

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

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

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

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

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

	A__							:    List[Any]        =		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 						UpperCamelCase					(lowercase_: List[Any]    ,      lowercase_: Tuple					)     ->      Dict:
	assert get_writer_batch_size(lowercase_					) == expected






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


A_       : Union[str, Any]         =			logging.get_logger(__name__)



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       **A__			):
						warnings.warn(
						    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
						    """ use DonutImageProcessor instead."""    ,       A__    ,       )
						super().__init__(*A__    ,       **A__			)



