



"""simple docstring"""






def     _lowercase       (						__snake_case			)		->     float:
			__lowerCAmelCase   :						List[str]				= 0
			while len(__snake_case			) > 1:
						__lowerCAmelCase   :						Optional[Any]				= 0
						# Consider two files with minimum cost to be merged
						for _ in range(2			):
									__lowerCAmelCase   :						Optional[Any]				= files.index(min(__snake_case			)			)
									temp += files[min_index]
									files.pop(__snake_case			)
						files.append(__snake_case			)
						optimal_merge_cost += temp
			return optimal_merge_cost


if __name__ == "__main__":
	import doctest

	doctest.testmod()




"""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     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
			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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Dict				= tmp_path / "cache"
			__lowerCAmelCase   :						Union[str, Any]				= {"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   :						Optional[Any]				= 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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
			__lowerCAmelCase   :						Tuple				= tmp_path / "cache"
			__lowerCAmelCase   :						Dict				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						List[Any]				= features.copy() if features else default_expected_features
			__lowerCAmelCase   :						Dict				= (
			    Features({feature: Value(__snake_case			) for feature, dtype in features.items()}			) if features is not None else None
			)
			__lowerCAmelCase   :						Optional[int]				= 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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= tmp_path / "cache"
			__lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						List[Any]				= 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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Dict:
			if issubclass(__snake_case      ,__snake_case			):
						__lowerCAmelCase   :						List[Any]				= parquet_path
			elif issubclass(__snake_case      ,__snake_case			):
						__lowerCAmelCase   :						List[Any]				= [parquet_path]
			__lowerCAmelCase   :						str				= tmp_path / "cache"
			__lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						Tuple				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)


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


def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						int				= str(shared_datadir / "test_image_rgb.jpg"			)
			__lowerCAmelCase   :						Any				= {"image": [image_path]}
			__lowerCAmelCase   :						List[Any]				= Features({"image": Image()}			)
			__lowerCAmelCase   :						Tuple				= Dataset.from_dict(__snake_case      ,features=__snake_case			)
			__lowerCAmelCase   :						Dict				= ParquetDatasetWriter(__snake_case      ,tmp_path / "foo.parquet"			)
			assert writer.write() > 0

			__lowerCAmelCase   :						List[Any]				= Dataset.from_parquet(str(tmp_path / "foo.parquet"			)			)
			assert dataset.features == reloaded_dataset.features

			__lowerCAmelCase   :						List[Any]				= 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     _lowercase       (						__snake_case      ,__snake_case			)		->     List[Any]:
			assert get_writer_batch_size(__snake_case			) == expected