






"""simple docstring"""

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

import datasets


lowercase__          = """\\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"""

lowercase__          = """\\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"""

lowercase__          = """\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       )						->    Tuple:

		"""simple docstring"""







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

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase       )						->    List[str]:

		"""simple docstring"""







		lowerCAmelCase_   :							Tuple									=			simple_accuracy(a_      ,       a_       )
		lowerCAmelCase_   :							Tuple									=			float(fa_score(y_true=a_      ,       y_pred=a_       )       )
		return {
		    "accuracy": acc,
		    "f1": fa,
		}

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase       )						->    Union[str, Any]:

		"""simple docstring"""







		lowerCAmelCase_   :							List[str]									=			float(pearsonr(a_      ,       a_       )[0]       )
		lowerCAmelCase_   :							int									=			float(spearmanr(a_      ,       a_       )[0]       )
		return {
		    "pearson": pearson_corr,
		    "spearmanr": spearman_corr,
		}



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





		'''simple docstring'''






		def    lowerCamelCase       (   self							:	str	):






				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    lowerCamelCase       (   self							:	Tuple						,			a_							:	str						,			a_							:	str	):






				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\"]"	)







"""simple docstring"""
from collections.abc import Callable

import numpy as np



def      __lowerCamelCase   (							a_     :		Callable ,   a_     :		float ,   a_     :		float ,   a_     :		float ,   a_     :		float							)			->      np.ndarray:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  int(np.ceil((x_end - xa) / step_size							)							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.zeros((n + 1,)							)
      __SCREAMING_SNAKE_CASE   :int						=  ya
      __SCREAMING_SNAKE_CASE   :str						=  xa

      for k in range(a_							):
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  y[k] + step_size * ode_func(a_ ,   y[k]							)
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()