


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


UpperCAmelCase_				:			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__":
   UpperCAmelCase_				:			Dict   =	'hopper-medium-v2'
   UpperCAmelCase_				:			List[Any]   =	gym.make(env_name)

   UpperCAmelCase_				:			Dict   =	ValueGuidedRLPipeline.from_pretrained(
       'bglick13/hopper-medium-v2-value-function-hor32',
       env=env,
   )

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

         # execute action in environment
         UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_				:			Optional[Any]   =	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_				:			str   =	next_observation
   except KeyboardInterrupt:
      pass

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






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					Dict					       =						1
      @register_to_config
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple=2_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1E-3					)		->      Optional[int]:
             a_ :		Tuple							=  None
             a_ :		int							=  None
             a_ :		Tuple							=  None
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, torch.device] = None					)		->      List[str]:
             a_ :		Tuple							=  torch.linspace(1					,							self.config.sampling_eps					,							SCREAMING_SNAKE_CASE__					,							device=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					)		->      Tuple:
             if self.timesteps is None:
                    raise ValueError(
                        '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             a_ :		Tuple							=  (
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             a_ :		int							=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
             a_ :		Dict							=  std.flatten()
             while len(std.shape					) < len(score.shape					):
                    a_ :		str							=  std.unsqueeze(-1					)
             a_ :		List[str]							=  -score / std

             # compute
             a_ :		List[str]							=  -1.0 / len(self.timesteps					)

             a_ :		Optional[int]							=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             a_ :		Optional[Any]							=  beta_t.flatten()
             while len(beta_t.shape					) < len(x.shape					):
                    a_ :		List[str]							=  beta_t.unsqueeze(-1					)
             a_ :		Optional[Any]							=  -0.5 * beta_t * x

             a_ :		Tuple							=  torch.sqrt(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  drift - diffusion**2 * score
             a_ :		List[str]							=  x + drift * dt

             # add noise
             a_ :		Optional[Any]							=  randn_tensor(x.shape					,							layout=x.layout					,							generator=SCREAMING_SNAKE_CASE__					,							device=x.device					,							dtype=x.dtype					)
             a_ :		Optional[Any]							=  x_mean + diffusion * math.sqrt(-dt					) * noise

             return x, x_mean

      def __len__(	self    :	int					)		->      Tuple:
             return self.config.num_train_timesteps


