



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__						(							_SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
				"""simple docstring"""







				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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			Any    ):
				"""simple docstring"""







				__a       =		tmp_path / """cache"""
				__a       =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__a       =		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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		tmp_path / """cache"""
				__a       =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__a       =		features.copy() if features else default_expected_features
				__a       =		(
				    Features({feature: Value(__snake_case    ) for feature, dtype in features.items()}    ) if features is not None else None
				)
				__a       =		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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[Any]				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







				__a       =		tmp_path / """cache"""
				__a       =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__a       =		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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			str=("train",)    ):
				"""simple docstring"""







				assert isinstance(__snake_case				,     __snake_case    )
				for split in splits:
								__a       =		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__						(							_SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Tuple    ):
				"""simple docstring"""







				__a       =		tmp_path / """cache"""
				__a       =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__a       =		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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			Dict    ):
				"""simple docstring"""







				__a       =		tmp_path / """cache"""
				__a       =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__a       =		features.copy() if features else default_expected_features
				__a       =		(
				    Features({feature: Value(__snake_case    ) for feature, dtype in features.items()}    ) if features is not None else None
				)
				__a       =		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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			List[Any]				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
				"""simple docstring"""







				if split:
								__a       =		{split: parquet_path}
				else:
								__a       =		"""train"""
								__a       =		{"""train""": parquet_path, """test""": parquet_path}
				__a       =		tmp_path / """cache"""
				__a       =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				__a       =		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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			Dict    ):
				"""simple docstring"""







				__a       =		ParquetDatasetWriter(__snake_case				,     tmp_path / """foo.parquet"""    )
				assert writer.write() > 0
				__a       =		pq.ParquetFile(tmp_path / """foo.parquet"""    )
				__a       =		pf.read()
				assert dataset.data.table == output_table

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			Optional[int]    ):
				"""simple docstring"""







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

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

				__a       =		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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				assert get_writer_batch_size(__snake_case    ) == expected





from math import sqrt

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5				,     int(sqrt(_SCREAMING_SNAKE_CASE    ) + 1    )				,     6    ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int = 1_0001    ):
				"""simple docstring"""







				__a       =		0
				__a       =		1
				while count != nth and number < 3:
								number += 1
								if is_prime(_SCREAMING_SNAKE_CASE    ):
												count += 1
				while count != nth:
								number += 2
								if is_prime(_SCREAMING_SNAKE_CASE    ):
												count += 1
				return number


if __name__ == "__main__":
						print(F"""{solution() = }""")
