
"""simple docstring"""




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase=5    ):






  """simple docstring"""



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


A__ :       Tuple				= CamembertTokenizer.from_pretrained('camembert-base')
A__ :       Any				= CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

A__ :       Optional[Any]				= 'Le camembert est <mask> :)'







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


"""simple docstring"""




from collections.abc import Callable




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      float   			=  a
  _lowercase:      float   			=  b
  if function(_UpperCamelCase    ) == 0:  # one of the a or b is a root for the function
    return a
  elif function(_UpperCamelCase    ) == 0:
    return b
  elif (
      function(_UpperCamelCase    ) * function(_UpperCamelCase    ) > 0
  ):  # if none of these are root and they are both positive or negative,
    # then this algorithm can't find the root
    raise ValueError('''could not find root in given interval.'''    )
  else:
    _lowercase:      float   			=  start + (end - start) / 2.0
    while abs(start - mid    ) > 10**-7:  # until precisely equals to 10^-7
      if function(_UpperCamelCase    ) == 0:
        return mid
      elif function(_UpperCamelCase    ) * function(_UpperCamelCase    ) < 0:
        _lowercase:      Union[str, Any]   			=  mid
      else:
        _lowercase:      Any   			=  mid
      _lowercase:      List[Any]   			=  start + (end - start) / 2.0
    return mid




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  return x**3 - 2 * x - 5


if __name__ == "__main__":
  print(bisection(f, 1, 1_0_0_0))

  import doctest

  doctest.testmod()
