

def   a   (    A__			:     List[str]   ,     A__			:     Dict   ,     A__			:     Optional[int]=False  )					->   Optional[int]:





      """simple docstring"""



      if isinstance(A__   ,     A__  ) and isinstance(A__   ,     A__  ):
            _lowercase			 =len(set_a.intersection(A__  )  )

            if alternative_union:
                  _lowercase			 =len(A__  ) + len(A__  )
            else:
                  _lowercase			 =len(set_a.union(A__  )  )

            return intersection / union

      if isinstance(A__   ,     (list, tuple)  ) and isinstance(A__   ,     (list, tuple)  ):
            _lowercase			 =[element for element in set_a if element in set_b]

            if alternative_union:
                  _lowercase			 =len(A__  ) + len(A__  )
                  return len(A__  ) / union
            else:
                  _lowercase			 =set_a + [element for element in set_b if element not in set_a]
                  return len(A__  ) / len(A__  )

            return len(A__  ) / len(A__  )
      return None


if __name__ == "__main__":
  lowercase_             =     {'a', 'b', 'c', 'd', 'e'}
  lowercase_             =     {'c', 'd', 'e', 'f', 'h', 'i'}
  print(jaccard_similarity(set_a, set_b))





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 				__lowerCAmelCase  (      unittest.TestCase							):




  @slow
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

        _lowercase			 =tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]							,     dtype=tf.intaa							,     )  # J'aime le camembert !"

        _lowercase			 =model(lowerCAmelCase      )['last_hidden_state']
        _lowercase			 =tf.TensorShape((1, 10, 768)      )
        self.assertEqual(output.shape							,     lowerCAmelCase      )
        # compare the actual values for a slice.
        _lowercase			 =tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]							,     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      )      )


