


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_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  lowerCamelCase			: Tuple   =    states.reshape(1				,						-1				,						model.config.state_dim     )
  lowerCamelCase			: str   =    actions.reshape(1				,						-1				,						model.config.act_dim     )
  lowerCamelCase			: int   =    returns_to_go.reshape(1				,						-1				,						1     )
  lowerCamelCase			: Dict   =    timesteps.reshape(1				,						-1     )

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

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

  lowerCamelCase			: Union[str, Any]   =    model(
      states=SCREAMING_SNAKE_CASE_				,						actions=SCREAMING_SNAKE_CASE_				,						rewards=SCREAMING_SNAKE_CASE_				,						returns_to_go=SCREAMING_SNAKE_CASE_				,						timesteps=SCREAMING_SNAKE_CASE_				,						attention_mask=SCREAMING_SNAKE_CASE_				,						return_dict=SCREAMING_SNAKE_CASE_				,						)

  return action_preds[0, -1]


# build the environment

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

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

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

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

      _snake_case					=					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),
      )
      _snake_case					=					action
      _snake_case					=					action.detach().cpu().numpy()

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

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

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







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
   from PIL import Image
class      UpperCAmelCase_ ( unittest.TestCase		,			UpperCamelCase   ):

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    load_tool("image-segmentation"			)
     self.tool.setup()
     lowerCamelCase			: Optional[Any]   =    load_tool("image-segmentation"						,    remote=__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: int   =    self.remote_tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Optional[Any]   =    self.tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.remote_tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)



