




'''simple docstring'''







from __future__ import annotations

import random
import string







class 				_a   :





     '''simple docstring'''



     def __init__(							self       ,__a = None       )			->							None:
          snake_case :			str					   = passcode or self.__passcode_creator()
          snake_case :			Any					   = self.__make_key_list()
          snake_case :			str					   = self.__make_shift_key()







     def __str__(							self       )			->							str:
          return "".join(self.__passcode       )







     def  snake_case_			(							self       ,__a       )			->							list[int]:
          for i in range(1       ,len(__a       )       ,2       ):
               iterlist[i] *= -1
          return iterlist







     def  snake_case_			(							self       )			->							list[str]:
          snake_case :			Optional[int]					   = string.ascii_letters + string.digits
          snake_case :			Optional[Any]					   = [random.choice(__a       ) for _ in range(random.randint(10       ,20       )       )]
          return password







     def  snake_case_			(							self       )			->							list[str]:
          snake_case :			str					   = (
              string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
          )

          snake_case :			Optional[Any]					   = []

          # creates points known as breakpoints to break the key_list_options at those
          # points and pivot each substring
          snake_case :			Optional[Any]					   = sorted(set(self.__passcode       )       )
          snake_case :			list[str]					   = []

          # 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  snake_case_			(							self       )			->							int:
          snake_case :			Any					   = sum(self.__neg_pos([ord(__a       ) for x in self.__passcode]       )       )
          return num if num > 0 else len(self.__passcode       )







     def  snake_case_			(							self       ,__a       )			->							str:
          snake_case :			Any					   = """"""

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

          return decoded_message







     def  snake_case_			(							self       ,__a       )			->							str:
          snake_case :			List[Any]					   = """"""

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

          return encoded_message



def 					lowerCamelCase__							(			__lowercase = "Hello, this is a modified Caesar cipher"							):
     snake_case :			Union[str, Any]					   = 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 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Tuple									=				["""image_processor""", """tokenizer"""]
     lowerCAmelCase_     :    Optional[Any]									=				"""OneFormerImageProcessor"""
     lowerCAmelCase_     :    int									=				("""CLIPTokenizer""", """CLIPTokenizerFast""")







     def __init__(							self       ,__a=None       ,__a=None       ,__a = 77       ,__a = 77       ,**__a       )			->							Optional[Any]:
          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`."""       )

          snake_case :			str					   = max_seq_length
          snake_case :			Any					   = task_seq_length

          super().__init__(__a       ,__a       )







     def  snake_case_			(							self       ,__a=None       ,__a=77       )			->							List[str]:
          if text_list is None:
               raise ValueError("""tokens cannot be None."""       )

          snake_case :			int					   = self.tokenizer(__a       ,padding="""max_length"""       ,max_length=__a       ,truncation=__a       )

          snake_case   ,		snake_case :			int					   = tokens["""attention_mask"""], tokens["""input_ids"""]

          snake_case :			Tuple					   = []
          for attn_mask, input_id in zip(__a       ,__a       ):
               snake_case :			Dict					   = torch.tensor(__a       ) * torch.tensor(__a       )
               token_inputs.append(token.unsqueeze(0       )       )

          snake_case :			Optional[int]					   = torch.cat(__a       ,dim=0       )
          return token_inputs







     def __call__(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							Optional[int]:

          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."""       )

          snake_case :			Union[str, Any]					   = self.image_processor(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Tuple					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Optional[Any]					   = []
               for task in task_inputs:
                    snake_case :			Any					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Optional[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"""       ):
               snake_case :			Optional[int]					   = encoded_inputs.text_inputs

               snake_case :			Dict					   = []
               for texts in texts_list:
                    snake_case :			Optional[Any]					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Tuple					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							List[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."""       )

          snake_case :			Any					   = self.image_processor.encode_inputs(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Optional[int]					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Dict					   = []
               for task in task_inputs:
                    snake_case :			str					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Any					   = 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"""       ):
               snake_case :			Dict					   = encoded_inputs.text_inputs

               snake_case :			Tuple					   = []
               for texts in texts_list:
                    snake_case :			Tuple					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Union[str, Any]					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[str]:
          return self.image_processor.post_process_semantic_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[Any]:
          return self.image_processor.post_process_instance_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							Optional[Any]:
          return self.image_processor.post_process_panoptic_segmentation(*__a       ,**__a       )



