






from __future__ import annotations

import random
import string
class 	a_:



     """simple docstring"""



     def __init__(		self					:	str  ,  lowerCAmelCase__					:	Any = None)							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		passcode or self.__passcode_creator()
      SCREAMING_SNAKE_CASE				    =		self.__make_key_list()
      SCREAMING_SNAKE_CASE				    =		self.__make_shift_key()





     def __str__(		self					:	List[str])							->	str:

      """simple docstring"""
      return "".join(self.__passcode)





     def     __UpperCamelCase						(		self					:	List[Any]  ,  lowerCAmelCase__					:	Optional[Any])							->	list[int]:

      """simple docstring"""
      for i in range(1  ,  len(a__)  ,  2):
       iterlist[i] *= -1
      return iterlist





     def     __UpperCamelCase						(		self					:	List[str])							->	list[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		string.ascii_letters + string.digits
      SCREAMING_SNAKE_CASE				    =		[random.choice(a__) for _ in range(random.randint(1_0  ,  2_0))]
      return password





     def     __UpperCamelCase						(		self					:	List[Any])							->	list[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		(
          string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
      )

      SCREAMING_SNAKE_CASE				    =		[]

      # creates points known as breakpoints to break the key_list_options at those
      # points and pivot each substring
      SCREAMING_SNAKE_CASE				    =		sorted(set(self.__passcode))
      SCREAMING_SNAKE_CASE				    =		[]

      # algorithm for creating a new shuffled list, keys_l, out of key_list_options
      for i in key_list_options:
       temp_list.extend(a__)

       # checking breakpoints at which to pivot temporary sublist and add it into
       # keys_l
       if i in breakpoints or i == key_list_options[-1]:
        keys_l.extend(temp_list[::-1])
        temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
      return keys_l





     def     __UpperCamelCase						(		self					:	List[str])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		sum(self.__neg_pos([ord(a__) for x in self.__passcode]))
      return num if num > 0 else len(self.__passcode)





     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	List[Any])							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		''''''

      # decoding shift like Caesar cipher algorithm implementing negative shift or
      # reverse shift or left shift
      for i in encoded_message:
       SCREAMING_SNAKE_CASE				    =		self.__key_list.index(a__)
       decoded_message += self.__key_list[
           (position - self.__shift_key) % -len(self.__key_list)
       ]

      return decoded_message





     def     __UpperCamelCase						(		self					:	Tuple  ,  lowerCAmelCase__					:	Tuple)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		''''''

      # encoding shift like Caesar cipher algorithm implementing positive shift or
      # forward shift or right shift
      for i in plaintext:
       SCREAMING_SNAKE_CASE				    =		self.__key_list.index(a__)
       encoded_message += self.__key_list[
           (position + self.__shift_key) % len(self.__key_list)
       ]

      return encoded_message





def 						A_					(						lowercase_ = "Hello, this is a modified Caesar cipher"			)				->Tuple:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		ShuffledShiftCipher()
 return cipa.decrypt(cipa.encrypt(lowercase_			)			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()










'''simple docstring'''





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
       import torch






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''OneFormerImageProcessor'''
      __lowercase					=	('''CLIPTokenizer''', '''CLIPTokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	a__ = 7_7			,	a__ = 7_7			,	**a__       )							-> str:
         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`.'''       )

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			max_seq_length
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			task_seq_length

         super().__init__(a__			,	a__       )






      def  A__(self			,	a__=None			,	a__=7_7       )							-> str:
         if text_list is None:
            raise ValueError('''tokens cannot be None.'''       )

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(a__			,	padding='''max_length'''			,	max_length=a__			,	truncation=a__       )

         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			tokens['''attention_mask'''], tokens['''input_ids''']

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         for attn_mask, input_id in zip(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.tensor(a__       ) * torch.tensor(a__       )
            token_inputs.append(token.unsqueeze(0       )       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.cat(a__			,	dim=0       )
         return token_inputs






      def __call__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> str:

         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.'''       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.image_processor(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[task_inputs]

         if isinstance(a__			,	a__       ) and all(isinstance(a__			,	a__       ) for task_input in task_inputs       ):
            SCREAMING_SNAKE_CASE__      :					Any							    =			[]
            for task in task_inputs:
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			f"""the task is {task}"""
               task_token_inputs.append(a__       )
            SCREAMING_SNAKE_CASE__      :					int							    =			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'''       ):
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					int							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> Any:

         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.'''       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.image_processor.encode_inputs(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[task_inputs]

         if isinstance(a__			,	a__       ) and all(isinstance(a__			,	a__       ) for task_input in task_inputs       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            for task in task_inputs:
               SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			f"""the task is {task}"""
               task_token_inputs.append(a__       )
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			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'''       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.image_processor.post_process_semantic_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.image_processor.post_process_instance_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Tuple:
         return self.image_processor.post_process_panoptic_segmentation(*a__			,	**a__       )

