
"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable

lowerCamelCase__           =	list[list[float | int]]
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Matrix		    =   [[0 for _ in range(size + 1	)] for _ in range(_UpperCamelCase	)]
	__lowerCAmelCase    :		int
	__lowerCAmelCase    :		int
	__lowerCAmelCase    :		int
	__lowerCAmelCase    :		int
	__lowerCAmelCase    :		int
	__lowerCAmelCase    :		float

	for row in range(_UpperCamelCase	):
		for col in range(_UpperCamelCase	):
			__lowerCAmelCase    :		Union[str, Any]		    =   matrix[row][col]

		__lowerCAmelCase    :		Optional[int]		    =   vector[row][0]

	__lowerCAmelCase    :		Union[str, Any]		    =   0
	__lowerCAmelCase    :		str		    =   0
	while row < size and col < size:
		# pivoting
		__lowerCAmelCase    :		Tuple		    =   max((abs(augmented[rowa][col]	), rowa) for rowa in range(_UpperCamelCase ,						_UpperCamelCase	)	)[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   augmented[pivot_row], augmented[row]

		for rowa in range(row + 1 ,						_UpperCamelCase	):
			__lowerCAmelCase    :		Tuple		    =   augmented[rowa][col] / augmented[row][col]
			__lowerCAmelCase    :		Optional[Any]		    =   0
			for cola in range(col + 1 ,						size + 1	):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1 ,						_UpperCamelCase	):
		for row in range(_UpperCamelCase	):
			__lowerCAmelCase    :		Optional[Any]		    =   augmented[row][col] / augmented[col][col]
			for cola in range(_UpperCamelCase ,						size + 1	):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row] ,						10	)] for row in range(_UpperCamelCase	)
	]
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Matrix		    =   [[0 for _ in range(_UpperCamelCase	)] for _ in range(_UpperCamelCase	)]
	__lowerCAmelCase    :		Matrix		    =   [[0] for _ in range(_UpperCamelCase	)]
	__lowerCAmelCase    :		Matrix
	__lowerCAmelCase    :		int
	__lowerCAmelCase    :		int
	__lowerCAmelCase    :		int

	for x_val, y_val in enumerate(_UpperCamelCase	):
		for col in range(_UpperCamelCase	):
			__lowerCAmelCase    :		List[Any]		    =   (x_val + 1) ** (size - col - 1)
		__lowerCAmelCase    :		int		    =   y_val

	__lowerCAmelCase    :		Union[str, Any]		    =   solve(_UpperCamelCase ,						_UpperCamelCase	)

	def interpolated_func(_UpperCamelCase	) -> int:
		return sum(
		    round(coeffs[x_val][0]	) * (var ** (size - x_val - 1))
		    for x_val in range(_UpperCamelCase	)	)

	return interpolated_func
def 					__lowerCAmelCase       (_UpperCamelCase	):
	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)
def 					__lowerCAmelCase       (_UpperCamelCase = question_function ,						_UpperCamelCase = 10	):
	__lowerCAmelCase    :		list[int]		    =   [func(_UpperCamelCase	) for x_val in range(1 ,						order + 1	)]

	__lowerCAmelCase    :		list[Callable[[int], int]]		    =   [
	    interpolate(data_points[:max_coeff]	) for max_coeff in range(1 ,						order + 1	)
	]

	__lowerCAmelCase    :		int		    =   0
	__lowerCAmelCase    :		Callable[[int], int]
	__lowerCAmelCase    :		int

	for poly in polynomials:
		__lowerCAmelCase    :		Optional[Any]		    =   1
		while func(_UpperCamelCase	) == poly(_UpperCamelCase	):
			x_val += 1

		ret += poly(_UpperCamelCase	)

	return ret


if __name__ == "__main__":
							print(f'{solution() = }')

"""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}')