
"""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


_SCREAMING_SNAKE_CASE	       =      (
    """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   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							warnings.warn(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							requires_backends(SCREAMING_SNAKE_CASE							,    "sklearn" )
							return (preds == labels).mean()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							warnings.warn(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							requires_backends(SCREAMING_SNAKE_CASE							,    "sklearn" )
							__snake_case     			=       simple_accuracy(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							__snake_case     			=       fa_score(y_true=SCREAMING_SNAKE_CASE							,    y_pred=SCREAMING_SNAKE_CASE )
							return {
							    "acc": acc,
							    "f1": fa,
							    "acc_and_f1": (acc + fa) / 2,
							}
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


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







							"""simple docstring"""


							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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							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 )


"""simple docstring"""







from __future__ import annotations

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







class 							__magic_name__     :





					def __init__(				self   :							Any							,						snake_case_   :							dict[str, list[str]]							,						snake_case_   :							str    ):
												__snake_case     			=       graph
												# mapping node to its parent in resulting breadth first tree
												__snake_case     			=       {}
												__snake_case     			=       source_vertex





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       {self.source_vertex}
												__snake_case     			=       None
												__snake_case     			=       [self.source_vertex]  # first in first out queue

												while queue:
																			__snake_case     			=       queue.pop(0    )
																			for adjacent_vertex in self.graph[vertex]:
																										if adjacent_vertex not in visited:
																																	visited.add(snake_case_    )
																																	__snake_case     			=       vertex
																																	queue.append(snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str    ):
												if target_vertex == self.source_vertex:
																			return self.source_vertex

												__snake_case     			=       self.parent.get(snake_case_    )
												if target_vertex_parent is None:
																			__snake_case     			=       (
																			    F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
																			)
																			raise ValueError(snake_case_    )

												return self.shortest_path(snake_case_    ) + F'''->{target_vertex}'''


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      Graph(graph, """G""")
		g.breath_first_search()
		print(g.shortest_path("""D"""))
		print(g.shortest_path("""G"""))
		print(g.shortest_path("""Foo"""))
