

'''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					(     __magic_name__ ,    __magic_name__							):



					'''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       lowercase					(     __magic_name__ ,    __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Tuple   					=   tmp_path / "cache"
					UpperCAmelCase		:							Tuple   					=   {"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		:							Dict   					=   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					(     __magic_name__ ,    __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







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



					'''simple docstring'''







					UpperCAmelCase		:							int   					=   tmp_path / "cache"
					UpperCAmelCase		:							List[Any]   					=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
					UpperCAmelCase		:							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					(     __magic_name__ ,    __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







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





def       lowercase					(     __magic_name__ ,    __magic_name__ ,    __magic_name__=("train",)							):



					'''simple docstring'''







					assert isinstance(_snake_case ,    _snake_case							)
					for split in splits:
										UpperCAmelCase		:							Any   					=   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					(     __magic_name__ ,    __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							List[Any]   					=   tmp_path / "cache"
					UpperCAmelCase		:							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():
										UpperCAmelCase		:							Tuple   					=   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					(     __magic_name__ ,    __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







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



					'''simple docstring'''







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



					'''simple docstring'''







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





def       lowercase					(     __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







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

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

					UpperCAmelCase		:							Union[str, 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					(     __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







					assert get_writer_batch_size(_snake_case							) == expected



from __future__ import annotations






def        UpperCamelCase_(							_snake_case   :  int	):




			"""simple docstring"""






			__a        =str(_snake_case	)
			return len(_snake_case	) == 9 and set(_snake_case	) == set('123456789'	)






def        UpperCamelCase_(							):




			"""simple docstring"""






			for base_num in range(9999       ,     4999       ,     -1	):
						__a        =100002 * base_num
						if is_9_pandigital(_snake_case	):
									return candidate

			for base_num in range(333       ,     99       ,     -1	):
						__a        =1002003 * base_num
						if is_9_pandigital(_snake_case	):
									return candidate

			return None


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






