



import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		int      							=     ["image_processor", "tokenizer"]
					A__					:		List[str]      							=     "BlipImageProcessor"
					A__					:		Union[str, Any]      							=     "AutoTokenizer"



					def __init__(  self       :							List[Any]					,       _snake_case       :							List[str]					,       _snake_case       :							List[Any]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								super().__init__(_snake_case					,       _snake_case       )

								# add QFormer tokenizer
								A__	  =     qformer_tokenizer



					def __call__(  self       :							int					,       _snake_case       :							ImageInput = None					,       _snake_case       :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,       _snake_case       :							bool = True					,       _snake_case       :							Union[bool, str, PaddingStrategy] = False					,       _snake_case       :							Union[bool, str, TruncationStrategy] = None					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							int = 0					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							bool = False					,       _snake_case       :							bool = False					,       _snake_case       :							bool = False					,       _snake_case       :							bool = False					,       _snake_case       :							bool = False					,       _snake_case       :							bool = True					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       **_snake_case       :							Any					,       ):






								"""simple docstring"""


								if images is None and text is None:
											raise ValueError('You have to specify at least images or text.'       )

								A__	  =     BatchFeature()

								if text is not None:
											A__	  =     self.tokenizer(
											    text=_snake_case					,       add_special_tokens=_snake_case					,       padding=_snake_case					,       truncation=_snake_case					,       max_length=_snake_case					,       stride=_snake_case					,       pad_to_multiple_of=_snake_case					,       return_attention_mask=_snake_case					,       return_overflowing_tokens=_snake_case					,       return_special_tokens_mask=_snake_case					,       return_offsets_mapping=_snake_case					,       return_token_type_ids=_snake_case					,       return_length=_snake_case					,       verbose=_snake_case					,       return_tensors=_snake_case					,       **_snake_case					,       )
											encoding.update(_snake_case       )
											A__	  =     self.qformer_tokenizer(
											    text=_snake_case					,       add_special_tokens=_snake_case					,       padding=_snake_case					,       truncation=_snake_case					,       max_length=_snake_case					,       stride=_snake_case					,       pad_to_multiple_of=_snake_case					,       return_attention_mask=_snake_case					,       return_overflowing_tokens=_snake_case					,       return_special_tokens_mask=_snake_case					,       return_offsets_mapping=_snake_case					,       return_token_type_ids=_snake_case					,       return_length=_snake_case					,       verbose=_snake_case					,       return_tensors=_snake_case					,       **_snake_case					,       )
											A__	  =     qformer_text_encoding.pop('input_ids'       )
											A__	  =     qformer_text_encoding.pop('attention_mask'       )

								if images is not None:
											A__	  =     self.image_processor(_snake_case					,       return_tensors=_snake_case       )
											encoding.update(_snake_case       )

								return encoding



					def      _a				(  self       :							Optional[int]					,       *_snake_case       :							List[str]					,       **_snake_case       :							List[str]       ):






								"""simple docstring"""


								return self.tokenizer.batch_decode(*_snake_case					,       **_snake_case       )



					def      _a				(  self       :							Any					,       *_snake_case       :							Union[str, Any]					,       **_snake_case       :							Any       ):






								"""simple docstring"""


								return self.tokenizer.decode(*_snake_case					,       **_snake_case       )



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     self.tokenizer.model_input_names
								A__	  =     self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



					def      _a				(  self       :							int					,       _snake_case       :							List[Any]					,       **_snake_case       :							List[Any]       ):






								"""simple docstring"""


								if os.path.isfile(_snake_case       ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''       )
								os.makedirs(_snake_case					,       exist_ok=_snake_case       )
								A__	  =     os.path.join(_snake_case					,       'qformer_tokenizer'       )
								self.qformer_tokenizer.save_pretrained(_snake_case       )
								return super().save_pretrained(_snake_case					,       **_snake_case       )



					@classmethod
					def      _a				(  cls       :							int					,       _snake_case       :							Optional[Any]					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     AutoTokenizer.from_pretrained(_snake_case					,       subfolder='qformer_tokenizer'       )
								A__	  =     cls._get_arguments_from_pretrained(_snake_case					,       **_snake_case       )
								args.append(_snake_case       )
								return cls(*_snake_case       )









from __future__ import annotations

from fractions import Fraction


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   bool:
			return (
			    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
			)


def A    (							__UpperCamelCase       )    ->   list[str]:
			A__	  =     []
			A__	  =     11
			A__	  =     int('1' + '0' * digit_len       )
			for num in range(__UpperCamelCase			,						__UpperCamelCase       ):
						while den <= 99:
									if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
												if is_digit_cancelling(__UpperCamelCase			,						__UpperCamelCase       ):
															solutions.append(f'''{num}/{den}'''       )
									den += 1
						num += 1
						A__	  =     10
			return solutions


def A    (							__UpperCamelCase = 2       )    ->   int:
			A__	  =     1.0
			for fraction in fraction_list(__UpperCamelCase       ):
						A__	  =     Fraction(__UpperCamelCase       )
						result *= frac.denominator / frac.numerator
			return int(__UpperCamelCase       )


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




