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

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

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

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

 __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					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

_UpperCamelCase				:		str       			=		gym.make('Hopper-v3')
_UpperCamelCase				:		List[Any]       			=		env.observation_space.shape[0]
_UpperCamelCase				:		int       			=		env.action_space.shape[0]
_UpperCamelCase				:		str       			=		10_00
_UpperCamelCase				:		Tuple       			=		'cuda'
_UpperCamelCase				:		List[Any]       			=		1000.0  # normalization for rewards/returns
_UpperCamelCase				:		Tuple       			=		36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase				:		List[str]       			=		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,
    ]
)
_UpperCamelCase				:		Optional[Any]       			=		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,
    ]
)
_UpperCamelCase				:		str       			=		torch.from_numpy(state_mean).to(device=device)
_UpperCamelCase				:		List[str]       			=		torch.from_numpy(state_std).to(device=device)

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

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

     _UpperCamelCase				:		List[str]       			=		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				:		List[str]       			=		torch.cat([rewards, torch.zeros(1, device=device)])

          _UpperCamelCase				:		Optional[int]       			=		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				:		Tuple       			=		action.detach().cpu().numpy()

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

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

          _UpperCamelCase				:		Tuple       			=		target_return[0, -1] - (reward / scale)
          _UpperCamelCase				:		int       			=		torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
          _UpperCamelCase				:		Tuple       			=		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				:		int       			=		{
    'configuration_align': [
        'ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AlignConfig',
        'AlignTextConfig',
        'AlignVisionConfig',
    ],
    'processing_align': ['AlignProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[Any]       			=		[
         'ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'AlignModel',
         'AlignPreTrainedModel',
         'AlignTextModel',
         'AlignVisionModel',
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

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



