





"""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_				(     SCREAMING_SNAKE_CASE__							):
       snake_case__				:							Tuple											=     ['image_processor', 'tokenizer']
       snake_case__				:							Tuple											=     'OneFormerImageProcessor'
       snake_case__				:							List[Any]											=     ('CLIPTokenizer', 'CLIPTokenizerFast')

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE = 77   ,					__SCREAMING_SNAKE_CASE = 77   ,					**__SCREAMING_SNAKE_CASE     ):






        '''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`."     )

        snake_case__    							=				max_seq_length
        snake_case__    							=				task_seq_length

        super().__init__(a_   ,					a_     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=77     ):






        '''simple docstring'''

        if text_list is None:
         raise ValueError("tokens cannot be None."     )

        snake_case__    							=				self.tokenizer(a_   ,					padding="max_length"   ,					max_length=a_   ,					truncation=a_     )

        snake_case__							,   snake_case__    							=				tokens["attention_mask"], tokens["input_ids"]

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

        snake_case__    							=				torch.cat(a_   ,					dim=0     )
        return token_inputs

       def __call__(			self   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					**__SCREAMING_SNAKE_CASE     ):






        '''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."     )

        snake_case__    							=				self.image_processor(a_   ,					a_   ,					a_   ,					**a_     )

        if isinstance(a_   ,					a_     ):
         snake_case__    							=				[task_inputs]

        if isinstance(a_   ,					a_     ) and all(isinstance(a_   ,					a_     ) for task_input in task_inputs     ):
         snake_case__    							=				[]
         for task in task_inputs:
          snake_case__    							=				F"the task is {task}"
          task_token_inputs.append(a_     )
         snake_case__    							=				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__    							=				encoded_inputs.text_inputs

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

         snake_case__    							=				torch.cat(a_   ,					dim=0     )

        return encoded_inputs

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					__SCREAMING_SNAKE_CASE=None   ,					**__SCREAMING_SNAKE_CASE     ):






        '''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."     )

        snake_case__    							=				self.image_processor.encode_inputs(a_   ,					a_   ,					a_   ,					**a_     )

        if isinstance(a_   ,					a_     ):
         snake_case__    							=				[task_inputs]

        if isinstance(a_   ,					a_     ) and all(isinstance(a_   ,					a_     ) for task_input in task_inputs     ):
         snake_case__    							=				[]
         for task in task_inputs:
          snake_case__    							=				F"the task is {task}"
          task_token_inputs.append(a_     )
         snake_case__    							=				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__    							=				encoded_inputs.text_inputs

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

         snake_case__    							=				torch.cat(a_   ,					dim=0     )

        return encoded_inputs

       def 		_SCREAMING_SNAKE_CASE(			self   ,					*__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return self.image_processor.post_process_semantic_segmentation(*a_   ,					**a_     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					*__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return self.image_processor.post_process_instance_segmentation(*a_   ,					**a_     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					*__SCREAMING_SNAKE_CASE   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return self.image_processor.post_process_panoptic_segmentation(*a_   ,					**a_     )








"""simple docstring"""


def 							a__			(  _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 return int(_UpperCAmelCase	) if x - int(_UpperCAmelCase	) <= 0 else int(_UpperCAmelCase	) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

