

'''simple docstring'''





import functools
def   a   (    A__			:     str   ,     A__			:     str  )					->   int:





      """simple docstring"""



      _lowercase			 =len(A__  )
      _lowercase			 =len(A__  )

      @functools.cache
      def min_distance(A__			:     int   ,     A__			:     int  ) -> int:
            # if first word index is overflow - delete all from the second word
            if indexa >= len_worda:
                  return len_worda - indexa
            # if second word index is overflow - delete all from the first word
            if indexa >= len_worda:
                  return len_worda - indexa
            _lowercase			 =int(worda[indexa] != worda[indexa]  )  # current letters not identical
            return min(
                1 + min_distance(indexa + 1   ,     A__  )   ,     1 + min_distance(A__   ,     indexa + 1  )   ,     diff + min_distance(indexa + 1   ,     indexa + 1  )   ,     )

      return min_distance(0   ,     0  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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      )      )


