

"""simple docstring"""





import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__UpperCamelCase  :      Optional[int]							      =		{
    '''n_samples''': 6_4,
    '''horizon''': 3_2,
    '''num_inference_steps''': 2_0,
    '''n_guide_steps''': 2,  # can set to 0 for faster sampling, does not use value network
    '''scale_grad_by_std''': True,
    '''scale''': 0.1,
    '''eta''': 0.0,
    '''t_grad_cutoff''': 2,
    '''device''': '''cpu''',
}


if __name__ == "__main__":
							__UpperCamelCase  :      Optional[int]							      =		'''hopper-medium-v2'''
							__UpperCamelCase  :      Optional[int]							      =		gym.make(env_name)

							__UpperCamelCase  :      List[Any]							      =		ValueGuidedRLPipeline.from_pretrained(
							    '''bglick13/hopper-medium-v2-value-function-hor32''',
							    env=env,
							)

							env.seed(0)
							__UpperCamelCase  :      List[Any]							      =		env.reset()
							__UpperCamelCase  :      Optional[Any]							      =		0
							__UpperCamelCase  :      Any							      =		0
							__UpperCamelCase  :      Any							      =		1_0_0_0
							__UpperCamelCase  :      Dict							      =		[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					__UpperCamelCase  :      Optional[int]							      =		pipeline(obs, planning_horizon=3_2)

																					# execute action in environment
																					__UpperCamelCase      ,    __UpperCamelCase      ,    __UpperCamelCase      ,    __UpperCamelCase  :      Optional[Any]							      =		env.step(denorm_actions)
																					__UpperCamelCase  :      Optional[Any]							      =		env.get_normalized_score(total_reward)

																					# update return
																					total_reward += reward
																					total_score += score
																					print(
																					    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
																					    F''' {total_score}'''
																					)

																					# save observations for rendering
																					rollout.append(next_observation.copy())

																					__UpperCamelCase  :      str							      =		next_observation
							except KeyboardInterrupt:
														pass

							print(F'''Total reward: {total_reward}''')









"""simple docstring"""






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Dict:

				"""simple docstring"""

				with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as input_file:
							__lowerCAmelCase      :  List[str]												=       re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)")
							__lowerCAmelCase      :  List[Any]												=       input_file.read()
							__lowerCAmelCase      :  Any												=       regexp.search(_SCREAMING_SNAKE_CASE)

				return match

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

				"""simple docstring"""

				with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as input_file:
							__lowerCAmelCase      :  Any												=       re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"       ,			re.DOTALL)
							__lowerCAmelCase      :  Optional[int]												=       input_file.read()
							# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
							__lowerCAmelCase      :  int												=       regexp.finditer(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Union[str, Any]												=       [match for match in matches if match is not None and match.group(1) is not None]
				return matches[0] if matches else None

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

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       Path("./datasets")
				__lowerCAmelCase      :  Optional[int]												=       list(dataset_paths.absolute().glob("**/*.py"))

				for dataset in dataset_files:
							if self._no_encoding_on_file_open(str(_SCREAMING_SNAKE_CASE)):
										raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""")







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       Path("./datasets")
				__lowerCAmelCase      :  Union[str, Any]												=       list(dataset_paths.absolute().glob("**/*.py"))

				for dataset in dataset_files:
							if self._no_print_statements(str(_SCREAMING_SNAKE_CASE)):
										raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead.""")