'''simple docstring'''





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     ,UpperCAmelCase					)     ->			Tuple:




						'''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     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			List[Any]:




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




						'''simple docstring'''
						_UpperCamelCase			:			int   			=						tmp_path / "cache"
						_UpperCamelCase			:			Tuple   			=						{"text": "string"}
						_UpperCamelCase			:			Dict   			=						features.copy() if features else default_expected_features
						_UpperCamelCase			:			List[str]   			=						(
						    Features({feature: Value(UpperCAmelCase					) for feature, dtype in features.items()}					) if features is not None else None
						)
						_UpperCamelCase			:			str   			=						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     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[int]   			=						tmp_path / "cache"
						_UpperCamelCase			:			Tuple   			=						{"text": "string"}
						_UpperCamelCase			:			int   			=						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     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			Union[str, Any]:




						'''simple docstring'''
						if issubclass(UpperCAmelCase     ,UpperCAmelCase					):
												_UpperCamelCase			:			Any   			=						text_path
						elif issubclass(UpperCAmelCase     ,UpperCAmelCase					):
												_UpperCamelCase			:			Dict   			=						[text_path]
						_UpperCamelCase			:			int   			=						tmp_path / "cache"
						_UpperCamelCase			:			List[Any]   			=						{"text": "string"}
						_UpperCamelCase			:			List[Any]   			=						TextDatasetReader(UpperCAmelCase     ,cache_dir=UpperCAmelCase					).read()
						_check_text_dataset(UpperCAmelCase     ,UpperCAmelCase					)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase=("train",)					)     ->			str:




						'''simple docstring'''
						assert isinstance(UpperCAmelCase     ,UpperCAmelCase					)
						for split in splits:
												_UpperCamelCase			:			List[Any]   			=						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     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			Tuple:




						'''simple docstring'''
						_UpperCamelCase			:			Tuple   			=						tmp_path / "cache"
						_UpperCamelCase			:			List[str]   			=						{"text": "string"}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCamelCase			:			Union[str, Any]   			=						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     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			List[Any]:




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




						'''simple docstring'''
						if split:
												_UpperCamelCase			:			List[str]   			=						{split: text_path}
						else:
												_UpperCamelCase			:			List[Any]   			=						"train"
												_UpperCamelCase			:			Optional[int]   			=						{"train": text_path, "test": text_path}
						_UpperCamelCase			:			int   			=						tmp_path / "cache"
						_UpperCamelCase			:			Optional[int]   			=						{"text": "string"}
						_UpperCamelCase			:			str   			=						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()					)



'''simple docstring'''





def 						__A							(     UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Optional[Any]   			=						set({"(", "[", "{"}					)
						_UpperCamelCase			:			int   			=						set({")", "]", "}"}					)
						_UpperCamelCase			:			Dict   			=						{"{": "}", "[": "]", "(": ")"}

						for i in range(len(UpperCAmelCase					)					):
												if s[i] in open_brackets:
																		stack.append(s[i]					)

												elif s[i] in closed_brackets and (
												    len(UpperCAmelCase					) == 0 or (len(UpperCAmelCase					) > 0 and open_to_closed[stack.pop()] != s[i])
												):
																		return False

						return len(UpperCAmelCase					) == 0
def 						__A							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						input("Enter sequence of brackets: "					)
						if is_balanced(UpperCAmelCase					):
												print(UpperCAmelCase     ,"is balanced"					)
						else:
												print(UpperCAmelCase     ,"is not balanced"					)


if __name__ == "__main__":
			main()


