



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


A__  :  Union[str, Any]								=					{
    '''n_samples''': 64,
    '''horizon''': 32,
    '''num_inference_steps''': 20,
    '''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__":
						A__  :  Optional[int]								=					'''hopper-medium-v2'''
						A__  :  int								=					gym.make(env_name)

						A__  :  Optional[int]								=					ValueGuidedRLPipeline.from_pretrained(
						    '''bglick13/hopper-medium-v2-value-function-hor32''',
						    env=env,
						)

						env.seed(0)
						A__  :  int								=					env.reset()
						A__  :  Optional[int]								=					0
						A__  :  Union[str, Any]								=					0
						A__  :  Union[str, Any]								=					1000
						A__  :  Optional[Any]								=					[obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__  :  Union[str, Any]								=					pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__					,     A__					,     A__					,     A__  :  str								=					env.step(denorm_actions)
																		A__  :  Dict								=					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())

																		A__  :  List[str]								=					next_observation
						except KeyboardInterrupt:
												pass

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













"""simple docstring"""



_UpperCamelCase		:  List[str]						=		{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_UpperCamelCase		:  str						=		{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 1_2,
    'Pm': 1_5,
    'Em': 1_8,
    'Zm': 2_1,
    'Ym': 2_4,
}
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  float		,					__snake_case			:  str		,					__snake_case			:  str    ):





  '''simple docstring'''





  lowercase							    =       from_type.lower().strip('s'    )
  lowercase							    =       to_type.lower().strip('s'    )

  lowercase							    =       UNIT_SYMBOL.get(__snake_case		,					__snake_case    )
  lowercase							    =       UNIT_SYMBOL.get(__snake_case		,					__snake_case    )

  if from_sanitized not in METRIC_CONVERSION:
    lowercase							    =       (
        f'Invalid \'from_type\' value: {from_type!r}.\n'
        f'Conversion abbreviations are: {", ".join(__snake_case    )}'
    )
    raise ValueError(__snake_case    )
  if to_sanitized not in METRIC_CONVERSION:
    lowercase							    =       (
        f'Invalid \'to_type\' value: {to_type!r}.\n'
        f'Conversion abbreviations are: {", ".join(__snake_case    )}'
    )
    raise ValueError(__snake_case    )
  lowercase							    =       METRIC_CONVERSION[from_sanitized]
  lowercase							    =       METRIC_CONVERSION[to_sanitized]
  lowercase							    =       1

  if from_exponent > to_exponent:
    lowercase							    =       from_exponent - to_exponent
  else:
    lowercase							    =       -(to_exponent - from_exponent)

  return value * pow(10		,					__snake_case    )


if __name__ == "__main__":
    from doctest import testmod

    testmod()


