





'''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						(a__					):







  def __init__(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						List[str]:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						Dict:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Dict:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Dict:






        """simple docstring"""





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

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

                          _snake_case		:	Dict					     =     self.scheduler._get_variance(lowercase__		)
                          _snake_case		:	Dict					     =     torch.exp(0.5 * posterior_variance		)
                          _snake_case		:	Union[str, Any]					     =     model_std * grad

                    _snake_case		:	str					     =     0
                    _snake_case		:	Tuple					     =     x.detach()
                    _snake_case		:	Dict					     =     x + scale * grad
                    _snake_case		:	Optional[int]					     =     self.reset_xa(lowercase__     ,     lowercase__     ,     self.action_dim		)

              _snake_case		:	Any					     =     self.unet(x.permute(0     ,     2     ,     1		)     ,     lowercase__		).sample.permute(0     ,     2     ,     1		)

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

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







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






        """simple docstring"""





        _snake_case		:	int					     =     self.normalize(lowercase__     ,     '''observations'''		)
        _snake_case		:	Dict					     =     obs[None].repeat(lowercase__     ,     axis=0		)

        _snake_case		:	int					     =     {0: self.to_torch(lowercase__		)}
        _snake_case		:	List[str]					     =     (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)
        _snake_case		:	Any					     =     randn_tensor(lowercase__     ,     device=self.unet.device		)
        _snake_case		:	Union[str, Any]					     =     self.reset_xa(lowercase__     ,     lowercase__     ,     self.action_dim		)
        _snake_case		:	Any					     =     self.to_torch(lowercase__		)

        # run the diffusion process
        _snake_case     ,							_snake_case		:	List[Any]					     =     self.run_diffusion(lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)

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

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

        _snake_case		:	int					     =     denorm_actions[selected_index, 0]
        return denorm_actions









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		int		=							{
    'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							[
       'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PegasusXForConditionalGeneration',
       'PegasusXModel',
       'PegasusXPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pegasus_x import (
          PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
          PegasusXForConditionalGeneration,
          PegasusXModel,
          PegasusXPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase					:		Dict		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


