






"""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_ :Any      ,     A_ :Union[str, Any]      ,     A_ :int      ,     A_ :Optional[int]      ,     A_ :int      ,     A_ :str ):







  '''simple docstring'''

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

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

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

  a,	a,	a	:     Optional[int]							=  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							:       Tuple       					=		gym.make('Hopper-v3')
_UpperCamelCase							:       int       					=		env.observation_space.shape[0]
_UpperCamelCase							:       Dict       					=		env.action_space.shape[0]
_UpperCamelCase							:       Dict       					=		1000
_UpperCamelCase							:       List[str]       					=		'cuda'
_UpperCamelCase							:       int       					=		1000.0  # normalization for rewards/returns
_UpperCamelCase							:       List[Any]       					=		3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase							:       Tuple       					=		np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_UpperCamelCase							:       List[Any]       					=		np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_UpperCamelCase							:       Optional[int]       					=		torch.from_numpy(state_mean).to(device=device)
_UpperCamelCase							:       Dict       					=		torch.from_numpy(state_std).to(device=device)

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

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

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

      _UpperCamelCase							:       Optional[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							:       Dict       					=		action
      _UpperCamelCase							:       Optional[Any]       					=		action.detach().cpu().numpy()

      _UpperCamelCase					,	_UpperCamelCase					,	_UpperCamelCase					,	_UpperCamelCase							:       List[str]       					=		env.step(action)

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

      _UpperCamelCase							:       List[Any]       					=		target_return[0, -1] - (reward / scale)
      _UpperCamelCase							:       List[str]       					=		torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
      _UpperCamelCase							:       List[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 json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info







def 							A     (						__UpperCAmelCase      )			->			Dict:



						'''simple docstring'''






						class 				a_    :

												def __init__(       self      :Optional[int]  ,		_lowercase      :Any)						->   None:
																		assert isinstance(_lowercase  ,		(bytes, str))
																		UpperCAmelCase_					=	content




												def       __a			(       self      :List[str])						->   Tuple:
																		return json.loads(self.content)

						def mock_response(*__UpperCAmelCase				,   **__UpperCAmelCase      ):
												assert args[0] == AUTHENTICATED_USER_ENDPOINT
												assert "Authorization" in kwargs["headers"]
												assert kwargs["headers"]["Authorization"].startswith('''token '''      )
												assert "Accept" in kwargs["headers"]
												return FakeResponse(b'''{"login":"test","id":1}'''      )

						monkeypatch.setattr(__UpperCAmelCase				,   '''get'''				,   __UpperCAmelCase      )
						UpperCAmelCase_					=	fetch_github_info('''token'''      )
						assert result["login"] == "test"
						assert result["id"] == 1





