





'''simple docstring'''



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    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Optional[Any]				):
			assert isinstance(UpperCamelCase__		,       UpperCamelCase__				)
			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    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Tuple				):
			__lowercase										=							tmp_path / '''cache'''
			__lowercase										=							{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						__lowercase										=							ParquetDatasetReader(UpperCamelCase__		,       cache_dir=UpperCamelCase__		,       keep_in_memory=UpperCamelCase__				).read()
			_check_parquet_dataset(UpperCamelCase__		,       UpperCamelCase__				)




@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    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  Tuple				):
			__lowercase										=							tmp_path / '''cache'''
			__lowercase										=							{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			__lowercase										=							features.copy() if features else default_expected_features
			__lowercase										=							(
			    Features({feature: Value(UpperCamelCase__				) for feature, dtype in features.items()}				) if features is not None else None
			)
			__lowercase										=							ParquetDatasetReader(UpperCamelCase__		,       features=UpperCamelCase__		,       cache_dir=UpperCamelCase__				).read()
			_check_parquet_dataset(UpperCamelCase__		,       UpperCamelCase__				)




@pytest.mark.parametrize('''split'''		,       [None, NamedSplit('''train'''				), '''train''', '''test''']				)
def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							tmp_path / '''cache'''
			__lowercase										=							{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			__lowercase										=							ParquetDatasetReader(UpperCamelCase__		,       cache_dir=UpperCamelCase__		,       split=UpperCamelCase__				).read()
			_check_parquet_dataset(UpperCamelCase__		,       UpperCamelCase__				)
			assert dataset.split == split if split else "train"




@pytest.mark.parametrize('''path_type'''		,       [str, list]				)
def    _lowerCAmelCase						(				lowerCamelCase_			:  Any		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Union[str, Any]				):
			if issubclass(UpperCamelCase__		,       UpperCamelCase__				):
						__lowercase										=							parquet_path
			elif issubclass(UpperCamelCase__		,       UpperCamelCase__				):
						__lowercase										=							[parquet_path]
			__lowercase										=							tmp_path / '''cache'''
			__lowercase										=							{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			__lowercase										=							ParquetDatasetReader(UpperCamelCase__		,       cache_dir=UpperCamelCase__				).read()
			_check_parquet_dataset(UpperCamelCase__		,       UpperCamelCase__				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Any=("train",)				):
			assert isinstance(UpperCamelCase__		,       UpperCamelCase__				)
			for split in splits:
						__lowercase										=							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    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Dict				):
			__lowercase										=							tmp_path / '''cache'''
			__lowercase										=							{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						__lowercase										=							ParquetDatasetReader(
						    {'''train''': parquet_path}		,       cache_dir=UpperCamelCase__		,       keep_in_memory=UpperCamelCase__				).read()
			_check_parquet_datasetdict(UpperCamelCase__		,       UpperCamelCase__				)




@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    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Tuple				):
			__lowercase										=							tmp_path / '''cache'''
			__lowercase										=							{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			__lowercase										=							features.copy() if features else default_expected_features
			__lowercase										=							(
			    Features({feature: Value(UpperCamelCase__				) for feature, dtype in features.items()}				) if features is not None else None
			)
			__lowercase										=							ParquetDatasetReader({'''train''': parquet_path}		,       features=UpperCamelCase__		,       cache_dir=UpperCamelCase__				).read()
			_check_parquet_datasetdict(UpperCamelCase__		,       UpperCamelCase__				)




@pytest.mark.parametrize('''split'''		,       [None, NamedSplit('''train'''				), '''train''', '''test''']				)
def    _lowerCAmelCase						(				lowerCamelCase_			:  Any		,       lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  str				):
			if split:
						__lowercase										=							{split: parquet_path}
			else:
						__lowercase										=							'''train'''
						__lowercase										=							{'''train''': parquet_path, '''test''': parquet_path}
			__lowercase										=							tmp_path / '''cache'''
			__lowercase										=							{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
			__lowercase										=							ParquetDatasetReader(UpperCamelCase__		,       cache_dir=UpperCamelCase__				).read()
			_check_parquet_datasetdict(UpperCamelCase__		,       UpperCamelCase__		,       splits=list(path.keys()				)				)
			assert all(dataset[split].split == split for split in path.keys()				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							ParquetDatasetWriter(UpperCamelCase__		,       tmp_path / '''foo.parquet'''				)
			assert writer.write() > 0
			__lowercase										=							pq.ParquetFile(tmp_path / '''foo.parquet'''				)
			__lowercase										=							pf.read()
			assert dataset.data.table == output_table




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  List[Any]				):
			__lowercase										=							str(shared_datadir / '''test_image_rgb.jpg'''				)
			__lowercase										=							{'''image''': [image_path]}
			__lowercase										=							Features({'''image''': Image()}				)
			__lowercase										=							Dataset.from_dict(UpperCamelCase__		,       features=UpperCamelCase__				)
			__lowercase										=							ParquetDatasetWriter(UpperCamelCase__		,       tmp_path / '''foo.parquet'''				)
			assert writer.write() > 0

			__lowercase										=							Dataset.from_parquet(str(tmp_path / '''foo.parquet'''				)				)
			assert dataset.features == reloaded_dataset.features

			__lowercase										=							ParquetDatasetReader(str(tmp_path / '''foo.parquet'''				)		,       streaming=UpperCamelCase__				).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    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Any				):
			assert get_writer_batch_size(UpperCamelCase__				) == expected












'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE					=    {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''VanForImageClassification''',
	    '''VanModel''',
	    '''VanPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_van import (
		    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VanForImageClassification,
		    VanModel,
		    VanPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)





