






'''simple docstring'''





from queue import PriorityQueue
from typing import Any

import numpy as np


def 					UpperCamelCase_(     snake_case							:	dict							,    snake_case							:	str							,    snake_case							:	set							,    snake_case							:	set							,    snake_case							:	dict							,    snake_case							:	dict							,    snake_case							:	PriorityQueue							,    snake_case							:	dict							,    snake_case							:	float | int							,    ):

							'''simple docstring'''






							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														snake_case_      =				cst_fwd.get(snake_case							,    np.inf					)
														snake_case_      =				cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)					)
																					snake_case_      =				new_cost_f
																					snake_case_      =				v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												snake_case_      =				cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	str							,    snake_case							:	dict							,    snake_case							:	dict					):

							'''simple docstring'''






							snake_case_      =				-1

							snake_case_      =				set()
							snake_case_      =				set()
							snake_case_      =				{source: 0}
							snake_case_      =				{destination: 0}
							snake_case_      =				{source: None}
							snake_case_      =				{destination: None}
							snake_case_      =				PriorityQueue()
							snake_case_      =				PriorityQueue()

							snake_case_      =				np.inf

							queue_forward.put((0, source)					)
							queue_backward.put((0, destination)					)

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														snake_case_ ,				snake_case_      =				queue_forward.get()
														visited_forward.add(snake_case					)

														snake_case_ ,				snake_case_      =				queue_backward.get()
														visited_backward.add(snake_case					)

														snake_case_      =				pass_and_relaxation(
														    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    )

														snake_case_      =				pass_and_relaxation(
														    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case							,    )

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														snake_case_      =				shortest_distance
							return shortest_path_distance


_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
_SCREAMING_SNAKE_CASE							:       Tuple						=			{
    "B": [["E", 1]],
    "C": [["B", 1]],
    "D": [["C", 1]],
    "F": [["D", 1], ["G", 1]],
    "E": [[None, np.inf]],
    "G": [["E", 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()










'''simple docstring'''





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


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

_SCREAMING_SNAKE_CASE							:       Optional[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 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Dict							=   "decision_transformer"
			lowerCAmelCase_					:				List[Any]							=   ["past_key_values"]
			lowerCAmelCase_					:				Tuple							=   {
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}




			def __init__( self    ,							a__=17    ,							a__=4    ,							a__=128    ,							a__=4_096    ,							a__=True    ,							a__=1    ,							a__=1_024    ,							a__=3    ,							a__=1    ,							a__=None    ,							a__="relu"    ,							a__=0.1    ,							a__=0.1    ,							a__=0.1    ,							a__=1e-5    ,							a__=0.0_2    ,							a__=True    ,							a__=True    ,							a__=50_256    ,							a__=50_256    ,							a__=False    ,							a__=False    ,							**a__    ,							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				state_dim
										snake_case_      =				act_dim
										snake_case_      =				hidden_size
										snake_case_      =				max_ep_len
										snake_case_      =				action_tanh
										snake_case_      =				vocab_size
										snake_case_      =				n_positions
										snake_case_      =				n_layer
										snake_case_      =				n_head
										snake_case_      =				n_inner
										snake_case_      =				activation_function
										snake_case_      =				resid_pdrop
										snake_case_      =				embd_pdrop
										snake_case_      =				attn_pdrop
										snake_case_      =				layer_norm_epsilon
										snake_case_      =				initializer_range
										snake_case_      =				scale_attn_weights
										snake_case_      =				use_cache
										snake_case_      =				scale_attn_by_inverse_layer_idx
										snake_case_      =				reorder_and_upcast_attn

										snake_case_      =				bos_token_id
										snake_case_      =				eos_token_id

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




