from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def     UpperCamelCase						(	__lowercase						:			float						,__lowercase						:			float						,__lowercase						:			bool = False     ):
		'''simple docstring'''




		if radian_mode:
				return [magnitude * cos(__lowercase     ), magnitude * sin(__lowercase     )]
		return [magnitude * cos(radians(__lowercase     )     ), magnitude * sin(radians(__lowercase     )     )]




def     UpperCamelCase						(	__lowercase						:			NDArray[floataa]						,__lowercase						:			NDArray[floataa]						,__lowercase						:			float = 10**-1     ):
		'''simple docstring'''




		A_		:    NDArray[floataa]         =				cross(__lowercase						,__lowercase     )
		A_		:    float         =				sum(__lowercase     )
		return abs(__lowercase     ) < eps


if __name__ == "__main__":
	# Test to check if it works
	_UpperCAmelCase            =		array(
	    [
	        polar_force(718.4, 180 - 30),
	        polar_force(879.54, 45),
	        polar_force(100, -90),
	    ]
	)

	_UpperCAmelCase            =		array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem 1 in image_data/2D_problems.jpg
	_UpperCAmelCase            =		array(
	    [
	        polar_force(30 * 9.81, 15),
	        polar_force(215, 180 - 45),
	        polar_force(264, 90 - 30),
	    ]
	)

	_UpperCAmelCase            =		array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem in image_data/2D_problems_1.jpg
	_UpperCAmelCase            =		array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

	_UpperCAmelCase            =		array([[0, 0], [6, 0], [10, 0], [12, 0]])

	assert in_static_equilibrium(forces, location)

	import doctest

	doctest.testmod()







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


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

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

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

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		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())

			_UpperCAmelCase            =		next_observation
	except KeyboardInterrupt:
		pass

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






