"""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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase							)				-> Optional[Any]:
				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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> Optional[Any]:
				SCREAMING_SNAKE_CASE__			=					tmp_path / "cache"
				SCREAMING_SNAKE_CASE__			=					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								SCREAMING_SNAKE_CASE__			=					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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> Optional[int]:
				SCREAMING_SNAKE_CASE__			=					tmp_path / "cache"
				SCREAMING_SNAKE_CASE__			=					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				SCREAMING_SNAKE_CASE__			=					features.copy() if features else default_expected_features
				SCREAMING_SNAKE_CASE__			=					(
				    Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
				)
				SCREAMING_SNAKE_CASE__			=					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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> Any:
				SCREAMING_SNAKE_CASE__			=					tmp_path / "cache"
				SCREAMING_SNAKE_CASE__			=					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				SCREAMING_SNAKE_CASE__			=					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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> Any:
				if issubclass(__snake_case	,       __snake_case							):
								SCREAMING_SNAKE_CASE__			=					parquet_path
				elif issubclass(__snake_case	,       __snake_case							):
								SCREAMING_SNAKE_CASE__			=					[parquet_path]
				SCREAMING_SNAKE_CASE__			=					tmp_path / "cache"
				SCREAMING_SNAKE_CASE__			=					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				SCREAMING_SNAKE_CASE__			=					ParquetDatasetReader(__snake_case	,       cache_dir=__snake_case							).read()
				_check_parquet_dataset(__snake_case	,       __snake_case							)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase=("train",)							)				-> List[str]:
				assert isinstance(__snake_case	,       __snake_case							)
				for split in splits:
								SCREAMING_SNAKE_CASE__			=					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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> Tuple:
				SCREAMING_SNAKE_CASE__			=					tmp_path / "cache"
				SCREAMING_SNAKE_CASE__			=					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								SCREAMING_SNAKE_CASE__			=					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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> Optional[Any]:
				SCREAMING_SNAKE_CASE__			=					tmp_path / "cache"
				SCREAMING_SNAKE_CASE__			=					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				SCREAMING_SNAKE_CASE__			=					features.copy() if features else default_expected_features
				SCREAMING_SNAKE_CASE__			=					(
				    Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
				)
				SCREAMING_SNAKE_CASE__			=					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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> List[Any]:
				if split:
								SCREAMING_SNAKE_CASE__			=					{split: parquet_path}
				else:
								SCREAMING_SNAKE_CASE__			=					"train"
								SCREAMING_SNAKE_CASE__			=					{"train": parquet_path, "test": parquet_path}
				SCREAMING_SNAKE_CASE__			=					tmp_path / "cache"
				SCREAMING_SNAKE_CASE__			=					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				SCREAMING_SNAKE_CASE__			=					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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase							)				-> Dict:
				SCREAMING_SNAKE_CASE__			=					ParquetDatasetWriter(__snake_case	,       tmp_path / "foo.parquet"							)
				assert writer.write() > 0
				SCREAMING_SNAKE_CASE__			=					pq.ParquetFile(tmp_path / "foo.parquet"							)
				SCREAMING_SNAKE_CASE__			=					pf.read()
				assert dataset.data.table == output_table




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

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

				SCREAMING_SNAKE_CASE__			=					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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase							)				-> List[Any]:
				assert get_writer_batch_size(__snake_case							) == expected












def       _SCREAMING_SNAKE_CASE (							__snake_case = 1_0_0_0 )				->						int:
       _UpperCAmelCase	,			_UpperCAmelCase							=    1, 1
       _UpperCAmelCase							=    []
       for i in range(1   ,      n + 1 ):
              _UpperCAmelCase							=    prev_numerator + 2 * prev_denominator
              _UpperCAmelCase							=    prev_numerator + prev_denominator
              if len(str(__snake_case ) ) > len(str(__snake_case ) ):
                     result.append(__snake_case )
              _UpperCAmelCase							=    numerator
              _UpperCAmelCase							=    denominator

       return len(__snake_case )


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