




"""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 	lowerCamelCase_							(				SCREAMING_SNAKE_CASE_ ):


 '''simple docstring'''







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



 def 				lowerCAmelCase_  (							self  :		List[Any]    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Optional[int]						):
       return (x_in - self.means[key]) / self.stds[key]



 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		List[str]						):
       return x_in * self.stds[key] + self.means[key]



 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		Optional[Any]						):
       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 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Optional[Any]						):
       for key, val in cond.items():
             SCREAMING_SNAKE_CASE_     						=      val.clone()
       return x_in



 def 				lowerCAmelCase_  (							self  :		List[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      x.shape[0]
       SCREAMING_SNAKE_CASE_     						=      None
       for i in tqdm.tqdm(self.scheduler.timesteps						):
             # create batch of timesteps to pass into model
             SCREAMING_SNAKE_CASE_     						=      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
                         SCREAMING_SNAKE_CASE_     						=      self.value_function(x.permute(0    ,    2    ,    1						)    ,    __a						).sample
                         SCREAMING_SNAKE_CASE_     						=      torch.autograd.grad([y.sum()]    ,    [x]						)[0]

                         SCREAMING_SNAKE_CASE_     						=      self.scheduler._get_variance(__a						)
                         SCREAMING_SNAKE_CASE_     						=      torch.exp(0.5 * posterior_variance						)
                         SCREAMING_SNAKE_CASE_     						=      model_std * grad

                   SCREAMING_SNAKE_CASE_     						=      0
                   SCREAMING_SNAKE_CASE_     						=      x.detach()
                   SCREAMING_SNAKE_CASE_     						=      x + scale * grad
                   SCREAMING_SNAKE_CASE_     						=      self.reset_xa(__a    ,    __a    ,    self.action_dim						)

             SCREAMING_SNAKE_CASE_     						=      self.unet(x.permute(0    ,    2    ,    1						)    ,    __a						).sample.permute(0    ,    2    ,    1						)

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

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




 def __call__(							self  :		Tuple    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Optional[Any]=64    ,    _lowerCAmelCase  :		List[Any]=32    ,    _lowerCAmelCase  :		Union[str, Any]=2    ,    _lowerCAmelCase  :		Dict=0.1						):
       SCREAMING_SNAKE_CASE_     						=      self.normalize(__a    ,    'observations'						)
       SCREAMING_SNAKE_CASE_     						=      obs[None].repeat(__a    ,    axis=0						)

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

       # run the diffusion process
       SCREAMING_SNAKE_CASE_     						=      self.run_diffusion(__a    ,    __a    ,    __a    ,    __a						)

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

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

       SCREAMING_SNAKE_CASE_     						=      denorm_actions[selected_index, 0]
       return denorm_actions





import random
from typing import Any

def  UpperCAmelCase_							(				__UpperCAmelCase						:     list							) ->			list[Any]:
      for _ in range(len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      data[b], data[a]
      return data


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