
for j in range(len(_lowerCAmelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
_lowerCamelCase : int = ds_c + ds_b
diff += addend
_lowerCamelCase : List[str] = 0
for j in range(_lowerCAmelCase ):
_lowerCamelCase : List[Any] = a_i[j] + addend
_lowerCamelCase , _lowerCamelCase : Any = divmod(_lowerCAmelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return diff, i - start_i
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : Tuple = digits[j] + addend
if s >= 10:
_lowerCamelCase , _lowerCamelCase : Optional[int] = divmod(_lowerCAmelCase , 10 )
_lowerCamelCase : Any = addend // 10 + quotient
else:
_lowerCamelCase : Tuple = s
_lowerCamelCase : List[Any] = addend // 10
if addend == 0:
break
while addend > 0:
_lowerCamelCase , _lowerCamelCase : str = divmod(_lowerCAmelCase , 10 )
digits.append(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : int = 10**15 ):
"""simple docstring"""
_lowerCamelCase : Tuple = [1]
_lowerCamelCase : List[Any] = 1
_lowerCamelCase : List[str] = 0
while True:
_lowerCamelCase , _lowerCamelCase : Dict = next_term(_lowerCAmelCase , 20 , i + dn , _lowerCAmelCase )
dn += terms_jumped
if dn == n - i:
break
_lowerCamelCase : Optional[Any] = 0
for j in range(len(_lowerCAmelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'glpn'
def __init__( self : Tuple,__A : Optional[int]=3,__A : Optional[int]=4,__A : str=[2, 2, 2, 2],__A : Union[str, Any]=[8, 4, 2, 1],__A : Tuple=[3_2, 6_4, 1_6_0, 2_5_6],__A : int=[7, 3, 3, 3],__A : str=[4, 2, 2, 2],__A : int=[1, 2, 5, 8],__A : List[Any]=[4, 4, 4, 4],__A : Optional[int]="gelu",__A : int=0.0,__A : Tuple=0.0,__A : Tuple=0.02,__A : Optional[int]=0.1,__A : Optional[int]=1e-6,__A : Optional[int]=6_4,__A : Optional[Any]=1_0,__A : Tuple=-1,**__A : List[str],):
super().__init__(**__A )
_lowerCamelCase : Tuple = num_channels
_lowerCamelCase : Union[str, Any] = num_encoder_blocks
_lowerCamelCase : Dict = depths
_lowerCamelCase : List[Any] = sr_ratios
_lowerCamelCase : str = hidden_sizes
_lowerCamelCase : Any = patch_sizes
_lowerCamelCase : Any = strides
_lowerCamelCase : Dict = mlp_ratios
_lowerCamelCase : int = num_attention_heads
_lowerCamelCase : List[Any] = hidden_act
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : Optional[int] = initializer_range
_lowerCamelCase : Union[str, Any] = drop_path_rate
_lowerCamelCase : str = layer_norm_eps
_lowerCamelCase : Tuple = decoder_hidden_size
_lowerCamelCase : int = max_depth
_lowerCamelCase : Dict = head_in_index
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
if len(_lowerCAmelCase ) < 2:
return collection
def circle_sort_util(_lowerCAmelCase : list , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> bool:
_lowerCamelCase : str = False
if low == high:
return swapped
_lowerCamelCase : str = low
_lowerCamelCase : Dict = high
while left < right:
if collection[left] > collection[right]:
_lowerCamelCase , _lowerCamelCase : Optional[int] = (
collection[right],
collection[left],
)
_lowerCamelCase : List[Any] = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
_lowerCamelCase , _lowerCamelCase : Any = (
collection[right + 1],
collection[left],
)
_lowerCamelCase : Optional[int] = True
_lowerCamelCase : List[Any] = low + int((high - low) / 2 )
_lowerCamelCase : List[Any] = circle_sort_util(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
_lowerCamelCase : Any = circle_sort_util(_lowerCAmelCase , mid + 1 , _lowerCAmelCase )
return swapped or left_swap or right_swap
_lowerCamelCase : str = True
while is_not_sorted is True:
_lowerCamelCase : str = circle_sort_util(_lowerCAmelCase , 0 , len(_lowerCAmelCase ) - 1 )
return collection
if __name__ == "__main__":
UpperCAmelCase_ : List[Any] = input('Enter numbers separated by a comma:\n').strip()
UpperCAmelCase_ : Tuple = [int(item) for item in user_input.split(',')]
print(circle_sort(unsorted))
| 44 |
'''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_ : str = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['input_features', 'attention_mask']
def __init__( self : Any,__A : List[Any]=8_0,__A : Dict=1_6_0_0_0,__A : Tuple=0.0,__A : Dict=1_0,__A : int=2_5,__A : Union[str, Any]="hamming_window",__A : List[str]=32768.0,__A : Union[str, Any]=0.97,__A : str=1.0,__A : Union[str, Any]=True,__A : Tuple=True,__A : Optional[Any]=False,**__A : Optional[Any],):
super().__init__(feature_size=__A,sampling_rate=__A,padding_value=__A,**__A )
_lowerCamelCase : Dict = feature_size
_lowerCamelCase : List[str] = sampling_rate
_lowerCamelCase : Any = padding_value
_lowerCamelCase : Dict = hop_length
_lowerCamelCase : Tuple = win_length
_lowerCamelCase : str = frame_signal_scale
_lowerCamelCase : List[str] = preemphasis_coeff
_lowerCamelCase : List[str] = mel_floor
_lowerCamelCase : str = normalize_means
_lowerCamelCase : Any = normalize_vars
_lowerCamelCase : List[str] = win_function
_lowerCamelCase : Tuple = return_attention_mask
_lowerCamelCase : List[Any] = win_length * sampling_rate // 1_0_0_0
_lowerCamelCase : List[Any] = hop_length * sampling_rate // 1_0_0_0
_lowerCamelCase : Any = optimal_fft_length(self.sample_size )
_lowerCamelCase : Dict = (self.n_fft // 2) + 1
def lowerCamelCase_ ( self : Any,__A : np.array ):
if self.win_function == "hamming_window":
_lowerCamelCase : Any = window_function(window_length=self.sample_size,name=self.win_function,periodic=__A )
else:
_lowerCamelCase : Optional[int] = window_function(window_length=self.sample_size,name=self.win_function )
_lowerCamelCase : int = 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 : List[str] = spectrogram(
one_waveform * self.frame_signal_scale,window=__A,frame_length=self.sample_size,hop_length=self.sample_stride,fft_length=self.n_fft,center=__A,preemphasis=self.preemphasis_coeff,mel_filters=__A,mel_floor=self.mel_floor,log_mel="log",)
return msfc_features.T
def lowerCamelCase_ ( self : Optional[int],__A : List[str],__A : Dict,__A : int ):
# make sure we normalize float32 arrays
if self.normalize_means:
_lowerCamelCase : Optional[Any] = x[:input_length].mean(axis=0 )
_lowerCamelCase : Optional[int] = np.subtract(__A,__A )
if self.normalize_vars:
_lowerCamelCase : int = x[:input_length].std(axis=0 )
_lowerCamelCase : Any = np.divide(__A,__A )
if input_length < x.shape[0]:
_lowerCamelCase : Tuple = padding_value
# make sure array is in float32
_lowerCamelCase : Optional[int] = x.astype(np.floataa )
return x
def lowerCamelCase_ ( self : Any,__A : List[np.ndarray],__A : Optional[np.ndarray] = None ):
_lowerCamelCase : Optional[int] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(__A,__A,self.padding_value ) for x, n in zip(__A,__A )]
def __call__( self : Optional[Any],__A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],__A : Union[bool, str, PaddingStrategy] = False,__A : Optional[int] = None,__A : bool = False,__A : Optional[int] = None,__A : Optional[bool] = None,__A : Optional[Union[str, TensorType]] = None,__A : Optional[int] = None,**__A : Optional[Any],):
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 : List[str] = isinstance(__A,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 : List[str] = is_batched_numpy or (
isinstance(__A,(list, tuple) ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list) ))
)
if is_batched:
_lowerCamelCase : List[Any] = [np.asarray(__A,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__A,np.ndarray ):
_lowerCamelCase : Dict = np.asarray(__A,dtype=np.floataa )
elif isinstance(__A,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowerCamelCase : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCamelCase : Tuple = [raw_speech]
# extract fbank features
_lowerCamelCase : str = [self._extract_mfsc_features(__A ) for one_waveform in raw_speech]
# convert into correct format for padding
_lowerCamelCase : Union[str, Any] = BatchFeature({"input_features": features} )
_lowerCamelCase : List[Any] = self.pad(
__A,padding=__A,max_length=__A,truncation=__A,pad_to_multiple_of=__A,return_attention_mask=__A,**__A,)
# make sure list is in array format
_lowerCamelCase : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0],__A ):
_lowerCamelCase : int = [np.asarray(__A,dtype=np.floataa ) for feature in input_features]
_lowerCamelCase : Dict = padded_inputs.get("attention_mask" )
if attention_mask is not None:
_lowerCamelCase : Dict = [np.asarray(__A,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
_lowerCamelCase : Dict = (
np.array(__A,dtype=np.intaa )
if self._get_padding_strategies(__A,max_length=__A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
_lowerCamelCase : Tuple = self.normalize(
padded_inputs["input_features"],attention_mask=__A )
if return_tensors is not None:
_lowerCamelCase : Dict = padded_inputs.convert_to_tensors(__A )
return padded_inputs
| 44 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def A_ ( _lowerCAmelCase : Callable[[int | float], int | float] , _lowerCAmelCase : int | float , _lowerCAmelCase : int | float , _lowerCAmelCase : int = 100 , ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = x_start
_lowerCamelCase : List[str] = fnc(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = 0.0
for _ in range(_lowerCAmelCase ):
# Approximates curve as a sequence of linear lines and sums their length
_lowerCamelCase : Optional[Any] = (x_end - x_start) / steps + xa
_lowerCamelCase : List[str] = fnc(_lowerCAmelCase )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
_lowerCamelCase : Any = xa
_lowerCamelCase : Union[str, Any] = fxa
return length
if __name__ == "__main__":
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
return math.sin(10 * x )
print('f(x) = sin(10 * x)')
print('The length of the curve from x = -10 to x = 10 is:')
UpperCAmelCase_ : Union[str, Any] = 10
while i <= 10_0000:
print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
UpperCAmelCase_ : int = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = torch.load(_lowerCAmelCase , map_location="cpu" )
return sd
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple=rename_keys_prefix ):
"""simple docstring"""
_lowerCamelCase : Any = OrderedDict()
_lowerCamelCase : str = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
_lowerCamelCase : Any = key
for name_pair in rename_keys_prefix:
_lowerCamelCase : Dict = new_key.replace(name_pair[0] , name_pair[1] )
_lowerCamelCase : Any = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
_lowerCamelCase : List[str] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Dict ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
_lowerCamelCase : Optional[int] = "pretraining"
if "vcr" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
_lowerCamelCase : int = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
_lowerCamelCase : Any = {"visual_embedding_dim": 512}
_lowerCamelCase : List[Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : Tuple = {"visual_embedding_dim": 2048}
_lowerCamelCase : Dict = "vqa_advanced"
elif "vqa" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 2048, "num_labels": 3129}
_lowerCamelCase : Optional[int] = "vqa"
elif "nlvr" in checkpoint_path:
_lowerCamelCase : Tuple = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
_lowerCamelCase : Optional[Any] = "nlvr"
_lowerCamelCase : str = VisualBertConfig(**_lowerCAmelCase )
# Load State Dict
_lowerCamelCase : str = load_state_dict(_lowerCAmelCase )
_lowerCamelCase : List[str] = get_new_dict(_lowerCAmelCase , _lowerCAmelCase )
if model_type == "pretraining":
_lowerCamelCase : List[Any] = VisualBertForPreTraining(_lowerCAmelCase )
elif model_type == "vqa":
_lowerCamelCase : Dict = VisualBertForQuestionAnswering(_lowerCAmelCase )
elif model_type == "nlvr":
_lowerCamelCase : Tuple = VisualBertForVisualReasoning(_lowerCAmelCase )
elif model_type == "multichoice":
_lowerCamelCase : str = VisualBertForMultipleChoice(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
# Save Checkpoints
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
UpperCAmelCase_ : Tuple = [
{'dataset': 'wikipedia', 'config_name': '20220301.de'},
{'dataset': 'wikipedia', 'config_name': '20220301.en'},
{'dataset': 'wikipedia', 'config_name': '20220301.fr'},
{'dataset': 'wikipedia', 'config_name': '20220301.frr'},
{'dataset': 'wikipedia', 'config_name': '20220301.it'},
{'dataset': 'wikipedia', 'config_name': '20220301.simple'},
{'dataset': 'snli', 'config_name': 'plain_text'},
{'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
{'dataset': 'wiki40b', 'config_name': 'en'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
{'dataset': 'natural_questions', 'config_name': 'default'},
]
def A_ ( _lowerCAmelCase : Optional[Any]=True ):
"""simple docstring"""
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=A ) )
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = None
lowerCAmelCase_ = None
def lowerCamelCase_ ( self : Union[str, Any],__A : str,__A : int ):
with TemporaryDirectory() as tmp_dir:
_lowerCamelCase : Union[str, Any] = dataset_module_factory(__A,cache_dir=__A )
_lowerCamelCase : Any = import_main_class(dataset_module.module_path,dataset=__A )
_lowerCamelCase : DatasetBuilder = builder_cls(
cache_dir=__A,config_name=__A,hash=dataset_module.hash,)
_lowerCamelCase : List[Any] = "/".join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=__A ).replace(os.sep,"/" ),
config.DATASET_INFO_FILENAME,
] )
_lowerCamelCase : Any = cached_path(__A,cache_dir=__A )
self.assertTrue(os.path.exists(__A ) )
@pytest.mark.integration
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : Tuple = tmp_path_factory.mktemp("test_hf_gcp" ) / "test_wikipedia_simple"
_lowerCamelCase : str = dataset_module_factory("wikipedia" , cache_dir=_lowerCAmelCase )
_lowerCamelCase : List[str] = import_main_class(dataset_module.module_path )
_lowerCamelCase : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name="20220301.frr" , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
_lowerCamelCase : List[str] = None
builder_instance.download_and_prepare()
_lowerCamelCase : str = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : List[Any] = dataset_module_factory("wikipedia" , cache_dir=_lowerCAmelCase )
_lowerCamelCase : Optional[int] = import_main_class(dataset_module.module_path , dataset=_lowerCAmelCase )
_lowerCamelCase : DatasetBuilder = builder_cls(
cache_dir=_lowerCAmelCase , config_name="20220301.frr" , hash=dataset_module.hash , )
_lowerCamelCase : str = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert "train" in ds
assert isinstance(ds["train"] , _lowerCAmelCase )
assert next(iter(ds["train"] ) )
| 44 |
'''simple docstring'''
import functools
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(_lowerCAmelCase ) != 3 or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(_lowerCAmelCase ) == 0:
return 0
if min(_lowerCAmelCase ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(_lowerCAmelCase ) >= 366:
raise ValueError("All days elements should be less than 366" )
_lowerCamelCase : Union[str, Any] = set(_lowerCAmelCase )
@functools.cache
def dynamic_programming(_lowerCAmelCase : int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 | 1 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
if not nums:
raise ValueError("List is empty" )
return sum(_lowerCAmelCase ) / len(_lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_lowerCamelCase : Dict = MaskFormerConfig(backbone_config=_lowerCAmelCase )
_lowerCamelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 847
_lowerCamelCase : str = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_lowerCamelCase : Optional[int] = 150
_lowerCamelCase : Union[str, Any] = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_lowerCamelCase : Union[str, Any] = 171
_lowerCamelCase : str = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_lowerCamelCase : Optional[int] = 133
_lowerCamelCase : Any = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_lowerCamelCase : str = 19
_lowerCamelCase : Tuple = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 65
_lowerCamelCase : Optional[int] = "mapillary-vistas-id2label.json"
_lowerCamelCase : Any = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : Optional[int] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
return config
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : Any = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.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.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight') )
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Tuple = dct.pop(_lowerCAmelCase )
_lowerCamelCase : str = val
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_lowerCamelCase : int = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_lowerCamelCase : Union[str, Any] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
_lowerCamelCase : List[str] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[int] = in_proj_weight[:dim, :]
_lowerCamelCase : Optional[int] = in_proj_bias[: dim]
_lowerCamelCase : List[str] = in_proj_weight[
dim : dim * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
dim : dim * 2
]
_lowerCamelCase : List[Any] = in_proj_weight[
-dim :, :
]
_lowerCamelCase : Union[str, Any] = in_proj_bias[-dim :]
# fmt: on
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : int = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Tuple = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[Any] = in_proj_weight[: hidden_size, :]
_lowerCamelCase : Optional[int] = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : Any = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Any = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
_lowerCamelCase : List[Any] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Tuple = in_proj_weight[: hidden_size, :]
_lowerCamelCase : str = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Optional[int] = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : int = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# fmt: on
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : List[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[Any] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : bool = False ):
"""simple docstring"""
_lowerCamelCase : Tuple = get_maskformer_config(_lowerCAmelCase )
# load original state_dict
with open(_lowerCAmelCase , "rb" ) as f:
_lowerCamelCase : List[Any] = pickle.load(_lowerCAmelCase )
_lowerCamelCase : Optional[Any] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_lowerCamelCase : List[Any] = create_rename_keys(_lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_swin_q_k_v(_lowerCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_lowerCAmelCase , _lowerCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_lowerCamelCase : Dict = torch.from_numpy(_lowerCAmelCase )
# load 🤗 model
_lowerCamelCase : int = MaskFormerForInstanceSegmentation(_lowerCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_lowerCAmelCase , param.shape )
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_lowerCAmelCase ) == 0, F'Unexpected keys: {unexpected_keys}'
# verify results
_lowerCamelCase : Any = prepare_img()
if "vistas" in model_name:
_lowerCamelCase : Any = 65
elif "cityscapes" in model_name:
_lowerCamelCase : Optional[Any] = 65535
else:
_lowerCamelCase : str = 255
_lowerCamelCase : List[str] = True if "ade" in model_name else False
_lowerCamelCase : Union[str, Any] = MaskFormerImageProcessor(ignore_index=_lowerCAmelCase , reduce_labels=_lowerCAmelCase )
_lowerCamelCase : int = image_processor(_lowerCAmelCase , return_tensors="pt" )
_lowerCamelCase : Tuple = model(**_lowerCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_lowerCamelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
image_processor.save_pretrained(_lowerCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F'nielsr/{model_name}' )
image_processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
UpperCAmelCase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='maskformer-swin-tiny-ade',
type=str,
help=('Name of the MaskFormer model you\'d like to convert',),
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
type=str,
help='Path to the original state dict (.pth file).',
)
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.'
)
UpperCAmelCase_ : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 44 | 1 |
'''simple docstring'''
UpperCAmelCase_ : Any = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def A_ ( _lowerCAmelCase : dict , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : Any = set()
# keep track of all the paths to be checked
_lowerCamelCase : Tuple = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
_lowerCamelCase : Tuple = queue.pop(0 )
# get the last node from the path
_lowerCamelCase : List[Any] = path[-1]
if node not in explored:
_lowerCamelCase : Optional[Any] = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
_lowerCamelCase : Any = list(_lowerCAmelCase )
new_path.append(_lowerCAmelCase )
queue.append(_lowerCAmelCase )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(_lowerCAmelCase )
# in case there's no path between the 2 nodes
return []
def A_ ( _lowerCAmelCase : dict , _lowerCAmelCase : Any , _lowerCAmelCase : int ):
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
_lowerCamelCase : Tuple = [start]
_lowerCamelCase : List[str] = set(_lowerCAmelCase )
# Keep tab on distances from `start` node.
_lowerCamelCase : Dict = {start: 0, target: -1}
while queue:
_lowerCamelCase : Any = queue.pop(0 )
if node == target:
_lowerCamelCase : Optional[Any] = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(_lowerCAmelCase )
queue.append(_lowerCAmelCase )
_lowerCamelCase : Dict = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, 'G', 'D')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, 'G', 'D')) # returns 4
| 44 |
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = range(2, 20 + 1)
UpperCAmelCase_ : str = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = sum(a_i[j] for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ) )
_lowerCamelCase : List[str] = sum(a_i[j] * base[j] for j in range(min(len(_lowerCAmelCase ) , _lowerCAmelCase ) ) )
_lowerCamelCase , _lowerCamelCase : int = 0, 0
_lowerCamelCase : Dict = n - i
_lowerCamelCase : int = memo.get(_lowerCAmelCase )
if sub_memo is not None:
_lowerCamelCase : List[str] = sub_memo.get(_lowerCAmelCase )
if jumps is not None and len(_lowerCAmelCase ) > 0:
# find and make the largest jump without going over
_lowerCamelCase : List[Any] = -1
for _k in range(len(_lowerCAmelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
_lowerCamelCase : Any = _k
break
if max_jump >= 0:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : str = jumps[max_jump]
# since the difference between jumps is cached, add c
_lowerCamelCase : str = diff + c
for j in range(min(_lowerCAmelCase , len(_lowerCAmelCase ) ) ):
_lowerCamelCase , _lowerCamelCase : List[Any] = divmod(_lowerCAmelCase , 10 )
if new_c > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_lowerCamelCase : int = []
else:
_lowerCamelCase : Tuple = {c: []}
_lowerCamelCase : Any = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_lowerCamelCase , _lowerCamelCase : Optional[int] = next_term(_lowerCAmelCase , k - 1 , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_lowerCamelCase , _lowerCamelCase : List[str] = compute(_lowerCAmelCase , _lowerCAmelCase , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
_lowerCamelCase : List[str] = sub_memo[c]
# keep jumps sorted by # of terms skipped
_lowerCamelCase : int = 0
while j < len(_lowerCAmelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_lowerCAmelCase , (diff, dn, k) )
return (diff, dn)
def A_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
if i >= n:
return 0, i
if k > len(_lowerCAmelCase ):
a_i.extend([0 for _ in range(k - len(_lowerCAmelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
_lowerCamelCase : List[str] = i
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Any = 0, 0, 0
for j in range(len(_lowerCAmelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
_lowerCamelCase : int = ds_c + ds_b
diff += addend
_lowerCamelCase : List[str] = 0
for j in range(_lowerCAmelCase ):
_lowerCamelCase : List[Any] = a_i[j] + addend
_lowerCamelCase , _lowerCamelCase : Any = divmod(_lowerCAmelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return diff, i - start_i
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : Tuple = digits[j] + addend
if s >= 10:
_lowerCamelCase , _lowerCamelCase : Optional[int] = divmod(_lowerCAmelCase , 10 )
_lowerCamelCase : Any = addend // 10 + quotient
else:
_lowerCamelCase : Tuple = s
_lowerCamelCase : List[Any] = addend // 10
if addend == 0:
break
while addend > 0:
_lowerCamelCase , _lowerCamelCase : str = divmod(_lowerCAmelCase , 10 )
digits.append(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : int = 10**15 ):
"""simple docstring"""
_lowerCamelCase : Tuple = [1]
_lowerCamelCase : List[Any] = 1
_lowerCamelCase : List[str] = 0
while True:
_lowerCamelCase , _lowerCamelCase : Dict = next_term(_lowerCAmelCase , 20 , i + dn , _lowerCAmelCase )
dn += terms_jumped
if dn == n - i:
break
_lowerCamelCase : Optional[Any] = 0
for j in range(len(_lowerCAmelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class UpperCAmelCase__ ( unittest.TestCase ):
lowerCAmelCase_ = inspect.getfile(accelerate.test_utils )
lowerCAmelCase_ = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_cli.py'] )
lowerCAmelCase_ = ['accelerate', 'launch']
lowerCAmelCase_ = Path.home() / '.cache/huggingface/accelerate'
lowerCAmelCase_ = 'default_config.yaml'
lowerCAmelCase_ = config_folder / config_file
lowerCAmelCase_ = config_folder / '_default_config.yaml'
lowerCAmelCase_ = Path('tests/test_configs' )
@classmethod
def lowerCamelCase_ ( cls : Dict ):
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase_ ( cls : str ):
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Tuple = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path],env=os.environ.copy() )
def lowerCamelCase_ ( self : Tuple ):
for config in sorted(self.test_config_path.glob("**/*.yaml" ) ):
with self.subTest(config_file=__A ):
execute_subprocess_async(
self.base_cmd + ["--config_file", str(__A ), self.test_file_path],env=os.environ.copy() )
def lowerCamelCase_ ( self : Dict ):
execute_subprocess_async(["accelerate", "test"],env=os.environ.copy() )
class UpperCAmelCase__ ( unittest.TestCase ):
lowerCAmelCase_ = 'test-tpu'
lowerCAmelCase_ = 'us-central1-a'
lowerCAmelCase_ = 'ls'
lowerCAmelCase_ = ['accelerate', 'tpu-config']
lowerCAmelCase_ = 'cd /usr/share'
lowerCAmelCase_ = 'tests/test_samples/test_command_file.sh'
lowerCAmelCase_ = 'Running gcloud compute tpus tpu-vm ssh'
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : List[Any] = run_command(
self.cmd
+ ["--command", self.command, "--tpu_zone", self.tpu_zone, "--tpu_name", self.tpu_name, "--debug"],return_stdout=__A,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all',__A,)
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : Dict = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command",
self.command,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
],return_stdout=__A,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all',__A,)
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : List[Any] = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--debug"],return_stdout=__A )
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all',__A,)
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : List[Any] = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--command", self.command, "--debug"],return_stdout=__A,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all',__A,)
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : List[Any] = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--command",
self.command,
"--command",
"echo \"Hello World\"",
"--debug",
],return_stdout=__A,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all',__A,)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Tuple = run_command(
self.cmd
+ ["--config_file", "tests/test_configs/latest.yaml", "--command_file", self.command_file, "--debug"],return_stdout=__A,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all',__A,)
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Optional[int] = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command_file",
self.command_file,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
],return_stdout=__A,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all',__A,)
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : Optional[Any] = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--install_accelerate", "--debug"],return_stdout=__A,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all',__A,)
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase : Union[str, Any] = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--install_accelerate",
"--accelerate_version",
"12.0.0",
"--debug",
],return_stdout=__A,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all',__A,)
| 44 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase_ : Any = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether tp freeze the encoder.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
lowerCAmelCase_ = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
lowerCAmelCase_ = field(
default=1024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Source language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Target language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': '# num_beams to use for evaluation.'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any ):
"""simple docstring"""
logger.info(F'***** {split} metrics *****' )
for key in sorted(metrics.keys() ):
logger.info(F' {key} = {metrics[key]}' )
save_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , F'{split}_results.json' ) )
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : str = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Optional[Any] = parser.parse_args_into_dataclasses()
check_output_dir(_lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , _lowerCAmelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCamelCase : Optional[int] = 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 , )
_lowerCamelCase : Tuple = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
assert hasattr(_lowerCAmelCase , _lowerCAmelCase ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
setattr(_lowerCAmelCase , _lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
_lowerCamelCase : List[Any] = 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 , )
_lowerCamelCase : int = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=_lowerCAmelCase , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(_lowerCAmelCase , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCamelCase : List[Any] = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(_lowerCAmelCase , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Any = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCamelCase : int = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(_lowerCAmelCase )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCamelCase : int = SeqaSeqDataset
# Get datasets
_lowerCamelCase : Tuple = (
dataset_class(
_lowerCAmelCase , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
_lowerCamelCase : List[Any] = (
dataset_class(
_lowerCAmelCase , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCamelCase : Optional[int] = (
dataset_class(
_lowerCAmelCase , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCamelCase : int = (
build_compute_metrics_fn(data_args.task , _lowerCAmelCase ) if training_args.predict_with_generate else None
)
_lowerCamelCase : List[Any] = SeqaSeqTrainer(
model=_lowerCAmelCase , args=_lowerCAmelCase , data_args=_lowerCAmelCase , train_dataset=_lowerCAmelCase , eval_dataset=_lowerCAmelCase , data_collator=SeqaSeqDataCollator(
_lowerCAmelCase , _lowerCAmelCase , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=_lowerCAmelCase , tokenizer=_lowerCAmelCase , )
_lowerCamelCase : Optional[Any] = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
_lowerCamelCase : Optional[Any] = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCamelCase : int = train_result.metrics
_lowerCamelCase : Optional[int] = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_lowerCamelCase : Optional[Any] = trainer.evaluate(metric_key_prefix="val" )
_lowerCamelCase : Dict = data_args.n_val
_lowerCamelCase : List[Any] = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.do_predict:
logger.info("*** Predict ***" )
_lowerCamelCase : Any = trainer.predict(test_dataset=_lowerCAmelCase , metric_key_prefix="test" )
_lowerCamelCase : Dict = test_output.metrics
_lowerCamelCase : Optional[int] = data_args.n_test
if trainer.is_world_process_zero():
_lowerCamelCase : int = round(metrics["test_loss"] , 4 )
handle_metrics("test" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.predict_with_generate:
_lowerCamelCase : List[str] = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
_lowerCamelCase : Any = lmap(str.strip , _lowerCAmelCase )
write_txt_file(_lowerCAmelCase , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(_lowerCAmelCase , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 44 | 1 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
UpperCAmelCase_ : Any = threading.Lock()
UpperCAmelCase_ : Optional[logging.Handler] = None
UpperCAmelCase_ : str = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
UpperCAmelCase_ : int = logging.WARNING
UpperCAmelCase_ : Optional[Any] = True
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = os.getenv("TRANSFORMERS_VERBOSITY" , _lowerCAmelCase )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F'Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '
F'has to be one of: { ", ".join(log_levels.keys() ) }' )
return _default_log_level
def A_ ( ):
"""simple docstring"""
return __name__.split("." )[0]
def A_ ( ):
"""simple docstring"""
return logging.getLogger(_get_library_name() )
def A_ ( ):
"""simple docstring"""
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
_lowerCamelCase : int = logging.StreamHandler() # Set sys.stderr as stream.
_lowerCamelCase : Tuple = sys.stderr.flush
# Apply our default configuration to the library root logger.
_lowerCamelCase : Optional[Any] = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
_lowerCamelCase : Dict = False
def A_ ( ):
"""simple docstring"""
global _default_handler
with _lock:
if not _default_handler:
return
_lowerCamelCase : Dict = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
_lowerCamelCase : int = None
def A_ ( ):
"""simple docstring"""
return log_levels
def A_ ( _lowerCAmelCase : Optional[str] = None ):
"""simple docstring"""
if name is None:
_lowerCamelCase : List[Any] = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_configure_library_root_logger()
_get_library_root_logger().setLevel(_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
return set_verbosity(_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
return set_verbosity(_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
return set_verbosity(_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
return set_verbosity(_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def A_ ( ):
"""simple docstring"""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def A_ ( _lowerCAmelCase : logging.Handler ):
"""simple docstring"""
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : logging.Handler ):
"""simple docstring"""
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
_configure_library_root_logger()
_lowerCamelCase : str = False
def A_ ( ):
"""simple docstring"""
_configure_library_root_logger()
_lowerCamelCase : int = True
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : List[Any] = _get_library_root_logger().handlers
for handler in handlers:
_lowerCamelCase : str = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s" )
handler.setFormatter(_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Dict = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(_lowerCAmelCase )
def A_ ( self : Optional[Any] , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS" , _lowerCAmelCase )
if no_advisory_warnings:
return
self.warning(*_lowerCAmelCase , **_lowerCAmelCase )
UpperCAmelCase_ : int = warning_advice
@functools.lru_cache(_lowerCAmelCase )
def A_ ( self : int , *_lowerCAmelCase : str , **_lowerCAmelCase : Tuple ):
"""simple docstring"""
self.warning(*_lowerCAmelCase , **_lowerCAmelCase )
UpperCAmelCase_ : Dict = warning_once
class UpperCAmelCase__ :
def __init__( self : Any,*__A : str,**__A : Tuple ): # pylint: disable=unused-argument
_lowerCamelCase : Any = args[0] if args else None
def __iter__( self : List[Any] ):
return iter(self._iterator )
def __getattr__( self : Any,__A : int ):
def empty_fn(*__A : int,**__A : str ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : Optional[int] ):
return self
def __exit__( self : List[Any],__A : List[str],__A : List[str],__A : List[str] ):
return
class UpperCAmelCase__ :
def __call__( self : List[Any],*__A : Dict,**__A : List[str] ):
if _tqdm_active:
return tqdm_lib.tqdm(*__A,**__A )
else:
return EmptyTqdm(*__A,**__A )
def lowerCamelCase_ ( self : Any,*__A : Tuple,**__A : List[Any] ):
_lowerCamelCase : str = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*__A,**__A )
def lowerCamelCase_ ( self : Any ):
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
UpperCAmelCase_ : str = _tqdm_cls()
def A_ ( ):
"""simple docstring"""
global _tqdm_active
return bool(_tqdm_active )
def A_ ( ):
"""simple docstring"""
global _tqdm_active
_lowerCamelCase : Any = True
hf_hub_utils.enable_progress_bars()
def A_ ( ):
"""simple docstring"""
global _tqdm_active
_lowerCamelCase : Any = False
hf_hub_utils.disable_progress_bars()
| 44 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase__ :
def __init__( self : List[Any],__A : str,__A : List[str]=1_3,__A : str=3_2,__A : Tuple=2,__A : Any=3,__A : Dict=1_6,__A : Dict=[3_2, 6_4, 1_2_8],__A : List[str]=[1, 2, 1],__A : str=[2, 2, 4],__A : Optional[int]=2,__A : Dict=2.0,__A : str=True,__A : Tuple=0.0,__A : int=0.0,__A : List[str]=0.1,__A : Any="gelu",__A : List[Any]=False,__A : Optional[Any]=True,__A : List[str]=0.02,__A : Tuple=1e-5,__A : Any=True,__A : Tuple=None,__A : Tuple=True,__A : Tuple=1_0,__A : List[Any]=8,__A : Optional[int]=["stage1", "stage2"],__A : int=[1, 2],):
_lowerCamelCase : List[Any] = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : Optional[int] = image_size
_lowerCamelCase : int = patch_size
_lowerCamelCase : Optional[Any] = num_channels
_lowerCamelCase : int = embed_dim
_lowerCamelCase : int = hidden_sizes
_lowerCamelCase : List[Any] = depths
_lowerCamelCase : Any = num_heads
_lowerCamelCase : List[str] = window_size
_lowerCamelCase : str = mlp_ratio
_lowerCamelCase : Any = qkv_bias
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : List[str] = drop_path_rate
_lowerCamelCase : str = hidden_act
_lowerCamelCase : Union[str, Any] = use_absolute_embeddings
_lowerCamelCase : List[Any] = patch_norm
_lowerCamelCase : Tuple = layer_norm_eps
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Optional[int] = is_training
_lowerCamelCase : Tuple = scope
_lowerCamelCase : List[Any] = use_labels
_lowerCamelCase : int = type_sequence_label_size
_lowerCamelCase : Tuple = encoder_stride
_lowerCamelCase : Any = out_features
_lowerCamelCase : Any = out_indices
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = None
if self.use_labels:
_lowerCamelCase : str = ids_tensor([self.batch_size],self.type_sequence_label_size )
_lowerCamelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Union[str, Any] ):
return FocalNetConfig(
image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,embed_dim=self.embed_dim,hidden_sizes=self.hidden_sizes,depths=self.depths,num_heads=self.num_heads,window_size=self.window_size,mlp_ratio=self.mlp_ratio,qkv_bias=self.qkv_bias,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,drop_path_rate=self.drop_path_rate,hidden_act=self.hidden_act,use_absolute_embeddings=self.use_absolute_embeddings,path_norm=self.patch_norm,layer_norm_eps=self.layer_norm_eps,initializer_range=self.initializer_range,encoder_stride=self.encoder_stride,out_features=self.out_features,out_indices=self.out_indices,)
def lowerCamelCase_ ( self : int,__A : Union[str, Any],__A : Tuple,__A : List[Any] ):
_lowerCamelCase : Optional[Any] = FocalNetModel(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[Any] = model(__A )
_lowerCamelCase : Optional[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_lowerCamelCase : Union[str, Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, expected_seq_len, expected_dim) )
def lowerCamelCase_ ( self : int,__A : Optional[int],__A : int,__A : Optional[int] ):
_lowerCamelCase : Any = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ),len(config.out_features ) )
self.parent.assertListEqual(model.channels,config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
_lowerCamelCase : List[str] = None
_lowerCamelCase : List[str] = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ),1 )
self.parent.assertListEqual(model.channels,[config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : Optional[int],__A : Optional[int],__A : Dict,__A : Dict ):
_lowerCamelCase : List[Any] = FocalNetForMaskedImageModeling(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
self.parent.assertEqual(
result.reconstruction.shape,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_lowerCamelCase : Dict = 1
_lowerCamelCase : Any = FocalNetForMaskedImageModeling(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : Optional[int] = model(__A )
self.parent.assertEqual(result.reconstruction.shape,(self.batch_size, 1, self.image_size, self.image_size) )
def lowerCamelCase_ ( self : List[Any],__A : Union[str, Any],__A : List[Any],__A : Optional[Any] ):
_lowerCamelCase : Union[str, Any] = self.type_sequence_label_size
_lowerCamelCase : Optional[Any] = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[int] = model(__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_lowerCamelCase : str = 1
_lowerCamelCase : str = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = model(__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : int = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Union[str, Any] = config_and_inputs
_lowerCamelCase : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[int] = FocalNetModelTester(self )
_lowerCamelCase : int = ConfigTester(self,config_class=__A,embed_dim=3_7,has_text_modality=__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
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 lowerCamelCase_ ( self : List[str] ):
return
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
@unittest.skip(reason="FocalNet does not use inputs_embeds" )
def lowerCamelCase_ ( self : Optional[int] ):
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking" )
def lowerCamelCase_ ( self : List[str] ):
pass
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase , _lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : str = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(),(nn.Module) )
_lowerCamelCase : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A,nn.Linear ) )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : Union[str, Any] = model_class(__A )
_lowerCamelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase : int = [*signature.parameters.keys()]
_lowerCamelCase : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1],__A )
def lowerCamelCase_ ( self : Tuple,__A : Any,__A : List[Any],__A : str,__A : Any ):
_lowerCamelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
_lowerCamelCase : Optional[int] = model(**self._prepare_for_class(__A,__A ) )
_lowerCamelCase : Optional[int] = outputs.hidden_states
_lowerCamelCase : int = getattr(
self.model_tester,"expected_num_hidden_layers",len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__A ),__A )
# FocalNet has a different seq_length
_lowerCamelCase : Optional[Any] = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : List[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
_lowerCamelCase : Any = outputs.reshaped_hidden_states
self.assertEqual(len(__A ),__A )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Tuple = reshaped_hidden_states[0].shape
_lowerCamelCase : List[str] = (
reshaped_hidden_states[0].view(__A,__A,height * width ).permute(0,2,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase , _lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase , _lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Tuple = 3
_lowerCamelCase : Optional[int] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
_lowerCamelCase : Tuple = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : Any = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_lowerCamelCase : int = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : Optional[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
@slow
def lowerCamelCase_ ( self : Tuple ):
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = FocalNetModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase , _lowerCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = _config_zero_init(__A )
for model_class in self.all_model_classes:
_lowerCamelCase : Any = model_class(config=__A )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),[0.0, 1.0],msg=f'Parameter {name} of model {model_class} seems not properly initialized',)
@require_vision
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : Union[str, Any] ):
# TODO update organization
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny" ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny" ).to(__A )
_lowerCamelCase : int = self.default_image_processor
_lowerCamelCase : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_lowerCamelCase : Dict = image_processor(images=__A,return_tensors="pt" ).to(__A )
# forward pass
with torch.no_grad():
_lowerCamelCase : Dict = model(**__A )
# verify the logits
_lowerCamelCase : List[Any] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape,__A )
_lowerCamelCase : List[str] = torch.tensor([0.2166, -0.4368, 0.2191] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3],__A,atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item(),2_8_1 )
@require_torch
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = (FocalNetBackbone,) if is_torch_available() else ()
lowerCAmelCase_ = FocalNetConfig
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : int = FocalNetModelTester(self )
| 44 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
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,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(_lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(_lowerCAmelCase ):
return [[videos]]
raise ValueError(F'Could not make batched video from {videos}' )
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['pixel_values']
def __init__( self : Dict,__A : bool = True,__A : Dict[str, int] = None,__A : PILImageResampling = PILImageResampling.BILINEAR,__A : bool = True,__A : Dict[str, int] = None,__A : bool = True,__A : Union[int, float] = 1 / 2_5_5,__A : bool = True,__A : bool = True,__A : Optional[Union[float, List[float]]] = None,__A : Optional[Union[float, List[float]]] = None,**__A : Optional[int],):
super().__init__(**__A )
_lowerCamelCase : Optional[int] = size if size is not None else {"shortest_edge": 2_5_6}
_lowerCamelCase : int = get_size_dict(__A,default_to_square=__A )
_lowerCamelCase : Optional[int] = crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
_lowerCamelCase : Tuple = get_size_dict(__A,param_name="crop_size" )
_lowerCamelCase : Tuple = do_resize
_lowerCamelCase : List[str] = size
_lowerCamelCase : List[str] = do_center_crop
_lowerCamelCase : Any = crop_size
_lowerCamelCase : int = resample
_lowerCamelCase : str = do_rescale
_lowerCamelCase : int = rescale_factor
_lowerCamelCase : Optional[int] = offset
_lowerCamelCase : Tuple = do_normalize
_lowerCamelCase : List[str] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCamelCase : List[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase_ ( self : Dict,__A : np.ndarray,__A : Dict[str, int],__A : PILImageResampling = PILImageResampling.BILINEAR,__A : Optional[Union[str, ChannelDimension]] = None,**__A : Dict,):
_lowerCamelCase : Any = get_size_dict(__A,default_to_square=__A )
if "shortest_edge" in size:
_lowerCamelCase : int = get_resize_output_image_size(__A,size["shortest_edge"],default_to_square=__A )
elif "height" in size and "width" in size:
_lowerCamelCase : str = (size["height"], size["width"])
else:
raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
return resize(__A,size=__A,resample=__A,data_format=__A,**__A )
def lowerCamelCase_ ( self : Tuple,__A : np.ndarray,__A : Dict[str, int],__A : Optional[Union[str, ChannelDimension]] = None,**__A : List[Any],):
_lowerCamelCase : Tuple = get_size_dict(__A )
if "height" not in size or "width" not in size:
raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}' )
return center_crop(__A,size=(size["height"], size["width"]),data_format=__A,**__A )
def lowerCamelCase_ ( self : List[Any],__A : np.ndarray,__A : Union[int, float],__A : bool = True,__A : Optional[Union[str, ChannelDimension]] = None,**__A : Tuple,):
_lowerCamelCase : List[str] = image.astype(np.floataa )
if offset:
_lowerCamelCase : Any = image - (scale / 2)
return rescale(__A,scale=__A,data_format=__A,**__A )
def lowerCamelCase_ ( self : Optional[Any],__A : np.ndarray,__A : Union[float, List[float]],__A : Union[float, List[float]],__A : Optional[Union[str, ChannelDimension]] = None,**__A : Optional[Any],):
return normalize(__A,mean=__A,std=__A,data_format=__A,**__A )
def lowerCamelCase_ ( self : Any,__A : ImageInput,__A : bool = None,__A : Dict[str, int] = None,__A : PILImageResampling = None,__A : bool = None,__A : Dict[str, int] = None,__A : bool = None,__A : float = None,__A : bool = None,__A : bool = None,__A : Optional[Union[float, List[float]]] = None,__A : Optional[Union[float, List[float]]] = None,__A : Optional[ChannelDimension] = ChannelDimension.FIRST,):
if do_resize and size is None or resample is None:
raise ValueError("Size and resample 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." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
_lowerCamelCase : Optional[int] = to_numpy_array(__A )
if do_resize:
_lowerCamelCase : Tuple = self.resize(image=__A,size=__A,resample=__A )
if do_center_crop:
_lowerCamelCase : Any = self.center_crop(__A,size=__A )
if do_rescale:
_lowerCamelCase : List[str] = self.rescale(image=__A,scale=__A,offset=__A )
if do_normalize:
_lowerCamelCase : Optional[Any] = self.normalize(image=__A,mean=__A,std=__A )
_lowerCamelCase : Dict = to_channel_dimension_format(__A,__A )
return image
def lowerCamelCase_ ( self : Dict,__A : ImageInput,__A : bool = None,__A : Dict[str, int] = None,__A : PILImageResampling = None,__A : bool = None,__A : Dict[str, int] = None,__A : bool = None,__A : float = None,__A : bool = None,__A : bool = None,__A : Optional[Union[float, List[float]]] = None,__A : Optional[Union[float, List[float]]] = None,__A : Optional[Union[str, TensorType]] = None,__A : ChannelDimension = ChannelDimension.FIRST,**__A : Optional[Any],):
_lowerCamelCase : List[str] = do_resize if do_resize is not None else self.do_resize
_lowerCamelCase : int = resample if resample is not None else self.resample
_lowerCamelCase : Optional[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
_lowerCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCamelCase : Union[str, Any] = offset if offset is not None else self.offset
_lowerCamelCase : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
_lowerCamelCase : Dict = image_mean if image_mean is not None else self.image_mean
_lowerCamelCase : Union[str, Any] = image_std if image_std is not None else self.image_std
_lowerCamelCase : Any = size if size is not None else self.size
_lowerCamelCase : Any = get_size_dict(__A,default_to_square=__A )
_lowerCamelCase : str = crop_size if crop_size is not None else self.crop_size
_lowerCamelCase : Optional[Any] = get_size_dict(__A,param_name="crop_size" )
if not valid_images(__A ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
_lowerCamelCase : str = make_batched(__A )
_lowerCamelCase : int = [
[
self._preprocess_image(
image=__A,do_resize=__A,size=__A,resample=__A,do_center_crop=__A,crop_size=__A,do_rescale=__A,rescale_factor=__A,offset=__A,do_normalize=__A,image_mean=__A,image_std=__A,data_format=__A,)
for img in video
]
for video in videos
]
_lowerCamelCase : Any = {"pixel_values": videos}
return BatchFeature(data=__A,tensor_type=__A )
| 44 |
'''simple docstring'''
class UpperCAmelCase__ :
def __init__( self : Any,__A : Any,__A : Any,__A : Any ):
_lowerCamelCase : List[Any] = name
_lowerCamelCase : Union[str, Any] = value
_lowerCamelCase : str = weight
def __repr__( self : Any ):
return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def lowerCamelCase_ ( self : Optional[int] ):
return self.value
def lowerCamelCase_ ( self : Any ):
return self.name
def lowerCamelCase_ ( self : List[Any] ):
return self.weight
def lowerCamelCase_ ( self : str ):
return self.value / self.weight
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = []
for i in range(len(_lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = sorted(_lowerCAmelCase , key=_lowerCAmelCase , reverse=_lowerCAmelCase )
_lowerCamelCase : Optional[int] = []
_lowerCamelCase , _lowerCamelCase : Optional[int] = 0.0, 0.0
for i in range(len(_lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def A_ ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 | 1 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_lowerCamelCase : Dict = MaskFormerConfig(backbone_config=_lowerCAmelCase )
_lowerCamelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 847
_lowerCamelCase : str = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_lowerCamelCase : Optional[int] = 150
_lowerCamelCase : Union[str, Any] = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_lowerCamelCase : Union[str, Any] = 171
_lowerCamelCase : str = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_lowerCamelCase : Optional[int] = 133
_lowerCamelCase : Any = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_lowerCamelCase : str = 19
_lowerCamelCase : Tuple = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 65
_lowerCamelCase : Optional[int] = "mapillary-vistas-id2label.json"
_lowerCamelCase : Any = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : Optional[int] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
return config
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : Any = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.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.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight') )
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Tuple = dct.pop(_lowerCAmelCase )
_lowerCamelCase : str = val
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_lowerCamelCase : int = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_lowerCamelCase : Union[str, Any] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
_lowerCamelCase : List[str] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[int] = in_proj_weight[:dim, :]
_lowerCamelCase : Optional[int] = in_proj_bias[: dim]
_lowerCamelCase : List[str] = in_proj_weight[
dim : dim * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
dim : dim * 2
]
_lowerCamelCase : List[Any] = in_proj_weight[
-dim :, :
]
_lowerCamelCase : Union[str, Any] = in_proj_bias[-dim :]
# fmt: on
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : int = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Tuple = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[Any] = in_proj_weight[: hidden_size, :]
_lowerCamelCase : Optional[int] = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : Any = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Any = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
_lowerCamelCase : List[Any] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Tuple = in_proj_weight[: hidden_size, :]
_lowerCamelCase : str = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Optional[int] = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : int = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# fmt: on
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : List[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[Any] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : bool = False ):
"""simple docstring"""
_lowerCamelCase : Tuple = get_maskformer_config(_lowerCAmelCase )
# load original state_dict
with open(_lowerCAmelCase , "rb" ) as f:
_lowerCamelCase : List[Any] = pickle.load(_lowerCAmelCase )
_lowerCamelCase : Optional[Any] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_lowerCamelCase : List[Any] = create_rename_keys(_lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_swin_q_k_v(_lowerCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_lowerCAmelCase , _lowerCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_lowerCamelCase : Dict = torch.from_numpy(_lowerCAmelCase )
# load 🤗 model
_lowerCamelCase : int = MaskFormerForInstanceSegmentation(_lowerCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_lowerCAmelCase , param.shape )
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_lowerCAmelCase ) == 0, F'Unexpected keys: {unexpected_keys}'
# verify results
_lowerCamelCase : Any = prepare_img()
if "vistas" in model_name:
_lowerCamelCase : Any = 65
elif "cityscapes" in model_name:
_lowerCamelCase : Optional[Any] = 65535
else:
_lowerCamelCase : str = 255
_lowerCamelCase : List[str] = True if "ade" in model_name else False
_lowerCamelCase : Union[str, Any] = MaskFormerImageProcessor(ignore_index=_lowerCAmelCase , reduce_labels=_lowerCAmelCase )
_lowerCamelCase : int = image_processor(_lowerCAmelCase , return_tensors="pt" )
_lowerCamelCase : Tuple = model(**_lowerCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_lowerCamelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
image_processor.save_pretrained(_lowerCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F'nielsr/{model_name}' )
image_processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
UpperCAmelCase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='maskformer-swin-tiny-ade',
type=str,
help=('Name of the MaskFormer model you\'d like to convert',),
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
type=str,
help='Path to the original state dict (.pth file).',
)
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.'
