

"""simple docstring"""




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

import datasets


__UpperCAmelCase =				'\\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 =				'\\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 =				'\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   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 return float((preds == labels).mean()      )
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					simple_accuracy(__UpperCamelCase							,    __UpperCamelCase      )
 UpperCAmelCase__					:  Optional[Any]					   =					float(fa_score(y_true=__UpperCamelCase							,    y_pred=__UpperCamelCase      )      )
 return {
     "accuracy": acc,
     "f1": fa,
 }
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[Any]					   =					float(pearsonr(__UpperCamelCase							,    __UpperCamelCase      )[0]      )
 UpperCAmelCase__					:  str					   =					float(spearmanr(__UpperCamelCase							,    __UpperCamelCase      )[0]      )
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }







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

  def    __lowercase       (  self		:			Any    ):






   '''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		:			int					,A		:			Optional[Any]					,A		:			Optional[Any]    ):






   '''simple docstring'''
   if self.config_name == "cola":
    return {"matthews_correlation": matthews_corrcoef(A					,A    )}
   elif self.config_name == "stsb":
    return pearson_and_spearman(A					,A    )
   elif self.config_name in ["mrpc", "qqp"]:
    return acc_and_fa(A					,A    )
   elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
    return {"accuracy": simple_accuracy(A					,A    )}
   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"""




# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCAmelCase =				abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 from diffusers.utils.testing_utils import pytest_addoption_shared

 pytest_addoption_shared(__UpperCamelCase      )




def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 from diffusers.utils.testing_utils import pytest_terminal_summary_main

 UpperCAmelCase__					:  Optional[Any]					   =					terminalreporter.config.getoption("""--make-reports"""      )
 if make_reports:
  pytest_terminal_summary_main(__UpperCamelCase							,    id=__UpperCamelCase      )





