




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

import datasets


__A        =    "\\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"

__A        =    "\\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"

__A        =    "\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__				:      Optional[int] ,     UpperCamelCase__				:      int      )     ->       Optional[int]:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Tuple      )     ->       Any:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[Any]      )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    float(pearsonr(UpperCamelCase__ ,     UpperCamelCase__      )[0]      )
				__lowerCamelCase		       =    float(spearmanr(UpperCamelCase__ ,     UpperCamelCase__      )[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       lowercase_    (      self							)    ->      Dict:



							'''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			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      List[str]:



							'''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"]'							)








class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    {}


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							if vertex not in self.adjacency:
											__lowerCamelCase		       =    {}
											self.num_vertices += 1


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							self.add_vertex(lowerCamelCase__							)
							self.add_vertex(lowerCamelCase__							)

							if head == tail:
											return

							__lowerCamelCase		       =    weight
							__lowerCamelCase		       =    weight


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_edges()
							for edge in edges:
											__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    edge
											edges.remove((tail, head, weight)							)
							for i in range(len(lowerCamelCase__							)							):
											__lowerCamelCase		       =    list(edges[i]							)

							edges.sort(key=lambda lowerCamelCase__							: e[2]							)
							for i in range(len(lowerCamelCase__							) - 1							):
											if edges[i][2] >= edges[i + 1][2]:
															__lowerCamelCase		       =    edges[i][2] + 1
							for edge in edges:
											__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    edge
											__lowerCamelCase		       =    weight
											__lowerCamelCase		       =    weight


			def __str__(      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    ''
							for tail in self.adjacency:
											for head in self.adjacency[tail]:
															__lowerCamelCase		       =    self.adjacency[head][tail]
															string += f"""{head} -> {tail} == {weight}\n"""
							return string.rstrip('\n'							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							for tail in self.adjacency:
											for head in self.adjacency[tail]:
															output.append((tail, head, self.adjacency[head][tail])							)
							return output


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							return self.adjacency.keys()


			@staticmethod
			def       lowercase_    (      lowerCamelCase__=None			,						lowerCamelCase__=None							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    Graph()
							if vertices is None:
											__lowerCamelCase		       =    []
							if edges is None:
											__lowerCamelCase		       =    []
							for vertex in vertices:
											g.add_vertex(lowerCamelCase__							)
							for edge in edges:
											g.add_edge(*lowerCamelCase__							)
							return g







			class     __lowerCAmelCase :

						"""simple docstring"""







						def __init__(      self							)    ->      Tuple:



										'''simple docstring'''
										__lowerCamelCase		       =    {}
										__lowerCamelCase		       =    {}


						def __len__(      self							)    ->      Tuple:



										'''simple docstring'''
										return len(self.parent							)


						def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[Any]:



										'''simple docstring'''
										if item in self.parent:
														return self.find(lowerCamelCase__							)

										__lowerCamelCase		       =    item
										__lowerCamelCase		       =    0
										return item


						def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[str]:



										'''simple docstring'''
										if item not in self.parent:
														return self.make_set(lowerCamelCase__							)
										if item != self.parent[item]:
														__lowerCamelCase		       =    self.find(self.parent[item]							)
										return self.parent[item]






						def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      int:



										'''simple docstring'''
										__lowerCamelCase		       =    self.find(lowerCamelCase__							)
										__lowerCamelCase		       =    self.find(lowerCamelCase__							)

										if roota == roota:
														return roota

										if self.rank[roota] > self.rank[roota]:
														__lowerCamelCase		       =    roota
														return roota

										if self.rank[roota] < self.rank[roota]:
														__lowerCamelCase		       =    roota
														return roota

										if self.rank[roota] == self.rank[roota]:
														self.rank[roota] += 1
														__lowerCamelCase		       =    roota
														return roota
										return None




			@staticmethod
			def       lowercase_    (      lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    graph.num_vertices

							__lowerCamelCase		       =    Graph.UnionFind()
							__lowerCamelCase		       =    []
							while num_components > 1:
											__lowerCamelCase		       =    {}
											for vertex in graph.get_vertices():
															__lowerCamelCase		       =    -1

											__lowerCamelCase		       =    graph.get_edges()
											for edge in edges:
															__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    edge
															edges.remove((tail, head, weight)							)
											for edge in edges:
															__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    edge
															__lowerCamelCase		       =    union_find.find(lowerCamelCase__							)
															__lowerCamelCase		       =    union_find.find(lowerCamelCase__							)
															if seta != seta:
																			if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																							__lowerCamelCase		       =    [head, tail, weight]

																			if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																							__lowerCamelCase		       =    [head, tail, weight]
											for vertex in cheap_edge:
															if cheap_edge[vertex] != -1:
																			__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    cheap_edge[vertex]
																			if union_find.find(lowerCamelCase__							) != union_find.find(lowerCamelCase__							):
																							union_find.union(lowerCamelCase__			,						lowerCamelCase__							)
																							mst_edges.append(cheap_edge[vertex]							)
																							__lowerCamelCase		       =    num_components - 1
							__lowerCamelCase		       =    Graph.build(edges=lowerCamelCase__							)
							return mst


