



'''simple docstring'''






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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= 42
	lowerCamelCase__    				= 42







	def __init__(		self    :  List[str]	,    __snake_case    :  UNetaDModel	,    __snake_case    :  ScoreSdeVeScheduler	)    ->	Tuple:
								super().__init__()
								self.register_modules(unet=__snake_case	,    scheduler=__snake_case	)







	@torch.no_grad()
	def __call__(		self    :  List[Any]	,    __snake_case    :  int = 1	,    __snake_case    :  int = 2000	,    __snake_case    :  Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    __snake_case    :  Optional[str] = "pil"	,    __snake_case    :  bool = True	,    **__snake_case    :  Optional[int]	,    )    ->	Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase				: Dict											=	self.unet.config.sample_size
								UpperCAmelCase				: str											=	(batch_size, 3, img_size, img_size)

								UpperCAmelCase				: Optional[int]											=	self.unet

								UpperCAmelCase				: int											=	randn_tensor(__snake_case	,    generator=__snake_case	) * self.scheduler.init_noise_sigma
								UpperCAmelCase				: int											=	sample.to(self.device	)

								self.scheduler.set_timesteps(__snake_case	)
								self.scheduler.set_sigmas(__snake_case	)

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

															# correction step
															for _ in range(self.scheduler.config.correct_steps	):
																						UpperCAmelCase				: Dict											=	self.unet(__snake_case	,    __snake_case	).sample
																						UpperCAmelCase				: int											=	self.scheduler.step_correct(__snake_case	,    __snake_case	,    generator=__snake_case	).prev_sample

															# prediction step
															UpperCAmelCase				: List[str]											=	model(__snake_case	,    __snake_case	).sample
															UpperCAmelCase				: Optional[Any]											=	self.scheduler.step_pred(__snake_case	,    __snake_case	,    __snake_case	,    generator=__snake_case	)

															UpperCAmelCase							,	UpperCAmelCase				: Dict											=	output.prev_sample, output.prev_sample_mean

								UpperCAmelCase				: Optional[Any]											=	sample_mean.clamp(0	,    1	)
								UpperCAmelCase				: List[str]											=	sample.cpu().permute(0	,    2	,    3	,    1	).numpy()
								if output_type == "pil":
															UpperCAmelCase				: List[str]											=	self.numpy_to_pil(__snake_case	)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case	)










'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    '''split_dict'''     ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42     ,   dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ]     ,   )
def 				snake_case_  (      _lowerCAmelCase						:		SplitDict						)   ->     List[Any]:
							UpperCAmelCase				: str											=	split_dict._to_yaml_list()
							assert len(_lowerCAmelCase						) == len(_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	SplitDict._from_yaml_list(_lowerCAmelCase						)
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														UpperCAmelCase				: Optional[Any]											=	None
														# the split name of split_dict takes over the name of the split info object
														UpperCAmelCase				: Dict											=	split_name
							assert split_dict == reloaded




@pytest.mark.parametrize(
    '''split_info'''     ,   [SplitInfo(), SplitInfo(dataset_name=_lowerCAmelCase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Optional[Any]:
							# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
							# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
							UpperCAmelCase				: Any											=	asdict(SplitDict({'''train''': split_info}						)						)
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





