


'''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 _SCREAMING_SNAKE_CASE      (A  , A  , A  , A       )       ->       Union[str, Any]:






       """simple docstring"""







       if isinstance(A  , A       ):
              lowercase__      			= np.full((len(A       ), sequence_length, 2)  , A       )
       else:
              lowercase__      			= np.full((len(A       ), sequence_length)  , A       )

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

       return out_tensor.tolist()

def _SCREAMING_SNAKE_CASE      (A       )       ->       Tuple:






       """simple docstring"""







       lowercase__      			= 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
       lowercase__      			= unicodedata.category(A       )
       if cat.startswith('''P'''       ):
              return True
       return False

@dataclass
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     PreTrainedTokenizerBase
      lowerCAmelCase__					:     Union[bool, str, PaddingStrategy]  					=							True
      lowerCAmelCase__					:     Optional[int]  					=							None
      lowerCAmelCase__					:     Optional[int]  					=							None
      lowerCAmelCase__					:     int  					=							-100
      lowerCAmelCase__					:     str  					=							"pt"



      def       UpperCamelCase__		(self    :    Dict   ,						UpperCamelCase    :    int ):

             '''simple docstring'''







             import torch

             lowercase__      			= '''label''' if '''label''' in features[0].keys() else '''labels'''
             lowercase__      			= [feature[label_name] for feature in features] if label_name in features[0].keys() else None
             lowercase__      			= self.tokenizer.pad(
                 UpperCamelCase   ,						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

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

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

             return batch








'''simple docstring'''

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       if not isinstance(A  , A       ):
              raise TypeError('''only integers accepted as input'''       )
       else:
              lowercase__      			= str(abs(A       )       )
              lowercase__      			= [list(A       ) for char in range(len(A       )       )]
              for index in range(len(A       )       ):
                     num_transpositions[index].pop(A       )
              return max(
                  int(''''''.join(list(A       )       )       ) for transposition in num_transpositions       )


if __name__ == "__main__":
 __import__('doctest').testmod()