)
UpperCAmelCase_ : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : List[Any] = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = ['ConditionalDetrFeatureExtractor']
UpperCAmelCase_ : str = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : str = [
'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConditionalDetrForObjectDetection',
'ConditionalDetrForSegmentation',
'ConditionalDetrModel',
'ConditionalDetrPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 | 1 |
'''simple docstring'''
UpperCAmelCase_ : List[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
UpperCAmelCase_ : str = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
UpperCAmelCase_ : str = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
UpperCAmelCase_ : Optional[Any] = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
UpperCAmelCase_ : int = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
UpperCAmelCase_ : Optional[int] = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
UpperCAmelCase_ : Optional[Any] = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
UpperCAmelCase_ : List[str] = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 44 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Tuple = tmp_path / "file.csv"
_lowerCamelCase : Optional[int] = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Any = tmp_path / "malformed_file.csv"
_lowerCamelCase : Any = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20,\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : int = tmp_path / "csv_with_image.csv"
_lowerCamelCase : int = textwrap.dedent(
F'\\n image\n {image_file}\n ' )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Dict = tmp_path / "csv_with_label.csv"
_lowerCamelCase : int = textwrap.dedent(
"\\n label\n good\n bad\n good\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Dict = tmp_path / "csv_with_int_list.csv"
_lowerCamelCase : Any = textwrap.dedent(
"\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : List[Any] = Csv()
_lowerCamelCase : Any = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(_lowerCAmelCase , match="Error tokenizing data" ):
for _ in generator:
pass
assert any(
record.levelname == "ERROR"
and "Failed to read file" in record.message
and os.path.basename(_lowerCAmelCase ) in record.message
for record in caplog.records )
@require_pil
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
with open(_lowerCAmelCase , encoding="utf-8" ) as f:
_lowerCamelCase : Any = f.read().splitlines()[1]
_lowerCamelCase : Optional[Any] = Csv(encoding="utf-8" , features=Features({"image": Image()} ) )
_lowerCamelCase : Union[str, Any] = csv._generate_tables([[csv_file_with_image]] )
_lowerCamelCase : List[str] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("image" ).type == Image()()
_lowerCamelCase : int = pa_table.to_pydict()["image"]
assert generated_content == [{"path": image_file, "bytes": None}]
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
with open(_lowerCAmelCase , encoding="utf-8" ) as f:
_lowerCamelCase : List[Any] = f.read().splitlines()[1:]
_lowerCamelCase : int = Csv(encoding="utf-8" , features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
_lowerCamelCase : Tuple = csv._generate_tables([[csv_file_with_label]] )
_lowerCamelCase : int = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
_lowerCamelCase : Union[str, Any] = pa_table.to_pydict()["label"]
assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(_lowerCAmelCase ) for label in labels]
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Dict = Csv(encoding="utf-8" , sep="," , converters={"int_list": lambda _lowerCAmelCase : [int(_lowerCAmelCase ) for i in x.split()]} )
_lowerCamelCase : List[Any] = csv._generate_tables([[csv_file_with_int_list]] )
_lowerCamelCase : Optional[int] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
_lowerCamelCase : Optional[Any] = pa_table.to_pydict()["int_list"]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 44 | 1 |
'''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
UpperCAmelCase_ : List[str] = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class UpperCAmelCase__ :
lowerCAmelCase_ = PegasusConfig
lowerCAmelCase_ = {}
lowerCAmelCase_ = 'gelu'
def __init__( self : Optional[int],__A : Tuple,__A : Dict=1_3,__A : List[Any]=7,__A : int=True,__A : Optional[int]=False,__A : Any=9_9,__A : Any=3_2,__A : Any=5,__A : List[Any]=4,__A : Tuple=3_7,__A : int=0.1,__A : Optional[Any]=0.1,__A : int=2_0,__A : Optional[Any]=2,__A : List[Any]=1,__A : Optional[int]=0,):
_lowerCamelCase : List[str] = parent
_lowerCamelCase : str = batch_size
_lowerCamelCase : Dict = seq_length
_lowerCamelCase : Dict = is_training
_lowerCamelCase : Dict = use_labels
_lowerCamelCase : int = vocab_size
_lowerCamelCase : int = hidden_size
_lowerCamelCase : Tuple = num_hidden_layers
_lowerCamelCase : Any = num_attention_heads
_lowerCamelCase : List[str] = intermediate_size
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : int = attention_probs_dropout_prob
_lowerCamelCase : Union[str, Any] = max_position_embeddings
_lowerCamelCase : Tuple = eos_token_id
_lowerCamelCase : Optional[int] = pad_token_id
_lowerCamelCase : Union[str, Any] = bos_token_id
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase : int = ids_tensor([self.batch_size, self.seq_length - 1],self.vocab_size ).clip(3,self.vocab_size )
_lowerCamelCase : Optional[int] = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ),1 )
_lowerCamelCase : List[str] = np.concatenate([input_ids, eos_tensor],axis=1 )
_lowerCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length],self.vocab_size )
_lowerCamelCase : Any = 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,)
_lowerCamelCase : List[Any] = prepare_pegasus_inputs_dict(__A,__A,__A )
return config, inputs_dict
def lowerCamelCase_ ( self : str,__A : List[Any],__A : List[str],__A : Dict ):
_lowerCamelCase : Union[str, Any] = 2_0
_lowerCamelCase : List[str] = model_class_name(__A )
_lowerCamelCase : Optional[Any] = model.encode(inputs_dict["input_ids"] )
_lowerCamelCase , _lowerCamelCase : str = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
_lowerCamelCase : List[Any] = model.init_cache(decoder_input_ids.shape[0],__A,__A )
_lowerCamelCase : List[str] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length),dtype="i4" )
_lowerCamelCase : List[Any] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)
_lowerCamelCase : Union[str, Any] = model.decode(
decoder_input_ids[:, :-1],__A,decoder_attention_mask=__A,past_key_values=__A,decoder_position_ids=__A,)
_lowerCamelCase : Union[str, Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4" )
_lowerCamelCase : Union[str, Any] = model.decode(
decoder_input_ids[:, -1:],__A,decoder_attention_mask=__A,past_key_values=outputs_cache.past_key_values,decoder_position_ids=__A,)
_lowerCamelCase : List[Any] = model.decode(__A,__A )
_lowerCamelCase : List[Any] = 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 lowerCamelCase_ ( self : Any,__A : List[str],__A : Tuple,__A : Dict ):
_lowerCamelCase : str = 2_0
_lowerCamelCase : Union[str, Any] = model_class_name(__A )
_lowerCamelCase : Dict = model.encode(inputs_dict["input_ids"] )
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
_lowerCamelCase : Tuple = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
],axis=-1,)
_lowerCamelCase : Optional[int] = model.init_cache(decoder_input_ids.shape[0],__A,__A )
_lowerCamelCase : Tuple = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)
_lowerCamelCase : Tuple = model.decode(
decoder_input_ids[:, :-1],__A,decoder_attention_mask=__A,past_key_values=__A,decoder_position_ids=__A,)
_lowerCamelCase : Any = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4" )
_lowerCamelCase : int = model.decode(
decoder_input_ids[:, -1:],__A,past_key_values=outputs_cache.past_key_values,decoder_attention_mask=__A,decoder_position_ids=__A,)
_lowerCamelCase : Optional[Any] = model.decode(__A,__A,decoder_attention_mask=__A )
_lowerCamelCase : List[str] = 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_ ( _lowerCAmelCase : str , _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : int=None , _lowerCAmelCase : Tuple=None , ):
"""simple docstring"""
if attention_mask is None:
_lowerCamelCase : List[str] = np.not_equal(_lowerCAmelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_lowerCamelCase : List[Any] = 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 UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowerCAmelCase_ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowerCAmelCase_ = True
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : List[str] = FlaxPegasusModelTester(self )
_lowerCamelCase : Dict = ConfigTester(self,config_class=__A )
def lowerCamelCase_ ( self : Optional[int] ):
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : str ):
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__A,__A,__A )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase , _lowerCamelCase : str = 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(__A,__A,__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase , _lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCamelCase : Optional[int] = self._prepare_for_class(__A,__A )
_lowerCamelCase : Union[str, Any] = model_class(__A )
@jax.jit
def encode_jitted(__A : Union[str, Any],__A : Dict=None,**__A : Union[str, Any] ):
return model.encode(input_ids=__A,attention_mask=__A )
with self.subTest("JIT Enabled" ):
_lowerCamelCase : Dict = encode_jitted(**__A ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
_lowerCamelCase : List[str] = encode_jitted(**__A ).to_tuple()
self.assertEqual(len(__A ),len(__A ) )
for jitted_output, output in zip(__A,__A ):
self.assertEqual(jitted_output.shape,output.shape )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase , _lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCamelCase : Union[str, Any] = model_class(__A )
_lowerCamelCase : Optional[Any] = model.encode(inputs_dict["input_ids"],inputs_dict["attention_mask"] )
_lowerCamelCase : Optional[int] = {
"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(__A : Optional[int],__A : str,__A : Optional[Any] ):
return model.decode(
decoder_input_ids=__A,decoder_attention_mask=__A,encoder_outputs=__A,)
with self.subTest("JIT Enabled" ):
_lowerCamelCase : Any = decode_jitted(**__A ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
_lowerCamelCase : List[str] = decode_jitted(**__A ).to_tuple()
self.assertEqual(len(__A ),len(__A ) )
for jitted_output, output in zip(__A,__A ):
self.assertEqual(jitted_output.shape,output.shape )
@slow
def lowerCamelCase_ ( self : Dict ):
for model_class_name in self.all_model_classes:
_lowerCamelCase : str = model_class_name.from_pretrained("google/pegasus-large",from_pt=__A )
_lowerCamelCase : Union[str, Any] = np.ones((1, 1) )
_lowerCamelCase : Optional[Any] = model(__A )
self.assertIsNotNone(__A )
@slow
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase : str = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum" )
_lowerCamelCase : Dict = PegasusTokenizer.from_pretrained("google/pegasus-xsum" )
_lowerCamelCase : Any = [
" 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!\" ",
]
_lowerCamelCase : Dict = [
"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.",
]
_lowerCamelCase : Optional[Any] = tokenizer(__A,return_tensors="np",truncation=__A,max_length=5_1_2,padding=__A )
_lowerCamelCase : int = model.generate(**__A,num_beams=2 ).sequences
_lowerCamelCase : Union[str, Any] = tokenizer.batch_decode(__A,skip_special_tokens=__A )
assert tgt_text == decoded
| 44 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = IFInpaintingSuperResolutionPipeline
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
lowerCAmelCase_ = PipelineTesterMixin.required_optional_params - {'latents'}
def lowerCamelCase_ ( self : List[str] ):
return self._get_superresolution_dummy_components()
def lowerCamelCase_ ( self : str,__A : List[str],__A : List[str]=0 ):
if str(__A ).startswith("mps" ):
_lowerCamelCase : List[str] = torch.manual_seed(__A )
else:
_lowerCamelCase : Optional[int] = torch.Generator(device=__A ).manual_seed(__A )
_lowerCamelCase : List[Any] = floats_tensor((1, 3, 1_6, 1_6),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Any = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Tuple = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Dict = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),reason="XFormers attention is only available with CUDA and `xformers` installed",)
def lowerCamelCase_ ( self : Optional[int] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowerCamelCase_ ( self : Dict ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda",reason="float16 requires CUDA" )
def lowerCamelCase_ ( self : Optional[Any] ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowerCamelCase_ ( self : Any ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowerCamelCase_ ( self : Dict ):
self._test_save_load_local()
def lowerCamelCase_ ( self : Any ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,)
| 44 | 1 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'M-CLIP'
def __init__( self : List[Any],__A : Dict=1_0_2_4,__A : List[Any]=7_6_8,**__A : Optional[int] ):
_lowerCamelCase : Union[str, Any] = transformerDimSize
_lowerCamelCase : List[Any] = imageDimSize
super().__init__(**__A )
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = MCLIPConfig
def __init__( self : List[str],__A : Any,*__A : Optional[int],**__A : Dict ):
super().__init__(__A,*__A,**__A )
_lowerCamelCase : Optional[Any] = XLMRobertaModel(__A )
_lowerCamelCase : str = torch.nn.Linear(
in_features=config.transformerDimensions,out_features=config.numDims )
def lowerCamelCase_ ( self : str,__A : Dict,__A : Optional[Any] ):
_lowerCamelCase : Optional[Any] = self.transformer(input_ids=__A,attention_mask=__A )[0]
_lowerCamelCase : int = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(__A ), embs
| 44 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class UpperCAmelCase__ ( A ):
def __init__( self : List[Any],__A : Tuple,__A : Optional[int],__A : Optional[int]=1_0_2_4,__A : int=1_0_2_4,__A : Any=3.6 ):
_lowerCamelCase : List[str] = tokenizer
_lowerCamelCase : Dict = tokenizer.bos_token_id
_lowerCamelCase : Tuple = dataset
_lowerCamelCase : Any = seq_length
_lowerCamelCase : List[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self : Tuple ):
_lowerCamelCase : Union[str, Any] = iter(self.dataset )
_lowerCamelCase : str = True
while more_examples:
_lowerCamelCase , _lowerCamelCase : Optional[int] = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__A )["content"] )
buffer_len += len(buffer[-1] )
except StopIteration:
_lowerCamelCase : Tuple = False
break
_lowerCamelCase : int = tokenizer(__A,truncation=__A )["input_ids"]
_lowerCamelCase : int = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0,len(__A ),self.seq_length ):
_lowerCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(__A ) == self.seq_length:
yield torch.tensor(__A )
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = {"streaming": True}
_lowerCamelCase : Optional[Any] = load_dataset(args.dataset_name , split="train" , **_lowerCAmelCase )
_lowerCamelCase : int = ConstantLengthDataset(_lowerCAmelCase , _lowerCAmelCase , seq_length=args.seq_length )
_lowerCamelCase : Dict = DataLoader(_lowerCAmelCase , batch_size=args.batch_size )
return eval_dataloader
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
model.eval()
_lowerCamelCase : Optional[int] = []
for step, batch in enumerate(_lowerCAmelCase ):
with torch.no_grad():
_lowerCamelCase : List[str] = model(_lowerCAmelCase , labels=_lowerCAmelCase )
_lowerCamelCase : List[Any] = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_lowerCAmelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
_lowerCamelCase : Dict = torch.mean(torch.cat(_lowerCAmelCase ) )
try:
_lowerCamelCase : List[Any] = torch.exp(_lowerCAmelCase )
except OverflowError:
_lowerCamelCase : Optional[int] = float("inf" )
return loss.item(), perplexity.item()
# Setup Accelerator
UpperCAmelCase_ : List[str] = Accelerator()
# Parse configuration
UpperCAmelCase_ : Tuple = HfArgumentParser(EvaluationArguments)
UpperCAmelCase_ : Dict = parser.parse_args()
set_seed(args.seed)
# Logging
UpperCAmelCase_ : Optional[int] = logging.getLogger(__name__)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
# Load model and tokenizer
UpperCAmelCase_ : Tuple = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase_ : Dict = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
UpperCAmelCase_ : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
UpperCAmelCase_, UpperCAmelCase_ : Dict = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCAmelCase_, UpperCAmelCase_ : str = evaluate(args)
logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 44 | 1 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any]=None ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
_lowerCamelCase , _lowerCamelCase : Optional[Any] = True, True
_lowerCamelCase : str = dfs(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return path
def A_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Tuple = 0
_lowerCamelCase : List[Any] = -1
for i in range(_lowerCAmelCase ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
_lowerCamelCase : Dict = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : List[Any] = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
_lowerCamelCase , _lowerCamelCase : Dict = check_circuit_or_path(_lowerCAmelCase , _lowerCAmelCase )
if check == 3:
print("graph is not Eulerian" )
print("no path" )
return
_lowerCamelCase : str = 1
if check == 2:
_lowerCamelCase : int = odd_node
print("graph has a Euler path" )
if check == 1:
print("graph has a Euler cycle" )
_lowerCamelCase : List[Any] = dfs(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
print(_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
_lowerCamelCase : Union[str, Any] = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
_lowerCamelCase : Tuple = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
_lowerCamelCase : Dict = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
_lowerCamelCase : List[Any] = {
1: [],
2: []
# all degree is zero
}
_lowerCamelCase : Optional[Any] = 10
check_euler(_lowerCAmelCase , _lowerCAmelCase )
check_euler(_lowerCAmelCase , _lowerCAmelCase )
check_euler(_lowerCAmelCase , _lowerCAmelCase )
check_euler(_lowerCAmelCase , _lowerCAmelCase )
check_euler(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 44 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCAmelCase_ : Union[str, Any] = {
'vocab_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
},
'merges_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
},
'tokenizer_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
},
}
UpperCAmelCase_ : List[str] = {
'allenai/led-base-16384': 1_6384,
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = LEDTokenizer
lowerCAmelCase_ = ['input_ids', 'attention_mask']
def __init__( self : Union[str, Any],__A : List[Any]=None,__A : str=None,__A : str=None,__A : Optional[int]="replace",__A : Union[str, Any]="<s>",__A : Union[str, Any]="</s>",__A : Any="</s>",__A : Optional[int]="<s>",__A : List[str]="<unk>",__A : str="<pad>",__A : Tuple="<mask>",__A : Union[str, Any]=False,__A : Optional[int]=True,**__A : Optional[int],):
super().__init__(
__A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)
_lowerCamelCase : List[str] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : str = getattr(__A,pre_tok_state.pop("type" ) )
_lowerCamelCase : List[Any] = add_prefix_space
_lowerCamelCase : Tuple = pre_tok_class(**__A )
_lowerCamelCase : Optional[int] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
_lowerCamelCase : List[str] = "post_processor"
_lowerCamelCase : int = getattr(self.backend_tokenizer,__A,__A )
if tokenizer_component_instance:
_lowerCamelCase : Tuple = 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 : str = tuple(state["sep"] )
if "cls" in state:
_lowerCamelCase : List[str] = tuple(state["cls"] )
_lowerCamelCase : Dict = False
if state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : List[str] = add_prefix_space
_lowerCamelCase : List[Any] = True
if state.get("trim_offsets",__A ) != trim_offsets:
_lowerCamelCase : List[str] = trim_offsets
_lowerCamelCase : List[str] = True
if changes_to_apply:
_lowerCamelCase : Tuple = getattr(__A,state.pop("type" ) )
_lowerCamelCase : Any = component_class(**__A )
setattr(self.backend_tokenizer,__A,__A )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def lowerCamelCase_ ( self : str ):
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 lowerCamelCase_ ( self : List[str],__A : str ):
_lowerCamelCase : Optional[Any] = AddedToken(__A,lstrip=__A,rstrip=__A ) if isinstance(__A,__A ) else value
_lowerCamelCase : str = value
def lowerCamelCase_ ( self : List[str],*__A : List[Any],**__A : int ):
_lowerCamelCase : List[str] = kwargs.get("is_split_into_words",__A )
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(*__A,**__A )
def lowerCamelCase_ ( self : Optional[int],*__A : Optional[Any],**__A : Union[str, Any] ):
_lowerCamelCase : List[Any] = kwargs.get("is_split_into_words",__A )
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(*__A,**__A )
def lowerCamelCase_ ( self : Dict,__A : str,__A : Optional[str] = None ):
_lowerCamelCase : List[str] = self._tokenizer.model.save(__A,name=__A )
return tuple(__A )
def lowerCamelCase_ ( self : List[str],__A : Optional[Any],__A : List[str]=None ):
_lowerCamelCase : Optional[Any] = [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 lowerCamelCase_ ( self : Dict,__A : List[int],__A : Optional[List[int]] = None ):
_lowerCamelCase : Tuple = [self.sep_token_id]
_lowerCamelCase : Union[str, Any] = [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 lowerCamelCase_ ( self : Any,__A : Union[Dict[str, EncodedInput], BatchEncoding],__A : Optional[int] = None,__A : PaddingStrategy = PaddingStrategy.DO_NOT_PAD,__A : Optional[int] = None,__A : Optional[bool] = None,):
_lowerCamelCase : List[str] = super()._pad(
encoded_inputs=__A,max_length=__A,padding_strategy=__A,pad_to_multiple_of=__A,return_attention_mask=__A,)
# Load from model defaults
if return_attention_mask is None:
_lowerCamelCase : Any = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_lowerCamelCase : Union[str, Any] = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_lowerCamelCase : Optional[Any] = len(encoded_inputs["global_attention_mask"] ) != len(__A )
if needs_to_be_padded:
_lowerCamelCase : str = len(__A ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_lowerCamelCase : Tuple = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
_lowerCamelCase : int = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 44 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class UpperCAmelCase__ :
def __init__( self : int,__A : int ):
_lowerCamelCase : List[str] = value
_lowerCamelCase : Node | None = None
_lowerCamelCase : Node | None = None
class UpperCAmelCase__ :
def __init__( self : str,__A : Node ):
_lowerCamelCase : Union[str, Any] = tree
def lowerCamelCase_ ( self : int,__A : Node | None ):
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : Optional[Any] ):
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
'''simple docstring'''
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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int]=False ):
"""simple docstring"""
_lowerCamelCase : List[Any] = []
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') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
] )
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"
_lowerCamelCase : Optional[int] = [(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"),
] )
return rename_keys
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any]=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
_lowerCamelCase : int = ""
else:
_lowerCamelCase : int = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase : Any = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_lowerCamelCase : Tuple = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : List[str] = in_proj_weight[
: config.hidden_size, :
]
_lowerCamelCase : List[str] = in_proj_bias[: config.hidden_size]
_lowerCamelCase : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
_lowerCamelCase : List[str] = in_proj_bias[-config.hidden_size :]
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = dct.pop(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = val
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : str = ViTConfig()
_lowerCamelCase : List[str] = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Optional[Any] = int(vit_name[-12:-10] )
_lowerCamelCase : str = int(vit_name[-9:-6] )
else:
_lowerCamelCase : List[Any] = 1000
_lowerCamelCase : str = "huggingface/label-files"
_lowerCamelCase : Any = "imagenet-1k-id2label.json"
_lowerCamelCase : int = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : str = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : Optional[Any] = idalabel
_lowerCamelCase : List[str] = {v: k for k, v in idalabel.items()}
_lowerCamelCase : List[str] = int(vit_name[-6:-4] )
_lowerCamelCase : str = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("tiny" ):
_lowerCamelCase : List[Any] = 192
_lowerCamelCase : Optional[int] = 768
_lowerCamelCase : Union[str, Any] = 12
_lowerCamelCase : Optional[Any] = 3
elif vit_name[9:].startswith("small" ):
_lowerCamelCase : Optional[Any] = 384
_lowerCamelCase : Optional[Any] = 1536
_lowerCamelCase : int = 12
_lowerCamelCase : List[str] = 6
else:
pass
else:
if vit_name[4:].startswith("small" ):
_lowerCamelCase : List[str] = 768
_lowerCamelCase : Optional[Any] = 2304
_lowerCamelCase : List[Any] = 8
_lowerCamelCase : List[Any] = 8
elif vit_name[4:].startswith("base" ):
pass
elif vit_name[4:].startswith("large" ):
_lowerCamelCase : List[Any] = 1024
_lowerCamelCase : Optional[Any] = 4096
_lowerCamelCase : List[Any] = 24
_lowerCamelCase : Union[str, Any] = 16
elif vit_name[4:].startswith("huge" ):
_lowerCamelCase : str = 1280
_lowerCamelCase : List[Any] = 5120
_lowerCamelCase : List[str] = 32
_lowerCamelCase : List[str] = 16
# load original model from timm
_lowerCamelCase : int = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
_lowerCamelCase : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = create_rename_keys(_lowerCAmelCase , _lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# load HuggingFace model
if vit_name[-5:] == "in21k":
_lowerCamelCase : int = ViTModel(_lowerCAmelCase ).eval()
else:
_lowerCamelCase : List[str] = ViTForImageClassification(_lowerCAmelCase ).eval()
model.load_state_dict(_lowerCAmelCase )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
_lowerCamelCase : Union[str, Any] = DeiTImageProcessor(size=config.image_size )
else:
_lowerCamelCase : Union[str, Any] = ViTImageProcessor(size=config.image_size )
_lowerCamelCase : Optional[int] = image_processor(images=prepare_img() , return_tensors="pt" )
_lowerCamelCase : Optional[int] = encoding["pixel_values"]
_lowerCamelCase : Union[str, Any] = model(_lowerCAmelCase )
if base_model:
_lowerCamelCase : int = timm_model.forward_features(_lowerCAmelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_lowerCAmelCase , outputs.pooler_output , atol=1E-3 )
else:
_lowerCamelCase : Union[str, Any] = timm_model(_lowerCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCAmelCase , outputs.logits , atol=1E-3 )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_lowerCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the 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.'
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : Union[str, Any]=None ):
"""simple docstring"""
return field(default_factory=lambda: default , metadata=_lowerCAmelCase )
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = 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'
)
} , )
lowerCAmelCase_ = list_field(
default=[8] , metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'} )
lowerCAmelCase_ = list_field(
default=[8, 32, 128, 512] , metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'} , )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'} , )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'} , )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Use FP16 to accelerate inference.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Benchmark training of model'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Verbose memory tracing'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'} , )
lowerCAmelCase_ = field(
default=A , metadata={
'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
} , )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Trace memory line by line'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Save result to a CSV file'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Save all print statements in a log file'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether to print environment information'} )
lowerCAmelCase_ = field(
default=A , 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.'
)
} , )
lowerCAmelCase_ = field(
default=F'''inference_time_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving time results to csv.'} , )
lowerCAmelCase_ = field(
default=F'''inference_memory_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving memory results to csv.'} , )
lowerCAmelCase_ = field(
default=F'''train_time_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving time results to csv for training.'} , )
lowerCAmelCase_ = field(
default=F'''train_memory_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving memory results to csv for training.'} , )
lowerCAmelCase_ = field(
default=F'''env_info_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving environment information.'} , )
lowerCAmelCase_ = field(
default=F'''log_{round(time() )}.csv''' , metadata={'help': 'Log filename used if print statements are saved in log.'} , )
lowerCAmelCase_ = field(default=3 , metadata={'help': 'Times an experiment will be run.'} )
lowerCAmelCase_ = field(
default=A , metadata={
'help': (
'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
' model weights.'
)
} , )
def lowerCamelCase_ ( self : Dict ):
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.",__A,)
def lowerCamelCase_ ( self : Union[str, Any] ):
return json.dumps(dataclasses.asdict(self ),indent=2 )
@property
def lowerCamelCase_ ( self : Tuple ):
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 lowerCamelCase_ ( self : List[str] ):
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
| 44 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( _lowerCAmelCase : int = 5000 ):
"""simple docstring"""
_lowerCamelCase : Dict = [(i * (3 * i - 1)) // 2 for i in range(1 , _lowerCAmelCase )]
for i, pentagonal_i in enumerate(_lowerCAmelCase ):
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : List[Any] = pentagonal_nums[j]
_lowerCamelCase : Any = pentagonal_i + pentagonal_j
_lowerCamelCase : Union[str, Any] = pentagonal_j - pentagonal_i
if is_pentagonal(_lowerCAmelCase ) and is_pentagonal(_lowerCAmelCase ):
return b
return -1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 | 1 |
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['image_processor', 'tokenizer']
lowerCAmelCase_ = 'AutoImageProcessor'
lowerCAmelCase_ = 'AutoTokenizer'
def __init__( self : str,__A : List[str]=None,__A : Optional[Any]=None,**__A : str ):
_lowerCamelCase : Optional[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead.",__A,)
_lowerCamelCase : Optional[int] = kwargs.pop("feature_extractor" )
_lowerCamelCase : List[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__A,__A )
_lowerCamelCase : Any = self.image_processor
_lowerCamelCase : str = False
def __call__( self : List[Any],*__A : Tuple,**__A : Optional[int] ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__A,**__A )
_lowerCamelCase : List[str] = kwargs.pop("images",__A )
_lowerCamelCase : Any = kwargs.pop("text",__A )
if len(__A ) > 0:
_lowerCamelCase : Dict = args[0]
_lowerCamelCase : Tuple = args[1:]
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
_lowerCamelCase : str = self.image_processor(__A,*__A,**__A )
if text is not None:
_lowerCamelCase : Optional[int] = self.tokenizer(__A,**__A )
if text is None:
return inputs
elif images is None:
return encodings
else:
_lowerCamelCase : Union[str, Any] = encodings["input_ids"]
return inputs
def lowerCamelCase_ ( self : int,*__A : List[str],**__A : Any ):
return self.tokenizer.batch_decode(*__A,**__A )
def lowerCamelCase_ ( self : Dict,*__A : List[Any],**__A : List[str] ):
return self.tokenizer.decode(*__A,**__A )
@contextmanager
def lowerCamelCase_ ( self : Optional[Any] ):
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your images inputs, or in a separate call." )
_lowerCamelCase : Tuple = True
_lowerCamelCase : Optional[int] = self.tokenizer
yield
_lowerCamelCase : Any = self.image_processor
_lowerCamelCase : List[str] = False
def lowerCamelCase_ ( self : List[Any],__A : str,__A : int=False,__A : int=None ):
if added_vocab is None:
_lowerCamelCase : List[Any] = self.tokenizer.get_added_vocab()
_lowerCamelCase : str = {}
while tokens:
_lowerCamelCase : Tuple = re.search(r"<s_(.*?)>",__A,re.IGNORECASE )
if start_token is None:
break
_lowerCamelCase : Any = start_token.group(1 )
_lowerCamelCase : Optional[Any] = re.search(rf'</s_{key}>',__A,re.IGNORECASE )
_lowerCamelCase : int = start_token.group()
if end_token is None:
_lowerCamelCase : Any = tokens.replace(__A,"" )
else:
_lowerCamelCase : Dict = end_token.group()
_lowerCamelCase : Union[str, Any] = re.escape(__A )
_lowerCamelCase : Union[str, Any] = re.escape(__A )
_lowerCamelCase : Union[str, Any] = re.search(f'{start_token_escaped}(.*?){end_token_escaped}',__A,re.IGNORECASE )
if content is not None:
_lowerCamelCase : str = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
_lowerCamelCase : Union[str, Any] = self.tokenajson(__A,is_inner_value=__A,added_vocab=__A )
if value:
if len(__A ) == 1:
_lowerCamelCase : Optional[Any] = value[0]
_lowerCamelCase : Dict = value
else: # leaf nodes
_lowerCamelCase : Tuple = []
for leaf in content.split(r"<sep/>" ):
_lowerCamelCase : List[Any] = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
_lowerCamelCase : int = leaf[1:-2] # for categorical special tokens
output[key].append(__A )
if len(output[key] ) == 1:
_lowerCamelCase : Any = output[key][0]
_lowerCamelCase : List[Any] = tokens[tokens.find(__A ) + len(__A ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:],is_inner_value=__A,added_vocab=__A )
if len(__A ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def lowerCamelCase_ ( self : Any ):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",__A,)
return self.image_processor_class
@property
def lowerCamelCase_ ( self : Dict ):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",__A,)
return self.image_processor
| 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileBertForMaskedLM',
'MobileBertForMultipleChoice',
'MobileBertForNextSentencePrediction',
'MobileBertForPreTraining',
'MobileBertForQuestionAnswering',
'MobileBertForSequenceClassification',
'MobileBertForTokenClassification',
'MobileBertLayer',
'MobileBertModel',
'MobileBertPreTrainedModel',
'load_tf_weights_in_mobilebert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileBertForMaskedLM',
'TFMobileBertForMultipleChoice',
'TFMobileBertForNextSentencePrediction',
'TFMobileBertForPreTraining',
'TFMobileBertForQuestionAnswering',
'TFMobileBertForSequenceClassification',
'TFMobileBertForTokenClassification',
'TFMobileBertMainLayer',
'TFMobileBertModel',
'TFMobileBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
UpperCAmelCase_ : List[str] = input('Enter image url: ').strip()
print(f'''Downloading image from {url} ...''')
