"""simple docstring"""





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def      lowerCamelCase_(*__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=2  )->    Dict:
							from .. import __version__

							_SCREAMING_SNAKE_CASE		:						int			  =    take_from
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ()
							if not isinstance(args[0]     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						int			  =    (args,)

							for attribute, version_name, message in args:
														if version.parse(version.parse(__SCREAMING_SNAKE_CASE  ).base_version  ) >= version.parse(__SCREAMING_SNAKE_CASE  ):
																					raise ValueError(
																					    F"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
																					    F""" version {__version__} is >= {version_name}"""  )

														_SCREAMING_SNAKE_CASE		:						str			  =    None
														if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) and attribute in deprecated_kwargs:
																					values += (deprecated_kwargs.pop(__SCREAMING_SNAKE_CASE  ),)
																					_SCREAMING_SNAKE_CASE		:						str			  =    F"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
														elif hasattr(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					values += (getattr(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ),)
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    F"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
														elif deprecated_kwargs is None:
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    F"""`{attribute}` is deprecated and will be removed in version {version_name}."""

														if warning is not None:
																					_SCREAMING_SNAKE_CASE		:						int			  =    warning + """ """ if standard_warn else """"""
																					warnings.warn(warning + message     ,	__SCREAMING_SNAKE_CASE     ,	stacklevel=__SCREAMING_SNAKE_CASE  )

							if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) and len(__SCREAMING_SNAKE_CASE  ) > 0:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    inspect.getouterframes(inspect.currentframe()  )[1]
														_SCREAMING_SNAKE_CASE		:						Dict			  =    call_frame.filename
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    call_frame.lineno
														_SCREAMING_SNAKE_CASE		:						str			  =    call_frame.function
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    next(iter(deprecated_kwargs.items()  )  )
														raise TypeError(F"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""  )

							if len(__SCREAMING_SNAKE_CASE  ) == 0:
														return
							elif len(__SCREAMING_SNAKE_CASE  ) == 1:
														return values[0]
							return values



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



@pytest.mark.parametrize("""split"""     ,	[None, NamedSplit("""train"""  ), """train""", """test"""]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						int			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	split=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							assert dataset.split == split if split else "train"



@pytest.mark.parametrize("""path_type"""     ,	[str, list]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							if issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Any			  =    parquet_path
							elif issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [parquet_path]
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						str			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=("train",)  )->    Union[str, Any]:
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							for split in splits:
														_SCREAMING_SNAKE_CASE		:						int			  =    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_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Optional[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():
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    ParquetDatasetReader(
														    {"""train""": parquet_path}     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    features.copy() if features else default_expected_features
							_SCREAMING_SNAKE_CASE		:						str			  =    (
							    Features({feature: Value(__SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							_SCREAMING_SNAKE_CASE		:						int			  =    ParquetDatasetReader({"""train""": parquet_path}     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@pytest.mark.parametrize("""split"""     ,	[None, NamedSplit("""train"""  ), """train""", """test"""]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							if split:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {split: parquet_path}
							else:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """train"""
														_SCREAMING_SNAKE_CASE		:						Any			  =    {"""train""": parquet_path, """test""": parquet_path}
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	splits=list(path.keys()  )  )
							assert all(dataset[split].split == split for split in path.keys()  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    ParquetDatasetWriter(__SCREAMING_SNAKE_CASE     ,	tmp_path / """foo.parquet"""  )
							assert writer.write() > 0
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    pq.ParquetFile(tmp_path / """foo.parquet"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    pf.read()
							assert dataset.data.table == output_table



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    str(shared_datadir / """test_image_rgb.jpg"""  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""image""": [image_path]}
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    Features({"""image""": Image()}  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    Dataset.from_dict(__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    ParquetDatasetWriter(__SCREAMING_SNAKE_CASE     ,	tmp_path / """foo.parquet"""  )
							assert writer.write() > 0

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

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ParquetDatasetReader(str(tmp_path / """foo.parquet"""  )     ,	streaming=__SCREAMING_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							assert get_writer_batch_size(__SCREAMING_SNAKE_CASE  ) == expected


