

from typing import List

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


if is_torch_available():
  import torch







class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Tuple      							=["""image_processor""", """tokenizer"""]
  UpperCAmelCase__   :			List[str]      							="""OneFormerImageProcessor"""
  UpperCAmelCase__   :			Optional[Any]      							=("""CLIPTokenizer""", """CLIPTokenizerFast""")


  def __init__(      self     :			Union[str, Any]					,					UpperCAmelCase__     :			Any=None					,					UpperCAmelCase__     :			Optional[Any]=None					,					UpperCAmelCase__     :			int = 7_7					,					UpperCAmelCase__     :			int = 7_7					,					**UpperCAmelCase__     :			Optional[Any]		)       ->List[str]:
        """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`."""		)

        SCREAMING_SNAKE_CASE			:   Tuple	  = max_seq_length
        SCREAMING_SNAKE_CASE			:   str	  = task_seq_length

        super().__init__(UpperCAmelCase__					,					UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Optional[Any]					,					UpperCAmelCase__     :			List[Any]=None					,					UpperCAmelCase__     :			str=7_7		)       ->List[Any]:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE			:   Dict	  = self.tokenizer(UpperCAmelCase__					,					padding="""max_length"""					,					max_length=UpperCAmelCase__					,					truncation=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Any	  = tokens["""attention_mask"""], tokens["""input_ids"""]

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = []
        for attn_mask, input_id in zip(UpperCAmelCase__					,					UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE			:   Dict	  = torch.tensor(UpperCAmelCase__		) * torch.tensor(UpperCAmelCase__		)
              token_inputs.append(token.unsqueeze(0		)		)

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = torch.cat(UpperCAmelCase__					,					dim=0		)
        return token_inputs


  def __call__(      self     :			Any					,					UpperCAmelCase__     :			Dict=None					,					UpperCAmelCase__     :			List[str]=None					,					UpperCAmelCase__     :			Tuple=None					,					**UpperCAmelCase__     :			int		)       ->Union[str, Any]:
        """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."""		)

        SCREAMING_SNAKE_CASE			:   List[str]	  = self.image_processor(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__					,					**UpperCAmelCase__		)

        if isinstance(UpperCAmelCase__					,					UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE			:   List[str]	  = [task_inputs]

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

        if hasattr(UpperCAmelCase__					,					"""text_inputs"""		):
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = encoded_inputs.text_inputs

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

              SCREAMING_SNAKE_CASE			:   List[Any]	  = torch.cat(UpperCAmelCase__					,					dim=0		)

        return encoded_inputs


  def 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			int=None					,					UpperCAmelCase__     :			int=None					,					UpperCAmelCase__     :			str=None					,					**UpperCAmelCase__     :			Dict		)       ->Optional[Any]:
        """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."""		)

        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.image_processor.encode_inputs(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__					,					**UpperCAmelCase__		)

        if isinstance(UpperCAmelCase__					,					UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE			:   Dict	  = [task_inputs]

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

        if hasattr(UpperCAmelCase__					,					"""text_inputs"""		):
              SCREAMING_SNAKE_CASE			:   List[str]	  = encoded_inputs.text_inputs

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

              SCREAMING_SNAKE_CASE			:   Any	  = torch.cat(UpperCAmelCase__					,					dim=0		)

        return encoded_inputs


  def 			_lowercase   (      self     :			Tuple					,					*UpperCAmelCase__     :			Any					,					**UpperCAmelCase__     :			Union[str, Any]		)       ->List[str]:
        """simple docstring"""







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


  def 			_lowercase   (      self     :			List[str]					,					*UpperCAmelCase__     :			Dict					,					**UpperCAmelCase__     :			Optional[Any]		)       ->Optional[Any]:
        """simple docstring"""







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




  def 			_lowercase   (      self     :			Dict					,					*UpperCAmelCase__     :			Optional[Any]					,					**UpperCAmelCase__     :			Union[str, Any]		)       ->Dict:
        """simple docstring"""







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







import safs







class 							a__							( safs.SaFileSystem							):







  """simple docstring"""




