




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


SCREAMING_SNAKE_CASE__       :      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      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Union[str, Any]:
       warnings.warn(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       requires_backends(_SCREAMING_SNAKE_CASE ,"sklearn"    )
       return (preds == labels).mean()

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       warnings.warn(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       requires_backends(_SCREAMING_SNAKE_CASE ,"sklearn"    )
       lowerCamelCase   :      Any			 =					simple_accuracy(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Dict			 =					fa_score(y_true=_SCREAMING_SNAKE_CASE ,y_pred=_SCREAMING_SNAKE_CASE    )
       return {
           "acc": acc,
           "f1": fa,
           "acc_and_f1": (acc + fa) / 2,
       }

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       warnings.warn(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       requires_backends(_SCREAMING_SNAKE_CASE ,"sklearn"    )
       lowerCamelCase   :      int			 =					pearsonr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )[0]
       lowerCamelCase   :      List[str]			 =					spearmanr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )[0]
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
           "corr": (pearson_corr + spearman_corr) / 2,
       }

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       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      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> List[Any]:
       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    )













from heapq import heappop, heappush

import numpy as np







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						)      ->      tuple[float | int, list[tuple[int, int]]]:
					__A    ,				__A	:						int       =				grid.shape
					__A	:						Any       =				[-1, 1, 0, 0]
					__A	:						Optional[Any]       =				[0, 0, -1, 1]
					if allow_diagonal:
										dx += [-1, -1, 1, 1]
										dy += [-1, 1, -1, 1]

					__A    ,				__A	:						Optional[int]       =				[(0, source)], set()
					__A	:						Any       =				np.full((rows, cols)					,						np.inf  )
					__A	:						Any       =				0
					__A	:						Any       =				np.empty((rows, cols)					,						dtype=a  )
					__A	:						Optional[Any]       =				None

					while queue:
										((__A)    ,				(__A))	:						List[str]       =				heappop(a  )
										if (x, y) in visited:
															continue
										visited.add((x, y)  )

										if (x, y) == destination:
															__A	:						int       =				[]
															while (x, y) != source:
																				path.append((x, y)  )
																				__A    ,				__A	:						Optional[int]       =				predecessors[x, y]
															path.append(a  )  # add the source manually
															path.reverse()
															return matrix[destination], path

										for i in range(len(a  )  ):
															__A    ,				__A	:						Union[str, Any]       =				x + dx[i], y + dy[i]
															if 0 <= nx < rows and 0 <= ny < cols:
																				__A	:						Optional[int]       =				grid[nx][ny]
																				if next_node == 1 and matrix[nx, ny] > dist + 1:
																									heappush(a					,						(dist + 1, (nx, ny))  )
																									__A	:						List[Any]       =				dist + 1
																									__A	:						Union[str, Any]       =				(x, y)

					return np.inf, []


if __name__ == "__main__":
				import doctest

				doctest.testmod()




