





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




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





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[str]:




    """simple docstring"""




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





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   int:




    """simple docstring"""




    _A    =       [float('inf'							) for _ in range(_SCREAMING_SNAKE_CASE							)]
    _A    =       [False for _ in range(_SCREAMING_SNAKE_CASE							)]
    _A    =       0.0

    for _ in range(v - 1							):
        _A    =       min_dist(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
        _A    =       True

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

    print_dist(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
 __A       : List[str]						    = int(input("Enter number of vertices: ").strip())
 __A       : Optional[int]						    = int(input("Enter number of edges: ").strip())

 __A       : str						    = [[float("inf") for i in range(V)] for j in range(V)]

 for i in range(V):
  __A       : Any						    = 0.0

 for i in range(E):
  print("\nEdge ", i + 1)
  __A       : Optional[Any]						    = int(input("Enter source:").strip())
  __A       : List[str]						    = int(input("Enter destination:").strip())
  __A       : Union[str, Any]						    = float(input("Enter weight:").strip())
  __A       : Union[str, Any]						    = weight

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












from typing import TYPE_CHECKING

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


__A       : Dict						    = {"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : List[str]						    = [
     "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "HubertForCTC",
     "HubertForSequenceClassification",
     "HubertModel",
     "HubertPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : Tuple						    = [
     "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFHubertForCTC",
     "TFHubertModel",
     "TFHubertPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A       : Dict						    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





