
"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_       :						Tuple     					=	{
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_       :						Dict     					=	[
          '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Data2VecAudioForAudioFrameClassification''',
          '''Data2VecAudioForCTC''',
          '''Data2VecAudioForSequenceClassification''',
          '''Data2VecAudioForXVector''',
          '''Data2VecAudioModel''',
          '''Data2VecAudioPreTrainedModel''',
      ]
      lowercase_       :						List[str]     					=	[
          '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Data2VecTextForCausalLM''',
          '''Data2VecTextForMaskedLM''',
          '''Data2VecTextForMultipleChoice''',
          '''Data2VecTextForQuestionAnswering''',
          '''Data2VecTextForSequenceClassification''',
          '''Data2VecTextForTokenClassification''',
          '''Data2VecTextModel''',
          '''Data2VecTextPreTrainedModel''',
      ]
      lowercase_       :						str     					=	[
          '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Data2VecVisionForImageClassification''',
          '''Data2VecVisionForMaskedImageModeling''',
          '''Data2VecVisionForSemanticSegmentation''',
          '''Data2VecVisionModel''',
          '''Data2VecVisionPreTrainedModel''',
      ]

if is_tf_available():
      lowercase_       :						int     					=	[
          '''TFData2VecVisionForImageClassification''',
          '''TFData2VecVisionForSemanticSegmentation''',
          '''TFData2VecVisionModel''',
          '''TFData2VecVisionPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

      lowercase_       :						Optional[int]     					=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Union[str, Any]:
  print("\nThe shortest path matrix using Floyd Warshall algorithm\n"				)
  for i in range(lowerCamelCase__				):
    for j in range(lowerCamelCase__				):
      if dist[i][j] != float("inf"				):
        print(int(dist[i][j]				),						end="\t"				)
      else:
        print("INF",						end="\t"				)
    print()





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							Any				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = [[float("inf"				) for _ in range(lowerCamelCase__				)] for _ in range(lowerCamelCase__				)]

  for i in range(lowerCamelCase__				):
    for j in range(lowerCamelCase__				):
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = graph[i][j]

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

  _print_dist(lowerCamelCase__,						lowerCamelCase__				)
  return dist, v


if __name__ == "__main__":
      lowercase_       :						Tuple     					=	int(input('''Enter number of vertices: '''))
      lowercase_       :						List[Any]     					=	int(input('''Enter number of edges: '''))

      lowercase_       :						Optional[Any]     					=	[[float('''inf''') for i in range(v)] for j in range(v)]

      for i in range(v):
            lowercase_       :						Tuple     					=	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)
            lowercase_       :						str     					=	int(input('''Enter source:'''))
            lowercase_       :						Optional[Any]     					=	int(input('''Enter destination:'''))
            lowercase_       :						Union[str, Any]     					=	float(input('''Enter weight:'''))
            lowercase_       :						str     					=	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



