code: 
  """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_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		List[Any]						  =	["""image_processor""", """tokenizer"""]
    SCREAMING_SNAKE_CASE__    :		Any						  =	"""LayoutLMv2ImageProcessor"""
    SCREAMING_SNAKE_CASE__    :		List[str]						  =	("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_=None       ,     lowercase_=None       ,     **lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					None
       if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."       ,     lowercase_       ,     )
          UpperCAmelCase_      :      Any      =					kwargs.pop("feature_extractor"						)
  
       UpperCAmelCase_      :      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__(lowercase_       ,     lowercase_						)
  
  
  
  
  
  
  
    def __call__(	self       ,     lowercase_       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = True       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = None       ,     lowercase_ = 0       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = True       ,     lowercase_ = None       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       # 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_      :      int      =					self.image_processor(images=lowercase_       ,     return_tensors=lowercase_						)
  
       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
          if isinstance(lowercase_       ,     lowercase_						):
             UpperCAmelCase_      :      List[Any]      =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
          UpperCAmelCase_      :      Optional[Any]      =					features["words"]
  
       UpperCAmelCase_      :      List[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=lowercase_       ,     add_special_tokens=lowercase_       ,     padding=lowercase_       ,     truncation=lowercase_       ,     max_length=lowercase_       ,     stride=lowercase_       ,     pad_to_multiple_of=lowercase_       ,     return_token_type_ids=lowercase_       ,     return_attention_mask=lowercase_       ,     return_overflowing_tokens=lowercase_       ,     return_special_tokens_mask=lowercase_       ,     return_offsets_mapping=lowercase_       ,     return_length=lowercase_       ,     verbose=lowercase_       ,     return_tensors=lowercase_       ,     **lowercase_       ,     )
  
       # add pixel values
       UpperCAmelCase_      :      List[str]      =					features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
          UpperCAmelCase_      :      List[Any]      =					self.get_overflowing_images(lowercase_       ,     encoded_inputs["overflow_to_sample_mapping"]						)
       UpperCAmelCase_      :      Optional[Any]      =					images
  
       return encoded_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Dict      =					[]
       for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx]						)
  
       if len(lowercase_						) != len(lowercase_						):
          raise ValueError(
              "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
              F""" {len(lowercase_						)} and {len(lowercase_						)}"""						)
  
       return images_with_overflow
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     *lowercase_       ,     **lowercase_						):
  
       """simple docstring"""
       return self.tokenizer.batch_decode(*lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     *lowercase_       ,     **lowercase_						):
  
       """simple docstring"""
       return self.tokenizer.decode(*lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,     lowercase_       ,     )
       return self.image_processor_class
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,     lowercase_       ,     )
       return self.image_processor
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  def 			__a  (	__lowerCamelCase = 1000						):
     UpperCAmelCase_      :      List[Any]      =					0
     UpperCAmelCase_      :      List[str]      =					(n - 1) // 3
     total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
     UpperCAmelCase_      :      Any      =					(n - 1) // 5
     total += ((terms) * (10 + (terms - 1) * 5)) // 2
     UpperCAmelCase_      :      Optional[int]      =					(n - 1) // 15
     total -= ((terms) * (30 + (terms - 1) * 15)) // 2
     return total
  
  
  if __name__ == "__main__":
      print(f"""{solution() = }""")
  
  
  