


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

import datasets


__snake_case            =   '\\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'

__snake_case            =   '\\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'

__snake_case            =   '\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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		Dict	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					float(pearsonr(lowerCamelCase_       ,  lowerCamelCase_	)[0]	)
			UpperCamelCase         =					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    ):




			def   lowerCamelCase_					(						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   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		List[Any]						):







						"""simple docstring"""







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








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




			def __init__(						self							:		int       ,       __magic_name__							:		List[Any]="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

						UpperCamelCase         =					AutoModel.from_pretrained(__magic_name__       ,       return_dict=__magic_name__						)
						UpperCamelCase         =					torch.nn.CosineSimilarity(3       ,       1e-08						)
						UpperCamelCase         =					torch.nn.Softmax(dim=1						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







						return self.softmax(T * self.cos(__magic_name__       ,       __magic_name__						)						)





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					W_supports["""sizes"""].tolist()
						UpperCamelCase         =					W_supports["""start_token_id"""].item()
						UpperCamelCase         =					W_supports["""end_token_id"""].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						UpperCamelCase         =					self.BERT(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						UpperCamelCase         =					None
						UpperCamelCase         =					None

						UpperCamelCase         =					W_supports["""input_ids"""] == start_token_id
						UpperCamelCase         =					W_supports["""input_ids"""] == end_token_id

						for i, size in enumerate(__magic_name__						):
									if i == 0:
												UpperCamelCase         =					0
									else:
												UpperCamelCase         =					support_sizes[i - 1]

									UpperCamelCase         =					S[s : s + size][start_token_masks[s : s + size]]
									UpperCamelCase         =					S[s : s + size][end_token_masks[s : s + size]]

									UpperCamelCase         =					torch.matmul(q[i]       ,       s_start.T						).sum(1						).softmax(0						)
									UpperCamelCase         =					torch.matmul(q[i]       ,       s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
												UpperCamelCase         =					torch.vstack((p_starts, p_start)						)
												UpperCamelCase         =					torch.vstack((p_ends, p_end)						)
									else:
												UpperCamelCase         =					p_start
												UpperCamelCase         =					p_end

						return p_starts, p_ends




