"""simple docstring"""




_a				:		int	   =       {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	str     )	->				Any:
  _lowerCAmelCase						:	List[Any]        =					set()
  # keep track of all the paths to be checked
  _lowerCAmelCase						:	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
    _lowerCAmelCase						:	Tuple        =					queue.pop(0     )
    # get the last node from the path
    _lowerCAmelCase						:	List[str]        =					path[-1]
    if node not in explored:
      _lowerCAmelCase						:	Any        =					graph[node]
      # go through all neighbour nodes, construct a new path and
      # push it into the queue
      for neighbour in neighbours:
        _lowerCAmelCase						:	List[Any]        =					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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	Tuple     )	->				List[Any]:



  if not graph or start not in graph or target not in graph:
    return -1
  if start == target:
    return 0
  _lowerCAmelCase						:	str        =					[start]
  _lowerCAmelCase						:	int        =					set(A__     )
  # Keep tab on distances from `start` node.
  _lowerCAmelCase						:	Dict        =					{start: 0, target: -1}
  while queue:
    _lowerCAmelCase						:	Union[str, Any]        =					queue.pop(0     )
    if node == target:
      _lowerCAmelCase						:	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(A__     )
        queue.append(A__     )
        _lowerCAmelCase						:	Any        =					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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_a				:		Any	   =       logging.get_logger(__name__)

_a				:		Any	   =       {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		"visual_bert"






     def __init__(		self    ,					a__=30522    ,					a__=768    ,					a__=512    ,					a__=12    ,					a__=12    ,					a__=3072    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=2    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=False    ,					a__=True    ,					a__=1    ,					a__=0    ,					a__=2    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )

       _lowerCAmelCase						:	Optional[Any]        =					vocab_size
       _lowerCAmelCase						:	int        =					max_position_embeddings
       _lowerCAmelCase						:	Optional[Any]        =					hidden_size
       _lowerCAmelCase						:	Optional[int]        =					visual_embedding_dim
       _lowerCAmelCase						:	Optional[int]        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	Any        =					intermediate_size
       _lowerCAmelCase						:	List[Any]        =					hidden_act
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Optional[Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					initializer_range
       _lowerCAmelCase						:	List[Any]        =					type_vocab_size
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps
       _lowerCAmelCase						:	Optional[Any]        =					bypass_transformer
       _lowerCAmelCase						:	List[Any]        =					special_visual_initialize




