



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 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

				assert isinstance(lowerCamelCase_ ,lowerCamelCase_)
				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 			lowerCAmelCase__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	Optional[int] ,lowerCamelCase_	:	List[str]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  tmp_path / '''cache'''
				lowerCAmelCase__  :       Tuple				=  {'''text''': '''string'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								lowerCAmelCase__  :       Optional[int]				=  TextDatasetReader(lowerCamelCase_ ,cache_dir=lowerCamelCase_ ,keep_in_memory=lowerCamelCase_).read()
				_check_text_dataset(lowerCamelCase_ ,lowerCamelCase_)






@pytest.mark.parametrize(
    '''features''' ,[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ] ,)
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	str ,lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  tmp_path / '''cache'''
				lowerCAmelCase__  :       Tuple				=  {'''text''': '''string'''}
				lowerCAmelCase__  :       Tuple				=  features.copy() if features else default_expected_features
				lowerCAmelCase__  :       Optional[int]				=  (
				    Features({feature: Value(lowerCamelCase_) for feature, dtype in features.items()}) if features is not None else None
				)
				lowerCAmelCase__  :       List[Any]				=  TextDatasetReader(lowerCamelCase_ ,features=lowerCamelCase_ ,cache_dir=lowerCamelCase_).read()
				_check_text_dataset(lowerCamelCase_ ,lowerCamelCase_)






@pytest.mark.parametrize('''split''' ,[None, NamedSplit('''train'''), '''train''', '''test'''])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  tmp_path / '''cache'''
				lowerCAmelCase__  :       List[Any]				=  {'''text''': '''string'''}
				lowerCAmelCase__  :       List[Any]				=  TextDatasetReader(lowerCamelCase_ ,cache_dir=lowerCamelCase_ ,split=lowerCamelCase_).read()
				_check_text_dataset(lowerCamelCase_ ,lowerCamelCase_)
				assert dataset.split == split if split else "train"






@pytest.mark.parametrize('''path_type''' ,[str, list])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				if issubclass(lowerCamelCase_ ,lowerCamelCase_):
								lowerCAmelCase__  :       List[Any]				=  text_path
				elif issubclass(lowerCamelCase_ ,lowerCamelCase_):
								lowerCAmelCase__  :       str				=  [text_path]
				lowerCAmelCase__  :       List[Any]				=  tmp_path / '''cache'''
				lowerCAmelCase__  :       Optional[Any]				=  {'''text''': '''string'''}
				lowerCAmelCase__  :       int				=  TextDatasetReader(lowerCamelCase_ ,cache_dir=lowerCamelCase_).read()
				_check_text_dataset(lowerCamelCase_ ,lowerCamelCase_)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	List[Any]=("train",)):





				'''simple docstring'''

				assert isinstance(lowerCamelCase_ ,lowerCamelCase_)
				for split in splits:
								lowerCAmelCase__  :       Optional[int]				=  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 			lowerCAmelCase__	(					lowerCamelCase_	:	str ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  tmp_path / '''cache'''
				lowerCAmelCase__  :       Tuple				=  {'''text''': '''string'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								lowerCAmelCase__  :       Dict				=  TextDatasetReader({'''train''': text_path} ,cache_dir=lowerCamelCase_ ,keep_in_memory=lowerCamelCase_).read()
				_check_text_datasetdict(lowerCamelCase_ ,lowerCamelCase_)






@pytest.mark.parametrize(
    '''features''' ,[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ] ,)
def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	List[str]):





				'''simple docstring'''

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






@pytest.mark.parametrize('''split''' ,[None, NamedSplit('''train'''), '''train''', '''test'''])
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

				if split:
								lowerCAmelCase__  :       Dict				=  {split: text_path}
				else:
								lowerCAmelCase__  :       Optional[int]				=  '''train'''
								lowerCAmelCase__  :       int				=  {'''train''': text_path, '''test''': text_path}
				lowerCAmelCase__  :       Optional[Any]				=  tmp_path / '''cache'''
				lowerCAmelCase__  :       str				=  {'''text''': '''string'''}
				lowerCAmelCase__  :       int				=  TextDatasetReader(lowerCamelCase_ ,cache_dir=lowerCamelCase_).read()
				_check_text_datasetdict(lowerCamelCase_ ,lowerCamelCase_ ,splits=list(path.keys()))
				assert all(dataset[split].split == split for split in path.keys())






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				if length <= 0 or not isinstance(lowerCamelCase_ ,lowerCamelCase_):
								raise ValueError('''Length must be a positive integer.''')
				return [n * (2 * n - 1) for n in range(lowerCamelCase_)]


if __name__ == "__main__":
							print(hexagonal_numbers(length=5))
							print(hexagonal_numbers(length=1_0))

