






"""simple docstring"""



import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
						from comet_ml import OfflineExperiment

if is_tensorboard_available():
						import struct

						import tensorboard.compat.proto.event_pba as event_pba

_snake_case							:   Tuple	  =       logging.getLogger(__name__)




@require_tensorboard
class 							__lowercase(							unittest.TestCase	):






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											lowerCamelCase__					='test_project_with_config'
											with tempfile.TemporaryDirectory() as dirpath:
																lowerCamelCase__					=Accelerator(log_with='tensorboard'     ,    project_dir=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
																accelerator.init_trackers(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
																accelerator.end_training()
																for child in Path(F"{dirpath}/{project_name}").glob('*/**'):
																					lowerCamelCase__					=list(filter(lambda _SCREAMING_SNAKE_CASE: x.is_file()     ,    child.iterdir()))[0]
																self.assertNotEqual(str(_SCREAMING_SNAKE_CASE)     ,    '')






						def 		lowerCAmelCase (       self)      ->	Union[str, Any]:




											"""simple docstring"""





											lowerCamelCase__					='test_project_with_log'
											with tempfile.TemporaryDirectory() as dirpath:
																lowerCamelCase__					=Accelerator(log_with='tensorboard'     ,    project_dir=_SCREAMING_SNAKE_CASE)
																accelerator.init_trackers(_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																accelerator.log(_SCREAMING_SNAKE_CASE     ,    step=0)
																accelerator.end_training()
																# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																# Names are randomly generated each time
																lowerCamelCase__					=list(filter(lambda _SCREAMING_SNAKE_CASE: x.is_file()     ,    Path(F"{dirpath}/{project_name}").iterdir()))[0]
																self.assertNotEqual(str(_SCREAMING_SNAKE_CASE)     ,    '')






						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					='test_project_with_log'
											with tempfile.TemporaryDirectory() as dirpath:
																lowerCamelCase__					=Accelerator(log_with='tensorboard'     ,    project_dir=_SCREAMING_SNAKE_CASE)
																accelerator.init_trackers(_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					={'tensor': torch.tensor(1)}
																accelerator.log(_SCREAMING_SNAKE_CASE     ,    step=0)
																accelerator.end_training()
																# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																# Names are randomly generated each time
																lowerCamelCase__					=list(filter(lambda _SCREAMING_SNAKE_CASE: x.is_file()     ,    Path(F"{dirpath}/{project_name}").iterdir()))[0]
																# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
																with open(_SCREAMING_SNAKE_CASE     ,    'rb') as f:
																					lowerCamelCase__					=f.read()
																lowerCamelCase__					=False
																while data:
																					lowerCamelCase__					=struct.unpack('Q'     ,    data[:8])

																					lowerCamelCase__					=data[12 : 12 + int(header[0])]  # 8+4
																					lowerCamelCase__					=data[12 + int(header[0]) + 4 :]
																					lowerCamelCase__					=event_pba.Event()

																					event.ParseFromString(_SCREAMING_SNAKE_CASE)
																					if event.HasField('summary'):
																										for value in event.summary.value:
																															if value.simple_value == 1.0 and value.tag == "tensor":
																																				lowerCamelCase__					=True
																self.assertTrue(_SCREAMING_SNAKE_CASE     ,    'Converted tensor was not found in the log file!')






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE     ,    'Logging with `tensorboard` requires a `logging_dir`'):
																lowerCamelCase__					=Accelerator(log_with='tensorboard')
											with tempfile.TemporaryDirectory() as dirpath:
																lowerCamelCase__					=Accelerator(log_with='tensorboard'     ,    project_dir=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											lowerCamelCase__					=ProjectConfiguration(total_limit=30)
											with tempfile.TemporaryDirectory() as dirpath:
																lowerCamelCase__					=Accelerator(log_with='tensorboard'     ,    project_dir=_SCREAMING_SNAKE_CASE     ,    project_config=_SCREAMING_SNAKE_CASE)




@require_wandb
@mock.patch.dict(os.environ    ,				{"""WANDB_MODE""": """offline"""}	)
class 							__lowercase(							_UpperCamelCase    ,				_UpperCamelCase	):






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											super().setUp()
											# wandb let's us override where logs are stored to via the WANDB_DIR env var
											self.add_mocks(mock.patch.dict(os.environ     ,    {'WANDB_DIR': self.tmpdir}))






						@staticmethod
						def 		lowerCAmelCase (       _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = True)      ->	Union[str, Any]:




											"""simple docstring"""





											lowerCamelCase__					=rF"{section} ([\S\s]*?)\n\n"
											if record:
																lowerCamelCase__					=rF"Record: {pattern}"
											lowerCamelCase__					=re.findall(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)[0]
											# A config
											if section == "config" or section == "history":
																lowerCamelCase__					=re.findall(r'"([a-zA-Z0-9_.,]+)'     ,    _SCREAMING_SNAKE_CASE)
																return {key: val for key, val in zip(cleaned_record[0::2]     ,    cleaned_record[1::2])}
											# Everything else
											else:
																return dict(re.findall(r'(\w+): "([^\s]+)"'     ,    _SCREAMING_SNAKE_CASE))






