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 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     ['image_processor', 'tokenizer']
    SCREAMING_SNAKE_CASE							=     'LayoutLMv2ImageProcessor'
    SCREAMING_SNAKE_CASE							=     ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')
  
  
  
    def __init__(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any   				=						None
          if "feature_extractor" in kwargs:
                warnings.warn(
                    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                    """ instead."""       ,					__lowerCamelCase       ,					)
                UpperCAmelCase__ :       List[Any]   				=						kwargs.pop("""feature_extractor"""  )
  
          UpperCAmelCase__ :       int   				=						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__(__lowerCamelCase       ,					__lowerCamelCase  )
  
  
  
    def __call__(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = True       ,					_lowerCamelCase = False       ,					_lowerCamelCase = False       ,					_lowerCamelCase = None       ,					_lowerCamelCase = 0       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = False       ,					_lowerCamelCase = False       ,					_lowerCamelCase = False       ,					_lowerCamelCase = False       ,					_lowerCamelCase = True       ,					_lowerCamelCase = None       ,					**_lowerCamelCase       ,					):
  
  
          """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
          UpperCAmelCase__ :       Dict   				=						self.image_processor(images=__lowerCamelCase       ,					return_tensors=__lowerCamelCase  )
  
          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
                if isinstance(__lowerCamelCase       ,					__lowerCamelCase  ):
                      UpperCAmelCase__ :       Any   				=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
                UpperCAmelCase__ :       str   				=						features["words"]
  
          UpperCAmelCase__ :       Dict   				=						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=__lowerCamelCase       ,					add_special_tokens=__lowerCamelCase       ,					padding=__lowerCamelCase       ,					truncation=__lowerCamelCase       ,					max_length=__lowerCamelCase       ,					stride=__lowerCamelCase       ,					pad_to_multiple_of=__lowerCamelCase       ,					return_token_type_ids=__lowerCamelCase       ,					return_attention_mask=__lowerCamelCase       ,					return_overflowing_tokens=__lowerCamelCase       ,					return_special_tokens_mask=__lowerCamelCase       ,					return_offsets_mapping=__lowerCamelCase       ,					return_length=__lowerCamelCase       ,					verbose=__lowerCamelCase       ,					return_tensors=__lowerCamelCase       ,					**__lowerCamelCase       ,					)
  
          # add pixel values
          UpperCAmelCase__ :       Dict   				=						features.pop("""pixel_values"""  )
          if return_overflowing_tokens is True:
                UpperCAmelCase__ :       List[Any]   				=						self.get_overflowing_images(__lowerCamelCase       ,					encoded_inputs["""overflow_to_sample_mapping"""]  )
          UpperCAmelCase__ :       Optional[Any]   				=						images
  
          return encoded_inputs
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict   				=						[]
          for sample_idx in overflow_to_sample_mapping:
                images_with_overflow.append(images[sample_idx]  )
  
          if len(__lowerCamelCase  ) != len(__lowerCamelCase  ):
                raise ValueError(
                    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
                    F""" {len(__lowerCamelCase  )} and {len(__lowerCamelCase  )}"""  )
  
          return images_with_overflow
  
  
  
    def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.tokenizer.batch_decode(*__lowerCamelCase       ,					**__lowerCamelCase  )
  
  
  
    def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.tokenizer.decode(*__lowerCamelCase       ,					**__lowerCamelCase  )
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,					__lowerCamelCase       ,					)
          return self.image_processor_class
  
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,					__lowerCamelCase       ,					)
          return self.image_processor
  
  
style_context: 
  
  """simple docstring"""
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
        if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
              raise TypeError("""plaintext must be a string"""						)
        if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
              raise TypeError("""key must be a string"""						)
  
        if not plaintext:
              raise ValueError("""plaintext is empty"""						)
        if not key:
              raise ValueError("""key is empty"""						)
  
        key += plaintext
        UpperCAmelCase__ :       Dict						=						plaintext.lower()
        UpperCAmelCase__ :       Tuple						=						key.lower()
        UpperCAmelCase__ :       Optional[int]						=						0
        UpperCAmelCase__ :       Tuple						=						0
        UpperCAmelCase__ :       Optional[int]						=						""""""
        while plaintext_iterator < len(lowerCAmelCase						):
              if (
                  ord(plaintext[plaintext_iterator]						) < 97
                  or ord(plaintext[plaintext_iterator]						) > 1_22
              ):
                    ciphertext += plaintext[plaintext_iterator]
                    plaintext_iterator += 1
              elif ord(key[key_iterator]						) < 97 or ord(key[key_iterator]						) > 1_22:
                    key_iterator += 1
              else:
                    ciphertext += chr(
                        (
                            (ord(plaintext[plaintext_iterator]						) - 97 + ord(key[key_iterator]						))
                            - 97
                        )
                        % 26
                        + 97						)
                    key_iterator += 1
                    plaintext_iterator += 1
        return ciphertext
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
        if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
              raise TypeError("""ciphertext must be a string"""						)
        if not isinstance(lowerCAmelCase ,  lowerCAmelCase						):
              raise TypeError("""key must be a string"""						)
  
        if not ciphertext:
              raise ValueError("""ciphertext is empty"""						)
        if not key:
              raise ValueError("""key is empty"""						)
  
        UpperCAmelCase__ :       Union[str, Any]						=						key.lower()
        UpperCAmelCase__ :       int						=						0
        UpperCAmelCase__ :       int						=						0
        UpperCAmelCase__ :       int						=						""""""
        while ciphertext_iterator < len(lowerCAmelCase						):
              if (
                  ord(ciphertext[ciphertext_iterator]						) < 97
                  or ord(ciphertext[ciphertext_iterator]						) > 1_22
              ):
                    plaintext += ciphertext[ciphertext_iterator]
              else:
                    plaintext += chr(
                        (ord(ciphertext[ciphertext_iterator]						) - ord(key[key_iterator]						)) % 26
                        + 97						)
                    key += chr(
                        (ord(ciphertext[ciphertext_iterator]						) - ord(key[key_iterator]						)) % 26
                        + 97						)
                    key_iterator += 1
              ciphertext_iterator += 1
        return plaintext
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
      _A						=     int(input("""Type 1 to encrypt or 2 to decrypt:"""))
      if operation == 1:
          _A						=     input("""Typeplaintext to be encrypted:\n""")
          _A						=     input("""Type the key:\n""")
          print(encrypt(plaintext, key))
      elif operation == 2:
          _A						=     input("""Type the ciphertext to be decrypted:\n""")
          _A						=     input("""Type the key:\n""")
          print(decrypt(ciphertext, key))
      decrypt("""jsqqs avvwo""", """coffee""")
  
  