



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__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							Optional[Any]    ):
		assert isinstance(UpperCamelCase__					,   UpperCamelCase__    )
		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__							(       UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Tuple    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__			=      {"""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__			=      ParquetDatasetReader(UpperCamelCase__					,   cache_dir=UpperCamelCase__					,   keep_in_memory=UpperCamelCase__    ).read()
		_check_parquet_dataset(UpperCamelCase__					,   UpperCamelCase__    )






@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__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							int					,   UpperCamelCase__:							Tuple    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__			=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
		SCREAMING_SNAKE_CASE__			=      features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE__			=      (
		    Features({feature: Value(UpperCamelCase__    ) for feature, dtype in features.items()}    ) if features is not None else None
		)
		SCREAMING_SNAKE_CASE__			=      ParquetDatasetReader(UpperCamelCase__					,   features=UpperCamelCase__					,   cache_dir=UpperCamelCase__    ).read()
		_check_parquet_dataset(UpperCamelCase__					,   UpperCamelCase__    )






@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__			=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
		SCREAMING_SNAKE_CASE__			=      ParquetDatasetReader(UpperCamelCase__					,   cache_dir=UpperCamelCase__					,   split=UpperCamelCase__    ).read()
		_check_parquet_dataset(UpperCamelCase__					,   UpperCamelCase__    )
		assert dataset.split == split if split else "train"






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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Dict					,   UpperCamelCase__:							Any=("train",)    ):
		assert isinstance(UpperCamelCase__					,   UpperCamelCase__    )
		for split in splits:
				SCREAMING_SNAKE_CASE__			=      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__							(       UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Dict    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__			=      {"""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__			=      ParquetDatasetReader(
				    {"""train""": parquet_path}					,   cache_dir=UpperCamelCase__					,   keep_in_memory=UpperCamelCase__    ).read()
		_check_parquet_datasetdict(UpperCamelCase__					,   UpperCamelCase__    )






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






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






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






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

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

		SCREAMING_SNAKE_CASE__			=      ParquetDatasetReader(str(tmp_path / """foo.parquet"""    )					,   streaming=UpperCamelCase__    ).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__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Any    ):
		assert get_writer_batch_size(UpperCamelCase__    ) == expected






import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_lowerCamelCase   				=			logging.get_logger(__name__)




class   UpperCamelCase_       (     UpperCamelCase__    ):


							def __init__(							self    :List[Any]							,				*__A    :Tuple							,				**__A    :Dict		)   ->   None:



									"""simple docstring"""



									warnings.warn(
									    """The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
									    """ Please use LayoutLMv2ImageProcessor instead."""							,				__A							,				)
									super().__init__(*__A							,				**__A		)