UpperCAmelCase_ : int = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
UpperCAmelCase_ : Any = soup.find('meta', {'property': 'og:image'})['content']
UpperCAmelCase_ : List[str] = requests.get(image_url).content
UpperCAmelCase_ : Tuple = f'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(f'''Done. Image saved to disk as {file_name}.''')
| 44 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase__ :
def __init__( self : Optional[Any],__A : list[tuple[float, float]] ):
_lowerCamelCase : Tuple = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
_lowerCamelCase : int = len(__A ) - 1
def lowerCamelCase_ ( self : Optional[int],__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree,__A ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__A ),5 ) == 1
return output_values
def lowerCamelCase_ ( self : int,__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : List[Any] = self.basis_function(__A )
_lowerCamelCase : str = 0.0
_lowerCamelCase : str = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowerCamelCase_ ( self : Optional[Any],__A : float = 0.01 ):
from matplotlib import pyplot as plt # type: ignore
_lowerCamelCase : list[float] = [] # x coordinates of points to plot
_lowerCamelCase : list[float] = [] # y coordinates of points to plot
_lowerCamelCase : Tuple = 0.0
while t <= 1:
_lowerCamelCase : str = self.bezier_curve_function(__A )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
_lowerCamelCase : List[str] = [i[0] for i in self.list_of_points]
_lowerCamelCase : Union[str, Any] = [i[1] for i in self.list_of_points]
plt.plot(
__A,__A,color="blue",label="Curve of Degree " + str(self.degree ),)
plt.scatter(__A,__A,color="red",label="Control Points" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 44 | 1 |
'''simple docstring'''
UpperCAmelCase_ : Tuple = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.13.2',
'requests-mock': 'requests-mock==1.10.0',
'importlib_metadata': 'importlib_metadata',
'invisible-watermark': 'invisible-watermark',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2',
'jaxlib': 'jaxlib>=0.1.65',
'Jinja2': 'Jinja2',
'k-diffusion': 'k-diffusion>=0.0.12',
'torchsde': 'torchsde',
'note_seq': 'note_seq',
'librosa': 'librosa',
'numpy': 'numpy',
'omegaconf': 'omegaconf',
'parameterized': 'parameterized',
'protobuf': 'protobuf>=3.20.3,<4',
'pytest': 'pytest',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'ruff': 'ruff>=0.0.241',
'safetensors': 'safetensors',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'scipy': 'scipy',
'onnx': 'onnx',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'tensorboard': 'tensorboard',
'torch': 'torch>=1.4',
'torchvision': 'torchvision',
'transformers': 'transformers>=4.25.1',
'urllib3': 'urllib3<=2.0.0',
}
| 44 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=A ):
lowerCAmelCase_ = ['transformers', 'torch', 'note_seq']
def __init__( self : str,*__A : List[str],**__A : List[Any] ):
requires_backends(self,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Optional[Any],*__A : str,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Dict,*__A : Dict,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
| 44 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
UpperCAmelCase_ : int = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = torch.load(_lowerCAmelCase , map_location="cpu" )
return sd
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple=rename_keys_prefix ):
"""simple docstring"""
_lowerCamelCase : Any = OrderedDict()
_lowerCamelCase : str = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
_lowerCamelCase : Any = key
for name_pair in rename_keys_prefix:
_lowerCamelCase : Dict = new_key.replace(name_pair[0] , name_pair[1] )
_lowerCamelCase : Any = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
_lowerCamelCase : List[str] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Dict ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
_lowerCamelCase : Optional[int] = "pretraining"
if "vcr" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
_lowerCamelCase : int = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
_lowerCamelCase : Any = {"visual_embedding_dim": 512}
_lowerCamelCase : List[Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : Tuple = {"visual_embedding_dim": 2048}
_lowerCamelCase : Dict = "vqa_advanced"
elif "vqa" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 2048, "num_labels": 3129}
_lowerCamelCase : Optional[int] = "vqa"
elif "nlvr" in checkpoint_path:
_lowerCamelCase : Tuple = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
_lowerCamelCase : Optional[Any] = "nlvr"
_lowerCamelCase : str = VisualBertConfig(**_lowerCAmelCase )
# Load State Dict
_lowerCamelCase : str = load_state_dict(_lowerCAmelCase )
_lowerCamelCase : List[str] = get_new_dict(_lowerCAmelCase , _lowerCAmelCase )
if model_type == "pretraining":
_lowerCamelCase : List[Any] = VisualBertForPreTraining(_lowerCAmelCase )
elif model_type == "vqa":
_lowerCamelCase : Dict = VisualBertForQuestionAnswering(_lowerCAmelCase )
elif model_type == "nlvr":
_lowerCamelCase : Tuple = VisualBertForVisualReasoning(_lowerCAmelCase )
elif model_type == "multichoice":
_lowerCamelCase : str = VisualBertForMultipleChoice(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
# Save Checkpoints
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = CodeGenTokenizer
lowerCAmelCase_ = CodeGenTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = {'add_prefix_space': True}
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : List[str] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_lowerCamelCase : Dict = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
_lowerCamelCase : Any = dict(zip(__A,range(len(__A ) ) ) )
_lowerCamelCase : Optional[int] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_lowerCamelCase : Tuple = {"unk_token": "<unk>"}
_lowerCamelCase : Optional[Any] = 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(__A ) + "\n" )
with open(self.merges_file,"w",encoding="utf-8" ) as fp:
fp.write("\n".join(__A ) )
def lowerCamelCase_ ( self : Dict,**__A : Tuple ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : Union[str, Any],**__A : int ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : str,__A : Dict ):
_lowerCamelCase : Optional[Any] = "lower newer"
_lowerCamelCase : Union[str, Any] = "lower newer"
return input_text, output_text
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : int = CodeGenTokenizer(self.vocab_file,self.merges_file,**self.special_tokens_map )
_lowerCamelCase : Any = "lower newer"
_lowerCamelCase : Optional[Any] = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
self.assertListEqual(__A,__A )
_lowerCamelCase : Union[str, Any] = tokens + [tokenizer.unk_token]
_lowerCamelCase : Dict = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Any ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase : str = self.get_tokenizer()
_lowerCamelCase : Optional[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = "lower newer"
# Testing tokenization
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
_lowerCamelCase : str = rust_tokenizer.tokenize(__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids without special tokens
_lowerCamelCase : str = tokenizer.encode(__A,add_special_tokens=__A,add_prefix_space=__A )
_lowerCamelCase : List[str] = rust_tokenizer.encode(__A,add_special_tokens=__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids with special tokens
_lowerCamelCase : List[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = tokenizer.encode(__A,add_prefix_space=__A )
_lowerCamelCase : Optional[int] = rust_tokenizer.encode(__A )
self.assertListEqual(__A,__A )
# Testing the unknown token
_lowerCamelCase : Optional[int] = tokens + [rust_tokenizer.unk_token]
_lowerCamelCase : Optional[Any] = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Tuple,*__A : Any,**__A : Any ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def lowerCamelCase_ ( self : int,__A : Optional[int]=1_5 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_lowerCamelCase : Tuple = self.rust_tokenizer_class.from_pretrained(__A,**__A )
# Simple input
_lowerCamelCase : Dict = "This is a simple input"
_lowerCamelCase : Any = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Tuple = ("This is a simple input", "This is a pair")
_lowerCamelCase : Tuple = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
# Pair input
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : str = CodeGenTokenizer.from_pretrained(self.tmpdirname,pad_token="<pad>" )
# Simple input
_lowerCamelCase : Tuple = "This is a simple input"
_lowerCamelCase : Dict = ["This is a simple input looooooooong", "This is a simple input"]
_lowerCamelCase : Dict = ("This is a simple input", "This is a pair")
_lowerCamelCase : Dict = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
_lowerCamelCase : Dict = tokenizer.pad_token_id
_lowerCamelCase : Dict = tokenizer(__A,padding="max_length",max_length=3_0,return_tensors="np" )
_lowerCamelCase : int = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
_lowerCamelCase : List[Any] = tokenizer(*__A,padding="max_length",max_length=6_0,return_tensors="np" )
_lowerCamelCase : Tuple = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1],3_0 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1],3_3 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1],6_0 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1],5_2 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : List[Any] = "$$$"
_lowerCamelCase : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname,bos_token=__A,add_bos_token=__A )
_lowerCamelCase : List[str] = "This is a simple input"
_lowerCamelCase : Optional[Any] = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Union[str, Any] = tokenizer.bos_token_id
_lowerCamelCase : Any = tokenizer(__A )
_lowerCamelCase : List[str] = tokenizer(__A )
self.assertEqual(out_s.input_ids[0],__A )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_lowerCamelCase : int = tokenizer.decode(out_s.input_ids )
_lowerCamelCase : str = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0],__A )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : int = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
_lowerCamelCase : Optional[Any] = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
_lowerCamelCase : Dict = "\nif len_a > len_b: result = a\nelse: result = b"
_lowerCamelCase : Any = tokenizer.encode(__A )
_lowerCamelCase : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
_lowerCamelCase : List[Any] = tokenizer.decode(__A,truncate_before_pattern=__A )
self.assertEqual(__A,__A )
def lowerCamelCase_ ( self : Any ):
pass
| 44 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
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 (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase__ :
def __init__( self : List[str],__A : int,__A : Optional[int]=1_3,__A : Any=7,__A : Union[str, Any]=True,__A : Optional[int]=True,__A : List[str]=True,__A : Tuple=True,__A : Any=9_9,__A : Any=3_2,__A : Any=5,__A : Tuple=4,__A : List[str]=3_7,__A : Union[str, Any]="gelu",__A : Tuple=0.1,__A : Tuple=0.1,__A : Optional[int]=5_1_2,__A : Union[str, Any]=1_6,__A : Dict=2,__A : Any=0.02,__A : Optional[Any]=3,__A : int=4,__A : Optional[Any]=None,):
_lowerCamelCase : Optional[int] = parent
_lowerCamelCase : List[str] = batch_size
_lowerCamelCase : Any = seq_length
_lowerCamelCase : int = is_training
_lowerCamelCase : int = use_input_mask
_lowerCamelCase : Any = use_token_type_ids
_lowerCamelCase : int = use_labels
_lowerCamelCase : Tuple = vocab_size
_lowerCamelCase : str = hidden_size
_lowerCamelCase : Any = num_hidden_layers
_lowerCamelCase : List[Any] = num_attention_heads
_lowerCamelCase : Any = intermediate_size
_lowerCamelCase : Optional[int] = hidden_act
_lowerCamelCase : List[str] = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : Any = max_position_embeddings
_lowerCamelCase : str = type_vocab_size
_lowerCamelCase : Dict = type_sequence_label_size
_lowerCamelCase : List[Any] = initializer_range
_lowerCamelCase : str = num_labels
_lowerCamelCase : List[str] = num_choices
_lowerCamelCase : Dict = scope
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : int = ids_tensor([self.batch_size, self.seq_length],self.vocab_size )
_lowerCamelCase : int = None
if self.use_input_mask:
_lowerCamelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : Optional[int] = None
if self.use_token_type_ids:
_lowerCamelCase : Any = ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size )
_lowerCamelCase : Dict = None
_lowerCamelCase : Union[str, Any] = None
_lowerCamelCase : Dict = None
if self.use_labels:
_lowerCamelCase : List[str] = ids_tensor([self.batch_size],self.type_sequence_label_size )
_lowerCamelCase : str = ids_tensor([self.batch_size, self.seq_length],self.num_labels )
_lowerCamelCase : Any = ids_tensor([self.batch_size],self.num_choices )
_lowerCamelCase : Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self : List[Any] ):
return NystromformerConfig(
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=__A,initializer_range=self.initializer_range,)
def lowerCamelCase_ ( self : str,__A : str,__A : Dict,__A : List[str],__A : List[Any],__A : Dict,__A : Optional[Any],__A : Any ):
_lowerCamelCase : int = NystromformerModel(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = model(__A,attention_mask=__A,token_type_ids=__A )
_lowerCamelCase : Optional[Any] = model(__A,token_type_ids=__A )
_lowerCamelCase : Optional[int] = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self : Any,__A : Union[str, Any],__A : Dict,__A : Union[str, Any],__A : Optional[int],__A : Tuple,__A : Union[str, Any],__A : List[Any] ):
_lowerCamelCase : str = NystromformerForMaskedLM(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[Any] = model(__A,attention_mask=__A,token_type_ids=__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase_ ( self : Union[str, Any],__A : List[Any],__A : Any,__A : Tuple,__A : List[str],__A : Union[str, Any],__A : Tuple,__A : Optional[int] ):
_lowerCamelCase : str = NystromformerForQuestionAnswering(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Any = model(
__A,attention_mask=__A,token_type_ids=__A,start_positions=__A,end_positions=__A,)
self.parent.assertEqual(result.start_logits.shape,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape,(self.batch_size, self.seq_length) )
def lowerCamelCase_ ( self : Dict,__A : Dict,__A : str,__A : List[Any],__A : List[str],__A : Union[str, Any],__A : Tuple,__A : Any ):
_lowerCamelCase : List[Any] = self.num_labels
_lowerCamelCase : int = NystromformerForSequenceClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A,attention_mask=__A,token_type_ids=__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : List[Any],__A : str,__A : Dict,__A : Tuple,__A : Optional[int],__A : Any,__A : Optional[Any],__A : List[Any] ):
_lowerCamelCase : int = self.num_labels
_lowerCamelCase : str = NystromformerForTokenClassification(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[int] = model(__A,attention_mask=__A,token_type_ids=__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase_ ( self : Union[str, Any],__A : int,__A : str,__A : List[Any],__A : Union[str, Any],__A : Optional[int],__A : Tuple,__A : List[str] ):
_lowerCamelCase : Optional[int] = self.num_choices
_lowerCamelCase : Dict = NystromformerForMultipleChoice(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
_lowerCamelCase : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
_lowerCamelCase : List[Any] = input_mask.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
_lowerCamelCase : List[Any] = model(
__A,attention_mask=__A,token_type_ids=__A,labels=__A,)
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices) )
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase : Any = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) : Any = config_and_inputs
_lowerCamelCase : Optional[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{
'feature-extraction': NystromformerModel,
'fill-mask': NystromformerForMaskedLM,
'question-answering': NystromformerForQuestionAnswering,
'text-classification': NystromformerForSequenceClassification,
'token-classification': NystromformerForTokenClassification,
'zero-shot': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : int = NystromformerModelTester(self )
_lowerCamelCase : Union[str, Any] = ConfigTester(self,config_class=__A,hidden_size=3_7 )
def lowerCamelCase_ ( self : int ):
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCamelCase : List[str] = type
self.model_tester.create_and_check_model(*__A )
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__A )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__A )
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__A )
@slow
def lowerCamelCase_ ( self : str ):
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Optional[Any] = NystromformerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase : Dict = NystromformerModel.from_pretrained("uw-madison/nystromformer-512" )
_lowerCamelCase : Optional[Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
_lowerCamelCase : Optional[int] = model(__A )[0]
_lowerCamelCase : int = torch.Size((1, 6, 7_6_8) )
self.assertEqual(output.shape,__A )
_lowerCamelCase : str = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3],__A,atol=1e-4 ) )
@slow
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : Optional[int] = "the [MASK] of Belgium is Brussels"
_lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained("uw-madison/nystromformer-512" )
_lowerCamelCase : Dict = NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512" )
_lowerCamelCase : Optional[Any] = tokenizer(__A,return_tensors="pt" )
with torch.no_grad():
_lowerCamelCase : Union[str, Any] = model(encoding.input_ids ).logits
_lowerCamelCase : Optional[Any] = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(__A ),"capital" )
| 44 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class UpperCAmelCase__ :
def __init__( self : Any,__A : int=2,__A : Any=3,__A : Optional[int]=6_4,__A : Tuple=None ):
_lowerCamelCase : int = np.random.default_rng(__A )
_lowerCamelCase : List[str] = length
_lowerCamelCase : Optional[Any] = rng.normal(size=(length,) ).astype(np.floataa )
_lowerCamelCase : Optional[int] = a * self.x + b + rng.normal(scale=0.1,size=(length,) ).astype(np.floataa )
def __len__( self : Dict ):
return self.length
def __getitem__( self : str,__A : List[str] ):
return {"x": self.x[i], "y": self.y[i]}
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : Optional[Any]=0,__A : Optional[int]=0,__A : Dict=False ):
super().__init__()
_lowerCamelCase : Tuple = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : List[str] = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : Optional[int] = True
def lowerCamelCase_ ( self : List[str],__A : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a[0] + self.b[0]
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : List[str]=0,__A : List[str]=0,__A : int=False ):
super().__init__()
_lowerCamelCase : Optional[int] = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Dict = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Tuple = True
def lowerCamelCase_ ( self : str,__A : List[Any]=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a + self.b
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
_lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("bert-base-cased" )
_lowerCamelCase : List[Any] = {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
_lowerCamelCase : int = load_dataset("csv" , data_files=_lowerCAmelCase )
_lowerCamelCase : Dict = datasets["train"].unique("label" )
_lowerCamelCase : Optional[Any] = {v: i for i, v in enumerate(_lowerCAmelCase )}
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Optional[int] = tokenizer(
examples["sentence1"] , examples["sentence2"] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase , padding="max_length" )
if "label" in examples:
_lowerCamelCase : str = [label_to_id[l] for l in examples["label"]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_lowerCamelCase : Optional[Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["sentence1", "sentence2", "label"] , )
def collate_fn(_lowerCAmelCase : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return tokenizer.pad(_lowerCAmelCase , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
_lowerCamelCase : str = DataLoader(tokenized_datasets["train"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=2 )
_lowerCamelCase : Optional[int] = DataLoader(tokenized_datasets["validation"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=1 )
return train_dataloader, eval_dataloader
| 44 | 1 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase_ : Dict = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : str = os.path.dirname(os.path.realpath(_lowerCAmelCase ) )
_lowerCamelCase : Tuple = os.path.join(_lowerCAmelCase , "words.txt" )
_lowerCamelCase : Dict = ""
with open(_lowerCAmelCase ) as f:
_lowerCamelCase : Optional[int] = f.readline()
_lowerCamelCase : Optional[Any] = [word.strip("\"" ) for word in words.strip("\r\n" ).split("," )]
_lowerCamelCase : Optional[int] = [
word
for word in [sum(ord(_lowerCAmelCase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(_lowerCAmelCase )
if __name__ == "__main__":
print(solution())
| 44 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Optional[Any] = False, False, False
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = None
lowerCAmelCase_ = True
lowerCAmelCase_ = True
lowerCAmelCase_ = None
# Automatically constructed
lowerCAmelCase_ = "dict"
lowerCAmelCase_ = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
lowerCAmelCase_ = field(default='Audio' , init=A , repr=A )
def __call__( self : Tuple ):
return self.pa_type
def lowerCamelCase_ ( self : Any,__A : Union[str, bytes, dict] ):
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
if isinstance(__A,__A ):
return {"bytes": None, "path": value}
elif isinstance(__A,__A ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
_lowerCamelCase : List[Any] = BytesIO()
sf.write(__A,value["array"],value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm" ):
# "PCM" only has raw audio bytes
if value.get("sampling_rate" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
if value.get("bytes" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
_lowerCamelCase : Dict = np.frombuffer(value["bytes"],dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
else:
_lowerCamelCase : str = np.memmap(value["path"],dtype="h",mode="r" ).astype(np.floataa ) / 3_2_7_6_7
_lowerCamelCase : Optional[int] = BytesIO(bytes() )
sf.write(__A,__A,value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' )
def lowerCamelCase_ ( self : Optional[Any],__A : dict,__A : Optional[Dict[str, Union[str, bool, None]]] = None ):
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )
_lowerCamelCase , _lowerCamelCase : Optional[Any] = (value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err
_lowerCamelCase : Tuple = xsplitext(__A )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
if file is None:
_lowerCamelCase : Tuple = token_per_repo_id or {}
_lowerCamelCase : Union[str, Any] = path.split("::" )[-1]
try:
_lowerCamelCase : str = string_to_dict(__A,config.HUB_DATASETS_URL )["repo_id"]
_lowerCamelCase : str = token_per_repo_id[repo_id]
except (ValueError, KeyError):
_lowerCamelCase : Any = None
with xopen(__A,"rb",use_auth_token=__A ) as f:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = sf.read(__A )
else:
_lowerCamelCase , _lowerCamelCase : str = sf.read(__A )
_lowerCamelCase : List[str] = array.T
if self.mono:
_lowerCamelCase : List[str] = librosa.to_mono(__A )
if self.sampling_rate and self.sampling_rate != sampling_rate:
_lowerCamelCase : List[str] = librosa.resample(__A,orig_sr=__A,target_sr=self.sampling_rate )
_lowerCamelCase : Optional[Any] = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def lowerCamelCase_ ( self : Any ):
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature." )
return {
"bytes": Value("binary" ),
"path": Value("string" ),
}
def lowerCamelCase_ ( self : List[str],__A : Union[pa.StringArray, pa.StructArray] ):
if pa.types.is_string(storage.type ):
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
_lowerCamelCase : int = pa.StructArray.from_arrays([bytes_array, storage],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_lowerCamelCase : Dict = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Any = pa.StructArray.from_arrays([storage, path_array],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
_lowerCamelCase : Tuple = pa.array([Audio().encode_example(__A ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
_lowerCamelCase : Tuple = storage.field("bytes" )
else:
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
_lowerCamelCase : List[str] = storage.field("path" )
else:
_lowerCamelCase : Tuple = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Tuple = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=storage.is_null() )
return array_cast(__A,self.pa_type )
def lowerCamelCase_ ( self : str,__A : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(__A : Dict ):
with xopen(__A,"rb" ) as f:
_lowerCamelCase : Any = f.read()
return bytes_
_lowerCamelCase : int = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
],type=pa.binary(),)
_lowerCamelCase : str = pa.array(
[os.path.basename(__A ) if path is not None else None for path in storage.field("path" ).to_pylist()],type=pa.string(),)
_lowerCamelCase : Dict = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=bytes_array.is_null() )
return array_cast(__A,self.pa_type )
| 44 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
@dataclass
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = [
'no_inference',
'no_cuda',
'no_tpu',
'no_speed',
'no_memory',
'no_env_print',
'no_multi_process',
]
def __init__( self : str,**__A : List[Any] ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
_lowerCamelCase : Any = deprecated_arg[3:]
setattr(self,__A,not kwargs.pop(__A ) )
logger.warning(
f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
f' {positive_arg}={kwargs[positive_arg]}' )
_lowerCamelCase : str = kwargs.pop("torchscript",self.torchscript )
_lowerCamelCase : Tuple = kwargs.pop("torch_xla_tpu_print_metrics",self.torch_xla_tpu_print_metrics )
_lowerCamelCase : str = kwargs.pop("fp16_opt_level",self.fpaa_opt_level )
super().__init__(**__A )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Trace the models using torchscript'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Print Xla/PyTorch tpu metrics'} )
lowerCAmelCase_ = field(
default='O1' , metadata={
'help': (
'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '
'See details at https://nvidia.github.io/apex/amp.html'
)
} , )
@cached_property
def lowerCamelCase_ ( self : Any ):
requires_backends(self,["torch"] )
logger.info("PyTorch: setting up devices" )
if not self.cuda:
_lowerCamelCase : Union[str, Any] = torch.device("cpu" )
_lowerCamelCase : List[str] = 0
elif is_torch_tpu_available():
_lowerCamelCase : Dict = xm.xla_device()
_lowerCamelCase : Dict = 0
else:
_lowerCamelCase : Union[str, Any] = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
_lowerCamelCase : Any = torch.cuda.device_count()
return device, n_gpu
@property
def lowerCamelCase_ ( self : Any ):
return is_torch_tpu_available() and self.tpu
@property
def lowerCamelCase_ ( self : Optional[Any] ):
requires_backends(self,["torch"] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def lowerCamelCase_ ( self : Dict ):
requires_backends(self,["torch"] )
return self._setup_devices[0]
@property
def lowerCamelCase_ ( self : Optional[int] ):
requires_backends(self,["torch"] )
return self._setup_devices[1]
@property
def lowerCamelCase_ ( self : str ):
return self.n_gpu > 0
| 44 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'glpn'
def __init__( self : Tuple,__A : Optional[int]=3,__A : Optional[int]=4,__A : str=[2, 2, 2, 2],__A : Union[str, Any]=[8, 4, 2, 1],__A : Tuple=[3_2, 6_4, 1_6_0, 2_5_6],__A : int=[7, 3, 3, 3],__A : str=[4, 2, 2, 2],__A : int=[1, 2, 5, 8],__A : List[Any]=[4, 4, 4, 4],__A : Optional[int]="gelu",__A : int=0.0,__A : Tuple=0.0,__A : Tuple=0.02,__A : Optional[int]=0.1,__A : Optional[int]=1e-6,__A : Optional[int]=6_4,__A : Optional[Any]=1_0,__A : Tuple=-1,**__A : List[str],):
super().__init__(**__A )
_lowerCamelCase : Tuple = num_channels
_lowerCamelCase : Union[str, Any] = num_encoder_blocks
_lowerCamelCase : Dict = depths
_lowerCamelCase : List[Any] = sr_ratios
_lowerCamelCase : str = hidden_sizes
_lowerCamelCase : Any = patch_sizes
_lowerCamelCase : Any = strides
_lowerCamelCase : Dict = mlp_ratios
_lowerCamelCase : int = num_attention_heads
_lowerCamelCase : List[Any] = hidden_act
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : Optional[int] = initializer_range
_lowerCamelCase : Union[str, Any] = drop_path_rate
_lowerCamelCase : str = layer_norm_eps
_lowerCamelCase : Tuple = decoder_hidden_size
_lowerCamelCase : int = max_depth
_lowerCamelCase : Dict = head_in_index
| 44 | 1 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] , _lowerCAmelCase : list[list[str]] , _lowerCAmelCase : int , ):
"""simple docstring"""
_lowerCamelCase : Dict = len(_lowerCAmelCase )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(_lowerCAmelCase ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , _lowerCAmelCase , _lowerCAmelCase , )
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : list[list[str]] = []
depth_first_search([] , [] , [] , _lowerCAmelCase , _lowerCAmelCase )
# Print all the boards
for board in boards:
for column in board:
print(_lowerCAmelCase )
print("" )
print(len(_lowerCAmelCase ) , "solutions were found." )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 44 |
'''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_ : str = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['input_features', 'attention_mask']
def __init__( self : Any,__A : List[Any]=8_0,__A : Dict=1_6_0_0_0,__A : Tuple=0.0,__A : Dict=1_0,__A : int=2_5,__A : Union[str, Any]="hamming_window",__A : List[str]=32768.0,__A : Union[str, Any]=0.97,__A : str=1.0,__A : Union[str, Any]=True,__A : Tuple=True,__A : Optional[Any]=False,**__A : Optional[Any],):
super().__init__(feature_size=__A,sampling_rate=__A,padding_value=__A,**__A )
_lowerCamelCase : Dict = feature_size
_lowerCamelCase : List[str] = sampling_rate
_lowerCamelCase : Any = padding_value
_lowerCamelCase : Dict = hop_length
_lowerCamelCase : Tuple = win_length
_lowerCamelCase : str = frame_signal_scale
_lowerCamelCase : List[str] = preemphasis_coeff
_lowerCamelCase : List[str] = mel_floor
_lowerCamelCase : str = normalize_means
_lowerCamelCase : Any = normalize_vars
_lowerCamelCase : List[str] = win_function
_lowerCamelCase : Tuple = return_attention_mask
_lowerCamelCase : List[Any] = win_length * sampling_rate // 1_0_0_0
_lowerCamelCase : List[Any] = hop_length * sampling_rate // 1_0_0_0
_lowerCamelCase : Any = optimal_fft_length(self.sample_size )
_lowerCamelCase : Dict = (self.n_fft // 2) + 1
def lowerCamelCase_ ( self : Any,__A : np.array ):
if self.win_function == "hamming_window":
_lowerCamelCase : Any = window_function(window_length=self.sample_size,name=self.win_function,periodic=__A )
else:
_lowerCamelCase : Optional[int] = window_function(window_length=self.sample_size,name=self.win_function )
_lowerCamelCase : int = 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 : List[str] = spectrogram(
one_waveform * self.frame_signal_scale,window=__A,frame_length=self.sample_size,hop_length=self.sample_stride,fft_length=self.n_fft,center=__A,preemphasis=self.preemphasis_coeff,mel_filters=__A,mel_floor=self.mel_floor,log_mel="log",)
return msfc_features.T
def lowerCamelCase_ ( self : Optional[int],__A : List[str],__A : Dict,__A : int ):
# make sure we normalize float32 arrays
if self.normalize_means:
_lowerCamelCase : Optional[Any] = x[:input_length].mean(axis=0 )
_lowerCamelCase : Optional[int] = np.subtract(__A,__A )
if self.normalize_vars:
_lowerCamelCase : int = x[:input_length].std(axis=0 )
_lowerCamelCase : Any = np.divide(__A,__A )
if input_length < x.shape[0]:
_lowerCamelCase : Tuple = padding_value
# make sure array is in float32
_lowerCamelCase : Optional[int] = x.astype(np.floataa )
return x
def lowerCamelCase_ ( self : Any,__A : List[np.ndarray],__A : Optional[np.ndarray] = None ):
_lowerCamelCase : Optional[int] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(__A,__A,self.padding_value ) for x, n in zip(__A,__A )]
def __call__( self : Optional[Any],__A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],__A : Union[bool, str, PaddingStrategy] = False,__A : Optional[int] = None,__A : bool = False,__A : Optional[int] = None,__A : Optional[bool] = None,__A : Optional[Union[str, TensorType]] = None,__A : Optional[int] = None,**__A : Optional[Any],):
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 : List[str] = isinstance(__A,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 : List[str] = is_batched_numpy or (
isinstance(__A,(list, tuple) ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list) ))
)
if is_batched:
_lowerCamelCase : List[Any] = [np.asarray(__A,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__A,np.ndarray ):
_lowerCamelCase : Dict = np.asarray(__A,dtype=np.floataa )
elif isinstance(__A,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowerCamelCase : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCamelCase : Tuple = [raw_speech]
# extract fbank features
_lowerCamelCase : str = [self._extract_mfsc_features(__A ) for one_waveform in raw_speech]
# convert into correct format for padding
_lowerCamelCase : Union[str, Any] = BatchFeature({"input_features": features} )
_lowerCamelCase : List[Any] = self.pad(
__A,padding=__A,max_length=__A,truncation=__A,pad_to_multiple_of=__A,return_attention_mask=__A,**__A,)
# make sure list is in array format
_lowerCamelCase : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0],__A ):
_lowerCamelCase : int = [np.asarray(__A,dtype=np.floataa ) for feature in input_features]
_lowerCamelCase : Dict = padded_inputs.get("attention_mask" )
if attention_mask is not None:
_lowerCamelCase : Dict = [np.asarray(__A,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
_lowerCamelCase : Dict = (
np.array(__A,dtype=np.intaa )
if self._get_padding_strategies(__A,max_length=__A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
_lowerCamelCase : Tuple = self.normalize(
padded_inputs["input_features"],attention_mask=__A )
if return_tensors is not None:
_lowerCamelCase : Dict = padded_inputs.convert_to_tensors(__A )
return padded_inputs
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : int = ""
for i in table:
res += inp[i - 1]
return res
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
return data[1:] + data[0]
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = ""
for i in range(len(_lowerCAmelCase ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = int("0b" + data[0] + data[-1] , 2 )
_lowerCamelCase : str = int("0b" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = message[:4]
_lowerCamelCase : Optional[Any] = message[4:]
_lowerCamelCase : int = apply_table(_lowerCAmelCase , _lowerCAmelCase )
_lowerCamelCase : List[str] = xor(_lowerCAmelCase , _lowerCAmelCase )
_lowerCamelCase : List[str] = apply_sbox(_lowerCAmelCase , temp[:4] ) # noqa: E741
_lowerCamelCase : int = apply_sbox(_lowerCAmelCase , temp[4:] )
_lowerCamelCase : Optional[int] = "0" * (2 - len(_lowerCAmelCase )) + l # noqa: E741
_lowerCamelCase : int = "0" * (2 - len(_lowerCAmelCase )) + r
_lowerCamelCase : str = apply_table(l + r , _lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = xor(_lowerCAmelCase , _lowerCAmelCase )
return temp + right
if __name__ == "__main__":
UpperCAmelCase_ : Dict = input('Enter 10 bit key: ')
UpperCAmelCase_ : str = input('Enter 8 bit message: ')
UpperCAmelCase_ : List[str] = [6, 3, 7, 4, 8, 5, 10, 9]
UpperCAmelCase_ : List[Any] = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
UpperCAmelCase_ : int = [2, 4, 3, 1]
UpperCAmelCase_ : Tuple = [2, 6, 3, 1, 4, 8, 5, 7]
UpperCAmelCase_ : Dict = [4, 1, 3, 5, 7, 2, 8, 6]
UpperCAmelCase_ : List[Any] = [4, 1, 2, 3, 2, 3, 4, 1]
UpperCAmelCase_ : Optional[Any] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
UpperCAmelCase_ : Optional[Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
UpperCAmelCase_ : List[str] = apply_table(key, paa_table)
UpperCAmelCase_ : Union[str, Any] = temp[:5]
UpperCAmelCase_ : Union[str, Any] = temp[5:]
UpperCAmelCase_ : Optional[int] = left_shift(left)
UpperCAmelCase_ : Optional[int] = left_shift(right)
UpperCAmelCase_ : Optional[Any] = apply_table(left + right, pa_table)
UpperCAmelCase_ : Optional[Any] = left_shift(left)
UpperCAmelCase_ : Optional[Any] = left_shift(right)
UpperCAmelCase_ : Union[str, Any] = left_shift(left)
UpperCAmelCase_ : List[str] = left_shift(right)
UpperCAmelCase_ : List[Any] = apply_table(left + right, pa_table)
# encryption
UpperCAmelCase_ : Optional[Any] = apply_table(message, IP)
UpperCAmelCase_ : int = function(expansion, sa, sa, keya, temp)
UpperCAmelCase_ : Tuple = temp[4:] + temp[:4]
UpperCAmelCase_ : Optional[Any] = function(expansion, sa, sa, keya, temp)
UpperCAmelCase_ : str = apply_table(temp, IP_inv)
print('Cipher text is:', CT)
# decryption
UpperCAmelCase_ : Union[str, Any] = apply_table(CT, IP)
UpperCAmelCase_ : Any = function(expansion, sa, sa, keya, temp)
UpperCAmelCase_ : Optional[int] = temp[4:] + temp[:4]
UpperCAmelCase_ : Tuple = function(expansion, sa, sa, keya, temp)
UpperCAmelCase_ : Optional[Any] = apply_table(temp, IP_inv)
print('Plain text after decypting is:', PT)
| 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
UpperCAmelCase_ : int = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = torch.load(_lowerCAmelCase , map_location="cpu" )
return sd
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple=rename_keys_prefix ):
"""simple docstring"""
_lowerCamelCase : Any = OrderedDict()
_lowerCamelCase : str = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
_lowerCamelCase : Any = key
for name_pair in rename_keys_prefix:
_lowerCamelCase : Dict = new_key.replace(name_pair[0] , name_pair[1] )
_lowerCamelCase : Any = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
_lowerCamelCase : List[str] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Dict ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
_lowerCamelCase : Optional[int] = "pretraining"
if "vcr" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
_lowerCamelCase : int = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
_lowerCamelCase : Any = {"visual_embedding_dim": 512}
_lowerCamelCase : List[Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : Tuple = {"visual_embedding_dim": 2048}
_lowerCamelCase : Dict = "vqa_advanced"
elif "vqa" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 2048, "num_labels": 3129}
_lowerCamelCase : Optional[int] = "vqa"
elif "nlvr" in checkpoint_path:
_lowerCamelCase : Tuple = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
_lowerCamelCase : Optional[Any] = "nlvr"
_lowerCamelCase : str = VisualBertConfig(**_lowerCAmelCase )
# Load State Dict
_lowerCamelCase : str = load_state_dict(_lowerCAmelCase )
_lowerCamelCase : List[str] = get_new_dict(_lowerCAmelCase , _lowerCAmelCase )
if model_type == "pretraining":
_lowerCamelCase : List[Any] = VisualBertForPreTraining(_lowerCAmelCase )
elif model_type == "vqa":
_lowerCamelCase : Dict = VisualBertForQuestionAnswering(_lowerCAmelCase )
elif model_type == "nlvr":
_lowerCamelCase : Tuple = VisualBertForVisualReasoning(_lowerCAmelCase )
elif model_type == "multichoice":
_lowerCamelCase : str = VisualBertForMultipleChoice(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
# Save Checkpoints
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def A_ ( *_lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Union[Dict, Any]] = None , _lowerCAmelCase : int=True , _lowerCAmelCase : Union[str, Any]=2 ):
"""simple docstring"""
from .. import __version__
_lowerCamelCase : Union[str, Any] = take_from
_lowerCamelCase : Union[str, Any] = ()
if not isinstance(args[0] , _lowerCAmelCase ):
_lowerCamelCase : int = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(_lowerCAmelCase ).base_version ) >= version.parse(_lowerCAmelCase ):
raise ValueError(
F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
F' version {__version__} is >= {version_name}' )
_lowerCamelCase : Dict = None
if isinstance(_lowerCAmelCase , _lowerCAmelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(_lowerCAmelCase ),)
_lowerCamelCase : Optional[int] = F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
elif hasattr(_lowerCAmelCase , _lowerCAmelCase ):
values += (getattr(_lowerCAmelCase , _lowerCAmelCase ),)
_lowerCamelCase : Tuple = F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
elif deprecated_kwargs is None:
_lowerCamelCase : int = F'`{attribute}` is deprecated and will be removed in version {version_name}.'
if warning is not None:
_lowerCamelCase : List[Any] = warning + " " if standard_warn else ""
warnings.warn(warning + message , _lowerCAmelCase , stacklevel=_lowerCAmelCase )
if isinstance(_lowerCAmelCase , _lowerCAmelCase ) and len(_lowerCAmelCase ) > 0:
_lowerCamelCase : Optional[Any] = inspect.getouterframes(inspect.currentframe() )[1]
_lowerCamelCase : Optional[Any] = call_frame.filename
_lowerCamelCase : Dict = call_frame.lineno
_lowerCamelCase : int = call_frame.function
_lowerCamelCase , _lowerCamelCase : Any = next(iter(deprecated_kwargs.items() ) )
raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`' )
if len(_lowerCAmelCase ) == 0:
return
elif len(_lowerCAmelCase ) == 1:
return values[0]
return values
| 44 |
'''simple docstring'''
import functools
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(_lowerCAmelCase ) != 3 or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(_lowerCAmelCase ) == 0:
return 0
if min(_lowerCAmelCase ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(_lowerCAmelCase ) >= 366:
raise ValueError("All days elements should be less than 366" )
_lowerCamelCase : Union[str, Any] = set(_lowerCAmelCase )
@functools.cache
def dynamic_programming(_lowerCAmelCase : int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 | 1 |
'''simple docstring'''
UpperCAmelCase_ : Dict = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
UpperCAmelCase_ : Dict = [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_ : Tuple = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 44 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_lowerCamelCase : Dict = MaskFormerConfig(backbone_config=_lowerCAmelCase )
_lowerCamelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 847
_lowerCamelCase : str = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_lowerCamelCase : Optional[int] = 150
_lowerCamelCase : Union[str, Any] = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_lowerCamelCase : Union[str, Any] = 171
_lowerCamelCase : str = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_lowerCamelCase : Optional[int] = 133
_lowerCamelCase : Any = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_lowerCamelCase : str = 19
_lowerCamelCase : Tuple = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 65
_lowerCamelCase : Optional[int] = "mapillary-vistas-id2label.json"
_lowerCamelCase : Any = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : Optional[int] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
return config
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : Any = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.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.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight') )
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Tuple = dct.pop(_lowerCAmelCase )
_lowerCamelCase : str = val
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_lowerCamelCase : int = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_lowerCamelCase : Union[str, Any] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
_lowerCamelCase : List[str] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[int] = in_proj_weight[:dim, :]
_lowerCamelCase : Optional[int] = in_proj_bias[: dim]
_lowerCamelCase : List[str] = in_proj_weight[
dim : dim * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
dim : dim * 2
]
_lowerCamelCase : List[Any] = in_proj_weight[
-dim :, :
]
_lowerCamelCase : Union[str, Any] = in_proj_bias[-dim :]
# fmt: on
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : int = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Tuple = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[Any] = in_proj_weight[: hidden_size, :]
_lowerCamelCase : Optional[int] = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : Any = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Any = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
_lowerCamelCase : List[Any] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Tuple = in_proj_weight[: hidden_size, :]
_lowerCamelCase : str = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Optional[int] = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : int = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# fmt: on
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : List[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[Any] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : bool = False ):
"""simple docstring"""
_lowerCamelCase : Tuple = get_maskformer_config(_lowerCAmelCase )
# load original state_dict
with open(_lowerCAmelCase , "rb" ) as f:
_lowerCamelCase : List[Any] = pickle.load(_lowerCAmelCase )
_lowerCamelCase : Optional[Any] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_lowerCamelCase : List[Any] = create_rename_keys(_lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_swin_q_k_v(_lowerCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_lowerCAmelCase , _lowerCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_lowerCamelCase : Dict = torch.from_numpy(_lowerCAmelCase )
# load 🤗 model
_lowerCamelCase : int = MaskFormerForInstanceSegmentation(_lowerCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_lowerCAmelCase , param.shape )
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_lowerCAmelCase ) == 0, F'Unexpected keys: {unexpected_keys}'
# verify results
_lowerCamelCase : Any = prepare_img()
if "vistas" in model_name:
_lowerCamelCase : Any = 65
elif "cityscapes" in model_name:
_lowerCamelCase : Optional[Any] = 65535
else:
_lowerCamelCase : str = 255
_lowerCamelCase : List[str] = True if "ade" in model_name else False
_lowerCamelCase : Union[str, Any] = MaskFormerImageProcessor(ignore_index=_lowerCAmelCase , reduce_labels=_lowerCAmelCase )
_lowerCamelCase : int = image_processor(_lowerCAmelCase , return_tensors="pt" )
_lowerCamelCase : Tuple = model(**_lowerCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_lowerCamelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
image_processor.save_pretrained(_lowerCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F'nielsr/{model_name}' )
image_processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
UpperCAmelCase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='maskformer-swin-tiny-ade',
type=str,
help=('Name of the MaskFormer model you\'d like to convert',),
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
type=str,
help='Path to the original state dict (.pth file).',
)
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.'
)
UpperCAmelCase_ : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 44 | 1 |
'''simple docstring'''
UpperCAmelCase_ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 100_0000,
"gigajoule": 10_0000_0000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 360_0000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr": 418_6800.00,
"electronvolt": 1.6_0217_6634E-19,
"britishthermalunit_it": 1055.0_5585,
"footpound": 1.35_5818,
}
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : float ):
"""simple docstring"""
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
_lowerCamelCase : List[str] = (
F'Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'
F'Valid values are: {", ".join(_lowerCAmelCase )}'
)
raise ValueError(_lowerCAmelCase )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = range(2, 20 + 1)
UpperCAmelCase_ : str = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = sum(a_i[j] for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ) )
_lowerCamelCase : List[str] = sum(a_i[j] * base[j] for j in range(min(len(_lowerCAmelCase ) , _lowerCAmelCase ) ) )
_lowerCamelCase , _lowerCamelCase : int = 0, 0
_lowerCamelCase : Dict = n - i
_lowerCamelCase : int = memo.get(_lowerCAmelCase )
if sub_memo is not None:
_lowerCamelCase : List[str] = sub_memo.get(_lowerCAmelCase )
if jumps is not None and len(_lowerCAmelCase ) > 0:
# find and make the largest jump without going over
_lowerCamelCase : List[Any] = -1
for _k in range(len(_lowerCAmelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
_lowerCamelCase : Any = _k
break
if max_jump >= 0:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : str = jumps[max_jump]
# since the difference between jumps is cached, add c
_lowerCamelCase : str = diff + c
for j in range(min(_lowerCAmelCase , len(_lowerCAmelCase ) ) ):
_lowerCamelCase , _lowerCamelCase : List[Any] = divmod(_lowerCAmelCase , 10 )
if new_c > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_lowerCamelCase : int = []
else:
_lowerCamelCase : Tuple = {c: []}
_lowerCamelCase : Any = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_lowerCamelCase , _lowerCamelCase : Optional[int] = next_term(_lowerCAmelCase , k - 1 , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_lowerCamelCase , _lowerCamelCase : List[str] = compute(_lowerCAmelCase , _lowerCAmelCase , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
_lowerCamelCase : List[str] = sub_memo[c]
# keep jumps sorted by # of terms skipped
_lowerCamelCase : int = 0
while j < len(_lowerCAmelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_lowerCAmelCase , (diff, dn, k) )
return (diff, dn)
def A_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
if i >= n:
return 0, i
if k > len(_lowerCAmelCase ):
a_i.extend([0 for _ in range(k - len(_lowerCAmelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
_lowerCamelCase : List[str] = i
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Any = 0, 0, 0
for j in range(len(_lowerCAmelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
_lowerCamelCase : int = ds_c + ds_b
diff += addend
_lowerCamelCase : List[str] = 0
for j in range(_lowerCAmelCase ):
_lowerCamelCase : List[Any] = a_i[j] + addend
_lowerCamelCase , _lowerCamelCase : Any = divmod(_lowerCAmelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return diff, i - start_i
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : Tuple = digits[j] + addend
if s >= 10:
_lowerCamelCase , _lowerCamelCase : Optional[int] = divmod(_lowerCAmelCase , 10 )
_lowerCamelCase : Any = addend // 10 + quotient
else:
_lowerCamelCase : Tuple = s
_lowerCamelCase : List[Any] = addend // 10
if addend == 0:
break
while addend > 0:
_lowerCamelCase , _lowerCamelCase : str = divmod(_lowerCAmelCase , 10 )
digits.append(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : int = 10**15 ):
"""simple docstring"""
_lowerCamelCase : Tuple = [1]
_lowerCamelCase : List[Any] = 1
_lowerCamelCase : List[str] = 0
while True:
_lowerCamelCase , _lowerCamelCase : Dict = next_term(_lowerCAmelCase , 20 , i + dn , _lowerCAmelCase )
dn += terms_jumped
if dn == n - i:
break
_lowerCamelCase : Optional[Any] = 0
for j in range(len(_lowerCAmelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 | 1 |
'''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 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase_ : Any = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether tp freeze the encoder.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
lowerCAmelCase_ = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
lowerCAmelCase_ = field(
default=1024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Source language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Target language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': '# num_beams to use for evaluation.'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any ):
"""simple docstring"""
logger.info(F'***** {split} metrics *****' )
for key in sorted(metrics.keys() ):
logger.info(F' {key} = {metrics[key]}' )
save_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , F'{split}_results.json' ) )
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : str = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Optional[Any] = parser.parse_args_into_dataclasses()
check_output_dir(_lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , _lowerCAmelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCamelCase : Optional[int] = 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 , )
_lowerCamelCase : Tuple = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
assert hasattr(_lowerCAmelCase , _lowerCAmelCase ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
setattr(_lowerCAmelCase , _lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
_lowerCamelCase : List[Any] = 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 , )
_lowerCamelCase : int = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=_lowerCAmelCase , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(_lowerCAmelCase , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCamelCase : List[Any] = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(_lowerCAmelCase , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Any = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCamelCase : int = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(_lowerCAmelCase )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCamelCase : int = SeqaSeqDataset
# Get datasets
_lowerCamelCase : Tuple = (
dataset_class(
_lowerCAmelCase , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
_lowerCamelCase : List[Any] = (
dataset_class(
_lowerCAmelCase , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCamelCase : Optional[int] = (
dataset_class(
_lowerCAmelCase , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCamelCase : int = (
build_compute_metrics_fn(data_args.task , _lowerCAmelCase ) if training_args.predict_with_generate else None
)
_lowerCamelCase : List[Any] = SeqaSeqTrainer(
model=_lowerCAmelCase , args=_lowerCAmelCase , data_args=_lowerCAmelCase , train_dataset=_lowerCAmelCase , eval_dataset=_lowerCAmelCase , data_collator=SeqaSeqDataCollator(
_lowerCAmelCase , _lowerCAmelCase , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=_lowerCAmelCase , tokenizer=_lowerCAmelCase , )
_lowerCamelCase : Optional[Any] = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
_lowerCamelCase : Optional[Any] = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCamelCase : int = train_result.metrics
_lowerCamelCase : Optional[int] = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_lowerCamelCase : Optional[Any] = trainer.evaluate(metric_key_prefix="val" )
_lowerCamelCase : Dict = data_args.n_val
_lowerCamelCase : List[Any] = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.do_predict:
logger.info("*** Predict ***" )
_lowerCamelCase : Any = trainer.predict(test_dataset=_lowerCAmelCase , metric_key_prefix="test" )
_lowerCamelCase : Dict = test_output.metrics
_lowerCamelCase : Optional[int] = data_args.n_test
if trainer.is_world_process_zero():
_lowerCamelCase : int = round(metrics["test_loss"] , 4 )
handle_metrics("test" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.predict_with_generate:
_lowerCamelCase : List[str] = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
_lowerCamelCase : Any = lmap(str.strip , _lowerCAmelCase )
write_txt_file(_lowerCAmelCase , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(_lowerCAmelCase , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 44 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'marian'
lowerCAmelCase_ = ['past_key_values']
lowerCAmelCase_ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Optional[Any],__A : str=5_8_1_0_1,__A : str=None,__A : List[str]=1_0_2_4,__A : int=1_2,__A : int=4_0_9_6,__A : List[Any]=1_6,__A : int=1_2,__A : List[str]=4_0_9_6,__A : Union[str, Any]=1_6,__A : Any=0.0,__A : Optional[int]=0.0,__A : Any=True,__A : Tuple=True,__A : List[Any]="gelu",__A : List[str]=1_0_2_4,__A : Any=0.1,__A : Dict=0.0,__A : Tuple=0.0,__A : Any=0.02,__A : int=5_8_1_0_0,__A : List[Any]=False,__A : List[str]=5_8_1_0_0,__A : List[Any]=0,__A : List[Any]=0,__A : Optional[int]=True,**__A : str,):
_lowerCamelCase : List[Any] = vocab_size
_lowerCamelCase : Tuple = decoder_vocab_size or vocab_size
_lowerCamelCase : Optional[int] = max_position_embeddings
_lowerCamelCase : List[str] = d_model
_lowerCamelCase : Any = encoder_ffn_dim
_lowerCamelCase : List[str] = encoder_layers
_lowerCamelCase : Optional[Any] = encoder_attention_heads
_lowerCamelCase : Optional[Any] = decoder_ffn_dim
_lowerCamelCase : Optional[int] = decoder_layers
_lowerCamelCase : List[str] = decoder_attention_heads
_lowerCamelCase : Optional[int] = dropout
_lowerCamelCase : Optional[Any] = attention_dropout
_lowerCamelCase : Union[str, Any] = activation_dropout
_lowerCamelCase : Union[str, Any] = activation_function
_lowerCamelCase : Union[str, Any] = init_std
_lowerCamelCase : Optional[int] = encoder_layerdrop
_lowerCamelCase : Union[str, Any] = decoder_layerdrop
_lowerCamelCase : str = use_cache
_lowerCamelCase : str = encoder_layers
_lowerCamelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
_lowerCamelCase : Optional[Any] = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=__A,eos_token_id=__A,is_encoder_decoder=__A,decoder_start_token_id=__A,forced_eos_token_id=__A,**__A,)
class UpperCAmelCase__ ( A ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def lowerCamelCase_ ( self : List[Any] ):
if self.task in ["default", "seq2seq-lm"]:
_lowerCamelCase : Dict = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
_lowerCamelCase : int = {0: "batch"}
_lowerCamelCase : Union[str, Any] = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
_lowerCamelCase : Union[str, Any] = {0: "batch", 1: "decoder_sequence"}
_lowerCamelCase : List[Any] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(__A,direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
_lowerCamelCase : int = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
_lowerCamelCase , _lowerCamelCase : int = self.num_layers
for i in range(__A ):
_lowerCamelCase : Union[str, Any] = {0: "batch", 2: "past_sequence + sequence"}
_lowerCamelCase : List[Any] = {0: "batch", 2: "past_sequence + sequence"}
else:
_lowerCamelCase : List[str] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def lowerCamelCase_ ( self : Tuple ):
if self.task in ["default", "seq2seq-lm"]:
_lowerCamelCase : Optional[int] = super().outputs
else:
_lowerCamelCase : int = super(__A,self ).outputs
if self.use_past:
_lowerCamelCase , _lowerCamelCase : Optional[int] = self.num_layers
for i in range(__A ):
_lowerCamelCase : Union[str, Any] = {0: "batch", 2: "past_sequence + sequence"}
_lowerCamelCase : List[Any] = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def lowerCamelCase_ ( self : Tuple,__A : PreTrainedTokenizer,__A : int = -1,__A : int = -1,__A : bool = False,__A : Optional[TensorType] = None,):
_lowerCamelCase : Optional[Any] = self._generate_dummy_inputs_for_encoder_and_decoder(
__A,__A,__A,__A,__A )
# Generate decoder inputs
_lowerCamelCase : Tuple = seq_length if not self.use_past else 1
_lowerCamelCase : Dict = self._generate_dummy_inputs_for_encoder_and_decoder(
__A,__A,__A,__A,__A )
_lowerCamelCase : Union[str, Any] = {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
_lowerCamelCase : List[Any] = dict(**__A,**__A )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
_lowerCamelCase , _lowerCamelCase : Tuple = common_inputs["input_ids"].shape
_lowerCamelCase : Tuple = common_inputs["decoder_input_ids"].shape[1]
_lowerCamelCase , _lowerCamelCase : Optional[Any] = self.num_attention_heads
_lowerCamelCase : List[str] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_lowerCamelCase : str = decoder_seq_length + 3
_lowerCamelCase : List[str] = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
_lowerCamelCase : Optional[Any] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(__A,__A )],dim=1 )
_lowerCamelCase : Any = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
_lowerCamelCase , _lowerCamelCase : Tuple = self.num_layers
_lowerCamelCase : Optional[int] = min(__A,__A )
_lowerCamelCase : Optional[Any] = max(__A,__A ) - min_num_layers
_lowerCamelCase : str = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(__A ):
common_inputs["past_key_values"].append(
(
torch.zeros(__A ),
torch.zeros(__A ),
torch.zeros(__A ),
torch.zeros(__A ),
) )
# TODO: test this.
_lowerCamelCase : Optional[Any] = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(__A,__A ):
common_inputs["past_key_values"].append((torch.zeros(__A ), torch.zeros(__A )) )
return common_inputs
def lowerCamelCase_ ( self : Tuple,__A : PreTrainedTokenizer,__A : int = -1,__A : int = -1,__A : bool = False,__A : Optional[TensorType] = None,):
_lowerCamelCase : Any = self._generate_dummy_inputs_for_encoder_and_decoder(
__A,__A,__A,__A,__A )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
_lowerCamelCase , _lowerCamelCase : List[str] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
_lowerCamelCase : Union[str, Any] = seqlen + 2
_lowerCamelCase , _lowerCamelCase : Any = self.num_layers
_lowerCamelCase , _lowerCamelCase : Any = self.num_attention_heads
_lowerCamelCase : int = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_lowerCamelCase : List[str] = common_inputs["attention_mask"].dtype
_lowerCamelCase : Any = torch.cat(
[common_inputs["attention_mask"], torch.ones(__A,__A,dtype=__A )],dim=1 )
_lowerCamelCase : List[Any] = [
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(__A )
]
return common_inputs
def lowerCamelCase_ ( self : Optional[Any],__A : PreTrainedTokenizer,__A : int = -1,__A : int = -1,__A : bool = False,__A : Optional[TensorType] = None,):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_lowerCamelCase : List[str] = compute_effective_axis_dimension(
__A,fixed_dimension=OnnxConfig.default_fixed_batch,num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_lowerCamelCase : Any = tokenizer.num_special_tokens_to_add(__A )
_lowerCamelCase : List[str] = compute_effective_axis_dimension(
__A,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=__A )
# Generate dummy inputs according to compute batch and sequence
_lowerCamelCase : Optional[Any] = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
_lowerCamelCase : str = dict(tokenizer(__A,return_tensors=__A ) )
return common_inputs
def lowerCamelCase_ ( self : Union[str, Any],__A : PreTrainedTokenizer,__A : int = -1,__A : int = -1,__A : bool = False,__A : Optional[TensorType] = None,):
if self.task in ["default", "seq2seq-lm"]:
_lowerCamelCase : Any = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__A,batch_size=__A,seq_length=__A,is_pair=__A,framework=__A )
else:
_lowerCamelCase : Dict = self._generate_dummy_inputs_for_causal_lm(
__A,batch_size=__A,seq_length=__A,is_pair=__A,framework=__A )
return common_inputs
def lowerCamelCase_ ( self : Optional[Any],__A : str,__A : Optional[int],__A : str,__A : Any ):
if self.task in ["default", "seq2seq-lm"]:
_lowerCamelCase : int = super()._flatten_past_key_values_(__A,__A,__A,__A )
else:
_lowerCamelCase : Union[str, Any] = super(__A,self )._flatten_past_key_values_(
__A,__A,__A,__A )
@property
def lowerCamelCase_ ( self : List[str] ):
return 1e-4
| 44 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase__ :
def __init__( self : List[Any],__A : str,__A : List[str]=1_3,__A : str=3_2,__A : Tuple=2,__A : Any=3,__A : Dict=1_6,__A : Dict=[3_2, 6_4, 1_2_8],__A : List[str]=[1, 2, 1],__A : str=[2, 2, 4],__A : Optional[int]=2,__A : Dict=2.0,__A : str=True,__A : Tuple=0.0,__A : int=0.0,__A : List[str]=0.1,__A : Any="gelu",__A : List[Any]=False,__A : Optional[Any]=True,__A : List[str]=0.02,__A : Tuple=1e-5,__A : Any=True,__A : Tuple=None,__A : Tuple=True,__A : Tuple=1_0,__A : List[Any]=8,__A : Optional[int]=["stage1", "stage2"],__A : int=[1, 2],):
_lowerCamelCase : List[Any] = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : Optional[int] = image_size
_lowerCamelCase : int = patch_size
_lowerCamelCase : Optional[Any] = num_channels
_lowerCamelCase : int = embed_dim
_lowerCamelCase : int = hidden_sizes
_lowerCamelCase : List[Any] = depths
_lowerCamelCase : Any = num_heads
_lowerCamelCase : List[str] = window_size
_lowerCamelCase : str = mlp_ratio
_lowerCamelCase : Any = qkv_bias
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : List[str] = drop_path_rate
_lowerCamelCase : str = hidden_act
_lowerCamelCase : Union[str, Any] = use_absolute_embeddings
_lowerCamelCase : List[Any] = patch_norm
_lowerCamelCase : Tuple = layer_norm_eps
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Optional[int] = is_training
_lowerCamelCase : Tuple = scope
_lowerCamelCase : List[Any] = use_labels
_lowerCamelCase : int = type_sequence_label_size
_lowerCamelCase : Tuple = encoder_stride
_lowerCamelCase : Any = out_features
_lowerCamelCase : Any = out_indices
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = None
if self.use_labels:
_lowerCamelCase : str = ids_tensor([self.batch_size],self.type_sequence_label_size )
_lowerCamelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Union[str, Any] ):
return FocalNetConfig(
image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,embed_dim=self.embed_dim,hidden_sizes=self.hidden_sizes,depths=self.depths,num_heads=self.num_heads,window_size=self.window_size,mlp_ratio=self.mlp_ratio,qkv_bias=self.qkv_bias,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,drop_path_rate=self.drop_path_rate,hidden_act=self.hidden_act,use_absolute_embeddings=self.use_absolute_embeddings,path_norm=self.patch_norm,layer_norm_eps=self.layer_norm_eps,initializer_range=self.initializer_range,encoder_stride=self.encoder_stride,out_features=self.out_features,out_indices=self.out_indices,)
def lowerCamelCase_ ( self : int,__A : Union[str, Any],__A : Tuple,__A : List[Any] ):
_lowerCamelCase : Optional[Any] = FocalNetModel(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[Any] = model(__A )
_lowerCamelCase : Optional[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_lowerCamelCase : Union[str, Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, expected_seq_len, expected_dim) )
def lowerCamelCase_ ( self : int,__A : Optional[int],__A : int,__A : Optional[int] ):
_lowerCamelCase : Any = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ),len(config.out_features ) )
self.parent.assertListEqual(model.channels,config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
_lowerCamelCase : List[str] = None
_lowerCamelCase : List[str] = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ),1 )
self.parent.assertListEqual(model.channels,[config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : Optional[int],__A : Optional[int],__A : Dict,__A : Dict ):
_lowerCamelCase : List[Any] = FocalNetForMaskedImageModeling(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
self.parent.assertEqual(
result.reconstruction.shape,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_lowerCamelCase : Dict = 1
_lowerCamelCase : Any = FocalNetForMaskedImageModeling(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : Optional[int] = model(__A )
self.parent.assertEqual(result.reconstruction.shape,(self.batch_size, 1, self.image_size, self.image_size) )
def lowerCamelCase_ ( self : List[Any],__A : Union[str, Any],__A : List[Any],__A : Optional[Any] ):
_lowerCamelCase : Union[str, Any] = self.type_sequence_label_size
_lowerCamelCase : Optional[Any] = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[int] = model(__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_lowerCamelCase : str = 1
_lowerCamelCase : str = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = model(__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : int = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Union[str, Any] = config_and_inputs
_lowerCamelCase : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[int] = FocalNetModelTester(self )
_lowerCamelCase : int = ConfigTester(self,config_class=__A,embed_dim=3_7,has_text_modality=__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
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 lowerCamelCase_ ( self : List[str] ):
return
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
@unittest.skip(reason="FocalNet does not use inputs_embeds" )
def lowerCamelCase_ ( self : Optional[int] ):
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking" )
def lowerCamelCase_ ( self : List[str] ):
pass
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase , _lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : str = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(),(nn.Module) )
_lowerCamelCase : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A,nn.Linear ) )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : Union[str, Any] = model_class(__A )
_lowerCamelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase : int = [*signature.parameters.keys()]
_lowerCamelCase : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1],__A )
def lowerCamelCase_ ( self : Tuple,__A : Any,__A : List[Any],__A : str,__A : Any ):
_lowerCamelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
_lowerCamelCase : Optional[int] = model(**self._prepare_for_class(__A,__A ) )
_lowerCamelCase : Optional[int] = outputs.hidden_states
_lowerCamelCase : int = getattr(
self.model_tester,"expected_num_hidden_layers",len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__A ),__A )
# FocalNet has a different seq_length
_lowerCamelCase : Optional[Any] = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : List[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
_lowerCamelCase : Any = outputs.reshaped_hidden_states
self.assertEqual(len(__A ),__A )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Tuple = reshaped_hidden_states[0].shape
_lowerCamelCase : List[str] = (
reshaped_hidden_states[0].view(__A,__A,height * width ).permute(0,2,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase , _lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase , _lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Tuple = 3
_lowerCamelCase : Optional[int] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
_lowerCamelCase : Tuple = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : Any = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_lowerCamelCase : int = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : Optional[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
@slow
def lowerCamelCase_ ( self : Tuple ):
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = FocalNetModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase , _lowerCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = _config_zero_init(__A )
for model_class in self.all_model_classes:
_lowerCamelCase : Any = model_class(config=__A )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),[0.0, 1.0],msg=f'Parameter {name} of model {model_class} seems not properly initialized',)
@require_vision
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : Union[str, Any] ):
# TODO update organization
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny" ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny" ).to(__A )
_lowerCamelCase : int = self.default_image_processor
_lowerCamelCase : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_lowerCamelCase : Dict = image_processor(images=__A,return_tensors="pt" ).to(__A )
# forward pass
with torch.no_grad():
_lowerCamelCase : Dict = model(**__A )
# verify the logits
_lowerCamelCase : List[Any] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape,__A )
_lowerCamelCase : List[str] = torch.tensor([0.2166, -0.4368, 0.2191] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3],__A,atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item(),2_8_1 )
@require_torch
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = (FocalNetBackbone,) if is_torch_available() else ()
lowerCAmelCase_ = FocalNetConfig
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : int = FocalNetModelTester(self )
| 44 | 1 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Tuple = F'Input value of [number={number}] must be an integer'
raise TypeError(_lowerCAmelCase )
if number < 1:
_lowerCamelCase : Any = F'Input value of [number={number}] must be > 0'
raise ValueError(_lowerCAmelCase )
elif number == 1:
return 3
elif number == 2:
return 5
else:
_lowerCamelCase : int = int(math.log(number // 3 , 2 ) ) + 2
_lowerCamelCase : str = [3, 5]
_lowerCamelCase : Optional[int] = 2
_lowerCamelCase : Union[str, Any] = 3
for block in range(1 , _lowerCAmelCase ):
for _ in range(_lowerCAmelCase ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
UpperCAmelCase_ : int = 0
try:
UpperCAmelCase_ : List[Any] = proth(number)
except ValueError:
print(f'''ValueError: there is no {number}th Proth number''')
continue
print(f'''The {number}th Proth number: {value}''')
| 44 |
'''simple docstring'''
class UpperCAmelCase__ :
def __init__( self : Any,__A : Any,__A : Any,__A : Any ):
_lowerCamelCase : List[Any] = name
_lowerCamelCase : Union[str, Any] = value
_lowerCamelCase : str = weight
def __repr__( self : Any ):
return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def lowerCamelCase_ ( self : Optional[int] ):
return self.value
def lowerCamelCase_ ( self : Any ):
return self.name
def lowerCamelCase_ ( self : List[Any] ):
return self.weight
def lowerCamelCase_ ( self : str ):
return self.value / self.weight
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = []
for i in range(len(_lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = sorted(_lowerCAmelCase , key=_lowerCAmelCase , reverse=_lowerCAmelCase )
_lowerCamelCase : Optional[int] = []
_lowerCamelCase , _lowerCamelCase : Optional[int] = 0.0, 0.0
for i in range(len(_lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def A_ ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 | 1 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
UpperCAmelCase_ : Union[str, Any] = [
'cross_validation.py',
'gradient_accumulation.py',
'local_sgd.py',
'multi_process_metrics.py',
'memory.py',
'automatic_gradient_accumulation.py',
'fsdp_with_peak_mem_tracking.py',
'deepspeed_with_config_support.py',
'megatron_lm_gpt_pretraining.py',
]
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCase_ ( self : str,__A : str,__A : bool,__A : str = None,__A : list = None ):
_lowerCamelCase : List[str] = None
_lowerCamelCase : Optional[Any] = os.path.abspath(os.path.join("examples","by_feature" ) )
_lowerCamelCase : Optional[Any] = os.path.abspath("examples" )
for item in os.listdir(__A ):
if item not in EXCLUDE_EXAMPLES:
_lowerCamelCase : Any = os.path.join(__A,__A )
if os.path.isfile(__A ) and ".py" in item_path:
with self.subTest(
tested_script=__A,feature_script=__A,tested_section="main()" if parser_only else "training_function()",):
_lowerCamelCase : List[Any] = compare_against_test(
os.path.join(__A,__A ),__A,__A,__A )
_lowerCamelCase : Optional[Any] = "\n".join(__A )
if special_strings is not None:
for string in special_strings:
_lowerCamelCase : Union[str, Any] = diff.replace(__A,"" )
self.assertEqual(__A,"" )
def lowerCamelCase_ ( self : Union[str, Any] ):
self.one_complete_example("complete_nlp_example.py",__A )
self.one_complete_example("complete_nlp_example.py",__A )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : List[str] = os.path.abspath(os.path.join("examples","cv_example.py" ) )
_lowerCamelCase : Tuple = [
" " * 1_6 + "{\n\n",
" " * 2_0 + "\"accuracy\": eval_metric[\"accuracy\"],\n\n",
" " * 2_0 + "\"f1\": eval_metric[\"f1\"],\n\n",
" " * 2_0 + "\"train_loss\": total_loss.item() / len(train_dataloader),\n\n",
" " * 2_0 + "\"epoch\": epoch,\n\n",
" " * 1_6 + "},\n\n",
" " * 1_6 + "step=epoch,\n",
" " * 1_2,
" " * 8 + "for step, batch in enumerate(active_dataloader):\n",
]
self.one_complete_example("complete_cv_example.py",__A,__A,__A )
self.one_complete_example("complete_cv_example.py",__A,__A,__A )
@mock.patch.dict(os.environ , {'TESTING_MOCKED_DATALOADERS': '1'} )
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = False
@classmethod
def lowerCamelCase_ ( cls : int ):
super().setUpClass()
_lowerCamelCase : Tuple = tempfile.mkdtemp()
_lowerCamelCase : List[Any] = os.path.join(cls._tmpdir,"default_config.yml" )
write_basic_config(save_location=cls.configPath )
_lowerCamelCase : str = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def lowerCamelCase_ ( cls : Tuple ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : Tuple = f'\n examples/by_feature/checkpointing.py\n --checkpointing_steps epoch\n --output_dir {self.tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir,"epoch_0" ) ) )
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase : List[str] = f'\n examples/by_feature/checkpointing.py\n --checkpointing_steps 1\n --output_dir {self.tmpdir}\n '.split()
_lowerCamelCase : List[str] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir,"step_2" ) ) )
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase : Optional[Any] = f'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir,"epoch_0" )}\n '.split()
_lowerCamelCase : Optional[Any] = run_command(self._launch_args + testargs,return_stdout=__A )
self.assertNotIn("epoch 0:",__A )
self.assertIn("epoch 1:",__A )
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : Any = f'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir,"step_2" )}\n '.split()
_lowerCamelCase : List[Any] = run_command(self._launch_args + testargs,return_stdout=__A )
if torch.cuda.is_available():
_lowerCamelCase : Union[str, Any] = torch.cuda.device_count()
else:
_lowerCamelCase : Any = 1
if num_processes > 1:
self.assertNotIn("epoch 0:",__A )
self.assertIn("epoch 1:",__A )
else:
self.assertIn("epoch 0:",__A )
self.assertIn("epoch 1:",__A )
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : List[str] = "\n examples/by_feature/cross_validation.py\n --num_folds 2\n ".split()
with mock.patch.dict(os.environ,{"TESTING_MOCKED_DATALOADERS": "0"} ):
_lowerCamelCase : Optional[Any] = run_command(self._launch_args + testargs,return_stdout=__A )
_lowerCamelCase : List[Any] = re.findall("({.+})",__A )
_lowerCamelCase : int = [r for r in results if "accuracy" in r][-1]
_lowerCamelCase : Optional[Any] = ast.literal_eval(__A )
self.assertGreaterEqual(results["accuracy"],0.75 )
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : Tuple = ["examples/by_feature/multi_process_metrics.py"]
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ,{"WANDB_MODE": "offline"} )
def lowerCamelCase_ ( self : Optional[Any] ):
with tempfile.TemporaryDirectory() as tmpdir:
_lowerCamelCase : Tuple = f'\n examples/by_feature/tracking.py\n --with_tracking\n --project_dir {tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(__A,"tracking" ) ) )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : List[str] = ["examples/by_feature/gradient_accumulation.py"]
run_command(self._launch_args + testargs )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : Optional[int] = ["examples/by_feature/local_sgd.py"]
run_command(self._launch_args + testargs )
| 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : List[Any] = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = ['ConditionalDetrFeatureExtractor']
UpperCAmelCase_ : str = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : str = [
'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConditionalDetrForObjectDetection',
'ConditionalDetrForSegmentation',
'ConditionalDetrModel',
'ConditionalDetrPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
UpperCAmelCase_ : Optional[Any] = None
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : str = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
UpperCAmelCase_ : str = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
),
},
}
UpperCAmelCase_ : int = {
'facebook/nllb-large-en-ro': 1024,
'facebook/nllb-200-distilled-600M': 1024,
}
# fmt: off
UpperCAmelCase_ : List[str] = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = ['input_ids', 'attention_mask']
lowerCAmelCase_ = NllbTokenizer
lowerCAmelCase_ = []
lowerCAmelCase_ = []
def __init__( self : Dict,__A : Any=None,__A : List[Any]=None,__A : List[str]="<s>",__A : Union[str, Any]="</s>",__A : Optional[int]="</s>",__A : str="<s>",__A : List[Any]="<unk>",__A : Any="<pad>",__A : List[str]="<mask>",__A : Any=None,__A : List[Any]=None,__A : Union[str, Any]=None,__A : Dict=False,**__A : Union[str, Any],):
# Mask token behave like a normal word, i.e. include the space before it
_lowerCamelCase : Optional[int] = AddedToken(__A,lstrip=__A,rstrip=__A ) if isinstance(__A,__A ) else mask_token
_lowerCamelCase : Tuple = legacy_behaviour
super().__init__(
vocab_file=__A,tokenizer_file=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,src_lang=__A,tgt_lang=__A,additional_special_tokens=__A,legacy_behaviour=__A,**__A,)
_lowerCamelCase : Dict = vocab_file
_lowerCamelCase : int = False if not self.vocab_file else True
_lowerCamelCase : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
_lowerCamelCase : Dict = {
lang_code: self.convert_tokens_to_ids(__A ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
_lowerCamelCase : Union[str, Any] = src_lang if src_lang is not None else "eng_Latn"
_lowerCamelCase : Optional[int] = self.convert_tokens_to_ids(self._src_lang )
_lowerCamelCase : str = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def lowerCamelCase_ ( self : Optional[int] ):
return self._src_lang
@src_lang.setter
def lowerCamelCase_ ( self : List[str],__A : str ):
_lowerCamelCase : List[Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowerCamelCase_ ( self : List[Any],__A : List[int],__A : Optional[List[int]] = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowerCamelCase_ ( self : List[str],__A : List[int],__A : Optional[List[int]] = None ):
_lowerCamelCase : Optional[int] = [self.sep_token_id]
_lowerCamelCase : Dict = [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 lowerCamelCase_ ( self : str,__A : Tuple,__A : str,__A : Optional[str],__A : Optional[str],**__A : str ):
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
_lowerCamelCase : int = src_lang
_lowerCamelCase : Optional[int] = self(__A,add_special_tokens=__A,return_tensors=__A,**__A )
_lowerCamelCase : Optional[Any] = self.convert_tokens_to_ids(__A )
_lowerCamelCase : Any = tgt_lang_id
return inputs
def lowerCamelCase_ ( self : int,__A : List[str],__A : str = "eng_Latn",__A : Optional[List[str]] = None,__A : str = "fra_Latn",**__A : Tuple,):
_lowerCamelCase : Any = src_lang
_lowerCamelCase : Tuple = tgt_lang
return super().prepare_seqaseq_batch(__A,__A,**__A )
def lowerCamelCase_ ( self : List[Any] ):
return self.set_src_lang_special_tokens(self.src_lang )
def lowerCamelCase_ ( self : Optional[Any] ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowerCamelCase_ ( self : Any,__A : str ):
_lowerCamelCase : int = self.convert_tokens_to_ids(__A )
if self.legacy_behaviour:
_lowerCamelCase : Optional[int] = []
_lowerCamelCase : Dict = [self.eos_token_id, self.cur_lang_code]
else:
_lowerCamelCase : Optional[int] = [self.cur_lang_code]
_lowerCamelCase : Any = [self.eos_token_id]
_lowerCamelCase : int = self.convert_ids_to_tokens(self.prefix_tokens )
_lowerCamelCase : List[str] = self.convert_ids_to_tokens(self.suffix_tokens )
_lowerCamelCase : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str,pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,self.prefix_tokens + self.suffix_tokens ) ),)
def lowerCamelCase_ ( self : List[str],__A : str ):
_lowerCamelCase : Dict = self.convert_tokens_to_ids(__A )
if self.legacy_behaviour:
_lowerCamelCase : Dict = []
_lowerCamelCase : Union[str, Any] = [self.eos_token_id, self.cur_lang_code]
else:
_lowerCamelCase : List[Any] = [self.cur_lang_code]
_lowerCamelCase : Tuple = [self.eos_token_id]
_lowerCamelCase : int = self.convert_ids_to_tokens(self.prefix_tokens )
_lowerCamelCase : Tuple = self.convert_ids_to_tokens(self.suffix_tokens )
_lowerCamelCase : List[str] = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str,pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,self.prefix_tokens + self.suffix_tokens ) ),)
def lowerCamelCase_ ( self : str,__A : str,__A : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(__A ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
return
_lowerCamelCase : List[str] = os.path.join(
__A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ):
copyfile(self.vocab_file,__A )
return (out_vocab_file,)
| 44 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Tuple = tmp_path / "file.csv"
_lowerCamelCase : Optional[int] = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Any = tmp_path / "malformed_file.csv"
_lowerCamelCase : Any = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20,\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : int = tmp_path / "csv_with_image.csv"
_lowerCamelCase : int = textwrap.dedent(
F'\\n image\n {image_file}\n ' )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Dict = tmp_path / "csv_with_label.csv"
_lowerCamelCase : int = textwrap.dedent(
"\\n label\n good\n bad\n good\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Dict = tmp_path / "csv_with_int_list.csv"
_lowerCamelCase : Any = textwrap.dedent(
"\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : List[Any] = Csv()
_lowerCamelCase : Any = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(_lowerCAmelCase , match="Error tokenizing data" ):
for _ in generator:
pass
assert any(
record.levelname == "ERROR"
and "Failed to read file" in record.message
and os.path.basename(_lowerCAmelCase ) in record.message
for record in caplog.records )
@require_pil
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
with open(_lowerCAmelCase , encoding="utf-8" ) as f:
_lowerCamelCase : Any = f.read().splitlines()[1]
_lowerCamelCase : Optional[Any] = Csv(encoding="utf-8" , features=Features({"image": Image()} ) )
_lowerCamelCase : Union[str, Any] = csv._generate_tables([[csv_file_with_image]] )
_lowerCamelCase : List[str] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("image" ).type == Image()()
_lowerCamelCase : int = pa_table.to_pydict()["image"]
assert generated_content == [{"path": image_file, "bytes": None}]
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
with open(_lowerCAmelCase , encoding="utf-8" ) as f:
_lowerCamelCase : List[Any] = f.read().splitlines()[1:]
_lowerCamelCase : int = Csv(encoding="utf-8" , features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
_lowerCamelCase : Tuple = csv._generate_tables([[csv_file_with_label]] )
_lowerCamelCase : int = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
_lowerCamelCase : Union[str, Any] = pa_table.to_pydict()["label"]
assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(_lowerCAmelCase ) for label in labels]
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Dict = Csv(encoding="utf-8" , sep="," , converters={"int_list": lambda _lowerCAmelCase : [int(_lowerCAmelCase ) for i in x.split()]} )
_lowerCamelCase : List[Any] = csv._generate_tables([[csv_file_with_int_list]] )
_lowerCamelCase : Optional[int] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
_lowerCamelCase : Optional[Any] = pa_table.to_pydict()["int_list"]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 44 | 1 |
'''simple docstring'''
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 transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Dict = SwinvaConfig()
_lowerCamelCase : List[Any] = swinva_name.split("_" )
_lowerCamelCase : Optional[int] = name_split[1]
if "to" in name_split[3]:
_lowerCamelCase : Optional[int] = int(name_split[3][-3:] )
else:
_lowerCamelCase : Tuple = int(name_split[3] )
if "to" in name_split[2]:
_lowerCamelCase : str = int(name_split[2][-2:] )
else:
_lowerCamelCase : str = int(name_split[2][6:] )
if model_size == "tiny":
_lowerCamelCase : Optional[Any] = 96
_lowerCamelCase : List[Any] = (2, 2, 6, 2)
_lowerCamelCase : int = (3, 6, 12, 24)
elif model_size == "small":
_lowerCamelCase : Optional[int] = 96
_lowerCamelCase : int = (2, 2, 18, 2)
_lowerCamelCase : List[str] = (3, 6, 12, 24)
elif model_size == "base":
_lowerCamelCase : Dict = 128
_lowerCamelCase : List[str] = (2, 2, 18, 2)
_lowerCamelCase : Dict = (4, 8, 16, 32)
else:
_lowerCamelCase : List[Any] = 192
_lowerCamelCase : Any = (2, 2, 18, 2)
_lowerCamelCase : Optional[int] = (6, 12, 24, 48)
if "to" in swinva_name:
_lowerCamelCase : Optional[int] = (12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
_lowerCamelCase : Dict = 21841
_lowerCamelCase : List[Any] = "huggingface/label-files"
_lowerCamelCase : List[Any] = "imagenet-22k-id2label.json"
_lowerCamelCase : Union[str, Any] = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : int = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : Union[str, Any] = idalabel
_lowerCamelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
else:
_lowerCamelCase : str = 1000
_lowerCamelCase : int = "huggingface/label-files"
_lowerCamelCase : Optional[Any] = "imagenet-1k-id2label.json"
_lowerCamelCase : Dict = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : Tuple = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : Union[str, Any] = idalabel
_lowerCamelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
_lowerCamelCase : Any = img_size
_lowerCamelCase : Tuple = num_classes
_lowerCamelCase : Any = embed_dim
_lowerCamelCase : str = depths
_lowerCamelCase : List[Any] = num_heads
_lowerCamelCase : List[Any] = window_size
return config
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if "patch_embed.proj" in name:
_lowerCamelCase : Union[str, Any] = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
_lowerCamelCase : Optional[int] = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
_lowerCamelCase : List[str] = "encoder." + name
if "attn.proj" in name:
_lowerCamelCase : Optional[Any] = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
_lowerCamelCase : int = name.replace("attn" , "attention.self" )
if "norm1" in name:
_lowerCamelCase : str = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
_lowerCamelCase : List[str] = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
_lowerCamelCase : str = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
_lowerCamelCase : int = name.replace("mlp.fc2" , "output.dense" )
if "q_bias" in name:
_lowerCamelCase : Union[str, Any] = name.replace("q_bias" , "query.bias" )
if "k_bias" in name:
_lowerCamelCase : List[Any] = name.replace("k_bias" , "key.bias" )
if "v_bias" in name:
_lowerCamelCase : Any = name.replace("v_bias" , "value.bias" )
if "cpb_mlp" in name:
_lowerCamelCase : Any = name.replace("cpb_mlp" , "continuous_position_bias_mlp" )
if name == "norm.weight":
_lowerCamelCase : List[str] = "layernorm.weight"
if name == "norm.bias":
_lowerCamelCase : List[Any] = "layernorm.bias"
if "head" in name:
_lowerCamelCase : Tuple = name.replace("head" , "classifier" )
else:
_lowerCamelCase : Any = "swinv2." + name
return name
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCamelCase : Any = orig_state_dict.pop(_lowerCAmelCase )
if "mask" in key:
continue
elif "qkv" in key:
_lowerCamelCase : str = key.split("." )
_lowerCamelCase : Any = int(key_split[1] )
_lowerCamelCase : Optional[Any] = int(key_split[3] )
_lowerCamelCase : Tuple = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_lowerCamelCase : Optional[int] = val[:dim, :]
_lowerCamelCase : Dict = val[dim : dim * 2, :]
_lowerCamelCase : Union[str, Any] = val[-dim:, :]
else:
_lowerCamelCase : int = val[:dim]
_lowerCamelCase : str = val[
dim : dim * 2
]
_lowerCamelCase : str = val[-dim:]
else:
_lowerCamelCase : List[Any] = val
return orig_state_dict
def A_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : List[str] = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
_lowerCamelCase : Dict = get_swinva_config(_lowerCAmelCase )
_lowerCamelCase : Any = SwinvaForImageClassification(_lowerCAmelCase )
model.eval()
_lowerCamelCase : Optional[int] = convert_state_dict(timm_model.state_dict() , _lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
_lowerCamelCase : List[str] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Tuple = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_" , "-" ) ) )
_lowerCamelCase : Tuple = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
_lowerCamelCase : Any = image_processor(images=_lowerCAmelCase , return_tensors="pt" )
_lowerCamelCase : Dict = timm_model(inputs["pixel_values"] )
_lowerCamelCase : List[Any] = model(**_lowerCAmelCase ).logits
assert torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1E-3 )
print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_lowerCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_lowerCAmelCase )
model.push_to_hub(
repo_path_or_name=Path(_lowerCAmelCase , _lowerCAmelCase ) , organization="nandwalritik" , commit_message="Add model" , )
if __name__ == "__main__":
UpperCAmelCase_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swinv2_name',
default='swinv2_tiny_patch4_window8_256',
type=str,
help='Name of the Swinv2 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.'
