



'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


lowerCAmelCase_     :       Any								=						logging.get_logger(__name__)  # pylint: disable=invalid-name
class 	lowerCamelCase_							(					snake_case_			):







						def __init__(  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													super().__init__()
													self.register_modules(unet=lowerCAmelCase__				,    scheduler=lowerCAmelCase__				)







						@torch.no_grad()
						def __call__(  self   :      Tuple				,    lowerCAmelCase__   :      int = 1				,    lowerCAmelCase__   :      int = 1_00				,    lowerCAmelCase__   :      Optional[Union[torch.Generator, List[torch.Generator]]] = None				,    lowerCAmelCase__   :      Optional[float] = None				,    lowerCAmelCase__   :      bool = True				,    ):

													"""simple docstring"""

													if audio_length_in_s is None:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.unet.config.sample_size / self.unet.config.sample_rate

													SCREAMING_SNAKE_CASE					:      List[str]     					=			audio_length_in_s * self.unet.config.sample_rate

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			2 ** len(self.unet.up_blocks				)
													if sample_size < 3 * down_scale_factor:
																				raise ValueError(
																				    F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
																				    F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			int(lowerCAmelCase__				)
													if sample_size % down_scale_factor != 0:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			(
																				    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
																				) * down_scale_factor
																				logger.info(
																				    F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
																				    F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
																				    ''' process.'''				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			int(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			next(iter(self.unet.parameters()				)				).dtype
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			(batch_size, self.unet.config.in_channels, sample_size)
													if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				) and len(lowerCAmelCase__				) != batch_size:
																				raise ValueError(
																				    F"""You have passed a list of generators of length {len(lowerCAmelCase__				)}, but requested an effective batch"""
																				    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			randn_tensor(lowerCAmelCase__				,    generator=lowerCAmelCase__				,    device=self.device				,    dtype=lowerCAmelCase__				)

													# set step values
													self.scheduler.set_timesteps(lowerCAmelCase__				,    device=audio.device				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.scheduler.timesteps.to(lowerCAmelCase__				)

													for t in self.progress_bar(self.scheduler.timesteps				):
																				# 1. predict noise model_output
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			self.unet(lowerCAmelCase__				,    lowerCAmelCase__				).sample

																				# 2. compute previous image: x_t -> t_t-1
																				SCREAMING_SNAKE_CASE					:      str     					=			self.scheduler.step(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				).prev_sample

													SCREAMING_SNAKE_CASE					:      Any     					=			audio.clamp(-1				,    1				).float().cpu().numpy()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			audio[:, :, :original_sample_size]

													if not return_dict:
																				return (audio,)

													return AudioPipelineOutput(audios=lowerCAmelCase__				)









'''simple docstring'''



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


lowerCAmelCase_     :       int								=						{
    '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_     :       int								=						'hopper-medium-v2'
							lowerCAmelCase_     :       str								=						gym.make(env_name)

							lowerCAmelCase_     :       Union[str, Any]								=						ValueGuidedRLPipeline.from_pretrained(
							    'bglick13/hopper-medium-v2-value-function-hor32',
							    env=env,
							)

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

																					# execute action in environment
																					lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_     :       Union[str, Any]								=						env.step(denorm_actions)
																					lowerCAmelCase_     :       Optional[int]								=						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_     :       Union[str, Any]								=						next_observation
							except KeyboardInterrupt:
														pass

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




