



"""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 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Any    ):


						'''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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			tmp_path / """cache"""
						snake_case_   : List[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():
												snake_case_   : str						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			tmp_path / """cache"""
						snake_case_   : Union[str, Any]						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						snake_case_   : Optional[Any]						=			features.copy() if features else default_expected_features
						snake_case_   : Union[str, Any]						=			(
						    Features({feature: Value(__UpperCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						snake_case_   : Optional[Any]						=			ParquetDatasetReader(__UpperCamelCase					, features=__UpperCamelCase					, cache_dir=__UpperCamelCase    ).read()
						_check_parquet_dataset(__UpperCamelCase					, __UpperCamelCase    )
@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : Dict						=			tmp_path / """cache"""
						snake_case_   : Tuple						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						snake_case_   : str						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						if issubclass(__UpperCamelCase					, __UpperCamelCase    ):
												snake_case_   : Union[str, Any]						=			parquet_path
						elif issubclass(__UpperCamelCase					, __UpperCamelCase    ):
												snake_case_   : Any						=			[parquet_path]
						snake_case_   : int						=			tmp_path / """cache"""
						snake_case_   : Any						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						snake_case_   : Union[str, Any]						=			ParquetDatasetReader(__UpperCamelCase					, cache_dir=__UpperCamelCase    ).read()
						_check_parquet_dataset(__UpperCamelCase					, __UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Optional[int]=("train",)    ):


						'''simple docstring'''






						assert isinstance(__UpperCamelCase					, __UpperCamelCase    )
						for split in splits:
												snake_case_   : Optional[int]						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			tmp_path / """cache"""
						snake_case_   : Dict						=			{"""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_   : Optional[int]						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






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


						'''simple docstring'''






						if split:
												snake_case_   : Optional[int]						=			{split: parquet_path}
						else:
												snake_case_   : Tuple						=			"""train"""
												snake_case_   : List[Any]						=			{"""train""": parquet_path, """test""": parquet_path}
						snake_case_   : Any						=			tmp_path / """cache"""
						snake_case_   : Union[str, Any]						=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						snake_case_   : str						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






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


						'''simple docstring'''






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

						snake_case_   : Optional[int]						=			Dataset.from_parquet(str(tmp_path / """foo.parquet"""    )    )
						assert dataset.features == reloaded_dataset.features

						snake_case_   : int						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						assert get_writer_batch_size(__UpperCamelCase    ) == expected






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			bool = False    ):


						'''simple docstring'''






						if n == 2:
												return True
						if not n % 2 or n < 2:
												return False
						if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
												return False
						if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
												raise ValueError(
												    """Warning: upper bound of deterministic test is exceeded. """
												    """Pass allow_probable=True to allow probabilistic test. """
												    """A return value of True indicates a probable prime."""    )
						# array bounds provided by analysis
						snake_case_   : List[Any]						=			[
						    2_0_4_7,
						    1_3_7_3_6_5_3,
						    2_5_3_2_6_0_0_1,
						    3_2_1_5_0_3_1_7_5_1,
						    2_1_5_2_3_0_2_8_9_8_7_4_7,
						    3_4_7_4_7_4_9_6_6_0_3_8_3,
						    3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
						    1,
						    3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
						    1,
						    1,
						    3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
						    3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
						]

						snake_case_   : Dict						=			[2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
						for idx, _p in enumerate(__UpperCamelCase					, 1    ):
												if n < _p:
																		# then we have our last prime to check
																		snake_case_   : Optional[int]						=			primes[:idx]
																		break
						snake_case_			,		snake_case_   : Tuple						=			n - 1, 0
						# break up n -1 into a power of 2 (s) and
						# remaining odd component
						# essentially, solve for d * 2 ** s == n - 1
						while d % 2 == 0:
												d //= 2
												s += 1
						for prime in plist:
												snake_case_   : List[str]						=			False
												for r in range(__UpperCamelCase    ):
																		snake_case_   : int						=			pow(__UpperCamelCase					, d * 2**r					, __UpperCamelCase    )
																		# see article for analysis explanation for m
																		if (r == 0 and m == 1) or ((m + 1) % n == 0):
																								snake_case_   : Optional[Any]						=			True
																								# this loop will not determine compositeness
																								break
												if pr:
																		continue
												# if pr is False, then the above loop never evaluated to true,
												# and the n MUST be composite
												return False
						return True
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						assert not miller_rabin(5_6_1    )
						assert miller_rabin(5_6_3    )
						# 2047

						assert not miller_rabin(8_3_8_2_0_1    )
						assert miller_rabin(8_3_8_2_0_7    )
						# 1_373_653

						assert not miller_rabin(1_7_3_1_6_0_0_1    )
						assert miller_rabin(1_7_3_1_6_0_1_7    )
						# 25_326_001

						assert not miller_rabin(3_0_7_8_3_8_6_6_4_1    )
						assert miller_rabin(3_0_7_8_3_8_6_6_5_3    )
						# 3_215_031_751

						assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1    )
						assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9    )
						# 2_152_302_898_747

						assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7    )
						assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7    )
						# 3_474_749_660_383

						assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1    )
						assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7    )
						# 341_550_071_728_321

						assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1    )
						assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1    )
						# 3_825_123_056_546_413_051

						assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7    )
						assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1    )
						# 318_665_857_834_031_151_167_461

						assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3    )
						assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9    )
						# 3_317_044_064_679_887_385_961_981
						# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()

