



'''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  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    if isinstance(__lowerCAmelCase							,    __lowerCAmelCase			):
        lowerCAmelCase__ :  Any							      =					np.full((len(__lowerCAmelCase			), sequence_length, 2)							,    __lowerCAmelCase			)
    else:
        lowerCAmelCase__ :  Tuple							      =					np.full((len(__lowerCAmelCase			), sequence_length)							,    __lowerCAmelCase			)

    for i, tensor in enumerate(__lowerCAmelCase			):
        if padding_side == "right":
            if isinstance(__lowerCAmelCase							,    __lowerCAmelCase			):
                lowerCAmelCase__ :  Optional[int]							      =					tensor[:sequence_length]
            else:
                lowerCAmelCase__ :  List[str]							      =					tensor[:sequence_length]
        else:
            if isinstance(__lowerCAmelCase							,    __lowerCAmelCase			):
                lowerCAmelCase__ :  List[Any]							      =					tensor[:sequence_length]
            else:
                lowerCAmelCase__ :  str							      =					tensor[:sequence_length]

    return out_tensor.tolist()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					ord(__lowerCAmelCase			)
    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
    lowerCAmelCase__ :  int							      =					unicodedata.category(__lowerCAmelCase			)
    if cat.startswith("""P"""			):
        return True
    return False






@dataclass
class        lowerCAmelCase_(				A__     ):
  '''simple docstring'''




  __lowercase							:      PreTrainedTokenizerBase
  __lowercase							:      Union[bool, str, PaddingStrategy]				=      True
  __lowercase							:      Optional[int]				=      None
  __lowercase							:      Optional[int]				=      None
  __lowercase							:      int				=      -1_0_0
  __lowercase							:      str				=      "pt"




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      import torch

      lowerCAmelCase__ :  List[str]							      =					"label" if "label" in features[0].keys() else "labels"
      lowerCAmelCase__ :  Union[str, Any]							      =					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      lowerCAmelCase__ :  Tuple							      =					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

      lowerCAmelCase__ :  Dict							      =					torch.tensor(batch["""entity_ids"""]      ).shape[1]
      lowerCAmelCase__ :  Optional[int]							      =					self.tokenizer.padding_side
      if padding_side == "right":
          lowerCAmelCase__ :  Optional[Any]							      =					[
              list(__UpperCAmelCase      ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase      )) for label in labels
          ]
      else:
          lowerCAmelCase__ :  List[Any]							      =					[
              [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase      )) + list(__UpperCAmelCase      ) for label in labels
          ]

      lowerCAmelCase__ :  List[str]							      =					[feature["ner_tags"] for feature in features]
      lowerCAmelCase__ :  Optional[Any]							      =					padding_tensor(__UpperCAmelCase  ,-1  ,__UpperCAmelCase  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					[feature["original_entity_spans"] for feature in features]
      lowerCAmelCase__ :  Optional[int]							      =					padding_tensor(__UpperCAmelCase  ,(-1, -1)  ,__UpperCAmelCase  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Tuple							      =					{k: torch.tensor(__UpperCAmelCase  ,dtype=torch.intaa      ) for k, v in batch.items()}

      return batch










import numpy as np







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return 1 / (1 + np.exp(-vector			))







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return vector * sigmoid(1.7_0_2 * vector			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()