





'''simple docstring'''

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


__lowerCamelCase			:			Optional[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__":
			__lowerCamelCase			:			List[str]									=      "hopper-medium-v2"
			__lowerCamelCase			:			Dict									=      gym.make(env_name)

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

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

									# execute action in environment
									__lowerCamelCase					,     __lowerCamelCase					,     __lowerCamelCase					,     __lowerCamelCase			:			Union[str, Any]									=      env.step(denorm_actions)
									__lowerCamelCase			:			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			:			Tuple									=      next_observation
			except KeyboardInterrupt:
						pass

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









import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

			# extract state_dict for VQVAE
			A__	  =     {}
			A__	  =     'first_stage_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

    # extract state_dict for UNetLDM
			A__	  =     {}
			A__	  =     'model.diffusion_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

			A__	  =     VQModel(**__UpperCamelCase       ).eval()
			vqvae.load_state_dict(__UpperCamelCase       )

			A__	  =     UNetLDMModel(**__UpperCamelCase       ).eval()
			unet.load_state_dict(__UpperCamelCase       )

			A__	  =     DDIMScheduler(
			    timesteps=config.model.params.timesteps			,						beta_schedule='scaled_linear'			,						beta_start=config.model.params.linear_start			,						beta_end=config.model.params.linear_end			,						clip_sample=__UpperCamelCase			,						)

			A__	  =     LDMPipeline(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			pipeline.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--checkpoint_path''', type=str, required=True)
							parser.add_argument('''--config_path''', type=str, required=True)
							parser.add_argument('''--output_path''', type=str, required=True)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)




