



"""simple docstring"""







import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


__snake_case	:				Dict  					=			threading.Lock()
__snake_case	:				Optional[logging.Handler]  					=			None

__snake_case	:				Optional[int]  					=			{
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

__snake_case	:				Tuple  					=			logging.WARNING

__snake_case	:				str  					=			True



def   a_	(			):
     A__				=   os.getenv('''DIFFUSERS_VERBOSITY'''		,							__a      )
     if env_level_str:
          if env_level_str in log_levels:
               return log_levels[env_level_str]
          else:
               logging.getLogger().warning(
                   f'''Unknown option DIFFUSERS_VERBOSITY={env_level_str}, '''
                   f'''has to be one of: { ', '.join(log_levels.keys()      ) }'''      )
     return _default_log_level



def   a_	(			):
     return __name__.split('''.'''      )[0]



def   a_	(			):
     return logging.getLogger(_get_library_name()      )



def   a_	(			):
     global _default_handler

     with _lock:
          if _default_handler:
               # This library has already configured the library root logger.
               return
          A__				=   logging.StreamHandler()  # Set sys.stderr as stream.
          A__				=   sys.stderr.flush

          # Apply our default configuration to the library root logger.
          A__				=   _get_library_root_logger()
          library_root_logger.addHandler(_default_handler      )
          library_root_logger.setLevel(_get_default_logging_level()      )
          A__				=   False



def   a_	(			):
     global _default_handler

     with _lock:
          if not _default_handler:
               return

          A__				=   _get_library_root_logger()
          library_root_logger.removeHandler(_default_handler      )
          library_root_logger.setLevel(logging.NOTSET      )
          A__				=   None



def   a_	(			):
     return log_levels



def   a_	(			__a = None      ):

     if name is None:
          A__				=   _get_library_name()

     _configure_library_root_logger()
     return logging.getLogger(__a      )



def   a_	(			):
     _configure_library_root_logger()
     return _get_library_root_logger().getEffectiveLevel()



def   a_	(			__a      ):
     _configure_library_root_logger()
     _get_library_root_logger().setLevel(__a      )



def   a_	(			):
     return set_verbosity(__a      )



def   a_	(			):
     return set_verbosity(__a      )



def   a_	(			):
     return set_verbosity(__a      )



def   a_	(			):
     return set_verbosity(__a      )



def   a_	(			):
     _configure_library_root_logger()

     assert _default_handler is not None
     _get_library_root_logger().removeHandler(_default_handler      )



def   a_	(			):
     _configure_library_root_logger()

     assert _default_handler is not None
     _get_library_root_logger().addHandler(_default_handler      )



def   a_	(			__a      ):
     _configure_library_root_logger()

     assert handler is not None
     _get_library_root_logger().addHandler(__a      )



def   a_	(			__a      ):
     _configure_library_root_logger()

     assert handler is not None and handler not in _get_library_root_logger().handlers
     _get_library_root_logger().removeHandler(__a      )



def   a_	(			):
     _configure_library_root_logger()
     A__				=   False



def   a_	(			):
     _configure_library_root_logger()
     A__				=   True



def   a_	(			):
     A__				=   _get_library_root_logger().handlers

     for handler in handlers:
          A__				=   logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'''      )
          handler.setFormatter(__a      )



def   a_	(			):
     A__				=   _get_library_root_logger().handlers

     for handler in handlers:
          handler.setFormatter(__a      )



def   a_	(			self		,							*__a		,							**__a      ):
     A__				=   os.getenv('''DIFFUSERS_NO_ADVISORY_WARNINGS'''		,							__a      )
     if no_advisory_warnings:
          return
     self.warning(*__a		,							**__a      )


__snake_case	:				Union[str, Any]  					=			warning_advice

class       UpperCamelCase  :





     """simple docstring"""






     def __init__(					self :							Any	,      *_lowerCamelCase :							int	,      **_lowerCamelCase :							str ):  # pylint: disable=unused-argument
          A__				=   args[0] if args else None





     def __iter__(					self :							Optional[Any] ):
          return iter(self._iterator )





     def __getattr__(					self :							int	,      _lowerCamelCase :							List[Any] ):

          def empty_fn(*_lowerCamelCase :							Union[str, Any]	,      **_lowerCamelCase :							Union[str, Any] ):  # pylint: disable=unused-argument
               return

          return empty_fn





     def __enter__(					self :							int ):
          return self





     def __exit__(					self :							Optional[Any]	,      _lowerCamelCase :							str	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							List[str] ):
          return


class       UpperCamelCase  :





     """simple docstring"""






     def __call__(					self :							List[str]	,      *_lowerCamelCase :							Tuple	,      **_lowerCamelCase :							List[str] ):
          if _tqdm_active:
               return tqdm_lib.tqdm(*_lowerCamelCase	,      **_lowerCamelCase )
          else:
               return EmptyTqdm(*_lowerCamelCase	,      **_lowerCamelCase )





     def 						A__  (					self :							Dict	,      *_lowerCamelCase :							List[str]	,      **_lowerCamelCase :							List[Any] ):
          A__				=   None
          if _tqdm_active:
               return tqdm_lib.tqdm.set_lock(*_lowerCamelCase	,      **_lowerCamelCase )





     def 						A__  (					self :							Tuple ):
          if _tqdm_active:
               return tqdm_lib.tqdm.get_lock()


__snake_case	:				Any  					=			_tqdm_cls()



def   a_	(			):
     global _tqdm_active
     return bool(_tqdm_active      )



def   a_	(			):
     global _tqdm_active
     A__				=   True



def   a_	(			):
     global _tqdm_active
     A__				=   False








"""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   a_	(			__a		,							__a		,							__a		,							__a		,							__a		,							__a      ):
     # we don't care about the past rewards in this model

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

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

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

     A__	,     A__	,     A__				=   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

__snake_case	:				List[str]  					=			gym.make('Hopper-v3')
__snake_case	:				Optional[Any]  					=			env.observation_space.shape[0]
__snake_case	:				List[str]  					=			env.action_space.shape[0]
__snake_case	:				Optional[int]  					=			1_000
__snake_case	:				Any  					=			'cuda'
__snake_case	:				str  					=			1_000.0  # normalization for rewards/returns
__snake_case	:				str  					=			3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case	:				int  					=			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,
    ]
)
__snake_case	:				str  					=			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,
    ]
)
__snake_case	:				str  					=			torch.from_numpy(state_mean).to(device=device)
__snake_case	:				str  					=			torch.from_numpy(state_std).to(device=device)

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

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

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

  __snake_case	:				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),
  )
  __snake_case	:				List[str]  					=			action
  __snake_case	:				int  					=			action.detach().cpu().numpy()

  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case	:				Union[str, Any]  					=			env.step(action)

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

  __snake_case	:				Union[str, Any]  					=			target_return[0, -1] - (reward / scale)
  __snake_case	:				Dict  					=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __snake_case	:				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



