





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    (			__lowercase							):


  '''simple docstring'''







  def __init__(      self      :					List[Any]      ,				UpperCamelCase      :					Tuple      ,				UpperCamelCase      :					Dict      ,				UpperCamelCase      :					List[Any]      ,				UpperCamelCase      :					Tuple      ,				):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					List[Any]      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					Union[str, Any]      ,				UpperCamelCase      :					List[Any]							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    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 UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					Optional[int]      ,				UpperCamelCase      :					List[Any]      ,				UpperCamelCase      :					Optional[int]							):


    '''simple docstring'''





    for key, val in cond.items():
      _snake_case   :				List[Any]														=	val.clone()
    return x_in







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Union[str, Any]      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					Union[str, Any]      ,				UpperCamelCase      :					Any							):


    '''simple docstring'''





    _snake_case   :				Union[str, 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   :				str														=	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
          _snake_case   :				Union[str, Any]														=	self.value_function(x.permute(0      ,				2      ,				1							)      ,				__A							).sample
          _snake_case   :				Tuple														=	torch.autograd.grad([y.sum()]      ,				[x]							)[0]

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

        _snake_case   :				List[Any]														=	0
        _snake_case   :				int														=	x.detach()
        _snake_case   :				Union[str, Any]														=	x + scale * grad
        _snake_case   :				Dict														=	self.reset_xa(__A      ,				__A      ,				self.action_dim							)

      _snake_case   :				Optional[Any]														=	self.unet(x.permute(0      ,				2      ,				1							)      ,				__A							).sample.permute(0      ,				2      ,				1							)

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

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







  def __call__(      self      :					Optional[int]      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					Dict=64      ,				UpperCamelCase      :					Union[str, Any]=32      ,				UpperCamelCase      :					List[Any]=2      ,				UpperCamelCase      :					Optional[Any]=0.1							):


    '''simple docstring'''





    _snake_case   :				int														=	self.normalize(__A      ,				'observations'							)
    _snake_case   :				List[str]														=	obs[None].repeat(__A      ,				axis=0							)

    _snake_case   :				List[str]														=	{0: self.to_torch(__A							)}
    _snake_case   :				Optional[int]														=	(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(__A      ,				device=self.unet.device							)
    _snake_case   :				Tuple														=	self.reset_xa(__A      ,				__A      ,				self.action_dim							)
    _snake_case   :				List[str]														=	self.to_torch(__A							)

    # run the diffusion process
    _snake_case							,					_snake_case   :				int														=	self.run_diffusion(__A      ,				__A      ,				__A      ,				__A							)

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

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

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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Tuple      					="""audio-spectrogram-transformer"""







  def __init__(      self      :					List[Any]      ,				UpperCamelCase      :					Union[str, Any]=7_68      ,				UpperCamelCase      :					int=12      ,				UpperCamelCase      :					str=12      ,				UpperCamelCase      :					Tuple=30_72      ,				UpperCamelCase      :					Optional[Any]="gelu"      ,				UpperCamelCase      :					Any=0.0      ,				UpperCamelCase      :					Dict=0.0      ,				UpperCamelCase      :					List[Any]=0.02      ,				UpperCamelCase      :					Dict=1e-1_2      ,				UpperCamelCase      :					str=16      ,				UpperCamelCase      :					List[Any]=True      ,				UpperCamelCase      :					Any=10      ,				UpperCamelCase      :					Optional[int]=10      ,				UpperCamelCase      :					int=10_24      ,				UpperCamelCase      :					Optional[Any]=1_28      ,				**UpperCamelCase      :					Optional[Any]      ,				):


    '''simple docstring'''





    super().__init__(**UpperCamelCase							)

    _snake_case   :				Tuple														=	hidden_size
    _snake_case   :				str														=	num_hidden_layers
    _snake_case   :				Optional[Any]														=	num_attention_heads
    _snake_case   :				Optional[Any]														=	intermediate_size
    _snake_case   :				Optional[Any]														=	hidden_act
    _snake_case   :				List[str]														=	hidden_dropout_prob
    _snake_case   :				Union[str, Any]														=	attention_probs_dropout_prob
    _snake_case   :				Any														=	initializer_range
    _snake_case   :				List[str]														=	layer_norm_eps
    _snake_case   :				int														=	patch_size
    _snake_case   :				List[str]														=	qkv_bias
    _snake_case   :				int														=	frequency_stride
    _snake_case   :				List[Any]														=	time_stride
    _snake_case   :				List[Any]														=	max_length
    _snake_case   :				List[str]														=	num_mel_bins




