

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    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							tmp_path / 'cache'
					__lowerCAmelCase					=							{'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					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							tmp_path / 'cache'
					__lowerCAmelCase					=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					__lowerCAmelCase					=							features.copy() if features else default_expected_features
					__lowerCAmelCase					=							(
					    Features({feature: Value(UpperCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__lowerCAmelCase					=							ParquetDatasetReader(UpperCAmelCase__		,					features=UpperCAmelCase__		,					cache_dir=UpperCAmelCase__      ).read()
					_check_parquet_dataset(UpperCAmelCase__		,					UpperCAmelCase__      )


@pytest.mark.parametrize('split'		,					[None, NamedSplit('train'      ), 'train', 'test']      )
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							tmp_path / 'cache'
					__lowerCAmelCase					=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=("train",)      ):

					"""simple docstring"""






					assert isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					for split in splits:
										__lowerCAmelCase					=							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    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							tmp_path / 'cache'
					__lowerCAmelCase					=							{'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					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							tmp_path / 'cache'
					__lowerCAmelCase					=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					__lowerCAmelCase					=							features.copy() if features else default_expected_features
					__lowerCAmelCase					=							(
					    Features({feature: Value(UpperCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					if split:
										__lowerCAmelCase					=							{split: parquet_path}
					else:
										__lowerCAmelCase					=							'train'
										__lowerCAmelCase					=							{'train': parquet_path, 'test': parquet_path}
					__lowerCAmelCase					=							tmp_path / 'cache'
					__lowerCAmelCase					=							{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

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

					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					assert get_writer_batch_size(UpperCAmelCase__      ) == expected









from graphs.minimum_spanning_tree_kruskal import kruskal


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							9
					__lowerCAmelCase					=							[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					__lowerCAmelCase					=							kruskal(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					assert sorted(UpperCAmelCase__      ) == sorted(UpperCAmelCase__      )






