






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	:		List[Any]		,    _lowerCamelCase	:		str		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Dict		,    _lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		List[Any]			)				->    Union[str, Any]:
  # 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

_SCREAMING_SNAKE_CASE :    List[Any]    =						gym.make('''Hopper-v3''')
_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						env.observation_space.shape[0]
_SCREAMING_SNAKE_CASE :    Any    =						env.action_space.shape[0]
_SCREAMING_SNAKE_CASE :    Optional[Any]    =						1000
_SCREAMING_SNAKE_CASE :    str    =						'''cuda'''
_SCREAMING_SNAKE_CASE :    str    =						1000.0  # normalization for rewards/returns
_SCREAMING_SNAKE_CASE :    Tuple    =						3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_SCREAMING_SNAKE_CASE :    List[Any]    =						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,
    ]
)
_SCREAMING_SNAKE_CASE :    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,
    ]
)
_SCREAMING_SNAKE_CASE :    Optional[Any]    =						torch.from_numpy(state_mean).to(device=device)
_SCREAMING_SNAKE_CASE :    Dict    =						torch.from_numpy(state_std).to(device=device)

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

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

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

    _SCREAMING_SNAKE_CASE :    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),
    )
    _SCREAMING_SNAKE_CASE :    Tuple    =						action
    _SCREAMING_SNAKE_CASE :    Tuple    =						action.detach().cpu().numpy()

    _SCREAMING_SNAKE_CASE :    Any    =						env.step(action)

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

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












"""simple docstring"""





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union






class 					a						( enum.Enum		):
    SCREAMING_SNAKE_CASE						:					str						=			"""timm"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			"""transformers"""







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[Iterable[str]]		,    _lowerCamelCase	:		Optional[Iterable[int]]		,    _lowerCamelCase	:		Optional[Iterable[str]]			)				->    List[Any]:
  if stage_names is None:
    raise ValueError('Stage_names must be set for transformers backbones'			)

  if out_features is not None:
    if not isinstance(_lowerCamelCase		,    (list,)			):
      raise ValueError(F'''out_features must be a list {type(_lowerCamelCase			)}'''			)
    if any(feat not in stage_names for feat in out_features			):
      raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''			)

  if out_indices is not None:
    if not isinstance(_lowerCamelCase		,    (list, tuple)			):
      raise ValueError(F'''out_indices must be a list or tuple, got {type(_lowerCamelCase			)}'''			)
    if any(idx >= len(_lowerCamelCase			) for idx in out_indices			):
      raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'			)

  if out_features is not None and out_indices is not None:
    if len(_lowerCamelCase			) != len(_lowerCamelCase			):
      raise ValueError('out_features and out_indices should have the same length if both are set'			)
    if out_features != [stage_names[idx] for idx in out_indices]:
      raise ValueError('out_features and out_indices should correspond to the same stages if both are set'			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Union[str, Any]:
  if out_indices is None and out_features is None:
    lowerCamelCase_   					=						[len(_lowerCamelCase			) - 1]
    lowerCamelCase_   					=						[stage_names[-1]]
  elif out_indices is None and out_features is not None:
    lowerCamelCase_   					=						[stage_names.index(_lowerCamelCase			) for layer in out_features]
  elif out_features is None and out_indices is not None:
    lowerCamelCase_   					=						[stage_names[idx] for idx in out_indices]
  return out_features, out_indices







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Tuple[List[str], List[int]]:
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  lowerCamelCase_					,     lowerCamelCase_   					=						_align_output_features_output_indices(
      out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  # Verify that the aligned out_features and out_indices are valid
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  return output_features, output_indices






class 					a						:
    SCREAMING_SNAKE_CASE						:					Optional[BackboneType]						=			None






    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		None:
      if getattr(self	,						'_backbone'	,						__SCREAMING_SNAKE_CASE ) is None:
        raise ValueError('self._backbone must be set before calling _init_timm_backbone' )

      # These will diagree with the defaults for the transformers models e.g. for resnet50
      # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
      # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
      lowerCamelCase_   					=						[stage['module'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						[stage['num_chs'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						self._backbone.feature_info.out_indices
      lowerCamelCase_   					=						self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )
      lowerCamelCase_					,     lowerCamelCase_   					=						out_features, out_indices






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Any )					->		None:
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'stage_names' )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_features'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_indices'	,						__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						stage_names
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=__SCREAMING_SNAKE_CASE )
      # Number of channels for each stage. This is set in the transformer backbone model init
      lowerCamelCase_   					=						None






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict )					->		None:
      lowerCamelCase_   					=						config

      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'use_timm_backbone'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

      if self.backbone_type == BackboneType.TIMM:
        self._init_timm_backbone(__SCREAMING_SNAKE_CASE )
      elif self.backbone_type == BackboneType.TRANSFORMERS:
        self._init_transformers_backbone(__SCREAMING_SNAKE_CASE )
      else:
        raise ValueError(F'''backbone_type {self.backbone_type} not supported.''' )






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		Any:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[Any]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							List[Any] )					->		Dict:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Tuple )					->		Tuple:
      # the current backbones will output the number of channels for each stage
      # even if that stage is not in the out_features list.
      return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names )}






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		List[Any]:
      return [self.out_feature_channels[name] for name in self.out_features]






    def        UpperCamelCase	(	self					:							Optional[int]	,						*__SCREAMING_SNAKE_CASE					:							Any	,						**__SCREAMING_SNAKE_CASE					:							str )					->		Tuple:
      lowerCamelCase_   					=						dict(inspect.signature(self.forward ).parameters )
      lowerCamelCase_   					=						{k: v for k, v in kwargs.items() if k in signature}
      return self(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						)					->		int:
      raise NotImplementedError('This method should be implemented by the derived class.' )






    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		List[str]:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output






class 					a						:






    @property
    def        UpperCamelCase	(	self					:							int )					->		Optional[Any]:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		Tuple:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		str:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    def        UpperCamelCase	(	self					:							int )					->		str:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output




