




def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> int:

							if len(A_				) < len(A_				):
														return levenshtein_distance(A_				,A_				)

							if len(A_				) == 0:
														return len(A_				)

							lowercase__  :       Optional[int]   					=							list(range(len(A_				) + 1				)				)

							for i, ca in enumerate(A_				):
														lowercase__  :       int   					=							[i + 1]

														for j, ca in enumerate(A_				):
																					# Calculate insertions, deletions and substitutions
																					lowercase__  :       Union[str, Any]   					=							previous_row[j + 1] + 1
																					lowercase__  :       Tuple   					=							current_row[j] + 1
																					lowercase__  :       Optional[Any]   					=							previous_row[j] + (ca != ca)

																					# Get the minimum to append to the current row
																					current_row.append(min(A_				,A_				,A_				)				)

														# Store the previous row
														lowercase__  :       Dict   					=							current_row

							# Returns the last element (distance)
							return previous_row[-1]


if __name__ == "__main__":
						__a  :		Any			=    input('''Enter the first word:\n''').strip()
						__a  :		int			=    input('''Enter the second word:\n''').strip()

						__a  :		Optional[Any]			=    levenshtein_distance(first_word, second_word)
						print(f'Levenshtein distance between {first_word} and {second_word} is {result}')







"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]				,		device=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   torch.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]]]				,		device=A				,		dtype=torch.float				,		)
      # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
      # camembert.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

      self.assertTrue(torch.allclose(output[:, :3, :3]				,		A				,		atol=1E-4			)			)




