





'''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       (a__				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      ):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          for key, val in cond.items():
               __A		:					List[Any]						=			val.clone()
          return x_in


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






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

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

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

               __A		:					int						=			self.unet(x.permute(0		,      2		,      1)		,      _UpperCAmelCase).sample.permute(0		,      2		,      1)

               # TODO: verify deprecation of this kwarg
               __A		:					int						=			self.scheduler.step(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      predict_epsilon=_UpperCAmelCase)['prev_sample']

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


     def __call__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.1):

          '''simple docstring'''






          __A		:					str						=			self.normalize(_UpperCAmelCase		,      'observations')
          __A		:					str						=			obs[None].repeat(_UpperCAmelCase		,      axis=0)

          __A		:					Any						=			{0: self.to_torch(_UpperCAmelCase)}
          __A		:					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)
          __A		:					Tuple						=			randn_tensor(_UpperCAmelCase		,      device=self.unet.device)
          __A		:					Tuple						=			self.reset_xa(_UpperCAmelCase		,      _UpperCAmelCase		,      self.action_dim)
          __A		:					Tuple						=			self.to_torch(_UpperCAmelCase)

          # run the diffusion process
          __A					,__A		:					Any						=			self.run_diffusion(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

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

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

          __A		:					str						=			denorm_actions[selected_index, 0]
          return denorm_actions




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowercase    	=		"2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
   raise ImportWarning(
       "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
   )

if version.parse(pyarrow.__version__).major < 8:
   raise ImportWarning(
       "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
       "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
   )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowercase    	=		concatenate_datasets
_lowercase    	=		DownloadConfig
_lowercase    	=		DownloadManager
_lowercase    	=		DownloadMode
_lowercase    	=		DownloadConfig
_lowercase    	=		DownloadMode
_lowercase    	=		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager
