



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,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     )	->						List[str]:
          super().__init__()
          UpperCamelCase       :    str		      =      value_function
          UpperCamelCase       :    Tuple		      =      unet
          UpperCamelCase       :    Dict		      =      scheduler
          UpperCamelCase       :    Dict		      =      env
          UpperCamelCase       :    List[str]		      =      env.get_dataset()
          UpperCamelCase       :    Optional[int]		      =      {}
          for key in self.data.keys():
             try:
                UpperCamelCase       :    Dict		      =      self.data[key].mean()
             except:  # noqa: E722
                pass
          UpperCamelCase       :    Optional[Any]		      =      {}
          for key in self.data.keys():
             try:
                UpperCamelCase       :    Tuple		      =      self.data[key].std()
             except:  # noqa: E722
                pass
          UpperCamelCase       :    Optional[int]		      =      env.observation_space.shape[0]
          UpperCamelCase       :    List[str]		      =      env.action_space.shape[0]
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						str:
          return (x_in - self.means[key]) / self.stds[key]
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						int:
          return x_in * self.stds[key] + self.means[key]
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          if type(SCREAMING_SNAKE_CASE_			) is dict:
             return {k: self.to_torch(SCREAMING_SNAKE_CASE_			) for k, v in x_in.items()}
          elif torch.is_tensor(SCREAMING_SNAKE_CASE_			):
             return x_in.to(self.unet.device			)
          return torch.tensor(SCREAMING_SNAKE_CASE_,     device=self.unet.device			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Any:
          for key, val in cond.items():
             UpperCamelCase       :    List[Any]		      =      val.clone()
          return x_in
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    str		      =      x.shape[0]
          UpperCamelCase       :    str		      =      None
          for i in tqdm.tqdm(self.scheduler.timesteps			):
             # create batch of timesteps to pass into model
             UpperCamelCase       :    Optional[Any]		      =      torch.full((batch_size,),     SCREAMING_SNAKE_CASE_,     device=self.unet.device,     dtype=torch.long			)
             for _ in range(SCREAMING_SNAKE_CASE_			):
                with torch.enable_grad():
                   x.requires_grad_()

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

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

                UpperCamelCase       :    List[Any]		      =      0
                UpperCamelCase       :    str		      =      x.detach()
                UpperCamelCase       :    Dict		      =      x + scale * grad
                UpperCamelCase       :    Optional[int]		      =      self.reset_xa(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     self.action_dim			)

             UpperCamelCase       :    Dict		      =      self.unet(x.permute(0,     2,     1			),     SCREAMING_SNAKE_CASE_			).sample.permute(0,     2,     1			)

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

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






       def __call__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=64,     SCREAMING_SNAKE_CASE_=32,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=0.1			)	->						Dict:
          # normalize the observations and create  batch dimension
          UpperCamelCase       :    Union[str, Any]		      =      self.normalize(SCREAMING_SNAKE_CASE_,     'observations'			)
          UpperCamelCase       :    int		      =      obs[None].repeat(SCREAMING_SNAKE_CASE_,     axis=0			)

          UpperCamelCase       :    List[str]		      =      {0: self.to_torch(SCREAMING_SNAKE_CASE_			)}
          UpperCamelCase       :    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)
          UpperCamelCase       :    Union[str, Any]		      =      randn_tensor(SCREAMING_SNAKE_CASE_,     device=self.unet.device			)
          UpperCamelCase       :    Dict		      =      self.reset_xa(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     self.action_dim			)
          UpperCamelCase       :    List[Any]		      =      self.to_torch(SCREAMING_SNAKE_CASE_			)

          # run the diffusion process
          UpperCamelCase      ,	UpperCamelCase       :    Optional[Any]		      =      self.run_diffusion(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

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

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

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









"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''vit_mae'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]=768       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			Optional[Any]=12       ,				_UpperCAmelCase			:			Optional[int]=3_072       ,				_UpperCAmelCase			:			Any="gelu"       ,				_UpperCAmelCase			:			Optional[Any]=0.0       ,				_UpperCAmelCase			:			Optional[int]=0.0       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			List[Any]=1E-1_2       ,				_UpperCAmelCase			:			Optional[Any]=224       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=3       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=512       ,				_UpperCAmelCase			:			int=8       ,				_UpperCAmelCase			:			List[Any]=2_048       ,				_UpperCAmelCase			:			Optional[Any]=0.75       ,				_UpperCAmelCase			:			List[str]=False       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       super().__init__(**_UpperCAmelCase						)

       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  image_size
       _A     =  patch_size
       _A     =  num_channels
       _A     =  qkv_bias
       _A     =  decoder_num_attention_heads
       _A     =  decoder_hidden_size
       _A     =  decoder_num_hidden_layers
       _A     =  decoder_intermediate_size
       _A     =  mask_ratio
       _A     =  norm_pix_loss

