



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  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Optional[Any]:
						return float((preds == labels).mean()   )






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      str:
						__lowercase					=				simple_accuracy(lowercase__						,       lowercase__   )
						__lowercase					=				float(fa_score(y_true=lowercase__						,       y_pred=lowercase__   )   )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      List[str]:
						__lowercase					=				float(pearsonr(lowercase__						,       lowercase__   )[0]   )
						__lowercase					=				float(spearmanr(lowercase__						,       lowercase__   )[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 		snake_case__			(				self	:       Dict   )						->					Optional[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 		snake_case__			(				self	:       Tuple			,     lowercase	:       Tuple			,     lowercase	:       int   )						->					str:
									"""simple docstring"""







									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\"]"""   )










def  UpperCAmelCase__    (    lowercase__   ) ->      Optional[int]:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				sum(lowercase__   )

						__lowercase					=				[[False for x in range(s + 1   )] for y in range(n + 1   )]

						for i in range(1						,       n + 1   ):
												__lowercase					=				True

						for i in range(1						,       s + 1   ):
												__lowercase					=				False

						for i in range(1						,       n + 1   ):
												for j in range(1						,       s + 1   ):
																		__lowercase					=				dp[i][j - 1]

																		if arr[i - 1] <= j:
																								__lowercase					=				dp[i][j] or dp[i - 1][j - arr[i - 1]]

						for j in range(int(s / 2   )						,       -1						,       -1   ):
												if dp[n][j] is True:
																		__lowercase					=				s - 2 * j
																		break

						return diff





