
import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class   lowerCamelCase	(						tf.keras.layers.Layer						):





      def __init__(self   :					Union[str, Any]       ,					_A   :					Dict[str, int]       ,					_A   :					List[str]       ,					_A   :					int = None       ,					_A   :					int = None     )		->		Optional[int]:
          super().__init__()
          snake_case				  =      pad_token_id
          snake_case				  =      max_length
          snake_case				  =      vocab
          snake_case				  =      merges
          snake_case				  =      BytePairTokenizer(_A       ,					_A       ,					sequence_length=_A     )





      @classmethod
      def 	UpperCAmelCase(cls   :					str       ,					_A   :					GPTaTokenizer       ,					*_A   :					Optional[Any]       ,					**_A   :					List[Any]     )		->		Dict:
          snake_case				  =      [" ".join(_A     ) for m in tokenizer.bpe_ranks.keys()]
          snake_case				  =      tokenizer.get_vocab()
          return cls(_A       ,					_A       ,					*_A       ,					**_A     )





      @classmethod
      def 	UpperCAmelCase(cls   :					str       ,					_A   :					Union[str, os.PathLike]       ,					*_A   :					int       ,					**_A   :					Any     )		->		str:
          snake_case				  =      GPTaTokenizer.from_pretrained(_A       ,					*_A       ,					**_A     )
          return cls.from_tokenizer(_A       ,					*_A       ,					**_A     )





      @classmethod
      def 	UpperCAmelCase(cls   :					int       ,					_A   :					List[Any]     )		->		Union[str, Any]:
          return cls(**_A     )





      def 	UpperCAmelCase(self   :					Any     )		->		Optional[Any]:
          return {
              "vocab": self.vocab,
              "merges": self.merges,
              "max_length": self.max_length,
              "pad_token_id": self.pad_token_id,
          }





      def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					str       ,					_A   :					int = None     )		->		Union[str, Any]:
          snake_case				  =      self.tf_tokenizer(_A     )
          snake_case				  =      tf.ones_like(_A     )

          if self.pad_token_id is not None:
              # pad the tokens up to max length
              snake_case				  =      max_length if max_length is not None else self.max_length

              if max_length is not None:
                  snake_case  ,							snake_case				  =      pad_model_inputs(
                      _A       ,					max_seq_length=_A       ,					pad_value=self.pad_token_id     )

          return {"attention_mask": attention_mask, "input_ids": input_ids}



_A						      =				0  # The first color of the flag.
_A						      =				1  # The second color of the flag.
_A						      =				2  # The third color of the flag.
_A						      =				(red, white, blue)



def 		lowercase_    (     A__	)							->							list:
    """simple docstring"""

    if not sequence:
        return []
    if len(A__	) == 1:
        return list(A__	)
    snake_case				  =      0
    snake_case				  =      len(A__	) - 1
    snake_case				  =      0
    while mid <= high:
        if sequence[mid] == colors[0]:
            snake_case  ,							snake_case				  =      sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            snake_case  ,							snake_case				  =      sequence[high], sequence[mid]
            high -= 1
        else:
            snake_case				  =      F'The elements inside the sequence must contains only {colors} values'
            raise ValueError(A__	)
    return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _A						      =				input("Enter numbers separated by commas:\n").strip()
   _A						      =				[int(item.strip()) for item in user_input.split(",")]
   print(f"{dutch_national_flag_sort(unsorted)}")