)
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 44 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = IFInpaintingSuperResolutionPipeline
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
lowerCAmelCase_ = PipelineTesterMixin.required_optional_params - {'latents'}
def lowerCamelCase_ ( self : List[str] ):
return self._get_superresolution_dummy_components()
def lowerCamelCase_ ( self : str,__A : List[str],__A : List[str]=0 ):
if str(__A ).startswith("mps" ):
_lowerCamelCase : List[str] = torch.manual_seed(__A )
else:
_lowerCamelCase : Optional[int] = torch.Generator(device=__A ).manual_seed(__A )
_lowerCamelCase : List[Any] = floats_tensor((1, 3, 1_6, 1_6),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Any = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Tuple = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Dict = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),reason="XFormers attention is only available with CUDA and `xformers` installed",)
def lowerCamelCase_ ( self : Optional[int] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowerCamelCase_ ( self : Dict ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda",reason="float16 requires CUDA" )
def lowerCamelCase_ ( self : Optional[Any] ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowerCamelCase_ ( self : Any ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowerCamelCase_ ( self : Dict ):
self._test_save_load_local()
def lowerCamelCase_ ( self : Any ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,)
| 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : List[str] = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[Any] = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class UpperCAmelCase__ ( A ):
def __init__( self : List[Any],__A : Tuple,__A : Optional[int],__A : Optional[int]=1_0_2_4,__A : int=1_0_2_4,__A : Any=3.6 ):
_lowerCamelCase : List[str] = tokenizer
_lowerCamelCase : Dict = tokenizer.bos_token_id
_lowerCamelCase : Tuple = dataset
_lowerCamelCase : Any = seq_length
_lowerCamelCase : List[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self : Tuple ):
_lowerCamelCase : Union[str, Any] = iter(self.dataset )
_lowerCamelCase : str = True
while more_examples:
_lowerCamelCase , _lowerCamelCase : Optional[int] = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__A )["content"] )
buffer_len += len(buffer[-1] )
except StopIteration:
_lowerCamelCase : Tuple = False
break
_lowerCamelCase : int = tokenizer(__A,truncation=__A )["input_ids"]
_lowerCamelCase : int = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0,len(__A ),self.seq_length ):
_lowerCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(__A ) == self.seq_length:
yield torch.tensor(__A )
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = {"streaming": True}
_lowerCamelCase : Optional[Any] = load_dataset(args.dataset_name , split="train" , **_lowerCAmelCase )
_lowerCamelCase : int = ConstantLengthDataset(_lowerCAmelCase , _lowerCAmelCase , seq_length=args.seq_length )
_lowerCamelCase : Dict = DataLoader(_lowerCAmelCase , batch_size=args.batch_size )
return eval_dataloader
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
model.eval()
_lowerCamelCase : Optional[int] = []
for step, batch in enumerate(_lowerCAmelCase ):
with torch.no_grad():
_lowerCamelCase : List[str] = model(_lowerCAmelCase , labels=_lowerCAmelCase )
_lowerCamelCase : List[Any] = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_lowerCAmelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
_lowerCamelCase : Dict = torch.mean(torch.cat(_lowerCAmelCase ) )
try:
_lowerCamelCase : List[Any] = torch.exp(_lowerCAmelCase )
except OverflowError:
_lowerCamelCase : Optional[int] = float("inf" )
return loss.item(), perplexity.item()
# Setup Accelerator
UpperCAmelCase_ : List[str] = Accelerator()
# Parse configuration
UpperCAmelCase_ : Tuple = HfArgumentParser(EvaluationArguments)
UpperCAmelCase_ : Dict = parser.parse_args()
set_seed(args.seed)
# Logging
UpperCAmelCase_ : Optional[int] = logging.getLogger(__name__)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
# Load model and tokenizer
UpperCAmelCase_ : Tuple = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase_ : Dict = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
UpperCAmelCase_ : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
UpperCAmelCase_, UpperCAmelCase_ : Dict = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCAmelCase_, UpperCAmelCase_ : str = evaluate(args)
logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 44 | 1 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase__ :
def __init__( self : Optional[Any],__A : list[tuple[float, float]] ):
_lowerCamelCase : Tuple = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
_lowerCamelCase : int = len(__A ) - 1
def lowerCamelCase_ ( self : Optional[int],__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree,__A ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__A ),5 ) == 1
return output_values
def lowerCamelCase_ ( self : int,__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : List[Any] = self.basis_function(__A )
_lowerCamelCase : str = 0.0
_lowerCamelCase : str = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowerCamelCase_ ( self : Optional[Any],__A : float = 0.01 ):
from matplotlib import pyplot as plt # type: ignore
_lowerCamelCase : list[float] = [] # x coordinates of points to plot
_lowerCamelCase : list[float] = [] # y coordinates of points to plot
_lowerCamelCase : Tuple = 0.0
while t <= 1:
_lowerCamelCase : str = self.bezier_curve_function(__A )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
_lowerCamelCase : List[str] = [i[0] for i in self.list_of_points]
_lowerCamelCase : Union[str, Any] = [i[1] for i in self.list_of_points]
plt.plot(
__A,__A,color="blue",label="Curve of Degree " + str(self.degree ),)
plt.scatter(__A,__A,color="red",label="Control Points" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 44 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCAmelCase_ : Union[str, Any] = {
'vocab_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
},
'merges_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
},
'tokenizer_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
},
}
UpperCAmelCase_ : List[str] = {
'allenai/led-base-16384': 1_6384,
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = LEDTokenizer
lowerCAmelCase_ = ['input_ids', 'attention_mask']
def __init__( self : Union[str, Any],__A : List[Any]=None,__A : str=None,__A : str=None,__A : Optional[int]="replace",__A : Union[str, Any]="<s>",__A : Union[str, Any]="</s>",__A : Any="</s>",__A : Optional[int]="<s>",__A : List[str]="<unk>",__A : str="<pad>",__A : Tuple="<mask>",__A : Union[str, Any]=False,__A : Optional[int]=True,**__A : Optional[int],):
super().__init__(
__A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)
_lowerCamelCase : List[str] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : str = getattr(__A,pre_tok_state.pop("type" ) )
_lowerCamelCase : List[Any] = add_prefix_space
_lowerCamelCase : Tuple = pre_tok_class(**__A )
_lowerCamelCase : Optional[int] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
_lowerCamelCase : List[str] = "post_processor"
_lowerCamelCase : int = getattr(self.backend_tokenizer,__A,__A )
if tokenizer_component_instance:
_lowerCamelCase : Tuple = 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 : str = tuple(state["sep"] )
if "cls" in state:
_lowerCamelCase : List[str] = tuple(state["cls"] )
_lowerCamelCase : Dict = False
if state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : List[str] = add_prefix_space
_lowerCamelCase : List[Any] = True
if state.get("trim_offsets",__A ) != trim_offsets:
_lowerCamelCase : List[str] = trim_offsets
_lowerCamelCase : List[str] = True
if changes_to_apply:
_lowerCamelCase : Tuple = getattr(__A,state.pop("type" ) )
_lowerCamelCase : Any = component_class(**__A )
setattr(self.backend_tokenizer,__A,__A )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def lowerCamelCase_ ( self : str ):
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 lowerCamelCase_ ( self : List[str],__A : str ):
_lowerCamelCase : Optional[Any] = AddedToken(__A,lstrip=__A,rstrip=__A ) if isinstance(__A,__A ) else value
_lowerCamelCase : str = value
def lowerCamelCase_ ( self : List[str],*__A : List[Any],**__A : int ):
_lowerCamelCase : List[str] = kwargs.get("is_split_into_words",__A )
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(*__A,**__A )
def lowerCamelCase_ ( self : Optional[int],*__A : Optional[Any],**__A : Union[str, Any] ):
_lowerCamelCase : List[Any] = kwargs.get("is_split_into_words",__A )
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(*__A,**__A )
def lowerCamelCase_ ( self : Dict,__A : str,__A : Optional[str] = None ):
_lowerCamelCase : List[str] = self._tokenizer.model.save(__A,name=__A )
return tuple(__A )
def lowerCamelCase_ ( self : List[str],__A : Optional[Any],__A : List[str]=None ):
_lowerCamelCase : Optional[Any] = [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 lowerCamelCase_ ( self : Dict,__A : List[int],__A : Optional[List[int]] = None ):
_lowerCamelCase : Tuple = [self.sep_token_id]
_lowerCamelCase : Union[str, Any] = [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 lowerCamelCase_ ( self : Any,__A : Union[Dict[str, EncodedInput], BatchEncoding],__A : Optional[int] = None,__A : PaddingStrategy = PaddingStrategy.DO_NOT_PAD,__A : Optional[int] = None,__A : Optional[bool] = None,):
_lowerCamelCase : List[str] = super()._pad(
encoded_inputs=__A,max_length=__A,padding_strategy=__A,pad_to_multiple_of=__A,return_attention_mask=__A,)
# Load from model defaults
if return_attention_mask is None:
_lowerCamelCase : Any = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_lowerCamelCase : Union[str, Any] = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_lowerCamelCase : Optional[Any] = len(encoded_inputs["global_attention_mask"] ) != len(__A )
if needs_to_be_padded:
_lowerCamelCase : str = len(__A ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_lowerCamelCase : Tuple = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
_lowerCamelCase : int = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : str = len(_lowerCAmelCase ) + 1
_lowerCamelCase : Dict = len(_lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
_lowerCamelCase : List[Any] = [[0 for i in range(_lowerCAmelCase )] for j in range(_lowerCAmelCase )]
# since string of zero length match pattern of zero length
_lowerCamelCase : Optional[int] = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _lowerCAmelCase ):
_lowerCamelCase : List[Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _lowerCAmelCase ):
_lowerCamelCase : List[Any] = dp[0][j - 2] if pattern[j - 1] == "*" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _lowerCAmelCase ):
for j in range(1 , _lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
_lowerCamelCase : List[Any] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
_lowerCamelCase : Tuple = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
_lowerCamelCase : Tuple = dp[i - 1][j]
else:
_lowerCamelCase : Dict = 0
else:
_lowerCamelCase : int = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
UpperCAmelCase_ : Optional[Any] = 'aab'
UpperCAmelCase_ : List[str] = 'c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'''{input_string} matches the given pattern {pattern}''')
else:
print(f'''{input_string} does not match with the given pattern {pattern}''')
| 44 |
'''simple docstring'''
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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int]=False ):
"""simple docstring"""
_lowerCamelCase : List[Any] = []
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') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
] )
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"
_lowerCamelCase : Optional[int] = [(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"),
] )
return rename_keys
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any]=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
_lowerCamelCase : int = ""
else:
_lowerCamelCase : int = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase : Any = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_lowerCamelCase : Tuple = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : List[str] = in_proj_weight[
: config.hidden_size, :
]
_lowerCamelCase : List[str] = in_proj_bias[: config.hidden_size]
_lowerCamelCase : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
_lowerCamelCase : List[str] = in_proj_bias[-config.hidden_size :]
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = dct.pop(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = val
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : str = ViTConfig()
_lowerCamelCase : List[str] = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Optional[Any] = int(vit_name[-12:-10] )
_lowerCamelCase : str = int(vit_name[-9:-6] )
else:
_lowerCamelCase : List[Any] = 1000
_lowerCamelCase : str = "huggingface/label-files"
_lowerCamelCase : Any = "imagenet-1k-id2label.json"
_lowerCamelCase : int = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : str = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : Optional[Any] = idalabel
_lowerCamelCase : List[str] = {v: k for k, v in idalabel.items()}
_lowerCamelCase : List[str] = int(vit_name[-6:-4] )
_lowerCamelCase : str = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("tiny" ):
_lowerCamelCase : List[Any] = 192
_lowerCamelCase : Optional[int] = 768
_lowerCamelCase : Union[str, Any] = 12
_lowerCamelCase : Optional[Any] = 3
elif vit_name[9:].startswith("small" ):
_lowerCamelCase : Optional[Any] = 384
_lowerCamelCase : Optional[Any] = 1536
_lowerCamelCase : int = 12
_lowerCamelCase : List[str] = 6
else:
pass
else:
if vit_name[4:].startswith("small" ):
_lowerCamelCase : List[str] = 768
_lowerCamelCase : Optional[Any] = 2304
_lowerCamelCase : List[Any] = 8
_lowerCamelCase : List[Any] = 8
elif vit_name[4:].startswith("base" ):
pass
elif vit_name[4:].startswith("large" ):
_lowerCamelCase : List[Any] = 1024
_lowerCamelCase : Optional[Any] = 4096
_lowerCamelCase : List[Any] = 24
_lowerCamelCase : Union[str, Any] = 16
elif vit_name[4:].startswith("huge" ):
_lowerCamelCase : str = 1280
_lowerCamelCase : List[Any] = 5120
_lowerCamelCase : List[str] = 32
_lowerCamelCase : List[str] = 16
# load original model from timm
_lowerCamelCase : int = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
_lowerCamelCase : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = create_rename_keys(_lowerCAmelCase , _lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# load HuggingFace model
if vit_name[-5:] == "in21k":
_lowerCamelCase : int = ViTModel(_lowerCAmelCase ).eval()
else:
_lowerCamelCase : List[str] = ViTForImageClassification(_lowerCAmelCase ).eval()
model.load_state_dict(_lowerCAmelCase )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
_lowerCamelCase : Union[str, Any] = DeiTImageProcessor(size=config.image_size )
else:
_lowerCamelCase : Union[str, Any] = ViTImageProcessor(size=config.image_size )
_lowerCamelCase : Optional[int] = image_processor(images=prepare_img() , return_tensors="pt" )
_lowerCamelCase : Optional[int] = encoding["pixel_values"]
_lowerCamelCase : Union[str, Any] = model(_lowerCAmelCase )
if base_model:
_lowerCamelCase : int = timm_model.forward_features(_lowerCAmelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_lowerCAmelCase , outputs.pooler_output , atol=1E-3 )
else:
_lowerCamelCase : Union[str, Any] = timm_model(_lowerCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCAmelCase , outputs.logits , atol=1E-3 )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_lowerCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the 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.'
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
UpperCAmelCase_ : Tuple = getLogger(__name__)
UpperCAmelCase_ : Dict = 'cuda' if torch.cuda.is_available() else 'cpu'
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : int = 8 , _lowerCAmelCase : str = DEFAULT_DEVICE , _lowerCAmelCase : Dict=False , _lowerCAmelCase : str="summarization" , _lowerCAmelCase : Dict=None , **_lowerCAmelCase : Tuple , ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = Path(_lowerCAmelCase ).open("w" , encoding="utf-8" )
_lowerCamelCase : str = str(_lowerCAmelCase )
_lowerCamelCase : Dict = AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase ).to(_lowerCAmelCase )
if fpaa:
_lowerCamelCase : int = model.half()
_lowerCamelCase : Any = AutoTokenizer.from_pretrained(_lowerCAmelCase )
logger.info(F'Inferred tokenizer type: {tokenizer.__class__}' ) # if this is wrong, check config.model_type.
_lowerCamelCase : Union[str, Any] = time.time()
# update config with task specific params
use_task_specific_params(_lowerCAmelCase , _lowerCAmelCase )
if prefix is None:
_lowerCamelCase : str = prefix or getattr(model.config , "prefix" , "" ) or ""
for examples_chunk in tqdm(list(chunks(_lowerCAmelCase , _lowerCAmelCase ) ) ):
_lowerCamelCase : Tuple = [prefix + text for text in examples_chunk]
_lowerCamelCase : Any = tokenizer(_lowerCAmelCase , return_tensors="pt" , truncation=_lowerCAmelCase , padding="longest" ).to(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **_lowerCAmelCase , )
_lowerCamelCase : Tuple = tokenizer.batch_decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
for hypothesis in dec:
fout.write(hypothesis + "\n" )
fout.flush()
fout.close()
_lowerCamelCase : Any = int(time.time() - start_time ) # seconds
_lowerCamelCase : Optional[int] = len(_lowerCAmelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def A_ ( ):
"""simple docstring"""
return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )
def A_ ( _lowerCAmelCase : Dict=True ):
"""simple docstring"""
_lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument("model_name" , type=_lowerCAmelCase , help="like facebook/bart-large-cnn,t5-base, etc." )
parser.add_argument("input_path" , type=_lowerCAmelCase , help="like cnn_dm/test.source" )
parser.add_argument("save_path" , type=_lowerCAmelCase , help="where to save summaries" )
parser.add_argument("--reference_path" , type=_lowerCAmelCase , required=_lowerCAmelCase , help="like cnn_dm/test.target" )
parser.add_argument("--score_path" , type=_lowerCAmelCase , required=_lowerCAmelCase , default="metrics.json" , help="where to save metrics" )
parser.add_argument("--device" , type=_lowerCAmelCase , required=_lowerCAmelCase , default=_lowerCAmelCase , help="cuda, cuda:1, cpu etc." )
parser.add_argument(
"--prefix" , type=_lowerCAmelCase , required=_lowerCAmelCase , default=_lowerCAmelCase , help="will be added to the begininng of src examples" )
parser.add_argument("--task" , type=_lowerCAmelCase , default="summarization" , help="used for task_specific_params + metrics" )
parser.add_argument("--bs" , type=_lowerCAmelCase , default=8 , required=_lowerCAmelCase , help="batch size" )
parser.add_argument(
"--n_obs" , type=_lowerCAmelCase , default=-1 , required=_lowerCAmelCase , help="How many observations. Defaults to all." )
parser.add_argument("--fp16" , action="store_true" )
parser.add_argument("--dump-args" , action="store_true" , help="print the custom hparams with the results" )
parser.add_argument(
"--info" , nargs="?" , type=_lowerCAmelCase , const=datetime_now() , help=(
"use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
" lang=en-ru. If no value is passed, the current datetime string will be used."
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
_lowerCamelCase , _lowerCamelCase : List[Any] = parser.parse_known_args()
_lowerCamelCase : List[str] = parse_numeric_n_bool_cl_kwargs(_lowerCAmelCase )
if parsed_args and verbose:
print(F'parsed the following generate kwargs: {parsed_args}' )
_lowerCamelCase : Optional[int] = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
_lowerCamelCase : Optional[Any] = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_lowerCAmelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F'score_path {args.score_path} will be overwritten unless you type ctrl-c.' )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError("Can't mix --fp16 and --device cpu" )
_lowerCamelCase : int = generate_summaries_or_translations(
_lowerCAmelCase , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **_lowerCAmelCase , )
if args.reference_path is None:
return {}
# Compute scores
_lowerCamelCase : List[str] = calculate_bleu if "translation" in args.task else calculate_rouge
_lowerCamelCase : Any = [x.rstrip() for x in open(args.save_path ).readlines()]
_lowerCamelCase : List[Any] = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_lowerCAmelCase )]
_lowerCamelCase : dict = score_fn(_lowerCAmelCase , _lowerCAmelCase )
scores.update(_lowerCAmelCase )
if args.dump_args:
scores.update(_lowerCAmelCase )
if args.info:
_lowerCamelCase : Union[str, Any] = args.info
if verbose:
print(_lowerCAmelCase )
if args.score_path is not None:
json.dump(_lowerCAmelCase , open(args.score_path , "w" ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 44 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( _lowerCAmelCase : int = 5000 ):
"""simple docstring"""
_lowerCamelCase : Dict = [(i * (3 * i - 1)) // 2 for i in range(1 , _lowerCAmelCase )]
for i, pentagonal_i in enumerate(_lowerCAmelCase ):
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : List[Any] = pentagonal_nums[j]
_lowerCamelCase : Any = pentagonal_i + pentagonal_j
_lowerCamelCase : Union[str, Any] = pentagonal_j - pentagonal_i
if is_pentagonal(_lowerCAmelCase ) and is_pentagonal(_lowerCAmelCase ):
return b
return -1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'MCTCTFeatureExtractor'
lowerCAmelCase_ = 'AutoTokenizer'
def __init__( self : Union[str, Any],__A : int,__A : Tuple ):
super().__init__(__A,__A )
_lowerCamelCase : Any = self.feature_extractor
_lowerCamelCase : Dict = False
def __call__( self : Optional[Any],*__A : int,**__A : Union[str, Any] ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__A,**__A )
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
_lowerCamelCase : int = kwargs.pop("raw_speech" )
else:
_lowerCamelCase : int = kwargs.pop("audio",__A )
_lowerCamelCase : Optional[Any] = kwargs.pop("sampling_rate",__A )
_lowerCamelCase : Tuple = kwargs.pop("text",__A )
if len(__A ) > 0:
_lowerCamelCase : List[Any] = args[0]
_lowerCamelCase : int = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
_lowerCamelCase : Tuple = self.feature_extractor(__A,*__A,sampling_rate=__A,**__A )
if text is not None:
_lowerCamelCase : Optional[Any] = self.tokenizer(__A,**__A )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_lowerCamelCase : str = encodings["input_ids"]
return inputs
def lowerCamelCase_ ( self : Optional[Any],*__A : Tuple,**__A : List[str] ):
return self.tokenizer.batch_decode(*__A,**__A )
def lowerCamelCase_ ( self : Union[str, Any],*__A : List[Any],**__A : Optional[Any] ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*__A,**__A )
_lowerCamelCase : Optional[int] = kwargs.pop("input_features",__A )
_lowerCamelCase : int = kwargs.pop("labels",__A )
if len(__A ) > 0:
_lowerCamelCase : str = args[0]
_lowerCamelCase : str = args[1:]
if input_features is not None:
_lowerCamelCase : Tuple = self.feature_extractor.pad(__A,*__A,**__A )
if labels is not None:
_lowerCamelCase : Optional[int] = self.tokenizer.pad(__A,**__A )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
_lowerCamelCase : List[Any] = labels["input_ids"]
return input_features
def lowerCamelCase_ ( self : int,*__A : Dict,**__A : List[str] ):
return self.tokenizer.decode(*__A,**__A )
@contextmanager
def lowerCamelCase_ ( self : List[Any] ):
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your audio inputs, or in a separate call." )
_lowerCamelCase : Union[str, Any] = True
_lowerCamelCase : Union[str, Any] = self.tokenizer
yield
_lowerCamelCase : List[str] = self.feature_extractor
_lowerCamelCase : Optional[Any] = False
| 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileBertForMaskedLM',
'MobileBertForMultipleChoice',
'MobileBertForNextSentencePrediction',
'MobileBertForPreTraining',
'MobileBertForQuestionAnswering',
'MobileBertForSequenceClassification',
'MobileBertForTokenClassification',
'MobileBertLayer',
'MobileBertModel',
'MobileBertPreTrainedModel',
'load_tf_weights_in_mobilebert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileBertForMaskedLM',
'TFMobileBertForMultipleChoice',
'TFMobileBertForNextSentencePrediction',
'TFMobileBertForPreTraining',
'TFMobileBertForQuestionAnswering',
'TFMobileBertForSequenceClassification',
'TFMobileBertForTokenClassification',
'TFMobileBertMainLayer',
'TFMobileBertModel',
'TFMobileBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 | 1 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
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 (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class UpperCAmelCase__ :
def __init__( self : Union[str, Any],__A : Optional[int],__A : Union[str, Any]=1_3,__A : str=7,__A : str=True,__A : Dict=True,__A : int=False,__A : str=True,__A : Optional[Any]=9_9,__A : int=6_4,__A : Tuple=5,__A : Union[str, Any]=4,__A : Tuple=6_4,__A : Union[str, Any]="gelu",__A : Dict=0.1,__A : int=0.1,__A : List[str]=5_1_2,__A : Tuple=1_6,__A : List[str]=2,__A : int=0.02,__A : int=3,__A : int=4,__A : Any=None,):
_lowerCamelCase : Dict = parent
_lowerCamelCase : Tuple = batch_size
_lowerCamelCase : str = seq_length
_lowerCamelCase : Any = is_training
_lowerCamelCase : Dict = use_input_mask
_lowerCamelCase : int = use_token_type_ids
_lowerCamelCase : Optional[Any] = use_labels
_lowerCamelCase : int = vocab_size
_lowerCamelCase : Any = hidden_size
_lowerCamelCase : Tuple = num_hidden_layers
_lowerCamelCase : Optional[int] = num_attention_heads
_lowerCamelCase : List[Any] = intermediate_size
_lowerCamelCase : int = hidden_act
_lowerCamelCase : List[str] = hidden_dropout_prob
_lowerCamelCase : List[str] = attention_probs_dropout_prob
_lowerCamelCase : Optional[int] = max_position_embeddings
_lowerCamelCase : int = type_vocab_size
_lowerCamelCase : List[Any] = type_sequence_label_size
_lowerCamelCase : int = initializer_range
_lowerCamelCase : Any = num_labels
_lowerCamelCase : str = num_choices
_lowerCamelCase : Optional[int] = scope
def lowerCamelCase_ ( self : str ):
return MPNetConfig.from_pretrained("microsoft/mpnet-base" )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length],self.vocab_size )
_lowerCamelCase : Optional[Any] = None
if self.use_input_mask:
_lowerCamelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : int = None
_lowerCamelCase : Dict = None
_lowerCamelCase : int = None
if self.use_labels:
_lowerCamelCase : List[Any] = ids_tensor([self.batch_size],self.type_sequence_label_size )
_lowerCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length],self.num_labels )
_lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size],self.num_choices )
_lowerCamelCase : List[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self : str ):
return MPNetConfig(
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,initializer_range=self.initializer_range,)
def lowerCamelCase_ ( self : Optional[int],__A : Union[str, Any],__A : Any,__A : Dict,__A : List[Any],__A : Optional[int],__A : str ):
_lowerCamelCase : Any = MPNetModel(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A,__A )
_lowerCamelCase : List[str] = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape,(self.batch_size, self.hidden_size) )
def lowerCamelCase_ ( self : str,__A : Tuple,__A : List[str],__A : List[Any],__A : str,__A : Optional[Any],__A : Union[str, Any] ):
_lowerCamelCase : Tuple = MPNetForQuestionAnswering(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[Any] = model(
__A,attention_mask=__A,start_positions=__A,end_positions=__A,)
self.parent.assertEqual(result.start_logits.shape,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape,(self.batch_size, self.seq_length) )
def lowerCamelCase_ ( self : Dict,__A : str,__A : Any,__A : Any,__A : int,__A : List[Any],__A : Optional[Any] ):
_lowerCamelCase : Union[str, Any] = self.num_labels
_lowerCamelCase : Dict = MPNetForSequenceClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Tuple = model(__A,attention_mask=__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : Union[str, Any],__A : Optional[int],__A : List[Any],__A : Tuple,__A : int,__A : Tuple,__A : Any ):
_lowerCamelCase : Union[str, Any] = self.num_choices
_lowerCamelCase : Optional[Any] = MPNetForMultipleChoice(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[Any] = input_ids.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
_lowerCamelCase : int = input_mask.unsqueeze(1 ).expand(-1,self.num_choices,-1 ).contiguous()
_lowerCamelCase : Union[str, Any] = model(
__A,attention_mask=__A,labels=__A,)
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices) )
def lowerCamelCase_ ( self : List[Any],__A : str,__A : List[str],__A : int,__A : Optional[int],__A : Any,__A : Any ):
_lowerCamelCase : Any = self.num_labels
_lowerCamelCase : List[Any] = MPNetForTokenClassification(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A,attention_mask=__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : int = self.prepare_config_and_inputs()
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) : List[Any] = config_and_inputs
_lowerCamelCase : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{
'feature-extraction': MPNetModel,
'fill-mask': MPNetForMaskedLM,
'question-answering': MPNetForQuestionAnswering,
'text-classification': MPNetForSequenceClassification,
'token-classification': MPNetForTokenClassification,
'zero-shot': MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = True
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : Any = MPNetModelTester(self )
_lowerCamelCase : Tuple = ConfigTester(self,config_class=__A,hidden_size=3_7 )
def lowerCamelCase_ ( self : List[Any] ):
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*__A )
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*__A )
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*__A )
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*__A )
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : List[Any] = MPNetModel.from_pretrained("microsoft/mpnet-base" )
_lowerCamelCase : Optional[Any] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
_lowerCamelCase : Dict = model(__A )[0]
_lowerCamelCase : int = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape,__A )
_lowerCamelCase : int = torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3],__A,atol=1e-4 ) )
| 44 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase__ :
def __init__( self : Optional[Any],__A : list[tuple[float, float]] ):
_lowerCamelCase : Tuple = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
_lowerCamelCase : int = len(__A ) - 1
def lowerCamelCase_ ( self : Optional[int],__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree,__A ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__A ),5 ) == 1
return output_values
def lowerCamelCase_ ( self : int,__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : List[Any] = self.basis_function(__A )
_lowerCamelCase : str = 0.0
_lowerCamelCase : str = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowerCamelCase_ ( self : Optional[Any],__A : float = 0.01 ):
from matplotlib import pyplot as plt # type: ignore
_lowerCamelCase : list[float] = [] # x coordinates of points to plot
_lowerCamelCase : list[float] = [] # y coordinates of points to plot
_lowerCamelCase : Tuple = 0.0
while t <= 1:
_lowerCamelCase : str = self.bezier_curve_function(__A )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
_lowerCamelCase : List[str] = [i[0] for i in self.list_of_points]
_lowerCamelCase : Union[str, Any] = [i[1] for i in self.list_of_points]
plt.plot(
__A,__A,color="blue",label="Curve of Degree " + str(self.degree ),)
plt.scatter(__A,__A,color="red",label="Control Points" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : int = [
'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'AdaptiveEmbedding',
'TransfoXLForSequenceClassification',
'TransfoXLLMHeadModel',
'TransfoXLModel',
'TransfoXLPreTrainedModel',
'load_tf_weights_in_transfo_xl',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Tuple = [
'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAdaptiveEmbedding',
'TFTransfoXLForSequenceClassification',
'TFTransfoXLLMHeadModel',
'TFTransfoXLMainLayer',
'TFTransfoXLModel',
'TFTransfoXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=A ):
lowerCAmelCase_ = ['transformers', 'torch', 'note_seq']
def __init__( self : str,*__A : List[str],**__A : List[Any] ):
requires_backends(self,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Optional[Any],*__A : str,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Dict,*__A : Dict,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( _lowerCAmelCase : int = 5000 ):
"""simple docstring"""
_lowerCamelCase : Dict = [(i * (3 * i - 1)) // 2 for i in range(1 , _lowerCAmelCase )]
for i, pentagonal_i in enumerate(_lowerCAmelCase ):
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : List[Any] = pentagonal_nums[j]
_lowerCamelCase : Any = pentagonal_i + pentagonal_j
_lowerCamelCase : Union[str, Any] = pentagonal_j - pentagonal_i
if is_pentagonal(_lowerCAmelCase ) and is_pentagonal(_lowerCAmelCase ):
return b
return -1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = CodeGenTokenizer
lowerCAmelCase_ = CodeGenTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = {'add_prefix_space': True}
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : List[str] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_lowerCamelCase : Dict = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
_lowerCamelCase : Any = dict(zip(__A,range(len(__A ) ) ) )
_lowerCamelCase : Optional[int] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_lowerCamelCase : Tuple = {"unk_token": "<unk>"}
_lowerCamelCase : Optional[Any] = 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(__A ) + "\n" )
with open(self.merges_file,"w",encoding="utf-8" ) as fp:
fp.write("\n".join(__A ) )
def lowerCamelCase_ ( self : Dict,**__A : Tuple ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : Union[str, Any],**__A : int ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : str,__A : Dict ):
_lowerCamelCase : Optional[Any] = "lower newer"
_lowerCamelCase : Union[str, Any] = "lower newer"
return input_text, output_text
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : int = CodeGenTokenizer(self.vocab_file,self.merges_file,**self.special_tokens_map )
_lowerCamelCase : Any = "lower newer"
_lowerCamelCase : Optional[Any] = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
self.assertListEqual(__A,__A )
_lowerCamelCase : Union[str, Any] = tokens + [tokenizer.unk_token]
_lowerCamelCase : Dict = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Any ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase : str = self.get_tokenizer()
_lowerCamelCase : Optional[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = "lower newer"
# Testing tokenization
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
_lowerCamelCase : str = rust_tokenizer.tokenize(__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids without special tokens
_lowerCamelCase : str = tokenizer.encode(__A,add_special_tokens=__A,add_prefix_space=__A )
_lowerCamelCase : List[str] = rust_tokenizer.encode(__A,add_special_tokens=__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids with special tokens
_lowerCamelCase : List[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = tokenizer.encode(__A,add_prefix_space=__A )
_lowerCamelCase : Optional[int] = rust_tokenizer.encode(__A )
self.assertListEqual(__A,__A )
# Testing the unknown token
_lowerCamelCase : Optional[int] = tokens + [rust_tokenizer.unk_token]
_lowerCamelCase : Optional[Any] = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Tuple,*__A : Any,**__A : Any ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def lowerCamelCase_ ( self : int,__A : Optional[int]=1_5 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_lowerCamelCase : Tuple = self.rust_tokenizer_class.from_pretrained(__A,**__A )
# Simple input
_lowerCamelCase : Dict = "This is a simple input"
_lowerCamelCase : Any = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Tuple = ("This is a simple input", "This is a pair")
_lowerCamelCase : Tuple = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
# Pair input
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : str = CodeGenTokenizer.from_pretrained(self.tmpdirname,pad_token="<pad>" )
# Simple input
_lowerCamelCase : Tuple = "This is a simple input"
_lowerCamelCase : Dict = ["This is a simple input looooooooong", "This is a simple input"]
_lowerCamelCase : Dict = ("This is a simple input", "This is a pair")
_lowerCamelCase : Dict = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
_lowerCamelCase : Dict = tokenizer.pad_token_id
_lowerCamelCase : Dict = tokenizer(__A,padding="max_length",max_length=3_0,return_tensors="np" )
_lowerCamelCase : int = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
_lowerCamelCase : List[Any] = tokenizer(*__A,padding="max_length",max_length=6_0,return_tensors="np" )
_lowerCamelCase : Tuple = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1],3_0 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1],3_3 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1],6_0 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1],5_2 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : List[Any] = "$$$"
_lowerCamelCase : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname,bos_token=__A,add_bos_token=__A )
_lowerCamelCase : List[str] = "This is a simple input"
_lowerCamelCase : Optional[Any] = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Union[str, Any] = tokenizer.bos_token_id
_lowerCamelCase : Any = tokenizer(__A )
_lowerCamelCase : List[str] = tokenizer(__A )
self.assertEqual(out_s.input_ids[0],__A )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_lowerCamelCase : int = tokenizer.decode(out_s.input_ids )
_lowerCamelCase : str = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0],__A )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : int = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
_lowerCamelCase : Optional[Any] = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
_lowerCamelCase : Dict = "\nif len_a > len_b: result = a\nelse: result = b"
_lowerCamelCase : Any = tokenizer.encode(__A )
_lowerCamelCase : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
_lowerCamelCase : List[Any] = tokenizer.decode(__A,truncate_before_pattern=__A )
self.assertEqual(__A,__A )
def lowerCamelCase_ ( self : Any ):
pass
| 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : str = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[Any] = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class UpperCAmelCase__ :
def __init__( self : Any,__A : int=2,__A : Any=3,__A : Optional[int]=6_4,__A : Tuple=None ):
_lowerCamelCase : int = np.random.default_rng(__A )
_lowerCamelCase : List[str] = length
_lowerCamelCase : Optional[Any] = rng.normal(size=(length,) ).astype(np.floataa )
_lowerCamelCase : Optional[int] = a * self.x + b + rng.normal(scale=0.1,size=(length,) ).astype(np.floataa )
def __len__( self : Dict ):
return self.length
def __getitem__( self : str,__A : List[str] ):
return {"x": self.x[i], "y": self.y[i]}
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : Optional[Any]=0,__A : Optional[int]=0,__A : Dict=False ):
super().__init__()
_lowerCamelCase : Tuple = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : List[str] = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : Optional[int] = True
def lowerCamelCase_ ( self : List[str],__A : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a[0] + self.b[0]
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : List[str]=0,__A : List[str]=0,__A : int=False ):
super().__init__()
_lowerCamelCase : Optional[int] = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Dict = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Tuple = True
def lowerCamelCase_ ( self : str,__A : List[Any]=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a + self.b
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
_lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("bert-base-cased" )
_lowerCamelCase : List[Any] = {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
_lowerCamelCase : int = load_dataset("csv" , data_files=_lowerCAmelCase )
_lowerCamelCase : Dict = datasets["train"].unique("label" )
_lowerCamelCase : Optional[Any] = {v: i for i, v in enumerate(_lowerCAmelCase )}
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Optional[int] = tokenizer(
examples["sentence1"] , examples["sentence2"] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase , padding="max_length" )
if "label" in examples:
_lowerCamelCase : str = [label_to_id[l] for l in examples["label"]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_lowerCamelCase : Optional[Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["sentence1", "sentence2", "label"] , )
def collate_fn(_lowerCAmelCase : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return tokenizer.pad(_lowerCAmelCase , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
_lowerCamelCase : str = DataLoader(tokenized_datasets["train"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=2 )
_lowerCamelCase : Optional[int] = DataLoader(tokenized_datasets["validation"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=1 )
return train_dataloader, eval_dataloader
| 44 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'gpt_neox'
def __init__( self : Optional[Any],__A : Any=5_0_4_3_2,__A : Optional[Any]=6_1_4_4,__A : Optional[int]=4_4,__A : int=6_4,__A : List[str]=2_4_5_7_6,__A : Union[str, Any]="gelu",__A : Optional[int]=0.25,__A : List[str]=1_0_0_0_0,__A : Optional[Any]=0.0,__A : Optional[Any]=0.0,__A : Tuple=0.1,__A : List[Any]=2_0_4_8,__A : int=0.02,__A : Tuple=1e-5,__A : Dict=True,__A : int=0,__A : Optional[int]=2,__A : int=False,__A : List[Any]=True,__A : List[Any]=None,**__A : Dict,):
super().__init__(bos_token_id=__A,eos_token_id=__A,**__A )
_lowerCamelCase : int = vocab_size
_lowerCamelCase : Dict = max_position_embeddings
_lowerCamelCase : List[Any] = hidden_size
_lowerCamelCase : Optional[int] = num_hidden_layers
_lowerCamelCase : str = num_attention_heads
_lowerCamelCase : Optional[Any] = intermediate_size
_lowerCamelCase : Optional[int] = hidden_act
_lowerCamelCase : List[str] = rotary_pct
_lowerCamelCase : Tuple = rotary_emb_base
_lowerCamelCase : Optional[int] = attention_dropout
_lowerCamelCase : List[Any] = hidden_dropout
_lowerCamelCase : str = classifier_dropout
_lowerCamelCase : List[str] = initializer_range
_lowerCamelCase : Any = layer_norm_eps
_lowerCamelCase : Dict = use_cache
_lowerCamelCase : Dict = tie_word_embeddings
_lowerCamelCase : List[str] = use_parallel_residual
_lowerCamelCase : Tuple = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"The hidden size is not divisble by the number of attention heads! Make sure to update them!" )
def lowerCamelCase_ ( self : Optional[Any] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling,__A ) or len(self.rope_scaling ) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
f'got {self.rope_scaling}' )
_lowerCamelCase : Union[str, Any] = self.rope_scaling.get("type",__A )
_lowerCamelCase : Tuple = self.rope_scaling.get("factor",__A )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(__A,__A ) or rope_scaling_factor <= 1.0:
raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 44 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Optional[Any] = False, False, False
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = None
lowerCAmelCase_ = True
lowerCAmelCase_ = True
lowerCAmelCase_ = None
# Automatically constructed
lowerCAmelCase_ = "dict"
lowerCAmelCase_ = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
lowerCAmelCase_ = field(default='Audio' , init=A , repr=A )
def __call__( self : Tuple ):
return self.pa_type
def lowerCamelCase_ ( self : Any,__A : Union[str, bytes, dict] ):
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
if isinstance(__A,__A ):
return {"bytes": None, "path": value}
elif isinstance(__A,__A ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
