


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

import datasets


lowerCAmelCase	=							'\\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'

lowerCAmelCase	=							'\\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'

lowerCAmelCase	=							'\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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		return float((preds == labels).mean()					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				simple_accuracy(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		lowercase__						=				float(fa_score(y_true=SCREAMING_SNAKE_CASE						,    y_pred=SCREAMING_SNAKE_CASE					)					)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				float(pearsonr(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)[0]					)
		lowercase__						=				float(spearmanr(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)[0]					)
		return {
		    "pearson": pearson_corr,
		    "spearmanr": spearman_corr,
		}


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

							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       Union[str, 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]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Optional[Any]							)      ->       Optional[Any]:





									"""simple docstring"""







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






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class 	_a							:
							_lowercase     :      int
							_lowercase     :      TreeNode | None  				=			None
							_lowercase     :      TreeNode | None  				=			None
lowerCAmelCase	=							namedtuple('CoinsDistribResult', 'moves excess')
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if root is None:
				return 0

		# Validation
		def count_nodes(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_nodes(node.left					) + count_nodes(node.right					) + 1

		def count_coins(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_coins(node.left					) + count_coins(node.right					) + node.data

		if count_nodes(SCREAMING_SNAKE_CASE					) != count_coins(SCREAMING_SNAKE_CASE					):
				raise ValueError('''The nodes number should be same as the number of coins'''					)

		# Main calculation
		def get_distrib(SCREAMING_SNAKE_CASE					) -> CoinsDistribResult:

				if node is None:
						return CoinsDistribResult(0						,    1					)

				lowercase__					,		lowercase__						=				get_distrib(node.left					)
				lowercase__					,		lowercase__						=				get_distrib(node.right					)

				lowercase__						=				1 - left_distrib_excess
				lowercase__						=				1 - right_distrib_excess

				lowercase__						=				(
				    left_distrib_moves
				    + right_distrib_moves
				    + abs(SCREAMING_SNAKE_CASE					)
				    + abs(SCREAMING_SNAKE_CASE					)
				)
				lowercase__						=				node.data - coins_to_left - coins_to_right

				return CoinsDistribResult(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		return get_distrib(SCREAMING_SNAKE_CASE					)[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


