'''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_:					Dict  =(
    '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_	:			Any ,   snake_case_	:			List[str]      )   ->				int:


     '''simple docstring'''




     warnings.warn(snake_case_ ,   snake_case_      )
     requires_backends(snake_case_ ,   "sklearn"      )
     return (preds == labels).mean()





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Any      )   ->				Any:


     '''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_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				List[str]:


     '''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_	:			Tuple ,   snake_case_	:			int ,   snake_case_	:			List[str]      )   ->				Tuple:


     '''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_	:			str ,   snake_case_	:			Optional[Any] ,   snake_case_	:			Tuple      )   ->				Optional[Any]:


     '''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_      )





'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 	__A				(			unittest.TestCase     ):



    @slow
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       TFCamembertModel.from_pretrained("jplu/tf-camembert-base"			)

         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]       ,      dtype=tf.intaa       ,      )  # J'aime le camembert !"

         UpperCAmelCase_	   =       model(__a			)["last_hidden_state"]
         UpperCAmelCase_	   =       tf.TensorShape((1, 10, 768)			)
         self.assertEqual(output.shape       ,      __a			)
         # compare the actual values for a slice.
         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]       ,      dtype=tf.floataa       ,      )
         # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
         # camembert.eval()
         # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

         self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,      expected_slice.numpy()       ,      atol=1E-4			)			)




