

from typing import List

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


if is_torch_available():
    import torch

class 			lowerCamelCase  (					lowerCAmelCase__      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     ['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE							=     'OneFormerImageProcessor'
  SCREAMING_SNAKE_CASE							=     ('CLIPTokenizer', 'CLIPTokenizerFast')



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


        """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`."""  )

        UpperCAmelCase__ :       List[Any]						=						max_seq_length
        UpperCAmelCase__ :       Tuple						=						task_seq_length

        super().__init__(_lowerCamelCase       ,					_lowerCamelCase  )



  def    _a			(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=77  ):


        """simple docstring"""

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

        UpperCAmelCase__ :       Any						=						self.tokenizer(_lowerCamelCase       ,					padding="""max_length"""       ,					max_length=_lowerCamelCase       ,					truncation=_lowerCamelCase  )

        UpperCAmelCase__ :       List[str]						=						tokens["""attention_mask"""], tokens["""input_ids"""]

        UpperCAmelCase__ :       str						=						[]
        for attn_mask, input_id in zip(_lowerCamelCase       ,					_lowerCamelCase  ):
              UpperCAmelCase__ :       List[str]						=						torch.tensor(_lowerCamelCase  ) * torch.tensor(_lowerCamelCase  )
              token_inputs.append(token.unsqueeze(0  )  )

        UpperCAmelCase__ :       Tuple						=						torch.cat(_lowerCamelCase       ,					dim=0  )
        return token_inputs



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


        """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."""  )

        UpperCAmelCase__ :       Dict						=						self.image_processor(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					**_lowerCamelCase  )

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

        if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) and all(isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) for task_input in task_inputs  ):
              UpperCAmelCase__ :       str						=						[]
              for task in task_inputs:
                    UpperCAmelCase__ :       Optional[Any]						=						F"""the task is {task}"""
                    task_token_inputs.append(_lowerCamelCase  )
              UpperCAmelCase__ :       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"""  ):
              UpperCAmelCase__ :       str						=						encoded_inputs.text_inputs

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

              UpperCAmelCase__ :       List[str]						=						torch.cat(_lowerCamelCase       ,					dim=0  )

        return encoded_inputs



  def    _a			(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					**_lowerCamelCase  ):


        """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."""  )

        UpperCAmelCase__ :       str						=						self.image_processor.encode_inputs(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					**_lowerCamelCase  )

        if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
              UpperCAmelCase__ :       Tuple						=						[task_inputs]

        if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) and all(isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) for task_input in task_inputs  ):
              UpperCAmelCase__ :       Dict						=						[]
              for task in task_inputs:
                    UpperCAmelCase__ :       Union[str, Any]						=						F"""the task is {task}"""
                    task_token_inputs.append(_lowerCamelCase  )
              UpperCAmelCase__ :       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"""  ):
              UpperCAmelCase__ :       Tuple						=						encoded_inputs.text_inputs

              UpperCAmelCase__ :       str						=						[]
              for texts in texts_list:
                    UpperCAmelCase__ :       Union[str, Any]						=						self._preprocess_text(_lowerCamelCase       ,					max_length=self.max_seq_length  )
                    text_inputs.append(text_input_list.unsqueeze(0  )  )

              UpperCAmelCase__ :       Union[str, Any]						=						torch.cat(_lowerCamelCase       ,					dim=0  )

        return encoded_inputs



  def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):


        """simple docstring"""

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



  def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):


        """simple docstring"""

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




  def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):


        """simple docstring"""

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




"""simple docstring"""
from __future__ import annotations




def 				a__	(	lowerCAmelCase						)  ->   set[int]:
      UpperCAmelCase__ :       List[Any]						=						{1}
      UpperCAmelCase__ :       Optional[int]						=						[1]
      for _ in range(2 ,  depth + 1						):
            UpperCAmelCase__ :       Tuple						=						[*previous_coefficients, 0]
            UpperCAmelCase__ :       List[str]						=						[0, *previous_coefficients]
            UpperCAmelCase__ :       Dict						=						[]
            for x, y in zip(lowerCAmelCase ,  lowerCAmelCase						):
                  coefficients.add(x + y						)
                  previous_coefficients.append(x + y						)
      return coefficients




def 				a__	(	lowerCAmelCase						)  ->   set[int]:
      UpperCAmelCase__ :       List[str]						=						set()
      for number in unique_coefficients:
            UpperCAmelCase__ :       int						=						2
            UpperCAmelCase__ :       List[Any]						=						number
            while divisor**2 <= copy_number:
                  UpperCAmelCase__ :       Tuple						=						0
                  while copy_number % divisor == 0:
                        copy_number //= divisor
                        multiplicity += 1
                  if multiplicity >= 2:
                        non_squarefrees.add(lowerCAmelCase						)
                        break
                  divisor += 1

      return unique_coefficients.difference(lowerCAmelCase						)




def 				a__	(	lowerCAmelCase = 51						)  ->   int:
      UpperCAmelCase__ :       Any						=						get_pascal_triangle_unique_coefficients(lowerCAmelCase						)
      UpperCAmelCase__ :       Dict						=						get_squarefrees(lowerCAmelCase						)
      return sum(lowerCAmelCase						)


if __name__ == "__main__":
    print(f'''{solution() = }''')

