



'''simple docstring'''




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


UpperCamelCase__       :	int    = logging.get_logger(__name__)

UpperCamelCase__       :	int    = {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class 						_UpperCamelCase  (							__UpperCamelCase    ):


			'''simple docstring'''

			_A      :		Optional[Any]			    =		'decision_transformer'
			_A      :		List[Any]			    =		['past_key_values']
			_A      :		Dict			    =		{
			    'max_position_embeddings': 'n_positions',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}

			def __init__(							self     : List[str]							,     lowerCAmelCase__     : Tuple=1_7							,     lowerCAmelCase__     : Tuple=4							,     lowerCAmelCase__     : List[Any]=1_2_8							,     lowerCAmelCase__     : Optional[int]=4_0_9_6							,     lowerCAmelCase__     : Tuple=True							,     lowerCAmelCase__     : str=1							,     lowerCAmelCase__     : int=1_0_2_4							,     lowerCAmelCase__     : Dict=3							,     lowerCAmelCase__     : Optional[int]=1							,     lowerCAmelCase__     : Dict=None							,     lowerCAmelCase__     : Tuple="relu"							,     lowerCAmelCase__     : List[Any]=0.1							,     lowerCAmelCase__     : Optional[Any]=0.1							,     lowerCAmelCase__     : int=0.1							,     lowerCAmelCase__     : List[Any]=1E-5							,     lowerCAmelCase__     : List[str]=0.02							,     lowerCAmelCase__     : Union[str, Any]=True							,     lowerCAmelCase__     : Optional[Any]=True							,     lowerCAmelCase__     : Dict=5_0_2_5_6							,     lowerCAmelCase__     : Optional[Any]=5_0_2_5_6							,     lowerCAmelCase__     : List[str]=False							,     lowerCAmelCase__     : Dict=False							,     **lowerCAmelCase__     : int							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		state_dim
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		act_dim
					__SCREAMING_SNAKE_CASE       :		str           =		hidden_size
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		max_ep_len
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		action_tanh
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		vocab_size
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		n_positions
					__SCREAMING_SNAKE_CASE       :		str           =		n_layer
					__SCREAMING_SNAKE_CASE       :		int           =		n_head
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		n_inner
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		activation_function
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		resid_pdrop
					__SCREAMING_SNAKE_CASE       :		Dict           =		embd_pdrop
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		attn_pdrop
					__SCREAMING_SNAKE_CASE       :		int           =		layer_norm_epsilon
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		initializer_range
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		scale_attn_weights
					__SCREAMING_SNAKE_CASE       :		List[str]           =		use_cache
					__SCREAMING_SNAKE_CASE       :		Any           =		scale_attn_by_inverse_layer_idx
					__SCREAMING_SNAKE_CASE       :		List[str]           =		reorder_and_upcast_attn

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		bos_token_id
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		eos_token_id

					super().__init__(bos_token_id=lowerCAmelCase__							,     eos_token_id=lowerCAmelCase__							,     **lowerCAmelCase__						)



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

            # mark node as explored
                     explored.add(snake_case     )

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




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       lowercase__:   Tuple  			=   [start]
       lowercase__:   List[Any]  			=   set(snake_case     )
       # Keep tab on distances from `start` node.
       lowercase__:   Tuple  			=   {start: 0, target: -1}
       while queue:
              lowercase__:   Dict  			=   queue.pop(0     )
              if node == target:
                     lowercase__:   str  			=   (
                         dist[node] if dist[target] == -1 else min(dist[target]			,							dist[node]     )
                     )
              for adjacent in graph[node]:
                     if adjacent not in visited:
                            visited.add(snake_case     )
                            queue.append(snake_case     )
                            lowercase__:   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


