




from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__A						:							List[str]							=  logging.get_logger(__name__)


__A						:							Dict							=  TypeVar("""DatasetType""", Dataset, IterableDataset)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = None					,	SCREAMING_SNAKE_CASE = None					,	SCREAMING_SNAKE_CASE = None					,	SCREAMING_SNAKE_CASE = None					,	SCREAMING_SNAKE_CASE = "first_exhausted"					,	):
			'''simple docstring'''
			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("""Unable to interleave an empty list of datasets."""   )
			for i, dataset in enumerate(SCREAMING_SNAKE_CASE   ):
						if not isinstance(SCREAMING_SNAKE_CASE					,	(Dataset, IterableDataset)   ):
									if isinstance(SCREAMING_SNAKE_CASE					,	(DatasetDict, IterableDatasetDict)   ):
												if not dataset:
															raise ValueError(
															    f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    """is an empty dataset dictionary."""   )
												raise ValueError(
												    f"""Dataset at position {i} has at least one split: {list(SCREAMING_SNAKE_CASE   )}\n"""
												    f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(SCREAMING_SNAKE_CASE   )   )}']"""   )
									raise ValueError(
									    f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(SCREAMING_SNAKE_CASE   ).__name__}."""   )
						if i == 0:
									SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					(
									    (Dataset, IterableDataset) if isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) else (IterableDataset, Dataset)
									)
						elif not isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
									raise ValueError(
									    f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""   )
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(f"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""   )
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	info=SCREAMING_SNAKE_CASE					,	split=SCREAMING_SNAKE_CASE					,	stopping_strategy=SCREAMING_SNAKE_CASE   )
			else:
						return _interleave_iterable_datasets(
						    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	info=SCREAMING_SNAKE_CASE					,	split=SCREAMING_SNAKE_CASE					,	stopping_strategy=SCREAMING_SNAKE_CASE   )




def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = None					,	SCREAMING_SNAKE_CASE = None					,	SCREAMING_SNAKE_CASE = 0					,	):
			'''simple docstring'''

			if not dsets:
						raise ValueError("""Unable to concatenate an empty list of datasets."""   )
			for i, dataset in enumerate(SCREAMING_SNAKE_CASE   ):
						if not isinstance(SCREAMING_SNAKE_CASE					,	(Dataset, IterableDataset)   ):
									if isinstance(SCREAMING_SNAKE_CASE					,	(DatasetDict, IterableDatasetDict)   ):
												if not dataset:
															raise ValueError(
															    f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    """is an empty dataset dictionary."""   )
												raise ValueError(
												    f"""Dataset at position {i} has at least one split: {list(SCREAMING_SNAKE_CASE   )}\n"""
												    f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(SCREAMING_SNAKE_CASE   )   )}']"""   )
									raise ValueError(
									    f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(SCREAMING_SNAKE_CASE   ).__name__}."""   )
						if i == 0:
									SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					(
									    (Dataset, IterableDataset) if isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) else (IterableDataset, Dataset)
									)
						elif not isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
									raise ValueError(
									    f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""   )
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(SCREAMING_SNAKE_CASE					,	info=SCREAMING_SNAKE_CASE					,	split=SCREAMING_SNAKE_CASE					,	axis=SCREAMING_SNAKE_CASE   )
			else:
						return _concatenate_iterable_datasets(SCREAMING_SNAKE_CASE					,	info=SCREAMING_SNAKE_CASE					,	split=SCREAMING_SNAKE_CASE					,	axis=SCREAMING_SNAKE_CASE   )












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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     42
							a__						  =     42





							def __init__(					self       :	Any       ,     a       :	UNetaDModel       ,     a       :	ScoreSdeVeScheduler			)			->					str:
										super().__init__()
										self.register_modules(unet=a       ,     scheduler=a			)







							@torch.no_grad()
							def __call__(					self       :	List[Any]       ,     a       :	int = 1       ,     a       :	int = 2_000       ,     a       :	Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     a       :	Optional[str] = "pil"       ,     a       :	bool = True       ,     **a       :	Optional[int]       ,     )			->					Union[ImagePipelineOutput, Tuple]:
										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			)






