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 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  def __init__(				self:			Optional[Any]				,				__lowerCAmelCase:			UNetaDModel				,				__lowerCAmelCase:			UNetaDModel				,				__lowerCAmelCase:			DDPMScheduler				,				__lowerCAmelCase:			int				,				) -> int:




    '''simple docstring'''




    super().__init__()
    __UpperCAmelCase						    =       value_function
    __UpperCAmelCase						    =       unet
    __UpperCAmelCase						    =       scheduler
    __UpperCAmelCase						    =       env
    __UpperCAmelCase						    =       env.get_dataset()
    __UpperCAmelCase						    =       {}
    for key in self.data.keys():
      try:
        __UpperCAmelCase						    =       self.data[key].mean()
      except:  # noqa: E722
        pass
    __UpperCAmelCase						    =       {}
    for key in self.data.keys():
      try:
        __UpperCAmelCase						    =       self.data[key].std()
      except:  # noqa: E722
        pass
    __UpperCAmelCase						    =       env.observation_space.shape[0]
    __UpperCAmelCase						    =       env.action_space.shape[0]






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			List[str]				,				__lowerCAmelCase:			Optional[Any]   ) -> List[str]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Tuple				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Optional[Any]   ) -> Optional[int]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			int   ) -> Tuple:




    '''simple docstring'''




    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 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			List[str]				,				__lowerCAmelCase:			Optional[int]				,				__lowerCAmelCase:			List[Any]   ) -> str:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			Union[str, Any]				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Optional[Any]				,				__lowerCAmelCase:			Optional[Any]   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       x.shape[0]
    __UpperCAmelCase						    =       None
    for i in tqdm.tqdm(self.scheduler.timesteps   ):
      # create batch of timesteps to pass into model
      __UpperCAmelCase						    =       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
          __UpperCAmelCase						    =       self.value_function(x.permute(0				,				2				,				1   )				,				__lowerCAmelCase   ).sample
          __UpperCAmelCase						    =       torch.autograd.grad([y.sum()]				,				[x]   )[0]

          __UpperCAmelCase						    =       self.scheduler._get_variance(__lowerCAmelCase   )
          __UpperCAmelCase						    =       torch.exp(0.5 * posterior_variance   )
          __UpperCAmelCase						    =       model_std * grad

        __UpperCAmelCase						    =       0
        __UpperCAmelCase						    =       x.detach()
        __UpperCAmelCase						    =       x + scale * grad
        __UpperCAmelCase						    =       self.reset_xa(__lowerCAmelCase				,				__lowerCAmelCase				,				self.action_dim   )

      __UpperCAmelCase						    =       self.unet(x.permute(0				,				2				,				1   )				,				__lowerCAmelCase   ).sample.permute(0				,				2				,				1   )

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

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






  def __call__(				self:			Optional[int]				,				__lowerCAmelCase:			Optional[Any]				,				__lowerCAmelCase:			Union[str, Any]=64				,				__lowerCAmelCase:			Tuple=32				,				__lowerCAmelCase:			int=2				,				__lowerCAmelCase:			str=0.1   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.normalize(__lowerCAmelCase				,				"observations"   )
    __UpperCAmelCase						    =       obs[None].repeat(__lowerCAmelCase				,				axis=0   )

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

    # run the diffusion process
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.run_diffusion(__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase   )

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

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

    __UpperCAmelCase						    =       denorm_actions[selected_index, 0]
    return denorm_actions




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= ['image_processor', 'tokenizer']
  lowerCAmelCase__		:	Any    		= 'CLIPImageProcessor'
  lowerCAmelCase__		:	Any    		= ('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(				self:			str				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Any=None				,				**__lowerCAmelCase:			str   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       None
    if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."				,				__lowerCAmelCase				,				)
      __UpperCAmelCase						    =       kwargs.pop("feature_extractor"   )

    __UpperCAmelCase						    =       image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."   )
    if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."   )

    super().__init__(__lowerCAmelCase				,				__lowerCAmelCase   )






  def __call__(				self:			Optional[int]				,				__lowerCAmelCase:			Union[str, Any]=None				,				__lowerCAmelCase:			Dict=None				,				__lowerCAmelCase:			Optional[int]=None				,				**__lowerCAmelCase:			Optional[int]   ) -> Dict:




    '''simple docstring'''




    if text is None and images is None:
      raise ValueError("You have to specify either text or images. Both cannot be none."   )

    if text is not None:
      __UpperCAmelCase						    =       self.tokenizer(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    if images is not None:
      __UpperCAmelCase						    =       self.image_processor(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    if text is not None and images is not None:
      __UpperCAmelCase						    =       image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**__lowerCAmelCase   )				,				tensor_type=__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			str				,				*__lowerCAmelCase:			str				,				**__lowerCAmelCase:			List[Any]   ) -> Optional[Any]:




    '''simple docstring'''




    return self.tokenizer.batch_decode(*__lowerCAmelCase				,				**__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Any				,				*__lowerCAmelCase:			List[Any]				,				**__lowerCAmelCase:			Dict   ) -> Union[str, Any]:




    '''simple docstring'''




    return self.tokenizer.decode(*__lowerCAmelCase				,				**__lowerCAmelCase   )






  @property
  def 	_UpperCAmelCase				(				self:			Tuple   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer.model_input_names
    __UpperCAmelCase						    =       self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






  @property
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    warnings.warn(
        "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,				__lowerCAmelCase				,				)
    return self.image_processor_class






  @property
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[str]:




    '''simple docstring'''




    warnings.warn(
        "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,				__lowerCAmelCase				,				)
    return self.image_processor



