





lowercase      :       Union[str, Any]									=  {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}



def A_	(		A__		,      A__		,      A__				)  ->		list[str]:
       a__						:	List[str] =				set()
       # keep track of all the paths to be checked
       a__						:	Union[str, 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
              a__						:	Tuple =				queue.pop(0				)
              # get the last node from the path
              a__						:	Optional[int] =				path[-1]
              if node not in explored:
                     a__						:	List[str] =				graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            a__						:	Optional[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 A_	(		A__		,      A__		,      A__				)  ->		int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       a__						:	Tuple =				[start]
       a__						:	Union[str, Any] =				set(A__				)
       # Keep tab on distances from `start` node.
       a__						:	Optional[Any] =				{start: 0, target: -1}
       while queue:
              a__						:	str =				queue.pop(0				)
              if node == target:
                     a__						:	List[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(A__				)
                            queue.append(A__				)
                            a__						:	int =				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


__snake_case							:   List[Any]      =    logging.get_logger(__name__)

__snake_case							:   Any      =    {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'mctct'

	def __init__(     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		str=8065       ,			_SCREAMING_SNAKE_CASE:		str=1536       ,			_SCREAMING_SNAKE_CASE:		str=36       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=6144       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=4       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=384       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=920       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1e-5       ,			_SCREAMING_SNAKE_CASE:		List[Any]=0.3       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]="relu"       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=0.02       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0.3       ,			_SCREAMING_SNAKE_CASE:		Dict=0.3       ,			_SCREAMING_SNAKE_CASE:		List[Any]=1       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0       ,			_SCREAMING_SNAKE_CASE:		List[str]=2       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.3       ,			_SCREAMING_SNAKE_CASE:		Dict=1       ,			_SCREAMING_SNAKE_CASE:		int=(7,)       ,			_SCREAMING_SNAKE_CASE:		str=(3,)       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=80       ,			_SCREAMING_SNAKE_CASE:		Tuple=1       ,			_SCREAMING_SNAKE_CASE:		Dict=None       ,			_SCREAMING_SNAKE_CASE:		Tuple="sum"       ,			_SCREAMING_SNAKE_CASE:		List[str]=False       ,			**_SCREAMING_SNAKE_CASE:		Tuple       ,			)				->       Tuple:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE       ,			pad_token_id=_SCREAMING_SNAKE_CASE       ,			bos_token_id=_SCREAMING_SNAKE_CASE       ,			eos_token_id=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       vocab_size
				__lowerCAmelCase      :  str												=       hidden_size
				__lowerCAmelCase      :  str												=       num_hidden_layers
				__lowerCAmelCase      :  str												=       intermediate_size
				__lowerCAmelCase      :  List[Any]												=       num_attention_heads
				__lowerCAmelCase      :  Dict												=       attention_head_dim
				__lowerCAmelCase      :  Optional[int]												=       max_position_embeddings
				__lowerCAmelCase      :  str												=       layer_norm_eps
				__lowerCAmelCase      :  Tuple												=       layerdrop
				__lowerCAmelCase      :  str												=       hidden_act
				__lowerCAmelCase      :  List[Any]												=       initializer_range
				__lowerCAmelCase      :  int												=       hidden_dropout_prob
				__lowerCAmelCase      :  Optional[Any]												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  str												=       pad_token_id
				__lowerCAmelCase      :  Optional[int]												=       bos_token_id
				__lowerCAmelCase      :  Union[str, Any]												=       eos_token_id
				__lowerCAmelCase      :  Any												=       conv_glu_dim
				__lowerCAmelCase      :  Optional[int]												=       conv_dropout
				__lowerCAmelCase      :  Union[str, Any]												=       num_conv_layers
				__lowerCAmelCase      :  Optional[int]												=       input_feat_per_channel
				__lowerCAmelCase      :  Union[str, Any]												=       input_channels
				__lowerCAmelCase      :  Optional[Any]												=       conv_channels
				__lowerCAmelCase      :  Dict												=       ctc_loss_reduction
				__lowerCAmelCase      :  int												=       ctc_zero_infinity

				# prevents config testing fail with exporting to json
				__lowerCAmelCase      :  List[str]												=       list(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       list(_SCREAMING_SNAKE_CASE)

				if len(self.conv_kernel) != self.num_conv_layers:
							raise ValueError(
							    "Configuration for convolutional module is incorrect. "
							    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
							    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, """
							    F"""`config.num_conv_layers = {self.num_conv_layers}`.""")