



import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	states.reshape(1				,   -1				,   model.config.state_dim      )
						UpperCAmelCase_					=	actions.reshape(1				,   -1				,   model.config.act_dim      )
						UpperCAmelCase_					=	returns_to_go.reshape(1				,   -1				,   1      )
						UpperCAmelCase_					=	timesteps.reshape(1				,   -1      )

						if model.config.max_length is not None:
												UpperCAmelCase_					=	states[:, -model.config.max_length :]
												UpperCAmelCase_					=	actions[:, -model.config.max_length :]
												UpperCAmelCase_					=	returns_to_go[:, -model.config.max_length :]
												UpperCAmelCase_					=	timesteps[:, -model.config.max_length :]

												# pad all tokens to sequence length
												UpperCAmelCase_					=	torch.cat(
												    [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
												UpperCAmelCase_					=	attention_mask.to(dtype=torch.long				,   device=states.device      ).reshape(1				,   -1      )
												UpperCAmelCase_					=	torch.cat(
												    [
												        torch.zeros(
												            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)				,   device=states.device				,   ),
												        states,
												    ]				,   dim=1				,   ).to(dtype=torch.floataa      )
												UpperCAmelCase_					=	torch.cat(
												    [
												        torch.zeros(
												            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)				,   device=actions.device				,   ),
												        actions,
												    ]				,   dim=1				,   ).to(dtype=torch.floataa      )
												UpperCAmelCase_					=	torch.cat(
												    [
												        torch.zeros(
												            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)				,   device=returns_to_go.device				,   ),
												        returns_to_go,
												    ]				,   dim=1				,   ).to(dtype=torch.floataa      )
												UpperCAmelCase_					=	torch.cat(
												    [
												        torch.zeros(
												            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])				,   device=timesteps.device      ),
												        timesteps,
												    ]				,   dim=1				,   ).to(dtype=torch.long      )
						else:
												UpperCAmelCase_					=	None

						UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_					=	model(
						    states=__UpperCAmelCase				,   actions=__UpperCAmelCase				,   rewards=__UpperCAmelCase				,   returns_to_go=__UpperCAmelCase				,   timesteps=__UpperCAmelCase				,   attention_mask=__UpperCAmelCase				,   return_dict=__UpperCAmelCase				,   )

						return action_preds[0, -1]


# build the environment

UpperCamelCase_      			=  gym.make("Hopper-v3")
UpperCamelCase_      			=  env.observation_space.shape[0]
UpperCamelCase_      			=  env.action_space.shape[0]
UpperCamelCase_      			=  1_000
UpperCamelCase_      			=  "cuda"
UpperCamelCase_      			=  10_00.0  # normalization for rewards/returns
UpperCamelCase_      			=  3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase_      			=  np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
UpperCamelCase_      			=  np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
UpperCamelCase_      			=  torch.from_numpy(state_mean).to(device=device)
UpperCamelCase_      			=  torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
UpperCamelCase_      			=  DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
UpperCamelCase_      			=  model.to(device)
model.eval()

for ep in range(10):
							UpperCamelCase_			,     UpperCamelCase_      			=  0, 0
							UpperCamelCase_      			=  env.reset()
							UpperCamelCase_      			=  torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
							UpperCamelCase_      			=  torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
							UpperCamelCase_      			=  torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
							UpperCamelCase_      			=  torch.zeros(0, device=device, dtype=torch.floataa)

							UpperCamelCase_      			=  torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
							for t in range(max_ep_len):
														env.render()
														# add padding
														UpperCamelCase_      			=  torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
														UpperCamelCase_      			=  torch.cat([rewards, torch.zeros(1, device=device)])

														UpperCamelCase_      			=  get_action(
														    model,
														    (states.to(dtype=torch.floataa) - state_mean) / state_std,
														    actions.to(dtype=torch.floataa),
														    rewards.to(dtype=torch.floataa),
														    target_return.to(dtype=torch.floataa),
														    timesteps.to(dtype=torch.long),
														)
														UpperCamelCase_      			=  action
														UpperCamelCase_      			=  action.detach().cpu().numpy()

														UpperCamelCase_			,     UpperCamelCase_			,     UpperCamelCase_			,     UpperCamelCase_      			=  env.step(action)

														UpperCamelCase_      			=  torch.from_numpy(state).to(device=device).reshape(1, state_dim)
														UpperCamelCase_      			=  torch.cat([states, cur_state], dim=0)
														UpperCamelCase_      			=  reward

														UpperCamelCase_      			=  target_return[0, -1] - (reward / scale)
														UpperCamelCase_      			=  torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
														UpperCamelCase_      			=  torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

														episode_return += reward
														episode_length += 1

														if done:
																					break










def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			int:



						'''simple docstring'''






						print('''\nVertex Distance'''      )
						for i in range(__UpperCAmelCase      ):
												if dist[i] != float('''inf'''      ):
																		print(__UpperCAmelCase				,   '''\t'''				,   int(dist[i]      )				,   end='''\t'''      )
												else:
																		print(__UpperCAmelCase				,   '''\t'''				,   '''INF'''				,   end='''\t'''      )
												print()







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			str:



						'''simple docstring'''






						UpperCAmelCase_					=	float('''inf'''      )
						UpperCAmelCase_					=	-1
						for i in range(__UpperCAmelCase      ):
												if (not vset[i]) and mdist[i] < min_val:
																		UpperCAmelCase_					=	i
																		UpperCAmelCase_					=	mdist[i]
						return min_ind







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	[float('''inf'''      ) for _ in range(__UpperCAmelCase      )]
						UpperCAmelCase_					=	[False for _ in range(__UpperCAmelCase      )]
						UpperCAmelCase_					=	0.0

						for _ in range(v - 1      ):
												UpperCAmelCase_					=	min_dist(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
												UpperCAmelCase_					=	True

												for i in range(__UpperCAmelCase      ):
																		if (
																		    (not vset[i])
																		    and graph[u][i] != float('''inf'''      )
																		    and mdist[u] + graph[u][i] < mdist[i]
																		):
																								UpperCAmelCase_					=	mdist[u] + graph[u][i]

						print_dist(__UpperCAmelCase				,   __UpperCAmelCase      )


if __name__ == "__main__":
							UpperCamelCase_      			=  int(input("Enter number of vertices: ").strip())
							UpperCamelCase_      			=  int(input("Enter number of edges: ").strip())

							UpperCamelCase_      			=  [[float("inf") for i in range(V)] for j in range(V)]

							for i in range(V):
														UpperCamelCase_      			=  0.0

							for i in range(E):
														print("\nEdge ", i + 1)
														UpperCamelCase_      			=  int(input("Enter source:").strip())
														UpperCamelCase_      			=  int(input("Enter destination:").strip())
														UpperCamelCase_      			=  float(input("Enter weight:").strip())
														UpperCamelCase_      			=  weight

							UpperCamelCase_      			=  int(input("\nEnter shortest path source:").strip())
							dijkstra(graph, V, gsrc)





