


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 		_snake_case  (				tf.keras.layers.Layer							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = None   ):
       super().__init__()
       a						:List[str]										=	pad_token_id
       a						:int										=	max_length
       a						:Any										=	vocab
       a						:Tuple										=	merges
       a						:int										=	BytePairTokenizer(_lowerCamelCase ,  _lowerCamelCase ,  sequence_length=_lowerCamelCase   )




 @classmethod
 def 	SCREAMING_SNAKE_CASE__    (		cls ,  _lowerCamelCase ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       a						:List[Any]										=	[''' '''.join(_lowerCamelCase   ) for m in tokenizer.bpe_ranks.keys()]
       a						:Union[str, Any]										=	tokenizer.get_vocab()
       return cls(_lowerCamelCase ,  _lowerCamelCase ,  *_lowerCamelCase ,  **_lowerCamelCase   )




 @classmethod
 def 	SCREAMING_SNAKE_CASE__    (		cls ,  _lowerCamelCase ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       a						:str										=	GPTaTokenizer.from_pretrained(_lowerCamelCase ,  *_lowerCamelCase ,  **_lowerCamelCase   )
       return cls.from_tokenizer(_lowerCamelCase ,  *_lowerCamelCase ,  **_lowerCamelCase   )




 @classmethod
 def 	SCREAMING_SNAKE_CASE__    (		cls ,  _lowerCamelCase   ):
       return cls(**_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return {
           "vocab": self.vocab,
           "merges": self.merges,
           "max_length": self.max_length,
           "pad_token_id": self.pad_token_id,
       }




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:List[Any]										=	self.tf_tokenizer(_lowerCamelCase   )
       a						:Dict										=	tf.ones_like(_lowerCamelCase   )

       if self.pad_token_id is not None:
             # pad the tokens up to max length
             a						:List[str]										=	max_length if max_length is not None else self.max_length

             if max_length is not None:
                   a						:int										=	pad_model_inputs(
                       _lowerCamelCase ,  max_seq_length=_lowerCamelCase ,  pad_value=self.pad_token_id   )

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









def     __lowerCamelCase						(			UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_			,  UpperCAmelCase_						):
            raise TypeError('''Input value must be an \'int\' type'''						)

      a						:Optional[int]										=	0
      while number:
            position += 1
            number >>= 1

      return position


if __name__ == "__main__":
     import doctest

     doctest.testmod()





