


class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =name
											a__      =val





				def __str__(				self)  -> Tuple:
											return F"""{self.__class__.__name__}({self.name}, {self.val})"""





				def __lt__(				self       ,  lowercase_)  -> Any:
											return self.val < other.val







class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> Any:
											a__      ={}
											a__      ={}
											a__      =self.build_heap(lowercase_)





				def __getitem__(				self       ,  lowercase_)  -> List[str]:
											return self.get_value(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Optional[int]:
											return (idx - 1) // 2





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:
											return idx * 2 + 1





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> List[Any]:
											return idx * 2 + 2





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Any:
											return self.heap_dict[key]





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> str:
											a__      =len(lowercase_) - 1
											a__      =self.get_parent_idx(lowercase_)

											for idx, i in enumerate(lowercase_):
																		a__      =idx
																		a__      =i.val

											for i in range(lowercase_       ,  -1       ,  -1):
																		self.sift_down(lowercase_       ,  lowercase_)
											return array





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> List[str]:
											while True:
																		a__      =self.get_left_child_idx(lowercase_)  # noqa: E741
																		a__      =self.get_right_child_idx(lowercase_)

																		a__      =idx
																		if l < len(lowercase_) and array[l] < array[idx]:
																									a__      =l
																		if r < len(lowercase_) and array[r] < array[smallest]:
																									a__      =r

																		if smallest != idx:
																									a__    ,     a__      =array[smallest], array[idx]
																									(
																									    (
																									    a__
																									)    ,     (
																									    a__
																									)    ,     
																									)      =(
																									    self.idx_of_element[array[smallest]],
																									    self.idx_of_element[array[idx]],
																									)
																									a__      =smallest
																		else:
																									break





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Dict:
											a__      =self.get_parent_idx(lowercase_)
											while p >= 0 and self.heap[p] > self.heap[idx]:
																		a__    ,     a__      =self.heap[idx], self.heap[p]
																		a__    ,     a__      =(
																		    self.idx_of_element[self.heap[idx]],
																		    self.idx_of_element[self.heap[p]],
																		)
																		a__      =p
																		a__      =self.get_parent_idx(lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											return self.heap[0]





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__    ,     a__      =self.heap[-1], self.heap[0]
											a__    ,     a__      =(
											    self.idx_of_element[self.heap[-1]],
											    self.idx_of_element[self.heap[0]],
											)

											a__      =self.heap.pop()
											del self.idx_of_element[x]
											self.sift_down(0       ,  self.heap)
											return x





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Tuple:
											self.heap.append(lowercase_)
											a__      =len(self.heap) - 1
											a__      =node.val
											self.sift_up(len(self.heap) - 1)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											return len(self.heap) == 0





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> int:
											assert (
											    self.heap[self.idx_of_element[node]].val > new_value
											), "newValue must be less that current value"
											a__      =new_value
											a__      =new_value
											self.sift_up(self.idx_of_element[node])







_lowerCAmelCase:						Tuple   				=       Node('R', -1)
_lowerCAmelCase:						Optional[int]   				=       Node('B', 6)
_lowerCAmelCase:						Tuple   				=       Node('A', 3)
_lowerCAmelCase:						int   				=       Node('X', 1)
_lowerCAmelCase:						List[str]   				=       Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_lowerCAmelCase:						int   				=       MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
	print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
	print(i)

if __name__ == "__main__":
	import doctest

	doctest.testmod()





'''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__     (		__lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				str				):







   '''simple docstring'''




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





def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Union[str, Any]				):







   '''simple docstring'''




   _UpperCAmelCase  :  Union[str, Any]				       =simple_accuracy(__lowerCamelCase			,       __lowerCamelCase				)
   _UpperCAmelCase  :  List[Any]				       =float(fa_score(y_true=__lowerCamelCase			,       y_pred=__lowerCamelCase				)				)
   return {
       "accuracy": acc,
       "f1": fa,
   }





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =float(pearsonr(__lowerCamelCase			,       __lowerCamelCase				)[0]				)
   _UpperCAmelCase  :  int				       =float(spearmanr(__lowerCamelCase			,       __lowerCamelCase				)[0]				)
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
   }






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






       def      lowerCAmelCase						(     self)		->    str:




          '''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    ,				snake_case    ,				snake_case)		->    int:




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

