





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


snake_case__					:							Dict													=     {
    '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__":
       snake_case__					:							Optional[int]													=     'hopper-medium-v2'
       snake_case__					:							Optional[int]													=     gym.make(env_name)

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

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

                     # execute action in environment
                     snake_case__						,    snake_case__						,    snake_case__						,    snake_case__					:							int													=     env.step(denorm_actions)
                     snake_case__					:							List[str]													=     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())

                     snake_case__					:							int													=     next_observation
       except KeyboardInterrupt:
              pass

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












from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


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

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

       # add all rotated images in a list
       snake_case__					:							Optional[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
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['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()





