


'''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}""")



'''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_					)