



'''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_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Dict:
      return (x_in - self.means[key]) / self.stds[key]




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		str:
      return x_in * self.stds[key] + self.means[key]




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[Any]:
      if type(__UpperCAmelCase      ) is dict:
          return {k: self.to_torch(__UpperCAmelCase      ) for k, v in x_in.items()}
      elif torch.is_tensor(__UpperCAmelCase      ):
          return x_in.to(self.unet.device      )
      return torch.tensor(__UpperCAmelCase  ,device=self.unet.device      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		List[str]:
      for key, val in cond.items():
          lowerCAmelCase__ :  List[Any]							      =					val.clone()
      return x_in




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  Optional[int]							      =					x.shape[0]
      lowerCAmelCase__ :  int							      =					None
      for i in tqdm.tqdm(self.scheduler.timesteps      ):
          # create batch of timesteps to pass into model
          lowerCAmelCase__ :  Dict							      =					torch.full((batch_size,)  ,__UpperCAmelCase  ,device=self.unet.device  ,dtype=torch.long      )
          for _ in range(__UpperCAmelCase      ):
              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      )  ,__UpperCAmelCase      ).sample
                  lowerCAmelCase__ :  int							      =					torch.autograd.grad([y.sum()]  ,[x]      )[0]

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

              lowerCAmelCase__ :  Optional[Any]							      =					0
              lowerCAmelCase__ :  Tuple							      =					x.detach()
              lowerCAmelCase__ :  str							      =					x + scale * grad
              lowerCAmelCase__ :  Optional[Any]							      =					self.reset_xa(__UpperCAmelCase  ,__UpperCAmelCase  ,self.action_dim      )

          lowerCAmelCase__ :  Union[str, Any]							      =					self.unet(x.permute(0  ,2  ,1      )  ,__UpperCAmelCase      ).sample.permute(0  ,2  ,1      )

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

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






  def __call__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=64  ,__UpperCAmelCase=32  ,__UpperCAmelCase=2  ,__UpperCAmelCase=0.1      )    ->		Optional[int]:
      # normalize the observations and create  batch dimension
      lowerCAmelCase__ :  int							      =					self.normalize(__UpperCAmelCase  ,"""observations"""      )
      lowerCAmelCase__ :  Dict							      =					obs[None].repeat(__UpperCAmelCase  ,axis=0      )

      lowerCAmelCase__ :  Dict							      =					{0: self.to_torch(__UpperCAmelCase      )}
      lowerCAmelCase__ :  Union[str, 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__ :  Any							      =					randn_tensor(__UpperCAmelCase  ,device=self.unet.device      )
      lowerCAmelCase__ :  int							      =					self.reset_xa(__UpperCAmelCase  ,__UpperCAmelCase  ,self.action_dim      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.to_torch(__UpperCAmelCase      )

      # run the diffusion process
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					self.run_diffusion(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )

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

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

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







'''simple docstring'''





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    try:
        with open(UpperCamelCase							,    """rb"""			) as flax_state_f:
            lowerCAmelCase__ :  Union[str, Any]							      =					from_bytes(UpperCamelCase							,    flax_state_f.read()			)
    except UnpicklingError as e:
        try:
            with open(UpperCamelCase			) as f:
                if f.read().startswith("""version"""			):
                    raise OSError(
                        """You seem to have cloned a repository without having git-lfs installed. Please"""
                        """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
                        """ folder you cloned."""			)
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """			)

    return load_flax_weights_in_pytorch_model(UpperCamelCase							,    UpperCamelCase			)




def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
            """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
            """ instructions."""			)
        raise

    # check if we have bf16 weights
    lowerCAmelCase__ :  str							      =					flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase			: x.dtype == jnp.bfloataa							,    UpperCamelCase			)			).values()
    if any(UpperCamelCase			):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
            """before loading those in PyTorch model."""			)
        lowerCAmelCase__ :  Dict							      =					jax.tree_util.tree_map(
            lambda UpperCamelCase			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params							,    UpperCamelCase			)

    lowerCAmelCase__ :  Any							      =					""""""

    lowerCAmelCase__ :  Any							      =					flatten_dict(UpperCamelCase							,    sep="""."""			)
    lowerCAmelCase__ :  Optional[int]							      =					pt_model.state_dict()

    # keep track of unexpected & missing keys
    lowerCAmelCase__ :  Optional[Any]							      =					[]
    lowerCAmelCase__ :  int							      =					set(pt_model_dict.keys()			)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        lowerCAmelCase__ :  Union[str, Any]							      =					flax_key_tuple.split("""."""			)

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            lowerCAmelCase__ :  Optional[int]							      =					flax_key_tuple_array[:-1] + ["""weight"""]
            lowerCAmelCase__ :  Any							      =					jnp.transpose(UpperCamelCase							,    (3, 2, 0, 1)			)
        elif flax_key_tuple_array[-1] == "kernel":
            lowerCAmelCase__ :  str							      =					flax_key_tuple_array[:-1] + ["""weight"""]
            lowerCAmelCase__ :  Any							      =					flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            lowerCAmelCase__ :  int							      =					flax_key_tuple_array[:-1] + ["""weight"""]

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(UpperCamelCase			):
                lowerCAmelCase__ :  List[str]							      =					(
                    flax_key_tuple_string.replace("""_0"""							,    """.0"""			)
                    .replace("""_1"""							,    """.1"""			)
                    .replace("""_2"""							,    """.2"""			)
                    .replace("""_3"""							,    """.3"""			)
                    .replace("""_4"""							,    """.4"""			)
                    .replace("""_5"""							,    """.5"""			)
                    .replace("""_6"""							,    """.6"""			)
                    .replace("""_7"""							,    """.7"""			)
                    .replace("""_8"""							,    """.8"""			)
                    .replace("""_9"""							,    """.9"""			)
                )

        lowerCAmelCase__ :  Union[str, Any]							      =					""".""".join(UpperCamelCase			)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""			)
            else:
                # add weight to pytorch dict
                lowerCAmelCase__ :  int							      =					np.asarray(UpperCamelCase			) if not isinstance(UpperCamelCase							,    np.ndarray			) else flax_tensor
                lowerCAmelCase__ :  int							      =					torch.from_numpy(UpperCamelCase			)
                # remove from missing keys
                missing_keys.remove(UpperCamelCase			)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(UpperCamelCase			)

    pt_model.load_state_dict(UpperCamelCase			)

    # re-transform missing_keys to list
    lowerCAmelCase__ :  Optional[int]							      =					list(UpperCamelCase			)

    if len(UpperCamelCase			) > 0:
        logger.warning(
            """Some weights of the Flax model were not used when initializing the PyTorch model"""
            f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
            f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
            """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
            f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
            """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
            """ FlaxBertForSequenceClassification model)."""			)
    if len(UpperCamelCase			) > 0:
        logger.warning(
            f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
            f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
            """ use it for predictions and inference."""			)

    return pt_model


