


"""simple docstring"""







from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .notes_encoder import SpectrogramNotesEncoder
     from .continous_encoder import SpectrogramContEncoder
     from .pipeline_spectrogram_diffusion import (
         SpectrogramContEncoder,
         SpectrogramDiffusionPipeline,
         TaFilmDecoder,
     )

try:
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
     from .midi_utils import MidiProcessor









"""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__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  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__      (    SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					Optional[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():
    lowerCAmelCase					:					str								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				keep_in_memory=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@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__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					Optional[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[str]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					List[str]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				features=SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"    ), "train", "test"]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					int								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				split=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"    ,				[str, list]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Optional[Any]								=							parquet_path
  elif issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Dict								=							[parquet_path]
  lowerCAmelCase					:					Optional[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					Any								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Optional[Any]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   Tuple=("train",)    ):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  for split in splits:
    lowerCAmelCase					:					List[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__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							tmp_path / "cache"
  lowerCAmelCase					:					List[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():
    lowerCAmelCase					:					Optional[Any]								=							ParquetDatasetReader(
        {"train": parquet_path}    ,				cache_dir=SCREAMING_SNAKE_CASE    ,				keep_in_memory=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@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__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							tmp_path / "cache"
  lowerCAmelCase					:					Union[str, Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[Any]								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[Any]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetReader({"train": parquet_path}    ,				features=SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"    ), "train", "test"]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if split:
    lowerCAmelCase					:					List[str]								=							{split: parquet_path}
  else:
    lowerCAmelCase					:					List[str]								=							"train"
    lowerCAmelCase					:					str								=							{"train": parquet_path, "test": parquet_path}
  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					List[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[str]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_datasetdict(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				splits=list(path.keys()    )    )
  assert all(dataset[split].split == split for split in path.keys()    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,				tmp_path / "foo.parquet"    )
  assert writer.write() > 0
  lowerCAmelCase					:					List[str]								=							pq.ParquetFile(tmp_path / "foo.parquet"    )
  lowerCAmelCase					:					Union[str, Any]								=							pf.read()
  assert dataset.data.table == output_table
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







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

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

  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetReader(str(tmp_path / "foo.parquet"    )    ,				streaming=SCREAMING_SNAKE_CASE    ).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__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  assert get_writer_batch_size(SCREAMING_SNAKE_CASE    ) == expected





