






"""simple docstring"""


def      snake_case    (A_ :int      ,     A_ :Dict ):







  '''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      snake_case    (A_ :Optional[Any]      ,     A_ :Optional[Any]      ,     A_ :Dict ):







  '''simple docstring'''

  a	:     Optional[int]							=  float('inf' )
  a	:     List[str]							=  -1
  for i in range(__UpperCAmelCase ):
    if (not vset[i]) and mdist[i] < min_val:
      a	:     Optional[int]							=  i
      a	:     Union[str, Any]							=  mdist[i]
  return min_ind




def      snake_case    (A_ :Dict      ,     A_ :str      ,     A_ :Tuple ):







  '''simple docstring'''

  a	:     str							=  [float('inf' ) for _ in range(__UpperCAmelCase )]
  a	:     Dict							=  [False for _ in range(__UpperCAmelCase )]
  a	:     List[str]							=  0.0

  for _ in range(v - 1 ):
    a	:     Tuple							=  min_dist(__UpperCAmelCase      ,     __UpperCAmelCase      ,     __UpperCAmelCase )
    a	:     Any							=  True

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

  print_dist(__UpperCAmelCase      ,     __UpperCAmelCase )


if __name__ == "__main__":
   _UpperCamelCase							:       Dict       					=		int(input('Enter number of vertices: ').strip())
   _UpperCamelCase							:       Optional[Any]       					=		int(input('Enter number of edges: ').strip())

   _UpperCamelCase							:       Tuple       					=		[[float('inf') for i in range(V)] for j in range(V)]

   for i in range(V):
      _UpperCamelCase							:       Tuple       					=		0.0

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

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









from typing import TYPE_CHECKING

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


UpperCamelCase_      			=  {
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  ["MobileNetV1FeatureExtractor"]
							UpperCamelCase_      			=  ["MobileNetV1ImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MobileNetV1ForImageClassification",
							    "MobileNetV1Model",
							    "MobileNetV1PreTrainedModel",
							    "load_tf_weights_in_mobilenet_v1",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							UpperCamelCase_      			=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





