



import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Tuple ,       UpperCamelCase_				:       Dict ,       UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       Tuple=5	)->  int:
				assert masked_input.count('''<mask>'''	) == 1
				A__		 =					torch.tensor(tokenizer.encode(lowerCAmelCase__ ,       add_special_tokens=lowerCAmelCase__	)	).unsqueeze(0	)  # Batch size 1
				A__		 =					model(lowerCAmelCase__	)[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__		 =					prob.topk(k=lowerCAmelCase__ ,       dim=0	)
				A__		 =					' '.join(
				    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(lowerCAmelCase__	)	)]	)
				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(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


_lowercase						  =		CamembertTokenizer.from_pretrained("camembert-base")
_lowercase						  =		CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase						  =		"Le camembert est <mask> :)"



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





import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"▁"

_lowercase						  =		{"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

_lowercase						  =		{
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

_lowercase						  =		{
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

_lowercase						  =		{
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

_lowercase						  =		{
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				["input_ids"]

     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_INIT_CONFIGURATION
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				RESOURCE_FILES_NAMES




     def __init__(				self ,						a__ ,						a__=None ,						a__=False ,						a__="utf8" ,						a__="[UNK]" ,						a__="[SEP]" ,						a__="[PAD]" ,						a__="[CLS]" ,						a__="[MASK]" ,						a__ = None ,						**a__ ,						):
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.

         A__		 =					{} if sp_model_kwargs is None else sp_model_kwargs
         super().__init__(
             do_lower_case=a__ ,						unk_token=a__ ,						sep_token=a__ ,						pad_token=a__ ,						cls_token=a__ ,						mask_token=a__ ,						vocab_file=a__ ,						encoding=a__ ,						sp_model_kwargs=self.sp_model_kwargs ,						**a__ ,						)
         A__		 =					do_lower_case
         A__		 =					sentencepiece_model_ckpt
         A__		 =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(a__)

         # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
         if vocab_file is not None:
             A__		 =					self.load_vocab(filepath=a__)
         else:
             A__		 =					{self.sp_model.id_to_piece(a__): id for id in range(self.sp_model.get_piece_size())}
         A__		 =					{v: k for k, v in self.vocab.items()}




     def 		snake_case_		(				self ,						a__):
         if text is None:
             return None

         A__		 =					self.tokenize(a__)
         A__ ,	A__		 =					'''''', []

         for i, ch in enumerate(a__):
             if ch in self.SP_CHAR_MAPPING:
                 A__		 =					self.SP_CHAR_MAPPING.get(a__)
             else:
                 A__		 =					unicodedata.normalize('''NFKC''' ,						a__)
             if self.is_whitespace(a__):
                 continue
             normalized_text += ch
             char_mapping.extend([i] * len(a__))

         A__ ,	A__ ,	A__		 =					normalized_text, [], 0

         if self.do_lower_case:
             A__		 =					text.lower()

         for token in split_tokens:
             if token[:1] == "▁":
                 A__		 =					token[1:]
             A__		 =					text[offset:].index(a__) + offset
             A__		 =					start + len(a__)

             token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
             A__		 =					end
         return token_mapping




     @property
     def 		snake_case_		(				self):
         return len(self.vocab)




     def 		snake_case_		(				self):
         return dict(self.vocab ,						**self.added_tokens_encoder)




     def __getstate__(				self):
         A__		 =					self.__dict__.copy()
         A__		 =					None
         return state




     def __setstate__(				self ,						a__):
         A__		 =					d

         # for backward compatibility
         if not hasattr(self ,						'''sp_model_kwargs'''):
             A__		 =					{}

         A__		 =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.sentencepiece_model_ckpt)




     def 		snake_case_		(				self ,						a__):
         return "".join((self.SP_CHAR_MAPPING.get(a__ ,						a__) for c in text))




     def 		snake_case_		(				self ,						a__ ,						a__=False ,						a__=6_4 ,						a__=0.1):

         if self.sp_model_kwargs.get('''enable_sampling''') is True:
             A__		 =					True
         if self.sp_model_kwargs.get('''alpha''') is not None:
             A__		 =					self.sp_model_kwargs.get('''alpha''')
         if self.sp_model_kwargs.get('''nbest_size''') is not None:
             A__		 =					self.sp_model_kwargs.get('''nbest_size''')