_lowerCamelCase : List[Any] = BytesIO()
sf.write(__A,value["array"],value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm" ):
# "PCM" only has raw audio bytes
if value.get("sampling_rate" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
if value.get("bytes" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
_lowerCamelCase : Dict = np.frombuffer(value["bytes"],dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
else:
_lowerCamelCase : str = np.memmap(value["path"],dtype="h",mode="r" ).astype(np.floataa ) / 3_2_7_6_7
_lowerCamelCase : Optional[int] = BytesIO(bytes() )
sf.write(__A,__A,value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' )
def lowerCamelCase_ ( self : Optional[Any],__A : dict,__A : Optional[Dict[str, Union[str, bool, None]]] = None ):
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )
_lowerCamelCase , _lowerCamelCase : Optional[Any] = (value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err
_lowerCamelCase : Tuple = xsplitext(__A )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
if file is None:
_lowerCamelCase : Tuple = token_per_repo_id or {}
_lowerCamelCase : Union[str, Any] = path.split("::" )[-1]
try:
_lowerCamelCase : str = string_to_dict(__A,config.HUB_DATASETS_URL )["repo_id"]
_lowerCamelCase : str = token_per_repo_id[repo_id]
except (ValueError, KeyError):
_lowerCamelCase : Any = None
with xopen(__A,"rb",use_auth_token=__A ) as f:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = sf.read(__A )
else:
_lowerCamelCase , _lowerCamelCase : str = sf.read(__A )
_lowerCamelCase : List[str] = array.T
if self.mono:
_lowerCamelCase : List[str] = librosa.to_mono(__A )
if self.sampling_rate and self.sampling_rate != sampling_rate:
_lowerCamelCase : List[str] = librosa.resample(__A,orig_sr=__A,target_sr=self.sampling_rate )
_lowerCamelCase : Optional[Any] = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def lowerCamelCase_ ( self : Any ):
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature." )
return {
"bytes": Value("binary" ),
"path": Value("string" ),
}
def lowerCamelCase_ ( self : List[str],__A : Union[pa.StringArray, pa.StructArray] ):
if pa.types.is_string(storage.type ):
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
_lowerCamelCase : int = pa.StructArray.from_arrays([bytes_array, storage],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_lowerCamelCase : Dict = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Any = pa.StructArray.from_arrays([storage, path_array],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
_lowerCamelCase : Tuple = pa.array([Audio().encode_example(__A ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
_lowerCamelCase : Tuple = storage.field("bytes" )
else:
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
_lowerCamelCase : List[str] = storage.field("path" )
else:
_lowerCamelCase : Tuple = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Tuple = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=storage.is_null() )
return array_cast(__A,self.pa_type )
def lowerCamelCase_ ( self : str,__A : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(__A : Dict ):
with xopen(__A,"rb" ) as f:
_lowerCamelCase : Any = f.read()
return bytes_
_lowerCamelCase : int = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
],type=pa.binary(),)
_lowerCamelCase : str = pa.array(
[os.path.basename(__A ) if path is not None else None for path in storage.field("path" ).to_pylist()],type=pa.string(),)
_lowerCamelCase : Dict = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=bytes_array.is_null() )
return array_cast(__A,self.pa_type )
| 44 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCAmelCase_ : Optional[int] = 'http://www.mocksite.com/file1.txt'
UpperCAmelCase_ : Any = '"text": ["foo", "foo"]'
UpperCAmelCase_ : int = '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class UpperCAmelCase__ :
lowerCAmelCase_ = 200
lowerCAmelCase_ = {'Content-Length': '100'}
lowerCAmelCase_ = {}
def lowerCamelCase_ ( self : int,**__A : List[str] ):
return [bytes(__A,"utf-8" )]
def A_ ( *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : List[Any] ):
"""simple docstring"""
return MockResponse()
@pytest.mark.parametrize("urls_type" , [str, list, dict] )
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
import requests
monkeypatch.setattr(_lowerCAmelCase , "request" , _lowerCAmelCase )
_lowerCamelCase : Dict = URL
if issubclass(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : str = url
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Any = [url]
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Optional[Any] = {"train": url}
_lowerCamelCase : List[str] = "dummy"
_lowerCamelCase : List[Any] = "downloads"
_lowerCamelCase : Dict = tmp_path
_lowerCamelCase : Any = DownloadConfig(
cache_dir=os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , use_etag=_lowerCAmelCase , )
_lowerCamelCase : Optional[int] = DownloadManager(dataset_name=_lowerCAmelCase , download_config=_lowerCAmelCase )
_lowerCamelCase : str = dl_manager.download(_lowerCAmelCase )
_lowerCamelCase : Tuple = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Dict = [downloaded_paths]
_lowerCamelCase : Dict = [urls]
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
assert "train" in downloaded_paths.keys()
_lowerCamelCase : List[Any] = downloaded_paths.values()
_lowerCamelCase : Optional[Any] = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(_lowerCAmelCase , _lowerCAmelCase ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
_lowerCamelCase : List[Any] = Path(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
_lowerCamelCase : Optional[Any] = downloaded_path.read_text()
assert content == CONTENT
_lowerCamelCase : Any = downloaded_path.with_suffix(".json" )
assert metadata_downloaded_path.exists()
_lowerCamelCase : int = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize("paths_type" , [str, list, dict] )
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = str(_lowerCAmelCase )
if issubclass(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : str = filename
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : str = [filename]
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : str = {"train": filename}
_lowerCamelCase : Any = "dummy"
_lowerCamelCase : int = xz_file.parent
_lowerCamelCase : Optional[Any] = "extracted"
_lowerCamelCase : int = DownloadConfig(
cache_dir=_lowerCAmelCase , use_etag=_lowerCAmelCase , )
_lowerCamelCase : int = DownloadManager(dataset_name=_lowerCAmelCase , download_config=_lowerCAmelCase )
_lowerCamelCase : str = dl_manager.extract(_lowerCAmelCase )
_lowerCamelCase : str = paths
for extracted_paths in [extracted_paths]:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Optional[int] = [extracted_paths]
_lowerCamelCase : str = [paths]
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
assert "train" in extracted_paths.keys()
_lowerCamelCase : Tuple = extracted_paths.values()
_lowerCamelCase : List[str] = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(_lowerCAmelCase , _lowerCAmelCase ):
assert extracted_path == dl_manager.extracted_paths[input_path]
_lowerCamelCase : Optional[int] = Path(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = extracted_path.parts
assert parts[-1] == hash_url_to_filename(_lowerCAmelCase , etag=_lowerCAmelCase )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
_lowerCamelCase : List[str] = extracted_path.read_text()
_lowerCamelCase : Tuple = text_file.read_text()
assert extracted_file_content == expected_file_content
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : int ):
"""simple docstring"""
assert path.endswith(".jsonl" )
for num_items, line in enumerate(_lowerCAmelCase , start=1 ):
_lowerCamelCase : Dict = json.loads(line.decode("utf-8" ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize("archive_jsonl" , ["tar_jsonl_path", "zip_jsonl_path"] )
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = request.getfixturevalue(_lowerCAmelCase )
_lowerCamelCase : List[Any] = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_lowerCAmelCase ) , start=1 ):
_test_jsonl(_lowerCAmelCase , _lowerCAmelCase )
assert num_jsonl == 2
@pytest.mark.parametrize("archive_nested_jsonl" , ["tar_nested_jsonl_path", "zip_nested_jsonl_path"] )
def A_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Dict = request.getfixturevalue(_lowerCAmelCase )
_lowerCamelCase : List[Any] = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_lowerCAmelCase ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_lowerCAmelCase ) , start=1 ):
_test_jsonl(_lowerCAmelCase , _lowerCAmelCase )
assert num_tar == 1
assert num_jsonl == 2
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Any = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(_lowerCAmelCase ) , start=1 ):
assert os.path.basename(_lowerCAmelCase ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 44 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'glpn'
def __init__( self : Tuple,__A : Optional[int]=3,__A : Optional[int]=4,__A : str=[2, 2, 2, 2],__A : Union[str, Any]=[8, 4, 2, 1],__A : Tuple=[3_2, 6_4, 1_6_0, 2_5_6],__A : int=[7, 3, 3, 3],__A : str=[4, 2, 2, 2],__A : int=[1, 2, 5, 8],__A : List[Any]=[4, 4, 4, 4],__A : Optional[int]="gelu",__A : int=0.0,__A : Tuple=0.0,__A : Tuple=0.02,__A : Optional[int]=0.1,__A : Optional[int]=1e-6,__A : Optional[int]=6_4,__A : Optional[Any]=1_0,__A : Tuple=-1,**__A : List[str],):
super().__init__(**__A )
_lowerCamelCase : Tuple = num_channels
_lowerCamelCase : Union[str, Any] = num_encoder_blocks
_lowerCamelCase : Dict = depths
_lowerCamelCase : List[Any] = sr_ratios
_lowerCamelCase : str = hidden_sizes
_lowerCamelCase : Any = patch_sizes
_lowerCamelCase : Any = strides
_lowerCamelCase : Dict = mlp_ratios
_lowerCamelCase : int = num_attention_heads
_lowerCamelCase : List[Any] = hidden_act
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : Optional[int] = initializer_range
_lowerCamelCase : Union[str, Any] = drop_path_rate
_lowerCamelCase : str = layer_norm_eps
_lowerCamelCase : Tuple = decoder_hidden_size
_lowerCamelCase : int = max_depth
_lowerCamelCase : Dict = head_in_index
| 44 | 1 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase_ : Optional[int] = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n'
UpperCAmelCase_ : Tuple = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'
UpperCAmelCase_ : Tuple = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results["google_bleu"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results["google_bleu"], 2))\n 0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase__ ( datasets.Metric ):
def lowerCamelCase_ ( self : Any ):
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string",id="token" ),id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string",id="token" ),id="sequence" ),id="references" ),
} ),)
def lowerCamelCase_ ( self : int,__A : List[List[List[str]]],__A : List[List[str]],__A : int = 1,__A : int = 4,):
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=__A,hypotheses=__A,min_len=__A,max_len=__A )
}
| 44 |
'''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_ : str = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['input_features', 'attention_mask']
def __init__( self : Any,__A : List[Any]=8_0,__A : Dict=1_6_0_0_0,__A : Tuple=0.0,__A : Dict=1_0,__A : int=2_5,__A : Union[str, Any]="hamming_window",__A : List[str]=32768.0,__A : Union[str, Any]=0.97,__A : str=1.0,__A : Union[str, Any]=True,__A : Tuple=True,__A : Optional[Any]=False,**__A : Optional[Any],):
super().__init__(feature_size=__A,sampling_rate=__A,padding_value=__A,**__A )
_lowerCamelCase : Dict = feature_size
_lowerCamelCase : List[str] = sampling_rate
_lowerCamelCase : Any = padding_value
_lowerCamelCase : Dict = hop_length
_lowerCamelCase : Tuple = win_length
_lowerCamelCase : str = frame_signal_scale
_lowerCamelCase : List[str] = preemphasis_coeff
_lowerCamelCase : List[str] = mel_floor
_lowerCamelCase : str = normalize_means
_lowerCamelCase : Any = normalize_vars
_lowerCamelCase : List[str] = win_function
_lowerCamelCase : Tuple = return_attention_mask
_lowerCamelCase : List[Any] = win_length * sampling_rate // 1_0_0_0
_lowerCamelCase : List[Any] = hop_length * sampling_rate // 1_0_0_0
_lowerCamelCase : Any = optimal_fft_length(self.sample_size )
_lowerCamelCase : Dict = (self.n_fft // 2) + 1
def lowerCamelCase_ ( self : Any,__A : np.array ):
if self.win_function == "hamming_window":
_lowerCamelCase : Any = window_function(window_length=self.sample_size,name=self.win_function,periodic=__A )
else:
_lowerCamelCase : Optional[int] = window_function(window_length=self.sample_size,name=self.win_function )
_lowerCamelCase : int = 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 : List[str] = spectrogram(
one_waveform * self.frame_signal_scale,window=__A,frame_length=self.sample_size,hop_length=self.sample_stride,fft_length=self.n_fft,center=__A,preemphasis=self.preemphasis_coeff,mel_filters=__A,mel_floor=self.mel_floor,log_mel="log",)
return msfc_features.T
def lowerCamelCase_ ( self : Optional[int],__A : List[str],__A : Dict,__A : int ):
# make sure we normalize float32 arrays
if self.normalize_means:
_lowerCamelCase : Optional[Any] = x[:input_length].mean(axis=0 )
_lowerCamelCase : Optional[int] = np.subtract(__A,__A )
if self.normalize_vars:
_lowerCamelCase : int = x[:input_length].std(axis=0 )
_lowerCamelCase : Any = np.divide(__A,__A )
if input_length < x.shape[0]:
_lowerCamelCase : Tuple = padding_value
# make sure array is in float32
_lowerCamelCase : Optional[int] = x.astype(np.floataa )
return x
def lowerCamelCase_ ( self : Any,__A : List[np.ndarray],__A : Optional[np.ndarray] = None ):
_lowerCamelCase : Optional[int] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(__A,__A,self.padding_value ) for x, n in zip(__A,__A )]
def __call__( self : Optional[Any],__A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],__A : Union[bool, str, PaddingStrategy] = False,__A : Optional[int] = None,__A : bool = False,__A : Optional[int] = None,__A : Optional[bool] = None,__A : Optional[Union[str, TensorType]] = None,__A : Optional[int] = None,**__A : Optional[Any],):
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 : List[str] = isinstance(__A,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 : List[str] = is_batched_numpy or (
isinstance(__A,(list, tuple) ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list) ))
)
if is_batched:
_lowerCamelCase : List[Any] = [np.asarray(__A,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__A,np.ndarray ):
_lowerCamelCase : Dict = np.asarray(__A,dtype=np.floataa )
elif isinstance(__A,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowerCamelCase : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCamelCase : Tuple = [raw_speech]
# extract fbank features
_lowerCamelCase : str = [self._extract_mfsc_features(__A ) for one_waveform in raw_speech]
# convert into correct format for padding
_lowerCamelCase : Union[str, Any] = BatchFeature({"input_features": features} )
_lowerCamelCase : List[Any] = self.pad(
__A,padding=__A,max_length=__A,truncation=__A,pad_to_multiple_of=__A,return_attention_mask=__A,**__A,)
# make sure list is in array format
_lowerCamelCase : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0],__A ):
_lowerCamelCase : int = [np.asarray(__A,dtype=np.floataa ) for feature in input_features]
_lowerCamelCase : Dict = padded_inputs.get("attention_mask" )
if attention_mask is not None:
_lowerCamelCase : Dict = [np.asarray(__A,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
_lowerCamelCase : Dict = (
np.array(__A,dtype=np.intaa )
if self._get_padding_strategies(__A,max_length=__A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
_lowerCamelCase : Tuple = self.normalize(
padded_inputs["input_features"],attention_mask=__A )
if return_tensors is not None:
_lowerCamelCase : Dict = padded_inputs.convert_to_tensors(__A )
return padded_inputs
| 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileBertForMaskedLM',
'MobileBertForMultipleChoice',
'MobileBertForNextSentencePrediction',
'MobileBertForPreTraining',
'MobileBertForQuestionAnswering',
'MobileBertForSequenceClassification',
'MobileBertForTokenClassification',
'MobileBertLayer',
'MobileBertModel',
'MobileBertPreTrainedModel',
'load_tf_weights_in_mobilebert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileBertForMaskedLM',
'TFMobileBertForMultipleChoice',
'TFMobileBertForNextSentencePrediction',
'TFMobileBertForPreTraining',
'TFMobileBertForQuestionAnswering',
'TFMobileBertForSequenceClassification',
'TFMobileBertForTokenClassification',
'TFMobileBertMainLayer',
'TFMobileBertModel',
'TFMobileBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
UpperCAmelCase_ : int = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = torch.load(_lowerCAmelCase , map_location="cpu" )
return sd
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple=rename_keys_prefix ):
"""simple docstring"""
_lowerCamelCase : Any = OrderedDict()
_lowerCamelCase : str = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
_lowerCamelCase : Any = key
for name_pair in rename_keys_prefix:
_lowerCamelCase : Dict = new_key.replace(name_pair[0] , name_pair[1] )
_lowerCamelCase : Any = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
_lowerCamelCase : List[str] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Dict ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
_lowerCamelCase : Optional[int] = "pretraining"
if "vcr" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
_lowerCamelCase : int = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
_lowerCamelCase : Any = {"visual_embedding_dim": 512}
_lowerCamelCase : List[Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : Tuple = {"visual_embedding_dim": 2048}
_lowerCamelCase : Dict = "vqa_advanced"
elif "vqa" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 2048, "num_labels": 3129}
_lowerCamelCase : Optional[int] = "vqa"
elif "nlvr" in checkpoint_path:
_lowerCamelCase : Tuple = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
_lowerCamelCase : Optional[Any] = "nlvr"
_lowerCamelCase : str = VisualBertConfig(**_lowerCAmelCase )
# Load State Dict
_lowerCamelCase : str = load_state_dict(_lowerCAmelCase )
_lowerCamelCase : List[str] = get_new_dict(_lowerCAmelCase , _lowerCAmelCase )
if model_type == "pretraining":
_lowerCamelCase : List[Any] = VisualBertForPreTraining(_lowerCAmelCase )
elif model_type == "vqa":
_lowerCamelCase : Dict = VisualBertForQuestionAnswering(_lowerCAmelCase )
elif model_type == "nlvr":
_lowerCamelCase : Tuple = VisualBertForVisualReasoning(_lowerCAmelCase )
elif model_type == "multichoice":
_lowerCamelCase : str = VisualBertForMultipleChoice(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
# Save Checkpoints
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
import argparse
import os
import re
UpperCAmelCase_ : int = 'src/transformers'
# Pattern that looks at the indentation in a line.
UpperCAmelCase_ : int = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase_ : Optional[int] = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCAmelCase_ : Any = re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCAmelCase_ : int = re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCAmelCase_ : List[Any] = re.compile(R'\[([^\]]+)\]')
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : List[str] = _re_indent.search(_lowerCAmelCase )
return "" if search is None else search.groups()[0]
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any]="" , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Union[str, Any]=None ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = 0
_lowerCamelCase : Dict = code.split("\n" )
if start_prompt is not None:
while not lines[index].startswith(_lowerCAmelCase ):
index += 1
_lowerCamelCase : List[Any] = ["\n".join(lines[:index] )]
else:
_lowerCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
_lowerCamelCase : Optional[Any] = [lines[index]]
index += 1
while index < len(_lowerCAmelCase ) and (end_prompt is None or not lines[index].startswith(_lowerCAmelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(_lowerCAmelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ):
current_block.append(lines[index] )
blocks.append("\n".join(_lowerCAmelCase ) )
if index < len(_lowerCAmelCase ) - 1:
_lowerCamelCase : List[str] = [lines[index + 1]]
index += 1
else:
_lowerCamelCase : Any = []
else:
blocks.append("\n".join(_lowerCAmelCase ) )
_lowerCamelCase : Any = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(_lowerCAmelCase ) > 0:
blocks.append("\n".join(_lowerCAmelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(_lowerCAmelCase ):
blocks.append("\n".join(lines[index:] ) )
return blocks
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
def _inner(_lowerCAmelCase : List[str] ):
return key(_lowerCAmelCase ).lower().replace("_" , "" )
return _inner
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : str=None ):
"""simple docstring"""
def noop(_lowerCAmelCase : int ):
return x
if key is None:
_lowerCamelCase : Union[str, Any] = noop
# Constants are all uppercase, they go first.
_lowerCamelCase : List[str] = [obj for obj in objects if key(_lowerCAmelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
_lowerCamelCase : Optional[int] = [obj for obj in objects if key(_lowerCAmelCase )[0].isupper() and not key(_lowerCAmelCase ).isupper()]
# Functions begin with a lowercase, they go last.
_lowerCamelCase : Optional[int] = [obj for obj in objects if not key(_lowerCAmelCase )[0].isupper()]
_lowerCamelCase : Dict = ignore_underscore(_lowerCAmelCase )
return sorted(_lowerCAmelCase , key=_lowerCAmelCase ) + sorted(_lowerCAmelCase , key=_lowerCAmelCase ) + sorted(_lowerCAmelCase , key=_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
def _replace(_lowerCAmelCase : List[Any] ):
_lowerCamelCase : Tuple = match.groups()[0]
if "," not in imports:
return F'[{imports}]'
_lowerCamelCase : Optional[int] = [part.strip().replace("\"" , "" ) for part in imports.split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_lowerCamelCase : Optional[Any] = keys[:-1]
return "[" + ", ".join([F'"{k}"' for k in sort_objects(_lowerCAmelCase )] ) + "]"
_lowerCamelCase : int = import_statement.split("\n" )
if len(_lowerCAmelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
_lowerCamelCase : Dict = 2 if lines[1].strip() == "[" else 1
_lowerCamelCase : List[Any] = [(i, _re_strip_line.search(_lowerCAmelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
_lowerCamelCase : Optional[Any] = sort_objects(_lowerCAmelCase , key=lambda _lowerCAmelCase : x[1] )
_lowerCamelCase : Optional[Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(_lowerCAmelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
_lowerCamelCase : List[Any] = _re_bracket_content.sub(_replace , lines[1] )
else:
_lowerCamelCase : int = [part.strip().replace("\"" , "" ) for part in lines[1].split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_lowerCamelCase : int = keys[:-1]
_lowerCamelCase : str = get_indent(lines[1] ) + ", ".join([F'"{k}"' for k in sort_objects(_lowerCAmelCase )] )
return "\n".join(_lowerCAmelCase )
else:
# Finally we have to deal with imports fitting on one line
_lowerCamelCase : List[Any] = _re_bracket_content.sub(_replace , _lowerCAmelCase )
return import_statement
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Dict=True ):
"""simple docstring"""
with open(_lowerCAmelCase , encoding="utf-8" ) as f:
_lowerCamelCase : Tuple = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
_lowerCamelCase : int = split_code_in_indented_blocks(
_lowerCAmelCase , start_prompt="_import_structure = {" , end_prompt="if TYPE_CHECKING:" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(_lowerCAmelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
_lowerCamelCase : List[Any] = main_blocks[block_idx]
_lowerCamelCase : Tuple = block.split("\n" )
# Get to the start of the imports.
_lowerCamelCase : Tuple = 0
while line_idx < len(_lowerCAmelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
_lowerCamelCase : str = len(_lowerCAmelCase )
else:
line_idx += 1
if line_idx >= len(_lowerCAmelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
_lowerCamelCase : Dict = "\n".join(block_lines[line_idx:-1] )
_lowerCamelCase : int = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
_lowerCamelCase : Dict = split_code_in_indented_blocks(_lowerCAmelCase , indent_level=_lowerCAmelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
_lowerCamelCase : List[str] = _re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
_lowerCamelCase : str = [(pattern.search(_lowerCAmelCase ).groups()[0] if pattern.search(_lowerCAmelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
_lowerCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(_lowerCAmelCase ) if key is not None]
_lowerCamelCase : Optional[int] = [x[0] for x in sorted(_lowerCAmelCase , key=lambda _lowerCAmelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
_lowerCamelCase : Dict = 0
_lowerCamelCase : List[str] = []
for i in range(len(_lowerCAmelCase ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
_lowerCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(_lowerCAmelCase )
count += 1
# And we put our main block back together with its first and last line.
_lowerCamelCase : Optional[Any] = "\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(_lowerCAmelCase ):
if check_only:
return True
else:
print(F'Overwriting {file}.' )
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write("\n".join(_lowerCAmelCase ) )
def A_ ( _lowerCAmelCase : Optional[int]=True ):
"""simple docstring"""
_lowerCamelCase : Any = []
for root, _, files in os.walk(_lowerCAmelCase ):
if "__init__.py" in files:
_lowerCamelCase : Optional[Any] = sort_imports(os.path.join(_lowerCAmelCase , "__init__.py" ) , check_only=_lowerCAmelCase )
if result:
_lowerCamelCase : Optional[int] = [os.path.join(_lowerCAmelCase , "__init__.py" )]
if len(_lowerCAmelCase ) > 0:
raise ValueError(F'Would overwrite {len(_lowerCAmelCase )} files, run `make style`.' )
if __name__ == "__main__":
UpperCAmelCase_ : Any = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
UpperCAmelCase_ : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 44 |
'''simple docstring'''
import functools
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(_lowerCAmelCase ) != 3 or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(_lowerCAmelCase ) == 0:
return 0
if min(_lowerCAmelCase ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(_lowerCAmelCase ) >= 366:
raise ValueError("All days elements should be less than 366" )
_lowerCamelCase : Union[str, Any] = set(_lowerCAmelCase )
@functools.cache
def dynamic_programming(_lowerCAmelCase : int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 | 1 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : List[str] = year % 19
_lowerCamelCase : Dict = year % 4
_lowerCamelCase : str = year % 7
_lowerCamelCase : Any = math.floor(year / 100 )
_lowerCamelCase : Union[str, Any] = math.floor((13 + 8 * leap_day_inhibits) / 25 )
_lowerCamelCase : str = leap_day_inhibits / 4
_lowerCamelCase : Tuple = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
_lowerCamelCase : int = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
_lowerCamelCase : str = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
_lowerCamelCase : Any = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(_lowerCAmelCase , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(_lowerCAmelCase , 4 , 18 )
else:
return datetime(_lowerCAmelCase , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1994, 2000, 2010, 2021, 2023):
UpperCAmelCase_ : Any = 'will be' if year > datetime.now().year else 'was'
print(f'''Easter in {year} {tense} {gauss_easter(year)}''')
| 44 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_lowerCamelCase : Dict = MaskFormerConfig(backbone_config=_lowerCAmelCase )
_lowerCamelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 847
_lowerCamelCase : str = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_lowerCamelCase : Optional[int] = 150
_lowerCamelCase : Union[str, Any] = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_lowerCamelCase : Union[str, Any] = 171
_lowerCamelCase : str = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_lowerCamelCase : Optional[int] = 133
_lowerCamelCase : Any = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_lowerCamelCase : str = 19
_lowerCamelCase : Tuple = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_lowerCamelCase : List[Any] = 65
_lowerCamelCase : Optional[int] = "mapillary-vistas-id2label.json"
_lowerCamelCase : Any = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : Optional[int] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
return config
def A_ ( _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : Any = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.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.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight') )
rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias') )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight') )
rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight') )
rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias') )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias') )
# cross-attention out projection
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias') )
# MLP 1
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias') )
# MLP 2
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias') )
# layernorm 3 (final layernorm)
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight') )
rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias') )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight') )
rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias') )
# fmt: on
return rename_keys
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Tuple = dct.pop(_lowerCAmelCase )
_lowerCamelCase : str = val
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_lowerCamelCase : int = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_lowerCamelCase : Union[str, Any] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight' )
_lowerCamelCase : List[str] = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[int] = in_proj_weight[:dim, :]
_lowerCamelCase : Optional[int] = in_proj_bias[: dim]
_lowerCamelCase : List[str] = in_proj_weight[
dim : dim * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
dim : dim * 2
]
_lowerCamelCase : List[Any] = in_proj_weight[
-dim :, :
]
_lowerCamelCase : Union[str, Any] = in_proj_bias[-dim :]
# fmt: on
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : int = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Tuple = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight' )
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Optional[Any] = in_proj_weight[: hidden_size, :]
_lowerCamelCase : Optional[int] = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : Any = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Any = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowerCamelCase : Optional[int] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight' )
_lowerCamelCase : List[Any] = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : Tuple = in_proj_weight[: hidden_size, :]
_lowerCamelCase : str = in_proj_bias[:config.hidden_size]
_lowerCamelCase : str = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowerCamelCase : Optional[int] = in_proj_bias[hidden_size : hidden_size * 2]
_lowerCamelCase : int = in_proj_weight[-hidden_size :, :]
_lowerCamelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# fmt: on
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : List[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[Any] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : bool = False ):
"""simple docstring"""
_lowerCamelCase : Tuple = get_maskformer_config(_lowerCAmelCase )
# load original state_dict
with open(_lowerCAmelCase , "rb" ) as f:
_lowerCamelCase : List[Any] = pickle.load(_lowerCAmelCase )
_lowerCamelCase : Optional[Any] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_lowerCamelCase : List[Any] = create_rename_keys(_lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_swin_q_k_v(_lowerCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_lowerCAmelCase , _lowerCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_lowerCamelCase : Dict = torch.from_numpy(_lowerCAmelCase )
# load 🤗 model
_lowerCamelCase : int = MaskFormerForInstanceSegmentation(_lowerCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_lowerCAmelCase , param.shape )
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_lowerCAmelCase ) == 0, F'Unexpected keys: {unexpected_keys}'
# verify results
_lowerCamelCase : Any = prepare_img()
if "vistas" in model_name:
_lowerCamelCase : Any = 65
elif "cityscapes" in model_name:
_lowerCamelCase : Optional[Any] = 65535
else:
_lowerCamelCase : str = 255
_lowerCamelCase : List[str] = True if "ade" in model_name else False
_lowerCamelCase : Union[str, Any] = MaskFormerImageProcessor(ignore_index=_lowerCAmelCase , reduce_labels=_lowerCAmelCase )
_lowerCamelCase : int = image_processor(_lowerCAmelCase , return_tensors="pt" )
_lowerCamelCase : Tuple = model(**_lowerCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_lowerCamelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
image_processor.save_pretrained(_lowerCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F'nielsr/{model_name}' )
image_processor.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
UpperCAmelCase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='maskformer-swin-tiny-ade',
type=str,
help=('Name of the MaskFormer model you\'d like to convert',),
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
type=str,
help='Path to the original state dict (.pth file).',
)
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.'
)
UpperCAmelCase_ : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 44 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
@dataclass
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = [
'no_inference',
'no_cuda',
'no_tpu',
'no_speed',
'no_memory',
'no_env_print',
'no_multi_process',
]
def __init__( self : Dict,**__A : Optional[int] ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
_lowerCamelCase : Tuple = deprecated_arg[3:]
_lowerCamelCase : Union[str, Any] = not kwargs.pop(__A )
logger.warning(
f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
f' {positive_arg}={kwargs[positive_arg]}' )
_lowerCamelCase : List[Any] = kwargs.pop("tpu_name",self.tpu_name )
_lowerCamelCase : Union[str, Any] = kwargs.pop("device_idx",self.device_idx )
_lowerCamelCase : Any = kwargs.pop("eager_mode",self.eager_mode )
_lowerCamelCase : Tuple = kwargs.pop("use_xla",self.use_xla )
super().__init__(**__A )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Name of TPU'} , )
lowerCAmelCase_ = field(
default=0 , metadata={'help': 'CPU / GPU device index. Defaults to 0.'} , )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Benchmark models in eager model.'} )
lowerCAmelCase_ = field(
default=A , metadata={
'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
} , )
@cached_property
def lowerCamelCase_ ( self : Optional[Any] ):
requires_backends(self,["tf"] )
_lowerCamelCase : Optional[int] = None
if self.tpu:
try:
if self.tpu_name:
_lowerCamelCase : Union[str, Any] = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
_lowerCamelCase : Union[str, Any] = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
_lowerCamelCase : int = None
return tpu
@cached_property
def lowerCamelCase_ ( self : Optional[Any] ):
requires_backends(self,["tf"] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
_lowerCamelCase : str = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx],"GPU" )
_lowerCamelCase : str = tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}' )
else:
tf.config.set_visible_devices([],"GPU" ) # disable GPU
_lowerCamelCase : Tuple = tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}' )
return strategy
@property
def lowerCamelCase_ ( self : Any ):
requires_backends(self,["tf"] )
return self._setup_tpu is not None
@property
def lowerCamelCase_ ( self : List[str] ):
requires_backends(self,["tf"] )
return self._setup_strategy
@property
def lowerCamelCase_ ( self : Tuple ):
requires_backends(self,["tf"] )
return tf.config.list_physical_devices("GPU" )
@property
def lowerCamelCase_ ( self : int ):
requires_backends(self,["tf"] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def lowerCamelCase_ ( self : Dict ):
return self.n_gpu > 0
| 44 |
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = range(2, 20 + 1)
UpperCAmelCase_ : str = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = sum(a_i[j] for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ) )
_lowerCamelCase : List[str] = sum(a_i[j] * base[j] for j in range(min(len(_lowerCAmelCase ) , _lowerCAmelCase ) ) )
_lowerCamelCase , _lowerCamelCase : int = 0, 0
_lowerCamelCase : Dict = n - i
_lowerCamelCase : int = memo.get(_lowerCAmelCase )
if sub_memo is not None:
_lowerCamelCase : List[str] = sub_memo.get(_lowerCAmelCase )
if jumps is not None and len(_lowerCAmelCase ) > 0:
# find and make the largest jump without going over
_lowerCamelCase : List[Any] = -1
for _k in range(len(_lowerCAmelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
_lowerCamelCase : Any = _k
break
if max_jump >= 0:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : str = jumps[max_jump]
# since the difference between jumps is cached, add c
_lowerCamelCase : str = diff + c
for j in range(min(_lowerCAmelCase , len(_lowerCAmelCase ) ) ):
_lowerCamelCase , _lowerCamelCase : List[Any] = divmod(_lowerCAmelCase , 10 )
if new_c > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
_lowerCamelCase : int = []
else:
_lowerCamelCase : Tuple = {c: []}
_lowerCamelCase : Any = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_lowerCamelCase , _lowerCamelCase : Optional[int] = next_term(_lowerCAmelCase , k - 1 , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_lowerCamelCase , _lowerCamelCase : List[str] = compute(_lowerCAmelCase , _lowerCAmelCase , i + dn , _lowerCAmelCase )
diff += _diff
dn += terms_jumped
_lowerCamelCase : List[str] = sub_memo[c]
# keep jumps sorted by # of terms skipped
_lowerCamelCase : int = 0
while j < len(_lowerCAmelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_lowerCAmelCase , (diff, dn, k) )
return (diff, dn)
def A_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
if i >= n:
return 0, i
if k > len(_lowerCAmelCase ):
a_i.extend([0 for _ in range(k - len(_lowerCAmelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
_lowerCamelCase : List[str] = i
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Any = 0, 0, 0
for j in range(len(_lowerCAmelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
_lowerCamelCase : int = ds_c + ds_b
diff += addend
_lowerCamelCase : List[str] = 0
for j in range(_lowerCAmelCase ):
_lowerCamelCase : List[Any] = a_i[j] + addend
_lowerCamelCase , _lowerCamelCase : Any = divmod(_lowerCAmelCase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return diff, i - start_i
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : Tuple = digits[j] + addend
if s >= 10:
_lowerCamelCase , _lowerCamelCase : Optional[int] = divmod(_lowerCAmelCase , 10 )
_lowerCamelCase : Any = addend // 10 + quotient
else:
_lowerCamelCase : Tuple = s
_lowerCamelCase : List[Any] = addend // 10
if addend == 0:
break
while addend > 0:
_lowerCamelCase , _lowerCamelCase : str = divmod(_lowerCAmelCase , 10 )
digits.append(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : int = 10**15 ):
"""simple docstring"""
_lowerCamelCase : Tuple = [1]
_lowerCamelCase : List[Any] = 1
_lowerCamelCase : List[str] = 0
while True:
_lowerCamelCase , _lowerCamelCase : Dict = next_term(_lowerCAmelCase , 20 , i + dn , _lowerCAmelCase )
dn += terms_jumped
if dn == n - i:
break
_lowerCamelCase : Optional[Any] = 0
for j in range(len(_lowerCAmelCase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 | 1 |
'''simple docstring'''
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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int]=False ):
"""simple docstring"""
_lowerCamelCase : List[Any] = []
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') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
] )
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"
_lowerCamelCase : Optional[int] = [(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"),
] )
return rename_keys
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any]=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
_lowerCamelCase : int = ""
else:
_lowerCamelCase : int = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase : Any = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_lowerCamelCase : Tuple = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : List[str] = in_proj_weight[
: config.hidden_size, :
]
_lowerCamelCase : List[str] = in_proj_bias[: config.hidden_size]
_lowerCamelCase : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
_lowerCamelCase : List[str] = in_proj_bias[-config.hidden_size :]
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = dct.pop(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = val
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : str = ViTConfig()
_lowerCamelCase : List[str] = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Optional[Any] = int(vit_name[-12:-10] )
_lowerCamelCase : str = int(vit_name[-9:-6] )
else:
_lowerCamelCase : List[Any] = 1000
_lowerCamelCase : str = "huggingface/label-files"
_lowerCamelCase : Any = "imagenet-1k-id2label.json"
_lowerCamelCase : int = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : str = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : Optional[Any] = idalabel
_lowerCamelCase : List[str] = {v: k for k, v in idalabel.items()}
_lowerCamelCase : List[str] = int(vit_name[-6:-4] )
_lowerCamelCase : str = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("tiny" ):
_lowerCamelCase : List[Any] = 192
_lowerCamelCase : Optional[int] = 768
_lowerCamelCase : Union[str, Any] = 12
_lowerCamelCase : Optional[Any] = 3
elif vit_name[9:].startswith("small" ):
_lowerCamelCase : Optional[Any] = 384
_lowerCamelCase : Optional[Any] = 1536
_lowerCamelCase : int = 12
_lowerCamelCase : List[str] = 6
else:
pass
else:
if vit_name[4:].startswith("small" ):
_lowerCamelCase : List[str] = 768
_lowerCamelCase : Optional[Any] = 2304
_lowerCamelCase : List[Any] = 8
_lowerCamelCase : List[Any] = 8
elif vit_name[4:].startswith("base" ):
pass
elif vit_name[4:].startswith("large" ):
_lowerCamelCase : List[Any] = 1024
_lowerCamelCase : Optional[Any] = 4096
_lowerCamelCase : List[Any] = 24
_lowerCamelCase : Union[str, Any] = 16
elif vit_name[4:].startswith("huge" ):
_lowerCamelCase : str = 1280
_lowerCamelCase : List[Any] = 5120
_lowerCamelCase : List[str] = 32
_lowerCamelCase : List[str] = 16
# load original model from timm
_lowerCamelCase : int = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
_lowerCamelCase : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = create_rename_keys(_lowerCAmelCase , _lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# load HuggingFace model
if vit_name[-5:] == "in21k":
_lowerCamelCase : int = ViTModel(_lowerCAmelCase ).eval()
else:
_lowerCamelCase : List[str] = ViTForImageClassification(_lowerCAmelCase ).eval()
model.load_state_dict(_lowerCAmelCase )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
_lowerCamelCase : Union[str, Any] = DeiTImageProcessor(size=config.image_size )
else:
_lowerCamelCase : Union[str, Any] = ViTImageProcessor(size=config.image_size )
_lowerCamelCase : Optional[int] = image_processor(images=prepare_img() , return_tensors="pt" )
_lowerCamelCase : Optional[int] = encoding["pixel_values"]
_lowerCamelCase : Union[str, Any] = model(_lowerCAmelCase )
if base_model:
_lowerCamelCase : int = timm_model.forward_features(_lowerCAmelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_lowerCAmelCase , outputs.pooler_output , atol=1E-3 )
else:
_lowerCamelCase : Union[str, Any] = timm_model(_lowerCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCAmelCase , outputs.logits , atol=1E-3 )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_lowerCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the 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.'
