



import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case=5    )     ->     List[Any]:






      '''simple docstring'''







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





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


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""lxmert"""
       _lowerCamelCase			    =				{}



       def __init__(     self     ,lowercase=30522     ,lowercase=768     ,lowercase=12     ,lowercase=9500     ,lowercase=1600     ,lowercase=400     ,lowercase=3072     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=512     ,lowercase=2     ,lowercase=0.02     ,lowercase=1E-12     ,lowercase=9     ,lowercase=5     ,lowercase=5     ,lowercase=2048     ,lowercase=4     ,lowercase=6.67     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		vocab_size
             UpperCAmelCase_  :	str				      =		hidden_size
             UpperCAmelCase_  :	List[str]				      =		num_attention_heads
             UpperCAmelCase_  :	Dict				      =		hidden_act
             UpperCAmelCase_  :	List[Any]				      =		intermediate_size
             UpperCAmelCase_  :	Tuple				      =		hidden_dropout_prob
             UpperCAmelCase_  :	List[Any]				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Any				      =		max_position_embeddings
             UpperCAmelCase_  :	str				      =		type_vocab_size
             UpperCAmelCase_  :	Any				      =		initializer_range
             UpperCAmelCase_  :	Optional[Any]				      =		layer_norm_eps
             UpperCAmelCase_  :	Any				      =		num_qa_labels
             UpperCAmelCase_  :	str				      =		num_object_labels
             UpperCAmelCase_  :	Dict				      =		num_attr_labels
             UpperCAmelCase_  :	Tuple				      =		l_layers
             UpperCAmelCase_  :	Tuple				      =		x_layers
             UpperCAmelCase_  :	int				      =		r_layers
             UpperCAmelCase_  :	Optional[Any]				      =		visual_feat_dim
             UpperCAmelCase_  :	List[Any]				      =		visual_pos_dim
             UpperCAmelCase_  :	int				      =		visual_loss_normalizer
             UpperCAmelCase_  :	str				      =		task_matched
             UpperCAmelCase_  :	str				      =		task_mask_lm
             UpperCAmelCase_  :	int				      =		task_obj_predict
             UpperCAmelCase_  :	List[str]				      =		task_qa
             UpperCAmelCase_  :	Optional[int]				      =		visual_obj_loss
             UpperCAmelCase_  :	List[str]				      =		visual_attr_loss
             UpperCAmelCase_  :	str				      =		visual_feat_loss
             UpperCAmelCase_  :	List[Any]				      =		{"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
             super().__init__(**lowercase)
