



'''simple docstring'''



from collections.abc import Callable
def 					UpperCAmelCase   (						A  :				Callable[[float], float]					,		A  :				float					,		A  :				float				):

							'''simple docstring'''





							SCREAMING_SNAKE_CASE					:      float     					=			a
							SCREAMING_SNAKE_CASE					:      float     					=			b
							if function(A				) == 0:  # one of the a or b is a root for the function
														return a
							elif function(A				) == 0:
														return b
							elif (
							    function(A				) * function(A				) > 0
							):  # if none of these are root and they are both positive or negative,
														# then this algorithm can't find the root
														raise ValueError('''could not find root in given interval.'''				)
							else:
														SCREAMING_SNAKE_CASE					:      float     					=			start + (end - start) / 2.0
														while abs(start - mid				) > 10**-7:  # until precisely equals to 10^-7
																					if function(A				) == 0:
																												return mid
																					elif function(A				) * function(A				) < 0:
																												SCREAMING_SNAKE_CASE					:      Any     					=			mid
																					else:
																												SCREAMING_SNAKE_CASE					:      List[str]     					=			mid
																					SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			start + (end - start) / 2.0
														return mid
def 					UpperCAmelCase   (						A  :				float				):

							'''simple docstring'''





							return x**3 - 2 * x - 5


if __name__ == "__main__":
							print(bisection(f, 1, 1000))

							import doctest

							doctest.testmod()









'''simple docstring'''



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


lowerCAmelCase_     :       int								=						{
    '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__":
							lowerCAmelCase_     :       int								=						'hopper-medium-v2'
							lowerCAmelCase_     :       str								=						gym.make(env_name)

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

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

																					# execute action in environment
																					lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_     :       Union[str, Any]								=						env.step(denorm_actions)
																					lowerCAmelCase_     :       Optional[int]								=						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())

																					lowerCAmelCase_     :       Union[str, Any]								=						next_observation
							except KeyboardInterrupt:
														pass

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




