
"""simple docstring"""






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowerCamelCase__ :       List[str]						     =			logging.get_logger(__name__)




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Dict		, lowerCamelCase_    :Any		, lowerCamelCase_    :Any					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= question_encoder
			SCREAMING_SNAKE_CASE       :     str				= generator
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.question_encoder





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Any					)							->			Any:







			'''simple docstring'''




			if os.path.isfile(lowerCAmelCase_					):
					raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"					)
			os.makedirs(lowerCAmelCase_		, exist_ok=lowerCAmelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= os.path.join(lowerCAmelCase_		, '''question_encoder_tokenizer'''					)
			SCREAMING_SNAKE_CASE       :     Tuple				= os.path.join(lowerCAmelCase_		, '''generator_tokenizer'''					)
			self.question_encoder.save_pretrained(lowerCAmelCase_					)
			self.generator.save_pretrained(lowerCAmelCase_					)





	@classmethod
	def   __lowerCAmelCase (						cls    :Any		, lowerCamelCase_    :Union[str, Any]		, **lowerCamelCase_    :List[str]					)							->			Tuple:







			'''simple docstring'''




			from ..auto.tokenization_auto import AutoTokenizer

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= kwargs.pop('''config'''		, lowerCAmelCase_					)

			if config is None:
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= RagConfig.from_pretrained(lowerCAmelCase_					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= AutoTokenizer.from_pretrained(
			    lowerCAmelCase_		, config=config.question_encoder		, subfolder='''question_encoder_tokenizer'''					)
			SCREAMING_SNAKE_CASE       :     int				= AutoTokenizer.from_pretrained(
			    lowerCAmelCase_		, config=config.generator		, subfolder='''generator_tokenizer'''					)
			return cls(question_encoder=lowerCAmelCase_		, generator=lowerCAmelCase_					)





	def __call__(						self    :Optional[Any]		, *lowerCamelCase_    :List[Any]		, **lowerCamelCase_    :int					)							->			Tuple:







			'''simple docstring'''




			return self.current_tokenizer(*lowerCAmelCase_		, **lowerCAmelCase_					)





	def   __lowerCAmelCase (						self    :Dict		, *lowerCamelCase_    :Union[str, Any]		, **lowerCamelCase_    :Tuple					)							->			Optional[int]:







			'''simple docstring'''




			return self.generator.batch_decode(*lowerCAmelCase_		, **lowerCAmelCase_					)





	def   __lowerCAmelCase (						self    :str		, *lowerCamelCase_    :Optional[Any]		, **lowerCamelCase_    :Optional[int]					)							->			List[Any]:







			'''simple docstring'''




			return self.generator.decode(*lowerCAmelCase_		, **lowerCAmelCase_					)





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.question_encoder





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.generator







	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Any = None		, lowerCamelCase_    :List[Any] = None		, lowerCamelCase_    :Dict = None		, lowerCamelCase_    :List[str] = "longest"		, lowerCamelCase_    :Union[str, Any] = None		, lowerCamelCase_    :int = True		, **lowerCamelCase_    :Optional[Any]		, )							->			BatchEncoding:







			'''simple docstring'''




			warnings.warn(
			    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
			    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
			    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
			    '''details'''		, lowerCAmelCase_		, )
			if max_length is None:
					SCREAMING_SNAKE_CASE       :     int				= self.current_tokenizer.model_max_length
			SCREAMING_SNAKE_CASE       :     Tuple				= self(
			    lowerCAmelCase_		, add_special_tokens=lowerCAmelCase_		, return_tensors=lowerCAmelCase_		, max_length=lowerCAmelCase_		, padding=lowerCAmelCase_		, truncation=lowerCAmelCase_		, **lowerCAmelCase_		, )
			if tgt_texts is None:
					return model_inputs
			# Process tgt_texts
			if max_target_length is None:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.current_tokenizer.model_max_length
			SCREAMING_SNAKE_CASE       :     List[str]				= self(
			    text_target=lowerCAmelCase_		, add_special_tokens=lowerCAmelCase_		, return_tensors=lowerCAmelCase_		, padding=lowerCAmelCase_		, max_length=lowerCAmelCase_		, truncation=lowerCAmelCase_		, **lowerCAmelCase_		, )
			SCREAMING_SNAKE_CASE       :     Dict				= labels['''input_ids''']
			return model_inputs







"""simple docstring"""






from __future__ import annotations

from fractions import Fraction





def __A						(   a_ :							int		,		a_ :							int     )->							bool:




		'''simple docstring'''






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





def __A						(   a_ :							int     )->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
		SCREAMING_SNAKE_CASE       :     List[str]				= 11
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= int('''1''' + '''0''' * digit_len     )
		for num in range(a_		,		a_     ):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(a_		,		a_     ):
										solutions.append(F"{num}/{den}"     )
						den += 1
				num += 1
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10
		return solutions





def __A						(   a_ :							int = 2     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 1.0
		for fraction in fraction_list(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= Fraction(a_     )
				result *= frac.denominator / frac.numerator
		return int(a_     )


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





