



import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      assert isinstance(UpperCAmelCase__   ,   UpperCAmelCase__ )
      assert dataset.num_rows == 4
      assert dataset.num_columns == 1
      assert dataset.column_names == ["text"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("keep_in_memory"   ,   [False, True] )
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		tmp_path / "cache"
      lowerCamelCase								=		{"text": "string"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            lowerCamelCase								=		TextDatasetReader(UpperCAmelCase__   ,   cache_dir=UpperCAmelCase__   ,   keep_in_memory=UpperCAmelCase__ ).read()
      _check_text_dataset(UpperCAmelCase__   ,   UpperCAmelCase__ )

@pytest.mark.parametrize(
    "features"   ,   [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]   ,   )
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		tmp_path / "cache"
      lowerCamelCase								=		{"text": "string"}
      lowerCamelCase								=		features.copy() if features else default_expected_features
      lowerCamelCase								=		(
          Features({feature: Value(UpperCAmelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
      )
      lowerCamelCase								=		TextDatasetReader(UpperCAmelCase__   ,   features=UpperCAmelCase__   ,   cache_dir=UpperCAmelCase__ ).read()
      _check_text_dataset(UpperCAmelCase__   ,   UpperCAmelCase__ )

@pytest.mark.parametrize("split"   ,   [None, NamedSplit("train" ), "train", "test"] )
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		tmp_path / "cache"
      lowerCamelCase								=		{"text": "string"}
      lowerCamelCase								=		TextDatasetReader(UpperCAmelCase__   ,   cache_dir=UpperCAmelCase__   ,   split=UpperCAmelCase__ ).read()
      _check_text_dataset(UpperCAmelCase__   ,   UpperCAmelCase__ )
      assert dataset.split == split if split else "train"

@pytest.mark.parametrize("path_type"   ,   [str, list] )
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      if issubclass(UpperCAmelCase__   ,   UpperCAmelCase__ ):
            lowerCamelCase								=		text_path
      elif issubclass(UpperCAmelCase__   ,   UpperCAmelCase__ ):
            lowerCamelCase								=		[text_path]
      lowerCamelCase								=		tmp_path / "cache"
      lowerCamelCase								=		{"text": "string"}
      lowerCamelCase								=		TextDatasetReader(UpperCAmelCase__   ,   cache_dir=UpperCAmelCase__ ).read()
      _check_text_dataset(UpperCAmelCase__   ,   UpperCAmelCase__ )

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=("train",) ):


      """simple docstring"""



      assert isinstance(UpperCAmelCase__   ,   UpperCAmelCase__ )
      for split in splits:
            lowerCamelCase								=		dataset_dict[split]
            assert dataset.num_rows == 4
            assert dataset.num_columns == 1
            assert dataset.column_names == ["text"]
            for feature, expected_dtype in expected_features.items():
                  assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("keep_in_memory"   ,   [False, True] )
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		tmp_path / "cache"
      lowerCamelCase								=		{"text": "string"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            lowerCamelCase								=		TextDatasetReader({"train": text_path}   ,   cache_dir=UpperCAmelCase__   ,   keep_in_memory=UpperCAmelCase__ ).read()
      _check_text_datasetdict(UpperCAmelCase__   ,   UpperCAmelCase__ )

@pytest.mark.parametrize(
    "features"   ,   [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]   ,   )
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      lowerCamelCase								=		{"text": "string"}
      lowerCamelCase								=		features.copy() if features else default_expected_features
      lowerCamelCase								=		(
          Features({feature: Value(UpperCAmelCase__ ) for feature, dtype in features.items()} ) if features is not None else None
      )
      lowerCamelCase								=		TextDatasetReader({"train": text_path}   ,   features=UpperCAmelCase__   ,   cache_dir=UpperCAmelCase__ ).read()
      _check_text_datasetdict(UpperCAmelCase__   ,   UpperCAmelCase__ )






@pytest.mark.parametrize("split"   ,   [None, NamedSplit("train" ), "train", "test"] )
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      if split:
            lowerCamelCase								=		{split: text_path}
      else:
            lowerCamelCase								=		"train"
            lowerCamelCase								=		{"train": text_path, "test": text_path}
      lowerCamelCase								=		tmp_path / "cache"
      lowerCamelCase								=		{"text": "string"}
      lowerCamelCase								=		TextDatasetReader(UpperCAmelCase__   ,   cache_dir=UpperCAmelCase__ ).read()
      _check_text_datasetdict(UpperCAmelCase__   ,   UpperCAmelCase__   ,   splits=list(path.keys() ) )
      assert all(dataset[split].split == split for split in path.keys() )




from math import pi

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(9_0, 1_0))