



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_ (		__a       :			Optional[Any]	,						__a       :			List[str]	,						__a       :			Tuple	,						__a       :			List[str]	,						__a       :			Optional[Any]	,						__a       :			Any						):

				"""simple docstring"""
				a__			 =						states.reshape(1	,						-1	,						model.config.state_dim						)
				a__			 =						actions.reshape(1	,						-1	,						model.config.act_dim						)
				a__			 =						returns_to_go.reshape(1	,						-1	,						1						)
				a__			 =						timesteps.reshape(1	,						-1						)

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

								# pad all tokens to sequence length
								a__			 =						torch.cat(
								    [torch.zeros(model.config.max_length - states.shape[1]						), torch.ones(states.shape[1]						)]						)
								a__			 =						attention_mask.to(dtype=torch.long	,						device=states.device						).reshape(1	,						-1						)
								a__			 =						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						)
								a__			 =						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						)
								a__			 =						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						)
								a__			 =						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:
								a__			 =						None

				a__ ,     a__ ,     a__			 =						model(
				    states=__a	,						actions=__a	,						rewards=__a	,						returns_to_go=__a	,						timesteps=__a	,						attention_mask=__a	,						return_dict=__a	,						)

				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								=   1_000.0  # 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






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 	__snake_case (				utils.Parser):







							'''simple docstring'''






							UpperCamelCase__			: str             =   "halfcheetah-medium-expert-v2"
							UpperCamelCase__			: str             =   "config.offline"




def      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

				trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
				a__			 =						gpt.pos_emb
				trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()						)
				trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()						)
				trajectory_transformer.head.load_state_dict(gpt.head.state_dict()						)

				for i, block in enumerate(gpt.blocks						):
								trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
								trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
								trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()						)

								trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()						)
								trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()						)
								trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()						)
								trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()						)

				torch.save(trajectory_transformer.state_dict()	,						"""pytorch_model.bin"""						)


if __name__ == "__main__":
	UpperCAmelCase								=   Parser().parse_args("""plan""")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)

