
"""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								=						(
    """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__						(     __snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Tuple:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						Any:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





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









_lowerCAmelCase  :      int			 ="""
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_lowerCAmelCase  :      List[str]			 =[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowerCAmelCase  :      int			 ={
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}