

'''simple docstring'''






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

import datasets


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

lowerCAmelCase__											=	'''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

lowerCAmelCase__											=	'''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



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


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = simple_accuracy(__snake_case     ,				__snake_case      )
 __lowercase							  = float(fa_score(y_true=__snake_case     ,				y_pred=__snake_case      )      )
 return {
     "accuracy": acc,
     "f1": fa,
 }


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = float(pearsonr(__snake_case     ,				__snake_case      )[0]      )
 __lowercase							  = float(spearmanr(__snake_case     ,				__snake_case      )[0]      )
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,       _KWARGS_DESCRIPTION      )
class 			lowercase_	(datasets.Metric      ):




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   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 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     str					,lowercase__   :     int			):
   if self.config_name == "cola":
    return {"matthews_correlation": matthews_corrcoef(lowercase__					,lowercase__			)}
   elif self.config_name == "stsb":
    return pearson_and_spearman(lowercase__					,lowercase__			)
   elif self.config_name in ["mrpc", "qqp"]:
    return acc_and_fa(lowercase__					,lowercase__			)
   elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
    return {"accuracy": simple_accuracy(lowercase__					,lowercase__			)}
   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"]'''			)









"""simple docstring"""



__A		:       Any  				=		{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}



