
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 	a_		(	__lowercase       :     Optional[Any]	, __lowercase       :     Optional[int]			)			->  Optional[int]:
			assert isinstance(__lowercase	, __lowercase			)
			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 	a_		(	__lowercase       :     Dict	, __lowercase       :     Union[str, Any]	, __lowercase       :     int			)			->  Union[str, Any]:
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						_snake_case          =      ParquetDatasetReader(__lowercase	, cache_dir=__lowercase	, keep_in_memory=__lowercase			).read()
			_check_parquet_dataset(__lowercase	, __lowercase			)






@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 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     int	, __lowercase       :     List[str]			)			->  Optional[int]:
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			_snake_case          =      features.copy() if features else default_expected_features
			_snake_case          =      (
			    Features({feature: Value(__lowercase			) for feature, dtype in features.items()}			) if features is not None else None
			)
			_snake_case          =      ParquetDatasetReader(__lowercase	, features=__lowercase	, cache_dir=__lowercase			).read()
			_check_parquet_dataset(__lowercase	, __lowercase			)






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






@pytest.mark.parametrize('path_type'	, [str, list]			)
def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Optional[int]	, __lowercase       :     str			)			->  int:
			if issubclass(__lowercase	, __lowercase			):
						_snake_case          =      parquet_path
			elif issubclass(__lowercase	, __lowercase			):
						_snake_case          =      [parquet_path]
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			_snake_case          =      ParquetDatasetReader(__lowercase	, cache_dir=__lowercase			).read()
			_check_parquet_dataset(__lowercase	, __lowercase			)






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






@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 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Tuple	, __lowercase       :     Dict			)			->  Union[str, Any]:
			_snake_case          =      tmp_path / 'cache'
			_snake_case          =      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			_snake_case          =      features.copy() if features else default_expected_features
			_snake_case          =      (
			    Features({feature: Value(__lowercase			) for feature, dtype in features.items()}			) if features is not None else None
			)
			_snake_case          =      ParquetDatasetReader({'train': parquet_path}	, features=__lowercase	, cache_dir=__lowercase			).read()
			_check_parquet_datasetdict(__lowercase	, __lowercase			)






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






def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     Dict			)			->  int:
			_snake_case          =      ParquetDatasetWriter(__lowercase	, tmp_path / 'foo.parquet'			)
			assert writer.write() > 0
			_snake_case          =      pq.ParquetFile(tmp_path / 'foo.parquet'			)
			_snake_case          =      pf.read()
			assert dataset.data.table == output_table






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

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

			_snake_case          =      ParquetDatasetReader(str(tmp_path / 'foo.parquet'			)	, streaming=__lowercase			).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 	a_		(	__lowercase       :     Dict	, __lowercase       :     Optional[Any]			)			->  Dict:
			assert get_writer_batch_size(__lowercase			) == expected

def 	a_		(	__lowercase       :     List[Any]			)			->  Tuple:
			_snake_case          =      len(__lowercase			)
			for i in range(length - 1			):
						_snake_case          =      i
						for k in range(i + 1	, __lowercase			):
									if collection[k] < collection[least]:
												_snake_case          =      k
						if least != i:
									_snake_case     , _snake_case          =      (collection[i], collection[least])
			return collection


if __name__ == "__main__":
			_lowerCamelCase	:    str          =      input('''Enter numbers separated by a comma:\n''').strip()
			_lowerCamelCase	:    List[Any]          =      [int(item) for item in user_input.split(''',''')]
			print(selection_sort(unsorted))