




import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


_lowerCamelCase			:	Optional[int]							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    def __init__(    self   :			int      ,				*UpperCAmelCase__   :			Tuple      ,				**UpperCAmelCase__   :			List[str])		->None:





           '''simple docstring'''

           warnings.warn(
               '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use GLPNImageProcessor instead.'''      ,				UpperCAmelCase__      ,				)
           super().__init__(*UpperCAmelCase__      ,				**UpperCAmelCase__)








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_				,       lowercase_		)     -> Tuple:



       """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_				,       lowercase_				,       lowercase_		)     -> int:



       """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_				,       lowercase_				,       lowercase_		)     -> List[str]:



       """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_				,       lowercase_				,       lowercase_		)     -> str:



       """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_				,       lowercase_				,       lowercase_		)     -> List[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_				,       lowercase_				,       lowercase_=("train",)		)     -> List[str]:



       """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_				,       lowercase_				,       lowercase_		)     -> 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(
                  {'''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_				,       lowercase_				,       lowercase_		)     -> List[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({'''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_				,       lowercase_				,       lowercase_		)     -> Union[str, Any]:



       """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_				,       lowercase_		)     -> 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_				,       lowercase_		)     -> str:



       """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_				,       lowercase_		)     -> Tuple:



       """simple docstring"""

       assert get_writer_batch_size(lowercase_		) == expected


