




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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    ["image_processor", "tokenizer"]
      lowerCAmelCase__	:       Any			=    "LayoutLMv2ImageProcessor"
      lowerCAmelCase__	:       List[Any]			=    ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")







      def __init__(   self    :       List[str]       ,				snake_case    :       int=None       ,				snake_case    :       Optional[Any]=None       ,				**snake_case    :       List[str] ):
          __UpperCamelCase     						=  None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.'''       ,				snake_case       ,				)
              __UpperCamelCase     						=  kwargs.pop('''feature_extractor''' )

          __UpperCamelCase     						=  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__(snake_case       ,				snake_case )







      def __call__(   self    :       Optional[int]       ,				snake_case    :       Dict       ,				snake_case    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,				snake_case    :       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,				snake_case    :       Union[List[List[int]], List[List[List[int]]]] = None       ,				snake_case    :       Optional[Union[List[int], List[List[int]]]] = None       ,				snake_case    :       bool = True       ,				snake_case    :       Union[bool, str, PaddingStrategy] = False       ,				snake_case    :       Union[bool, str, TruncationStrategy] = False       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       int = 0       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       Optional[bool] = 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 = True       ,				snake_case    :       Optional[Union[str, TensorType]] = None       ,				**snake_case    :       Dict       ,				):
          # verify input
          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
          __UpperCamelCase     						=  self.image_processor(images=snake_case       ,				return_tensors=snake_case )

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

          __UpperCamelCase     						=  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=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_token_type_ids=snake_case       ,				return_attention_mask=snake_case       ,				return_overflowing_tokens=snake_case       ,				return_special_tokens_mask=snake_case       ,				return_offsets_mapping=snake_case       ,				return_length=snake_case       ,				verbose=snake_case       ,				return_tensors=snake_case       ,				**snake_case       ,				)

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

          return encoded_inputs







      def 					snake_case		(   self    :       Any       ,				snake_case    :       int       ,				snake_case    :       Optional[int] ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          __UpperCamelCase     						=  []
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx] )

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

          return images_with_overflow







      def 					snake_case		(   self    :       int       ,				*snake_case    :       List[Any]       ,				**snake_case    :       Union[str, Any] ):
          return self.tokenizer.batch_decode(*snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Any       ,				*snake_case    :       List[str]       ,				**snake_case    :       str ):
          return self.tokenizer.decode(*snake_case       ,				**snake_case )







      @property
      def 					snake_case		(   self    :       Optional[Any] ):
          return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







      @property
      def 					snake_case		(   self    :       Union[str, Any] ):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,				snake_case       ,				)
          return self.image_processor_class







      @property
      def 					snake_case		(   self    :       Tuple ):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,				snake_case       ,				)
          return self.image_processor







def 							lowerCAmelCase(	__lowerCamelCase = 10**6						):

					if n <= 0:
										raise ValueError('Please enter an integer greater than 0'						)

					__a							=   list(range(n + 1						)						)
					for number in range(2      ,				n + 1						):
										if phi[number] == number:
															phi[number] -= 1
															for multiple in range(number * 2      ,				n + 1      ,				__lowerCamelCase						):
																				__a							=   (phi[multiple] // number) * (number - 1)

					__a							=   1
					for number in range(1      ,				n + 1						):
										if (answer / phi[answer]) < (number / phi[number]):
															__a							=   number

					return answer


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




