

"""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__	,    A__	,    A__	,    A__   )      ->      Any:




   """simple docstring"""
   if isinstance(A__	,    A__   ):
      lowerCamelCase__						=			np.full((len(A__   ), sequence_length, 2)	,    A__   )
   else:
      lowerCamelCase__						=			np.full((len(A__   ), sequence_length)	,    A__   )

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

   return out_tensor.tolist()



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			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
   lowerCamelCase__						=			unicodedata.category(A__   )
   if cat.startswith("P"   ):
      return True
   return False


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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					str:
         import torch

         lowerCamelCase__						=			"label" if "label" in features[0].keys() else "labels"
         lowerCamelCase__						=			[feature[label_name] for feature in features] if label_name in features[0].keys() else None
         lowerCamelCase__						=			self.tokenizer.pad(
             SCREAMING_SNAKE_CASE__    ,						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__						=			torch.tensor(batch["entity_ids"]		).shape[1]
         lowerCamelCase__						=			self.tokenizer.padding_side
         if padding_side == "right":
            lowerCamelCase__						=			[
                list(SCREAMING_SNAKE_CASE__		) + [self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE__		)) for label in labels
            ]
         else:
            lowerCamelCase__						=			[
                [self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE__		)) + list(SCREAMING_SNAKE_CASE__		) for label in labels
            ]

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

         return batch







"""simple docstring"""




import numpy as np



def 	UpperCAmelCase__   (				A__   )      ->      np.array:




   """simple docstring"""
   return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
     import doctest

     doctest.testmod()




