

import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput






@dataclass
class    __A				(							lowerCAmelCase ):
    lowerCAmelCase_							:		float
    lowerCAmelCase_							:		Optional[float]						=							None
    lowerCAmelCase_							:		Optional[float]						=							None






class    __A				(							unittest.TestCase ):






    def 	lowercase__ (	self			:    Optional[Any]    ):
        lowerCAmelCase				:			str    			=      ModelOutputTest(a=30    )
        self.assertEqual(x.a    ,				30    )
        self.assertIsNone(x.b    )
        self.assertIsNone(x.c    )
        with self.assertRaises(UpperCAmelCase_    ):
            lowerCAmelCase				:			Optional[Any]    			=      x.d






    def 	lowercase__ (	self			:    Union[str, Any]    ):
        lowerCAmelCase				:			Any    			=      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)    )

        lowerCAmelCase				:			List[str]    			=      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 	lowercase__ (	self			:    Dict    ):
        lowerCAmelCase				:			Dict    			=      ModelOutputTest(a=30    ,				b=10    )
        self.assertEqual(x['a']    ,				30    )
        self.assertEqual(x['b']    ,				10    )
        with self.assertRaises(UpperCAmelCase_    ):
            lowerCAmelCase				:			str    			=      x['c']

        lowerCAmelCase				:			str    			=      ModelOutputTest(a=30    ,				c=10    )
        self.assertEqual(x['a']    ,				30    )
        self.assertEqual(x['c']    ,				10    )
        with self.assertRaises(UpperCAmelCase_    ):
            lowerCAmelCase				:			List[str]    			=      x['b']






    def 	lowercase__ (	self			:    Optional[Any]    ):
        lowerCAmelCase				:			Union[str, Any]    			=      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(UpperCAmelCase_    )    ,				['a']    )

        lowerCAmelCase				:			Dict    			=      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(UpperCAmelCase_    )    ,				['a', 'b']    )

        lowerCAmelCase				:			List[str]    			=      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(UpperCAmelCase_    )    ,				['a', 'c']    )

        with self.assertRaises(UpperCAmelCase_    ):
            lowerCAmelCase				:			Any    			=      x.update({'d': 20}    )
        with self.assertRaises(UpperCAmelCase_    ):
            del x["a"]
        with self.assertRaises(UpperCAmelCase_    ):
            lowerCAmelCase				:			Dict    			=      x.pop('a'    )
        with self.assertRaises(UpperCAmelCase_    ):
            lowerCAmelCase				:			Optional[Any]    			=      x.setdefault('d'    ,				32    )






    def 	lowercase__ (	self			:    List[Any]    ):
        lowerCAmelCase				:			Union[str, Any]    			=      ModelOutputTest(a=30    )
        lowerCAmelCase				:			Dict    			=      10
        self.assertEqual(x.a    ,				10    )
        self.assertEqual(x['a']    ,				10    )






    def 	lowercase__ (	self			:    Dict    ):
        lowerCAmelCase				:			str    			=      ModelOutputTest(a=30    )
        lowerCAmelCase				:			Optional[int]    			=      10
        self.assertEqual(x.a    ,				10    )
        self.assertEqual(x['a']    ,				10    )






    def 	lowercase__ (	self			:    List[Any]    ):
        lowerCAmelCase				:			int    			=      ModelOutputTest({'a': 30, 'b': 10}    )
        self.assertEqual(list(x.keys()    )    ,				['a', 'b']    )
        self.assertEqual(x.a    ,				30    )
        self.assertEqual(x.b    ,				10    )






    def 	lowercase__ (	self			:    int    ):
        lowerCAmelCase				:			int    			=      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(UpperCAmelCase_    ):
            lowerCAmelCase				:			List[str]    			=      ModelOutputTest([('a', 30), (10, 10)]    )

        lowerCAmelCase				:			Dict    			=      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 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:



    '''simple docstring'''






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

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

        # pad all tokens to sequence length
        lowerCAmelCase				:			List[str]    			=      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				:			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				:			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				:			Union[str, 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				:			Tuple    			=      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				:			Optional[Any]    			=      None

    lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      model(
        states=_UpperCAmelCase,      actions=_UpperCAmelCase,      rewards=_UpperCAmelCase,      returns_to_go=_UpperCAmelCase,      timesteps=_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      return_dict=_UpperCAmelCase,      )

    return action_preds[0, -1]


# build the environment

__A   :     Dict      =			gym.make('''Hopper-v3''')
__A   :     Any      =			env.observation_space.shape[0]
__A   :     Optional[Any]      =			env.action_space.shape[0]
__A   :     Any      =			1000
__A   :     Tuple      =			'''cuda'''
__A   :     Optional[int]      =			10_00.0  # normalization for rewards/returns
__A   :     Optional[Any]      =			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__A   :     str      =			np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
__A   :     Dict      =			np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
__A   :     int      =			torch.from_numpy(state_mean).to(device=device)
__A   :     Tuple      =			torch.from_numpy(state_std).to(device=device)

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

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

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

  __A   :     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),
  )
  __A   :     List[str]      =			action
  __A   :     List[str]      =			action.detach().cpu().numpy()

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

  __A   :     Union[str, Any]      =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  __A   :     Any      =			torch.cat([states, cur_state], dim=0)
  __A   :     Dict      =			reward

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





