




'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def 						snake_case_       (UpperCamelCase			:    List[str]					,		UpperCamelCase			:    str					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[int]=5 ):





							'''simple docstring'''


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


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

_snake_case : str  				=						'Le camembert est <mask> :)'

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







'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


_snake_case : Tuple  				=						logging.get_logger(__name__)

_snake_case : Optional[int]  				=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case : List[Any]  				=						{
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case : Union[str, Any]  				=						{
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


_snake_case : Tuple  				=						{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}

class      A       (						_a       ):
							lowercase_							     =							VOCAB_FILES_NAMES
							lowercase_							     =							PRETRAINED_VOCAB_FILES_MAP
							lowercase_							     =							PRETRAINED_INIT_CONFIGURATION
							lowercase_							     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							     =							SqueezeBertTokenizer







							def __init__(    self			:    str				,     lowerCAmelCase_			:    str=None				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    List[str]="[UNK]"				,     lowerCAmelCase_			:    Union[str, Any]="[SEP]"				,     lowerCAmelCase_			:    Optional[Any]="[PAD]"				,     lowerCAmelCase_			:    Any="[CLS]"				,     lowerCAmelCase_			:    List[str]="[MASK]"				,     lowerCAmelCase_			:    int=True				,     lowerCAmelCase_			:    List[Any]=None				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				int:


														"""simple docstring"""


														super().__init__(
														    lowerCAmelCase_				,     tokenizer_file=lowerCAmelCase_				,     do_lower_case=lowerCAmelCase_				,     unk_token=lowerCAmelCase_				,     sep_token=lowerCAmelCase_				,     pad_token=lowerCAmelCase_				,     cls_token=lowerCAmelCase_				,     mask_token=lowerCAmelCase_				,     tokenize_chinese_chars=lowerCAmelCase_				,     strip_accents=lowerCAmelCase_				,     **lowerCAmelCase_				,     )

														_a      	=							json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
														if (
														    normalizer_state.get('''lowercase'''				,     lowerCAmelCase_   ) != do_lower_case
														    or normalizer_state.get('''strip_accents'''				,     lowerCAmelCase_   ) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''				,     lowerCAmelCase_   ) != tokenize_chinese_chars
														):
																					_a      	=							getattr(lowerCAmelCase_				,     normalizer_state.pop('''type'''   )   )
																					_a      	=							do_lower_case
																					_a      	=							strip_accents
																					_a      	=							tokenize_chinese_chars
																					_a      	=							normalizer_class(**lowerCAmelCase_   )

														_a      	=							do_lower_case







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Optional[Any]=None   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    List[int]				,     lowerCAmelCase_			:    Optional[List[int]] = None   )     ->				List[int]:


														"""simple docstring"""


														_a      	=							[self.sep_token_id]
														_a      	=							[self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep   ) * [0]
														return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[str] = None   )     ->				Tuple[str]:


														"""simple docstring"""


														_a      	=							self._tokenizer.model.save(lowerCAmelCase_				,     name=lowerCAmelCase_   )
														return tuple(lowerCAmelCase_   )

