



'''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_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Union[str, Any]:
  # we don't care about the past rewards in this model

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

  if model.config.max_length is not None:
    __lowerCamelCase	:	str   	=			states[:, -model.config.max_length :]
    __lowerCamelCase	:	Optional[int]   	=			actions[:, -model.config.max_length :]
    __lowerCamelCase	:	List[str]   	=			returns_to_go[:, -model.config.max_length :]
    __lowerCamelCase	:	Union[str, Any]   	=			timesteps[:, -model.config.max_length :]

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

  __lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	:	Union[str, Any]   	=			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							    =						gym.make('Hopper-v3')
_UpperCamelCase							    =						env.observation_space.shape[0]
_UpperCamelCase							    =						env.action_space.shape[0]
_UpperCamelCase							    =						1000
_UpperCamelCase							    =						'cuda'
_UpperCamelCase							    =						1_0_0_0.0  # normalization for rewards/returns
_UpperCamelCase							    =						3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase							    =						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							    =						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							    =						torch.from_numpy(state_mean).to(device=device)
_UpperCamelCase							    =						torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
   _UpperCamelCase    ,    _UpperCamelCase							    =						0, 0
   _UpperCamelCase							    =						env.reset()
   _UpperCamelCase							    =						torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   _UpperCamelCase							    =						torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   _UpperCamelCase							    =						torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   _UpperCamelCase							    =						torch.zeros(0, device=device, dtype=torch.floataa)

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

      _UpperCamelCase							    =						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							    =						action
      _UpperCamelCase							    =						action.detach().cpu().numpy()

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

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

      _UpperCamelCase							    =						target_return[0, -1] - (reward / scale)
      _UpperCamelCase							    =						torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
      _UpperCamelCase							    =						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, is_vision_available


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

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






