

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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 					lowercase							(			lowercase_     ):




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case = None ,  ):
											super().__init__()
											self.register_modules(transformer=snake_case ,  vae=snake_case ,  scheduler=snake_case			)

											# create a imagenet -> id dictionary for easier use
											snake_case_     							=       {}
											if idalabel is not None:
																		for key, value in idalabel.items():
																									for label in value.split(','			):
																																snake_case_     							=       int(snake_case			)
																		snake_case_     							=       dict(sorted(self.labels.items()			)			)




				def        a				(   self ,  snake_case			):

											if not isinstance(snake_case ,  snake_case			):
																		snake_case_     							=       list(snake_case			)

											for l in label:
																		if l not in self.labels:
																									raise ValueError(
																									    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''			)

											return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(   self ,  snake_case ,  snake_case = 4.0 ,  snake_case = None ,  snake_case = 50 ,  snake_case = "pil" ,  snake_case = True ,  ):
											snake_case_     							=       len(snake_case			)
											snake_case_     							=       self.transformer.config.sample_size
											snake_case_     							=       self.transformer.config.in_channels

											snake_case_     							=       randn_tensor(
											    shape=(batch_size, latent_channels, latent_size, latent_size) ,  generator=snake_case ,  device=self.device ,  dtype=self.transformer.dtype ,  )
											snake_case_     							=       torch.cat([latents] * 2			) if guidance_scale > 1 else latents

											snake_case_     							=       torch.tensor(snake_case ,  device=self.device			).reshape(-1			)
											snake_case_     							=       torch.tensor([1000] * batch_size ,  device=self.device			)
											snake_case_     							=       torch.cat([class_labels, class_null] ,  0			) if guidance_scale > 1 else class_labels

											# set step values
											self.scheduler.set_timesteps(snake_case			)

											for t in self.progress_bar(self.scheduler.timesteps			):
																		if guidance_scale > 1:
																									snake_case_     							=       latent_model_input[: len(snake_case			) // 2]
																									snake_case_     							=       torch.cat([half, half] ,  dim=0			)
																		snake_case_     							=       self.scheduler.scale_model_input(snake_case ,  snake_case			)

																		snake_case_     							=       t
																		if not torch.is_tensor(snake_case			):
																									# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																									# This would be a good case for the `match` statement (Python 3.10+)
																									snake_case_     							=       latent_model_input.device.type == 'mps'
																									if isinstance(snake_case ,  snake_case			):
																																snake_case_     							=       torch.floataa if is_mps else torch.floataa
																									else:
																																snake_case_     							=       torch.intaa if is_mps else torch.intaa
																									snake_case_     							=       torch.tensor([timesteps] ,  dtype=snake_case ,  device=latent_model_input.device			)
																		elif len(timesteps.shape			) == 0:
																									snake_case_     							=       timesteps[None].to(latent_model_input.device			)
																		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																		snake_case_     							=       timesteps.expand(latent_model_input.shape[0]			)
																		# predict noise model_output
																		snake_case_     							=       self.transformer(
																		    snake_case ,  timestep=snake_case ,  class_labels=snake_case			).sample

																		# perform guidance
																		if guidance_scale > 1:
																									snake_case_							,       snake_case_     							=       noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																									snake_case_							,       snake_case_     							=       torch.split(snake_case ,  len(snake_case			) // 2 ,  dim=0			)

																									snake_case_     							=       uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																									snake_case_     							=       torch.cat([half_eps, half_eps] ,  dim=0			)

																									snake_case_     							=       torch.cat([eps, rest] ,  dim=1			)

																		# learned sigma
																		if self.transformer.config.out_channels // 2 == latent_channels:
																									snake_case_							,       snake_case_     							=       torch.split(snake_case ,  snake_case ,  dim=1			)
																		else:
																									snake_case_     							=       noise_pred

																		# compute previous image: x_t -> x_t-1
																		snake_case_     							=       self.scheduler.step(snake_case ,  snake_case ,  snake_case			).prev_sample

											if guidance_scale > 1:
																		snake_case_							,       snake_case_     							=       latent_model_input.chunk(2 ,  dim=0			)
											else:
																		snake_case_     							=       latent_model_input

											snake_case_     							=       1 / self.vae.config.scaling_factor * latents
											snake_case_     							=       self.vae.decode(snake_case			).sample

											snake_case_     							=       (samples / 2 + 0.5).clamp(0 ,  1			)

											# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
											snake_case_     							=       samples.cpu().permute(0 ,  2 ,  3 ,  1			).float().numpy()

											if output_type == "pil":
																		snake_case_     							=       self.numpy_to_pil(snake_case			)

											if not return_dict:
																		return (samples,)

											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}''')




