





'''simple docstring'''







import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

def    __UpperCamelCase	(	a    : List[str]					,   a    : str					,   a    : List[Any]					,   a    : List[Any]     ) ->Tuple:
 if isinstance(a					,   a     ):
  snake_case				=			np.full((len(a     ), sequence_length, 2)					,   a     )
 else:
  snake_case				=			np.full((len(a     ), sequence_length)					,   a     )

 for i, tensor in enumerate(a     ):
  if padding_side == "right":
   if isinstance(a					,   a     ):
    snake_case				=			tensor[:sequence_length]
   else:
    snake_case				=			tensor[:sequence_length]
  else:
   if isinstance(a					,   a     ):
    snake_case				=			tensor[:sequence_length]
   else:
    snake_case				=			tensor[:sequence_length]

 return out_tensor.tolist()

def    __UpperCamelCase	(	a    : List[Any]     ) ->Union[str, Any]:
 snake_case				=			ord(a     )
 if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
  return True
 snake_case				=			unicodedata.category(a     )
 if cat.startswith('''P'''     ):
  return True
 return False






@dataclass
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					42
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					-100
     _UpperCAmelCase										=					"pt"







     def     UpperCamelCase			( self    ,   A__    ) ->	Any:
      import torch

      snake_case				=			'''label''' if '''label''' in features[0].keys() else '''labels'''
      snake_case				=			[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      snake_case				=			self.tokenizer.pad(
          A__    ,   padding=self.padding    ,   max_length=self.max_length    ,   pad_to_multiple_of=self.pad_to_multiple_of    ,   return_tensors='''pt''' if labels is None else None    ,   )

      if labels is None:
       return batch

      snake_case				=			torch.tensor(batch['''entity_ids''']    ).shape[1]
      snake_case				=			self.tokenizer.padding_side
      if padding_side == "right":
       snake_case				=			[
           list(A__    ) + [self.label_pad_token_id] * (sequence_length - len(A__    )) for label in labels
       ]
      else:
       snake_case				=			[
           [self.label_pad_token_id] * (sequence_length - len(A__    )) + list(A__    ) for label in labels
       ]

      snake_case				=			[feature['''ner_tags'''] for feature in features]
      snake_case				=			padding_tensor(A__    ,   -1    ,   A__    ,   A__    )
      snake_case				=			[feature['''original_entity_spans'''] for feature in features]
      snake_case				=			padding_tensor(A__    ,   (-1, -1)    ,   A__    ,   A__    )
      snake_case				=			{k: torch.tensor(A__    ,   dtype=torch.intaa    ) for k, v in batch.items()}

      return batch








'''simple docstring'''







_lowercase       						=			{
    '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',
}

