



"""simple docstring"""


import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput







@dataclass
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				None
						__UpperCamelCase													=				None







class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[Any]:
												UpperCAmelCase			 =					ModelOutputTest(a=30   )
												self.assertEqual(x.a  ,    30   )
												self.assertIsNone(x.b   )
												self.assertIsNone(x.c   )
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x.d






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Any:
												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    b=10   )
												self.assertEqual(x[0]  ,    30   )
												self.assertEqual(x[1]  ,    10   )
												self.assertEqual(x[:2]  ,    (30, 10)   )
												self.assertEqual(x[:]  ,    (30, 10)   )

												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    c=10   )
												self.assertEqual(x[0]  ,    30   )
												self.assertEqual(x[1]  ,    10   )
												self.assertEqual(x[:2]  ,    (30, 10)   )
												self.assertEqual(x[:]  ,    (30, 10)   )






						def       UpperCAmelCase__	(   self					:Dict   )							->     int:
												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    b=10   )
												self.assertEqual(x['a']  ,    30   )
												self.assertEqual(x['b']  ,    10   )
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x['c']

												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    c=10   )
												self.assertEqual(x['a']  ,    30   )
												self.assertEqual(x['c']  ,    10   )
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x['b']






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Union[str, Any]:
												UpperCAmelCase			 =					ModelOutputTest(a=30   )
												self.assertEqual(list(x.keys()   )  ,    ['a']   )
												self.assertEqual(list(x.values()   )  ,    [30]   )
												self.assertEqual(list(x.items()   )  ,    [('a', 30)]   )
												self.assertEqual(list(lowercase_   )  ,    ['a']   )

												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    b=10   )
												self.assertEqual(list(x.keys()   )  ,    ['a', 'b']   )
												self.assertEqual(list(x.values()   )  ,    [30, 10]   )
												self.assertEqual(list(x.items()   )  ,    [('a', 30), ('b', 10)]   )
												self.assertEqual(list(lowercase_   )  ,    ['a', 'b']   )

												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    c=10   )
												self.assertEqual(list(x.keys()   )  ,    ['a', 'c']   )
												self.assertEqual(list(x.values()   )  ,    [30, 10]   )
												self.assertEqual(list(x.items()   )  ,    [('a', 30), ('c', 10)]   )
												self.assertEqual(list(lowercase_   )  ,    ['a', 'c']   )

												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x.update({'d': 20}   )
												with self.assertRaises(lowercase_   ):
																		del x["a"]
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x.pop('a'   )
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x.setdefault('d'  ,    32   )






						def       UpperCAmelCase__	(   self					:Any   )							->     Union[str, Any]:
												UpperCAmelCase			 =					ModelOutputTest(a=30   )
												UpperCAmelCase			 =					10
												self.assertEqual(x.a  ,    10   )
												self.assertEqual(x['a']  ,    10   )






						def       UpperCAmelCase__	(   self					:Any   )							->     int:
												UpperCAmelCase			 =					ModelOutputTest(a=30   )
												UpperCAmelCase			 =					10
												self.assertEqual(x.a  ,    10   )
												self.assertEqual(x['a']  ,    10   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[str]:
												UpperCAmelCase			 =					ModelOutputTest({'a': 30, 'b': 10}   )
												self.assertEqual(list(x.keys()   )  ,    ['a', 'b']   )
												self.assertEqual(x.a  ,    30   )
												self.assertEqual(x.b  ,    10   )






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Optional[int]:
												UpperCAmelCase			 =					ModelOutputTest([('a', 30), ('b', 10)]   )
												self.assertEqual(list(x.keys()   )  ,    ['a', 'b']   )
												self.assertEqual(x.a  ,    30   )
												self.assertEqual(x.b  ,    10   )

												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					ModelOutputTest([('a', 30), (10, 10)]   )

												UpperCAmelCase			 =					ModelOutputTest(a=(30, 30)   )
												self.assertEqual(list(x.keys()   )  ,    ['a']   )
												self.assertEqual(x.a  ,    (30, 30)   )






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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





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

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

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

      UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			int     	=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

__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
