






'''simple docstring'''




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


lowerCamelCase_												=					logging.get_logger(__name__)

lowerCamelCase_												=					{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}

class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   snake_case    				=						'''biogpt'''

   def __init__(		self :	int      ,      __UpperCAmelCase :	Optional[Any]=42384      ,      __UpperCAmelCase :	List[Any]=1024      ,      __UpperCAmelCase :	Dict=24      ,      __UpperCAmelCase :	int=16      ,      __UpperCAmelCase :	Optional[int]=4096      ,      __UpperCAmelCase :	Optional[int]="gelu"      ,      __UpperCAmelCase :	Dict=0.1      ,      __UpperCAmelCase :	Tuple=0.1      ,      __UpperCAmelCase :	List[Any]=1024      ,      __UpperCAmelCase :	Any=0.02      ,      __UpperCAmelCase :	Union[str, Any]=1E-12      ,      __UpperCAmelCase :	str=True      ,      __UpperCAmelCase :	int=True      ,      __UpperCAmelCase :	Dict=0.0      ,      __UpperCAmelCase :	Optional[Any]=0.0      ,      __UpperCAmelCase :	int=1      ,      __UpperCAmelCase :	Any=0      ,      __UpperCAmelCase :	str=2      ,      **__UpperCAmelCase :	Dict      ,      ):





         '''simple docstring'''





         _A			     =	vocab_size
         _A			     =	max_position_embeddings
         _A			     =	hidden_size
         _A			     =	num_hidden_layers
         _A			     =	num_attention_heads
         _A			     =	intermediate_size
         _A			     =	hidden_act
         _A			     =	hidden_dropout_prob
         _A			     =	attention_probs_dropout_prob
         _A			     =	initializer_range
         _A			     =	layer_norm_eps
         _A			     =	scale_embedding
         _A			     =	use_cache
         _A			     =	layerdrop
         _A			     =	activation_dropout
         super().__init__(pad_token_id=__UpperCAmelCase      ,      bos_token_id=__UpperCAmelCase      ,      eos_token_id=__UpperCAmelCase      ,      **__UpperCAmelCase		)








"""simple docstring"""


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






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


