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


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

_a:    List[Any] 							=			{
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     "trajectory_transformer"
	SCREAMING_SNAKE_CASE__    				=     ["past_key_values"]
	SCREAMING_SNAKE_CASE__    				=     {
	    "hidden_size": "n_embd",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						Tuple=100		,	lowerCAmelCase	:						List[str]=5		,	lowerCAmelCase	:						List[str]=1		,	lowerCAmelCase	:						str=1		,	lowerCAmelCase	:						int=249		,	lowerCAmelCase	:						List[str]=6		,	lowerCAmelCase	:						List[str]=17		,	lowerCAmelCase	:						Tuple=25		,	lowerCAmelCase	:						Dict=4		,	lowerCAmelCase	:						Any=4		,	lowerCAmelCase	:						Union[str, Any]=128		,	lowerCAmelCase	:						Optional[int]=0.1		,	lowerCAmelCase	:						int=0.1		,	lowerCAmelCase	:						Union[str, Any]=0.1		,	lowerCAmelCase	:						Any=0.0_006		,	lowerCAmelCase	:						str=512		,	lowerCAmelCase	:						Tuple=0.02		,	lowerCAmelCase	:						Tuple=1e-12		,	lowerCAmelCase	:						List[str]=1		,	lowerCAmelCase	:						Tuple=True		,	lowerCAmelCase	:						Dict=1		,	lowerCAmelCase	:						Dict=50_256		,	lowerCAmelCase	:						int=50_256		,	**lowerCAmelCase	:						Any		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			vocab_size
		UpperCAmelCase_       				=			action_weight
		UpperCAmelCase_       				=			reward_weight
		UpperCAmelCase_       				=			value_weight
		UpperCAmelCase_       				=			max_position_embeddings
		UpperCAmelCase_       				=			block_size
		UpperCAmelCase_       				=			action_dim
		UpperCAmelCase_       				=			observation_dim
		UpperCAmelCase_       				=			transition_dim
		UpperCAmelCase_       				=			learning_rate
		UpperCAmelCase_       				=			n_layer
		UpperCAmelCase_       				=			n_head
		UpperCAmelCase_       				=			n_embd
		UpperCAmelCase_       				=			embd_pdrop
		UpperCAmelCase_       				=			attn_pdrop
		UpperCAmelCase_       				=			resid_pdrop
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			layer_norm_eps
		UpperCAmelCase_       				=			kaiming_initializer_range
		UpperCAmelCase_       				=			use_cache
		super().__init__(pad_token_id=UpperCamelCase__		,	bos_token_id=UpperCamelCase__		,	eos_token_id=UpperCamelCase__		,	**UpperCamelCase__							)


from __future__ import annotations



def       __lowerCAmelCase      (						A       ,     A    ):
	print(F"Vertex\tShortest Distance from vertex {src}"    )
	for i, d in enumerate(A    ):
		print(F"{i}\t\t{d}"    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	for j in range(A    ):
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])
		if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
			return True
	return False



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[float("inf"    )] * vertex_count
	UpperCAmelCase_       				=			0.0

	for _ in range(vertex_count - 1    ):
		for j in range(A    ):
			UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])

			if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
				UpperCAmelCase_       				=			distance[u] + w

	UpperCAmelCase_       				=			check_negative_cycle(A       ,     A       ,     A    )
	if negative_cycle_exists:
		raise Exception("Negative cycle found"    )

	return distance


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a:    List[str] 							=			int(input("""Enter number of vertices: """).strip())
				_a:    str 							=			int(input("""Enter number of edges: """).strip())

				_a:    list[dict[str, int]] 							=			[{} for _ in range(E)]

				for i in range(E):
								print("""Edge """, i + 1)
								_a							,			_a							,			_a:    Any 							=			(
								    int(x)
								    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
								)
								_a:    str 							=			{"""src""": src, """dst""": dest, """weight""": weight}

				_a:    List[Any] 							=			int(input("""\nEnter shortest path source:""").strip())
				_a:    Optional[int] 							=			bellman_ford(graph, V, E, source)
				print_distance(shortest_distance, 0)