



"""simple docstring"""







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        __A       (	tf.keras.layers.Layer    ):







		'''simple docstring'''





		def __init__(	self						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Tuple			,_snake_case						:  str = None			,_snake_case						:  List[Any] = None						)					->		int:






				"""simple docstring"""



				super().__init__()
				lowercase__   :    List[Any]				=			pad_token_id
				lowercase__   :    Dict				=			max_length
				lowercase__   :    Any				=			vocab
				lowercase__   :    str				=			merges
				lowercase__   :    Tuple				=			BytePairTokenizer(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,sequence_length=_SCREAMING_SNAKE_CASE						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  Any			,_snake_case						:  Dict			,*_snake_case						:  Dict			,**_snake_case						:  Any						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			[''' '''.join(_SCREAMING_SNAKE_CASE						) for m in tokenizer.bpe_ranks.keys()]
				lowercase__   :    List[Any]				=			tokenizer.get_vocab()
				return cls(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,*_SCREAMING_SNAKE_CASE			,**_SCREAMING_SNAKE_CASE						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  str			,_snake_case						:  Optional[int]			,*_snake_case						:  Dict			,**_snake_case						:  Optional[int]						)					->		int:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			GPTaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			,*_SCREAMING_SNAKE_CASE			,**_SCREAMING_SNAKE_CASE						)
				return cls.from_tokenizer(_SCREAMING_SNAKE_CASE			,*_SCREAMING_SNAKE_CASE			,**_SCREAMING_SNAKE_CASE						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  Optional[int]			,_snake_case						:  Tuple						)					->		Any:






				"""simple docstring"""



				return cls(**_SCREAMING_SNAKE_CASE						)







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Tuple:






				"""simple docstring"""



				return {
				    "vocab": self.vocab,
				    "merges": self.merges,
				    "max_length": self.max_length,
				    "pad_token_id": self.pad_token_id,
				}







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Tuple			,_snake_case						:  Optional[Any] = None						)					->		int:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.tf_tokenizer(_SCREAMING_SNAKE_CASE						)
				lowercase__   :    List[Any]				=			tf.ones_like(_SCREAMING_SNAKE_CASE						)

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

						if max_length is not None:
								lowercase__   :    List[str]				=			pad_model_inputs(
								    _SCREAMING_SNAKE_CASE			,max_seq_length=_SCREAMING_SNAKE_CASE			,pad_value=self.pad_token_id						)

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




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not nums:  # Makes sure that the list is not empty
    raise ValueError('''List is empty'''       )

  A_      :				Optional[int]		     =					sum(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )  # Calculate the average
  return sum(abs(x - average       ) for x in nums       ) / len(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




