import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


lowerCamelCase_				:		Any       						=       logging.get_logger(__name__)


class 						a__							(   __snake_case		):



			def __init__(						self      ,						*UpperCAmelCase      ,						**UpperCAmelCase    ) ->    None:
								warnings.warn(
								    'The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
								    ' Please use ConvNextImageProcessor instead.'      ,						UpperCAmelCase      ,						)
								super().__init__(*UpperCAmelCase      ,						**UpperCAmelCase    )





from dataclasses import dataclass
from typing import Callable, Optional

import datasets


@dataclass
class 						a__							(   datasets.BuilderConfig		):
			A__				:   Optional[Callable]								=			None
			A__				:   Optional[dict]								=			None
			A__				:   Optional[datasets.Features]								=			None



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								assert self.generator is not None, "generator must be specified"

								if self.gen_kwargs is None:
													__a							=   {}






class 						a__							(   datasets.GeneratorBasedBuilder		):
			A__				:   List[Any]								=			GeneratorConfig



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								return datasets.DatasetInfo(features=self.config.features    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,						gen_kwargs=self.config.gen_kwargs    )]







			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    Tuple:
								for idx, ex in enumerate(self.config.generator(**UpperCAmelCase    )    ):
													yield idx, ex