						@skip
						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					='test_project_with_config'
											lowerCamelCase__					=Accelerator(log_with='wandb')
											lowerCamelCase__					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
											lowerCamelCase__					={'wandb': {'tags': ['my_tag']}}
											accelerator.init_trackers(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											lowerCamelCase__					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
											accelerator.log(_SCREAMING_SNAKE_CASE     ,    step=0)
											accelerator.end_training()
											# The latest offline log is stored at wandb/latest-run/*.wandb
											for child in Path(F"{self.tmpdir}/wandb/latest-run").glob('*'):
																if child.is_file() and child.suffix == ".wandb":
																					lowerCamelCase__					=subprocess.check_output(
																					    ['wandb', 'sync', '--view', '--verbose', str(_SCREAMING_SNAKE_CASE)]     ,    env=os.environ.copy()).decode('utf8'     ,    'ignore')
																					break

        # Check HPS through careful parsing and cleaning
											lowerCamelCase__					=self.parse_log(_SCREAMING_SNAKE_CASE     ,    'config')
											self.assertEqual(logged_items['num_iterations']     ,    '12')
											self.assertEqual(logged_items['learning_rate']     ,    '0.01')
											self.assertEqual(logged_items['some_boolean']     ,    'false')
											self.assertEqual(logged_items['some_string']     ,    'some_value')
											self.assertEqual(logged_items['some_string']     ,    'some_value')

											# Run tags
											lowerCamelCase__					=self.parse_log(_SCREAMING_SNAKE_CASE     ,    'run'     ,    _SCREAMING_SNAKE_CASE)
											self.assertEqual(logged_items['tags']     ,    'my_tag')

											# Actual logging
											lowerCamelCase__					=self.parse_log(_SCREAMING_SNAKE_CASE     ,    'history')
											self.assertEqual(logged_items['total_loss']     ,    '0.1')
											self.assertEqual(logged_items['iteration']     ,    '1')
											self.assertEqual(logged_items['my_text']     ,    'some_value')
											self.assertEqual(logged_items['_step']     ,    '0')




def     lowerCamelCase_					(	self	:		Optional[Any]      ,						__A	:		str      ,						__A	:		str      ):
					lowerCamelCase__					=run_name
					lowerCamelCase__					=OfflineExperiment(project_name=__A      ,						offline_directory=__A      )
					logger.info(f"Initialized offline CometML project {self.run_name}"      )
					logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'      )




@require_comet_ml
@mock.patch.object(_UpperCamelCase    ,				"""__init__"""    ,				_UpperCamelCase	)
class 							__lowercase(							unittest.TestCase	):






						@staticmethod
						def 		lowerCAmelCase (       _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = False)      ->	Tuple:




											"""simple docstring"""





											for log in log_list:
																lowerCamelCase__					=json.loads(_SCREAMING_SNAKE_CASE)['payload']
																if is_param and "param" in j.keys():
																					if j["param"]["paramName"] == key:
																										return j["param"]["paramValue"]
																if "log_other" in j.keys():
																					if j["log_other"]["key"] == key:
																										return j["log_other"]["val"]
																if "metric" in j.keys():
																					if j["metric"]["metricName"] == key:
																										return j["metric"]["metricValue"]






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											with tempfile.TemporaryDirectory() as d:
																lowerCamelCase__					=CometMLTracker('test_project_with_config'     ,    _SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=Accelerator(log_with=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
																accelerator.init_trackers(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
																accelerator.end_training()
																lowerCamelCase__					=os.listdir(_SCREAMING_SNAKE_CASE)[0]  # Comet is nice, it's just a zip file here
																# We parse the raw logs
																lowerCamelCase__					=os.path.join(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=zipfile.ZipFile(_SCREAMING_SNAKE_CASE     ,    'r')
																lowerCamelCase__					=archive.open('messages.json').read().decode('utf-8')
											lowerCamelCase__					=log.split('\n')[:-1]
											self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE     ,    'num_iterations'     ,    _SCREAMING_SNAKE_CASE)     ,    12)
											self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE     ,    'learning_rate'     ,    _SCREAMING_SNAKE_CASE)     ,    0.01)
											self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE     ,    'some_boolean'     ,    _SCREAMING_SNAKE_CASE)     ,    _SCREAMING_SNAKE_CASE)
											self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE     ,    'some_string'     ,    _SCREAMING_SNAKE_CASE)     ,    'some_value')






