






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       (    SCREAMING_SNAKE_CASE__  :     List[Any]					,  SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Tuple					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]					,  SCREAMING_SNAKE_CASE__  :     List[str]					,  SCREAMING_SNAKE_CASE__  :     int	):
      # we don't care about the past rewards in this model

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

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

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

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

      return action_preds[0, -1]


# build the environment

__snake_case							 =  gym.make("""Hopper-v3""")
__snake_case							 =  env.observation_space.shape[0]
__snake_case							 =  env.action_space.shape[0]
__snake_case							 =  10_00
__snake_case							 =  """cuda"""
__snake_case							 =  10_00.0  # normalization for rewards/returns
__snake_case							 =  36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case							 =  np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
__snake_case							 =  np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
__snake_case							 =  torch.from_numpy(state_mean).to(device=device)
__snake_case							 =  torch.from_numpy(state_std).to(device=device)

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

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

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

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

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

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

  __snake_case							 =  target_return[0, -1] - (reward / scale)
  __snake_case							 =  torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __snake_case							 =  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 warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

__snake_case							 =  0.2_5 + 0.0j
__snake_case							 =  -0.4 + 0.6j
__snake_case							 =  -0.1 + 0.651j
__snake_case							 =  -2.0
__snake_case							 =  56
__snake_case							 =  2.0
__snake_case							 =  6_66


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return numpy.exp(SCREAMING_SNAKE_CASE__	) + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return z_values * z_values + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:List[Any]     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       x.reshape((nb_pixels, 1)	)
    UpperCamelCase	:Tuple     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       y.reshape((1, nb_pixels)	)
    return x + 1.0J * y


def   _A       (    SCREAMING_SNAKE_CASE__  :     Callable[[Any, numpy.ndarray], numpy.ndarray]					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  SCREAMING_SNAKE_CASE__  :     float | None = None					,  ):
    UpperCamelCase	:Dict     							=       z_a.astype('''complex64'''	)
    for _ in range(SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:int     							=       eval_function(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
        if infinity is not None:
            numpy.nan_to_num(SCREAMING_SNAKE_CASE__					,  copy=SCREAMING_SNAKE_CASE__					,  nan=SCREAMING_SNAKE_CASE__	)
            UpperCamelCase	:Tuple     							=       infinity
    return z_n


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  ):
    UpperCamelCase	:Any     							=       (abs(SCREAMING_SNAKE_CASE__	)).transpose()
    UpperCamelCase	:Any     							=       abs_z_final[::-1, :]
    pyplot.matshow(abs_z_final < escape_radius	)
    pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$'''	)
    pyplot.show()


def   _A       (    ):
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in multiply'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''invalid value encountered in multiply'''					,  )
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in absolute'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in exp'''	)


if __name__ == "__main__":
 __snake_case							 =  prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 __snake_case							 =  24
 __snake_case							 =  2 * abs(c_cauliflower) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

 __snake_case							 =  64
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  1_61
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  12
 __snake_case							 =  1_0_0_0_0.0
 __snake_case							 =  iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0E1_0,
 )
 show_results("""e^z+c""", c_exponential, escape_radius, z_final)





