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       ( __lowerCamelCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Dict						=						['image_processor', 'tokenizer']
   __UpperCAmelCase  :       int						=						'OneFormerImageProcessor'
   __UpperCAmelCase  :       str						=						('CLIPTokenizer', 'CLIPTokenizerFast')







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


      '''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`.'  )

      __lowerCamelCase								=				max_seq_length
      __lowerCamelCase								=				task_seq_length

      super().__init__(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=77  ):


      '''simple docstring'''

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

      __lowerCamelCase								=				self.tokenizer(_SCREAMING_SNAKE_CASE   ,							padding='max_length'   ,							max_length=_SCREAMING_SNAKE_CASE   ,							truncation=_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase		,__lowerCamelCase								=				tokens['attention_mask'], tokens['input_ids']

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

      __lowerCamelCase								=				torch.cat(_SCREAMING_SNAKE_CASE   ,							dim=0  )
      return token_inputs







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


      '''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.'  )

      __lowerCamelCase								=				self.image_processor(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE  )

      if isinstance(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):
         __lowerCamelCase								=				[task_inputs]

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

      if hasattr(_SCREAMING_SNAKE_CASE   ,							'text_inputs'  ):
         __lowerCamelCase								=				encoded_inputs.text_inputs

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

         __lowerCamelCase								=				torch.cat(_SCREAMING_SNAKE_CASE   ,							dim=0  )

      return encoded_inputs







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=None   ,							**_SCREAMING_SNAKE_CASE  ):


      '''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.'  )

      __lowerCamelCase								=				self.image_processor.encode_inputs(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE  )

      if isinstance(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):
         __lowerCamelCase								=				[task_inputs]

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

      if hasattr(_SCREAMING_SNAKE_CASE   ,							'text_inputs'  ):
         __lowerCamelCase								=				encoded_inputs.text_inputs

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

         __lowerCamelCase								=				torch.cat(_SCREAMING_SNAKE_CASE   ,							dim=0  )

      return encoded_inputs







   def       _A							(  self   ,							*_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







   def       _A							(  self   ,							*_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







   def       _A							(  self   ,							*_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







"""simple docstring"""







_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_SCREAMING_SNAKE_CASE  :		List[str]        =      [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      [0 for x in range(len(graph))]
_SCREAMING_SNAKE_CASE  :		List[Any]        =      []



def 			A__						(  lowercase_    :  Optional[int]			,   lowercase_    :  List[str]	)     ->       List[str]:
   __lowerCamelCase								=				1
   while stack:
      __lowerCamelCase								=				stack.pop()
      print(lowercase_			,   clothes[current_clothing]	)
      order += 1



def 			A__						(  lowercase_    :  List[Any]			,   lowercase_    :  List[str]			,   lowercase_    :  Union[str, Any]	)     ->       List[Any]:
   __lowerCamelCase								=				1
   for v in graph[u]:
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)

   stack.append(lowercase_	)



def 			A__						(  lowercase_    :  Dict			,   lowercase_    :  Optional[Any]	)     ->       Union[str, Any]:
   for v in range(len(lowercase_	)	):
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)






