


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





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


lowerCamelCase					:		List[str]            =						CamembertTokenizer.from_pretrained('camembert-base')
lowerCamelCase					:		str            =						CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

lowerCamelCase					:		Tuple            =						'Le camembert est <mask> :)'


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










"""simple docstring"""






import os
from pathlib import Path




def 			snake_case		(		)						->				Tuple:
  from torch.utils.cpp_extension import load

  _snake_case   	=      Path(lowerCAmelCase_	).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
  _snake_case   	=      [
      root / filename
      for filename in [
          '''vision.cpp''',
          os.path.join('''cpu'''  ,						'''ms_deform_attn_cpu.cpp'''	),
          os.path.join('''cuda'''  ,						'''ms_deform_attn_cuda.cu'''	),
      ]
  ]

  load(
      '''MultiScaleDeformableAttention'''  ,						lowerCAmelCase_  ,						with_cuda=lowerCAmelCase_  ,						extra_include_paths=[str(lowerCAmelCase_	)]  ,						extra_cflags=['''-DWITH_CUDA=1''']  ,						extra_cuda_cflags=[
          '''-DCUDA_HAS_FP16=1''',
          '''-D__CUDA_NO_HALF_OPERATORS__''',
          '''-D__CUDA_NO_HALF_CONVERSIONS__''',
          '''-D__CUDA_NO_HALF2_OPERATORS__''',
      ]  ,						)

  import MultiScaleDeformableAttention as MSDA

  return MSDA





