

'''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  a__       (    _SCREAMING_SNAKE_CASE   :       Optional[int]	,				_SCREAMING_SNAKE_CASE   :       int				)     ->					List[Any]:


	"""simple docstring"""





	assert isinstance(_SCREAMING_SNAKE_CASE	,				_SCREAMING_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  a__       (    _SCREAMING_SNAKE_CASE   :       int	,				_SCREAMING_SNAKE_CASE   :       Union[str, Any]	,				_SCREAMING_SNAKE_CASE   :       Optional[int]				)     ->					Tuple:


	"""simple docstring"""





	UpperCAmelCase_     :      List[Any]						=  tmp_path / "cache"
	UpperCAmelCase_     :      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():
		UpperCAmelCase_     :      int						=  ParquetDatasetReader(_SCREAMING_SNAKE_CASE	,				cache_dir=_SCREAMING_SNAKE_CASE	,				keep_in_memory=_SCREAMING_SNAKE_CASE				).read()
	_check_parquet_dataset(_SCREAMING_SNAKE_CASE	,				_SCREAMING_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  a__       (    _SCREAMING_SNAKE_CASE   :       List[str]	,				_SCREAMING_SNAKE_CASE   :       Union[str, Any]	,				_SCREAMING_SNAKE_CASE   :       Optional[int]				)     ->					List[Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      Optional[Any]						=  tmp_path / "cache"
	UpperCAmelCase_     :      Any						=  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	UpperCAmelCase_     :      List[str]						=  features.copy() if features else default_expected_features
	UpperCAmelCase_     :      Optional[Any]						=  (
	    Features({feature: Value(_SCREAMING_SNAKE_CASE				) for feature, dtype in features.items()}				) if features is not None else None
	)
	UpperCAmelCase_     :      Optional[int]						=  ParquetDatasetReader(_SCREAMING_SNAKE_CASE	,				features=_SCREAMING_SNAKE_CASE	,				cache_dir=_SCREAMING_SNAKE_CASE				).read()
	_check_parquet_dataset(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)


@pytest.mark.parametrize("split"	,				[None, NamedSplit("train"				), "train", "test"]				)
def  a__       (    _SCREAMING_SNAKE_CASE   :       Union[str, Any]	,				_SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       Any				)     ->					Union[str, Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      str						=  tmp_path / "cache"
	UpperCAmelCase_     :      Optional[int]						=  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	UpperCAmelCase_     :      Optional[Any]						=  ParquetDatasetReader(_SCREAMING_SNAKE_CASE	,				cache_dir=_SCREAMING_SNAKE_CASE	,				split=_SCREAMING_SNAKE_CASE				).read()
	_check_parquet_dataset(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"	,				[str, list]				)
def  a__       (    _SCREAMING_SNAKE_CASE   :       int	,				_SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       Dict				)     ->					List[Any]:


	"""simple docstring"""





	if issubclass(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				):
		UpperCAmelCase_     :      Optional[int]						=  parquet_path
	elif issubclass(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				):
		UpperCAmelCase_     :      Dict						=  [parquet_path]
	UpperCAmelCase_     :      str						=  tmp_path / "cache"
	UpperCAmelCase_     :      List[Any]						=  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	UpperCAmelCase_     :      Union[str, Any]						=  ParquetDatasetReader(_SCREAMING_SNAKE_CASE	,				cache_dir=_SCREAMING_SNAKE_CASE				).read()
	_check_parquet_dataset(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)


def  a__       (    _SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]=("train",)				)     ->					Optional[Any]:


	"""simple docstring"""





	assert isinstance(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	for split in splits:
		UpperCAmelCase_     :      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  a__       (    _SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       Optional[int]	,				_SCREAMING_SNAKE_CASE   :       str				)     ->					Union[str, Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      List[Any]						=  tmp_path / "cache"
	UpperCAmelCase_     :      List[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():
		UpperCAmelCase_     :      Union[str, Any]						=  ParquetDatasetReader(
		    {"train": parquet_path}	,				cache_dir=_SCREAMING_SNAKE_CASE	,				keep_in_memory=_SCREAMING_SNAKE_CASE				).read()
	_check_parquet_datasetdict(_SCREAMING_SNAKE_CASE	,				_SCREAMING_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  a__       (    _SCREAMING_SNAKE_CASE   :       Tuple	,				_SCREAMING_SNAKE_CASE   :       Any	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]				)     ->					Dict:


