





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












from __future__ import annotations

from collections.abc import Generator


def  SCREAMING_SNAKE_CASE   ( )    ->  Generator[int, None, None]:
    lowerCamelCase__					:   dict[int, int]      				=	{}
    lowerCamelCase__					:   Union[str, Any]      				=	2
    while True:
        lowerCamelCase__					:   Optional[int]      				=	factor_map.pop(_UpperCAmelCase		,			_UpperCAmelCase				)
        if factor:
            lowerCamelCase__					:   Optional[Any]      				=	factor + prime
            while x in factor_map:
                x += factor
            lowerCamelCase__					:   Optional[Any]      				=	factor
        else:
            lowerCamelCase__					:   Union[str, Any]      				=	prime
            yield prime
        prime += 1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 1e10				)    ->  int:
    lowerCamelCase__					:   Tuple      				=	sieve()

    lowerCamelCase__					:   Dict      				=	1
    while True:
        lowerCamelCase__					:   List[Any]      				=	next(_UpperCAmelCase				)
        if (2 * prime * n) > limit:
            return n
        # Ignore the next prime as the reminder will be 2.
        next(_UpperCAmelCase				)
        n += 2


if __name__ == "__main__":
    print(solution())





