


"""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 						lowercase				(	_snake_case		: Tuple	,  _snake_case		: Tuple	,  _snake_case		: Optional[Any]	,  _snake_case		: str	,  _snake_case		: int	,  _snake_case		: List[str]	)	->Optional[int]:
	"""simple docstring"""





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

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

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

	__snake_case      ,				__snake_case      ,				__snake_case      :		Optional[int]         =							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

SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							gym.make("""Hopper-v3""")
SCREAMING_SNAKE_CASE					:       List[str]							   =							env.observation_space.shape[0]
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							env.action_space.shape[0]
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							1000
SCREAMING_SNAKE_CASE					:       Tuple							   =							"""cuda"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1_000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							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,
    ]
)
SCREAMING_SNAKE_CASE					:       List[Any]							   =							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,
    ]
)
SCREAMING_SNAKE_CASE					:       List[str]							   =							torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							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					:       Optional[int]							   =							model.to(device)
model.eval()

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

			SCREAMING_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
						SCREAMING_SNAKE_CASE					:       str							   =							torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							torch.cat([rewards, torch.zeros(1, device=device)])

						SCREAMING_SNAKE_CASE					:       Dict							   =							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					:       Optional[int]							   =							action
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							action.detach().cpu().numpy()

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

						SCREAMING_SNAKE_CASE					:       Dict							   =							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					:       List[str]							   =							reward

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












from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_       : List[Any]							=    {
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : str							=    ["""RetriBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[Any]							=    [
          """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RetriBertModel""",
          """RetriBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
      from .tokenization_retribert import RetriBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_retribert_fast import RetriBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_retribert import (
                RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RetriBertModel,
                RetriBertPreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_       : Tuple							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
