





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

            # mark node as explored
                  explored.add(__SCREAMING_SNAKE_CASE       )

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




def 						lowerCamelCase_	(		UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_       ):
      if not graph or start not in graph or target not in graph:
            return -1
      if start == target:
            return 0
      _a							:  Optional[Any]				= [start]
      _a							:  Optional[int]				= set(__SCREAMING_SNAKE_CASE       )
      # Keep tab on distances from `start` node.
      _a							:  Any				= {start: 0, target: -1}
      while queue:
            _a							:  List[str]				= queue.pop(0       )
            if node == target:
                  _a							:  Optional[int]				= (
                      dist[node] if dist[target] == -1 else min(dist[target]			,					dist[node]       )
                  )
            for adjacent in graph[node]:
                  if adjacent not in visited:
                        visited.add(__SCREAMING_SNAKE_CASE       )
                        queue.append(__SCREAMING_SNAKE_CASE       )
                        _a							:  Tuple				= 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_flax_available,
    is_tf_available,
    is_torch_available,
)


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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "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_			      =		[
          "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_			      =		[
          "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_			      =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