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 44 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase_ : Any = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether tp freeze the encoder.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
lowerCAmelCase_ = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
lowerCAmelCase_ = field(
default=1024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Source language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Target language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': '# num_beams to use for evaluation.'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any ):
"""simple docstring"""
logger.info(F'***** {split} metrics *****' )
for key in sorted(metrics.keys() ):
logger.info(F' {key} = {metrics[key]}' )
save_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , F'{split}_results.json' ) )
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : str = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Optional[Any] = parser.parse_args_into_dataclasses()
check_output_dir(_lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , _lowerCAmelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCamelCase : Optional[int] = 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 , )
_lowerCamelCase : Tuple = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
assert hasattr(_lowerCAmelCase , _lowerCAmelCase ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
setattr(_lowerCAmelCase , _lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
_lowerCamelCase : List[Any] = 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 , )
_lowerCamelCase : int = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=_lowerCAmelCase , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(_lowerCAmelCase , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCamelCase : List[Any] = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(_lowerCAmelCase , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Any = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCamelCase : int = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(_lowerCAmelCase )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCamelCase : int = SeqaSeqDataset
# Get datasets
_lowerCamelCase : Tuple = (
dataset_class(
_lowerCAmelCase , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
_lowerCamelCase : List[Any] = (
dataset_class(
_lowerCAmelCase , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCamelCase : Optional[int] = (
dataset_class(
_lowerCAmelCase , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCamelCase : int = (
build_compute_metrics_fn(data_args.task , _lowerCAmelCase ) if training_args.predict_with_generate else None
)
_lowerCamelCase : List[Any] = SeqaSeqTrainer(
model=_lowerCAmelCase , args=_lowerCAmelCase , data_args=_lowerCAmelCase , train_dataset=_lowerCAmelCase , eval_dataset=_lowerCAmelCase , data_collator=SeqaSeqDataCollator(
_lowerCAmelCase , _lowerCAmelCase , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=_lowerCAmelCase , tokenizer=_lowerCAmelCase , )
_lowerCamelCase : Optional[Any] = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
_lowerCamelCase : Optional[Any] = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCamelCase : int = train_result.metrics
_lowerCamelCase : Optional[int] = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_lowerCamelCase : Optional[Any] = trainer.evaluate(metric_key_prefix="val" )
_lowerCamelCase : Dict = data_args.n_val
_lowerCamelCase : List[Any] = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.do_predict:
logger.info("*** Predict ***" )
_lowerCamelCase : Any = trainer.predict(test_dataset=_lowerCAmelCase , metric_key_prefix="test" )
_lowerCamelCase : Dict = test_output.metrics
_lowerCamelCase : Optional[int] = data_args.n_test
if trainer.is_world_process_zero():
_lowerCamelCase : int = round(metrics["test_loss"] , 4 )
handle_metrics("test" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.predict_with_generate:
_lowerCamelCase : List[str] = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
_lowerCamelCase : Any = lmap(str.strip , _lowerCAmelCase )
write_txt_file(_lowerCAmelCase , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(_lowerCAmelCase , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 44 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'luke'
def __init__( self : Tuple,__A : str=5_0_2_6_7,__A : List[Any]=5_0_0_0_0_0,__A : int=7_6_8,__A : Optional[int]=2_5_6,__A : Optional[Any]=1_2,__A : Optional[Any]=1_2,__A : Dict=3_0_7_2,__A : int="gelu",__A : List[Any]=0.1,__A : Tuple=0.1,__A : Any=5_1_2,__A : List[Any]=2,__A : int=0.02,__A : Union[str, Any]=1e-12,__A : Any=True,__A : Any=None,__A : Optional[Any]=1,__A : Any=0,__A : Union[str, Any]=2,**__A : Any,):
super().__init__(pad_token_id=__A,bos_token_id=__A,eos_token_id=__A,**__A )
_lowerCamelCase : str = vocab_size
_lowerCamelCase : List[str] = entity_vocab_size
_lowerCamelCase : List[Any] = hidden_size
_lowerCamelCase : Dict = entity_emb_size
_lowerCamelCase : str = num_hidden_layers
_lowerCamelCase : str = num_attention_heads
_lowerCamelCase : str = hidden_act
_lowerCamelCase : str = intermediate_size
_lowerCamelCase : Union[str, Any] = hidden_dropout_prob
_lowerCamelCase : int = attention_probs_dropout_prob
_lowerCamelCase : Optional[Any] = max_position_embeddings
_lowerCamelCase : Optional[Any] = type_vocab_size
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : int = layer_norm_eps
_lowerCamelCase : Tuple = use_entity_aware_attention
_lowerCamelCase : Dict = classifier_dropout
| 44 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase__ :
def __init__( self : List[Any],__A : str,__A : List[str]=1_3,__A : str=3_2,__A : Tuple=2,__A : Any=3,__A : Dict=1_6,__A : Dict=[3_2, 6_4, 1_2_8],__A : List[str]=[1, 2, 1],__A : str=[2, 2, 4],__A : Optional[int]=2,__A : Dict=2.0,__A : str=True,__A : Tuple=0.0,__A : int=0.0,__A : List[str]=0.1,__A : Any="gelu",__A : List[Any]=False,__A : Optional[Any]=True,__A : List[str]=0.02,__A : Tuple=1e-5,__A : Any=True,__A : Tuple=None,__A : Tuple=True,__A : Tuple=1_0,__A : List[Any]=8,__A : Optional[int]=["stage1", "stage2"],__A : int=[1, 2],):
_lowerCamelCase : List[Any] = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : Optional[int] = image_size
_lowerCamelCase : int = patch_size
_lowerCamelCase : Optional[Any] = num_channels
_lowerCamelCase : int = embed_dim
_lowerCamelCase : int = hidden_sizes
_lowerCamelCase : List[Any] = depths
_lowerCamelCase : Any = num_heads
_lowerCamelCase : List[str] = window_size
_lowerCamelCase : str = mlp_ratio
_lowerCamelCase : Any = qkv_bias
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : List[str] = drop_path_rate
_lowerCamelCase : str = hidden_act
_lowerCamelCase : Union[str, Any] = use_absolute_embeddings
_lowerCamelCase : List[Any] = patch_norm
_lowerCamelCase : Tuple = layer_norm_eps
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Optional[int] = is_training
_lowerCamelCase : Tuple = scope
_lowerCamelCase : List[Any] = use_labels
_lowerCamelCase : int = type_sequence_label_size
_lowerCamelCase : Tuple = encoder_stride
_lowerCamelCase : Any = out_features
_lowerCamelCase : Any = out_indices
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = None
if self.use_labels:
_lowerCamelCase : str = ids_tensor([self.batch_size],self.type_sequence_label_size )
_lowerCamelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Union[str, Any] ):
return FocalNetConfig(
image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,embed_dim=self.embed_dim,hidden_sizes=self.hidden_sizes,depths=self.depths,num_heads=self.num_heads,window_size=self.window_size,mlp_ratio=self.mlp_ratio,qkv_bias=self.qkv_bias,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,drop_path_rate=self.drop_path_rate,hidden_act=self.hidden_act,use_absolute_embeddings=self.use_absolute_embeddings,path_norm=self.patch_norm,layer_norm_eps=self.layer_norm_eps,initializer_range=self.initializer_range,encoder_stride=self.encoder_stride,out_features=self.out_features,out_indices=self.out_indices,)
def lowerCamelCase_ ( self : int,__A : Union[str, Any],__A : Tuple,__A : List[Any] ):
_lowerCamelCase : Optional[Any] = FocalNetModel(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[Any] = model(__A )
_lowerCamelCase : Optional[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_lowerCamelCase : Union[str, Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, expected_seq_len, expected_dim) )
def lowerCamelCase_ ( self : int,__A : Optional[int],__A : int,__A : Optional[int] ):
_lowerCamelCase : Any = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ),len(config.out_features ) )
self.parent.assertListEqual(model.channels,config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
_lowerCamelCase : List[str] = None
_lowerCamelCase : List[str] = FocalNetBackbone(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = model(__A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ),1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ),[self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ),1 )
self.parent.assertListEqual(model.channels,[config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : Optional[int],__A : Optional[int],__A : Dict,__A : Dict ):
_lowerCamelCase : List[Any] = FocalNetForMaskedImageModeling(config=__A )
model.to(__A )
model.eval()
_lowerCamelCase : List[str] = model(__A )
self.parent.assertEqual(
result.reconstruction.shape,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_lowerCamelCase : Dict = 1
_lowerCamelCase : Any = FocalNetForMaskedImageModeling(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : Optional[int] = model(__A )
self.parent.assertEqual(result.reconstruction.shape,(self.batch_size, 1, self.image_size, self.image_size) )
def lowerCamelCase_ ( self : List[Any],__A : Union[str, Any],__A : List[Any],__A : Optional[Any] ):
_lowerCamelCase : Union[str, Any] = self.type_sequence_label_size
_lowerCamelCase : Optional[Any] = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : Optional[int] = model(__A,labels=__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_lowerCamelCase : str = 1
_lowerCamelCase : str = FocalNetForImageClassification(__A )
model.to(__A )
model.eval()
_lowerCamelCase : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCamelCase : List[Any] = model(__A )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : int = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Union[str, Any] = config_and_inputs
_lowerCamelCase : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[int] = FocalNetModelTester(self )
_lowerCamelCase : int = ConfigTester(self,config_class=__A,embed_dim=3_7,has_text_modality=__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
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 lowerCamelCase_ ( self : List[str] ):
return
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__A )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
@unittest.skip(reason="FocalNet does not use inputs_embeds" )
def lowerCamelCase_ ( self : Optional[int] ):
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking" )
def lowerCamelCase_ ( self : List[str] ):
pass
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase , _lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : str = model_class(__A )
self.assertIsInstance(model.get_input_embeddings(),(nn.Module) )
_lowerCamelCase : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A,nn.Linear ) )
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : Union[str, Any] = model_class(__A )
_lowerCamelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase : int = [*signature.parameters.keys()]
_lowerCamelCase : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1],__A )
def lowerCamelCase_ ( self : Tuple,__A : Any,__A : List[Any],__A : str,__A : Any ):
_lowerCamelCase : Union[str, Any] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
_lowerCamelCase : Optional[int] = model(**self._prepare_for_class(__A,__A ) )
_lowerCamelCase : Optional[int] = outputs.hidden_states
_lowerCamelCase : int = getattr(
self.model_tester,"expected_num_hidden_layers",len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__A ),__A )
# FocalNet has a different seq_length
_lowerCamelCase : Optional[Any] = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : List[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
_lowerCamelCase : Any = outputs.reshaped_hidden_states
self.assertEqual(len(__A ),__A )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Tuple = reshaped_hidden_states[0].shape
_lowerCamelCase : List[str] = (
reshaped_hidden_states[0].view(__A,__A,height * width ).permute(0,2,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase , _lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,__A )
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase , _lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Tuple = 3
_lowerCamelCase : Optional[int] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
_lowerCamelCase : Tuple = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_lowerCamelCase : Any = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_lowerCamelCase : int = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
_lowerCamelCase : List[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase : Optional[Any] = True
self.check_hidden_states_output(__A,__A,__A,(padded_height, padded_width) )
@slow
def lowerCamelCase_ ( self : Tuple ):
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = FocalNetModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase , _lowerCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase : Optional[Any] = _config_zero_init(__A )
for model_class in self.all_model_classes:
_lowerCamelCase : Any = model_class(config=__A )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),[0.0, 1.0],msg=f'Parameter {name} of model {model_class} seems not properly initialized',)
@require_vision
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : Union[str, Any] ):
# TODO update organization
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny" ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Any = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny" ).to(__A )
_lowerCamelCase : int = self.default_image_processor
_lowerCamelCase : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_lowerCamelCase : Dict = image_processor(images=__A,return_tensors="pt" ).to(__A )
# forward pass
with torch.no_grad():
_lowerCamelCase : Dict = model(**__A )
# verify the logits
_lowerCamelCase : List[Any] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape,__A )
_lowerCamelCase : List[str] = torch.tensor([0.2166, -0.4368, 0.2191] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3],__A,atol=1e-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item(),2_8_1 )
@require_torch
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = (FocalNetBackbone,) if is_torch_available() else ()
lowerCAmelCase_ = FocalNetConfig
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : int = FocalNetModelTester(self )
| 44 | 1 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCAmelCase_ : int = False
class UpperCAmelCase__ ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCase_ ( self : Dict ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : int = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion",torch_dtype=torch.floataa )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
_lowerCamelCase : int = torch.manual_seed(0 )
_lowerCamelCase : List[Any] = pipe.dual_guided(
prompt="first prompt",image=__A,text_to_image_strength=0.75,generator=__A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__A )
_lowerCamelCase : Dict = VersatileDiffusionPipeline.from_pretrained(__A,torch_dtype=torch.floataa )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Tuple = generator.manual_seed(0 )
_lowerCamelCase : Optional[Any] = pipe.dual_guided(
prompt="first prompt",image=__A,text_to_image_strength=0.75,generator=__A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def lowerCamelCase_ ( self : str ):
_lowerCamelCase : Dict = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion",torch_dtype=torch.floataa )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Tuple = "cyberpunk 2077"
_lowerCamelCase : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
_lowerCamelCase : Tuple = torch.manual_seed(0 )
_lowerCamelCase : Tuple = pipe.dual_guided(
prompt=__A,image=__A,text_to_image_strength=0.75,generator=__A,guidance_scale=7.5,num_inference_steps=5_0,output_type="numpy",).images
_lowerCamelCase : int = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : Union[str, Any] = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
_lowerCamelCase : List[str] = "A painting of a squirrel eating a burger "
_lowerCamelCase : Union[str, Any] = torch.manual_seed(0 )
_lowerCamelCase : Any = pipe.text_to_image(
prompt=__A,generator=__A,guidance_scale=7.5,num_inference_steps=5_0,output_type="numpy" ).images
_lowerCamelCase : Optional[int] = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : Tuple = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
_lowerCamelCase : int = pipe.image_variation(__A,generator=__A,output_type="numpy" ).images
_lowerCamelCase : int = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : Optional[int] = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 44 |
'''simple docstring'''
class UpperCAmelCase__ :
def __init__( self : Any,__A : Any,__A : Any,__A : Any ):
_lowerCamelCase : List[Any] = name
_lowerCamelCase : Union[str, Any] = value
_lowerCamelCase : str = weight
def __repr__( self : Any ):
return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def lowerCamelCase_ ( self : Optional[int] ):
return self.value
def lowerCamelCase_ ( self : Any ):
return self.name
def lowerCamelCase_ ( self : List[Any] ):
return self.weight
def lowerCamelCase_ ( self : str ):
return self.value / self.weight
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = []
for i in range(len(_lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = sorted(_lowerCAmelCase , key=_lowerCAmelCase , reverse=_lowerCAmelCase )
_lowerCamelCase : Optional[int] = []
_lowerCamelCase , _lowerCamelCase : Optional[int] = 0.0, 0.0
for i in range(len(_lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def A_ ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
_lowerCamelCase : Optional[int] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_lowerCamelCase : Any = 1
if upper_limit > 0:
_lowerCamelCase : List[str] = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(_lowerCAmelCase ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
print('\n*** Enter -1 at any time to quit ***')
print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
try:
while True:
UpperCAmelCase_ : Optional[Any] = int(input().strip())
if N < 0:
print('\n********* Goodbye!! ************')
break
else:
print(f'''The Catalan numbers from 0 through {N} are:''')
print(catalan_numbers(N))
print('Try another upper limit for the sequence: ', end='')
except (NameError, ValueError):
print('\n********* Invalid input, goodbye! ************\n')
import doctest
doctest.testmod()
| 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : List[Any] = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = ['ConditionalDetrFeatureExtractor']
UpperCAmelCase_ : str = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : str = [
'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConditionalDetrForObjectDetection',
'ConditionalDetrForSegmentation',
'ConditionalDetrModel',
'ConditionalDetrPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : int=False ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ) and isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : int = len(set_a.intersection(_lowerCAmelCase ) )
if alternative_union:
_lowerCamelCase : str = len(_lowerCAmelCase ) + len(_lowerCAmelCase )
else:
_lowerCamelCase : List[Any] = len(set_a.union(_lowerCAmelCase ) )
return intersection / union
if isinstance(_lowerCAmelCase , (list, tuple) ) and isinstance(_lowerCAmelCase , (list, tuple) ):
_lowerCamelCase : Dict = [element for element in set_a if element in set_b]
if alternative_union:
_lowerCamelCase : int = len(_lowerCAmelCase ) + len(_lowerCAmelCase )
return len(_lowerCAmelCase ) / union
else:
_lowerCamelCase : List[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_lowerCAmelCase ) / len(_lowerCAmelCase )
return len(_lowerCAmelCase ) / len(_lowerCAmelCase )
return None
if __name__ == "__main__":
UpperCAmelCase_ : Any = {'a', 'b', 'c', 'd', 'e'}
UpperCAmelCase_ : int = {'c', 'd', 'e', 'f', 'h', 'i'}
print(jaccard_similarity(set_a, set_b))
| 44 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Tuple = tmp_path / "file.csv"
_lowerCamelCase : Optional[int] = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : Any = tmp_path / "malformed_file.csv"
_lowerCamelCase : Any = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20,\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : int = tmp_path / "csv_with_image.csv"
_lowerCamelCase : int = textwrap.dedent(
F'\\n image\n {image_file}\n ' )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Dict = tmp_path / "csv_with_label.csv"
_lowerCamelCase : int = textwrap.dedent(
"\\n label\n good\n bad\n good\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
@pytest.fixture
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Dict = tmp_path / "csv_with_int_list.csv"
_lowerCamelCase : Any = textwrap.dedent(
"\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n " )
with open(_lowerCAmelCase , "w" ) as f:
f.write(_lowerCAmelCase )
return str(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Tuple ):
"""simple docstring"""
_lowerCamelCase : List[Any] = Csv()
_lowerCamelCase : Any = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(_lowerCAmelCase , match="Error tokenizing data" ):
for _ in generator:
pass
assert any(
record.levelname == "ERROR"
and "Failed to read file" in record.message
and os.path.basename(_lowerCAmelCase ) in record.message
for record in caplog.records )
@require_pil
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
with open(_lowerCAmelCase , encoding="utf-8" ) as f:
_lowerCamelCase : Any = f.read().splitlines()[1]
_lowerCamelCase : Optional[Any] = Csv(encoding="utf-8" , features=Features({"image": Image()} ) )
_lowerCamelCase : Union[str, Any] = csv._generate_tables([[csv_file_with_image]] )
_lowerCamelCase : List[str] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("image" ).type == Image()()
_lowerCamelCase : int = pa_table.to_pydict()["image"]
assert generated_content == [{"path": image_file, "bytes": None}]
def A_ ( _lowerCAmelCase : List[Any] ):
"""simple docstring"""
with open(_lowerCAmelCase , encoding="utf-8" ) as f:
_lowerCamelCase : List[Any] = f.read().splitlines()[1:]
_lowerCamelCase : int = Csv(encoding="utf-8" , features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
_lowerCamelCase : Tuple = csv._generate_tables([[csv_file_with_label]] )
_lowerCamelCase : int = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
_lowerCamelCase : Union[str, Any] = pa_table.to_pydict()["label"]
assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(_lowerCAmelCase ) for label in labels]
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Dict = Csv(encoding="utf-8" , sep="," , converters={"int_list": lambda _lowerCAmelCase : [int(_lowerCAmelCase ) for i in x.split()]} )
_lowerCamelCase : List[Any] = csv._generate_tables([[csv_file_with_int_list]] )
_lowerCamelCase : Optional[int] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
_lowerCamelCase : Optional[Any] = pa_table.to_pydict()["int_list"]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 44 | 1 |
'''simple docstring'''
class UpperCAmelCase__ :
def __init__( self : Any,__A : Any,__A : Any,__A : Any ):
_lowerCamelCase : List[Any] = name
_lowerCamelCase : Union[str, Any] = value
_lowerCamelCase : str = weight
def __repr__( self : Any ):
return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def lowerCamelCase_ ( self : Optional[int] ):
return self.value
def lowerCamelCase_ ( self : Any ):
return self.name
def lowerCamelCase_ ( self : List[Any] ):
return self.weight
def lowerCamelCase_ ( self : str ):
return self.value / self.weight
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : str = []
for i in range(len(_lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Dict = sorted(_lowerCAmelCase , key=_lowerCAmelCase , reverse=_lowerCAmelCase )
_lowerCamelCase : Optional[int] = []
_lowerCamelCase , _lowerCamelCase : Optional[int] = 0.0, 0.0
for i in range(len(_lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def A_ ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class UpperCAmelCase__ ( A , A , unittest.TestCase ):
lowerCAmelCase_ = IFInpaintingSuperResolutionPipeline
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
lowerCAmelCase_ = PipelineTesterMixin.required_optional_params - {'latents'}
def lowerCamelCase_ ( self : List[str] ):
return self._get_superresolution_dummy_components()
def lowerCamelCase_ ( self : str,__A : List[str],__A : List[str]=0 ):
if str(__A ).startswith("mps" ):
_lowerCamelCase : List[str] = torch.manual_seed(__A )
else:
_lowerCamelCase : Optional[int] = torch.Generator(device=__A ).manual_seed(__A )
_lowerCamelCase : List[Any] = floats_tensor((1, 3, 1_6, 1_6),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Any = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Tuple = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Dict = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),reason="XFormers attention is only available with CUDA and `xformers` installed",)
def lowerCamelCase_ ( self : Optional[int] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowerCamelCase_ ( self : Dict ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda",reason="float16 requires CUDA" )
def lowerCamelCase_ ( self : Optional[Any] ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowerCamelCase_ ( self : Any ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowerCamelCase_ ( self : Dict ):
self._test_save_load_local()
def lowerCamelCase_ ( self : Any ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2,)
| 44 | 1 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if is_torch_version("<" , "2.0.0" ) or not hasattr(_lowerCAmelCase , "_dynamo" ):
return False
return isinstance(_lowerCAmelCase , torch._dynamo.eval_frame.OptimizedModule )
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : bool = True ):
"""simple docstring"""
_lowerCamelCase : Tuple = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
_lowerCamelCase : Optional[Any] = is_compiled_module(_lowerCAmelCase )
if is_compiled:
_lowerCamelCase : Tuple = model
_lowerCamelCase : Dict = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Tuple = model.module
if not keep_fpaa_wrapper:
_lowerCamelCase : Optional[Any] = getattr(_lowerCAmelCase , "forward" )
_lowerCamelCase : Any = model.__dict__.pop("_original_forward" , _lowerCAmelCase )
if original_forward is not None:
while hasattr(_lowerCAmelCase , "__wrapped__" ):
_lowerCamelCase : List[str] = forward.__wrapped__
if forward == original_forward:
break
_lowerCamelCase : str = forward
if getattr(_lowerCAmelCase , "_converted_to_transformer_engine" , _lowerCAmelCase ):
convert_model(_lowerCAmelCase , to_transformer_engine=_lowerCAmelCase )
if is_compiled:
_lowerCamelCase : List[str] = model
_lowerCamelCase : Optional[int] = compiled_model
return model
def A_ ( ):
"""simple docstring"""
PartialState().wait_for_everyone()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Tuple ):
"""simple docstring"""
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_lowerCAmelCase , _lowerCAmelCase )
elif PartialState().local_process_index == 0:
torch.save(_lowerCAmelCase , _lowerCAmelCase )
@contextmanager
def A_ ( **_lowerCAmelCase : Any ):
"""simple docstring"""
for key, value in kwargs.items():
_lowerCamelCase : str = str(_lowerCAmelCase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
if not hasattr(_lowerCAmelCase , "__qualname__" ) and not hasattr(_lowerCAmelCase , "__name__" ):
_lowerCamelCase : Any = getattr(_lowerCAmelCase , "__class__" , _lowerCAmelCase )
if hasattr(_lowerCAmelCase , "__qualname__" ):
return obj.__qualname__
if hasattr(_lowerCAmelCase , "__name__" ):
return obj.__name__
return str(_lowerCAmelCase )
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str ):
"""simple docstring"""
for key, value in source.items():
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Optional[int] = destination.setdefault(_lowerCAmelCase , {} )
merge_dicts(_lowerCAmelCase , _lowerCAmelCase )
else:
_lowerCamelCase : Optional[Any] = value
return destination
def A_ ( _lowerCAmelCase : int = None ):
"""simple docstring"""
if port is None:
_lowerCamelCase : Dict = 29500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 44 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class UpperCAmelCase__ ( A ):
def __init__( self : List[Any],__A : Tuple,__A : Optional[int],__A : Optional[int]=1_0_2_4,__A : int=1_0_2_4,__A : Any=3.6 ):
_lowerCamelCase : List[str] = tokenizer
_lowerCamelCase : Dict = tokenizer.bos_token_id
_lowerCamelCase : Tuple = dataset
_lowerCamelCase : Any = seq_length
_lowerCamelCase : List[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self : Tuple ):
_lowerCamelCase : Union[str, Any] = iter(self.dataset )
_lowerCamelCase : str = True
while more_examples:
_lowerCamelCase , _lowerCamelCase : Optional[int] = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__A )["content"] )
buffer_len += len(buffer[-1] )
except StopIteration:
_lowerCamelCase : Tuple = False
break
_lowerCamelCase : int = tokenizer(__A,truncation=__A )["input_ids"]
_lowerCamelCase : int = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0,len(__A ),self.seq_length ):
_lowerCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(__A ) == self.seq_length:
yield torch.tensor(__A )
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = {"streaming": True}
_lowerCamelCase : Optional[Any] = load_dataset(args.dataset_name , split="train" , **_lowerCAmelCase )
_lowerCamelCase : int = ConstantLengthDataset(_lowerCAmelCase , _lowerCAmelCase , seq_length=args.seq_length )
_lowerCamelCase : Dict = DataLoader(_lowerCAmelCase , batch_size=args.batch_size )
return eval_dataloader
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
model.eval()
_lowerCamelCase : Optional[int] = []
for step, batch in enumerate(_lowerCAmelCase ):
with torch.no_grad():
_lowerCamelCase : List[str] = model(_lowerCAmelCase , labels=_lowerCAmelCase )
_lowerCamelCase : List[Any] = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_lowerCAmelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
_lowerCamelCase : Dict = torch.mean(torch.cat(_lowerCAmelCase ) )
try:
_lowerCamelCase : List[Any] = torch.exp(_lowerCAmelCase )
except OverflowError:
_lowerCamelCase : Optional[int] = float("inf" )
return loss.item(), perplexity.item()
# Setup Accelerator
UpperCAmelCase_ : List[str] = Accelerator()
# Parse configuration
UpperCAmelCase_ : Tuple = HfArgumentParser(EvaluationArguments)
UpperCAmelCase_ : Dict = parser.parse_args()
set_seed(args.seed)
# Logging
UpperCAmelCase_ : Optional[int] = logging.getLogger(__name__)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
# Load model and tokenizer
UpperCAmelCase_ : Tuple = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase_ : Dict = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
UpperCAmelCase_ : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
UpperCAmelCase_, UpperCAmelCase_ : Dict = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCAmelCase_, UpperCAmelCase_ : str = evaluate(args)
logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 44 | 1 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase__ :
@staticmethod
def lowerCamelCase_ ( *__A : Optional[Any],**__A : int ):
pass
def A_ ( _lowerCAmelCase : Image ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def A_ ( _lowerCAmelCase : Image ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = np.array(_lowerCAmelCase )
_lowerCamelCase : str = npimg.shape
return {"hash": hashimage(_lowerCAmelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
lowerCAmelCase_ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
lowerCAmelCase_ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def lowerCamelCase_ ( self : int,__A : Any,__A : Optional[int],__A : Optional[int] ):
_lowerCamelCase : Tuple = MaskGenerationPipeline(model=__A,image_processor=__A )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowerCamelCase_ ( self : Tuple,__A : List[Any],__A : Union[str, Any] ):
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def lowerCamelCase_ ( self : str ):
pass
@slow
@require_torch
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : int = pipeline("mask-generation",model="facebook/sam-vit-huge" )
_lowerCamelCase : Tuple = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg",points_per_batch=2_5_6 )
# Shortening by hashing
_lowerCamelCase : Optional[Any] = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(__A ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(__A,decimals=4 ),[
{"mask": {"hash": "115ad19f5f", "shape": (4_8_0, 6_4_0)}, "scores": 1.0444},
{"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.021},
{"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.0167},
{"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.0132},
{"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.0053},
{"mask": {"hash": "e2d0b7a0b7", "shape": (4_8_0, 6_4_0)}, "scores": 0.9967},
{"mask": {"hash": "453c7844bd", "shape": (4_8_0, 6_4_0)}, "scores": 0.993},
{"mask": {"hash": "3d44f2926d", "shape": (4_8_0, 6_4_0)}, "scores": 0.9909},
{"mask": {"hash": "64033ddc3f", "shape": (4_8_0, 6_4_0)}, "scores": 0.9879},
{"mask": {"hash": "801064ff79", "shape": (4_8_0, 6_4_0)}, "scores": 0.9834},
{"mask": {"hash": "6172f276ef", "shape": (4_8_0, 6_4_0)}, "scores": 0.9716},
{"mask": {"hash": "b49e60e084", "shape": (4_8_0, 6_4_0)}, "scores": 0.9612},
{"mask": {"hash": "a811e775fd", "shape": (4_8_0, 6_4_0)}, "scores": 0.9599},
{"mask": {"hash": "a6a8ebcf4b", "shape": (4_8_0, 6_4_0)}, "scores": 0.9552},
{"mask": {"hash": "9d8257e080", "shape": (4_8_0, 6_4_0)}, "scores": 0.9532},
{"mask": {"hash": "32de6454a8", "shape": (4_8_0, 6_4_0)}, "scores": 0.9516},
{"mask": {"hash": "af3d4af2c8", "shape": (4_8_0, 6_4_0)}, "scores": 0.9499},
{"mask": {"hash": "3c6db475fb", "shape": (4_8_0, 6_4_0)}, "scores": 0.9483},
{"mask": {"hash": "c290813fb9", "shape": (4_8_0, 6_4_0)}, "scores": 0.9464},
{"mask": {"hash": "b6f0b8f606", "shape": (4_8_0, 6_4_0)}, "scores": 0.943},
{"mask": {"hash": "92ce16bfdf", "shape": (4_8_0, 6_4_0)}, "scores": 0.943},
{"mask": {"hash": "c749b25868", "shape": (4_8_0, 6_4_0)}, "scores": 0.9408},
{"mask": {"hash": "efb6cab859", "shape": (4_8_0, 6_4_0)}, "scores": 0.9335},
{"mask": {"hash": "1ff2eafb30", "shape": (4_8_0, 6_4_0)}, "scores": 0.9326},
{"mask": {"hash": "788b798e24", "shape": (4_8_0, 6_4_0)}, "scores": 0.9262},
{"mask": {"hash": "abea804f0e", "shape": (4_8_0, 6_4_0)}, "scores": 0.8999},
{"mask": {"hash": "7b9e8ddb73", "shape": (4_8_0, 6_4_0)}, "scores": 0.8986},
{"mask": {"hash": "cd24047c8a", "shape": (4_8_0, 6_4_0)}, "scores": 0.8984},
{"mask": {"hash": "6943e6bcbd", "shape": (4_8_0, 6_4_0)}, "scores": 0.8873},
{"mask": {"hash": "b5f47c9191", "shape": (4_8_0, 6_4_0)}, "scores": 0.8871}
],)
# fmt: on
@require_torch
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : Union[str, Any] = "facebook/sam-vit-huge"
_lowerCamelCase : Tuple = pipeline("mask-generation",model=__A )
_lowerCamelCase : Union[str, Any] = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg",pred_iou_thresh=1,points_per_batch=2_5_6 )
# Shortening by hashing
_lowerCamelCase : List[Any] = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(__A ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(__A,decimals=4 ),[
{"mask": {"hash": "115ad19f5f", "shape": (4_8_0, 6_4_0)}, "scores": 1.0444},
{"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.0210},
{"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.0167},
{"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.0132},
{"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.0053},
],)
| 44 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCAmelCase_ : Union[str, Any] = {
'vocab_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
},
'merges_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
},
'tokenizer_file': {
'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
},
}
UpperCAmelCase_ : List[str] = {
'allenai/led-base-16384': 1_6384,
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = LEDTokenizer
lowerCAmelCase_ = ['input_ids', 'attention_mask']
def __init__( self : Union[str, Any],__A : List[Any]=None,__A : str=None,__A : str=None,__A : Optional[int]="replace",__A : Union[str, Any]="<s>",__A : Union[str, Any]="</s>",__A : Any="</s>",__A : Optional[int]="<s>",__A : List[str]="<unk>",__A : str="<pad>",__A : Tuple="<mask>",__A : Union[str, Any]=False,__A : Optional[int]=True,**__A : Optional[int],):
super().__init__(
__A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)
_lowerCamelCase : List[str] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : str = getattr(__A,pre_tok_state.pop("type" ) )
_lowerCamelCase : List[Any] = add_prefix_space
_lowerCamelCase : Tuple = pre_tok_class(**__A )
_lowerCamelCase : Optional[int] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
_lowerCamelCase : List[str] = "post_processor"
_lowerCamelCase : int = getattr(self.backend_tokenizer,__A,__A )
if tokenizer_component_instance:
_lowerCamelCase : Tuple = 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 : str = tuple(state["sep"] )
if "cls" in state:
_lowerCamelCase : List[str] = tuple(state["cls"] )
_lowerCamelCase : Dict = False
if state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : List[str] = add_prefix_space
_lowerCamelCase : List[Any] = True
if state.get("trim_offsets",__A ) != trim_offsets:
_lowerCamelCase : List[str] = trim_offsets
_lowerCamelCase : List[str] = True
if changes_to_apply:
_lowerCamelCase : Tuple = getattr(__A,state.pop("type" ) )
_lowerCamelCase : Any = component_class(**__A )
setattr(self.backend_tokenizer,__A,__A )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def lowerCamelCase_ ( self : str ):
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 lowerCamelCase_ ( self : List[str],__A : str ):
_lowerCamelCase : Optional[Any] = AddedToken(__A,lstrip=__A,rstrip=__A ) if isinstance(__A,__A ) else value
_lowerCamelCase : str = value
def lowerCamelCase_ ( self : List[str],*__A : List[Any],**__A : int ):
_lowerCamelCase : List[str] = kwargs.get("is_split_into_words",__A )
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(*__A,**__A )
def lowerCamelCase_ ( self : Optional[int],*__A : Optional[Any],**__A : Union[str, Any] ):
_lowerCamelCase : List[Any] = kwargs.get("is_split_into_words",__A )
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(*__A,**__A )
def lowerCamelCase_ ( self : Dict,__A : str,__A : Optional[str] = None ):
_lowerCamelCase : List[str] = self._tokenizer.model.save(__A,name=__A )
return tuple(__A )
def lowerCamelCase_ ( self : List[str],__A : Optional[Any],__A : List[str]=None ):
_lowerCamelCase : Optional[Any] = [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 lowerCamelCase_ ( self : Dict,__A : List[int],__A : Optional[List[int]] = None ):
_lowerCamelCase : Tuple = [self.sep_token_id]
_lowerCamelCase : Union[str, Any] = [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 lowerCamelCase_ ( self : Any,__A : Union[Dict[str, EncodedInput], BatchEncoding],__A : Optional[int] = None,__A : PaddingStrategy = PaddingStrategy.DO_NOT_PAD,__A : Optional[int] = None,__A : Optional[bool] = None,):
_lowerCamelCase : List[str] = super()._pad(
encoded_inputs=__A,max_length=__A,padding_strategy=__A,pad_to_multiple_of=__A,return_attention_mask=__A,)
# Load from model defaults
if return_attention_mask is None:
_lowerCamelCase : Any = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_lowerCamelCase : Union[str, Any] = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_lowerCamelCase : Optional[Any] = len(encoded_inputs["global_attention_mask"] ) != len(__A )
if needs_to_be_padded:
_lowerCamelCase : str = len(__A ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_lowerCamelCase : Tuple = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
_lowerCamelCase : int = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 44 | 1 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("In Malus Law, the angle is in the range 0-360 degrees" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(_lowerCAmelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 44 |
'''simple docstring'''
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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int]=False ):
"""simple docstring"""
_lowerCamelCase : List[Any] = []
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') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
] )
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"
_lowerCamelCase : Optional[int] = [(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"),
] )
return rename_keys
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any]=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
_lowerCamelCase : int = ""
else:
_lowerCamelCase : int = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase : Any = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_lowerCamelCase : Tuple = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase : List[str] = in_proj_weight[
: config.hidden_size, :
]
_lowerCamelCase : List[str] = in_proj_bias[: config.hidden_size]
_lowerCamelCase : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
_lowerCamelCase : List[str] = in_proj_bias[-config.hidden_size :]
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCamelCase : List[str] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = dct.pop(_lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = val
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCamelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : str = ViTConfig()
_lowerCamelCase : List[str] = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : Optional[Any] = int(vit_name[-12:-10] )
_lowerCamelCase : str = int(vit_name[-9:-6] )
else:
_lowerCamelCase : List[Any] = 1000
_lowerCamelCase : str = "huggingface/label-files"
_lowerCamelCase : Any = "imagenet-1k-id2label.json"
_lowerCamelCase : int = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) )
_lowerCamelCase : str = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : Optional[Any] = idalabel
_lowerCamelCase : List[str] = {v: k for k, v in idalabel.items()}
_lowerCamelCase : List[str] = int(vit_name[-6:-4] )
_lowerCamelCase : str = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("tiny" ):
_lowerCamelCase : List[Any] = 192
_lowerCamelCase : Optional[int] = 768
_lowerCamelCase : Union[str, Any] = 12
_lowerCamelCase : Optional[Any] = 3
elif vit_name[9:].startswith("small" ):
_lowerCamelCase : Optional[Any] = 384
_lowerCamelCase : Optional[Any] = 1536
_lowerCamelCase : int = 12
_lowerCamelCase : List[str] = 6
else:
pass
else:
if vit_name[4:].startswith("small" ):
_lowerCamelCase : List[str] = 768
_lowerCamelCase : Optional[Any] = 2304
_lowerCamelCase : List[Any] = 8
_lowerCamelCase : List[Any] = 8
elif vit_name[4:].startswith("base" ):
pass
elif vit_name[4:].startswith("large" ):
_lowerCamelCase : List[Any] = 1024
_lowerCamelCase : Optional[Any] = 4096
_lowerCamelCase : List[Any] = 24
_lowerCamelCase : Union[str, Any] = 16
elif vit_name[4:].startswith("huge" ):
_lowerCamelCase : str = 1280
_lowerCamelCase : List[Any] = 5120
_lowerCamelCase : List[str] = 32
_lowerCamelCase : List[str] = 16
# load original model from timm
_lowerCamelCase : int = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
_lowerCamelCase : Any = timm_model.state_dict()
if base_model:
remove_classification_head_(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = create_rename_keys(_lowerCAmelCase , _lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# load HuggingFace model
if vit_name[-5:] == "in21k":
_lowerCamelCase : int = ViTModel(_lowerCAmelCase ).eval()
else:
_lowerCamelCase : List[str] = ViTForImageClassification(_lowerCAmelCase ).eval()
model.load_state_dict(_lowerCAmelCase )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
_lowerCamelCase : Union[str, Any] = DeiTImageProcessor(size=config.image_size )
else:
_lowerCamelCase : Union[str, Any] = ViTImageProcessor(size=config.image_size )
_lowerCamelCase : Optional[int] = image_processor(images=prepare_img() , return_tensors="pt" )
_lowerCamelCase : Optional[int] = encoding["pixel_values"]
_lowerCamelCase : Union[str, Any] = model(_lowerCAmelCase )
if base_model:
_lowerCamelCase : int = timm_model.forward_features(_lowerCAmelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_lowerCAmelCase , outputs.pooler_output , atol=1E-3 )
else:
_lowerCamelCase : Union[str, Any] = timm_model(_lowerCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCAmelCase , outputs.logits , atol=1E-3 )
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_lowerCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the 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.'
