



'''simple docstring'''





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def   UpperCAmelCase			(  a_	,						a_	,						a_	,						a_=5						)					->    Optional[Any]:



   """simple docstring"""






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


UpperCamelCase__  :			str          =					CamembertTokenizer.from_pretrained('camembert-base')
UpperCamelCase__  :			Optional[Any]          =					CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

UpperCamelCase__  :			List[Any]          =					'Le camembert est <mask> :)'







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





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
				from .pipeline_kandinsky import KandinskyPipeline
				from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
				from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
				from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
				from .text_encoder import MultilingualCLIP





