






"""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'''






   UpperCamelCase__     	=							["""image_processor""", """tokenizer"""]
   UpperCamelCase__     	=							"""LayoutLMv2ImageProcessor"""
   UpperCamelCase__     	=							("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")





   def __init__(       self					, UpperCAmelCase_=None					, UpperCAmelCase_=None					, **UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							Dict         =     None
          if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."					, UpperCAmelCase_					, )
                 lowercase__:							int         =     kwargs.pop("feature_extractor")

          lowercase__:							Union[str, Any]         =     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__(UpperCAmelCase_					, UpperCAmelCase_)





   def __call__(       self					, UpperCAmelCase_					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = True					, UpperCAmelCase_ = False					, UpperCAmelCase_ = False					, UpperCAmelCase_ = None					, UpperCAmelCase_ = 0					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = False					, UpperCAmelCase_ = False					, UpperCAmelCase_ = False					, UpperCAmelCase_ = False					, UpperCAmelCase_ = True					, UpperCAmelCase_ = None					, **UpperCAmelCase_					, )			->   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
          lowercase__:							Optional[int]         =     self.image_processor(images=UpperCAmelCase_					, return_tensors=UpperCAmelCase_)

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

          lowercase__:							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=UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_					, padding=UpperCAmelCase_					, truncation=UpperCAmelCase_					, max_length=UpperCAmelCase_					, stride=UpperCAmelCase_					, pad_to_multiple_of=UpperCAmelCase_					, return_token_type_ids=UpperCAmelCase_					, return_attention_mask=UpperCAmelCase_					, return_overflowing_tokens=UpperCAmelCase_					, return_special_tokens_mask=UpperCAmelCase_					, return_offsets_mapping=UpperCAmelCase_					, return_length=UpperCAmelCase_					, verbose=UpperCAmelCase_					, return_tensors=UpperCAmelCase_					, **UpperCAmelCase_					, )

          # add pixel values
          lowercase__:							List[str]         =     features.pop("pixel_values")
          if return_overflowing_tokens is True:
                 lowercase__:							Dict         =     self.get_overflowing_images(UpperCAmelCase_					, encoded_inputs["overflow_to_sample_mapping"])
          lowercase__:							Optional[Any]         =     images

          return encoded_inputs





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Dict:




          '''simple docstring'''




          lowercase__:							str         =     []
          for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx])

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

          return images_with_overflow





   def __lowercase							(       self					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   str:




          '''simple docstring'''




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





   def __lowercase							(       self					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   int:




          '''simple docstring'''




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





   @property
   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




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





   @property
   def __lowercase							(       self)			->   str:




          '''simple docstring'''




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





   @property
   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




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








"""simple docstring"""





def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   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_   :   Optional[int]     	=       plaintext.lower()
   UpperCAmelCase_   :   Any     	=       key.lower()
   UpperCAmelCase_   :   Optional[int]     	=       0
   UpperCAmelCase_   :   List[str]     	=       0
   UpperCAmelCase_   :   Optional[int]     	=       ""
   while plaintext_iterator < len(__lowerCamelCase    ):
      if (
          ord(plaintext[plaintext_iterator]    ) < 97
          or ord(plaintext[plaintext_iterator]    ) > 122
      ):
         ciphertext += plaintext[plaintext_iterator]
         plaintext_iterator += 1
      elif ord(key[key_iterator]    ) < 97 or ord(key[key_iterator]    ) > 122:
         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    ):
   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_   :   List[str]     	=       key.lower()
   UpperCAmelCase_   :   Tuple     	=       0
   UpperCAmelCase_   :   Optional[int]     	=       0
   UpperCAmelCase_   :   Dict     	=       ""
   while ciphertext_iterator < len(__lowerCamelCase    ):
      if (
          ord(ciphertext[ciphertext_iterator]    ) < 97
          or ord(ciphertext[ciphertext_iterator]    ) > 122
      ):
         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')


