






'''simple docstring'''






import warnings
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_							):
   _snake_case        =							["""image_processor""", """tokenizer"""]
   _snake_case        =							"""LayoutLMv2ImageProcessor"""
   _snake_case        =							("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")







   def __init__(self					,   __a=None					,   __a=None					,   **__a)			->      Tuple:
      """simple docstring"""






      if "feature_extractor" in kwargs:
         warnings.warn(
             'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
             ' instead.'					,   __a					,   )
         __snake_case				:      List[str]     							= kwargs.pop('feature_extractor')

      __snake_case				:      Tuple     							= image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
         raise ValueError('You need to specify an `image_processor`.')
      if tokenizer is None:
         raise ValueError('You need to specify a `tokenizer`.')

      super().__init__(__a					,   __a)







   def __call__(self					,   __a					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = True					,   __a = False					,   __a = None					,   __a = None					,   __a = 0					,   __a = None					,   __a = None					,   __a = None					,   __a = False					,   __a = False					,   __a = False					,   __a = False					,   __a = True					,   __a = None					,   **__a					,   )			->      BatchEncoding:
      """simple docstring"""






      if self.image_processor.apply_ocr and (boxes is not None):
         raise ValueError(
             'You cannot provide bounding boxes '
             'if you initialized the image processor with apply_ocr set to True.')

      if self.image_processor.apply_ocr and (word_labels is not None):
         raise ValueError(
             'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.')

      if return_overflowing_tokens is True and return_offsets_mapping is False:
         raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.')

      # first, apply the image processor
      __snake_case				:      str     							= self.image_processor(images=__a					,   return_tensors=__a)

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(__a					,   __a):
            __snake_case				:      Optional[int]     							= [text]  # add batch dimension (as the image processor always adds a batch dimension)
         __snake_case				:      str     							= features['words']

      __snake_case				:      Union[str, Any]     							= self.tokenizer(
          text=text if text is not None else features['words']					,   text_pair=text_pair if text_pair is not None else None					,   boxes=boxes if boxes is not None else features['boxes']					,   word_labels=__a					,   add_special_tokens=__a					,   padding=__a					,   truncation=__a					,   max_length=__a					,   stride=__a					,   pad_to_multiple_of=__a					,   return_token_type_ids=__a					,   return_attention_mask=__a					,   return_overflowing_tokens=__a					,   return_special_tokens_mask=__a					,   return_offsets_mapping=__a					,   return_length=__a					,   verbose=__a					,   return_tensors=__a					,   **__a					,   )

      # add pixel values
      __snake_case				:      Any     							= features.pop('pixel_values')
      if return_overflowing_tokens is True:
         __snake_case				:      str     							= self.get_overflowing_images(__a					,   encoded_inputs['overflow_to_sample_mapping'])
      __snake_case				:      str     							= images

      return encoded_inputs







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      str:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= []
      for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx])

      if len(__a) != len(__a):
         raise ValueError(
             'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
             F""" {len(__a)} and {len(__a)}""")

      return images_with_overflow







   def     SCREAMING_SNAKE_CASE__					(self					,   *__a					,   **__a)			->      List[str]:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   *__a					,   **__a)			->      Any:
      """simple docstring"""






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







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return ["input_ids", "bbox", "attention_mask", "image"]







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,   __a					,   )
      return self.image_processor_class







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,   __a					,   )
      return self.image_processor







'''simple docstring'''






__A    	=   {str(digit): digit**5 for digit in range(1_0)}




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A  )  )




def        _SCREAMING_SNAKE_CASE  (				)    -> int:







   """simple docstring"""
   return sum(
       number
       for number in range(10_00  ,		1_00_00_00  )
       if number == digits_fifth_powers_sum(A  )  )


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