




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


lowerCAmelCase_					    = (
    """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      )     ->	List[Any]:
		warnings.warn(UpperCamelCase	,      UpperCamelCase      )
		requires_backends(UpperCamelCase	,      '''sklearn'''      )
		return (preds == labels).mean()



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	List[str]:
		warnings.warn(UpperCamelCase	,      UpperCamelCase      )
		requires_backends(UpperCamelCase	,      '''sklearn'''      )
		lowerCAmelCase__				:  Tuple		       =     simple_accuracy(UpperCamelCase	,      UpperCamelCase      )
		lowerCAmelCase__				:  Optional[int]		       =     fa_score(y_true=UpperCamelCase	,      y_pred=UpperCamelCase      )
		return {
		    "acc": acc,
		    "f1": fa,
		    "acc_and_f1": (acc + fa) / 2,
		}



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Tuple:
		warnings.warn(UpperCamelCase	,      UpperCamelCase      )
		requires_backends(UpperCamelCase	,      '''sklearn'''      )
		lowerCAmelCase__				:  List[Any]		       =     pearsonr(UpperCamelCase	,      UpperCamelCase      )[0]
		lowerCAmelCase__				:  Optional[Any]		       =     spearmanr(UpperCamelCase	,      UpperCamelCase      )[0]
		return {
		    "pearson": pearson_corr,
		    "spearmanr": spearman_corr,
		    "corr": (pearson_corr + spearman_corr) / 2,
		}



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



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	List[Any]:
		warnings.warn(UpperCamelCase	,      UpperCamelCase      )
		requires_backends(UpperCamelCase	,      '''sklearn'''      )
		if len(UpperCamelCase      ) != len(UpperCamelCase      ):
				raise ValueError(F"""Predictions and labels have mismatched lengths {len(UpperCamelCase      )} and {len(UpperCamelCase      )}"""      )
		if task_name == "xnli":
				return {"acc": simple_accuracy(UpperCamelCase	,      UpperCamelCase      )}
		else:
				raise KeyError(UpperCamelCase      )







from datetime import datetime

import matplotlib.pyplot as plt
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  for param in module.parameters():
    lowerCAmelCase__				:  int		       =     False



def      __lowerCAmelCase			(					)     ->	Optional[Any]:
  lowerCAmelCase__				:  List[Any]		       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    lowerCAmelCase__				:  Optional[Any]		       =     '''mps'''
  if device == "mps":
    print(
        '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
        ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
        ''' with generations.'''      )
  return device



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     plt.imshow(UpperCamelCase      )
  fig.axes.get_xaxis().set_visible(UpperCamelCase      )
  fig.axes.get_yaxis().set_visible(UpperCamelCase      )
  plt.show()



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  Dict		       =     datetime.now()
  lowerCAmelCase__				:  Optional[int]		       =     current_time.strftime('''%H:%M:%S'''      )
  return timestamp

