



'''simple docstring'''



from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt
def 					UpperCAmelCase   (						A  :				np.ndarray					,		A  :				np.ndarray					,		A  :				np.ndarray					,		A  :				int					,		A  :				int				):
							SCREAMING_SNAKE_CASE					:      str     					=			cva.getAffineTransform(A					,		A				)
							return cva.warpAffine(A					,		A					,		(rows, cols)				)


if __name__ == "__main__":
							# read original image
							lowerCAmelCase_     :       str								=						cva.imread(
							    str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
							)
							# turn image in gray scale value
							lowerCAmelCase_     :       int								=						cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							lowerCAmelCase_			,							lowerCAmelCase_     :       Optional[int]								=						gray_img.shape

							# set different points to rotate image
							lowerCAmelCase_     :       str								=						np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							lowerCAmelCase_     :       Any								=						np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							lowerCAmelCase_     :       Tuple								=						np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							lowerCAmelCase_     :       Optional[int]								=						np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							lowerCAmelCase_     :       Union[str, Any]								=						[
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							lowerCAmelCase_     :       Optional[int]								=						plt.figure(1)
							lowerCAmelCase_     :       Optional[Any]								=						['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
														plt.title(titles[i])
														plt.axis('off')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()









'''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}')




