





"""simple docstring"""





lowercase__				:List[str]				   =       {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						set()
  # keep track of all the paths to be checked
  __UpperCAmelCase    :						Any       =						[[start]]

  # return path if start is goal
  if start == goal:
    return [start]

  # keeps looping until all possible paths have been checked
  while queue:
    # pop the first path from the queue
    __UpperCAmelCase    :						str       =						queue.pop(0  )
    # get the last node from the path
    __UpperCAmelCase    :						Optional[int]       =						path[-1]
    if node not in explored:
      __UpperCAmelCase    :						List[Any]       =						graph[node]
      # go through all neighbour nodes, construct a new path and
      # push it into the queue
      for neighbour in neighbours:
        __UpperCAmelCase    :						int       =						list(UpperCamelCase__  )
        new_path.append(UpperCamelCase__  )
        queue.append(UpperCamelCase__  )
        # return path if neighbour is goal
        if neighbour == goal:
          return new_path

            # mark node as explored
      explored.add(UpperCamelCase__  )

    # in case there's no path between the 2 nodes
  return []


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  if not graph or start not in graph or target not in graph:
    return -1
  if start == target:
    return 0
  __UpperCAmelCase    :						Union[str, Any]       =						[start]
  __UpperCAmelCase    :						Optional[int]       =						set(UpperCamelCase__  )
  # Keep tab on distances from `start` node.
  __UpperCAmelCase    :						str       =						{start: 0, target: -1}
  while queue:
    __UpperCAmelCase    :						int       =						queue.pop(0  )
    if node == target:
      __UpperCAmelCase    :						Dict       =						(
          dist[node] if dist[target] == -1 else min(dist[target]  ,			dist[node]  )
      )
    for adjacent in graph[node]:
      if adjacent not in visited:
        visited.add(UpperCamelCase__  )
        queue.append(UpperCamelCase__  )
        __UpperCAmelCase    :						List[str]       =						dist[node] + 1
  return dist[target]


if __name__ == "__main__":
  print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
  print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4










"""simple docstring"""





from typing import TYPE_CHECKING

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


lowercase__				:Tuple				   =       {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__				:Optional[Any]				   =       [
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__				:List[Any]				   =       [
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  lowercase__				:List[Any]				   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)