






def    UpperCamelCase						(	_A = 1,   _A = 1000			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= 1
						__magic_name__      :       Dict       						= 0

						for divide_by_number in range(_A,   digit + 1			):
												__magic_name__      :       list[int]       						= []
												__magic_name__      :       Any       						= numerator
												for _ in range(1,   digit + 1			):
																		if now_divide in has_been_divided:
																								if longest_list_length < len(_A			):
																														__magic_name__      :       int       						= len(_A			)
																														__magic_name__      :       Dict       						= divide_by_number
																		else:
																								has_been_divided.append(_A			)
																								__magic_name__      :       Optional[int]       						= now_divide * 10 % divide_by_number

						return the_digit


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()








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			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

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

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

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