






"""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__					: Union[str, 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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Dict:
     """simple docstring"""

     warnings.warn(lowerCamelCase_ ,	lowerCamelCase_					)
     requires_backends(lowerCamelCase_ ,	'sklearn'					)
     return (preds == labels).mean()







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Any:
     """simple docstring"""

     warnings.warn(lowerCamelCase_ ,	lowerCamelCase_					)
     requires_backends(lowerCamelCase_ ,	'sklearn'					)
     UpperCamelCase__         =    simple_accuracy(lowerCamelCase_ ,	lowerCamelCase_					)
     UpperCamelCase__         =    fa_score(y_true=lowerCamelCase_ ,	y_pred=lowerCamelCase_					)
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[Any]:
     """simple docstring"""

     warnings.warn(lowerCamelCase_ ,	lowerCamelCase_					)
     requires_backends(lowerCamelCase_ ,	'sklearn'					)
     UpperCamelCase__         =    pearsonr(lowerCamelCase_ ,	lowerCamelCase_					)[0]
     UpperCamelCase__         =    spearmanr(lowerCamelCase_ ,	lowerCamelCase_					)[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
     """simple docstring"""

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Optional[Any]:
     """simple docstring"""

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












"""simple docstring"""





from collections import defaultdict
from math import ceil, sqrt







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 1_00_00_00 ,	SCREAMING_SNAKE_CASE = 10					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    defaultdict(SCREAMING_SNAKE_CASE					)

    for outer_width in range(3 ,	(t_limit // 4) + 2					):
        if outer_width * outer_width > t_limit:
            UpperCamelCase__         =    max(
                ceil(sqrt(outer_width * outer_width - t_limit					)					) ,	1					)
        else:
            UpperCamelCase__         =    1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(SCREAMING_SNAKE_CASE ,	outer_width - 1 ,	2					):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10					)


if __name__ == "__main__":
  print(F"""{solution() = }""")




