






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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    str:
							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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    str:
							_lowercase							:   str 							=   tmp_path / 'cache'
							_lowercase							:   Tuple 							=   {'text': 'string'}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														_lowercase							:   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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Union[str, Any]:
							_lowercase							:   str 							=   tmp_path / 'cache'
							_lowercase							:   List[Any] 							=   {'text': 'string'}
							_lowercase							:   Union[str, Any] 							=   features.copy() if features else default_expected_features
							_lowercase							:   int 							=   (
							    Features({feature: Value(_lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
							)
							_lowercase							:   Union[str, Any] 							=   TextDatasetReader(_lowerCAmelCase     ,     features=_lowerCAmelCase     ,     cache_dir=_lowerCAmelCase				).read()
							_check_text_dataset(_lowerCAmelCase     ,     _lowerCAmelCase				)






@pytest.mark.parametrize('split'     ,     [None, NamedSplit('train'				), 'train', 'test']				)
def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    List[Any]:
							_lowercase							:   List[str] 							=   tmp_path / 'cache'
							_lowercase							:   Optional[Any] 							=   {'text': 'string'}
							_lowercase							:   Dict 							=   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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    List[str]:
							if issubclass(_lowerCAmelCase     ,     _lowerCAmelCase				):
														_lowercase							:   Dict 							=   text_path
							elif issubclass(_lowerCAmelCase     ,     _lowerCAmelCase				):
														_lowercase							:   Dict 							=   [text_path]
							_lowercase							:   Dict 							=   tmp_path / 'cache'
							_lowercase							:   List[str] 							=   {'text': 'string'}
							_lowercase							:   Tuple 							=   TextDatasetReader(_lowerCAmelCase     ,     cache_dir=_lowerCAmelCase				).read()
							_check_text_dataset(_lowerCAmelCase     ,     _lowerCAmelCase				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=("train",)				)     ->    Dict:
							assert isinstance(_lowerCAmelCase     ,     _lowerCAmelCase				)
							for split in splits:
														_lowercase							:   Optional[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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Tuple:
							_lowercase							:   int 							=   tmp_path / 'cache'
							_lowercase							:   List[Any] 							=   {'text': 'string'}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														_lowercase							:   Tuple 							=   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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Optional[int]:
							_lowercase							:   Optional[int] 							=   tmp_path / 'cache'
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							_lowercase							:   Tuple 							=   {'text': 'string'}
							_lowercase							:   Union[str, Any] 							=   features.copy() if features else default_expected_features
							_lowercase							:   int 							=   (
							    Features({feature: Value(_lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
							)
							_lowercase							:   Tuple 							=   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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Dict:
							if split:
														_lowercase							:   Dict 							=   {split: text_path}
							else:
														_lowercase							:   Optional[Any] 							=   'train'
														_lowercase							:   Optional[Any] 							=   {'train': text_path, 'test': text_path}
							_lowercase							:   Tuple 							=   tmp_path / 'cache'
							_lowercase							:   Dict 							=   {'text': 'string'}
							_lowercase							:   Any 							=   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()				)









'''simple docstring'''





def 			a_	(				_lowerCAmelCase			) -> int:

  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    raise TypeError('only integers accepted as input'			)
  else:
    __lowerCamelCase			:			Any       			=					str(abs(_lowerCAmelCase			)			)
    __lowerCamelCase			:			Tuple       			=					[list(_lowerCAmelCase			) for char in range(len(_lowerCAmelCase			)			)]
    for index in range(len(_lowerCAmelCase			)			):
      num_transpositions[index].pop(_lowerCAmelCase			)
    return max(
        int(''.join(list(_lowerCAmelCase			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
   __import__('doctest').testmod()






