




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







@pytest.mark.parametrize('''split'''	,				[None, NamedSplit('''train'''	), '''train''', '''test''']	)
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  Optional[Any]								=       tmp_path / '''cache'''
			__lowercase			:  List[str]								=       {'''text''': '''string'''}
			__lowercase			:  Tuple								=       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      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			if issubclass(__UpperCamelCase	,				__UpperCamelCase	):
						__lowercase			:  Union[str, Any]								=       text_path
			elif issubclass(__UpperCamelCase	,				__UpperCamelCase	):
						__lowercase			:  Optional[int]								=       [text_path]
			__lowercase			:  Optional[Any]								=       tmp_path / '''cache'''
			__lowercase			:  Dict								=       {'''text''': '''string'''}
			__lowercase			:  List[Any]								=       TextDatasetReader(__UpperCamelCase	,				cache_dir=__UpperCamelCase	).read()
			_check_text_dataset(__UpperCamelCase	,				__UpperCamelCase	)







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=("train",)	):
			assert isinstance(__UpperCamelCase	,				__UpperCamelCase	)
			for split in splits:
						__lowercase			:  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      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  Optional[int]								=       tmp_path / '''cache'''
			__lowercase			:  List[str]								=       {'''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=__UpperCamelCase	,				keep_in_memory=__UpperCamelCase	).read()
			_check_text_datasetdict(__UpperCamelCase	,				__UpperCamelCase	)







@pytest.mark.parametrize(
    '''features'''	,				[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]	,				)
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  List[Any]								=       tmp_path / '''cache'''
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			__lowercase			:  Tuple								=       {'''text''': '''string'''}
			__lowercase			:  Optional[Any]								=       features.copy() if features else default_expected_features
			__lowercase			:  List[Any]								=       (
			    Features({feature: Value(__UpperCamelCase	) for feature, dtype in features.items()}	) if features is not None else None
			)
			__lowercase			:  int								=       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      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			if split:
						__lowercase			:  Optional[int]								=       {split: text_path}
			else:
						__lowercase			:  Any								=       '''train'''
						__lowercase			:  Tuple								=       {'''train''': text_path, '''test''': text_path}
			__lowercase			:  List[Any]								=       tmp_path / '''cache'''
			__lowercase			:  Any								=       {'''text''': '''string'''}
			__lowercase			:  Union[str, Any]								=       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'''





import string

def 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Union[str, Any]       			=					''
  for i in sequence:
    __lowerCamelCase			:			Tuple       			=					ord(_lowerCAmelCase			)
    if 65 <= extract <= 90:
      output += chr(155 - extract			)
    elif 97 <= extract <= 122:
      output += chr(219 - extract			)
    else:
      output += i
  return output

def 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Optional[Any]       			=					string.ascii_letters
  __lowerCamelCase			:			str       			=					string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(_lowerCAmelCase			)] if c in letters else c for c in sequence			)

def 			a_	(				) -> None:
  from timeit import timeit

  print('Running performance benchmarks...'			)
  __lowerCamelCase			:			Tuple       			=					'from string import printable ; from __main__ import atbash, atbash_slow'
  print(F'> atbash_slow(): {timeit("atbash_slow(printable)"			,setup=_lowerCAmelCase			)} seconds'			)
  print(F'>      atbash(): {timeit("atbash(printable)"			,setup=_lowerCAmelCase			)} seconds'			)


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f'''{example} encrypted in atbash: {atbash(example)}''')
   benchmark()






