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






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				Dict   ,    _lowercase    :				Union[str, Any]=("train",)     )							->	Optional[int]:
  assert isinstance(_lowerCAmelCase   ,    _lowerCAmelCase     )
  for split in splits:
    __UpperCAmelCase:					Union[str, 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__					(					_lowercase    :				Any   ,    _lowercase    :				int   ,    _lowercase    :				str     )							->	Optional[Any]:
  __UpperCAmelCase:					Dict		= tmp_path / "cache"
  __UpperCAmelCase:					Tuple		= {"text": "string"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    __UpperCAmelCase:					Any		= 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  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				Optional[int]   ,    _lowercase    :				Optional[Any]     )							->	int:
  __UpperCAmelCase:					Any		= tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  __UpperCAmelCase:					Dict		= {"text": "string"}
  __UpperCAmelCase:					Any		= features.copy() if features else default_expected_features
  __UpperCAmelCase:					int		= (
      Features({feature: Value(_lowerCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
  )
  __UpperCAmelCase:					str		= 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  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				Tuple   ,    _lowercase    :				List[str]     )							->	Dict:
  if split:
    __UpperCAmelCase:					Optional[int]		= {split: text_path}
  else:
    __UpperCAmelCase:					str		= "train"
    __UpperCAmelCase:					Dict		= {"train": text_path, "test": text_path}
  __UpperCAmelCase:					Dict		= tmp_path / "cache"
  __UpperCAmelCase:					List[str]		= {"text": "string"}
  __UpperCAmelCase:					Optional[int]		= 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 ....utils import logging


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=None   ,    snake_case_=2048					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= config.__dict__
    __UpperCAmelCase:					str		= modal_hidden_size
    if num_labels:
      __UpperCAmelCase:					int		= num_labels