
_A						      =				{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}



def 		lowercase_    (     A__				,	A__				,	A__	)							->							list[str]:
    """simple docstring"""

    snake_case				  =      set()
    # keep track of all the paths to be checked
    snake_case				  =      [[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
        snake_case				  =      queue.pop(0	)
        # get the last node from the path
        snake_case				  =      path[-1]
        if node not in explored:
            snake_case				  =      graph[node]
            # go through all neighbour nodes, construct a new path and
            # push it into the queue
            for neighbour in neighbours:
                snake_case				  =      list(A__	)
                new_path.append(A__	)
                queue.append(A__	)
                # return path if neighbour is goal
                if neighbour == goal:
                    return new_path

            # mark node as explored
            explored.add(A__	)

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



def 		lowercase_    (     A__				,	A__				,	A__	)							->							int:
    """simple docstring"""

    if not graph or start not in graph or target not in graph:
        return -1
    if start == target:
        return 0
    snake_case				  =      [start]
    snake_case				  =      set(A__	)
    # Keep tab on distances from `start` node.
    snake_case				  =      {start: 0, target: -1}
    while queue:
        snake_case				  =      queue.pop(0	)
        if node == target:
            snake_case				  =      (
                dist[node] if dist[target] == -1 else min(dist[target]				,	dist[node]	)
            )
        for adjacent in graph[node]:
            if adjacent not in visited:
                visited.add(A__	)
                queue.append(A__	)
                snake_case				  =      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



from typing import TYPE_CHECKING

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


_A						      =				{
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Wav2Vec2ForAudioFrameClassification",
       "Wav2Vec2ForCTC",
       "Wav2Vec2ForMaskedLM",
       "Wav2Vec2ForPreTraining",
       "Wav2Vec2ForSequenceClassification",
       "Wav2Vec2ForXVector",
       "Wav2Vec2Model",
       "Wav2Vec2PreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFWav2Vec2ForCTC",
       "TFWav2Vec2Model",
       "TFWav2Vec2PreTrainedModel",
       "TFWav2Vec2ForSequenceClassification",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "FlaxWav2Vec2ForCTC",
       "FlaxWav2Vec2ForPreTraining",
       "FlaxWav2Vec2Model",
       "FlaxWav2Vec2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

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

