


"""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							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    ["image_processor", "tokenizer"]
 __lowerCAmelCase					   =    "OneFormerImageProcessor"
 __lowerCAmelCase					   =    ("CLIPTokenizer", "CLIPTokenizerFast")


 def __init__(						self					,       __A=None					,       __A=None					,       __A = 77					,       __A = 77					,       **__A		)   ->	int:
     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`.'''		)

     a											=max_seq_length
     a											=task_seq_length

     super().__init__(__A					,       __A		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A=None					,       __A=77		)   ->	Tuple:
     if text_list is None:
         raise ValueError('''tokens cannot be None.'''		)

     a											=self.tokenizer(__A					,       padding='''max_length'''					,       max_length=__A					,       truncation=__A		)

     a						, a											=tokens['''attention_mask'''], tokens['''input_ids''']

     a											=[]
     for attn_mask, input_id in zip(__A					,       __A		):
         a											=torch.tensor(__A		) * torch.tensor(__A		)
         token_inputs.append(token.unsqueeze(0		)		)

     a											=torch.cat(__A					,       dim=0		)
     return token_inputs


 def __call__(						self					,       __A=None					,       __A=None					,       __A=None					,       **__A		)   ->	Tuple:

     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.'''		)

     a											=self.image_processor(__A					,       __A					,       __A					,       **__A		)

     if isinstance(__A					,       __A		):
         a											=[task_inputs]

     if isinstance(__A					,       __A		) and all(isinstance(__A					,       __A		) for task_input in task_inputs		):
         a											=[]
         for task in task_inputs:
             a											=f'''the task is {task}'''
             task_token_inputs.append(__A		)
         a											=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'''		):
         a											=encoded_inputs.text_inputs

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

         a											=torch.cat(__A					,       dim=0		)

     return encoded_inputs


 def  SCREAMING_SNAKE_CASE				(						self					,       __A=None					,       __A=None					,       __A=None					,       **__A		)   ->	Tuple:

     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.'''		)

     a											=self.image_processor.encode_inputs(__A					,       __A					,       __A					,       **__A		)

     if isinstance(__A					,       __A		):
         a											=[task_inputs]

     if isinstance(__A					,       __A		) and all(isinstance(__A					,       __A		) for task_input in task_inputs		):
         a											=[]
         for task in task_inputs:
             a											=f'''the task is {task}'''
             task_token_inputs.append(__A		)
         a											=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'''		):
         a											=encoded_inputs.text_inputs

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

         a											=torch.cat(__A					,       dim=0		)

     return encoded_inputs


 def  SCREAMING_SNAKE_CASE				(						self					,       *__A					,       **__A		)   ->	Optional[Any]:
     return self.image_processor.post_process_semantic_segmentation(*__A					,       **__A		)


 def  SCREAMING_SNAKE_CASE				(						self					,       *__A					,       **__A		)   ->	Optional[int]:
     return self.image_processor.post_process_instance_segmentation(*__A					,       **__A		)





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



"""simple docstring"""







from __future__ import annotations

import random
import string






class  __A							:






 """simple docstring"""




 def __init__(						self					,       __A = None		)   ->	None:
     a											=passcode or self.__passcode_creator()
     a											=self.__make_key_list()
     a											=self.__make_shift_key()


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


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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=string.ascii_letters + string.digits
     a											=[random.choice(__A		) for _ in range(random.randint(10					,       20		)		)]
     return password


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=(
         string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
     )

     a											=[]

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

     # 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  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=sum(self.__neg_pos([ord(__A		) for x in self.__passcode]		)		)
     return num if num > 0 else len(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

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

     return decoded_message





 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # encoding shift like Caesar cipher algorithm implementing positive shift or
     # forward shift or right shift
     for i in plaintext:
         a											=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"						):







    """simple docstring"""
    a											=ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(lowercase						)						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()