



'''simple docstring'''

import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
		from scipy.stats import pearsonr, spearmanr
		from sklearn.metrics import fa_score, matthews_corrcoef


UpperCAmelCase     		=   (
    'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
    'library. You can have a look at this example script for pointers: '
    'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Dict:







	"""simple docstring"""
	warnings.warn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	requires_backends(_SCREAMING_SNAKE_CASE							,		"""sklearn"""							)
	return (preds == labels).mean()






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						Tuple							)      ->						Optional[int]:







	"""simple docstring"""
	warnings.warn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	requires_backends(_SCREAMING_SNAKE_CASE							,		"""sklearn"""							)
	lowerCAmelCase   =     simple_accuracy(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     fa_score(y_true=_SCREAMING_SNAKE_CASE							,		y_pred=_SCREAMING_SNAKE_CASE							)
	return {
	    "acc": acc,
	    "f1": fa,
	    "acc_and_f1": (acc + fa) / 2,
	}






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						List[str]:







	"""simple docstring"""
	warnings.warn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	requires_backends(_SCREAMING_SNAKE_CASE							,		"""sklearn"""							)
	lowerCAmelCase   =     pearsonr(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)[0]
	lowerCAmelCase   =     spearmanr(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)[0]
	return {
	    "pearson": pearson_corr,
	    "spearmanr": spearman_corr,
	    "corr": (pearson_corr + spearman_corr) / 2,
	}






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							)      ->						Optional[int]:







	"""simple docstring"""
	warnings.warn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	requires_backends(_SCREAMING_SNAKE_CASE							,		"""sklearn"""							)
	assert len(_SCREAMING_SNAKE_CASE							) == len(_SCREAMING_SNAKE_CASE							), f'Predictions and labels have mismatched lengths {len(_SCREAMING_SNAKE_CASE							)} and {len(_SCREAMING_SNAKE_CASE							)}'
	if task_name == "cola":
		return {"mcc": matthews_corrcoef(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)}
	elif task_name == "sst-2":
		return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)}
	elif task_name == "mrpc":
		return acc_and_fa(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	elif task_name == "sts-b":
		return pearson_and_spearman(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	elif task_name == "qqp":
		return acc_and_fa(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	elif task_name == "mnli":
		return {"mnli/acc": simple_accuracy(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)}
	elif task_name == "mnli-mm":
		return {"mnli-mm/acc": simple_accuracy(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)}
	elif task_name == "qnli":
		return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)}
	elif task_name == "rte":
		return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)}
	elif task_name == "wnli":
		return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)}
	elif task_name == "hans":
		return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)}
	else:
		raise KeyError(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						Tuple							)      ->						List[Any]:







	"""simple docstring"""
	warnings.warn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	requires_backends(_SCREAMING_SNAKE_CASE							,		"""sklearn"""							)
	if len(_SCREAMING_SNAKE_CASE							) != len(_SCREAMING_SNAKE_CASE							):
		raise ValueError(f'Predictions and labels have mismatched lengths {len(_SCREAMING_SNAKE_CASE							)} and {len(_SCREAMING_SNAKE_CASE							)}'							)
	if task_name == "xnli":
		return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)}
	else:
		raise KeyError(_SCREAMING_SNAKE_CASE							)




'''simple docstring'''

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     int:
							lowerCAmelCase   =     [
							    [],
							    [],
							    [],
							]


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     None:
							try:
								if len(self.queues[priority]				) >= 100:
									raise OverflowError("""Maximum queue size is 100"""				)
								self.queues[priority].append(A_				)
							except IndexError:
								raise ValueError("""Valid priorities are 0, 1, and 2"""				)


						def     __snake_case   ( self				)     ->     int:
							for queue in self.queues:
								if queue:
									return queue.pop(0				)
							raise UnderFlowError("""All queues are empty"""				)




						def __str__( self				)     ->     str:
							return "\n".join(f'Priority {i}: {q}' for i, q in enumerate(self.queues				)				)







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     Dict:
							lowerCAmelCase   =     []


						def     __snake_case   ( self     ,	A_				)     ->     None:
							if len(self.queue				) == 100:
								raise OverFlowError("""Maximum queue size is 100"""				)
							self.queue.append(A_				)


						def     __snake_case   ( self				)     ->     int:
							if not self.queue:
								raise UnderFlowError("""The queue is empty"""				)
							else:
								lowerCAmelCase   =     min(self.queue				)
								self.queue.remove(A_				)
								return data




						def __str__( self				)     ->     str:
							return str(self.queue				)







def 		_snake_case		( )      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     FixedPriorityQueue()
	fpq.enqueue(0							,		10							)
	fpq.enqueue(1							,		70							)
	fpq.enqueue(0							,		100							)
	fpq.enqueue(2							,		1							)
	fpq.enqueue(2							,		5							)
	fpq.enqueue(1							,		7							)
	fpq.enqueue(2							,		4							)
	fpq.enqueue(1							,		64							)
	fpq.enqueue(0							,		128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)






def 		_snake_case		( )      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     ElementPriorityQueue()
	epq.enqueue(10							)
	epq.enqueue(70							)
	epq.enqueue(100							)
	epq.enqueue(1							)
	epq.enqueue(5							)
	epq.enqueue(7							)
	epq.enqueue(4							)
	epq.enqueue(64							)
	epq.enqueue(128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)


if __name__ == "__main__":
				fixed_priority_queue()
				element_priority_queue()