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







@pytest.mark.parametrize("""split"""     ,    [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				UpperCamelCase_:							str	=						tmp_path / """cache"""
				UpperCamelCase_:							int	=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Optional[Any]	=						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        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Any:
				if issubclass(lowerCamelCase     ,    lowerCamelCase						):
								UpperCamelCase_:							List[str]	=						parquet_path
				elif issubclass(lowerCamelCase     ,    lowerCamelCase						):
								UpperCamelCase_:							Tuple	=						[parquet_path]
				UpperCamelCase_:							Any	=						tmp_path / """cache"""
				UpperCamelCase_:							Optional[Any]	=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
				UpperCamelCase_:							Optional[Any]	=						ParquetDatasetReader(lowerCamelCase     ,    cache_dir=lowerCamelCase						).read()
				_check_parquet_dataset(lowerCamelCase     ,    lowerCamelCase						)







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







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

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

				UpperCamelCase_:							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        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[int]:
				assert get_writer_batch_size(lowerCamelCase						) == expected








def        A__					(						lowerCamelCase = 50						)  ->	int:
				UpperCamelCase_:							List[Any]	=						[[0] * 3 for _ in range(length + 1						)]

				for row_length in range(length + 1						):
								for tile_length in range(2     ,    5						):
												for tile_start in range(row_length - tile_length + 1						):
																different_colour_ways_number[row_length][tile_length - 2] += (
																    different_colour_ways_number[row_length - tile_start - tile_length][
																        tile_length - 2
																    ]
																    + 1
																)

				return sum(different_colour_ways_number[length]						)


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






