
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 		UpperCamelCase    (					_UpperCAmelCase     :      Union[str, Any]   , _UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      Union[str, Any]   , _UpperCAmelCase     :      Optional[Any]   , _UpperCAmelCase     :      Tuple   , _UpperCAmelCase     :      Optional[int]     )      -> str:






						'''simple docstring'''

						_lowercase					:  Optional[Any]    			=      states.reshape(1   , -1   , model.config.state_dim     )
						_lowercase					:  Optional[Any]    			=      actions.reshape(1   , -1   , model.config.act_dim     )
						_lowercase					:  str    			=      returns_to_go.reshape(1   , -1   , 1     )
						_lowercase					:  Any    			=      timesteps.reshape(1   , -1     )

						if model.config.max_length is not None:
												_lowercase					:  str    			=      states[:, -model.config.max_length :]
												_lowercase					:  int    			=      actions[:, -model.config.max_length :]
												_lowercase					:  Dict    			=      returns_to_go[:, -model.config.max_length :]
												_lowercase					:  str    			=      timesteps[:, -model.config.max_length :]

												# pad all tokens to sequence length
												_lowercase					:  Dict    			=      torch.cat(
												    [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
												_lowercase					:  str    			=      attention_mask.to(dtype=torch.long   , device=states.device     ).reshape(1   , -1     )
												_lowercase					:  Union[str, Any]    			=      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     )
												_lowercase					:  Any    			=      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     )
												_lowercase					:  Optional[int]    			=      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     )
												_lowercase					:  Tuple    			=      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:
												_lowercase					:  int    			=      None

						_lowercase					:  Optional[Any]    			=      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_	:    Optional[Any]  					=       gym.make("""Hopper-v3""")
UpperCamelCase_	:    List[Any]  					=       env.observation_space.shape[0]
UpperCamelCase_	:    Optional[int]  					=       env.action_space.shape[0]
UpperCamelCase_	:    Dict  					=       1000
UpperCamelCase_	:    Union[str, Any]  					=       """cuda"""
UpperCamelCase_	:    List[str]  					=       1000.0  # normalization for rewards/returns
UpperCamelCase_	:    str  					=       3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase_	:    Optional[Any]  					=       np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
UpperCamelCase_	:    Optional[int]  					=       np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
UpperCamelCase_	:    Dict  					=       torch.from_numpy(state_mean).to(device=device)
UpperCamelCase_	:    int  					=       torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
UpperCamelCase_	:    Tuple  					=       DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
UpperCamelCase_	:    List[Any]  					=       model.to(device)
model.eval()

for ep in range(10):
							UpperCamelCase_	:    List[str]  					=       0, 0
							UpperCamelCase_	:    Optional[Any]  					=       env.reset()
							UpperCamelCase_	:    int  					=       torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
							UpperCamelCase_	:    Any  					=       torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
							UpperCamelCase_	:    Tuple  					=       torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
							UpperCamelCase_	:    Union[str, Any]  					=       torch.zeros(0, device=device, dtype=torch.floataa)

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

														UpperCamelCase_	:    Any  					=       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_	:    List[Any]  					=       action
														UpperCamelCase_	:    Optional[int]  					=       action.detach().cpu().numpy()

														UpperCamelCase_	:    Tuple  					=       env.step(action)

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

														UpperCamelCase_	:    Optional[int]  					=       target_return[0, -1] - (reward / scale)
														UpperCamelCase_	:    Any  					=       torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
														UpperCamelCase_	:    str  					=       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 os



def 		UpperCamelCase    (					)      -> Optional[int]:






						'''simple docstring'''

						_lowercase					:  Tuple    			=      0
						_lowercase					:  List[Any]    			=      0
						with open(os.path.dirname(_UpperCAmelCase     ) + "/p022_names.txt"     ) as file:
												_lowercase					:  Any    			=      str(file.readlines()[0]     )
												_lowercase					:  int    			=      name.replace("\""   , ""     ).split(","     )

						name.sort()
						for i in range(len(_UpperCAmelCase     )     ):
												for j in name[i]:
																		temp_sum += ord(_UpperCAmelCase     ) - ord("A"     ) + 1
												total_sum += (i + 1) * temp_sum
												_lowercase					:  Tuple    			=      0
						return total_sum


if __name__ == "__main__":
							print(solution())




