
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					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Any    ):
       if isinstance(__lowerCamelCase				,					__lowerCamelCase    ):
              snake_case       :   int							=      np.full((len(__lowerCamelCase    ), sequence_length, 2)				,					__lowerCamelCase    )
       else:
              snake_case       :   List[Any]							=      np.full((len(__lowerCamelCase    ), sequence_length)				,					__lowerCamelCase    )

       for i, tensor in enumerate(__lowerCamelCase    ):
              if padding_side == "right":
                     if isinstance(__lowerCamelCase				,					__lowerCamelCase    ):
                            snake_case       :   Dict							=      tensor[:sequence_length]
                     else:
                            snake_case       :   Tuple							=      tensor[:sequence_length]
              else:
                     if isinstance(__lowerCamelCase				,					__lowerCamelCase    ):
                            snake_case       :   str							=      tensor[:sequence_length]
                     else:
                            snake_case       :   Optional[int]							=      tensor[:sequence_length]

       return out_tensor.tolist()


def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       snake_case       :   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
       snake_case       :   List[str]							=      unicodedata.category(__lowerCamelCase    )
       if cat.startswith("P"    ):
              return True
       return False






@dataclass
class   UpperCAmelCase		(						A_ ):
   A__					:		PreTrainedTokenizerBase
   A__					:		Union[bool, str, PaddingStrategy]						     =     True
   A__					:		Optional[int]						     =     None
   A__					:		Optional[int]						     =     None
   A__					:		int						     =     -1_00
   A__					:		str						     =     "pt"



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Tuple )		->	int:


          '''simple docstring'''


          import torch

          snake_case       :   Optional[Any]							=      "label" if "label" in features[0].keys() else "labels"
          snake_case       :   str							=      [feature[label_name] for feature in features] if label_name in features[0].keys() else None
          snake_case       :   List[Any]							=      self.tokenizer.pad(
              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

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

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

          return batch












"""simple docstring"""





from __future__ import annotations


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return [ord(SCREAMING_SNAKE_CASE			) - 96 for elem in plain]


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						list[int]			):







      '''simple docstring'''




      return "".join(chr(elem + 96			) for elem in encoded			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       encode(input("""-> """			).strip().lower()			)
      print("""Encoded: """							,						SCREAMING_SNAKE_CASE			)
      print("""Decoded:"""							,						decode(SCREAMING_SNAKE_CASE			)			)


if __name__ == "__main__":
       main()






