

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        UpperCamelCase_(							_snake_case   :  Union[str, Any]       ,     _snake_case   :  str	):




			"""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        UpperCamelCase_(							_snake_case   :  int       ,     _snake_case   :  Tuple       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =tmp_path / 'cache'
			__a        ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						__a        =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        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Dict       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =tmp_path / 'cache'
			__a        ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__a        =features.copy() if features else default_expected_features
			__a        =(
			    Features({feature: Value(_snake_case	) for feature, dtype in features.items()}	) if features is not None else None
			)
			__a        =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        UpperCamelCase_(							_snake_case   :  Optional[Any]       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =tmp_path / 'cache'
			__a        ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__a        =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        UpperCamelCase_(							_snake_case   :  Union[str, Any]       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  List[str]	):




			"""simple docstring"""






			if issubclass(_snake_case       ,     _snake_case	):
						__a        =parquet_path
			elif issubclass(_snake_case       ,     _snake_case	):
						__a        =[parquet_path]
			__a        =tmp_path / 'cache'
			__a        ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__a        =ParquetDatasetReader(_snake_case       ,     cache_dir=_snake_case	).read()
			_check_parquet_dataset(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Any       ,     _snake_case   :  int=("train",)	):




			"""simple docstring"""






			assert isinstance(_snake_case       ,     _snake_case	)
			for split in splits:
						__a        =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        UpperCamelCase_(							_snake_case   :  Optional[int]       ,     _snake_case   :  str       ,     _snake_case   :  Any	):




			"""simple docstring"""






			__a        =tmp_path / 'cache'
			__a        ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						__a        =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        UpperCamelCase_(							_snake_case   :  Optional[Any]       ,     _snake_case   :  List[str]       ,     _snake_case   :  Dict	):




			"""simple docstring"""






			__a        =tmp_path / 'cache'
			__a        ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__a        =features.copy() if features else default_expected_features
			__a        =(
			    Features({feature: Value(_snake_case	) for feature, dtype in features.items()}	) if features is not None else None
			)
			__a        =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        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			if split:
						__a        ={split: parquet_path}
			else:
						__a        ='train'
						__a        ={'train': parquet_path, 'test': parquet_path}
			__a        =tmp_path / 'cache'
			__a        ={'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__a        =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        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =ParquetDatasetWriter(_snake_case       ,     tmp_path / 'foo.parquet'	)
			assert writer.write() > 0
			__a        =pq.ParquetFile(tmp_path / 'foo.parquet'	)
			__a        =pf.read()
			assert dataset.data.table == output_table






def        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






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

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

			__a        =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        UpperCamelCase_(							_snake_case   :  Any       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			assert get_writer_batch_size(_snake_case	) == expected









from __future__ import annotations






def        UpperCamelCase_(							_snake_case   :  int	):




			"""simple docstring"""






			__a        =str(_snake_case	)
			return len(_snake_case	) == 9 and set(_snake_case	) == set('123456789'	)






def        UpperCamelCase_(							):




			"""simple docstring"""






			for base_num in range(9999       ,     4999       ,     -1	):
						__a        =100002 * base_num
						if is_9_pandigital(_snake_case	):
									return candidate

			for base_num in range(333       ,     99       ,     -1	):
						__a        =1002003 * base_num
						if is_9_pandigital(_snake_case	):
									return candidate

			return None


if __name__ == "__main__":
						print(f'''{solution() = }''')






