
import pytest

import datasets


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


def 						UpperCAmelCase      (     a_		,       a_						)		->     str:







				"""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      (     a_						)		->     Optional[int]:







				"""simple docstring"""



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


@pytest.fixture(autouse=a_						)
def 						UpperCAmelCase      (     a_		,       a_						)		->     Union[str, Any]:







				"""simple docstring"""



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


@pytest.fixture(autouse=a_		,       scope="session"						)
def 						UpperCAmelCase      (     )		->     str:







				"""simple docstring"""



				datasets.disable_progress_bar()


@pytest.fixture(autouse=a_						)
def 						UpperCAmelCase      (     a_						)		->     Optional[int]:







				"""simple docstring"""



				monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"		,       a_						)


@pytest.fixture
def 						UpperCAmelCase      (     a_						)		->     List[Any]:







				"""simple docstring"""



				monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"		,       a_						)


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

					# set step values
					self.scheduler.set_timesteps(A			)

					for t in self.progress_bar(self.scheduler.timesteps			):
									# 1. predict noise model_output
									__A             =			self.unet(A							,A			).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

					__A             =			(image / 2 + 0.5).clamp(0							,1			)
					__A             =			image.cpu().permute(0							,2							,3							,1			).numpy()
					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
