




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}""")












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       UNetaDModel
						UpperCamelCase_	:       KarrasVeScheduler






						def __init__( self :     Tuple				,							lowerCamelCase__ :     UNetaDModel				,							lowerCamelCase__ :     KarrasVeScheduler				)    ->						Optional[Any]:
													"""simple docstring"""


													super().__init__()
													self.register_modules(unet=lowerCamelCase__				,							scheduler=lowerCamelCase__				)






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							**lowerCamelCase__ :     int				,							)    ->						Union[Tuple, ImagePipelineOutput]:
													"""simple docstring"""


													__lowercase      =    self.unet.config.sample_size
													__lowercase      =    (batch_size, 3, img_size, img_size)

													__lowercase      =    self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													__lowercase      =    randn_tensor(lowerCamelCase__				,							generator=lowerCamelCase__				,							device=self.device				) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(lowerCamelCase__				)

													for t in self.progress_bar(self.scheduler.timesteps				):
																				# here sigma_t == t_i from the paper
																				__lowercase      =    self.scheduler.schedule[t]
																				__lowercase      =    self.scheduler.schedule[t - 1] if t > 0 else 0

																				# 1. Select temporarily increased noise level sigma_hat
																				# 2. Add new noise to move from sample_i to sample_hat
																				__lowercase     ,     __lowercase      =    self.scheduler.add_noise_to_input(lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				)

																				# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																				# The model inputs and output are adjusted by following eq. (213) in [1].
																				__lowercase      =    (sigma_hat / 2) * model((sample_hat + 1) / 2				,							sigma_hat / 2				).sample

																				# 4. Evaluate dx/dt at sigma_hat
																				# 5. Take Euler step from sigma to sigma_prev
																				__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

																				if sigma_prev != 0:
																											# 6. Apply 2nd order correction
																											# The model inputs and output are adjusted by following eq. (213) in [1].
																											__lowercase      =    (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,							sigma_prev / 2				).sample
																											__lowercase      =    self.scheduler.step_correct(
																											    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							step_output.prev_sample				,							step_output['''derivative''']				,							)
																				__lowercase      =    step_output.prev_sample

													__lowercase      =    (sample / 2 + 0.5).clamp(0				,							1				)
													__lowercase      =    sample.cpu().permute(0				,							2				,							3				,							1				).numpy()
													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)






