


"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


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

			'''simple docstring'''




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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

      _UpperCamelCase 							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

