

"""simple docstring"""


from __future__ import annotations




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				List[Any]:
      print(F'''Vertex\tShortest Distance from vertex {src}'''							)
      for i, d in enumerate(__lowerCAmelCase							):
            print(F'''{i}\t\t{d}'''							)




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Tuple:
      for j in range(__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	(graph[j][k] for k in ["""src""", """dst""", """weight"""])
            if distance[u] != float("""inf"""							) and distance[u] + w < distance[v]:
                  return True
      return False




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				list[float]:
      SCREAMING_SNAKE_CASE__   :      int      		=	[float("""inf"""							)] * vertex_count
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	0.0

      for _ in range(vertex_count - 1							):
            for j in range(__lowerCAmelCase							):
                  SCREAMING_SNAKE_CASE__   :      int      		=	(graph[j][k] for k in ["""src""", """dst""", """weight"""])

                  if distance[u] != float("""inf"""							) and distance[u] + w < distance[v]:
                        SCREAMING_SNAKE_CASE__   :      int      		=	distance[u] + w

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	check_negative_cycle(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)
      if negative_cycle_exists:
            raise Exception("""Negative cycle found"""							)

      return distance


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a					:Dict    = int(input("Enter number of vertices: ").strip())
  a					:Union[str, Any]    = int(input("Enter number of edges: ").strip())

  a					:list[dict[str, int]]    = [{} for _ in range(E)]

  for i in range(E):
    print("Edge ", i + 1)
    a					:Union[str, Any]    = (
        int(x)
        for x in input("Enter source, destination, weight: ").strip().split(" ")
    )
    a					:List[str]    = {"src": src, "dst": dest, "weight": weight}

  a					:str    = int(input("\nEnter shortest path source:").strip())
  a					:Optional[Any]    = bellman_ford(graph, V, E, source)
  print_distance(shortest_distance, 0)





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a					:Any    = {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Union[str, Any]    = [
      "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RobertaPreLayerNormForCausalLM",
      "RobertaPreLayerNormForMaskedLM",
      "RobertaPreLayerNormForMultipleChoice",
      "RobertaPreLayerNormForQuestionAnswering",
      "RobertaPreLayerNormForSequenceClassification",
      "RobertaPreLayerNormForTokenClassification",
      "RobertaPreLayerNormModel",
      "RobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Optional[Any]    = [
      "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRobertaPreLayerNormForCausalLM",
      "TFRobertaPreLayerNormForMaskedLM",
      "TFRobertaPreLayerNormForMultipleChoice",
      "TFRobertaPreLayerNormForQuestionAnswering",
      "TFRobertaPreLayerNormForSequenceClassification",
      "TFRobertaPreLayerNormForTokenClassification",
      "TFRobertaPreLayerNormMainLayer",
      "TFRobertaPreLayerNormModel",
      "TFRobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:List[Any]    = [
      "FlaxRobertaPreLayerNormForCausalLM",
      "FlaxRobertaPreLayerNormForMaskedLM",
      "FlaxRobertaPreLayerNormForMultipleChoice",
      "FlaxRobertaPreLayerNormForQuestionAnswering",
      "FlaxRobertaPreLayerNormForSequenceClassification",
      "FlaxRobertaPreLayerNormForTokenClassification",
      "FlaxRobertaPreLayerNormModel",
      "FlaxRobertaPreLayerNormPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      RobertaPreLayerNormConfig,
      RobertaPreLayerNormOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaPreLayerNormForCausalLM,
        RobertaPreLayerNormForMaskedLM,
        RobertaPreLayerNormForMultipleChoice,
        RobertaPreLayerNormForQuestionAnswering,
        RobertaPreLayerNormForSequenceClassification,
        RobertaPreLayerNormForTokenClassification,
        RobertaPreLayerNormModel,
        RobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta_prelayernorm import (
        TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormMainLayer,
        TFRobertaPreLayerNormModel,
        TFRobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
        FlaxRobertaPreLayerNormPreTrainedModel,
    )

else:
  import sys

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




