

from typing import List

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


if is_torch_available():
    import torch







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      List[Any]          =					['image_processor', 'tokenizer']
      lowerCamelCase_	:      Optional[int]          =					'OneFormerImageProcessor'
      lowerCamelCase_	:      str          =					('CLIPTokenizer', 'CLIPTokenizerFast')







      def __init__(     self							,  lowerCamelCase=None							,  lowerCamelCase=None							,  lowerCamelCase = 77							,  lowerCamelCase = 77							,  **lowerCamelCase   )						->       Tuple:
        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__(lowerCamelCase							,  lowerCamelCase   )







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

        snake_case_       						=      self.tokenizer(lowerCamelCase							,  padding="""max_length"""							,  max_length=lowerCamelCase							,  truncation=lowerCamelCase   )

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

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

        snake_case_       						=      torch.cat(lowerCamelCase							,  dim=0   )
        return token_inputs







      def __call__(     self							,  lowerCamelCase=None							,  lowerCamelCase=None							,  lowerCamelCase=None							,  **lowerCamelCase   )						->       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_       						=      self.image_processor(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  **lowerCamelCase   )

        if isinstance(lowerCamelCase							,  lowerCamelCase   ):
          snake_case_       						=      [task_inputs]

        if isinstance(lowerCamelCase							,  lowerCamelCase   ) and all(isinstance(lowerCamelCase							,  lowerCamelCase   ) for task_input in task_inputs   ):
          snake_case_       						=      []
          for task in task_inputs:
            snake_case_       						=      f'''the task is {task}'''
            task_token_inputs.append(lowerCamelCase   )
          snake_case_       						=      self._preprocess_text(lowerCamelCase							,  max_length=self.task_seq_length   )
        else:
          raise TypeError("""Task Inputs should be a string or a list of strings."""   )

        if hasattr(lowerCamelCase							,  """text_inputs"""   ):
          snake_case_       						=      encoded_inputs.text_inputs

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

          snake_case_       						=      torch.cat(lowerCamelCase							,  dim=0   )

        return encoded_inputs







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase=None							,  lowerCamelCase=None							,  lowerCamelCase=None							,  **lowerCamelCase   )						->       Dict:

        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(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  **lowerCamelCase   )

        if isinstance(lowerCamelCase							,  lowerCamelCase   ):
          snake_case_       						=      [task_inputs]

        if isinstance(lowerCamelCase							,  lowerCamelCase   ) and all(isinstance(lowerCamelCase							,  lowerCamelCase   ) for task_input in task_inputs   ):
          snake_case_       						=      []
          for task in task_inputs:
            snake_case_       						=      f'''the task is {task}'''
            task_token_inputs.append(lowerCamelCase   )
          snake_case_       						=      self._preprocess_text(lowerCamelCase							,  max_length=self.task_seq_length   )
        else:
          raise TypeError("""Task Inputs should be a string or a list of strings."""   )

        if hasattr(lowerCamelCase							,  """text_inputs"""   ):
          snake_case_       						=      encoded_inputs.text_inputs

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

          snake_case_       						=      torch.cat(lowerCamelCase							,  dim=0   )

        return encoded_inputs







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







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







      def 	lowerCAmelCase_    (     self							,  *lowerCamelCase							,  **lowerCamelCase   )						->       Tuple:
        return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase							,  **lowerCamelCase   )


def    UpperCamelCase(					lowercase_							)	->				int:





  '''simple docstring'''





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


if __name__ == "__main__":
    import doctest

    doctest.testmod()