

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__    				=     {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__    				=     ['MobileBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__    				=     [
      'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MobileBertForMaskedLM',
      'MobileBertForMultipleChoice',
      'MobileBertForNextSentencePrediction',
      'MobileBertForPreTraining',
      'MobileBertForQuestionAnswering',
      'MobileBertForSequenceClassification',
      'MobileBertForTokenClassification',
      'MobileBertLayer',
      'MobileBertModel',
      'MobileBertPreTrainedModel',
      'load_tf_weights_in_mobilebert',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__    				=     [
      'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFMobileBertForMaskedLM',
      'TFMobileBertForMultipleChoice',
      'TFMobileBertForNextSentencePrediction',
      'TFMobileBertForPreTraining',
      'TFMobileBertForQuestionAnswering',
      'TFMobileBertForSequenceClassification',
      'TFMobileBertForTokenClassification',
      'TFMobileBertMainLayer',
      'TFMobileBertModel',
      'TFMobileBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

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)






