






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''ChineseCLIPImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,       __lowerCAmelCase							,       )
												__magic_name__		:Optional[Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:Tuple   =   image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.'''  )
								if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.'''  )

								super().__init__(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""

								if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''  )

								if text is not None:
												__magic_name__		:int   =   self.tokenizer(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if images is not None:
												__magic_name__		:Dict   =   self.image_processor(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if text is not None and images is not None:
												__magic_name__		:Union[str, Any]   =   image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__lowerCAmelCase  )							,       tensor_type=__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor_class














import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				assert isinstance(snake_case,	snake_case			)
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory''',	[False, True]			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   tmp_path / '''cache'''
				__magic_name__		:int   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__		:Optional[Any]   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case,	keep_in_memory=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

@pytest.mark.parametrize(
    '''features''',	[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],	)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   tmp_path / '''cache'''
				__magic_name__		:int   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:Tuple   =   features.copy() if features else default_expected_features
				__magic_name__		:Union[str, Any]   =   (
				    Features({feature: Value(snake_case			) for feature, dtype in features.items()}			) if features is not None else None
				)
				__magic_name__		:int   =   ParquetDatasetReader(snake_case,	features=snake_case,	cache_dir=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   tmp_path / '''cache'''
				__magic_name__		:List[Any]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:int   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case,	split=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)
				assert dataset.split == split if split else "train"

@pytest.mark.parametrize('''path_type''',	[str, list]			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				if issubclass(snake_case,	snake_case			):
								__magic_name__		:Union[str, Any]   =   parquet_path
				elif issubclass(snake_case,	snake_case			):
								__magic_name__		:Union[str, Any]   =   [parquet_path]
				__magic_name__		:Optional[int]   =   tmp_path / '''cache'''
				__magic_name__		:Optional[int]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:str   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case=("train",)			):







				"""simple docstring"""






				assert isinstance(snake_case,	snake_case			)
				for split in splits:
								__magic_name__		:Optional[Any]   =   dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory''',	[False, True]			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Any   =   tmp_path / '''cache'''
				__magic_name__		:Optional[int]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__		:Tuple   =   ParquetDatasetReader(
								    {'''train''': parquet_path},	cache_dir=snake_case,	keep_in_memory=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case			)

@pytest.mark.parametrize(
    '''features''',	[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],	)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   tmp_path / '''cache'''
				__magic_name__		:Dict   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:int   =   features.copy() if features else default_expected_features
				__magic_name__		:List[Any]   =   (
				    Features({feature: Value(snake_case			) for feature, dtype in features.items()}			) if features is not None else None
				)
				__magic_name__		:Optional[int]   =   ParquetDatasetReader({'''train''': parquet_path},	features=snake_case,	cache_dir=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case			)

@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				if split:
								__magic_name__		:Dict   =   {split: parquet_path}
				else:
								__magic_name__		:Optional[int]   =   '''train'''
								__magic_name__		:Dict   =   {'''train''': parquet_path, '''test''': parquet_path}
				__magic_name__		:List[Any]   =   tmp_path / '''cache'''
				__magic_name__		:Optional[Any]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:Optional[Any]   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case,	splits=list(path.keys()			)			)
				assert all(dataset[split].split == split for split in path.keys()			)

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   ParquetDatasetWriter(snake_case,	tmp_path / '''foo.parquet'''			)
				assert writer.write() > 0
				__magic_name__		:List[Any]   =   pq.ParquetFile(tmp_path / '''foo.parquet'''			)
				__magic_name__		:List[Any]   =   pf.read()
				assert dataset.data.table == output_table

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   str(shared_datadir / '''test_image_rgb.jpg'''			)
				__magic_name__		:Tuple   =   {'''image''': [image_path]}
				__magic_name__		:List[Any]   =   Features({'''image''': Image()}			)
				__magic_name__		:Tuple   =   Dataset.from_dict(snake_case,	features=snake_case			)
				__magic_name__		:Union[str, Any]   =   ParquetDatasetWriter(snake_case,	tmp_path / '''foo.parquet'''			)
				assert writer.write() > 0

				__magic_name__		:List[str]   =   Dataset.from_parquet(str(tmp_path / '''foo.parquet'''			)			)
				assert dataset.features == reloaded_dataset.features

				__magic_name__		:List[str]   =   ParquetDatasetReader(str(tmp_path / '''foo.parquet'''			),	streaming=snake_case			).read()
				assert dataset.features == reloaded_iterable_dataset.features

@pytest.mark.parametrize(
    '''feature, expected''',	[
        (Features({'''foo''': Value('''int32'''			)}			), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ],	)
def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				assert get_writer_batch_size(snake_case			) == expected






