
'''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 snake_case_     (					lowercase__   ,       lowercase__		):
  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 snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Any				       =							tmp_path / "cache"
  UpperCAmelCase__      :					Dict				       =							{"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__      :					List[Any]				       =							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 snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					List[Any]				       =							tmp_path / "cache"
  UpperCAmelCase__      :					Any				       =							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  UpperCAmelCase__      :					List[str]				       =							features.copy() if features else default_expected_features
  UpperCAmelCase__      :					Union[str, Any]				       =							(
      Features({feature: Value(lowercase__		) for feature, dtype in features.items()}		) if features is not None else None
  )
  UpperCAmelCase__      :					List[str]				       =							ParquetDatasetReader(lowercase__   ,       features=lowercase__   ,       cache_dir=lowercase__		).read()
  _check_parquet_dataset(lowercase__   ,       lowercase__		)







@pytest.mark.parametrize("split"   ,       [None, NamedSplit("train"		), "train", "test"]		)
def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Optional[Any]				       =							tmp_path / "cache"
  UpperCAmelCase__      :					Tuple				       =							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  UpperCAmelCase__      :					List[Any]				       =							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 snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  if issubclass(lowercase__   ,       lowercase__		):
    UpperCAmelCase__      :					int				       =							parquet_path
  elif issubclass(lowercase__   ,       lowercase__		):
    UpperCAmelCase__      :					Dict				       =							[parquet_path]
  UpperCAmelCase__      :					Dict				       =							tmp_path / "cache"
  UpperCAmelCase__      :					str				       =							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  UpperCAmelCase__      :					List[Any]				       =							ParquetDatasetReader(lowercase__   ,       cache_dir=lowercase__		).read()
  _check_parquet_dataset(lowercase__   ,       lowercase__		)







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







def snake_case_     (					lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Tuple				       =							str(shared_datadir / "test_image_rgb.jpg"		)
  UpperCAmelCase__      :					Dict				       =							{"image": [image_path]}
  UpperCAmelCase__      :					Union[str, Any]				       =							Features({"image": Image()}		)
  UpperCAmelCase__      :					Optional[Any]				       =							Dataset.from_dict(lowercase__   ,       features=lowercase__		)
  UpperCAmelCase__      :					Optional[int]				       =							ParquetDatasetWriter(lowercase__   ,       tmp_path / "foo.parquet"		)
  assert writer.write() > 0

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

  UpperCAmelCase__      :					List[Any]				       =							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 snake_case_     (					lowercase__   ,       lowercase__		):
  assert get_writer_batch_size(lowercase__		) == expected





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE     =       {}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       ["""GPTSw3Tokenizer"""]


if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_swa import GPTSwaTokenizer

else:
      import sys

      SCREAMING_SNAKE_CASE     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



