


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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


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




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




      def 					_snake_case     (    self:	Optional[int]						,				a:	Union[str, Any]						,				a:	Union[str, Any]    ):
       return x_in * self.stds[key] + self.means[key]




      def 					_snake_case     (    self:	Optional[Any]						,				a:	Tuple    ):
       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:	List[str]						,				a:	List[Any]						,				a:	Optional[Any]						,				a:	List[Any]    ):
       for key, val in cond.items():
        __lowerCamelCase		:							Dict		 =						val.clone()
       return x_in




      def 					_snake_case     (    self:	List[Any]						,				a:	Any						,				a:	Tuple						,				a:	str						,				a:	Union[str, Any]    ):
       __lowerCamelCase		:							List[str]		 =						x.shape[0]
       __lowerCamelCase		:							Any		 =						None
       for i in tqdm.tqdm(self.scheduler.timesteps    ):
        # create batch of timesteps to pass into model
        __lowerCamelCase		:							Optional[int]		 =						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
          __lowerCamelCase		:							Tuple		 =						self.value_function(x.permute(0						,				2						,				1    )						,				a    ).sample
          __lowerCamelCase		:							Tuple		 =						torch.autograd.grad([y.sum()]						,				[x]    )[0]

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

         __lowerCamelCase		:							str		 =						0
         __lowerCamelCase		:							Any		 =						x.detach()
         __lowerCamelCase		:							Dict		 =						x + scale * grad
         __lowerCamelCase		:							int		 =						self.reset_xa(a						,				a						,				self.action_dim    )

        __lowerCamelCase		:							List[Any]		 =						self.unet(x.permute(0						,				2						,				1    )						,				a    ).sample.permute(0						,				2						,				1    )

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

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




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

       __lowerCamelCase		:							Optional[Any]		 =						{0: self.to_torch(a    )}
       __lowerCamelCase		:							Dict		 =						(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)
       __lowerCamelCase		:							List[str]		 =						randn_tensor(a						,				device=self.unet.device    )
       __lowerCamelCase		:							Any		 =						self.reset_xa(a						,				a						,				self.action_dim    )
       __lowerCamelCase		:							int		 =						self.to_torch(a    )

       # run the diffusion process
       __lowerCamelCase ,					__lowerCamelCase		:							Optional[int]		 =						self.run_diffusion(a						,				a						,				a						,				a    )

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

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

       __lowerCamelCase		:							List[str]		 =						denorm_actions[selected_index, 0]
       return denorm_actions





from __future__ import annotations
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						str(SCREAMING_SNAKE_CASE__						)
 return len(SCREAMING_SNAKE_CASE__						) == 9 and set(SCREAMING_SNAKE_CASE__						) == set('123456789'						)
def 				UpperCamelCase__					(  ):
 for base_num in range(9_999      ,	4_999      ,	-1						):
  __lowerCamelCase		:							Tuple		 =						100_002 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 for base_num in range(333      ,	99      ,	-1						):
  __lowerCamelCase		:							Union[str, Any]		 =						1_002_003 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 return None


if __name__ == "__main__":
       print(F"""{solution() = }""")

