from typing import List

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


if is_torch_available():
  import torch






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						Tuple 			=   ['image_processor', 'tokenizer']
   UpperCamelCase_				:						Optional[Any] 			=   'OneFormerImageProcessor'
   UpperCamelCase_				:						Dict 			=   ('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`.'''    )

        a  :    Optional[Any]				=						max_seq_length
        a  :    List[str]				=						task_seq_length

        super().__init__(lowerCAmelCase_							,     lowerCAmelCase_    )
   def _lowercase(    self							,     lowerCAmelCase_=None							,     lowerCAmelCase_=77    )	->Union[str, Any]:
        if text_list is None:
             raise ValueError('''tokens cannot be None.'''    )

        a  :    Optional[int]				=						self.tokenizer(lowerCAmelCase_							,     padding='''max_length'''							,     max_length=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )

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

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

        a  :    Dict				=						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.'''    )

        a  :    Optional[int]				=						self.image_processor(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     **lowerCAmelCase_    )

        if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Union[str, Any]				=						[task_inputs]

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

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

             a  :    int				=						torch.cat(lowerCAmelCase_							,     dim=0    )

        return encoded_inputs
   def _lowercase(    self							,     lowerCAmelCase_=None							,     lowerCAmelCase_=None							,     lowerCAmelCase_=None							,     **lowerCAmelCase_    )	->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.'''    )

        a  :    Tuple				=						self.image_processor.encode_inputs(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     **lowerCAmelCase_    )

        if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Optional[int]				=						[task_inputs]

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

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

             a  :    int				=						torch.cat(lowerCAmelCase_							,     dim=0    )

        return encoded_inputs
   def _lowercase(    self							,     *lowerCAmelCase_							,     **lowerCAmelCase_    )	->Union[str, Any]:
        return self.image_processor.post_process_semantic_segmentation(*lowerCAmelCase_							,     **lowerCAmelCase_    )
   def _lowercase(    self							,     *lowerCAmelCase_							,     **lowerCAmelCase_    )	->Union[str, Any]:
        return self.image_processor.post_process_instance_segmentation(*lowerCAmelCase_							,     **lowerCAmelCase_    )


   def _lowercase(    self							,     *lowerCAmelCase_							,     **lowerCAmelCase_    )	->Optional[Any]:
        return self.image_processor.post_process_panoptic_segmentation(*lowerCAmelCase_							,     **lowerCAmelCase_    )
def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	int:
     return 1 if input_a == 0 else 0







def 	UpperCAmelCase       (		)	->	None:
     assert not_gate(0       ) == 1
     assert not_gate(1       ) == 0


if __name__ == "__main__":
  print(not_gate(0))
  print(not_gate(1))