




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       ["""image_processor""", """tokenizer"""]
						UpperCamelCase      =       """Pix2StructImageProcessor"""
						UpperCamelCase      =       ("""T5Tokenizer""", """T5TokenizerFast""")

						def __init__(		self :Optional[Any]     ,			__snake_case :int     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=False
										super().__init__(__snake_case     ,			__snake_case			)

						def __call__(		self :Any     ,			__snake_case :List[Any]=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 :Optional[int] = 20_48     ,			__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 either images or text."""			)

										# Get only text
										if images is None and not self.image_processor.is_vqa:
														__magic_name__						:						Any										=self.tokenizer
														__magic_name__						:						Dict										=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     ,			)
														return text_encoding

										if not self.image_processor.is_vqa:
														# add pixel_values
														__magic_name__						:						Dict										=self.image_processor(
														    __snake_case     ,			return_tensors=__snake_case     ,			max_patches=__snake_case     ,			**__snake_case			)
										else:
														# add pixel_values and bbox
														__magic_name__						:						Optional[int]										=self.image_processor(
														    __snake_case     ,			return_tensors=__snake_case     ,			max_patches=__snake_case     ,			header_text=__snake_case     ,			**__snake_case			)

										if text is not None and not self.image_processor.is_vqa:
														__magic_name__						:						int										=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     ,			)

														if "attention_mask" in text_encoding:
																		__magic_name__						:						Tuple										=text_encoding.pop("""attention_mask"""			)
														if "input_ids" in text_encoding:
																		__magic_name__						:						List[str]										=text_encoding.pop("""input_ids"""			)
										else:
														__magic_name__						:						Any										=None

										if text_encoding is not None:
														encoding_image_processor.update(__snake_case			)

										return encoding_image_processor

						def      A__    (		self :List[Any]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

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

						def      A__    (		self :Optional[Any]     ,			*__snake_case :Optional[int]     ,			**__snake_case :str			):
										'''simple docstring'''

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


						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						int										=self.tokenizer.model_input_names
										__magic_name__						:						Any										=self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)










from __future__ import annotations

from fractions import Fraction




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				return (
				    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
				)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						List[str]										=[]
				__magic_name__						:						List[Any]										=11
				__magic_name__						:						Tuple										=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
								__magic_name__						:						List[str]										=10
				return solutions




def 		lowerCAmelCase_ (						lowerCamelCase = 2 ):
				__magic_name__						:						str										=1.0
				for fraction in fraction_list(lowerCamelCase ):
								__magic_name__						:						int										=Fraction(lowerCamelCase )
								result *= frac.denominator / frac.numerator
				return int(lowerCamelCase )


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




