






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						( __lowercase   :			Optional[int]			,				__lowercase   :			Optional[int]    )						->      Any:





    """simple docstring"""

    assert isinstance(__lowercase			,				__lowercase    )
    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						( __lowercase   :			Any			,				__lowercase   :			Dict			,				__lowercase   :			str    )						->      List[str]:





    """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(__lowercase			,				cache_dir=__lowercase			,				keep_in_memory=__lowercase    ).read()
    _check_parquet_dataset(__lowercase			,				__lowercase    )



@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						( __lowercase   :			Optional[Any]			,				__lowercase   :			Optional[Any]			,				__lowercase   :			Optional[Any]    )						->      Any:





    """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(__lowercase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    __A         =	ParquetDatasetReader(__lowercase			,				features=__lowercase			,				cache_dir=__lowercase    ).read()
    _check_parquet_dataset(__lowercase			,				__lowercase    )



@pytest.mark.parametrize("""split"""			,				[None, NamedSplit("""train"""    ), """train""", """test"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			Tuple			,				__lowercase   :			Optional[int]    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	tmp_path / """cache"""
    __A         =	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    __A         =	ParquetDatasetReader(__lowercase			,				cache_dir=__lowercase			,				split=__lowercase    ).read()
    _check_parquet_dataset(__lowercase			,				__lowercase    )
    assert dataset.split == split if split else "train"



@pytest.mark.parametrize("""path_type"""			,				[str, list]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			Any			,				__lowercase   :			Dict    )						->      Any:





    """simple docstring"""

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]			,				__lowercase   :			List[Any]			,				__lowercase   :			Any=("train",)    )						->      Optional[int]:





    """simple docstring"""

    assert isinstance(__lowercase			,				__lowercase    )
    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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]			,				__lowercase   :			Optional[Any]			,				__lowercase   :			str    )						->      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(
            {"""train""": parquet_path}			,				cache_dir=__lowercase			,				keep_in_memory=__lowercase    ).read()
    _check_parquet_datasetdict(__lowercase			,				__lowercase    )



@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						( __lowercase   :			Any			,				__lowercase   :			Union[str, Any]			,				__lowercase   :			str    )						->      Tuple:





    """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(__lowercase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    __A         =	ParquetDatasetReader({"""train""": parquet_path}			,				features=__lowercase			,				cache_dir=__lowercase    ).read()
    _check_parquet_datasetdict(__lowercase			,				__lowercase    )



@pytest.mark.parametrize("""split"""			,				[None, NamedSplit("""train"""    ), """train""", """test"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Any			,				__lowercase   :			Optional[int]    )						->      Optional[int]:





    """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(__lowercase			,				cache_dir=__lowercase    ).read()
    _check_parquet_datasetdict(__lowercase			,				__lowercase			,				splits=list(path.keys()    )    )
    assert all(dataset[split].split == split for split in path.keys()    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			List[str]    )						->      List[str]:





    """simple docstring"""

    __A         =	ParquetDatasetWriter(__lowercase			,				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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			Any    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	str(shared_datadir / """test_image_rgb.jpg"""    )
    __A         =	{"""image""": [image_path]}
    __A         =	Features({"""image""": Image()}    )
    __A         =	Dataset.from_dict(__lowercase			,				features=__lowercase    )
    __A         =	ParquetDatasetWriter(__lowercase			,				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=__lowercase    ).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						( __lowercase   :			List[Any]			,				__lowercase   :			List[Any]    )						->      Optional[Any]:





    """simple docstring"""

    assert get_writer_batch_size(__lowercase    ) == expected












from typing import TYPE_CHECKING

from ...utils import _LazyModule


__a      :       Optional[int]				  =   {"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
  from .tokenization_bertweet import BertweetTokenizer

else:
  import sys

  __a      :       int				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