	"""simple docstring"""





	UpperCAmelCase_     :      Any						=  tmp_path / "cache"
	UpperCAmelCase_     :      Optional[int]						=  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	UpperCAmelCase_     :      List[str]						=  features.copy() if features else default_expected_features
	UpperCAmelCase_     :      str						=  (
	    Features({feature: Value(_SCREAMING_SNAKE_CASE				) for feature, dtype in features.items()}				) if features is not None else None
	)
	UpperCAmelCase_     :      Optional[int]						=  ParquetDatasetReader({"train": parquet_path}	,				features=_SCREAMING_SNAKE_CASE	,				cache_dir=_SCREAMING_SNAKE_CASE				).read()
	_check_parquet_datasetdict(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)


@pytest.mark.parametrize("split"	,				[None, NamedSplit("train"				), "train", "test"]				)
def  a__       (    _SCREAMING_SNAKE_CASE   :       Tuple	,				_SCREAMING_SNAKE_CASE   :       Dict	,				_SCREAMING_SNAKE_CASE   :       Any				)     ->					int:


	"""simple docstring"""





	if split:
		UpperCAmelCase_     :      Tuple						=  {split: parquet_path}
	else:
		UpperCAmelCase_     :      int						=  "train"
		UpperCAmelCase_     :      int						=  {"train": parquet_path, "test": parquet_path}
	UpperCAmelCase_     :      List[Any]						=  tmp_path / "cache"
	UpperCAmelCase_     :      int						=  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	UpperCAmelCase_     :      Any						=  ParquetDatasetReader(_SCREAMING_SNAKE_CASE	,				cache_dir=_SCREAMING_SNAKE_CASE				).read()
	_check_parquet_datasetdict(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				splits=list(path.keys()				)				)
	assert all(dataset[split].split == split for split in path.keys()				)


def  a__       (    _SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       Union[str, Any]				)     ->					List[str]:


	"""simple docstring"""





	UpperCAmelCase_     :      str						=  ParquetDatasetWriter(_SCREAMING_SNAKE_CASE	,				tmp_path / "foo.parquet"				)
	assert writer.write() > 0
	UpperCAmelCase_     :      Optional[int]						=  pq.ParquetFile(tmp_path / "foo.parquet"				)
	UpperCAmelCase_     :      Optional[Any]						=  pf.read()
	assert dataset.data.table == output_table


def  a__       (    _SCREAMING_SNAKE_CASE   :       Optional[Any]	,				_SCREAMING_SNAKE_CASE   :       Optional[int]				)     ->					int:


	"""simple docstring"""





	UpperCAmelCase_     :      Optional[Any]						=  str(shared_datadir / "test_image_rgb.jpg"				)
	UpperCAmelCase_     :      Optional[int]						=  {"image": [image_path]}
	UpperCAmelCase_     :      List[str]						=  Features({"image": Image()}				)
	UpperCAmelCase_     :      Any						=  Dataset.from_dict(_SCREAMING_SNAKE_CASE	,				features=_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      List[str]						=  ParquetDatasetWriter(_SCREAMING_SNAKE_CASE	,				tmp_path / "foo.parquet"				)
	assert writer.write() > 0

	UpperCAmelCase_     :      Tuple						=  Dataset.from_parquet(str(tmp_path / "foo.parquet"				)				)
	assert dataset.features == reloaded_dataset.features

	UpperCAmelCase_     :      str						=  ParquetDatasetReader(str(tmp_path / "foo.parquet"				)	,				streaming=_SCREAMING_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  a__       (    _SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       Any				)     ->					Tuple:


	"""simple docstring"""





	assert get_writer_batch_size(_SCREAMING_SNAKE_CASE				) == expected







SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

SCREAMING_SNAKE_CASE		:Optional[Any]						=     [
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE		:Optional[int]						=     [
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]
