






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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Tuple:

     '''simple docstring'''
     warnings.warn(__lowerCamelCase ,							__lowerCamelCase       )
     requires_backends(__lowerCamelCase ,							"""sklearn"""       )
     return (preds == labels).mean()





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Any:

     '''simple docstring'''
     warnings.warn(__lowerCamelCase ,							__lowerCamelCase       )
     requires_backends(__lowerCamelCase ,							"""sklearn"""       )
     UpperCAmelCase__      :  Optional[int]				      =			simple_accuracy(__lowerCamelCase ,							__lowerCamelCase       )
     UpperCAmelCase__      :  str				      =			fa_score(y_true=__lowerCamelCase ,							y_pred=__lowerCamelCase       )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Union[str, Any]:

     '''simple docstring'''
     warnings.warn(__lowerCamelCase ,							__lowerCamelCase       )
     requires_backends(__lowerCamelCase ,							"""sklearn"""       )
     UpperCAmelCase__      :  Tuple				      =			pearsonr(__lowerCamelCase ,							__lowerCamelCase       )[0]
     UpperCAmelCase__      :  Any				      =			spearmanr(__lowerCamelCase ,							__lowerCamelCase       )[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			str:

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







def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			Optional[Any]:

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








'''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 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Any     			=						TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''      )

				snake_case  :       List[Any]     			=						tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,	dtype=tf.intaa  ,	)  # J'aime le camembert !"

				snake_case  :       Any     			=						model(UpperCamelCase__      )['''last_hidden_state''']
				snake_case  :       int     			=						tf.TensorShape((1, 10, 768)      )
				self.assertEqual(output.shape  ,	UpperCamelCase__      )
				# compare the actual values for a slice.
				snake_case  :       Dict     			=						tf.convert_to_tensor(
				    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,	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      )      )


