






'''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        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




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




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							Union[str, Any]   , __lowercase		:							str )		->  Tuple:
       return (x_in - self.means[key]) / self.stds[key]




 def        __magic_name__		( self		:							Dict   , __lowercase		:							Tuple   , __lowercase		:							List[str] )		->  Any:
       return x_in * self.stds[key] + self.means[key]




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




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							Tuple   , __lowercase		:							int   , __lowercase		:							Dict   , __lowercase		:							int )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=x.shape[0]
       SCREAMING_SNAKE_CASE__       :			List[str]													=None
       for i in tqdm.tqdm(self.scheduler.timesteps ):
             # create batch of timesteps to pass into model
             SCREAMING_SNAKE_CASE__       :			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
                         SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.value_function(x.permute(0   , 2   , 1 )   , __lowercase ).sample
                         SCREAMING_SNAKE_CASE__       :			int													=torch.autograd.grad([y.sum()]   , [x] )[0]

                         SCREAMING_SNAKE_CASE__       :			Tuple													=self.scheduler._get_variance(__lowercase )
                         SCREAMING_SNAKE_CASE__       :			Any													=torch.exp(0.5 * posterior_variance )
                         SCREAMING_SNAKE_CASE__       :			int													=model_std * grad

                   SCREAMING_SNAKE_CASE__       :			int													=0
                   SCREAMING_SNAKE_CASE__       :			int													=x.detach()
                   SCREAMING_SNAKE_CASE__       :			Dict													=x + scale * grad
                   SCREAMING_SNAKE_CASE__       :			Dict													=self.reset_xa(__lowercase   , __lowercase   , self.action_dim )

             SCREAMING_SNAKE_CASE__       :			List[Any]													=self.unet(x.permute(0   , 2   , 1 )   , __lowercase ).sample.permute(0   , 2   , 1 )

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

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




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

       SCREAMING_SNAKE_CASE__       :			Any													={0: self.to_torch(__lowercase )}
       SCREAMING_SNAKE_CASE__       :			Dict													=(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)
       SCREAMING_SNAKE_CASE__       :			Dict													=randn_tensor(__lowercase   , device=self.unet.device )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.reset_xa(__lowercase   , __lowercase   , self.action_dim )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.to_torch(__lowercase )

       # run the diffusion process
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Dict													=self.run_diffusion(__lowercase   , __lowercase   , __lowercase   , __lowercase )

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

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

       SCREAMING_SNAKE_CASE__       :			Tuple													=denorm_actions[selected_index, 0]
       return denorm_actions







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					=	{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)