




"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=5				)						->    Union[str, Any]:
      # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
      assert masked_input.count("""<mask>"""				) == 1
      lowerCAmelCase__      :			Tuple   =	torch.tensor(tokenizer.encode(__UpperCAmelCase    ,		add_special_tokens=__UpperCAmelCase				)				).unsqueeze(0				)  # Batch size 1
      lowerCAmelCase__      :			Optional[int]   =	model(__UpperCAmelCase				)[0]  # The last hidden-state is the first element of the output tuple
      lowerCAmelCase__      :			str   =	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      lowerCAmelCase__      :			List[Any]   =	logits[0, masked_index, :]
      lowerCAmelCase__      :			List[Any]   =	logits.softmax(dim=0				)
      lowerCAmelCase__ ,   lowerCAmelCase__      :			str   =	prob.topk(k=__UpperCAmelCase    ,		dim=0				)
      lowerCAmelCase__      :			int   =	""" """.join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCAmelCase				)				)]				)
      lowerCAmelCase__      :			Dict   =	tokenizer.mask_token
      lowerCAmelCase__      :			Dict   =	[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """				)				):
            lowerCAmelCase__      :			Optional[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													=      CamembertTokenizer.from_pretrained("""camembert-base""")
_A													=      CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

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

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











"""simple docstring"""


def 	lowercase_				(			__UpperCAmelCase				)						->    str:
      if not all(char in """01""" for char in bin_string				):
            raise ValueError("""Non-binary value was passed to the function"""				)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""				)
      lowerCAmelCase__      :			List[str]   =	""""""
      while len(__UpperCAmelCase				) % 3 != 0:
            lowerCAmelCase__      :			Optional[Any]   =	"""0""" + bin_string
      lowerCAmelCase__      :			List[Any]   =	[
          bin_string[index : index + 3]
          for index in range(len(__UpperCAmelCase				)				)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            lowerCAmelCase__      :			Optional[int]   =	0
            for index, val in enumerate(__UpperCAmelCase				):
                  oct_val += int(2 ** (2 - index) * int(__UpperCAmelCase				)				)
            oct_string += str(__UpperCAmelCase				)
      return oct_string


if __name__ == "__main__":
  from doctest import testmod

  testmod()





