"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Any=5   ):
     # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
     assert masked_input.count('<mask>'   ) == 1
     lowercase_			:       str									=			torch.tensor(tokenizer.encode(__SCREAMING_SNAKE_CASE  ,    add_special_tokens=__SCREAMING_SNAKE_CASE   )   ).unsqueeze(0   )  # Batch size 1
     lowercase_			:       Any									=			model(__SCREAMING_SNAKE_CASE   )[0]  # The last hidden-state is the first element of the output tuple
     lowercase_			:       int									=			(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     lowercase_			:       Any									=			logits[0, masked_index, :]
     lowercase_			:       Any									=			logits.softmax(dim=0   )
     lowercase_							,						lowercase_			:       Tuple									=			prob.topk(k=__SCREAMING_SNAKE_CASE  ,    dim=0   )
     lowercase_			:       str									=			' '.join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()   ) for i in range(len(__SCREAMING_SNAKE_CASE   )   )]   )
     lowercase_			:       Optional[Any]									=			tokenizer.mask_token
     lowercase_			:       Optional[int]									=			[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '   )   ):
          lowercase_			:       Union[str, Any]									=			predicted_token_bpe.replace('\u2581'  ,    ' '   )
          if " {0}".format(__SCREAMING_SNAKE_CASE   ) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(' {0}'.format(__SCREAMING_SNAKE_CASE   )  ,    __SCREAMING_SNAKE_CASE   ),
                       values[index].item(),
                       predicted_token,
                   )   )
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ),
                       values[index].item(),
                       predicted_token,
                   )   )
     return topk_filled_outputs


__SCREAMING_SNAKE_CASE								=CamembertTokenizer.from_pretrained("camembert-base")
__SCREAMING_SNAKE_CASE								=CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

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







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


"""simple docstring"""







__SCREAMING_SNAKE_CASE								={
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}

