






"""simple docstring"""
from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Union[str, Any]	,	lowerCAmelCase_					: NestedDataStructureLike[PathLike]	,	lowerCAmelCase_					: Optional[NamedSplit] = None	,	lowerCAmelCase_					: Optional[Features] = None	,	lowerCAmelCase_					: str = None	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: Optional[int] = None	,	**lowerCAmelCase_					: List[Any]	,	)						->			List[str]:
				super().__init__(
				    lowerCAmelCase_	,	split=lowerCAmelCase_	,	features=lowerCAmelCase_	,	cache_dir=lowerCAmelCase_	,	keep_in_memory=lowerCAmelCase_	,	streaming=lowerCAmelCase_	,	num_proc=lowerCAmelCase_	,	**lowerCAmelCase_	,	)
				UpperCAmelCase_     : List[Any]			     =					path_or_paths if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else {self.split: path_or_paths}
				UpperCAmelCase_     : List[Any]			     =					Text(
				    cache_dir=lowerCAmelCase_	,	data_files=lowerCAmelCase_	,	features=lowerCAmelCase_	,	**lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Dict:
				# Build iterable dataset
				if self.streaming:
							UpperCAmelCase_     : str			     =					self.builder.as_streaming_dataset(split=self.split						)
				# Build regular (map-style) dataset
				else:
							UpperCAmelCase_     : Any			     =					None
							UpperCAmelCase_     : int			     =					None
							UpperCAmelCase_     : int			     =					None
							UpperCAmelCase_     : Any			     =					None

							self.builder.download_and_prepare(
							    download_config=lowerCAmelCase_	,	download_mode=lowerCAmelCase_	,	verification_mode=lowerCAmelCase_	,	base_path=lowerCAmelCase_	,	num_proc=self.num_proc	,	)
							UpperCAmelCase_     : List[str]			     =					self.builder.as_dataset(
							    split=self.split	,	verification_mode=lowerCAmelCase_	,	in_memory=self.keep_in_memory						)
				return dataset











"""simple docstring"""
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)



