
'''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_       (	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




      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 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      _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 		A_       (	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      _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 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _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 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    List[Any]							):



      """simple docstring"""




      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 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    List[Any]=("train",)							):



      """simple docstring"""




      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 		A_       (	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Any							):



      """simple docstring"""




      _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 		A_       (	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _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 		A_       (	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




      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'''






import random
from typing import Any







def 		A_       (	_lowerCAmelCase   :    list							):



      """simple docstring"""




      for _ in range(len(_lowerCAmelCase							)							):
            _lowerCamelCase				:   Any								=   random.randint(0				,	len(_lowerCAmelCase							) - 1							)
            _lowerCamelCase				:   List[str]								=   random.randint(0				,	len(_lowerCAmelCase							) - 1							)
            _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   data[b], data[a]
      return data


if __name__ == "__main__":
 UpperCAmelCase_       :   Any   =       [0, 1, 2, 3, 4, 5, 6, 7]
 UpperCAmelCase_       :   Dict   =       ['python', 'says', 'hello', '!']
 print('Fisher-Yates Shuffle:')
 print('List', integers, strings)
 print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))