"""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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     assert isinstance(__lowercase     ,			__lowercase      )
     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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     lowercase_	:				int													=	tmp_path / 'cache'
     lowercase_	:				Optional[Any]													=	{'text': 'string'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          lowercase_	:				str													=	TextDatasetReader(__lowercase     ,			cache_dir=__lowercase     ,			keep_in_memory=__lowercase      ).read()
     _check_text_dataset(__lowercase     ,			__lowercase      )



@pytest.mark.parametrize(
    'features'     ,			[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]     ,			)
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple      ):
     lowercase_	:				Optional[int]													=	tmp_path / 'cache'
     lowercase_	:				Optional[Any]													=	{'text': 'string'}
     lowercase_	:				str													=	features.copy() if features else default_expected_features
     lowercase_	:				Any													=	(
         Features({feature: Value(__lowercase      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     lowercase_	:				str													=	TextDatasetReader(__lowercase     ,			features=__lowercase     ,			cache_dir=__lowercase      ).read()
     _check_text_dataset(__lowercase     ,			__lowercase      )



@pytest.mark.parametrize('split'     ,			[None, NamedSplit('train'      ), 'train', 'test']      )
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     lowercase_	:				int													=	tmp_path / 'cache'
     lowercase_	:				Tuple													=	{'text': 'string'}
     lowercase_	:				List[Any]													=	TextDatasetReader(__lowercase     ,			cache_dir=__lowercase     ,			split=__lowercase      ).read()
     _check_text_dataset(__lowercase     ,			__lowercase      )
     assert dataset.split == split if split else "train"



@pytest.mark.parametrize('path_type'     ,			[str, list]      )
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):



     if issubclass(__lowercase     ,			__lowercase      ):
          lowercase_	:				Tuple													=	text_path
     elif issubclass(__lowercase     ,			__lowercase      ):
          lowercase_	:				Any													=	[text_path]
     lowercase_	:				Tuple													=	tmp_path / 'cache'
     lowercase_	:				List[str]													=	{'text': 'string'}
     lowercase_	:				List[Any]													=	TextDatasetReader(__lowercase     ,			cache_dir=__lowercase      ).read()
     _check_text_dataset(__lowercase     ,			__lowercase      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]=("train",)      ):
     assert isinstance(__lowercase     ,			__lowercase      )
     for split in splits:
          lowercase_	:				List[str]													=	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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]      ):
     lowercase_	:				Tuple													=	tmp_path / 'cache'
     lowercase_	:				Dict													=	{'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=__lowercase     ,			keep_in_memory=__lowercase      ).read()
     _check_text_datasetdict(__lowercase     ,			__lowercase      )



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







@pytest.mark.parametrize('split'     ,			[None, NamedSplit('train'      ), 'train', 'test']      )
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Any      ):



     if split:
          lowercase_	:				List[str]													=	{split: text_path}
     else:
          lowercase_	:				Optional[int]													=	'train'
          lowercase_	:				Optional[int]													=	{'train': text_path, 'test': text_path}
     lowercase_	:				Union[str, Any]													=	tmp_path / 'cache'
     lowercase_	:				Optional[int]													=	{'text': 'string'}
     lowercase_	:				int													=	TextDatasetReader(__lowercase     ,			cache_dir=__lowercase      ).read()
     _check_text_datasetdict(__lowercase     ,			__lowercase     ,			splits=list(path.keys()      )      )
     assert all(dataset[split].split == split for split in path.keys()      )








'''simple docstring'''




import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
  from scipy.stats import pearsonr, spearmanr
  from sklearn.metrics import fa_score, matthews_corrcoef


__SCREAMING_SNAKE_CASE     :List[str]       =				(
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      Tuple       )		->       int:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     return (preds == labels).mean()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      str       )		->       Optional[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   simple_accuracy(__lowercase			,     __lowercase       )
     _UpperCAmelCase     							=   fa_score(y_true=__lowercase			,     y_pred=__lowercase       )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      List[str]       )		->       List[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   pearsonr(__lowercase			,     __lowercase       )[0]
     _UpperCAmelCase     							=   spearmanr(__lowercase			,     __lowercase       )[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      str			,     __lowercase							:      str       )		->       Tuple:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     assert len(__lowercase       ) == len(__lowercase       ), f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'
     if task_name == "cola":
          return {"mcc": matthews_corrcoef(__lowercase			,     __lowercase       )}
     elif task_name == "sst-2":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mrpc":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "sts-b":
          return pearson_and_spearman(__lowercase			,     __lowercase       )
     elif task_name == "qqp":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "mnli":
          return {"mnli/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mnli-mm":
          return {"mnli-mm/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "qnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "rte":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "wnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "hans":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )






def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Dict			,     __lowercase							:      str       )		->       Union[str, Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     if len(__lowercase       ) != len(__lowercase       ):
          raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'       )
     if task_name == "xnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )




