

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						:  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__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''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      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''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 __future__ import annotations


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						lowerCAmelCase__	:str						)					->       set[str]:
      '''simple docstring'''


      lowercase				,				lowercase								=      set(lowerCAmelCase__						), [start]

      while stack:
            lowercase								=      stack.pop()
            explored.add(lowerCAmelCase__						)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]						):
                  if adj not in explored:
                        stack.append(lowerCAmelCase__						)
      return explored


__lowerCAmelCase       :			str   	={
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, """A"""))



