




'''simple docstring'''

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    snake_case__     (  a       ,      a       ,      a       ,      a       ,      a       ,      a	)			->       List[Any]:





			'''simple docstring'''






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

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

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

			snake_case__    ,			snake_case__    ,			snake_case__          =						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

a__         =     gym.make('''Hopper-v3''')
a__         =     env.observation_space.shape[0]
a__         =     env.action_space.shape[0]
a__         =     1_000
a__         =     '''cuda'''
a__         =     1_000.0  # normalization for rewards/returns
a__         =     3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a__         =     np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
a__         =     np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
a__         =     torch.from_numpy(state_mean).to(device=device)
a__         =     torch.from_numpy(state_std).to(device=device)

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

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

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

				a__         =     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),
				)
				a__         =     action
				a__         =     action.detach().cpu().numpy()

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

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

				a__         =     target_return[0, -1] - (reward / scale)
				a__         =     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
				a__         =     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

from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available


@dataclass
class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
					from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
					from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
