import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def 	__A		(      _A				,					_A				,					_A				,					_A=5       ):



 """simple docstring"""


 assert masked_input.count("<mask>"       ) == 1
 __a							=	torch.tensor(tokenizer.encode(_A				,					add_special_tokens=_A       )       ).unsqueeze(0       )  # Batch size 1
 __a							=	model(_A       )[0]  # The last hidden-state is the first element of the output tuple
 __a							=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
 __a							=	logits[0, masked_index, :]
 __a							=	logits.softmax(dim=0       )
 __a							,  __a							=	prob.topk(k=_A				,					dim=0       )
 __a							=	" ".join(
     [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(_A       )       )]       )
 __a							=	tokenizer.mask_token
 __a							=	[]
 for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
  __a							=	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


SCREAMING_SNAKE_CASE			:						List[Any]				  =		CamembertTokenizer.from_pretrained("""camembert-base""")
SCREAMING_SNAKE_CASE			:						int				  =		CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

SCREAMING_SNAKE_CASE			:						str				  =		"""Le camembert est <mask> :)"""






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



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


