code: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				dict[str, str]   )		->					str:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						"", "", ""
     UpperCamelCase__ 						=						[]
  
     for character in message_part:
        tmp.append(character_to_number[character]   )
  
     for each in tmp:
        one += each[0]
        two += each[1]
        three += each[2]
  
     return one + two + three
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				dict[str, str]   )		->					tuple[str, str, str]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__						,		UpperCamelCase__ 						=						"", ""
     UpperCamelCase__ 						=						[]
  
     for character in message_part:
        this_part += character_to_number[character]
  
     for digit in this_part:
        tmp += digit
        if len(_UpperCamelCase   ) == len(_UpperCamelCase   ):
           result.append(_UpperCamelCase   )
           UpperCamelCase__ 						=						""
  
     return result[0], result[1], result[2]
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				str   )		->					tuple[str, str, dict[str, str], dict[str, str]]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						alphabet.replace(" "    ,		""   ).upper()
     UpperCamelCase__ 						=						message.replace(" "    ,		""   ).upper()
  
     # Check length and characters
     if len(_UpperCamelCase   ) != 27:
        raise KeyError("Length of alphabet has to be 27."   )
     for each in message:
        if each not in alphabet:
           raise ValueError("Each message character has to be included in alphabet!"   )
  
      # Generate dictionares
     UpperCamelCase__ 						=						(
         "111",
         "112",
         "113",
         "121",
         "122",
         "123",
         "131",
         "132",
         "133",
         "211",
         "212",
         "213",
         "221",
         "222",
         "223",
         "231",
         "232",
         "233",
         "311",
         "312",
         "313",
         "321",
         "322",
         "323",
         "331",
         "332",
         "333",
     )
     UpperCamelCase__ 						=						{}
     UpperCamelCase__ 						=						{}
     for letter, number in zip(_UpperCamelCase    ,		_UpperCamelCase   ):
        UpperCamelCase__ 						=						number
        UpperCamelCase__ 						=						letter
  
     return message, alphabet, character_to_number, number_to_character
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."    ,		_UpperCamelCase      :				int = 5   )		->					str:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						__prepare(
         _UpperCamelCase    ,		_UpperCamelCase   )
     UpperCamelCase__						,		UpperCamelCase__ 						=						"", ""
  
     for i in range(0    ,		len(_UpperCamelCase   ) + 1    ,		_UpperCamelCase   ):
        encrypted_numeric += __encrypt_part(
            message[i : i + period]    ,		_UpperCamelCase   )
  
     for i in range(0    ,		len(_UpperCamelCase   )    ,		3   ):
        encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
     return encrypted
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."    ,		_UpperCamelCase      :				int = 5   )		->					str:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						__prepare(
         _UpperCamelCase    ,		_UpperCamelCase   )
     UpperCamelCase__ 						=						[]
     UpperCamelCase__ 						=						""
  
     for i in range(0    ,		len(_UpperCamelCase   ) + 1    ,		_UpperCamelCase   ):
        UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						__decrypt_part(message[i : i + period]    ,		_UpperCamelCase   )
  
        for j in range(0    ,		len(_UpperCamelCase   )   ):
           decrypted_numeric.append(a[j] + b[j] + c[j]   )
  
     for each in decrypted_numeric:
        decrypted += number_to_character[each]
  
     return decrypted
  
  
  if __name__ == "__main__":
         __lowercase:		Optional[int]							=     "DEFEND THE EAST WALL OF THE CASTLE."
         __lowercase:		str							=     encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
         __lowercase:		List[str]							=     decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
         print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
style_context: 
  
  
  '''simple docstring'''
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
         import torch
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
   _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
   _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')
  
   def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      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`."					)
  
      UpperCamelCase__ 						=						max_seq_length
      UpperCamelCase__ 						=						task_seq_length
  
      super().__init__(a_,					a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if text_list is None:
         raise ValueError("tokens cannot be None."					)
  
      UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)
  
      UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]
  
      UpperCamelCase__ 						=						[]
      for attn_mask, input_id in zip(a_,					a_					):
         UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
         token_inputs.append(token.unsqueeze(0					)					)
  
      UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
      return token_inputs
  
   def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)