


'''simple docstring'''





from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__UpperCAmelCase : Optional[Any] 			=				'\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

__UpperCAmelCase : Optional[int] 			=				'\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

__UpperCAmelCase : Optional[int] 			=				'\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       return float((preds == labels).mean()       )


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Optional[int]       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		simple_accuracy(_lowerCamelCase							,			_lowerCamelCase       )
       UpperCamelCase    :	Tuple   	=		float(fa_score(y_true=_lowerCamelCase							,			y_pred=_lowerCamelCase       )       )
       return {
           "accuracy": acc,
           "f1": fa,
       }


def      a	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		Optional[int]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		float(pearsonr(_lowerCamelCase							,			_lowerCamelCase       )[0]       )
       UpperCamelCase    :	Optional[Any]   	=		float(spearmanr(_lowerCamelCase							,			_lowerCamelCase       )[0]       )
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,     _KWARGS_DESCRIPTION)
class    UpperCAmelCase_	(							datasets.Metric):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              if self.config_name not in [
                  "sst2",
                  "mnli",
                  "mnli_mismatched",
                  "mnli_matched",
                  "cola",
                  "stsb",
                  "mrpc",
                  "qqp",
                  "qnli",
                  "rte",
                  "wnli",
                  "hans",
              ]:
                     raise KeyError(
                         '''You should supply a configuration name selected in '''
                         '''[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", '''
                         '''\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]'''						)
              return datasets.MetricInfo(
                  description=_DESCRIPTION ,					citation=_CITATION ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
                      {
                          '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''						),
                          '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''						),
                      }						) ,					codebase_urls=[] ,					reference_urls=[] ,					format='''numpy''' ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if self.config_name == "cola":
                     return {"matthews_correlation": matthews_corrcoef(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)}
              elif self.config_name == "stsb":
                     return pearson_and_spearman(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              elif self.config_name in ["mrpc", "qqp"]:
                     return acc_and_fa(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                     return {"accuracy": simple_accuracy(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)}
              else:
                     raise KeyError(
                         '''You should supply a configuration name selected in '''
                         '''[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", '''
                         '''\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]'''						)





from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState


def      a	(							SCREAMING_SNAKE_CASE_      :		bool = True							,			*SCREAMING_SNAKE_CASE_      :		List[str]							,			**SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       if not is_tqdm_available():
              raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'''       )
       UpperCamelCase    :	int   	=		False
       if main_process_only:
              UpperCamelCase    :	int   	=		PartialState().local_process_index == 0
       return _tqdm(*SCREAMING_SNAKE_CASE_							,			**SCREAMING_SNAKE_CASE_							,			disable=SCREAMING_SNAKE_CASE_       )

