



'''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   a_   (		_UpperCAmelCase    :      Dict ,_UpperCAmelCase    :      Optional[int]				)    ->  Dict:
      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   a_   (		_UpperCAmelCase    :      List[str] ,_UpperCAmelCase    :      List[Any] ,_UpperCAmelCase    :      Tuple				)    ->  Optional[int]:
      __snake_case :   Dict													=    tmp_path / 'cache'
      __snake_case :   List[str]													=    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            __snake_case :   str													=    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   a_   (		_UpperCAmelCase    :      Optional[Any] ,_UpperCAmelCase    :      int ,_UpperCAmelCase    :      List[Any]				)    ->  Optional[int]:
      __snake_case :   List[str]													=    tmp_path / 'cache'
      __snake_case :   List[str]													=    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      __snake_case :   Dict													=    features.copy() if features else default_expected_features
      __snake_case :   Tuple													=    (
          Features({feature: Value(_UpperCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
      )
      __snake_case :   Optional[int]													=    ParquetDatasetReader(_UpperCAmelCase ,features=_UpperCAmelCase ,cache_dir=_UpperCAmelCase				).read()
      _check_parquet_dataset(_UpperCAmelCase ,_UpperCAmelCase				)





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





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





def   a_   (		_UpperCAmelCase    :      Optional[Any] ,_UpperCAmelCase    :      List[str]				)    ->  Dict:
      __snake_case :   Optional[int]													=    str(shared_datadir / 'test_image_rgb.jpg'				)
      __snake_case :   List[str]													=    {'image': [image_path]}
      __snake_case :   List[str]													=    Features({'image': Image()}				)
      __snake_case :   Any													=    Dataset.from_dict(_UpperCAmelCase ,features=_UpperCAmelCase				)
      __snake_case :   Optional[Any]													=    ParquetDatasetWriter(_UpperCAmelCase ,tmp_path / 'foo.parquet'				)
      assert writer.write() > 0

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

      __snake_case :   Tuple													=    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   a_   (		_UpperCAmelCase    :      Any ,_UpperCAmelCase    :      List[str]				)    ->  List[Any]:
      assert get_writer_batch_size(_UpperCAmelCase				) == expected








'''simple docstring'''





from ..utils import DummyObject, requires_backends



class  snake_case__						(    metaclass=SCREAMING_SNAKE_CASE_				):
 A__				= ['''note_seq''']
 def __init__(   self						:							Tuple      ,     *__a						:							int      ,     **__a						:							List[str]  )      ->   Any:







       '''simple docstring'''
       requires_backends(self      ,     ['note_seq']  )
 @classmethod
 def A_   (   cls						:							Optional[Any]      ,     *__a						:							Optional[Any]      ,     **__a						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       requires_backends(cls      ,     ['note_seq']  )





 @classmethod
 def A_   (   cls						:							List[Any]      ,     *__a						:							Union[str, Any]      ,     **__a						:							List[str]  )      ->   Any:







       '''simple docstring'''
       requires_backends(cls      ,     ['note_seq']  )



