


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


@pytest.mark.parametrize('''split''' ,   [None, NamedSplit('''train'''			), '''train''', '''test''']			)
def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			str ,   _snake_case				:			int			)     ->   List[Any]:
      lowerCAmelCase					:	Union[str, Any]       =   tmp_path / "cache"
      lowerCAmelCase					:	List[Any]       =   {"text": "string"}
      lowerCAmelCase					:	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      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			Union[str, Any] ,   _snake_case				:			List[Any]			)     ->   Dict:




      if issubclass(_lowerCAmelCase ,   _lowerCAmelCase			):
            lowerCAmelCase					:	Dict       =   text_path
      elif issubclass(_lowerCAmelCase ,   _lowerCAmelCase			):
            lowerCAmelCase					:	int       =   [text_path]
      lowerCAmelCase					:	List[Any]       =   tmp_path / "cache"
      lowerCAmelCase					:	Optional[int]       =   {"text": "string"}
      lowerCAmelCase					:	Union[str, Any]       =   TextDatasetReader(_lowerCAmelCase ,   cache_dir=_lowerCAmelCase			).read()
      _check_text_dataset(_lowerCAmelCase ,   _lowerCAmelCase			)


def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			int ,   _snake_case				:			List[Any]=("train",)			)     ->   Dict:
      assert isinstance(_lowerCAmelCase ,   _lowerCAmelCase			)
      for split in splits:
            lowerCAmelCase					:	Dict       =   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      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			Optional[Any] ,   _snake_case				:			Any			)     ->   Dict:
      lowerCAmelCase					:	Optional[int]       =   tmp_path / "cache"
      lowerCAmelCase					:	Dict       =   {"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      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			List[Any] ,   _snake_case				:			int			)     ->   str:
      lowerCAmelCase					:	List[Any]       =   tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      lowerCAmelCase					:	Optional[int]       =   {"text": "string"}
      lowerCAmelCase					:	Optional[int]       =   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					:	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      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			Any ,   _snake_case				:			Union[str, Any]			)     ->   int:




      if split:
            lowerCAmelCase					:	Tuple       =   {split: text_path}
      else:
            lowerCAmelCase					:	List[str]       =   "train"
            lowerCAmelCase					:	List[str]       =   {"train": text_path, "test": text_path}
      lowerCAmelCase					:	str       =   tmp_path / "cache"
      lowerCAmelCase					:	int       =   {"text": "string"}
      lowerCAmelCase					:	str       =   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"""





snake_case__		:     List[Any]    				=				'''Tobias Carryer'''

from time import time



class 		snake_case_:






     def __init__(	self  :					Optional[Any]					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Dict=int(time()				)				):  # noqa: B008
           lowerCAmelCase					:	str       =   multiplier
           lowerCAmelCase					:	Optional[int]       =   increment
           lowerCAmelCase					:	Optional[Any]       =   modulo
           lowerCAmelCase					:	Optional[Any]       =   seed






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Optional[int]       =   (self.multiplier * self.seed + self.increment) % self.modulo
           return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__		:     int    				=				LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
 while True:
  print(lcg.next_number())


