






"""simple docstring"""







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   UpperCamelCase_ (       a_  ):



  def __init__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			int:



         """simple docstring"""





         super().__init__()
         UpperCAmelCase	    =			value_function
         UpperCAmelCase	    =			unet
         UpperCAmelCase	    =			scheduler
         UpperCAmelCase	    =			env
         UpperCAmelCase	    =			env.get_dataset()
         UpperCAmelCase	    =			{}
         for key in self.data.keys():
                try:
                       UpperCAmelCase	    =			self.data[key].mean()
                except:  # noqa: E722
                       pass
         UpperCAmelCase	    =			{}
         for key in self.data.keys():
                try:
                       UpperCAmelCase	    =			self.data[key].std()
                except:  # noqa: E722
                       pass
         UpperCAmelCase	    =			env.observation_space.shape[0]
         UpperCAmelCase	    =			env.action_space.shape[0]



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			Any:



         """simple docstring"""





         return (x_in - self.means[key]) / self.stds[key]



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         return x_in * self.stds[key] + self.means[key]



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Tuple:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			str:



         """simple docstring"""





         for key, val in cond.items():
                UpperCAmelCase	    =			val.clone()
         return x_in



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[str]:



         """simple docstring"""





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

                              # permute to match dimension for pre-trained models
                              UpperCAmelCase	    =			self.value_function(x.permute(0 ,		2 ,		1				) ,		snake_case__				).sample
                              UpperCAmelCase	    =			torch.autograd.grad([y.sum()] ,		[x]				)[0]

                              UpperCAmelCase	    =			self.scheduler._get_variance(snake_case__				)
                              UpperCAmelCase	    =			torch.exp(0.5 * posterior_variance				)
                              UpperCAmelCase	    =			model_std * grad

                       UpperCAmelCase	    =			0
                       UpperCAmelCase	    =			x.detach()
                       UpperCAmelCase	    =			x + scale * grad
                       UpperCAmelCase	    =			self.reset_xa(snake_case__ ,		snake_case__ ,		self.action_dim				)

                UpperCAmelCase	    =			self.unet(x.permute(0 ,		2 ,		1				) ,		snake_case__				).sample.permute(0 ,		2 ,		1				)

                # TODO: verify deprecation of this kwarg
                UpperCAmelCase	    =			self.scheduler.step(snake_case__ ,		snake_case__ ,		snake_case__ ,		predict_epsilon=snake_case__				)["""prev_sample"""]

                # apply conditions to the trajectory (set the initial state)
                UpperCAmelCase	    =			self.reset_xa(snake_case__ ,		snake_case__ ,		self.action_dim				)
                UpperCAmelCase	    =			self.to_torch(snake_case__				)
         return x, y



  def __call__(      self ,		snake_case__ ,		snake_case__=64 ,		snake_case__=32 ,		snake_case__=2 ,		snake_case__=0.1				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.normalize(snake_case__ ,		"""observations"""				)
         UpperCAmelCase	    =			obs[None].repeat(snake_case__ ,		axis=0				)

         UpperCAmelCase	    =			{0: self.to_torch(snake_case__				)}
         UpperCAmelCase	    =			(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)
         UpperCAmelCase	    =			randn_tensor(snake_case__ ,		device=self.unet.device				)
         UpperCAmelCase	    =			self.reset_xa(snake_case__ ,		snake_case__ ,		self.action_dim				)
         UpperCAmelCase	    =			self.to_torch(snake_case__				)

         # run the diffusion process
         UpperCAmelCase    ,       UpperCAmelCase	    =			self.run_diffusion(snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)

         # sort output trajectories by value
         UpperCAmelCase	    =			y.argsort(0 ,		descending=snake_case__				).squeeze()
         UpperCAmelCase	    =			x[sorted_idx]
         UpperCAmelCase	    =			sorted_values[:, :, : self.action_dim]
         UpperCAmelCase	    =			actions.detach().cpu().numpy()
         UpperCAmelCase	    =			self.de_normalize(snake_case__ ,		key="""actions"""				)

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

         UpperCAmelCase	    =			denorm_actions[selected_index, 0]
         return denorm_actions








"""simple docstring"""







import random
from typing import Any






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       for _ in range(len(lowerCAmelCase		)		):
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase	    =			random.randint(0	,      len(lowerCAmelCase		) - 1		)
              UpperCAmelCase    ,       UpperCAmelCase	    =			data[b], data[a]
       return data


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      [0, 1, 2, 3, 4, 5, 6, 7]
 lowerCAmelCase_	:    List[str]            =      ['''python''', '''says''', '''hello''', '''!''']
 print('''Fisher-Yates Shuffle:''')
 print('''List''', integers, strings)
 print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
