





"""simple docstring"""


def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		int:




      """simple docstring"""

      def update_area_of_max_square(lowercase_		,lowercase_      ) -> int:
            # BASE CASE
            if row >= rows or col >= cols:
                  return 0

            _UpperCamelCase   :						Tuple    =							update_area_of_max_square(lowercase_		,col + 1      )
            _UpperCamelCase   :						int    =							update_area_of_max_square(row + 1		,col + 1      )
            _UpperCamelCase   :						str    =							update_area_of_max_square(row + 1		,lowercase_      )

            if mat[row][col]:
                  _UpperCamelCase   :						List[str]    =							1 + min([right, diagonal, down]      )
                  _UpperCamelCase   :						Optional[int]    =							max(largest_square_area[0]		,lowercase_      )
                  return sub_problem_sol
            else:
                  return 0

      _UpperCamelCase   :						Tuple    =							[0]
      update_area_of_max_square(0		,0      )
      return largest_square_area[0]




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		int:




      """simple docstring"""

      def update_area_of_max_square_using_dp_array(
          lowercase_		,lowercase_		,lowercase_      ) -> int:
            if row >= rows or col >= cols:
                  return 0
            if dp_array[row][col] != -1:
                  return dp_array[row][col]

            _UpperCamelCase   :						Any    =							update_area_of_max_square_using_dp_array(lowercase_		,col + 1		,lowercase_      )
            _UpperCamelCase   :						Any    =							update_area_of_max_square_using_dp_array(row + 1		,col + 1		,lowercase_      )
            _UpperCamelCase   :						int    =							update_area_of_max_square_using_dp_array(row + 1		,lowercase_		,lowercase_      )

            if mat[row][col]:
                  _UpperCamelCase   :						int    =							1 + min([right, diagonal, down]      )
                  _UpperCamelCase   :						List[Any]    =							max(largest_square_area[0]		,lowercase_      )
                  _UpperCamelCase   :						Any    =							sub_problem_sol
                  return sub_problem_sol
            else:
                  return 0

      _UpperCamelCase   :						Tuple    =							[0]
      _UpperCamelCase   :						str    =							[[-1] * cols for _ in range(lowercase_      )]
      update_area_of_max_square_using_dp_array(0		,0		,lowercase_      )

      return largest_square_area[0]




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Tuple    =							[[0] * (cols + 1) for _ in range(rows + 1      )]
      _UpperCamelCase   :						List[str]    =							0
      for row in range(rows - 1		,-1		,-1      ):
            for col in range(cols - 1		,-1		,-1      ):
                  _UpperCamelCase   :						str    =							dp_array[row][col + 1]
                  _UpperCamelCase   :						Union[str, Any]    =							dp_array[row + 1][col + 1]
                  _UpperCamelCase   :						Tuple    =							dp_array[row + 1][col]

                  if mat[row][col] == 1:
                        _UpperCamelCase   :						str    =							1 + min(lowercase_		,lowercase_		,lowercase_      )
                        _UpperCamelCase   :						int    =							max(dp_array[row][col]		,lowercase_      )
                  else:
                        _UpperCamelCase   :						Optional[int]    =							0

      return largest_square_area




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Dict    =							[0] * (cols + 1)
      _UpperCamelCase   :						Optional[int]    =							[0] * (cols + 1)
      _UpperCamelCase   :						Any    =							0
      for row in range(rows - 1		,-1		,-1      ):
            for col in range(cols - 1		,-1		,-1      ):
                  _UpperCamelCase   :						Tuple    =							current_row[col + 1]
                  _UpperCamelCase   :						int    =							next_row[col + 1]
                  _UpperCamelCase   :						Optional[Any]    =							next_row[col]

                  if mat[row][col] == 1:
                        _UpperCamelCase   :						List[str]    =							1 + min(lowercase_		,lowercase_		,lowercase_      )
                        _UpperCamelCase   :						List[Any]    =							max(current_row[col]		,lowercase_      )
                  else:
                        _UpperCamelCase   :						Union[str, Any]    =							0
            _UpperCamelCase   :						Dict    =							current_row

      return largest_square_area


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))







"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

      assert masked_input.count("<mask>"      ) == 1
      _UpperCamelCase   :						str    =							torch.tensor(tokenizer.encode(lowercase_		,add_special_tokens=lowercase_      )      ).unsqueeze(0      )  # Batch size 1
      _UpperCamelCase   :						Union[str, Any]    =							model(lowercase_      )[0]  # The last hidden-state is the first element of the output tuple
      _UpperCamelCase   :						List[Any]    =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      _UpperCamelCase   :						Tuple    =							logits[0, masked_index, :]
      _UpperCamelCase   :						List[str]    =							logits.softmax(dim=0      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							prob.topk(k=lowercase_		,dim=0      )
      _UpperCamelCase   :						str    =							" ".join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(lowercase_      )      )]      )
      _UpperCamelCase   :						Any    =							tokenizer.mask_token
      _UpperCamelCase   :						List[Any]    =							[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "      )      ):
            _UpperCamelCase   :						Optional[int]    =							predicted_token_bpe.replace("\u2581"		," "      )
            if " {0}".format(lowercase_      ) in masked_input:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(" {0}".format(lowercase_      )		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
            else:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(lowercase_		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
      return topk_filled_outputs


lowerCamelCase__						= CamembertTokenizer.from_pretrained("camembert-base")
lowerCamelCase__						= CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowerCamelCase__						= "Le camembert est <mask> :)"

print(fill_mask(masked_input, model, tokenizer, topk=3))
