

import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a						,       a						,       a						,       a						,       ):
         super().__init__()
         lowercase__   :       Dict				=	value_function
         lowercase__   :       Union[str, Any]				=	unet
         lowercase__   :       Optional[int]				=	scheduler
         lowercase__   :       Dict				=	env
         lowercase__   :       Union[str, Any]				=	env.get_dataset()
         lowercase__   :       Any				=	{}
         for key in self.data.keys():
              try:
                   lowercase__   :       List[str]				=	self.data[key].mean()
              except:  # noqa: E722
                   pass
         lowercase__   :       List[Any]				=	{}
         for key in self.data.keys():
              try:
                   lowercase__   :       Optional[Any]				=	self.data[key].std()
              except:  # noqa: E722
                   pass
         lowercase__   :       List[Any]				=	env.observation_space.shape[0]
         lowercase__   :       Union[str, Any]				=	env.action_space.shape[0]





    def 				snake_case_    (				self						,       a						,       a):
         return (x_in - self.means[key]) / self.stds[key]





    def 				snake_case_    (				self						,       a						,       a):
         return x_in * self.stds[key] + self.means[key]





    def 				snake_case_    (				self						,       a):
         if type(a) is dict:
              return {k: self.to_torch(a) for k, v in x_in.items()}
         elif torch.is_tensor(a):
              return x_in.to(self.unet.device)
         return torch.tensor(a						,       device=self.unet.device)





    def 				snake_case_    (				self						,       a						,       a						,       a):
         for key, val in cond.items():
              lowercase__   :       Optional[Any]				=	val.clone()
         return x_in





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a):
         lowercase__   :       List[Any]				=	x.shape[0]
         lowercase__   :       List[Any]				=	None
         for i in tqdm.tqdm(self.scheduler.timesteps):
              # create batch of timesteps to pass into model
              lowercase__   :       Tuple				=	torch.full((batch_size,)						,       a						,       device=self.unet.device						,       dtype=torch.long)
              for _ in range(a):
                   with torch.enable_grad():
                        x.requires_grad_()

                        # permute to match dimension for pre-trained models
                        lowercase__   :       List[str]				=	self.value_function(x.permute(0						,       2						,       1)						,       a).sample
                        lowercase__   :       Optional[Any]				=	torch.autograd.grad([y.sum()]						,       [x])[0]

                        lowercase__   :       Optional[Any]				=	self.scheduler._get_variance(a)
                        lowercase__   :       int				=	torch.exp(0.5 * posterior_variance)
                        lowercase__   :       List[str]				=	model_std * grad

                   lowercase__   :       Optional[Any]				=	0
                   lowercase__   :       List[Any]				=	x.detach()
                   lowercase__   :       str				=	x + scale * grad
                   lowercase__   :       List[str]				=	self.reset_xa(a						,       a						,       self.action_dim)

              lowercase__   :       Union[str, Any]				=	self.unet(x.permute(0						,       2						,       1)						,       a).sample.permute(0						,       2						,       1)

              # TODO: verify deprecation of this kwarg
              lowercase__   :       Dict				=	self.scheduler.step(a						,       a						,       a						,       predict_epsilon=a)['prev_sample']

              # apply conditions to the trajectory (set the initial state)
              lowercase__   :       int				=	self.reset_xa(a						,       a						,       self.action_dim)
              lowercase__   :       str				=	self.to_torch(a)
         return x, y





    def __call__(				self						,       a						,       a=64						,       a=32						,       a=2						,       a=0.1):
         # normalize the observations and create  batch dimension
         lowercase__   :       str				=	self.normalize(a						,       'observations')
         lowercase__   :       List[Any]				=	obs[None].repeat(a						,       axis=0)

         lowercase__   :       Any				=	{0: self.to_torch(a)}
         lowercase__   :       Any				=	(batch_size, planning_horizon, self.state_dim + self.action_dim)

         # generate initial noise and apply our conditions (to make the trajectories start at current state)
         lowercase__   :       int				=	randn_tensor(a						,       device=self.unet.device)
         lowercase__   :       List[Any]				=	self.reset_xa(a						,       a						,       self.action_dim)
         lowercase__   :       Union[str, Any]				=	self.to_torch(a)

         # run the diffusion process
         lowercase__				,			lowercase__   :       Dict				=	self.run_diffusion(a						,       a						,       a						,       a)

         # sort output trajectories by value
         lowercase__   :       Any				=	y.argsort(0						,       descending=a).squeeze()
         lowercase__   :       int				=	x[sorted_idx]
         lowercase__   :       Union[str, Any]				=	sorted_values[:, :, : self.action_dim]
         lowercase__   :       int				=	actions.detach().cpu().numpy()
         lowercase__   :       int				=	self.de_normalize(a						,       key='actions')

         # select the action with the highest value
         if y is not None:
              lowercase__   :       List[str]				=	0
         else:
              # if we didn't run value guiding, select a random action
              lowercase__   :       List[str]				=	np.random.randint(0						,       a)

         lowercase__   :       Optional[Any]				=	denorm_actions[selected_index, 0]
         return denorm_actions




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)

