





"""simple docstring"""


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__						):
     SCREAMING_SNAKE_CASE__					: Optional[int]									=     ["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE__					: List[str]									=     "Pix2StructImageProcessor"
     SCREAMING_SNAKE_CASE__					: str									=     ("T5Tokenizer", "T5TokenizerFast")



     def __init__(			self   ,	a__   ,	a__  ):
            _lowerCamelCase             =   False
            super().__init__(__snake_case   ,	__snake_case  )



     def __call__(			self   ,	a__=None   ,	a__ = None   ,	a__ = True   ,	a__ = False   ,	a__ = None   ,	a__ = None   ,	a__ = 20_48   ,	a__ = 0   ,	a__ = None   ,	a__ = None   ,	a__ = False   ,	a__ = False   ,	a__ = False   ,	a__ = False   ,	a__ = False   ,	a__ = True   ,	a__ = None   ,	**a__   ,	):

            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:
                   _lowerCamelCase             =   self.tokenizer
                   _lowerCamelCase             =   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
                   _lowerCamelCase             =   self.image_processor(
                       __snake_case   ,	return_tensors=__snake_case   ,	max_patches=__snake_case   ,	**__snake_case  )
            else:
                   # add pixel_values and bbox
                   _lowerCamelCase             =   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:
                   _lowerCamelCase             =   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:
                          _lowerCamelCase             =   text_encoding.pop('attention_mask'  )
                   if "input_ids" in text_encoding:
                          _lowerCamelCase             =   text_encoding.pop('input_ids'  )
            else:
                   _lowerCamelCase             =   None

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

            return encoding_image_processor



     def 		snake_case_     (			self   ,	*a__   ,	**a__  ):
            return self.tokenizer.batch_decode(*__snake_case   ,	**__snake_case  )



     def 		snake_case_     (			self   ,	*a__   ,	**a__  ):
            return self.tokenizer.decode(*__snake_case   ,	**__snake_case  )



     @property
     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   self.tokenizer.model_input_names
            _lowerCamelCase             =   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())




