






"""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      :  Tuple		, __snake_case      :  Dict		, __snake_case      :  List[Any]		, __snake_case      :  int		, __snake_case      :  Tuple		, __snake_case      :  Dict   ):





  '''simple docstring'''





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

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

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

  lowercase   ,					lowercase   ,					lowercase							   =	model(
      states=__snake_case		, actions=__snake_case		, rewards=__snake_case		, returns_to_go=__snake_case		, timesteps=__snake_case		, attention_mask=__snake_case		, return_dict=__snake_case		, )

  return action_preds[0, -1]


# build the environment

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

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

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

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

        _UpperCamelCase		:  str						=		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		:  Any						=		action
        _UpperCamelCase		:  int						=		action.detach().cpu().numpy()

        _UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase,		_UpperCamelCase		:  Tuple						=		env.step(action)

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

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase		:  str						=		{
    'configuration_wav2vec2_conformer': [
        'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Wav2Vec2ConformerConfig',
    ],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  str						=		[
        'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Wav2Vec2ConformerForAudioFrameClassification',
        'Wav2Vec2ConformerForCTC',
        'Wav2Vec2ConformerForPreTraining',
        'Wav2Vec2ConformerForSequenceClassification',
        'Wav2Vec2ConformerForXVector',
        'Wav2Vec2ConformerModel',
        'Wav2Vec2ConformerPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_wavaveca_conformer import (
        WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        WavaVecaConformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_wavaveca_conformer import (
            WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavaVecaConformerForAudioFrameClassification,
            WavaVecaConformerForCTC,
            WavaVecaConformerForPreTraining,
            WavaVecaConformerForSequenceClassification,
            WavaVecaConformerForXVector,
            WavaVecaConformerModel,
            WavaVecaConformerPreTrainedModel,
        )

else:
    import sys

    _UpperCamelCase		:  Union[str, Any]						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


