



from pathlib import Path

import numpy as np
from PIL import Image







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
						return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b







def       A_				(   _UpperCAmelCase   ):
						return (gray > 1_27) & (gray <= 2_55)







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	np.zeros_like(__lowerCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.zeros(
						    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)   )

						# Copy image to padded image
						SCREAMING_SNAKE_CASE_:			List[str]  		=	image

						# Iterate over image & apply kernel
						for x in range(image.shape[1]   ):
												for y in range(image.shape[0]   ):
																		SCREAMING_SNAKE_CASE_:			Dict  		=	(
																		    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																		).sum()
																		SCREAMING_SNAKE_CASE_:			Dict  		=	int(summation > 0   )
						return output


if __name__ == "__main__":
						# read original image
						lowerCAmelCase							:  List[Any]            =						Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
						lowerCAmelCase							:  List[Any]            =						np.array(Image.open(lena_path))
						# kernel to be applied
						lowerCAmelCase							:  str            =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
						lowerCAmelCase							:  str            =						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
						# Save the output image
						lowerCAmelCase							:  Optional[Any]            =						Image.fromarray(output).convert("""RGB""")
						pil_img.save("""result_dilation.png""")









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


lowerCAmelCase							:  Tuple            =						{
    """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							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

						lowerCAmelCase							:  Optional[int]            =						ValueGuidedRLPipeline.from_pretrained(
						    """bglick13/hopper-medium-v2-value-function-hor32""",
						    env=env,
						)

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

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

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






