



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

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=("train",)			) -> Dict:
  assert isinstance(_lowerCAmelCase			,_lowerCAmelCase			)
  for split in splits:
    __lowerCamelCase			:			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 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Tuple:
  __lowerCamelCase			:			int       			=					tmp_path / 'cache'
  __lowerCamelCase			:			List[Any]       			=					{'text': 'string'}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    __lowerCamelCase			:			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 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			Optional[int]       			=					tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  __lowerCamelCase			:			Tuple       			=					{'text': 'string'}
  __lowerCamelCase			:			Union[str, Any]       			=					features.copy() if features else default_expected_features
  __lowerCamelCase			:			int       			=					(
      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			,_lowerCAmelCase			,_lowerCAmelCase			) -> Dict:
  if split:
    __lowerCamelCase			:			Dict       			=					{split: text_path}
  else:
    __lowerCamelCase			:			Optional[Any]       			=					'train'
    __lowerCamelCase			:			Optional[Any]       			=					{'train': text_path, 'test': text_path}
  __lowerCamelCase			:			Tuple       			=					tmp_path / 'cache'
  __lowerCamelCase			:			Dict       			=					{'text': 'string'}
  __lowerCamelCase			:			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'''





from __future__ import annotations

_UpperCamelCase											=		10

def 			a_	(				_lowerCAmelCase			) -> list[int]:
  __lowerCamelCase			:			str       			=					1
  __lowerCamelCase			:			Union[str, Any]       			=					max(_lowerCAmelCase			)
  while placement <= max_digit:
    # declare and initialize empty buckets
    __lowerCamelCase			:			list[list]       			=					[[] for _ in range(_lowerCAmelCase			)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      __lowerCamelCase			:			List[str]       			=					int((i / placement) % RADIX			)
      buckets[tmp].append(_lowerCAmelCase			)
    # put each buckets' contents into list_of_ints
    __lowerCamelCase			:			Dict       			=					0
    for b in range(_lowerCAmelCase			):
      for i in buckets[b]:
        __lowerCamelCase			:			List[str]       			=					i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()






