

from ...processing_utils import ProcessorMixin





class    lowerCamelCase						(__lowercase			):
			lowerCamelCase__   :		Dict							=							['''image_processor''', '''feature_extractor''']
			lowerCamelCase__   :		Dict							=							'''TvltImageProcessor'''
			lowerCamelCase__   :		Any							=							'''TvltFeatureExtractor'''

			def __init__(					self				:						Dict    ,				__UpperCAmelCase				:						Dict    ,				__UpperCAmelCase				:						List[Any]       )   ->    Tuple:
						super().__init__(image_processor=__a    ,				feature_extractor=__a       )

						SCREAMING_SNAKE_CASE__								=      image_processor
						SCREAMING_SNAKE_CASE__								=      feature_extractor

			def __call__(					self				:						Tuple    ,				__UpperCAmelCase				:						Dict=None    ,				__UpperCAmelCase				:						Optional[int]=None    ,				__UpperCAmelCase				:						Any=None    ,				__UpperCAmelCase				:						Any=None    ,				__UpperCAmelCase				:						Tuple=False    ,				__UpperCAmelCase				:						Union[str, Any]=False    ,				*__UpperCAmelCase				:						Optional[Any]    ,				**__UpperCAmelCase				:						Any    ,				)   ->    str:

						if images is None and audio is None:
									raise ValueError("""You need to specify either an `images` or `audio` input to process."""       )

						SCREAMING_SNAKE_CASE__								=      None
						if images is not None:
									SCREAMING_SNAKE_CASE__								=      self.image_processor(__a    ,				mask_pixel=__a    ,				*__a    ,				**__a       )
						if images_mixed is not None:
									SCREAMING_SNAKE_CASE__								=      self.image_processor(__a    ,				is_mixed=__a    ,				*__a    ,				**__a       )
						if audio is not None:
									SCREAMING_SNAKE_CASE__								=      self.feature_extractor(
									    __a    ,				*__a    ,				sampling_rate=__a    ,				mask_audio=__a    ,				**__a       )

						SCREAMING_SNAKE_CASE__								=      {}
						if audio is not None:
									output_dict.update(__a       )
						if images is not None:
									output_dict.update(__a       )
						if images_mixed_dict is not None:
									output_dict.update(__a       )
						return output_dict





			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      self.image_processor.model_input_names
						SCREAMING_SNAKE_CASE__								=      self.feature_extractor.model_input_names
						return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names       )       )





"""simple docstring"""




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

A_							:			Tuple											= "2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
						raise ImportWarning(
						    "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
						    "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

A_							:			Tuple											= concatenate_datasets
A_							:			List[str]											= DownloadConfig
A_							:			int											= DownloadManager
A_							:			Optional[Any]											= DownloadMode
A_							:			Optional[int]											= DownloadConfig
A_							:			List[Any]											= DownloadMode
A_							:			Any											= DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


