

"""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




"""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""")

