





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 			__A       (      _snake_case  ):




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__      ,							a__      ,							a__      ,							):



             """simple docstring"""





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


      def 				__snake_case			(		self      ,							a__      ,							a__):



             """simple docstring"""





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


      def 				__snake_case			(		self      ,							a__      ,							a__):



             """simple docstring"""





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


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             if type(snake_case_) is dict:
                    return {k: self.to_torch(snake_case_) for k, v in x_in.items()}
             elif torch.is_tensor(snake_case_):
                    return x_in.to(self.unet.device)
             return torch.tensor(snake_case_      ,							device=self.unet.device)


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             for key, val in cond.items():
                    _lowerCamelCase							:     Optional[int]	=		val.clone()
             return x_in


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		x.shape[0]
             _lowerCamelCase							:     Optional[Any]	=		None
             for i in tqdm.tqdm(self.scheduler.timesteps):
                    # create batch of timesteps to pass into model
                    _lowerCamelCase							:     Union[str, Any]	=		torch.full((batch_size,)      ,							snake_case_      ,							device=self.unet.device      ,							dtype=torch.long)
                    for _ in range(snake_case_):
                           with torch.enable_grad():
                                  x.requires_grad_()

                                  # permute to match dimension for pre-trained models
                                  _lowerCamelCase							:     Optional[Any]	=		self.value_function(x.permute(0      ,							2      ,							1)      ,							snake_case_).sample
                                  _lowerCamelCase							:     Tuple	=		torch.autograd.grad([y.sum()]      ,							[x])[0]

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

                           _lowerCamelCase							:     List[Any]	=		0
                           _lowerCamelCase							:     int	=		x.detach()
                           _lowerCamelCase							:     int	=		x + scale * grad
                           _lowerCamelCase							:     Dict	=		self.reset_xa(snake_case_      ,							snake_case_      ,							self.action_dim)

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

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

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



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



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.normalize(snake_case_      ,							'''observations''')
             _lowerCamelCase							:     Union[str, Any]	=		obs[None].repeat(snake_case_      ,							axis=0)

             _lowerCamelCase							:     Dict	=		{0: self.to_torch(snake_case_)}
             _lowerCamelCase							:     Optional[int]	=		(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							:     List[str]	=		randn_tensor(snake_case_      ,							device=self.unet.device)
             _lowerCamelCase							:     Optional[int]	=		self.reset_xa(snake_case_      ,							snake_case_      ,							self.action_dim)
             _lowerCamelCase							:     Optional[int]	=		self.to_torch(snake_case_)

             # run the diffusion process
             _lowerCamelCase							:     Optional[int]	=		self.run_diffusion(snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_)

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

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

             _lowerCamelCase							:     List[Any]	=		denorm_actions[selected_index, 0]
             return denorm_actions










import pytest

import datasets


# Import fixture modules as plugins
_lowerCamelCase			       =      ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
       for item in items:
              if any(marker in item.keywords for marker in ['''integration''', '''unit''']      ):
                     continue
              item.add_marker(pytest.mark.unit      )




def    __UpperCAmelCase(      lowercase_      ):
       config.addinivalue_line('''markers''' ,    '''torchaudio_latest: mark test to run with torchaudio>=0.12'''      )




@pytest.fixture(autouse=lowercase_      )
def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
       _lowerCamelCase							:     Optional[Any]	=		tmp_path_factory.getbasetemp() / '''cache'''
       _lowerCamelCase							:     Optional[Any]	=		test_hf_cache_home / '''datasets'''
       _lowerCamelCase							:     Union[str, Any]	=		test_hf_cache_home / '''metrics'''
       _lowerCamelCase							:     Dict	=		test_hf_cache_home / '''modules'''
       monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' ,    str(lowercase_      )      )
       monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' ,    str(lowercase_      )      )
       monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' ,    str(lowercase_      )      )
       _lowerCamelCase							:     str	=		test_hf_datasets_cache / '''downloads'''
       monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' ,    str(lowercase_      )      )
       _lowerCamelCase							:     Optional[int]	=		test_hf_datasets_cache / '''downloads''' / '''extracted'''
       monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' ,    str(lowercase_      )      )




@pytest.fixture(autouse=lowercase_ ,    scope='''session'''      )
def    __UpperCAmelCase(      ):
       datasets.disable_progress_bar()




@pytest.fixture(autouse=lowercase_      )
def    __UpperCAmelCase(      lowercase_      ):
       # don't take tests into account when counting downloads
       monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' ,    lowercase_      )




@pytest.fixture
def    __UpperCAmelCase(      lowercase_      ):
       # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
       # To be removed once SQLAlchemy 2.0 supported
       monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' ,    lowercase_      )

