

'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			str:
       print("""\nThe shortest path matrix using Floyd Warshall algorithm\n"""				)
       for i in range(UpperCAmelCase__				):
              for j in range(UpperCAmelCase__				):
                     if dist[i][j] != float("""inf"""				):
                            print(int(dist[i][j]				),       end="""\t"""				)
                     else:
                            print("""INF""",       end="""\t"""				)
              print()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[int]:
       A_						=      [[float("""inf"""				) for _ in range(UpperCAmelCase__				)] for _ in range(UpperCAmelCase__				)]

       for i in range(UpperCAmelCase__				):
              for j in range(UpperCAmelCase__				):
                     A_						=      graph[i][j]

                     # check vertex k against all other vertices (i, j)
       for k in range(UpperCAmelCase__				):
              # looping through rows of graph array
              for i in range(UpperCAmelCase__				):
                     # looping through columns of graph array
                     for j in range(UpperCAmelCase__				):
                            if (
                                dist[i][k] != float("""inf"""				)
                                and dist[k][j] != float("""inf"""				)
                                and dist[i][k] + dist[k][j] < dist[i][j]
                            ):
                                   A_						=      dist[i][k] + dist[k][j]

       _print_dist(UpperCAmelCase__,       UpperCAmelCase__				)
       return dist, v


if __name__ == "__main__":
      __lowerCamelCase		       =    int(input('''Enter number of vertices: '''))
      __lowerCamelCase		       =    int(input('''Enter number of edges: '''))

      __lowerCamelCase		       =    [[float('''inf''') for i in range(v)] for j in range(v)]

      for i in range(v):
            __lowerCamelCase		       =    0.0

            # src and dst are indices that must be within the array size graph[e][v]
            # failure to follow this will result in an error
      for i in range(e):
            print('''\nEdge ''', i + 1)
            __lowerCamelCase		       =    int(input('''Enter source:'''))
            __lowerCamelCase		       =    int(input('''Enter destination:'''))
            __lowerCamelCase		       =    float(input('''Enter weight:'''))
            __lowerCamelCase		       =    weight

      floyd_warshall(graph, v)

      # Example Input
      # Enter number of vertices: 3
      # Enter number of edges: 2

      # # generated graph from vertex and edge inputs
      # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
      # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

      # specify source, destination and weight for edge #1
      # Edge  1
      # Enter source:1
      # Enter destination:2
      # Enter weight:2

      # specify source, destination and weight for edge #2
      # Edge  2
      # Enter source:2
      # Enter destination:1
      # Enter weight:1

      # # Expected Output from the vertice, edge and src, dst, weight inputs!!
      # 0		INF	INF
      # INF	0	2
      # INF	1	0



'''simple docstring'''







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 					A__      (      _snake_case			):
    lowercase						=		"ClapFeatureExtractor"
    lowercase						=		("RobertaTokenizer", "RobertaTokenizerFast")



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           super().__init__(UpperCamelCase__		,				UpperCamelCase__       )



    def __call__(					self		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_						=      kwargs.pop("""sampling_rate"""		,				UpperCamelCase__       )

           if text is None and audios is None:
                  raise ValueError("""You have to specify either text or audios. Both cannot be none."""       )

           if text is not None:
                  A_						=      self.tokenizer(UpperCamelCase__		,				return_tensors=UpperCamelCase__		,				**UpperCamelCase__       )

           if audios is not None:
                  A_						=      self.feature_extractor(
                      UpperCamelCase__		,				sampling_rate=UpperCamelCase__		,				return_tensors=UpperCamelCase__		,				**UpperCamelCase__       )

           if text is not None and audios is not None:
                  A_						=      audio_features.input_features
                  return encoding
           elif text is not None:
                  return encoding
           else:
                  return BatchEncoding(data=dict(**UpperCamelCase__       )		,				tensor_type=UpperCamelCase__       )



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           return self.tokenizer.batch_decode(*UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      str:





           '''simple docstring'''
           return self.tokenizer.decode(*UpperCamelCase__		,				**UpperCamelCase__       )







    @property
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.tokenizer.model_input_names
           A_						=      self.feature_extractor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names       )       )
