


'''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 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       Optional[Any]		)					->	Union[str, 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 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       List[str]							,    __UpperCamelCase					:       int		)					->	Optional[Any]:







     """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(__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 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       int							,    __UpperCamelCase					:       Any		)					->	Optional[int]:







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

@pytest.mark.parametrize('split'							,    [None, NamedSplit('train'		), 'train', 'test']		)
def 					lowerCamelCase_ ( __UpperCamelCase					:       str							,    __UpperCamelCase					:       List[str]							,    __UpperCamelCase					:       Tuple		)					->	str:







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







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

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[str]							,    __UpperCamelCase					:       int							,    __UpperCamelCase					:       int=("train",)		)					->	int:







     """simple docstring"""
     assert isinstance(__UpperCamelCase							,    __UpperCamelCase		)
     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 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]							,    __UpperCamelCase					:       Union[str, Any]							,    __UpperCamelCase					:       List[Any]		)					->	List[str]:







     """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=__UpperCamelCase							,    keep_in_memory=__UpperCamelCase		).read()
     _check_text_datasetdict(__UpperCamelCase							,    __UpperCamelCase		)

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







     """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(__UpperCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
     )
     _A				=							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 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       str		)					->	str:







     """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(__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'''
from __future__ import annotations

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int | None = None							,    __UpperCamelCase					:       int | None = None		)					->	None:







     """simple docstring"""
     if start is None:
          _A				=							0

     if end is None:
          _A				=							len(__UpperCamelCase		) - 1

     if start >= end:
          return

     _A				=							(start + end) // 2

     slowsort(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)
     slowsort(__UpperCamelCase							,    mid + 1							,    __UpperCamelCase		)

     if sequence[end] < sequence[mid]:
          _A		,				_A				=							sequence[mid], sequence[end]

     slowsort(__UpperCamelCase							,    __UpperCamelCase							,    end - 1		)


if __name__ == "__main__":
    from doctest import testmod

    testmod()