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int = 1000 ):
"""simple docstring"""
_lowerCamelCase : List[str] = 2**power
_lowerCamelCase : Any = str(_lowerCAmelCase )
_lowerCamelCase : Optional[int] = list(_lowerCAmelCase )
_lowerCamelCase : Any = 0
for i in list_num:
sum_of_num += int(_lowerCAmelCase )
return sum_of_num
if __name__ == "__main__":
UpperCAmelCase_ : Optional[int] = int(input('Enter the power of 2: ').strip())
print('2 ^ ', power, ' = ', 2**power)
UpperCAmelCase_ : Optional[int] = solution(power)
print('Sum of the digits is: ', result)
| 44 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( _lowerCAmelCase : int = 5000 ):
"""simple docstring"""
_lowerCamelCase : Dict = [(i * (3 * i - 1)) // 2 for i in range(1 , _lowerCAmelCase )]
for i, pentagonal_i in enumerate(_lowerCAmelCase ):
for j in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
_lowerCamelCase : List[Any] = pentagonal_nums[j]
_lowerCamelCase : Any = pentagonal_i + pentagonal_j
_lowerCamelCase : Union[str, Any] = pentagonal_j - pentagonal_i
if is_pentagonal(_lowerCAmelCase ) and is_pentagonal(_lowerCAmelCase ):
return b
return -1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 44 | 1 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase__ ( yaml.SafeLoader ):
def lowerCamelCase_ ( self : Optional[int],__A : int ):
_lowerCamelCase : List[Any] = [self.constructed_objects[key_node] for key_node, _ in node.value]
_lowerCamelCase : Optional[int] = [tuple(__A ) if isinstance(__A,__A ) else key for key in keys]
_lowerCamelCase : List[str] = Counter(__A )
_lowerCamelCase : List[Any] = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(f'Got duplicate yaml keys: {duplicate_keys}' )
def lowerCamelCase_ ( self : int,__A : List[Any],__A : Tuple=False ):
_lowerCamelCase : List[str] = super().construct_mapping(__A,deep=__A )
self._check_no_duplicates_on_constructed_node(__A )
return mapping
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Any = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
_lowerCamelCase : List[Any] = full_content[1:].index("---" ) + 1
_lowerCamelCase : Dict = "\n".join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(_lowerCAmelCase )
class UpperCAmelCase__ ( A ):
# class attributes
lowerCAmelCase_ = {'train_eval_index'} # train-eval-index in the YAML metadata
@classmethod
def lowerCamelCase_ ( cls : List[str],__A : Path ):
with open(__A,encoding="utf-8" ) as readme_file:
_lowerCamelCase , _lowerCamelCase : Optional[int] = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(__A )
else:
return cls()
def lowerCamelCase_ ( self : str,__A : Path ):
if path.exists():
with open(__A,encoding="utf-8" ) as readme_file:
_lowerCamelCase : Tuple = readme_file.read()
else:
_lowerCamelCase : Optional[Any] = None
_lowerCamelCase : Union[str, Any] = self._to_readme(__A )
with open(__A,"w",encoding="utf-8" ) as readme_file:
readme_file.write(__A )
def lowerCamelCase_ ( self : Optional[int],__A : Optional[str] = None ):
if readme_content is not None:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = _split_yaml_from_readme(__A )
_lowerCamelCase : List[str] = "---\n" + self.to_yaml_string() + "---\n" + content
else:
_lowerCamelCase : List[str] = "---\n" + self.to_yaml_string() + "---\n"
return full_content
@classmethod
def lowerCamelCase_ ( cls : List[Any],__A : str ):
_lowerCamelCase : Any = yaml.load(__A,Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
_lowerCamelCase : Optional[Any] = {
(key.replace("-","_" ) if key.replace("-","_" ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**__A )
def lowerCamelCase_ ( self : str ):
return yaml.safe_dump(
{
(key.replace("_","-" ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
},sort_keys=__A,allow_unicode=__A,encoding="utf-8",).decode("utf-8" )
UpperCAmelCase_ : Optional[int] = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
UpperCAmelCase_ : Union[str, Any] = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
UpperCAmelCase_ : str = ap.parse_args()
UpperCAmelCase_ : int = Path(args.readme_filepath)
UpperCAmelCase_ : Optional[Any] = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[Any] = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileBertForMaskedLM',
'MobileBertForMultipleChoice',
'MobileBertForNextSentencePrediction',
'MobileBertForPreTraining',
'MobileBertForQuestionAnswering',
'MobileBertForSequenceClassification',
'MobileBertForTokenClassification',
'MobileBertLayer',
'MobileBertModel',
'MobileBertPreTrainedModel',
'load_tf_weights_in_mobilebert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileBertForMaskedLM',
'TFMobileBertForMultipleChoice',
'TFMobileBertForNextSentencePrediction',
'TFMobileBertForPreTraining',
'TFMobileBertForQuestionAnswering',
'TFMobileBertForSequenceClassification',
'TFMobileBertForTokenClassification',
'TFMobileBertMainLayer',
'TFMobileBertModel',
'TFMobileBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 44 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'trocr'
lowerCAmelCase_ = ['past_key_values']
lowerCAmelCase_ = {
'num_attention_heads': 'decoder_attention_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'decoder_layers',
}
def __init__( self : List[Any],__A : int=5_0_2_6_5,__A : Union[str, Any]=1_0_2_4,__A : Any=1_2,__A : Any=1_6,__A : List[str]=4_0_9_6,__A : Tuple="gelu",__A : List[str]=5_1_2,__A : Tuple=0.1,__A : str=0.0,__A : Tuple=0.0,__A : List[Any]=2,__A : List[Any]=0.02,__A : Union[str, Any]=0.0,__A : Optional[int]=True,__A : List[Any]=False,__A : int=True,__A : Optional[int]=True,__A : List[str]=1,__A : Tuple=0,__A : Tuple=2,**__A : List[Any],):
_lowerCamelCase : Optional[Any] = vocab_size
_lowerCamelCase : str = d_model
_lowerCamelCase : Dict = decoder_layers
_lowerCamelCase : Dict = decoder_attention_heads
_lowerCamelCase : Union[str, Any] = decoder_ffn_dim
_lowerCamelCase : int = activation_function
_lowerCamelCase : Union[str, Any] = max_position_embeddings
_lowerCamelCase : Optional[int] = dropout
_lowerCamelCase : Any = attention_dropout
_lowerCamelCase : Tuple = activation_dropout
_lowerCamelCase : Dict = init_std
_lowerCamelCase : str = decoder_layerdrop
_lowerCamelCase : Optional[int] = use_cache
_lowerCamelCase : Any = scale_embedding
_lowerCamelCase : int = use_learned_position_embeddings
_lowerCamelCase : Optional[int] = layernorm_embedding
super().__init__(
pad_token_id=__A,bos_token_id=__A,eos_token_id=__A,decoder_start_token_id=__A,**__A,)
| 44 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase__ :
def __init__( self : Optional[Any],__A : list[tuple[float, float]] ):
_lowerCamelCase : Tuple = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
_lowerCamelCase : int = len(__A ) - 1
def lowerCamelCase_ ( self : Optional[int],__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree,__A ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__A ),5 ) == 1
return output_values
def lowerCamelCase_ ( self : int,__A : float ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
_lowerCamelCase : List[Any] = self.basis_function(__A )
_lowerCamelCase : str = 0.0
_lowerCamelCase : str = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowerCamelCase_ ( self : Optional[Any],__A : float = 0.01 ):
from matplotlib import pyplot as plt # type: ignore
_lowerCamelCase : list[float] = [] # x coordinates of points to plot
_lowerCamelCase : list[float] = [] # y coordinates of points to plot
_lowerCamelCase : Tuple = 0.0
while t <= 1:
_lowerCamelCase : str = self.bezier_curve_function(__A )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
_lowerCamelCase : List[str] = [i[0] for i in self.list_of_points]
_lowerCamelCase : Union[str, Any] = [i[1] for i in self.list_of_points]
plt.plot(
__A,__A,color="blue",label="Curve of Degree " + str(self.degree ),)
plt.scatter(__A,__A,color="red",label="Control Points" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 44 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self : List[Any] ):
_lowerCamelCase : str = XLMRobertaModel.from_pretrained("xlm-roberta-base" )
_lowerCamelCase : Dict = torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]] )
# The dog is cute and lives in the garden house
_lowerCamelCase : Union[str, Any] = torch.Size((1, 1_2, 7_6_8) ) # batch_size, sequence_length, embedding_vector_dim
_lowerCamelCase : str = torch.tensor(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
_lowerCamelCase : Optional[Any] = model(__A )["last_hidden_state"].detach()
self.assertEqual(output.shape,__A )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1],__A,atol=1e-3 ) )
@slow
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : Optional[Any] = XLMRobertaModel.from_pretrained("xlm-roberta-large" )
_lowerCamelCase : int = torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]] )
# The dog is cute and lives in the garden house
_lowerCamelCase : Optional[Any] = torch.Size((1, 1_2, 1_0_2_4) ) # batch_size, sequence_length, embedding_vector_dim
_lowerCamelCase : List[str] = torch.tensor(
[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
_lowerCamelCase : Tuple = model(__A )["last_hidden_state"].detach()
self.assertEqual(output.shape,__A )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1],__A,atol=1e-3 ) )
| 44 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=A ):
lowerCAmelCase_ = ['transformers', 'torch', 'note_seq']
def __init__( self : str,*__A : List[str],**__A : List[Any] ):
requires_backends(self,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Optional[Any],*__A : str,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
@classmethod
def lowerCamelCase_ ( cls : Dict,*__A : Dict,**__A : Tuple ):
requires_backends(cls,["transformers", "torch", "note_seq"] )
| 44 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase_ : Any = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether tp freeze the encoder.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
lowerCAmelCase_ = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
lowerCAmelCase_ = field(
default=1024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
lowerCAmelCase_ = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Source language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': 'Target language id for translation.'} )
lowerCAmelCase_ = field(default=A , metadata={'help': '# num_beams to use for evaluation.'} )
lowerCAmelCase_ = field(
default=A , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any ):
"""simple docstring"""
logger.info(F'***** {split} metrics *****' )
for key in sorted(metrics.keys() ):
logger.info(F' {key} = {metrics[key]}' )
save_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , F'{split}_results.json' ) )
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : str = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Optional[Any] = parser.parse_args_into_dataclasses()
check_output_dir(_lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , _lowerCAmelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCamelCase : Optional[int] = 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 , )
_lowerCamelCase : Tuple = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
assert hasattr(_lowerCAmelCase , _lowerCAmelCase ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
setattr(_lowerCAmelCase , _lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
_lowerCamelCase : List[Any] = 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 , )
_lowerCamelCase : int = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=_lowerCAmelCase , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(_lowerCAmelCase , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCamelCase : List[Any] = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(_lowerCAmelCase , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
_lowerCamelCase : Any = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCamelCase : int = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(_lowerCAmelCase )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCamelCase : int = SeqaSeqDataset
# Get datasets
_lowerCamelCase : Tuple = (
dataset_class(
_lowerCAmelCase , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
_lowerCamelCase : List[Any] = (
dataset_class(
_lowerCAmelCase , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCamelCase : Optional[int] = (
dataset_class(
_lowerCAmelCase , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCamelCase : int = (
build_compute_metrics_fn(data_args.task , _lowerCAmelCase ) if training_args.predict_with_generate else None
)
_lowerCamelCase : List[Any] = SeqaSeqTrainer(
model=_lowerCAmelCase , args=_lowerCAmelCase , data_args=_lowerCAmelCase , train_dataset=_lowerCAmelCase , eval_dataset=_lowerCAmelCase , data_collator=SeqaSeqDataCollator(
_lowerCAmelCase , _lowerCAmelCase , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=_lowerCAmelCase , tokenizer=_lowerCAmelCase , )
_lowerCamelCase : Optional[Any] = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
_lowerCamelCase : Optional[Any] = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCamelCase : int = train_result.metrics
_lowerCamelCase : Optional[int] = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_lowerCamelCase : Optional[Any] = trainer.evaluate(metric_key_prefix="val" )
_lowerCamelCase : Dict = data_args.n_val
_lowerCamelCase : List[Any] = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.do_predict:
logger.info("*** Predict ***" )
_lowerCamelCase : Any = trainer.predict(test_dataset=_lowerCAmelCase , metric_key_prefix="test" )
_lowerCamelCase : Dict = test_output.metrics
_lowerCamelCase : Optional[int] = data_args.n_test
if trainer.is_world_process_zero():
_lowerCamelCase : int = round(metrics["test_loss"] , 4 )
handle_metrics("test" , _lowerCAmelCase , training_args.output_dir )
all_metrics.update(_lowerCAmelCase )
if training_args.predict_with_generate:
_lowerCamelCase : List[str] = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
_lowerCamelCase : Any = lmap(str.strip , _lowerCAmelCase )
write_txt_file(_lowerCAmelCase , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(_lowerCAmelCase , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 44 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = CodeGenTokenizer
lowerCAmelCase_ = CodeGenTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = {'add_prefix_space': True}
lowerCAmelCase_ = False
def lowerCamelCase_ ( self : List[str] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_lowerCamelCase : Dict = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
_lowerCamelCase : Any = dict(zip(__A,range(len(__A ) ) ) )
_lowerCamelCase : Optional[int] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_lowerCamelCase : Tuple = {"unk_token": "<unk>"}
_lowerCamelCase : Optional[Any] = 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(__A ) + "\n" )
with open(self.merges_file,"w",encoding="utf-8" ) as fp:
fp.write("\n".join(__A ) )
def lowerCamelCase_ ( self : Dict,**__A : Tuple ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : Union[str, Any],**__A : int ):
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname,**__A )
def lowerCamelCase_ ( self : str,__A : Dict ):
_lowerCamelCase : Optional[Any] = "lower newer"
_lowerCamelCase : Union[str, Any] = "lower newer"
return input_text, output_text
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : int = CodeGenTokenizer(self.vocab_file,self.merges_file,**self.special_tokens_map )
_lowerCamelCase : Any = "lower newer"
_lowerCamelCase : Optional[Any] = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
self.assertListEqual(__A,__A )
_lowerCamelCase : Union[str, Any] = tokens + [tokenizer.unk_token]
_lowerCamelCase : Dict = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Any ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase : str = self.get_tokenizer()
_lowerCamelCase : Optional[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = "lower newer"
# Testing tokenization
_lowerCamelCase : List[Any] = tokenizer.tokenize(__A,add_prefix_space=__A )
_lowerCamelCase : str = rust_tokenizer.tokenize(__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids without special tokens
_lowerCamelCase : str = tokenizer.encode(__A,add_special_tokens=__A,add_prefix_space=__A )
_lowerCamelCase : List[str] = rust_tokenizer.encode(__A,add_special_tokens=__A )
self.assertListEqual(__A,__A )
# Testing conversion to ids with special tokens
_lowerCamelCase : List[Any] = self.get_rust_tokenizer(add_prefix_space=__A )
_lowerCamelCase : Union[str, Any] = tokenizer.encode(__A,add_prefix_space=__A )
_lowerCamelCase : Optional[int] = rust_tokenizer.encode(__A )
self.assertListEqual(__A,__A )
# Testing the unknown token
_lowerCamelCase : Optional[int] = tokens + [rust_tokenizer.unk_token]
_lowerCamelCase : Optional[Any] = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__A ),__A )
def lowerCamelCase_ ( self : Tuple,*__A : Any,**__A : Any ):
# It's very difficult to mix/test pretokenization with byte-level
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def lowerCamelCase_ ( self : int,__A : Optional[int]=1_5 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_lowerCamelCase : Tuple = self.rust_tokenizer_class.from_pretrained(__A,**__A )
# Simple input
_lowerCamelCase : Dict = "This is a simple input"
_lowerCamelCase : Any = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Tuple = ("This is a simple input", "This is a pair")
_lowerCamelCase : Tuple = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Simple input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
# Pair input
self.assertRaises(__A,tokenizer_r.encode,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(__A,tokenizer_r.encode_plus,__A,max_length=__A,padding="max_length" )
# Pair input
self.assertRaises(
__A,tokenizer_r.batch_encode_plus,__A,max_length=__A,padding="max_length",)
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : str = CodeGenTokenizer.from_pretrained(self.tmpdirname,pad_token="<pad>" )
# Simple input
_lowerCamelCase : Tuple = "This is a simple input"
_lowerCamelCase : Dict = ["This is a simple input looooooooong", "This is a simple input"]
_lowerCamelCase : Dict = ("This is a simple input", "This is a pair")
_lowerCamelCase : Dict = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
_lowerCamelCase : Dict = tokenizer.pad_token_id
_lowerCamelCase : Dict = tokenizer(__A,padding="max_length",max_length=3_0,return_tensors="np" )
_lowerCamelCase : int = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
_lowerCamelCase : List[Any] = tokenizer(*__A,padding="max_length",max_length=6_0,return_tensors="np" )
_lowerCamelCase : Tuple = tokenizer(__A,padding=__A,truncate=__A,return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1],3_0 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1],3_3 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1],6_0 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1],5_2 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : List[Any] = "$$$"
_lowerCamelCase : Tuple = CodeGenTokenizer.from_pretrained(self.tmpdirname,bos_token=__A,add_bos_token=__A )
_lowerCamelCase : List[str] = "This is a simple input"
_lowerCamelCase : Optional[Any] = ["This is a simple input 1", "This is a simple input 2"]
_lowerCamelCase : Union[str, Any] = tokenizer.bos_token_id
_lowerCamelCase : Any = tokenizer(__A )
_lowerCamelCase : List[str] = tokenizer(__A )
self.assertEqual(out_s.input_ids[0],__A )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_lowerCamelCase : int = tokenizer.decode(out_s.input_ids )
_lowerCamelCase : str = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0],__A )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : int = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
_lowerCamelCase : Optional[Any] = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
_lowerCamelCase : Dict = "\nif len_a > len_b: result = a\nelse: result = b"
_lowerCamelCase : Any = tokenizer.encode(__A )
_lowerCamelCase : str = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
_lowerCamelCase : List[Any] = tokenizer.decode(__A,truncate_before_pattern=__A )
self.assertEqual(__A,__A )
def lowerCamelCase_ ( self : Any ):
pass
| 44 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=A )
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = field(default='automatic-speech-recognition' , metadata={'include_in_asdict_even_if_is_default': True} )
lowerCAmelCase_ = Features({'audio': Audio()} )
lowerCAmelCase_ = Features({'transcription': Value('string' )} )
lowerCAmelCase_ = "audio"
lowerCAmelCase_ = "transcription"
def lowerCamelCase_ ( self : int,__A : Union[str, Any] ):
if self.audio_column not in features:
raise ValueError(f'Column {self.audio_column} is not present in features.' )
if not isinstance(features[self.audio_column],__A ):
raise ValueError(f'Column {self.audio_column} is not an Audio type.' )
_lowerCamelCase : Optional[int] = copy.deepcopy(self )
_lowerCamelCase : int = self.input_schema.copy()
_lowerCamelCase : Any = features[self.audio_column]
_lowerCamelCase : Dict = input_schema
return task_template
@property
def lowerCamelCase_ ( self : Union[str, Any] ):
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 44 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class UpperCAmelCase__ :
def __init__( self : Any,__A : int=2,__A : Any=3,__A : Optional[int]=6_4,__A : Tuple=None ):
_lowerCamelCase : int = np.random.default_rng(__A )
_lowerCamelCase : List[str] = length
_lowerCamelCase : Optional[Any] = rng.normal(size=(length,) ).astype(np.floataa )
_lowerCamelCase : Optional[int] = a * self.x + b + rng.normal(scale=0.1,size=(length,) ).astype(np.floataa )
def __len__( self : Dict ):
return self.length
def __getitem__( self : str,__A : List[str] ):
return {"x": self.x[i], "y": self.y[i]}
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : Optional[Any]=0,__A : Optional[int]=0,__A : Dict=False ):
super().__init__()
_lowerCamelCase : Tuple = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : List[str] = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
_lowerCamelCase : Optional[int] = True
def lowerCamelCase_ ( self : List[str],__A : Tuple=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a[0] + self.b[0]
class UpperCAmelCase__ ( torch.nn.Module ):
def __init__( self : Union[str, Any],__A : List[str]=0,__A : List[str]=0,__A : int=False ):
super().__init__()
_lowerCamelCase : Optional[int] = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Dict = torch.nn.Parameter(torch.tensor(__A ).float() )
_lowerCamelCase : Tuple = True
def lowerCamelCase_ ( self : str,__A : List[Any]=None ):
if self.first_batch:
print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}' )
_lowerCamelCase : Optional[Any] = False
return x * self.a + self.b
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
_lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("bert-base-cased" )
_lowerCamelCase : List[Any] = {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
_lowerCamelCase : int = load_dataset("csv" , data_files=_lowerCAmelCase )
_lowerCamelCase : Dict = datasets["train"].unique("label" )
_lowerCamelCase : Optional[Any] = {v: i for i, v in enumerate(_lowerCAmelCase )}
def tokenize_function(_lowerCAmelCase : int ):
# max_length=None => use the model max length (it's actually the default)
_lowerCamelCase : Optional[int] = tokenizer(
examples["sentence1"] , examples["sentence2"] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase , padding="max_length" )
if "label" in examples:
_lowerCamelCase : str = [label_to_id[l] for l in examples["label"]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_lowerCamelCase : Optional[Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["sentence1", "sentence2", "label"] , )
def collate_fn(_lowerCAmelCase : str ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_lowerCAmelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return tokenizer.pad(_lowerCAmelCase , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
_lowerCamelCase : str = DataLoader(tokenized_datasets["train"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=2 )
_lowerCamelCase : Optional[int] = DataLoader(tokenized_datasets["validation"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=1 )
return train_dataloader, eval_dataloader
| 44 | 1 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : Dict=None , **_lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : int = [x.strip() for x in open(_lowerCAmelCase ).readlines()]
_lowerCamelCase : Dict = [x.strip() for x in open(_lowerCAmelCase ).readlines()][: len(_lowerCAmelCase )]
_lowerCamelCase : List[str] = calculate_rouge(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
if save_path is not None:
save_json(_lowerCAmelCase , _lowerCAmelCase , indent=_lowerCAmelCase )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 44 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Optional[Any] = False, False, False
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = None
lowerCAmelCase_ = True
lowerCAmelCase_ = True
lowerCAmelCase_ = None
# Automatically constructed
lowerCAmelCase_ = "dict"
lowerCAmelCase_ = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
lowerCAmelCase_ = field(default='Audio' , init=A , repr=A )
def __call__( self : Tuple ):
return self.pa_type
def lowerCamelCase_ ( self : Any,__A : Union[str, bytes, dict] ):
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
if isinstance(__A,__A ):
return {"bytes": None, "path": value}
elif isinstance(__A,__A ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
_lowerCamelCase : List[Any] = BytesIO()
sf.write(__A,value["array"],value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm" ):
# "PCM" only has raw audio bytes
if value.get("sampling_rate" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
if value.get("bytes" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
_lowerCamelCase : Dict = np.frombuffer(value["bytes"],dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
else:
_lowerCamelCase : str = np.memmap(value["path"],dtype="h",mode="r" ).astype(np.floataa ) / 3_2_7_6_7
_lowerCamelCase : Optional[int] = BytesIO(bytes() )
sf.write(__A,__A,value["sampling_rate"],format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' )
def lowerCamelCase_ ( self : Optional[Any],__A : dict,__A : Optional[Dict[str, Union[str, bool, None]]] = None ):
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )
_lowerCamelCase , _lowerCamelCase : Optional[Any] = (value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err
_lowerCamelCase : Tuple = xsplitext(__A )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
if file is None:
_lowerCamelCase : Tuple = token_per_repo_id or {}
_lowerCamelCase : Union[str, Any] = path.split("::" )[-1]
try:
_lowerCamelCase : str = string_to_dict(__A,config.HUB_DATASETS_URL )["repo_id"]
_lowerCamelCase : str = token_per_repo_id[repo_id]
except (ValueError, KeyError):
_lowerCamelCase : Any = None
with xopen(__A,"rb",use_auth_token=__A ) as f:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = sf.read(__A )
else:
_lowerCamelCase , _lowerCamelCase : str = sf.read(__A )
_lowerCamelCase : List[str] = array.T
if self.mono:
_lowerCamelCase : List[str] = librosa.to_mono(__A )
if self.sampling_rate and self.sampling_rate != sampling_rate:
_lowerCamelCase : List[str] = librosa.resample(__A,orig_sr=__A,target_sr=self.sampling_rate )
_lowerCamelCase : Optional[Any] = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def lowerCamelCase_ ( self : Any ):
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature." )
return {
"bytes": Value("binary" ),
"path": Value("string" ),
}
def lowerCamelCase_ ( self : List[str],__A : Union[pa.StringArray, pa.StructArray] ):
if pa.types.is_string(storage.type ):
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
_lowerCamelCase : int = pa.StructArray.from_arrays([bytes_array, storage],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_lowerCamelCase : Dict = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Any = pa.StructArray.from_arrays([storage, path_array],["bytes", "path"],mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
_lowerCamelCase : Tuple = pa.array([Audio().encode_example(__A ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
_lowerCamelCase : Tuple = storage.field("bytes" )
else:
_lowerCamelCase : Any = pa.array([None] * len(__A ),type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
_lowerCamelCase : List[str] = storage.field("path" )
else:
_lowerCamelCase : Tuple = pa.array([None] * len(__A ),type=pa.string() )
_lowerCamelCase : Tuple = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=storage.is_null() )
return array_cast(__A,self.pa_type )
def lowerCamelCase_ ( self : str,__A : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(__A : Dict ):
with xopen(__A,"rb" ) as f:
_lowerCamelCase : Any = f.read()
return bytes_
_lowerCamelCase : int = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
],type=pa.binary(),)
_lowerCamelCase : str = pa.array(
[os.path.basename(__A ) if path is not None else None for path in storage.field("path" ).to_pylist()],type=pa.string(),)
_lowerCamelCase : Dict = pa.StructArray.from_arrays([bytes_array, path_array],["bytes", "path"],mask=bytes_array.is_null() )
return array_cast(__A,self.pa_type )
| 44 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'glpn'
def __init__( self : Tuple,__A : Optional[int]=3,__A : Optional[int]=4,__A : str=[2, 2, 2, 2],__A : Union[str, Any]=[8, 4, 2, 1],__A : Tuple=[3_2, 6_4, 1_6_0, 2_5_6],__A : int=[7, 3, 3, 3],__A : str=[4, 2, 2, 2],__A : int=[1, 2, 5, 8],__A : List[Any]=[4, 4, 4, 4],__A : Optional[int]="gelu",__A : int=0.0,__A : Tuple=0.0,__A : Tuple=0.02,__A : Optional[int]=0.1,__A : Optional[int]=1e-6,__A : Optional[int]=6_4,__A : Optional[Any]=1_0,__A : Tuple=-1,**__A : List[str],):
super().__init__(**__A )
_lowerCamelCase : Tuple = num_channels
_lowerCamelCase : Union[str, Any] = num_encoder_blocks
_lowerCamelCase : Dict = depths
_lowerCamelCase : List[Any] = sr_ratios
_lowerCamelCase : str = hidden_sizes
_lowerCamelCase : Any = patch_sizes
_lowerCamelCase : Any = strides
_lowerCamelCase : Dict = mlp_ratios
_lowerCamelCase : int = num_attention_heads
_lowerCamelCase : List[Any] = hidden_act
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : Optional[int] = initializer_range
_lowerCamelCase : Union[str, Any] = drop_path_rate
_lowerCamelCase : str = layer_norm_eps
_lowerCamelCase : Tuple = decoder_hidden_size
_lowerCamelCase : int = max_depth
_lowerCamelCase : Dict = head_in_index
| 44 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'glpn'
def __init__( self : Tuple,__A : Optional[int]=3,__A : Optional[int]=4,__A : str=[2, 2, 2, 2],__A : Union[str, Any]=[8, 4, 2, 1],__A : Tuple=[3_2, 6_4, 1_6_0, 2_5_6],__A : int=[7, 3, 3, 3],__A : str=[4, 2, 2, 2],__A : int=[1, 2, 5, 8],__A : List[Any]=[4, 4, 4, 4],__A : Optional[int]="gelu",__A : int=0.0,__A : Tuple=0.0,__A : Tuple=0.02,__A : Optional[int]=0.1,__A : Optional[int]=1e-6,__A : Optional[int]=6_4,__A : Optional[Any]=1_0,__A : Tuple=-1,**__A : List[str],):
super().__init__(**__A )
_lowerCamelCase : Tuple = num_channels
_lowerCamelCase : Union[str, Any] = num_encoder_blocks
_lowerCamelCase : Dict = depths
_lowerCamelCase : List[Any] = sr_ratios
_lowerCamelCase : str = hidden_sizes
_lowerCamelCase : Any = patch_sizes
_lowerCamelCase : Any = strides
_lowerCamelCase : Dict = mlp_ratios
_lowerCamelCase : int = num_attention_heads
_lowerCamelCase : List[Any] = hidden_act
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : Optional[int] = initializer_range
_lowerCamelCase : Union[str, Any] = drop_path_rate
_lowerCamelCase : str = layer_norm_eps
_lowerCamelCase : Tuple = decoder_hidden_size
_lowerCamelCase : int = max_depth
_lowerCamelCase : Dict = head_in_index
| 44 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
),
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'gptsan-japanese'
lowerCAmelCase_ = [
'past_key_values',
]
lowerCAmelCase_ = {
'hidden_size': 'd_model',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : List[str],__A : Union[str, Any]=3_6_0_0_0,__A : Any=1_2_8_0,__A : List[str]=1_0_2_4,__A : List[str]=8_1_9_2,__A : Any=4_0_9_6,__A : int=1_2_8,__A : List[Any]=1_0,__A : Any=0,__A : int=1_6,__A : str=1_6,__A : str=1_2_8,__A : List[str]=0.0,__A : int=1e-5,__A : List[str]=False,__A : List[Any]=0.0,__A : Optional[int]="float32",__A : Any=False,__A : List[Any]=False,__A : Any=False,__A : Dict=0.002,__A : Tuple=False,__A : Optional[Any]=True,__A : Union[str, Any]=3_5_9_9_8,__A : List[Any]=3_5_9_9_5,__A : Tuple=3_5_9_9_9,**__A : List[Any],):
_lowerCamelCase : int = vocab_size
_lowerCamelCase : List[str] = max_position_embeddings
_lowerCamelCase : Dict = d_model
_lowerCamelCase : List[str] = d_ff
_lowerCamelCase : int = d_ext
_lowerCamelCase : Optional[Any] = d_spout
_lowerCamelCase : int = num_switch_layers
_lowerCamelCase : Dict = num_ext_layers
_lowerCamelCase : List[str] = num_switch_layers + num_ext_layers
_lowerCamelCase : List[str] = num_heads
_lowerCamelCase : Tuple = num_experts
_lowerCamelCase : List[str] = expert_capacity
_lowerCamelCase : str = dropout_rate
_lowerCamelCase : List[Any] = layer_norm_epsilon
_lowerCamelCase : Optional[int] = router_bias
_lowerCamelCase : List[str] = router_jitter_noise
_lowerCamelCase : int = router_dtype
_lowerCamelCase : Optional[int] = router_ignore_padding_tokens
_lowerCamelCase : Optional[Any] = output_hidden_states
_lowerCamelCase : Optional[int] = output_attentions
_lowerCamelCase : List[Any] = initializer_factor
_lowerCamelCase : Union[str, Any] = output_router_logits
_lowerCamelCase : Optional[Any] = use_cache
super().__init__(
separator_token_id=__A,pad_token_id=__A,eos_token_id=__A,**__A,)
| 44 |
'''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_ : str = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['input_features', 'attention_mask']
def __init__( self : Any,__A : List[Any]=8_0,__A : Dict=1_6_0_0_0,__A : Tuple=0.0,__A : Dict=1_0,__A : int=2_5,__A : Union[str, Any]="hamming_window",__A : List[str]=32768.0,__A : Union[str, Any]=0.97,__A : str=1.0,__A : Union[str, Any]=True,__A : Tuple=True,__A : Optional[Any]=False,**__A : Optional[Any],):
super().__init__(feature_size=__A,sampling_rate=__A,padding_value=__A,**__A )
_lowerCamelCase : Dict = feature_size
_lowerCamelCase : List[str] = sampling_rate
_lowerCamelCase : Any = padding_value
_lowerCamelCase : Dict = hop_length
_lowerCamelCase : Tuple = win_length
_lowerCamelCase : str = frame_signal_scale
_lowerCamelCase : List[str] = preemphasis_coeff
_lowerCamelCase : List[str] = mel_floor
_lowerCamelCase : str = normalize_means
_lowerCamelCase : Any = normalize_vars
_lowerCamelCase : List[str] = win_function
_lowerCamelCase : Tuple = return_attention_mask
_lowerCamelCase : List[Any] = win_length * sampling_rate // 1_0_0_0
_lowerCamelCase : List[Any] = hop_length * sampling_rate // 1_0_0_0
_lowerCamelCase : Any = optimal_fft_length(self.sample_size )
_lowerCamelCase : Dict = (self.n_fft // 2) + 1
def lowerCamelCase_ ( self : Any,__A : np.array ):
if self.win_function == "hamming_window":
_lowerCamelCase : Any = window_function(window_length=self.sample_size,name=self.win_function,periodic=__A )
else:
_lowerCamelCase : Optional[int] = window_function(window_length=self.sample_size,name=self.win_function )
_lowerCamelCase : int = 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 : List[str] = spectrogram(
one_waveform * self.frame_signal_scale,window=__A,frame_length=self.sample_size,hop_length=self.sample_stride,fft_length=self.n_fft,center=__A,preemphasis=self.preemphasis_coeff,mel_filters=__A,mel_floor=self.mel_floor,log_mel="log",)
return msfc_features.T
def lowerCamelCase_ ( self : Optional[int],__A : List[str],__A : Dict,__A : int ):
# make sure we normalize float32 arrays
if self.normalize_means:
_lowerCamelCase : Optional[Any] = x[:input_length].mean(axis=0 )
_lowerCamelCase : Optional[int] = np.subtract(__A,__A )
if self.normalize_vars:
_lowerCamelCase : int = x[:input_length].std(axis=0 )
_lowerCamelCase : Any = np.divide(__A,__A )
if input_length < x.shape[0]:
_lowerCamelCase : Tuple = padding_value
# make sure array is in float32
_lowerCamelCase : Optional[int] = x.astype(np.floataa )
return x
def lowerCamelCase_ ( self : Any,__A : List[np.ndarray],__A : Optional[np.ndarray] = None ):
_lowerCamelCase : Optional[int] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(__A,__A,self.padding_value ) for x, n in zip(__A,__A )]
def __call__( self : Optional[Any],__A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],__A : Union[bool, str, PaddingStrategy] = False,__A : Optional[int] = None,__A : bool = False,__A : Optional[int] = None,__A : Optional[bool] = None,__A : Optional[Union[str, TensorType]] = None,__A : Optional[int] = None,**__A : Optional[Any],):
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 : List[str] = isinstance(__A,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 : List[str] = is_batched_numpy or (
isinstance(__A,(list, tuple) ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list) ))
)
if is_batched:
_lowerCamelCase : List[Any] = [np.asarray(__A,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__A,np.ndarray ):
_lowerCamelCase : Dict = np.asarray(__A,dtype=np.floataa )
elif isinstance(__A,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowerCamelCase : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCamelCase : Tuple = [raw_speech]
# extract fbank features
_lowerCamelCase : str = [self._extract_mfsc_features(__A ) for one_waveform in raw_speech]
# convert into correct format for padding
_lowerCamelCase : Union[str, Any] = BatchFeature({"input_features": features} )
_lowerCamelCase : List[Any] = self.pad(
__A,padding=__A,max_length=__A,truncation=__A,pad_to_multiple_of=__A,return_attention_mask=__A,**__A,)
# make sure list is in array format
_lowerCamelCase : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0],__A ):
_lowerCamelCase : int = [np.asarray(__A,dtype=np.floataa ) for feature in input_features]
_lowerCamelCase : Dict = padded_inputs.get("attention_mask" )
if attention_mask is not None:
_lowerCamelCase : Dict = [np.asarray(__A,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
_lowerCamelCase : Dict = (
np.array(__A,dtype=np.intaa )
if self._get_padding_strategies(__A,max_length=__A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
_lowerCamelCase : Tuple = self.normalize(
padded_inputs["input_features"],attention_mask=__A )
if return_tensors is not None:
_lowerCamelCase : Dict = padded_inputs.convert_to_tensors(__A )
return padded_inputs
| 44 | 1 |
'''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_ : str = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['input_features', 'attention_mask']
def __init__( self : Any,__A : List[Any]=8_0,__A : Dict=1_6_0_0_0,__A : Tuple=0.0,__A : Dict=1_0,__A : int=2_5,__A : Union[str, Any]="hamming_window",__A : List[str]=32768.0,__A : Union[str, Any]=0.97,__A : str=1.0,__A : Union[str, Any]=True,__A : Tuple=True,__A : Optional[Any]=False,**__A : Optional[Any],):
super().__init__(feature_size=__A,sampling_rate=__A,padding_value=__A,**__A )
_lowerCamelCase : Dict = feature_size
_lowerCamelCase : List[str] = sampling_rate
_lowerCamelCase : Any = padding_value
_lowerCamelCase : Dict = hop_length
_lowerCamelCase : Tuple = win_length
_lowerCamelCase : str = frame_signal_scale
_lowerCamelCase : List[str] = preemphasis_coeff
_lowerCamelCase : List[str] = mel_floor
_lowerCamelCase : str = normalize_means
_lowerCamelCase : Any = normalize_vars
_lowerCamelCase : List[str] = win_function
_lowerCamelCase : Tuple = return_attention_mask
_lowerCamelCase : List[Any] = win_length * sampling_rate // 1_0_0_0
_lowerCamelCase : List[Any] = hop_length * sampling_rate // 1_0_0_0
_lowerCamelCase : Any = optimal_fft_length(self.sample_size )
_lowerCamelCase : Dict = (self.n_fft // 2) + 1
def lowerCamelCase_ ( self : Any,__A : np.array ):
if self.win_function == "hamming_window":
_lowerCamelCase : Any = window_function(window_length=self.sample_size,name=self.win_function,periodic=__A )
else:
_lowerCamelCase : Optional[int] = window_function(window_length=self.sample_size,name=self.win_function )
_lowerCamelCase : int = 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 : List[str] = spectrogram(
one_waveform * self.frame_signal_scale,window=__A,frame_length=self.sample_size,hop_length=self.sample_stride,fft_length=self.n_fft,center=__A,preemphasis=self.preemphasis_coeff,mel_filters=__A,mel_floor=self.mel_floor,log_mel="log",)
return msfc_features.T
def lowerCamelCase_ ( self : Optional[int],__A : List[str],__A : Dict,__A : int ):
# make sure we normalize float32 arrays
if self.normalize_means:
_lowerCamelCase : Optional[Any] = x[:input_length].mean(axis=0 )
_lowerCamelCase : Optional[int] = np.subtract(__A,__A )
if self.normalize_vars:
_lowerCamelCase : int = x[:input_length].std(axis=0 )
_lowerCamelCase : Any = np.divide(__A,__A )
if input_length < x.shape[0]:
_lowerCamelCase : Tuple = padding_value
# make sure array is in float32
_lowerCamelCase : Optional[int] = x.astype(np.floataa )
return x
def lowerCamelCase_ ( self : Any,__A : List[np.ndarray],__A : Optional[np.ndarray] = None ):
_lowerCamelCase : Optional[int] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(__A,__A,self.padding_value ) for x, n in zip(__A,__A )]
def __call__( self : Optional[Any],__A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],__A : Union[bool, str, PaddingStrategy] = False,__A : Optional[int] = None,__A : bool = False,__A : Optional[int] = None,__A : Optional[bool] = None,__A : Optional[Union[str, TensorType]] = None,__A : Optional[int] = None,**__A : Optional[Any],):
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 : List[str] = isinstance(__A,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 : List[str] = is_batched_numpy or (
isinstance(__A,(list, tuple) ) and (isinstance(raw_speech[0],(np.ndarray, tuple, list) ))
)
if is_batched:
_lowerCamelCase : List[Any] = [np.asarray(__A,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__A,np.ndarray ):
_lowerCamelCase : Dict = np.asarray(__A,dtype=np.floataa )
elif isinstance(__A,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowerCamelCase : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCamelCase : Tuple = [raw_speech]
# extract fbank features
_lowerCamelCase : str = [self._extract_mfsc_features(__A ) for one_waveform in raw_speech]
# convert into correct format for padding
_lowerCamelCase : Union[str, Any] = BatchFeature({"input_features": features} )
_lowerCamelCase : List[Any] = self.pad(
__A,padding=__A,max_length=__A,truncation=__A,pad_to_multiple_of=__A,return_attention_mask=__A,**__A,)
# make sure list is in array format
_lowerCamelCase : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0],__A ):
_lowerCamelCase : int = [np.asarray(__A,dtype=np.floataa ) for feature in input_features]
_lowerCamelCase : Dict = padded_inputs.get("attention_mask" )
if attention_mask is not None:
_lowerCamelCase : Dict = [np.asarray(__A,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
_lowerCamelCase : Dict = (
np.array(__A,dtype=np.intaa )
if self._get_padding_strategies(__A,max_length=__A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
_lowerCamelCase : Tuple = self.normalize(
padded_inputs["input_features"],attention_mask=__A )
if return_tensors is not None:
_lowerCamelCase : Dict = padded_inputs.convert_to_tensors(__A )
return padded_inputs
| 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
UpperCAmelCase_ : int = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def A_ ( _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = torch.load(_lowerCAmelCase , map_location="cpu" )
return sd
def A_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple=rename_keys_prefix ):
"""simple docstring"""
_lowerCamelCase : Any = OrderedDict()
_lowerCamelCase : str = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
_lowerCamelCase : Any = key
for name_pair in rename_keys_prefix:
_lowerCamelCase : Dict = new_key.replace(name_pair[0] , name_pair[1] )
_lowerCamelCase : Any = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
_lowerCamelCase : List[str] = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Dict ):
"""simple docstring"""
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
_lowerCamelCase : Optional[int] = "pretraining"
if "vcr" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
_lowerCamelCase : int = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
_lowerCamelCase : List[str] = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
_lowerCamelCase : Any = {"visual_embedding_dim": 512}
_lowerCamelCase : List[Any] = "multichoice"
elif "vqa_advanced" in checkpoint_path:
_lowerCamelCase : Tuple = {"visual_embedding_dim": 2048}
_lowerCamelCase : Dict = "vqa_advanced"
elif "vqa" in checkpoint_path:
_lowerCamelCase : Union[str, Any] = {"visual_embedding_dim": 2048, "num_labels": 3129}
_lowerCamelCase : Optional[int] = "vqa"
elif "nlvr" in checkpoint_path:
_lowerCamelCase : Tuple = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
_lowerCamelCase : Optional[Any] = "nlvr"
_lowerCamelCase : str = VisualBertConfig(**_lowerCAmelCase )
# Load State Dict
_lowerCamelCase : str = load_state_dict(_lowerCAmelCase )
_lowerCamelCase : List[str] = get_new_dict(_lowerCAmelCase , _lowerCAmelCase )
if model_type == "pretraining":
_lowerCamelCase : List[Any] = VisualBertForPreTraining(_lowerCAmelCase )
elif model_type == "vqa":
_lowerCamelCase : Dict = VisualBertForQuestionAnswering(_lowerCAmelCase )
elif model_type == "nlvr":
_lowerCamelCase : Tuple = VisualBertForVisualReasoning(_lowerCAmelCase )
elif model_type == "multichoice":
_lowerCamelCase : str = VisualBertForMultipleChoice(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
# Save Checkpoints
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 44 | 1 |
'''simple docstring'''
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
UpperCAmelCase_ : List[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 ):
def __init__( self : List[Any],__A : List[str] ):
super().__init__()
_lowerCamelCase : Optional[Any] = torchvision.models.resnetaaa(pretrained=__A )
_lowerCamelCase : Optional[Any] = list(model.children() )[:-2]
_lowerCamelCase : Optional[Any] = nn.Sequential(*__A )
_lowerCamelCase : Optional[int] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def lowerCamelCase_ ( self : int,__A : List[str] ):
# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
_lowerCamelCase : Optional[Any] = self.pool(self.model(__A ) )
_lowerCamelCase : Optional[Any] = torch.flatten(__A,start_dim=2 )
_lowerCamelCase : Optional[Any] = out.transpose(1,2 ).contiguous()
return out # BxNx2048
class UpperCAmelCase__ ( A ):
def __init__( self : List[str],__A : Union[str, Any],__A : Tuple,__A : str,__A : str,__A : Optional[int] ):
_lowerCamelCase : List[Any] = [json.loads(__A ) for l in open(__A )]
_lowerCamelCase : Dict = os.path.dirname(__A )
_lowerCamelCase : Union[str, Any] = tokenizer
_lowerCamelCase : str = labels
_lowerCamelCase : int = len(__A )
_lowerCamelCase : str = max_seq_length
_lowerCamelCase : Tuple = transforms
def __len__( self : Union[str, Any] ):
return len(self.data )
def __getitem__( self : List[str],__A : Any ):
_lowerCamelCase : List[Any] = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"],add_special_tokens=__A ) )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : List[Any] = sentence[0], sentence[1:-1], sentence[-1]
_lowerCamelCase : Tuple = sentence[: self.max_seq_length]
_lowerCamelCase : Optional[int] = torch.zeros(self.n_classes )
_lowerCamelCase : List[Any] = 1
_lowerCamelCase : str = Image.open(os.path.join(self.data_dir,self.data[index]["img"] ) ).convert("RGB" )
_lowerCamelCase : Optional[int] = self.transforms(__A )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : str = Counter()
for row in self.data:
label_freqs.update(row["label"] )
return label_freqs
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = [len(row["sentence"] ) for row in batch]
_lowerCamelCase , _lowerCamelCase : List[Any] = len(_lowerCAmelCase ), max(_lowerCAmelCase )
_lowerCamelCase : Tuple = torch.zeros(_lowerCAmelCase , _lowerCAmelCase , dtype=torch.long )
_lowerCamelCase : Tuple = torch.zeros(_lowerCAmelCase , _lowerCAmelCase , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(_lowerCAmelCase , _lowerCAmelCase ) ):
_lowerCamelCase : Optional[int] = input_row["sentence"]
_lowerCamelCase : Tuple = 1
_lowerCamelCase : Any = torch.stack([row["image"] for row in batch] )
_lowerCamelCase : Union[str, Any] = torch.stack([row["label"] for row in batch] )
_lowerCamelCase : int = torch.stack([row["image_start_token"] for row in batch] )
_lowerCamelCase : Optional[Any] = 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 A_ ( ):
"""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 A_ ( ):
"""simple docstring"""
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7] , std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9] , ),
] )
| 44 |
'''simple docstring'''
import functools
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(_lowerCAmelCase ) != 3 or not all(isinstance(_lowerCAmelCase , _lowerCAmelCase ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(_lowerCAmelCase ) == 0:
return 0
if min(_lowerCAmelCase ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(_lowerCAmelCase ) >= 366:
raise ValueError("All days elements should be less than 366" )
_lowerCamelCase : Union[str, Any] = set(_lowerCAmelCase )
@functools.cache
def dynamic_programming(_lowerCAmelCase : int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 | 1 |
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