





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    ):



      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    Dict[str, int]		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    int = None		,	_lowerCAmelCase:    int = None							)    ->     List[Any]:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =pad_token_id
         UpperCAmelCase_           =max_length
         UpperCAmelCase_           =vocab
         UpperCAmelCase_           =merges
         UpperCAmelCase_           =BytePairTokenizer(_lowerCAmelCase		,	_lowerCAmelCase		,	sequence_length=_lowerCAmelCase							)



      @classmethod
      def 						lowerCAmelCase__     (						cls:    Union[str, Any]		,	_lowerCAmelCase:    GPTaTokenizer		,	*_lowerCAmelCase:    List[Any]		,	**_lowerCAmelCase:    Dict							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =[" ".join(_lowerCAmelCase							) for m in tokenizer.bpe_ranks.keys()]
         UpperCAmelCase_           =tokenizer.get_vocab()
         return cls(_lowerCAmelCase		,	_lowerCAmelCase		,	*_lowerCAmelCase		,	**_lowerCAmelCase							)



      @classmethod
      def 						lowerCAmelCase__     (						cls:    Dict		,	_lowerCAmelCase:    Union[str, os.PathLike]		,	*_lowerCAmelCase:    List[Any]		,	**_lowerCAmelCase:    List[Any]							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =GPTaTokenizer.from_pretrained(_lowerCAmelCase		,	*_lowerCAmelCase		,	**_lowerCAmelCase							)
         return cls.from_tokenizer(_lowerCAmelCase		,	*_lowerCAmelCase		,	**_lowerCAmelCase							)



      @classmethod
      def 						lowerCAmelCase__     (						cls:    List[str]		,	_lowerCAmelCase:    Optional[int]							)    ->     List[Any]:
         '''simple docstring'''

         return cls(**_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     int:
         '''simple docstring'''

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






      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    int = None							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.tf_tokenizer(_lowerCAmelCase							)
         UpperCAmelCase_           =tf.ones_like(_lowerCAmelCase							)

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

            if max_length is not None:
               UpperCAmelCase_    ,							UpperCAmelCase_           =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__			(       _a):
	if not isinstance(_a				,      _a):
		SCREAMING_SNAKE_CASE :   Tuple					  =							f"Input value of [number={number}] must be an integer"
		raise TypeError(_a)
	if number < 0:
		return False
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
			import doctest

			doctest.testmod()