




"""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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      ):
							super().__init__()
							_lowerCamelCase     :			Any    =    value_function
							_lowerCamelCase     :			str    =    unet
							_lowerCamelCase     :			Union[str, Any]    =    scheduler
							_lowerCamelCase     :			Optional[Any]    =    env
							_lowerCamelCase     :			Tuple    =    env.get_dataset()
							_lowerCamelCase     :			str    =    {}
							for key in self.data.keys():
											try:
															_lowerCamelCase     :			List[str]    =    self.data[key].mean()
											except:  # noqa: E722
															pass
							_lowerCamelCase     :			List[Any]    =    {}
							for key in self.data.keys():
											try:
															_lowerCamelCase     :			List[str]    =    self.data[key].std()
											except:  # noqa: E722
															pass
							_lowerCamelCase     :			int    =    env.observation_space.shape[0]
							_lowerCamelCase     :			str    =    env.action_space.shape[0]
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							return (x_in - self.means[key]) / self.stds[key]
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							return x_in * self.stds[key] + self.means[key]
			def 					A_    (  self  ,      lowercase  ):
							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 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							for key, val in cond.items():
											_lowerCamelCase     :			Dict    =    val.clone()
							return x_in
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[Any]    =    x.shape[0]
							_lowerCamelCase     :			Any    =    None
							for i in tqdm.tqdm(self.scheduler.timesteps  ):
											# create batch of timesteps to pass into model
											_lowerCamelCase     :			Tuple    =    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
																			_lowerCamelCase     :			str    =    self.value_function(x.permute(0  ,      2  ,      1  )  ,      lowercase  ).sample
																			_lowerCamelCase     :			Optional[int]    =    torch.autograd.grad([y.sum()]  ,      [x]  )[0]

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

															_lowerCamelCase     :			Tuple    =    0
															_lowerCamelCase     :			int    =    x.detach()
															_lowerCamelCase     :			List[str]    =    x + scale * grad
															_lowerCamelCase     :			Tuple    =    self.reset_xa(lowercase  ,      lowercase  ,      self.action_dim  )

											_lowerCamelCase     :			Tuple    =    self.unet(x.permute(0  ,      2  ,      1  )  ,      lowercase  ).sample.permute(0  ,      2  ,      1  )

											# TODO: verify deprecation of this kwarg
											_lowerCamelCase     :			List[str]    =    self.scheduler.step(lowercase  ,      lowercase  ,      lowercase  ,      predict_epsilon=lowercase  )['prev_sample']

											# apply conditions to the trajectory (set the initial state)
											_lowerCamelCase     :			Any    =    self.reset_xa(lowercase  ,      lowercase  ,      self.action_dim  )
											_lowerCamelCase     :			Union[str, Any]    =    self.to_torch(lowercase  )
							return x, y






			def __call__(  self  ,      lowercase  ,      lowercase=64  ,      lowercase=32  ,      lowercase=2  ,      lowercase=0.1  ):
							# normalize the observations and create  batch dimension
							_lowerCamelCase     :			List[str]    =    self.normalize(lowercase  ,      'observations'  )
							_lowerCamelCase     :			Dict    =    obs[None].repeat(lowercase  ,      axis=0  )

							_lowerCamelCase     :			Dict    =    {0: self.to_torch(lowercase  )}
							_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     :			Tuple    =    randn_tensor(lowercase  ,      device=self.unet.device  )
							_lowerCamelCase     :			int    =    self.reset_xa(lowercase  ,      lowercase  ,      self.action_dim  )
							_lowerCamelCase     :			Dict    =    self.to_torch(lowercase  )

							# run the diffusion process
							_lowerCamelCase, _lowerCamelCase     :			Any    =    self.run_diffusion(lowercase  ,      lowercase  ,      lowercase  ,      lowercase  )

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

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

							_lowerCamelCase     :			Union[str, Any]    =    denorm_actions[selected_index, 0]
							return denorm_actions



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

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


UpperCamelCase__    					=      {
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''RemBertTokenizer''']

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''RemBertForCausalLM''',
      '''RemBertForMaskedLM''',
      '''RemBertForMultipleChoice''',
      '''RemBertForQuestionAnswering''',
      '''RemBertForSequenceClassification''',
      '''RemBertForTokenClassification''',
      '''RemBertLayer''',
      '''RemBertModel''',
      '''RemBertPreTrainedModel''',
      '''load_tf_weights_in_rembert''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFRemBertForCausalLM''',
      '''TFRemBertForMaskedLM''',
      '''TFRemBertForMultipleChoice''',
      '''TFRemBertForQuestionAnswering''',
      '''TFRemBertForSequenceClassification''',
      '''TFRemBertForTokenClassification''',
      '''TFRemBertLayer''',
      '''TFRemBertModel''',
      '''TFRemBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert import RemBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert_fast import RemBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rembert import (
        REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertLayer,
        RemBertModel,
        RemBertPreTrainedModel,
        load_tf_weights_in_rembert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rembert import (
        TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRemBertForCausalLM,
        TFRemBertForMaskedLM,
        TFRemBertForMultipleChoice,
        TFRemBertForQuestionAnswering,
        TFRemBertForSequenceClassification,
        TFRemBertForTokenClassification,
        TFRemBertLayer,
        TFRemBertModel,
        TFRemBertPreTrainedModel,
    )


else:
  import sys

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