




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(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Any				)    -> Any:
							'''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 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Union[str, Any]				)    -> str:
							'''simple docstring'''


							__lowercase      =    tmp_path / '''cache'''
							__lowercase      =    {'''text''': '''string'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														__lowercase      =    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 		_A(       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			Optional[Any]				)    -> List[str]:
							'''simple docstring'''


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







@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''				), '''train''', '''test''']				)
def 		_A(       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Dict				)    -> List[Any]:
							'''simple docstring'''


							__lowercase      =    tmp_path / '''cache'''
							__lowercase      =    {'''text''': '''string'''}
							__lowercase      =    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 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			Union[str, Any]				)    -> List[str]:
							'''simple docstring'''


							if issubclass(UpperCamelCase__				,       UpperCamelCase__				):
														__lowercase      =    text_path
							elif issubclass(UpperCamelCase__				,       UpperCamelCase__				):
														__lowercase      =    [text_path]
							__lowercase      =    tmp_path / '''cache'''
							__lowercase      =    {'''text''': '''string'''}
							__lowercase      =    TextDatasetReader(UpperCamelCase__				,       cache_dir=UpperCamelCase__				).read()
							_check_text_dataset(UpperCamelCase__				,       UpperCamelCase__				)







def 		_A(       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Union[str, Any]=("train",)				)    -> List[str]:
							'''simple docstring'''


							assert isinstance(UpperCamelCase__				,       UpperCamelCase__				)
							for split in splits:
														__lowercase      =    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(       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Any				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    tmp_path / '''cache'''
							__lowercase      =    {'''text''': '''string'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														__lowercase      =    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 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    tmp_path / '''cache'''
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							__lowercase      =    {'''text''': '''string'''}
							__lowercase      =    features.copy() if features else default_expected_features
							__lowercase      =    (
							    Features({feature: Value(UpperCamelCase__				) for feature, dtype in features.items()}				) if features is not None else None
							)
							__lowercase      =    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 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			Optional[int]				)    -> List[str]:
							'''simple docstring'''


							if split:
														__lowercase      =    {split: text_path}
							else:
														__lowercase      =    '''train'''
														__lowercase      =    {'''train''': text_path, '''test''': text_path}
							__lowercase      =    tmp_path / '''cache'''
							__lowercase      =    {'''text''': '''string'''}
							__lowercase      =    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 .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels






