"""simple docstring"""







from torch import nn







class        UpperCamelCase		(			nn.Module  ):
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[Any]:


        '''simple docstring'''


        super().__init__()
        lowercase_	:				List[str]													=	class_size
        lowercase_	:				Tuple													=	embed_size
        # self.mlp1 = nn.Linear(embed_size, embed_size)
        # self.mlp2 = (nn.Linear(embed_size, class_size))
        lowercase_	:				List[str]													=	nn.Linear(snake_case_     ,snake_case_			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.mlp(snake_case_			)
        return logits








'''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     :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 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      Tuple       )		->       int:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     return (preds == labels).mean()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      str       )		->       Optional[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   simple_accuracy(__lowercase			,     __lowercase       )
     _UpperCAmelCase     							=   fa_score(y_true=__lowercase			,     y_pred=__lowercase       )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      List[str]       )		->       List[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   pearsonr(__lowercase			,     __lowercase       )[0]
     _UpperCAmelCase     							=   spearmanr(__lowercase			,     __lowercase       )[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      str			,     __lowercase							:      str       )		->       Tuple:


     '''simple docstring'''



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






def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Dict			,     __lowercase							:      str       )		->       Union[str, Any]:


     '''simple docstring'''



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




