





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				:     List[str]							,	a				:     Dict[str, int]							,	a				:     List[str]							,	a				:     int = None							,	a				:     int = None    )	->Dict:
													super().__init__()
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pad_token_id
													SCREAMING_SNAKE_CASE__     :				Tuple   =					max_length
													SCREAMING_SNAKE_CASE__     :				Tuple   =					vocab
													SCREAMING_SNAKE_CASE__     :				List[str]   =					merges
													SCREAMING_SNAKE_CASE__     :				Tuple   =					BytePairTokenizer(a							,	a							,	sequence_length=a    )

							@classmethod
							def      A_  (      cls				:     Union[str, Any]							,	a				:     GPTaTokenizer							,	*a				:     Tuple							,	**a				:     Optional[int]    )	->Any:
													SCREAMING_SNAKE_CASE__     :				int   =					[" ".join(a    ) for m in tokenizer.bpe_ranks.keys()]
													SCREAMING_SNAKE_CASE__     :				Dict   =					tokenizer.get_vocab()
													return cls(a							,	a							,	*a							,	**a    )

							@classmethod
							def      A_  (      cls				:     Union[str, Any]							,	a				:     Union[str, os.PathLike]							,	*a				:     int							,	**a				:     Dict    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					GPTaTokenizer.from_pretrained(a							,	*a							,	**a    )
													return cls.from_tokenizer(a							,	*a							,	**a    )

							@classmethod
							def      A_  (      cls				:     List[str]							,	a				:     Optional[int]    )	->int:
													return cls(**a    )

							def      A_  (      self				:     List[str]    )	->Tuple:
													return {
													    "vocab": self.vocab,
													    "merges": self.merges,
													    "max_length": self.max_length,
													    "pad_token_id": self.pad_token_id,
													}






							def      A_  (      self				:     Tuple							,	a				:     Tuple							,	a				:     int = None    )	->int:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.tf_tokenizer(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					tf.ones_like(a    )

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

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

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






from __future__ import annotations

from fractions import Fraction







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						return (
						    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
						)







def       UpperCAmelCase							(     _lowerCamelCase      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				int   =					[]
						SCREAMING_SNAKE_CASE__     :				str   =					11
						SCREAMING_SNAKE_CASE__     :				Any   =					int("1" + "0" * digit_len     )
						for num in range(_lowerCamelCase			,   _lowerCamelCase     ):
												while den <= 99:
																		if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																								if is_digit_cancelling(_lowerCamelCase			,   _lowerCamelCase     ):
																														solutions.append(f"""{num}/{den}"""     )
																		den += 1
												num += 1
												SCREAMING_SNAKE_CASE__     :				str   =					10
						return solutions







def       UpperCAmelCase							(     _lowerCamelCase      :  int = 2     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					1.0
						for fraction in fraction_list(_lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Any   =					Fraction(_lowerCamelCase     )
												result *= frac.denominator / frac.numerator
						return int(_lowerCamelCase     )


if __name__ == "__main__":
				print(solution())