

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	UNetaDModel
  __lowerCAmelCase     :	ScoreSdeVeScheduler

  def __init__(					self						,		_A						,		_A):
      super().__init__()
      self.register_modules(unet=_A						,		scheduler=_A)


  @torch.no_grad()
  def __call__(					self						,		_A = 1						,		_A = 2000						,		_A = None						,		_A = "pil"						,		_A = True						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						self.unet.config.sample_size
      SCREAMING_SNAKE_CASE_									=						(batch_size, 3, img_size, img_size)

      SCREAMING_SNAKE_CASE_									=						self.unet

      SCREAMING_SNAKE_CASE_									=						randn_tensor(_A						,		generator=_A) * self.scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_									=						sample.to(self.device)

      self.scheduler.set_timesteps(_A)
      self.scheduler.set_sigmas(_A)

      for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
          SCREAMING_SNAKE_CASE_									=						self.scheduler.sigmas[i] * torch.ones(shape[0]						,		device=self.device)

          # correction step
          for _ in range(self.scheduler.config.correct_steps):
              SCREAMING_SNAKE_CASE_									=						self.unet(_A						,		_A).sample
              SCREAMING_SNAKE_CASE_									=						self.scheduler.step_correct(_A						,		_A						,		generator=_A).prev_sample

          # prediction step
          SCREAMING_SNAKE_CASE_									=						model(_A						,		_A).sample
          SCREAMING_SNAKE_CASE_									=						self.scheduler.step_pred(_A						,		_A						,		_A						,		generator=_A)

          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						output.prev_sample, output.prev_sample_mean

      SCREAMING_SNAKE_CASE_									=						sample_mean.clamp(0						,		1)
      SCREAMING_SNAKE_CASE_									=						sample.cpu().permute(0						,		2						,		3						,		1).numpy()
      if output_type == "pil":
          SCREAMING_SNAKE_CASE_									=						self.numpy_to_pil(_A)

      if not return_dict:
          return (sample,)

      return ImagePipelineOutput(images=_A)





import pytest

import datasets


# Import fixture modules as plugins
UpperCamelCase__  :      Union[str, Any]			=    ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    for item in items:
        if any(marker in item.keywords for marker in ['integration', 'unit']       ):
            continue
        item.add_marker(pytest.mark.unit       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


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


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE       )
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						tmp_path_factory.getbasetemp() / 'cache'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'datasets'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'metrics'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'modules'
    monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    SCREAMING_SNAKE_CASE_									=						test_hf_datasets_cache / 'downloads'
    monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' ,   str(_SCREAMING_SNAKE_CASE       )       )
    SCREAMING_SNAKE_CASE_									=						test_hf_datasets_cache / 'downloads' / 'extracted'
    monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' ,   str(_SCREAMING_SNAKE_CASE       )       )


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE ,   scope='session'       )
def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    datasets.disable_progress_bar()


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE       )
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any       ):





    """simple docstring"""


    monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' ,   _SCREAMING_SNAKE_CASE       )





@pytest.fixture
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


    monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' ,   _SCREAMING_SNAKE_CASE       )


