


"""simple docstring"""







from heapq import heappop, heappush

import numpy as np


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       )->				tuple[float | int, list[tuple[int, int]]]:
  '''simple docstring'''







  a							,		a						:		Optional[int]											=      grid.shape
  a						:		Optional[int]											=      [-1, 1, 0, 0]
  a						:		int											=      [0, 0, -1, 1]
  if allow_diagonal:
    dx += [-1, -1, 1, 1]
    dy += [-1, 1, -1, 1]

  a							,		a						:		Dict											=      [(0, source)], set()
  a						:		Any											=      np.full((rows, cols)  ,       np.inf	)
  a						:		Optional[Any]											=      0
  a						:		str											=      np.empty((rows, cols)  ,       dtype=A_	)
  a						:		int											=      None

  while queue:
    ((a)							,		(a))						:		Any											=      heappop(A_	)
    if (x, y) in visited:
      continue
    visited.add((x, y)	)

    if (x, y) == destination:
      a						:		Union[str, Any]											=      []
      while (x, y) != source:
        path.append((x, y)	)
        a							,		a						:		int											=      predecessors[x, y]
      path.append(A_	)  # add the source manually
      path.reverse()
      return matrix[destination], path

    for i in range(len(A_	)	):
      a							,		a						:		List[Any]											=      x + dx[i], y + dy[i]
      if 0 <= nx < rows and 0 <= ny < cols:
        a						:		int											=      grid[nx][ny]
        if next_node == 1 and matrix[nx, ny] > dist + 1:
          heappush(A_  ,       (dist + 1, (nx, ny))	)
          a						:		Optional[Any]											=      dist + 1
          a						:		Tuple											=      (x, y)

  return np.inf, []


if __name__ == "__main__":
 import doctest

 doctest.testmod()










"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







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


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

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







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






