

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


A_ :				List[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_ :				str						 =    'hopper-medium-v2'
	A_ :				Dict						 =    gym.make(env_name)

	A_ :				str						 =    ValueGuidedRLPipeline.from_pretrained(
	    'bglick13/hopper-medium-v2-value-function-hor32',
	    env=env,
	)

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

			# execute action in environment
			A_ ,    A_ ,    A_ ,    A_ :				Any						 =    env.step(denorm_actions)
			A_ :				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())

			A_ :				List[Any]						 =    next_observation
	except KeyboardInterrupt:
		pass

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






class 					_a		:


					'''simple docstring'''






					def __init__(				self    ):
						A__							:					str       =	""""""
						A__							:					Any       =	""""""
						A__							:					List[Any]       =	[]






					def 						__A    (				self					, A__					, A__    ):
						if m == -1:
							return n + 1
						elif n == -1:
							return m + 1
						elif self.dp[m][n] > -1:
							return self.dp[m][n]
						else:
							if self.worda[m] == self.worda[n]:
								A__							:					Optional[Any]       =	self.__min_dist_top_down_dp(m - 1					, n - 1    )
							else:
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(A__					, n - 1    )
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(m - 1					, A__    )
								A__							:					Union[str, Any]       =	self.__min_dist_top_down_dp(m - 1					, n - 1    )
								A__							:					List[Any]       =	1 + min(A__					, A__					, A__    )

							return self.dp[m][n]






					def 						__A    (				self					, A__					, A__    ):
						A__							:					Tuple       =	worda
						A__							:					Dict       =	worda
						A__							:					Optional[Any]       =	[[-1 for _ in range(len(A__    )    )] for _ in range(len(A__    )    )]

						return self.__min_dist_top_down_dp(len(A__    ) - 1					, len(A__    ) - 1    )






					def 						__A    (				self					, A__					, A__    ):
						A__							:					Optional[Any]       =	worda
						A__							:					Dict       =	worda
						A__							:					Union[str, Any]       =	len(A__    )
						A__							:					List[str]       =	len(A__    )
						A__							:					int       =	[[0 for _ in range(n + 1    )] for _ in range(m + 1    )]

						for i in range(m + 1    ):
							for j in range(n + 1    ):
								if i == 0:  # first string is empty
									A__							:					Tuple       =	j
								elif j == 0:  # second string is empty
									A__							:					Dict       =	i
								elif worda[i - 1] == worda[j - 1]:  # last characters are equal
									A__							:					str       =	self.dp[i - 1][j - 1]
								else:
									A__							:					Union[str, Any]       =	self.dp[i][j - 1]
									A__							:					str       =	self.dp[i - 1][j]
									A__							:					Union[str, Any]       =	self.dp[i - 1][j - 1]
									A__							:					Tuple       =	1 + min(A__					, A__					, A__    )
						return self.dp[m][n]


if __name__ == "__main__":
	A_ :				Union[str, Any]						 =    EditDistance()

	print('****************** Testing Edit Distance DP Algorithm ******************')
	print()

	A_ :				int						 =    input('Enter the first string: ').strip()
	A_ :				List[str]						 =    input('Enter the second string: ').strip()

	print()
	print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
	print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
	print()
	print('*************** End of Testing Edit Distance DP Algorithm ***************')



