





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	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  assert isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  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	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  a__     : str  =tmp_path / "cache"
  a__     : int  ={"text": "string"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a__     : Any  =TextDatasetReader(SCREAMING_SNAKE_CASE	,	cache_dir=SCREAMING_SNAKE_CASE	,	keep_in_memory=SCREAMING_SNAKE_CASE						).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
@pytest.mark.parametrize(
    "features"	,	[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]	,	)
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[str]						):





  """simple docstring"""



  a__     : Any  =tmp_path / "cache"
  a__     : Any  ={"text": "string"}
  a__     : List[str]  =features.copy() if features else default_expected_features
  a__     : Optional[int]  =(
      Features({feature: Value(SCREAMING_SNAKE_CASE						) for feature, dtype in features.items()}						) if features is not None else None
  )
  a__     : str  =TextDatasetReader(SCREAMING_SNAKE_CASE	,	features=SCREAMING_SNAKE_CASE	,	cache_dir=SCREAMING_SNAKE_CASE						).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
@pytest.mark.parametrize("split"	,	[None, NamedSplit("train"						), "train", "test"]						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  a__     : Dict  =tmp_path / "cache"
  a__     : int  ={"text": "string"}
  a__     : Optional[int]  =TextDatasetReader(SCREAMING_SNAKE_CASE	,	cache_dir=SCREAMING_SNAKE_CASE	,	split=SCREAMING_SNAKE_CASE						).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"	,	[str, list]						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  if issubclass(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
    a__     : int  =text_path
  elif issubclass(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
    a__     : List[str]  =[text_path]
  a__     : int  =tmp_path / "cache"
  a__     : Any  ={"text": "string"}
  a__     : Union[str, Any]  =TextDatasetReader(SCREAMING_SNAKE_CASE	,	cache_dir=SCREAMING_SNAKE_CASE						).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       List[Any]=("train",)						):





  """simple docstring"""



  assert isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  for split in splits:
    a__     : 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	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  a__     : Dict  =tmp_path / "cache"
  a__     : str  ={"text": "string"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a__     : Dict  =TextDatasetReader({"train": text_path}	,	cache_dir=SCREAMING_SNAKE_CASE	,	keep_in_memory=SCREAMING_SNAKE_CASE						).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
@pytest.mark.parametrize(
    "features"	,	[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]	,	)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



  a__     : Optional[int]  =tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  a__     : Optional[int]  ={"text": "string"}
  a__     : Any  =features.copy() if features else default_expected_features
  a__     : List[str]  =(
      Features({feature: Value(SCREAMING_SNAKE_CASE						) for feature, dtype in features.items()}						) if features is not None else None
  )
  a__     : List[str]  =TextDatasetReader({"train": text_path}	,	features=SCREAMING_SNAKE_CASE	,	cache_dir=SCREAMING_SNAKE_CASE						).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)





@pytest.mark.parametrize("split"	,	[None, NamedSplit("train"						), "train", "test"]						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



  if split:
    a__     : Any  ={split: text_path}
  else:
    a__     : Tuple  ="train"
    a__     : List[Any]  ={"train": text_path, "test": text_path}
  a__     : int  =tmp_path / "cache"
  a__     : Dict  ={"text": "string"}
  a__     : List[Any]  =TextDatasetReader(SCREAMING_SNAKE_CASE	,	cache_dir=SCREAMING_SNAKE_CASE						).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	splits=list(path.keys()						)						)
  assert all(dataset[split].split == split for split in path.keys()						)













import os
def 					_A	(		):





  """simple docstring"""



  a__     : Optional[int]  =os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE						)	,	"num.txt"						)
  with open(SCREAMING_SNAKE_CASE						) as file_hand:
    return str(sum(int(SCREAMING_SNAKE_CASE						) for line in file_hand						)						)[:10]


if __name__ == "__main__":
     print(solution())






