


'''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   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



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

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    List[Any]						)  ->    Optional[int]:
      return (x_in - self.means[key]) / self.stds[key]

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Optional[Any]						)  ->    Optional[int]:
      return x_in * self.stds[key] + self.means[key]

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    Any						)  ->    Any:
      if type(lowerCamelCase						) is dict:
        return {k: self.to_torch(lowerCamelCase						) for k, v in x_in.items()}
      elif torch.is_tensor(lowerCamelCase						):
        return x_in.to(self.unet.device						)
      return torch.tensor(lowerCamelCase       ,		device=self.unet.device						)

    def __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    Dict       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Any						)  ->    List[str]:
      for key, val in cond.items():
        lowerCAmelCase_     : Optional[int]     =  val.clone()
      return x_in

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

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

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

          lowerCAmelCase_     : Tuple     =  0
          lowerCAmelCase_     : Optional[int]     =  x.detach()
          lowerCAmelCase_     : Optional[int]     =  x + scale * grad
          lowerCAmelCase_     : int     =  self.reset_xa(lowerCamelCase       ,		lowerCamelCase       ,		self.action_dim						)

        lowerCAmelCase_     : Any     =  self.unet(x.permute(0       ,		2       ,		1						)       ,		lowerCamelCase						).sample.permute(0       ,		2       ,		1						)

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

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





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

      lowerCAmelCase_     : Optional[Any]     =  {0: self.to_torch(lowerCamelCase						)}
      lowerCAmelCase_     : 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)
      lowerCAmelCase_     : Any     =  randn_tensor(lowerCamelCase       ,		device=self.unet.device						)
      lowerCAmelCase_     : List[Any]     =  self.reset_xa(lowerCamelCase       ,		lowerCamelCase       ,		self.action_dim						)
      lowerCAmelCase_     : Optional[int]     =  self.to_torch(lowerCamelCase						)

      # run the diffusion process
      lowerCAmelCase_,				lowerCAmelCase_     : Dict     =  self.run_diffusion(lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase						)

      # sort output trajectories by value
      lowerCAmelCase_     : List[Any]     =  y.argsort(0       ,		descending=lowerCamelCase						).squeeze()
      lowerCAmelCase_     : Dict     =  x[sorted_idx]
      lowerCAmelCase_     : Union[str, Any]     =  sorted_values[:, :, : self.action_dim]
      lowerCAmelCase_     : List[Any]     =  actions.detach().cpu().numpy()
      lowerCAmelCase_     : Tuple     =  self.de_normalize(lowerCamelCase       ,		key="""actions"""						)

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

      lowerCAmelCase_     : Any     =  denorm_actions[selected_index, 0]
      return denorm_actions







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__A   :    str											= {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    List[str]											= [
      "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ASTForAudioClassification",
      "ASTModel",
      "ASTPreTrainedModel",
  ]

try:
  if not is_speech_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    Union[str, Any]											= ["ASTFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ASTConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_audio_spectrogram_transformer import (
        AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ASTForAudioClassification,
        ASTModel,
        ASTPreTrainedModel,
    )

  try:
    if not is_speech_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
  import sys

  __A   :    List[Any]											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



