
"""simple docstring"""







from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__           =	200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase__           =	50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase__           =	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Dict		    =   len([g for position, g in enumerate(_UpperCamelCase	) if g == main_target[position]]	)
	return (item, float(_UpperCamelCase	))
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   random.randint(0 ,						len(_UpperCamelCase	) - 1	)
	__lowerCAmelCase    :		int		    =   parent_a[:random_slice] + parent_a[random_slice:]
	__lowerCAmelCase    :		int		    =   parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Any		    =   list(_UpperCamelCase	)
	if random.uniform(0 ,						1	) < MUTATION_PROBABILITY:
		__lowerCAmelCase    :		str		    =   random.choice(_UpperCamelCase	)
	return "".join(_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						):
	__lowerCAmelCase    :		Dict		    =   []
	# Generate more children proportionally to the fitness score.
	__lowerCAmelCase    :		Union[str, Any]		    =   int(parent_a[1] * 100	) + 1
	__lowerCAmelCase    :		int		    =   10 if child_n >= 10 else child_n
	for _ in range(_UpperCamelCase	):
		__lowerCAmelCase    :		List[str]		    =   population_score[random.randint(0 ,						_UpperCamelCase	)][0]

		__lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   crossover(parent_a[0] ,						_UpperCamelCase	)
		# Append new string to the population list.
		pop.append(mutate(_UpperCamelCase ,						_UpperCamelCase	)	)
		pop.append(mutate(_UpperCamelCase ,						_UpperCamelCase	)	)
	return pop
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase = True	):

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		__lowerCAmelCase    :		int		    =   F"{N_POPULATION} must be bigger than {N_SELECTED}"
		raise ValueError(_UpperCamelCase	)
	# Verify that the target contains no genes besides the ones inside genes variable.
	__lowerCAmelCase    :		Optional[Any]		    =   sorted({c for c in target if c not in genes}	)
	if not_in_genes_list:
		__lowerCAmelCase    :		List[Any]		    =   F"{not_in_genes_list} is not in genes list, evolution cannot converge"
		raise ValueError(_UpperCamelCase	)

	# Generate random starting population.
	__lowerCAmelCase    :		List[str]		    =   []
	for _ in range(_UpperCamelCase	):
		population.append(''.join([random.choice(_UpperCamelCase	) for i in range(len(_UpperCamelCase	)	)]	)	)

	# Just some logs to know what the algorithms is doing.
	__lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   0, 0

	# This loop will end when we find a perfect match for our target.
	while True:
		generation += 1
		total_population += len(_UpperCamelCase	)

		# Random population created. Now it's time to evaluate.

		# Adding a bit of concurrency can make everything faster,
		#
		# import concurrent.futures
		# population_score: list[tuple[str, float]] = []
		# with concurrent.futures.ThreadPoolExecutor(
		#                                   max_workers=NUM_WORKERS) as executor:
		#     futures = {executor.submit(evaluate, item) for item in population}
		#     concurrent.futures.wait(futures)
		#     population_score = [item.result() for item in futures]
		#
		# but with a simple algorithm like this, it will probably be slower.
		# We just need to call evaluate for every item inside the population.
		__lowerCAmelCase    :		Optional[int]		    =   [evaluate(_UpperCamelCase ,						_UpperCamelCase	) for item in population]

		# Check if there is a matching evolution.
		__lowerCAmelCase    :		List[Any]		    =   sorted(_UpperCamelCase ,						key=lambda _UpperCamelCase	: x[1] ,						reverse=_UpperCamelCase	)
		if population_score[0][0] == target:
			return (generation, total_population, population_score[0][0])

		# Print the best result every 10 generation.
		# Just to know that the algorithm is working.
		if debug and generation % 10 == 0:
			print(
			    F"\nGeneration: {generation}"
			    F"\nTotal Population:{total_population}"
			    F"\nBest score: {population_score[0][1]}"
			    F"\nBest string: {population_score[0][0]}"	)

		# Flush the old population, keeping some of the best evolutions.
		# Keeping this avoid regression of evolution.
		__lowerCAmelCase    :		List[Any]		    =   population[: int(N_POPULATION / 3	)]
		population.clear()
		population.extend(_UpperCamelCase	)
		# Normalize population score to be between 0 and 1.
		__lowerCAmelCase    :		Union[str, Any]		    =   [
		    (item, score / len(_UpperCamelCase	)) for item, score in population_score
		]

		# This is selection
		for i in range(_UpperCamelCase	):
			population.extend(select(population_score[int(_UpperCamelCase	)] ,						_UpperCamelCase ,						_UpperCamelCase	)	)
			# Check if the population has already reached the maximum value and if so,
			# break the cycle.  If this check is disabled, the algorithm will take
			# forever to compute large strings, but will also calculate small strings in
			# a far fewer generations.
			if len(_UpperCamelCase	) > N_POPULATION:
				break


if __name__ == "__main__":
							lowerCamelCase__           =	(
							    """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
							)
							lowerCamelCase__           =	list(
							    """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
							    """nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
							)
							lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__           =	basic(target_str, genes_list)
							print(
							    f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
							)

"""simple docstring"""







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


lowerCamelCase__           =	{
    """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__           =	"""hopper-medium-v2"""
							lowerCamelCase__           =	gym.make(env_name)

							lowerCamelCase__           =	ValueGuidedRLPipeline.from_pretrained(
							    """bglick13/hopper-medium-v2-value-function-hor32""",
							    env=env,
							)

							env.seed(0)
							lowerCamelCase__           =	env.reset()
							lowerCamelCase__           =	0
							lowerCamelCase__           =	0
							lowerCamelCase__           =	1_000
							lowerCamelCase__           =	[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					lowerCamelCase__           =	pipeline(obs, planning_horizon=32)

																					# execute action in environment
																					lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__           =	env.step(denorm_actions)
																					lowerCamelCase__           =	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__           =	next_observation
							except KeyboardInterrupt:
														pass

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