






"""simple docstring"""


from typing import List

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


if is_torch_available():
   import torch






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     ['''image_processor''', '''tokenizer''']
    __magic_name__         =     '''OneFormerImageProcessor'''
    __magic_name__         =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





    def __init__(							self	:					Dict				,		A	:					Optional[Any]=None				,		A	:					Dict=None				,		A	:					int = 7_7				,		A	:					int = 7_7				,		**A	:					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`.'			)

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

      super().__init__(A				,		A			)





    def 				lowerCamelCase__      (							self	:					Any				,		A	:					Tuple=None				,		A	:					str=7_7			):


      '''simple docstring'''




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

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

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

      a     :			Dict				=   []
      for attn_mask, input_id in zip(A				,		A			):
        a     :			Union[str, Any]				=   torch.tensor(A			) * torch.tensor(A			)
        token_inputs.append(token.unsqueeze(0			)			)

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





    def __call__(							self	:					Union[str, Any]				,		A	:					List[Any]=None				,		A	:					Optional[Any]=None				,		A	:					Optional[Any]=None				,		**A	:					Dict			):


      '''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.'			)

      a     :			Dict				=   self.image_processor(A				,		A				,		A				,		**A			)

      if isinstance(A				,		A			):
        a     :			str				=   [task_inputs]

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

        a     :			Optional[int]				=   []
        for texts in texts_list:
          a     :			List[str]				=   self._preprocess_text(A				,		max_length=self.max_seq_length			)
          text_inputs.append(text_input_list.unsqueeze(0			)			)

        a     :			Optional[int]				=   torch.cat(A				,		dim=0			)

      return encoded_inputs





    def 				lowerCamelCase__      (							self	:					str				,		A	:					Tuple=None				,		A	:					Any=None				,		A	:					Dict=None				,		**A	:					Tuple			):


      '''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.'			)

      a     :			List[Any]				=   self.image_processor.encode_inputs(A				,		A				,		A				,		**A			)

      if isinstance(A				,		A			):
        a     :			List[str]				=   [task_inputs]

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

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

        a     :			Union[str, Any]				=   torch.cat(A				,		dim=0			)

      return encoded_inputs





    def 				lowerCamelCase__      (							self	:					str				,		*A	:					Union[str, Any]				,		**A	:					Union[str, Any]			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict				,		*A	:					Dict				,		**A	:					int			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					List[str]				,		*A	:					List[str]				,		**A	:					Tuple			):


      '''simple docstring'''




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












"""simple docstring"""


def 			snake_case  (A_      :int = 1_0_0_0      ):







  '''simple docstring'''

  a     :			Union[str, Any]				=   0
  a     :			int				=   (n - 1) // 3
  total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
  a     :			Tuple				=   (n - 1) // 5
  total += ((terms) * (1_0 + (terms - 1) * 5)) // 2
  a     :			Dict				=   (n - 1) // 1_5
  total -= ((terms) * (3_0 + (terms - 1) * 1_5)) // 2
  return total


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




