

'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 						_A	(       A__	,							A__	,							A__	,							A__=5 ):






 """simple docstring"""



 assert masked_input.count('''<mask>''' ) == 1
 __lowercase				    =					torch.tensor(tokenizer.encode(A__	,							add_special_tokens=A__ ) ).unsqueeze(0 )  # Batch size 1
 __lowercase				    =					model(A__ )[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=A__	,							dim=0 )
 __lowercase				    =					''' '''.join(
     [tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(A__ ) )] )
 __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(A__ ) in masked_input:
   topk_filled_outputs.append(
       (
           masked_input.replace(''' {0}'''.format(A__ )	,							A__ ),
           values[index].item(),
           predicted_token,
       ) )
  else:
   topk_filled_outputs.append(
       (
           masked_input.replace(A__	,							A__ ),
           values[index].item(),
           predicted_token,
       ) )
 return topk_filled_outputs


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

lowerCAmelCase__   		=      '''Le camembert est <mask> :)'''






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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xlm-roberta-xl"""

						def __init__(		self :Dict     ,			__snake_case :Optional[Any]=25_08_80     ,			__snake_case :List[Any]=25_60     ,			__snake_case :Optional[Any]=36     ,			__snake_case :Any=32     ,			__snake_case :int=1_02_40     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :str=5_14     ,			__snake_case :Union[str, Any]=1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :str=1E-05     ,			__snake_case :str=1     ,			__snake_case :int=0     ,			__snake_case :Tuple=2     ,			__snake_case :Optional[int]="absolute"     ,			__snake_case :str=True     ,			__snake_case :Any=None     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case			)
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Union[str, Any]										=num_hidden_layers
										__magic_name__						:						Any										=num_attention_heads
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						List[str]										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						Union[str, Any]										=attention_probs_dropout_prob
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						Any										=type_vocab_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Dict										=position_embedding_type
										__magic_name__						:						Any										=use_cache
										__magic_name__						:						Dict										=classifier_dropout




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						str										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						Optional[Any]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]			)




