





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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  int:
    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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   str      				=	tmp_path / 'cache'
    lowerCamelCase__					:   str      				=	{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        lowerCamelCase__					:   int      				=	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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Dict:
    lowerCamelCase__					:   List[Any]      				=	tmp_path / 'cache'
    lowerCamelCase__					:   Tuple      				=	{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    lowerCamelCase__					:   Optional[Any]      				=	features.copy() if features else default_expected_features
    lowerCamelCase__					:   str      				=	(
        Features({feature: Value(_UpperCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
    )
    lowerCamelCase__					:   Union[str, Any]      				=	ParquetDatasetReader(_UpperCAmelCase		,			features=_UpperCAmelCase		,			cache_dir=_UpperCAmelCase				).read()
    _check_parquet_dataset(_UpperCAmelCase		,			_UpperCAmelCase				)


@pytest.mark.parametrize('split'		,			[None, NamedSplit('train'				), 'train', 'test']				)
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  List[str]:
    lowerCamelCase__					:   Union[str, Any]      				=	tmp_path / 'cache'
    lowerCamelCase__					:   Union[str, Any]      				=	{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    lowerCamelCase__					:   List[Any]      				=	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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Union[str, Any]:
    if issubclass(_UpperCAmelCase		,			_UpperCAmelCase				):
        lowerCamelCase__					:   Optional[int]      				=	parquet_path
    elif issubclass(_UpperCAmelCase		,			_UpperCAmelCase				):
        lowerCamelCase__					:   List[Any]      				=	[parquet_path]
    lowerCamelCase__					:   Union[str, Any]      				=	tmp_path / 'cache'
    lowerCamelCase__					:   Dict      				=	{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    lowerCamelCase__					:   Any      				=	ParquetDatasetReader(_UpperCAmelCase		,			cache_dir=_UpperCAmelCase				).read()
    _check_parquet_dataset(_UpperCAmelCase		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=("train",)				)    ->  Dict:
    assert isinstance(_UpperCAmelCase		,			_UpperCAmelCase				)
    for split in splits:
        lowerCamelCase__					:   List[str]      				=	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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Dict:
    lowerCamelCase__					:   int      				=	tmp_path / 'cache'
    lowerCamelCase__					:   str      				=	{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        lowerCamelCase__					:   List[str]      				=	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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__					:   List[Any]      				=	tmp_path / 'cache'
    lowerCamelCase__					:   List[Any]      				=	{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    lowerCamelCase__					:   Dict      				=	features.copy() if features else default_expected_features
    lowerCamelCase__					:   List[Any]      				=	(
        Features({feature: Value(_UpperCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
    )
    lowerCamelCase__					:   List[str]      				=	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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    if split:
        lowerCamelCase__					:   Optional[Any]      				=	{split: parquet_path}
    else:
        lowerCamelCase__					:   Any      				=	'train'
        lowerCamelCase__					:   Optional[Any]      				=	{'train': parquet_path, 'test': parquet_path}
    lowerCamelCase__					:   str      				=	tmp_path / 'cache'
    lowerCamelCase__					:   Tuple      				=	{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
    lowerCamelCase__					:   Optional[int]      				=	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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   int      				=	ParquetDatasetWriter(_UpperCAmelCase		,			tmp_path / 'foo.parquet'				)
    assert writer.write() > 0
    lowerCamelCase__					:   int      				=	pq.ParquetFile(tmp_path / 'foo.parquet'				)
    lowerCamelCase__					:   Optional[int]      				=	pf.read()
    assert dataset.data.table == output_table


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Dict:
    lowerCamelCase__					:   Tuple      				=	str(shared_datadir / 'test_image_rgb.jpg'				)
    lowerCamelCase__					:   Tuple      				=	{'image': [image_path]}
    lowerCamelCase__					:   List[str]      				=	Features({'image': Image()}				)
    lowerCamelCase__					:   Optional[int]      				=	Dataset.from_dict(_UpperCAmelCase		,			features=_UpperCAmelCase				)
    lowerCamelCase__					:   Tuple      				=	ParquetDatasetWriter(_UpperCAmelCase		,			tmp_path / 'foo.parquet'				)
    assert writer.write() > 0

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

    lowerCamelCase__					:   Dict      				=	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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  str:
    assert get_writer_batch_size(_UpperCAmelCase				) == expected












_UpperCAmelCase       : str			     =       """Tobias Carryer"""

from time import time

class      lowerCAmelCase							:
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   str=int(time()			)			)    ->			Optional[int]:  # noqa: B008
     lowerCamelCase__					:   List[Any]      				=	multiplier
     lowerCamelCase__					:   Tuple      				=	increment
     lowerCamelCase__					:   int      				=	modulo
     lowerCamelCase__					:   Any      				=	seed







 def   A_			(   self						:   Optional[int]			)    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	(self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
    # Show the LCG in action.
    _UpperCAmelCase       : List[Any]			     =       LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
    while True:
        print(lcg.next_number())





