




'''simple docstring'''





from typing import List

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


if is_torch_available():
  import torch




class      UpperCamelCase       (	snake_case__       ):
  a     :Tuple          =					['image_processor', 'tokenizer']
  a     :int          =					'OneFormerImageProcessor'
  a     :int          =					('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`.""" )

      _a				:							int							       =      max_seq_length
      _a				:							Optional[Any]							       =      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.""" )

      _a				:							Any							       =      self.tokenizer(__lowerCAmelCase		,     padding="""max_length"""		,     max_length=__lowerCAmelCase		,     truncation=__lowerCAmelCase )

      _a       , _a				:							Optional[Any]							       =      tokens["""attention_mask"""], tokens["""input_ids"""]

      _a				:							str							       =      []
      for attn_mask, input_id in zip(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Tuple							       =      torch.tensor(__lowerCAmelCase ) * torch.tensor(__lowerCAmelCase )
          token_inputs.append(token.unsqueeze(0 ) )

      _a				:							List[str]							       =      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.""" )

      _a				:							int							       =      self.image_processor(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase )

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							List[str]							       =      [task_inputs]

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) and all(isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) for task_input in task_inputs ):
          _a				:							List[str]							       =      []
          for task in task_inputs:
              _a				:							Dict							       =      f'''the task is {task}'''
              task_token_inputs.append(__lowerCAmelCase )
          _a				:							str							       =      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				:							int							       =      encoded_inputs.text_inputs

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

          _a				:							Any							       =      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.""" )

      _a				:							Dict							       =      self.image_processor.encode_inputs(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase )

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Optional[Any]							       =      [task_inputs]

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

          _a				:							int							       =      []
          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				:							Dict							       =      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 )







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			int:



						'''simple docstring'''






						print('''\nVertex Distance'''      )
						for i in range(__UpperCAmelCase      ):
												if dist[i] != float('''inf'''      ):
																		print(__UpperCAmelCase				,   '''\t'''				,   int(dist[i]      )				,   end='''\t'''      )
												else:
																		print(__UpperCAmelCase				,   '''\t'''				,   '''INF'''				,   end='''\t'''      )
												print()







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			str:



						'''simple docstring'''






						UpperCAmelCase_					=	float('''inf'''      )
						UpperCAmelCase_					=	-1
						for i in range(__UpperCAmelCase      ):
												if (not vset[i]) and mdist[i] < min_val:
																		UpperCAmelCase_					=	i
																		UpperCAmelCase_					=	mdist[i]
						return min_ind







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	[float('''inf'''      ) for _ in range(__UpperCAmelCase      )]
						UpperCAmelCase_					=	[False for _ in range(__UpperCAmelCase      )]
						UpperCAmelCase_					=	0.0

						for _ in range(v - 1      ):
												UpperCAmelCase_					=	min_dist(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
												UpperCAmelCase_					=	True

												for i in range(__UpperCAmelCase      ):
																		if (
																		    (not vset[i])
																		    and graph[u][i] != float('''inf'''      )
																		    and mdist[u] + graph[u][i] < mdist[i]
																		):
																								UpperCAmelCase_					=	mdist[u] + graph[u][i]

						print_dist(__UpperCAmelCase				,   __UpperCAmelCase      )


if __name__ == "__main__":
							UpperCamelCase_      			=  int(input("Enter number of vertices: ").strip())
							UpperCamelCase_      			=  int(input("Enter number of edges: ").strip())

							UpperCamelCase_      			=  [[float("inf") for i in range(V)] for j in range(V)]

							for i in range(V):
														UpperCamelCase_      			=  0.0

							for i in range(E):
														print("\nEdge ", i + 1)
														UpperCamelCase_      			=  int(input("Enter source:").strip())
														UpperCamelCase_      			=  int(input("Enter destination:").strip())
														UpperCamelCase_      			=  float(input("Enter weight:").strip())
														UpperCamelCase_      			=  weight

							UpperCamelCase_      			=  int(input("\nEnter shortest path source:").strip())
							dijkstra(graph, V, gsrc)





