





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= {
    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       "falcon"
  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       ["past_key_values"]

  def __init__(		self :							Optional[Any]       , __a :							int=6_5024       , __a :							Any=4544       , __a :							Tuple=32       , __a :							Optional[int]=71       , __a :							Optional[Any]=1e-5       , __a :							Dict=0.02       , __a :							Optional[Any]=True       , __a :							Tuple=0.0       , __a :							str=0.0       , __a :							List[str]=None       , __a :							Optional[int]=False       , __a :							Optional[int]=False       , __a :							List[Any]=True       , __a :							List[str]=True       , __a :							List[Any]=False       , __a :							Union[str, Any]=11       , __a :							Optional[Any]=11       , **__a :							List[str]       , )		->					Dict:
        _UpperCamelCase   :						List[str]    =							vocab_size
        # Backward compatibility with n_embed kwarg
        _UpperCamelCase   :						List[Any]    =							kwargs.pop("n_embed"       , __a						)
        _UpperCamelCase   :						Tuple    =							hidden_size if n_embed is None else n_embed
        _UpperCamelCase   :						List[str]    =							num_hidden_layers
        _UpperCamelCase   :						int    =							num_attention_heads
        _UpperCamelCase   :						int    =							layer_norm_epsilon
        _UpperCamelCase   :						Any    =							initializer_range
        _UpperCamelCase   :						str    =							use_cache
        _UpperCamelCase   :						int    =							hidden_dropout
        _UpperCamelCase   :						Optional[Any]    =							attention_dropout

        _UpperCamelCase   :						Any    =							bos_token_id
        _UpperCamelCase   :						Any    =							eos_token_id
        _UpperCamelCase   :						Union[str, Any]    =							num_attention_heads if num_kv_heads is None else num_kv_heads
        _UpperCamelCase   :						Union[str, Any]    =							alibi
        _UpperCamelCase   :						Union[str, Any]    =							new_decoder_architecture
        _UpperCamelCase   :						Optional[Any]    =							multi_query  # Ignored when new_decoder_architecture is True
        _UpperCamelCase   :						Tuple    =							parallel_attn
        _UpperCamelCase   :						Tuple    =							bias

        super().__init__(bos_token_id=__a       , eos_token_id=__a       , **__a						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					List[str]:
        return self.hidden_size // self.num_attention_heads


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					str:
        return not self.alibi







"""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))
