






A_		:		Tuple		     =			{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    list[str]:
  UpperCamelCase_: List[str]   				=    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_: Tuple   				=    queue.pop(0  )
    # get the last node from the path
    UpperCamelCase_: Any   				=    path[-1]
    if node not in explored:
      UpperCamelCase_: Any   				=    graph[node]
      # go through all neighbour nodes, construct a new path and
      # push it into the queue
      for neighbour in neighbours:
        UpperCamelCase_: Dict   				=    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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  if not graph or start not in graph or target not in graph:
    return -1
  if start == target:
    return 0
  UpperCamelCase_: List[Any]   				=    [start]
  UpperCamelCase_: str   				=    set(UpperCAmelCase__  )
  # Keep tab on distances from `start` node.
  UpperCamelCase_: Union[str, Any]   				=    {start: 0, target: -1}
  while queue:
    UpperCamelCase_: Optional[int]   				=    queue.pop(0  )
    if node == target:
      UpperCamelCase_: Optional[Any]   				=    (
          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_: 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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      decoder_start_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )