



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 (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				assert isinstance(lowerCamelCase__					,			lowerCamelCase__						)
				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 (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   List[Any]													=					tmp_path / "cache"
				lowercase__					:   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():
								lowercase__					:   Optional[Any]													=					ParquetDatasetReader(lowerCamelCase__					,			cache_dir=lowerCamelCase__					,			keep_in_memory=lowerCamelCase__						).read()
				_check_parquet_dataset(lowerCamelCase__					,			lowerCamelCase__						)


@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 (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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


@pytest.mark.parametrize("split"					,			[None, NamedSplit("train"						), "train", "test"]						)
def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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


@pytest.mark.parametrize("path_type"					,			[str, list]						)
def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=("train",)						):

				"""simple docstring"""


				assert isinstance(lowerCamelCase__					,			lowerCamelCase__						)
				for split in splits:
								lowercase__					:   List[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  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   str													=					tmp_path / "cache"
				lowercase__					:   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():
								lowercase__					:   Any													=					ParquetDatasetReader(
								    {"train": parquet_path}					,			cache_dir=lowerCamelCase__					,			keep_in_memory=lowerCamelCase__						).read()
				_check_parquet_datasetdict(lowerCamelCase__					,			lowerCamelCase__						)


@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 (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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


@pytest.mark.parametrize("split"					,			[None, NamedSplit("train"						), "train", "test"]						)
def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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

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

				lowercase__					:   str													=					ParquetDatasetReader(str(tmp_path / "foo.parquet"						)					,			streaming=lowerCamelCase__						).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 (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				assert get_writer_batch_size(lowerCamelCase__						) == expected









from math import ceil, sqrt


def  __lowerCamelCase (       lowerCamelCase__ = 1_000_000						):

				"""simple docstring"""


				lowercase__					:   int													=					0

				for outer_width in range(3					,			(limit // 4) + 2						):
								if outer_width**2 > limit:
												lowercase__					:   List[str]													=					max(ceil(sqrt(outer_width**2 - limit						)						)					,			1						)
								else:
												lowercase__					:   List[str]													=					1
								if (outer_width - hole_width_lower_bound) % 2:
												hole_width_lower_bound += 1

								answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

				return answer


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