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											with tempfile.TemporaryDirectory() as d:
																lowerCamelCase__					=CometMLTracker('test_project_with_config'     ,    _SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=Accelerator(log_with=_SCREAMING_SNAKE_CASE)
																accelerator.init_trackers(_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																accelerator.log(_SCREAMING_SNAKE_CASE     ,    step=0)
																accelerator.end_training()
																lowerCamelCase__					=os.listdir(_SCREAMING_SNAKE_CASE)[0]  # Comet is nice, it's just a zip file here
																# We parse the raw logs
																lowerCamelCase__					=os.path.join(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=zipfile.ZipFile(_SCREAMING_SNAKE_CASE     ,    'r')
																lowerCamelCase__					=archive.open('messages.json').read().decode('utf-8')
											lowerCamelCase__					=log.split('\n')[:-1]
											self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE     ,    'curr_step'     ,    _SCREAMING_SNAKE_CASE)     ,    0)
											self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE     ,    'total_loss')     ,    0.1)
											self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE     ,    'iteration')     ,    1)
											self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE     ,    'my_text')     ,    'some_value')




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				str=							[
						    """total_loss""",
						    """iteration""",
						    """my_text""",
						    """learning_rate""",
						    """num_iterations""",
						    """some_boolean""",
						    """some_string""",
						]

						lowerCAmelCase_		:				Dict=							"""my_custom_tracker"""
						lowerCAmelCase_		:				Optional[int]=							False






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					=open(F"{dir}/log.csv"     ,    'w+')
											lowerCamelCase__					=csv.DictWriter(self.f     ,    fieldnames=self._col_names)
											self.writer.writeheader()






						@property
						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											return self.writer






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	List[Any]:




											"""simple docstring"""





											logger.info('Call init')
											self.writer.writerow(_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	str:




											"""simple docstring"""





											logger.info('Call log')
											self.writer.writerow(_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											self.f.close()




class 							__lowercase(							unittest.TestCase	):






						def 		lowerCAmelCase (       self)      ->	Optional[int]:




											"""simple docstring"""





											with tempfile.TemporaryDirectory() as d:
																lowerCamelCase__					=MyCustomTracker(_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=Accelerator(log_with=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
																accelerator.init_trackers('Some name'     ,    _SCREAMING_SNAKE_CASE)
																accelerator.end_training()
																with open(F"{d}/log.csv"     ,    'r') as f:
																					lowerCamelCase__					=csv.DictReader(_SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					=next(_SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					={
																					    'total_loss': '',
																					    'iteration': '',
																					    'my_text': '',
																					    'learning_rate': '0.01',
																					    'num_iterations': '12',
																					    'some_boolean': 'False',
																					    'some_string': 'some_value',
																					}
																					self.assertDictEqual(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	int:




											"""simple docstring"""





											with tempfile.TemporaryDirectory() as d:
																lowerCamelCase__					=MyCustomTracker(_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=Accelerator(log_with=_SCREAMING_SNAKE_CASE)
																accelerator.init_trackers('Some name')
																lowerCamelCase__					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																accelerator.log(_SCREAMING_SNAKE_CASE     ,    step=0)
																accelerator.end_training()
																with open(F"{d}/log.csv"     ,    'r') as f:
																					lowerCamelCase__					=csv.DictReader(_SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					=next(_SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					={
																					    'total_loss': '0.1',
																					    'iteration': '1',
																					    'my_text': 'some_value',
																					    'learning_rate': '',
																					    'num_iterations': '',
																					    'some_boolean': '',
																					    'some_string': '',
																					}
																					self.assertDictEqual(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)







"""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     lowerCamelCase_					(	__A	:		Tuple      ,						__A	:		Optional[int]      ,						__A	:		str      ,						__A	:		Optional[Any]      ,						__A	:		Optional[int]      ,						__A	:		Union[str, Any]      ):
					# we don't care about the past rewards in this model

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

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

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

					lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					=model(
					    states=__A      ,						actions=__A      ,						rewards=__A      ,						returns_to_go=__A      ,						timesteps=__A      ,						attention_mask=__A      ,						return_dict=__A      ,						)

					return action_preds[0, -1]


# build the environment

_snake_case							:   List[str]	  =       gym.make("Hopper-v3")
_snake_case							:   Optional[int]	  =       env.observation_space.shape[0]
_snake_case							:   Tuple	  =       env.action_space.shape[0]
_snake_case							:   Union[str, Any]	  =       1_000
_snake_case							:   Dict	  =       "cuda"
_snake_case							:   Dict	  =       1000.0  # normalization for rewards/returns
_snake_case							:   Optional[int]	  =       3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_snake_case							:   Optional[Any]	  =       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							:   Tuple	  =       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							:   int	  =       torch.from_numpy(state_mean).to(device=device)
_snake_case							:   int	  =       torch.from_numpy(state_std).to(device=device)

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

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

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

												_snake_case							:   List[str]	  =       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							:   List[str]	  =       action
												_snake_case							:   Optional[int]	  =       action.detach().cpu().numpy()

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

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

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