





"""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 SCREAMING_SNAKE_CASE_   (       snake_case   :					int						,  snake_case   :					str						,  snake_case   :					Optional[Any]						,  snake_case   :					List[str]						,  snake_case   :					Dict						,  snake_case   :					Any		)->		List[str]:
       # we don't care about the past rewards in this model

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

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

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

       _lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase    	=					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

A_     :      str								=gym.make("""Hopper-v3""")
A_     :      str								=env.observation_space.shape[0]
A_     :      Optional[Any]								=env.action_space.shape[0]
A_     :      List[Any]								=1_0_0_0
A_     :      Union[str, Any]								="""cuda"""
A_     :      str								=1_0_0_0.0  # normalization for rewards/returns
A_     :      List[Any]								=3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
A_     :      str								=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,
    ]
)
A_     :      List[str]								=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,
    ]
)
A_     :      Dict								=torch.from_numpy(state_mean).to(device=device)
A_     :      Any								=torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
      A_     :      List[str]								=0, 0
      A_     :      Optional[Any]								=env.reset()
      A_     :      Union[str, Any]								=torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
      A_     :      Optional[Any]								=torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
      A_     :      Optional[int]								=torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
      A_     :      Any								=torch.zeros(0, device=device, dtype=torch.floataa)

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

            A_     :      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),
            )
            A_     :      int								=action
            A_     :      List[Any]								=action.detach().cpu().numpy()

            A_     :      Optional[int]								=env.step(action)

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

            A_     :      str								=target_return[0, -1] - (reward / scale)
            A_     :      Tuple								=torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            A_     :      Any								=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 typing import Any

import numpy as np
def 							lowerCAmelCase(	__lowerCamelCase						):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(__lowerCamelCase      ,				matrix.T						) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					__a					,      __a							=   np.linalg.eigh(__lowerCamelCase						)

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0						)						)
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a							=   np.random.randn(__lowerCamelCase      ,				__lowerCamelCase						)
					__a							=   np.dot(__lowerCamelCase      ,				random_matrix.T						)
					assert _is_matrix_spd(__lowerCamelCase						)
					return spd_matrix
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase = 1000      ,				__lowerCamelCase = 1e-8      ,				):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[0]
					assert _is_matrix_spd(__lowerCamelCase						)

					# Initialize solution guess, residual, search direction.
					__a							=   np.zeros((np.shape(__lowerCamelCase						)[0], 1)						)
					__a							=   np.copy(__lowerCamelCase						)
					__a							=   np.copy(__lowerCamelCase						)

					# Set initial errors in solution guess and residual.
					__a							=   1e9
					__a							=   1e9
					__a							=   1e9

					# Set iteration counter to threshold number of iterations.
					__a							=   0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

										# The main algorithm.

										# Update search direction magnitude.
										__a							=   np.dot(ra.T      ,				__lowerCamelCase						) / np.dot(pa.T      ,				__lowerCamelCase						)
										# Update solution guess.
										__a							=   xa + alpha * pa
										# Calculate new residual.
										__a							=   ra - alpha * w
										# Calculate new Krylov subspace scale.
										__a							=   np.dot(r.T      ,				__lowerCamelCase						) / np.dot(ra.T      ,				__lowerCamelCase						)
										# Calculate new A conjuage search direction.
										__a							=   r + beta * pa

										# Calculate errors.
										__a							=   np.linalg.norm(r - ra						)
										__a							=   np.linalg.norm(x - xa						)
										__a							=   np.maximum(__lowerCamelCase      ,				__lowerCamelCase						)

										# Update variables.
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x
def 							lowerCAmelCase(	):
					__a							=   3
					__a							=   _create_spd_matrix(__lowerCamelCase						)
					__a							=   np.random.randn(__lowerCamelCase      ,				1						)
					__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

					# Numpy solution.
					__a							=   np.linalg.solve(__lowerCamelCase      ,				__lowerCamelCase						)

					# Our implementation.
					__a							=   conjugate_gradient(__lowerCamelCase      ,				__lowerCamelCase						)

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true						) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true						) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()




