

'''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 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    List[Any]	)		->	Any:




  '''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 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    int     ,					lowercase_						:    Optional[Any]	)		->	Tuple:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase									=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_						:    str     ,					lowercase_						:    Any	)		->	int:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=features.copy() if features else default_expected_features
  lowercase									=(
      Features({feature: Value(lowercase_	) for feature, dtype in features.items()}	) if features is not None else None
  )
  lowercase									=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_						:    Optional[int]     ,					lowercase_						:    List[str]     ,					lowercase_						:    List[Any]	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=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_						:    Optional[Any]     ,					lowercase_						:    Tuple     ,					lowercase_						:    Optional[Any]	)		->	Any:




  '''simple docstring'''



  if issubclass(lowercase_     ,					lowercase_	):
    lowercase									=parquet_path
  elif issubclass(lowercase_     ,					lowercase_	):
    lowercase									=[parquet_path]
  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=ParquetDatasetReader(lowercase_     ,					cache_dir=lowercase_	).read()
  _check_parquet_dataset(lowercase_     ,					lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Any=("train",)	)		->	Optional[Any]:




  '''simple docstring'''



  assert isinstance(lowercase_     ,					lowercase_	)
  for split in splits:
    lowercase									=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_						:    Dict     ,					lowercase_						:    Dict     ,					lowercase_						:    Optional[int]	)		->	int:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase									=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_						:    Union[str, Any]     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Optional[int]	)		->	List[str]:




  '''simple docstring'''



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




  '''simple docstring'''



  if split:
    lowercase									={split: parquet_path}
  else:
    lowercase									='''train'''
    lowercase									={'''train''': parquet_path, '''test''': parquet_path}
  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=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[int]     ,					lowercase_						:    Optional[Any]	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=ParquetDatasetWriter(lowercase_     ,					tmp_path / '''foo.parquet'''	)
  assert writer.write() > 0
  lowercase									=pq.ParquetFile(tmp_path / '''foo.parquet'''	)
  lowercase									=pf.read()
  assert dataset.data.table == output_table




def 	UpperCamelCase   (       lowercase_						:    Tuple     ,					lowercase_						:    List[str]	)		->	str:




  '''simple docstring'''



  lowercase									=str(shared_datadir / '''test_image_rgb.jpg'''	)
  lowercase									={'''image''': [image_path]}
  lowercase									=Features({'''image''': Image()}	)
  lowercase									=Dataset.from_dict(lowercase_     ,					features=lowercase_	)
  lowercase									=ParquetDatasetWriter(lowercase_     ,					tmp_path / '''foo.parquet'''	)
  assert writer.write() > 0

  lowercase									=Dataset.from_parquet(str(tmp_path / '''foo.parquet'''	)	)
  assert dataset.features == reloaded_dataset.features

  lowercase									=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_						:    str     ,					lowercase_						:    List[Any]	)		->	List[Any]:




  '''simple docstring'''



  assert get_writer_batch_size(lowercase_	) == expected


"""simple docstring"""




from ..utils import DummyObject, requires_backends





class 			snake_case  (				metaclass=__lowercase ):
							UpperCAmelCase__      =   ['''note_seq''']

							def __init__(self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									requires_backends(self							,							['''note_seq''']     )

							@classmethod
							def _lowercase       (cls							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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







							@classmethod
							def _lowercase       (cls							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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