


"""simple docstring"""


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


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

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

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

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

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







"""simple docstring"""

from pathlib import Path

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


def lowerCAmelCase_   (					UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						cva.getAffineTransform(UpperCamelCase__					, UpperCamelCase__			)
 return cva.warpAffine(UpperCamelCase__					, UpperCamelCase__					, (rows, cols)			)


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

       # set different points to rotate image
       UpperCAmelCase__					=np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       UpperCAmelCase__					=np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       UpperCAmelCase__					=np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       UpperCAmelCase__					=np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       UpperCAmelCase__					=[
           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
       UpperCAmelCase__					=plt.figure(1)
       UpperCAmelCase__					=["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()
