

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__    				=     {
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__    				=     [
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__    				=     [
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









from __future__ import annotations







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







     print(f'''Vertex\tShortest Distance from vertex {src}'''				)
     for i, d in enumerate(SCREAMING_SNAKE_CASE				):
          print(f'''{i}\t\t{d}'''				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     for j in range(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Any        =    (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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      list[float]:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    [float("inf"				)] * vertex_count
     _lowerCAmelCase   :					int        =    0.0

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

               if distance[u] != float("inf"				) and distance[u] + w < distance[v]:
                    _lowerCAmelCase   :					List[str]        =    distance[u] + w

     _lowerCAmelCase   :					int        =    check_negative_cycle(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     if negative_cycle_exists:
          raise Exception("Negative cycle found"				)

     return distance


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __UpperCAmelCase    				=     int(input('Enter number of vertices: ').strip())
  __UpperCAmelCase    				=     int(input('Enter number of edges: ').strip())

  __UpperCAmelCase    				=     [{} for _ in range(E)]

  for i in range(E):
    print('Edge ', i + 1)
    __UpperCAmelCase				,__UpperCAmelCase				,__UpperCAmelCase    				=     (
        int(x)
        for x in input('Enter source, destination, weight: ').strip().split(' ')
    )
    __UpperCAmelCase    				=     {'src': src, 'dst': dest, 'weight': weight}

  __UpperCAmelCase    				=     int(input('\nEnter shortest path source:').strip())
  __UpperCAmelCase    				=     bellman_ford(graph, V, E, source)
  print_distance(shortest_distance, 0)






