"""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


_a  :  Optional[Any]=				(
    "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 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       List[Any]						)							->    Optional[int]:

 '''simple docstring'''





 warnings.warn(UpperCAmelCase_    ,							UpperCAmelCase_						)
 requires_backends(UpperCAmelCase_    ,							'sklearn'						)
 return (preds == labels).mean()
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       Dict						)							->    str:

 '''simple docstring'''





 warnings.warn(UpperCAmelCase_    ,							UpperCAmelCase_						)
 requires_backends(UpperCAmelCase_    ,							'sklearn'						)
 __snake_case    :							List[str]   	=				simple_accuracy(UpperCAmelCase_    ,							UpperCAmelCase_						)
 __snake_case    :							Union[str, Any]   	=				fa_score(y_true=UpperCAmelCase_    ,							y_pred=UpperCAmelCase_						)
 return {
     "acc": acc,
     "f1": fa,
     "acc_and_f1": (acc + fa) / 2,
 }
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       Any						)							->    Any:

 '''simple docstring'''





 warnings.warn(UpperCAmelCase_    ,							UpperCAmelCase_						)
 requires_backends(UpperCAmelCase_    ,							'sklearn'						)
 __snake_case    :							List[Any]   	=				pearsonr(UpperCAmelCase_    ,							UpperCAmelCase_						)[0]
 __snake_case    :							Any   	=				spearmanr(UpperCAmelCase_    ,							UpperCAmelCase_						)[0]
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
     "corr": (pearson_corr + spearman_corr) / 2,
 }
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       Any						)							->    Optional[int]:

 '''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 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       Union[str, Any]						)							->    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_						)






"""simple docstring"""







import math
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				len(UpperCAmelCase_						)
	__snake_case    :							List[Any]   	=				int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
	__snake_case    :							Any   	=				0
	while arr[min(UpperCAmelCase_    ,							UpperCAmelCase_						) - 1] < x:
		__snake_case    :							Tuple   	=				step
		step += int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
		if prev >= n:
			return -1

	while arr[prev] < x:
		__snake_case    :							Union[str, Any]   	=				prev + 1
		if prev == min(UpperCAmelCase_    ,							UpperCAmelCase_						):
			return -1
	if arr[prev] == x:
		return prev
	return -1


if __name__ == "__main__":
	_a  :  str=				input("Enter numbers separated by a comma:\n").strip()
	_a  :  int=				[int(item) for item in user_input.split(",")]
	_a  :  Optional[Any]=				int(input("Enter the number to be searched:\n"))
	_a  :  Tuple=				jump_search(arr, x)
	if res == -1:
		print("Number not found!")
	else:
		print(f'''Number {x} is at index {res}''')





