






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   __A       (  _lowercase       ,      _lowercase			):

					'''simple docstring'''







					assert isinstance(_lowercase       ,      _lowercase			)
					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   __A       (  _lowercase       ,      _lowercase       ,      _lowercase			):

					'''simple docstring'''







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




@pytest.mark.parametrize(
    '''features'''       ,      [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]       ,      )
def   __A       (  _lowercase       ,      _lowercase       ,      _lowercase			):

					'''simple docstring'''







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




@pytest.mark.parametrize('''split'''       ,      [None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __A       (  _lowercase       ,      _lowercase       ,      _lowercase			):

					'''simple docstring'''







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




@pytest.mark.parametrize('''path_type'''       ,      [str, list]			)
def   __A       (  _lowercase       ,      _lowercase       ,      _lowercase			):

					'''simple docstring'''







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




def   __A       (  _lowercase       ,      _lowercase       ,      _lowercase=("train",)			):

					'''simple docstring'''







					assert isinstance(_lowercase       ,      _lowercase			)
					for split in splits:
										_A										=			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   __A       (  _lowercase       ,      _lowercase       ,      _lowercase			):

					'''simple docstring'''







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




@pytest.mark.parametrize(
    '''features'''       ,      [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]       ,      )
def   __A       (  _lowercase       ,      _lowercase       ,      _lowercase			):

					'''simple docstring'''







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




@pytest.mark.parametrize('''split'''       ,      [None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __A       (  _lowercase       ,      _lowercase       ,      _lowercase			):

					'''simple docstring'''







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




def 		snake_case__	(		lowercase							,				lowercase					):

   if density <= 0:
      raise ValueError("Impossible fluid density"					)
   if bulk_modulus <= 0:
      raise ValueError("Impossible bulk modulus"					)

   return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()