





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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     # we don't care about the past rewards in this model

     _SCREAMING_SNAKE_CASE				:		List[str]  			=							states.reshape(1,				-1,				model.config.state_dim		)
     _SCREAMING_SNAKE_CASE				:		int  			=							actions.reshape(1,				-1,				model.config.act_dim		)
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							returns_to_go.reshape(1,				-1,				1		)
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							timesteps.reshape(1,				-1		)

     if model.config.max_length is not None:
          _SCREAMING_SNAKE_CASE				:		List[Any]  			=							states[:, -model.config.max_length :]
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							actions[:, -model.config.max_length :]
          _SCREAMING_SNAKE_CASE				:		str  			=							returns_to_go[:, -model.config.max_length :]
          _SCREAMING_SNAKE_CASE				:		Tuple  			=							timesteps[:, -model.config.max_length :]

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

     _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							model(
         states=__lowerCamelCase,				actions=__lowerCamelCase,				rewards=__lowerCamelCase,				returns_to_go=__lowerCamelCase,				timesteps=__lowerCamelCase,				attention_mask=__lowerCamelCase,				return_dict=__lowerCamelCase,				)

     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__          =1000
UpperCamelCase__          ='cuda'
UpperCamelCase__          =1000.0  # normalization for rewards/returns
UpperCamelCase__          =3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase__          =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__          =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__          =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



from __future__ import annotations

from collections.abc import Callable







def   lowerCamelCase(      a__		,a__		,a__		,a__ = 100		,):
				_SCREAMING_SNAKE_CASE	     =x_start
				_SCREAMING_SNAKE_CASE	     =fnc(a__)
				_SCREAMING_SNAKE_CASE	     =0.0

				for _ in range(a__):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_SCREAMING_SNAKE_CASE	     =(x_end - x_start) / steps + xa
								_SCREAMING_SNAKE_CASE	     =fnc(a__)
								area += abs(fxa + fxa) * (xa - xa) / 2

								# Increment step
								_SCREAMING_SNAKE_CASE	     =xa
								_SCREAMING_SNAKE_CASE	     =fxa
				return area


if __name__ == "__main__":







							def   lowerCamelCase(      a__):
											return x**3

							print('''f(x) = x^3''')
							print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
							snake_case_  :	Any							  =							10
							while i <= 10_00_00:
														snake_case_  :	Tuple							  =							trapezoidal_area(f, -5, 5, i)
														print(f"""with {i} steps: {area}""")
														i *= 10