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     :	int     , a_     :	str     , a_     :	Dict     , a_     :	Union[str, Any] = None     , a_     :	List[str] = None				)->       Tuple:




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		pad_token_id
							SCREAMING_SNAKE_CASE__				:				List[str]			=		max_length
							SCREAMING_SNAKE_CASE__				:				Dict			=		vocab
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		merges
							SCREAMING_SNAKE_CASE__				:				Dict			=		BytePairTokenizer(_lowercase     , _lowercase     , sequence_length=_lowercase				)




		@classmethod
		def 	__lowercase(      cls     :	Any     , a_     :	int     , *a_     :	Optional[Any]     , **a_     :	Tuple				)->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				str			=		[""" """.join(_lowercase				) for m in tokenizer.bpe_ranks.keys()]
							SCREAMING_SNAKE_CASE__				:				List[str]			=		tokenizer.get_vocab()
							return cls(_lowercase     , _lowercase     , *_lowercase     , **_lowercase				)




		@classmethod
		def 	__lowercase(      cls     :	Dict     , a_     :	Optional[int]     , *a_     :	Dict     , **a_     :	Optional[Any]				)->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Any			=		GPTaTokenizer.from_pretrained(_lowercase     , *_lowercase     , **_lowercase				)
							return cls.from_tokenizer(_lowercase     , *_lowercase     , **_lowercase				)




		@classmethod
		def 	__lowercase(      cls     :	Dict     , a_     :	Optional[Any]				)->       Dict:




							"""simple docstring"""
							return cls(**_lowercase				)




		def 	__lowercase(      self     :	int				)->       Optional[int]:




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




		def 	__lowercase(      self     :	Union[str, Any]     , a_     :	int     , a_     :	Union[str, Any] = None				)->       int:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				str			=		self.tf_tokenizer(_lowercase				)
							SCREAMING_SNAKE_CASE__				:				Dict			=		tf.ones_like(_lowercase				)

							if self.pad_token_id is not None:
												# pad the tokens up to max length
												SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		max_length if max_length is not None else self.max_length

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

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




from __future__ import annotations







def     _a				(      lowercase__  :			list[int | float]     ,  lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     if len(lowercase__ ) == 0:
          raise ValueError('find_max() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(left + right) >> 1  # the middle
     SCREAMING_SNAKE_CASE__				:				int			=		find_max(lowercase__     ,  lowercase__     ,  lowercase__ )  # find max in range[left, mid]
     SCREAMING_SNAKE_CASE__				:				Tuple			=		find_max(lowercase__     ,  mid + 1     ,  lowercase__ )  # find max in range[mid + 1, right]

     return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



