






'''simple docstring'''

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						List[Any]=5				)	->							Any:
      '''simple docstring'''


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


lowerCAmelCase_ : Optional[int]						  =      CamembertTokenizer.from_pretrained("camembert-base")
lowerCAmelCase_ : Dict						  =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowerCAmelCase_ : Optional[Any]						  =      "Le camembert est <mask> :)"






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







'''simple docstring'''

from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class 							lowercase						(						__lowerCamelCase ):


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	Callable   ,		__lowerCAmelCase				:	Optional[Features] = None   ,		__lowerCAmelCase				:	str = None   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[dict] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					List[Any]:
         super().__init__(
             features=__lowerCAmelCase   ,		cache_dir=__lowerCAmelCase   ,		keep_in_memory=__lowerCAmelCase   ,		streaming=__lowerCAmelCase   ,		num_proc=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)
         lowercase_     =      Generator(
             cache_dir=__lowerCAmelCase   ,		features=__lowerCAmelCase   ,		generator=__lowerCAmelCase   ,		gen_kwargs=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					List[str]:
         # Build iterable dataset
         if self.streaming:
               lowercase_     =      self.builder.as_streaming_dataset(split="train")
         # Build regular (map-style) dataset
         else:
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None
               lowercase_     =      None

               self.builder.download_and_prepare(
                   download_config=__lowerCAmelCase   ,		download_mode=__lowerCAmelCase   ,		verification_mode=__lowerCAmelCase   ,		base_path=__lowerCAmelCase   ,		num_proc=self.num_proc   ,		)
               lowercase_     =      self.builder.as_dataset(
                   split="train"   ,		verification_mode=__lowerCAmelCase   ,		in_memory=self.keep_in_memory)
         return dataset






