



"""simple docstring"""


def 			lowercase						(   _SCREAMING_SNAKE_CASE :    Union[str, Any]					):






				'''simple docstring'''



				if not head:
								return True
				# split the list to two parts
				_UpperCAmelCase      		=     head.next, head
				while fast and fast.next:
								_UpperCAmelCase      		=     fast.next.next
								_UpperCAmelCase      		=     slow.next
				_UpperCAmelCase      		=     slow.next
				_UpperCAmelCase      		=     None  # Don't forget here! But forget still works!
				# reverse the second part
				_UpperCAmelCase      		=     None
				while second:
								_UpperCAmelCase      		=     second.next
								_UpperCAmelCase      		=     node
								_UpperCAmelCase      		=     second
								_UpperCAmelCase      		=     nxt
				# compare two parts
				# second part has the same or one less node
				while node:
								if node.val != head.val:
												return False
								_UpperCAmelCase      		=     node.next
								_UpperCAmelCase      		=     head.next
				return True







def 			lowercase						(   _SCREAMING_SNAKE_CASE :    Union[str, Any]					):






				'''simple docstring'''



				if not head or not head.next:
								return True

				# 1. Get the midpoint (slow)
				_UpperCAmelCase      		=     head
				while fast and fast.next:
								_UpperCAmelCase      		=     fast.next.next, slow.next

				# 2. Push the second half into the stack
				_UpperCAmelCase      		=     [slow.val]
				while slow.next:
								_UpperCAmelCase      		=     slow.next
								stack.append(slow.val					)

				# 3. Comparison
				while stack:
								if stack.pop() != cur.val:
												return False
								_UpperCAmelCase      		=     cur.next

				return True







def 			lowercase						(   _SCREAMING_SNAKE_CASE :    int					):






				'''simple docstring'''



				if not head or not head.next:
								return True
				_UpperCAmelCase      		=     {}
				_UpperCAmelCase      		=     0
				while head:
								if head.val in d:
												d[head.val].append(UpperCAmelCase_					)
								else:
												_UpperCAmelCase      		=     [pos]
								_UpperCAmelCase      		=     head.next
								pos += 1
				_UpperCAmelCase      		=     pos - 1
				_UpperCAmelCase      		=     0
				for v in d.values():
								if len(UpperCAmelCase_					) % 2 != 0:
												middle += 1
								else:
												_UpperCAmelCase      		=     0
												for i in range(0       ,  len(UpperCAmelCase_					)					):
																if v[i] + v[len(UpperCAmelCase_					) - 1 - step] != checksum:
																				return False
																step += 1
								if middle > 1:
												return False
				return True







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


snake_case							:       List[str]										=	(
    '''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 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      return (preds == labels).mean()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:Dict									=			simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)
      a		:Any									=			fa_score(y_true=UpperCAmelCase_					,    y_pred=UpperCAmelCase_		)
      return {
          "acc": acc,
          "f1": fa,
          "acc_and_f1": (acc + fa) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:str									=			pearsonr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      a		:Union[str, Any]									=			spearmanr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
          "corr": (pearson_corr + spearman_corr) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						Dict		):




      """simple docstring"""





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






def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





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





