



"""simple docstring"""






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=5	)						->				List[str]:
  # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
  assert masked_input.count('''<mask>'''	) == 1
  _snake_case   	=      torch.tensor(tokenizer.encode(lowerCAmelCase_  ,						add_special_tokens=lowerCAmelCase_	)	).unsqueeze(0	)  # Batch size 1
  _snake_case   	=      model(lowerCAmelCase_	)[0]  # The last hidden-state is the first element of the output tuple
  _snake_case   	=      (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  _snake_case   	=      logits[0, masked_index, :]
  _snake_case   	=      logits.softmax(dim=0	)
  _snake_case	,     _snake_case   	=      prob.topk(k=lowerCAmelCase_  ,						dim=0	)
  _snake_case   	=      ''' '''.join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(lowerCAmelCase_	)	)]	)
  _snake_case   	=      tokenizer.mask_token
  _snake_case   	=      []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
    _snake_case   	=      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


snake_case   					=				CamembertTokenizer.from_pretrained('''camembert-base''')
snake_case   					=				CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

snake_case   					=				'''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





