
_UpperCAmelCase      :				int          = {
    "Pillow": "Pillow<10.0.0",
    "accelerate": "accelerate>=0.20.3",
    "av": "av==9.2.0",
    "beautifulsoup4": "beautifulsoup4",
    "black": "black~=23.1",
    "codecarbon": "codecarbon==1.2.0",
    "cookiecutter": "cookiecutter==1.7.3",
    "dataclasses": "dataclasses",
    "datasets": "datasets!=2.5.0",
    "decord": "decord==0.6.0",
    "deepspeed": "deepspeed>=0.9.3",
    "diffusers": "diffusers",
    "dill": "dill<0.3.5",
    "evaluate": "evaluate>=0.2.0",
    "fairscale": "fairscale>0.3",
    "faiss-cpu": "faiss-cpu",
    "fastapi": "fastapi",
    "filelock": "filelock",
    "flax": "flax>=0.4.1,<=0.7.0",
    "ftfy": "ftfy",
    "fugashi": "fugashi>=1.0",
    "GitPython": "GitPython<3.1.19",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
    "importlib_metadata": "importlib_metadata",
    "ipadic": "ipadic>=1.0.0,<2.0",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
    "jaxlib": "jaxlib>=0.1.65,<=0.4.13",
    "jieba": "jieba",
    "kenlm": "kenlm",
    "keras-nlp": "keras-nlp>=0.3.1",
    "librosa": "librosa",
    "nltk": "nltk",
    "natten": "natten>=0.14.6",
    "numpy": "numpy>=1.17",
    "onnxconverter-common": "onnxconverter-common",
    "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
    "onnxruntime": "onnxruntime>=1.4.0",
    "opencv-python": "opencv-python",
    "optuna": "optuna",
    "optax": "optax>=0.0.8,<=0.1.4",
    "packaging": "packaging>=20.0",
    "parameterized": "parameterized",
    "phonemizer": "phonemizer",
    "protobuf": "protobuf",
    "psutil": "psutil",
    "pyyaml": "pyyaml>=5.1",
    "pydantic": "pydantic<2",
    "pytest": "pytest>=7.2.0",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "python": "python>=3.8.0",
    "ray[tune]": "ray[tune]",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "rhoknp": "rhoknp>=1.1.0,<1.3.1",
    "rjieba": "rjieba",
    "rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
    "ruff": "ruff>=0.0.241,<=0.0.259",
    "sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
    "sacremoses": "sacremoses",
    "safetensors": "safetensors>=0.3.1",
    "sagemaker": "sagemaker>=2.31.0",
    "scikit-learn": "scikit-learn",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "sigopt": "sigopt",
    "starlette": "starlette",
    "sudachipy": "sudachipy>=0.6.6",
    "sudachidict_core": "sudachidict_core>=20220729",
    "tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
    "tensorflow": "tensorflow>=2.6,<2.14",
    "tensorflow-text": "tensorflow-text<2.14",
    "tf2onnx": "tf2onnx",
    "timeout-decorator": "timeout-decorator",
    "timm": "timm",
    "tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
    "torch": "torch>=1.9,!=1.12.0",
    "torchaudio": "torchaudio",
    "torchvision": "torchvision",
    "pyctcdecode": "pyctcdecode>=0.4.0",
    "tqdm": "tqdm>=4.27",
    "unidic": "unidic>=1.0.2",
    "unidic_lite": "unidic_lite>=1.0.7",
    "urllib3": "urllib3<2.0.0",
    "uvicorn": "uvicorn",
}


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 (      lowercase					, lowercase				)     ->						Optional[int]:
		'''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    A (      lowercase					, lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  features.copy() if features else default_expected_features
		UpperCamelCase						=  (
		    Features({feature: Value(lowercase				) for feature, dtype in features.items()}				) if features is not None else None
		)
		UpperCamelCase						=  ParquetDatasetReader(lowercase					, features=lowercase					, cache_dir=lowercase				).read()
		_check_parquet_dataset(lowercase					, lowercase				)


@pytest.mark.parametrize('split'					, [None, NamedSplit('train'				), 'train', 'test']				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

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


def    A (      lowercase					, lowercase					, lowercase=("train",)				)     ->						Tuple:
		'''simple docstring'''

		assert isinstance(lowercase					, lowercase				)
		for split in splits:
				UpperCamelCase						=  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 (      lowercase					, lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

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

		if split:
				UpperCamelCase						=  {split: parquet_path}
		else:
				UpperCamelCase						=  'train'
				UpperCamelCase						=  {'train': parquet_path, 'test': parquet_path}
		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  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    A (      lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

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


def    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

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

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

		UpperCamelCase						=  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    A (      lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		assert get_writer_batch_size(lowercase				) == expected