         if not enable_sampling:
             A__		 =					self.sp_model.EncodeAsPieces(a__)
         else:
             A__		 =					self.sp_model.SampleEncodeAsPieces(a__ ,						a__ ,						a__)
         A__		 =					[]
         for pi, piece in enumerate(a__):
             if piece == SPIECE_UNDERLINE:
                 if not pieces[pi + 1].startswith(a__) and pi != 0:
                     new_pieces.append(a__)
                     continue
                 else:
                     continue
             A__		 =					0
             for i, chunk in enumerate(a__):
                 if chunk == SPIECE_UNDERLINE:
                     continue
                 if self.is_ch_char(a__) or self.is_punct(a__):
                     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                         new_pieces.append(piece[lst_i:i])
                     new_pieces.append(a__)
                     A__		 =					i + 1
                 elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                         new_pieces.append(piece[lst_i:i])
                     A__		 =					i
                 elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                         new_pieces.append(piece[lst_i:i])
                     A__		 =					i
             if len(a__) > lst_i:
                 new_pieces.append(piece[lst_i:])
         return new_pieces




     def 		snake_case_		(				self ,						a__):
         A__		 =					''''''.join(a__).replace(a__ ,						''' ''').strip()
         return out_string




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.convert_ids_to_tokens(a__)
         A__		 =					''''''.join(a__).replace(a__ ,						''' ''').strip()
         return out_string




     def 		snake_case_		(				self ,						a__):
         return self.vocab.get(a__ ,						self.vocab.get(self.unk_token))




     def 		snake_case_		(				self ,						a__):
         return self.reverse_vocab.get(a__ ,						self.unk_token)




     def 		snake_case_		(				self ,						a__ ,						a__=None):
         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A__		 =					[self.cls_token_id]
         A__		 =					[self.sep_token_id]
         return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep




     def 		snake_case_		(				self ,						a__ ,						a__=None):
         if offset_mapping_a is None:
             return [(0, 0)] + offset_mapping_a + [(0, 0)]

         return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]




     def 		snake_case_		(				self ,						a__ ,						a__=None ,						a__=False):

         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     '''You should not supply a second sequence if the provided sequence of '''
                     '''ids is already formatted with special tokens for the model.''')
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
             return [1] + ([0] * len(a__)) + [1, 1] + ([0] * len(a__)) + [1]
         return [1] + ([0] * len(a__)) + [1]




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
         if token_ids_a is None:
             # [CLS] X [SEP]
             return (len(a__) + 2) * [0]

         # [CLS] A [SEP] [SEP] B [SEP]
         return [0] * (len(a__) + 1) + [1] * (len(a__) + 3)




     def 		snake_case_		(				self ,						a__):
         if "\u4e00" <= char <= "\u9fff":
             return True
         return False




     def 		snake_case_		(				self ,						a__):
         if ("a" <= char <= "z") or ("A" <= char <= "Z"):
             return True
         return False




     def 		snake_case_		(				self ,						a__):
         if char in ",;:.?!~，；：。？！《》【】":
             return True
         return False




     def 		snake_case_		(				self ,						a__):
         if char == " " or char == "\t" or char == "\n" or char == "\r":
             return True
         if len(a__) == 1:
             A__		 =					unicodedata.category(a__)
             if cat == "Zs":
                 return True
         return False




     def 		snake_case_		(				self ,						a__):
         A__		 =					{}
         with io.open(a__ ,						'''r''' ,						encoding='''utf-8''') as f:
             for index, line in enumerate(a__):
                 A__		 =					line.rstrip('''\n''')
                 A__		 =					int(a__)

         return token_to_idx




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         A__		 =					0
         if os.path.isdir(a__):
             A__		 =					os.path.join(
                 a__ ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
         else:
             A__		 =					(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
         with open(a__ ,						'''w''' ,						encoding='''utf-8''') as writer:
             for token, token_index in sorted(self.vocab.items() ,						key=lambda a__: kv[1]):
                 if index != token_index:
                     logger.warning(
                         F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                         ''' Please check that the vocabulary is not corrupted!''')
                     A__		 =					token_index
                 writer.write(token + '''\n''')
                 index += 1

         A__		 =					os.path.join(a__ ,						'''sentencepiece.bpe.model''')
         with open(a__ ,						'''wb''') as fi:
             A__		 =					self.sp_model.serialized_model_proto()
             fi.write(a__)

         return (vocab_file,)
