

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_UpperCAmelCase						:  Tuple									=			logging.get_logger(__name__)  # pylint: disable=invalid-name





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							warnings.warn(
							    'The preprocess method is deprecated and will be removed in a future version. Please'
							    ' use VaeImageProcessor.preprocess instead'  ,				UpperCamelCase__  ,				)
							if isinstance(UpperCamelCase__  ,				torch.Tensor ):
														return image
							elif isinstance(UpperCamelCase__  ,				PIL.Image.Image ):
														snake_case_     							=       [image]

							if isinstance(image[0]  ,				PIL.Image.Image ):
														snake_case_							,       snake_case_     							=       image[0].size
														snake_case_							,       snake_case_     							=       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														snake_case_     							=       [np.array(i.resize((w, h)  ,				resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
														snake_case_     							=       np.concatenate(UpperCamelCase__  ,				axis=0 )
														snake_case_     							=       np.array(UpperCamelCase__ ).astype(np.floataa ) / 2_55.0
														snake_case_     							=       image.transpose(0  ,				3  ,				1  ,				2 )
														snake_case_     							=       2.0 * image - 1.0
														snake_case_     							=       torch.from_numpy(UpperCamelCase__ )
							elif isinstance(image[0]  ,				torch.Tensor ):
														snake_case_     							=       torch.cat(UpperCamelCase__  ,				dim=0 )
							return image





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							if isinstance(UpperCamelCase__  ,				torch.Tensor ):
														return mask
							elif isinstance(UpperCamelCase__  ,				PIL.Image.Image ):
														snake_case_     							=       [mask]

							if isinstance(mask[0]  ,				PIL.Image.Image ):
														snake_case_							,       snake_case_     							=       mask[0].size
														snake_case_							,       snake_case_     							=       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														snake_case_     							=       [np.array(m.convert('L' ).resize((w, h)  ,				resample=PIL_INTERPOLATION['nearest'] ) )[None, :] for m in mask]
														snake_case_     							=       np.concatenate(UpperCamelCase__  ,				axis=0 )
														snake_case_     							=       mask.astype(np.floataa ) / 2_55.0
														snake_case_     							=       0
														snake_case_     							=       1
														snake_case_     							=       torch.from_numpy(UpperCamelCase__ )
							elif isinstance(mask[0]  ,				torch.Tensor ):
														snake_case_     							=       torch.cat(UpperCamelCase__  ,				dim=0 )
							return mask







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					UNetaDModel
				__SCREAMING_SNAKE_CASE   :					RePaintScheduler




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__()
											self.register_modules(unet=snake_case ,  scheduler=snake_case			)




				@torch.no_grad()
				def __call__(   self ,  snake_case ,  snake_case ,  snake_case = 250 ,  snake_case = 0.0 ,  snake_case = 10 ,  snake_case = 10 ,  snake_case = None ,  snake_case = "pil" ,  snake_case = True ,  ):
											snake_case_     							=       image

											snake_case_     							=       _preprocess_image(snake_case			)
											snake_case_     							=       original_image.to(device=self.device ,  dtype=self.unet.dtype			)
											snake_case_     							=       _preprocess_mask(snake_case			)
											snake_case_     							=       mask_image.to(device=self.device ,  dtype=self.unet.dtype			)

											snake_case_     							=       original_image.shape[0]

											# sample gaussian noise to begin the loop
											if isinstance(snake_case ,  snake_case			) and len(snake_case			) != batch_size:
																		raise ValueError(
																		    F'''You have passed a list of generators of length {len(snake_case			)}, but requested an effective batch'''
																		    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

											snake_case_     							=       original_image.shape
											snake_case_     							=       randn_tensor(snake_case ,  generator=snake_case ,  device=self.device ,  dtype=self.unet.dtype			)

											# set step values
											self.scheduler.set_timesteps(snake_case ,  snake_case ,  snake_case ,  self.device			)
											snake_case_     							=       eta

											snake_case_     							=       self.scheduler.timesteps[0] + 1
											snake_case_     							=       generator[0] if isinstance(snake_case ,  snake_case			) else generator
											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
																		if t < t_last:
																									# predict the noise residual
																									snake_case_     							=       self.unet(snake_case ,  snake_case			).sample
																									# compute previous image: x_t -> x_t-1
																									snake_case_     							=       self.scheduler.step(snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case			).prev_sample

																		else:
																									# compute the reverse: x_t-1 -> x_t
																									snake_case_     							=       self.scheduler.undo_step(snake_case ,  snake_case ,  snake_case			)
																		snake_case_     							=       t

											snake_case_     							=       (image / 2 + 0.5).clamp(0 ,  1			)
											snake_case_     							=       image.cpu().permute(0 ,  2 ,  3 ,  1			).numpy()
											if output_type == "pil":
																		snake_case_     							=       self.numpy_to_pil(snake_case			)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=snake_case			)







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


_UpperCAmelCase						:  List[Any]									=			{
    """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						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

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

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

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  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())

															_UpperCAmelCase						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

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




