


"""simple docstring"""







import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowerCAmelCase__ 							=  logging.get_logger(__name__)

lowerCAmelCase__ 							=  '''▁'''

lowerCAmelCase__ 							=  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

lowerCAmelCase__ 							=  {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

lowerCAmelCase__ 							=  {
    '''facebook/s2t-small-librispeech-asr''': 1_024,
}

lowerCAmelCase__ 							=  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

lowerCAmelCase__ 							=  {'''mustc''': MUSTC_LANGS}


class  SCREAMING_SNAKE_CASE__							(						_UpperCamelCase							):
       """simple docstring"""


       a    :							List[Any]  =VOCAB_FILES_NAMES
       a    :							Tuple  =PRETRAINED_VOCAB_FILES_MAP
       a    :							Dict  =MAX_MODEL_INPUT_SIZES
       a    :							Tuple  =["input_ids", "attention_mask"]

       a    :							List[int]  =[]

       def __init__(		self						,	snake_case__						,	snake_case__						,	snake_case__="<s>"						,	snake_case__="</s>"						,	snake_case__="<pad>"						,	snake_case__="<unk>"						,	snake_case__=False						,	snake_case__=False						,	snake_case__=None						,	snake_case__=None						,	snake_case__ = None						,	**snake_case__						,	):






         """simple docstring"""





         lowerCAmelCase	:	List[Any]								=				{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__a						,	eos_token=__a						,	unk_token=__a						,	pad_token=__a						,	do_upper_case=__a						,	do_lower_case=__a						,	tgt_lang=__a						,	lang_codes=__a						,	sp_model_kwargs=self.sp_model_kwargs						,	**__a						,	)
         lowerCAmelCase	:	str								=				do_upper_case
         lowerCAmelCase	:	List[str]								=				do_lower_case

         lowerCAmelCase	:	List[str]								=				load_json(__a		)
         lowerCAmelCase	:	List[str]								=				{v: k for k, v in self.encoder.items()}
         lowerCAmelCase	:	List[Any]								=				spm_file
         lowerCAmelCase	:	Union[str, Any]								=				load_spm(__a						,	self.sp_model_kwargs		)

         if lang_codes is not None:
           lowerCAmelCase	:	Any								=				lang_codes
           lowerCAmelCase	:	int								=				LANGUAGES[lang_codes]
           lowerCAmelCase	:	Any								=				[f"""<lang:{lang}>""" for lang in self.langs]
           lowerCAmelCase	:	Dict								=				{lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""		) for lang in self.langs}

           lowerCAmelCase	:	Optional[int]								=				self.lang_tokens
           lowerCAmelCase	:	Optional[int]								=				tgt_lang if tgt_lang is not None else self.langs[0]

           self.set_tgt_lang_special_tokens(self._tgt_lang		)
         else:
           lowerCAmelCase	:	int								=				{}

       @property
       def 			lowercase__				(		self		):






         """simple docstring"""





         return len(self.encoder		)

       @property
       def 			lowercase__				(		self		):






         """simple docstring"""





         return self._tgt_lang

       @tgt_lang.setter
       def 			lowercase__				(		self						,	snake_case__		):






         """simple docstring"""





         lowerCAmelCase	:	Tuple								=				new_tgt_lang
         self.set_tgt_lang_special_tokens(__a		)

       def 			lowercase__				(		self						,	snake_case__		):






         """simple docstring"""





         lowerCAmelCase	:	int								=				self.lang_code_to_id[tgt_lang]
         lowerCAmelCase	:	Any								=				[lang_code_id]

       def 			lowercase__				(		self						,	snake_case__		):






         """simple docstring"""





         return self.sp_model.encode(__a						,	out_type=__a		)

       def 			lowercase__				(		self						,	snake_case__		):






         """simple docstring"""





         return self.encoder.get(__a						,	self.encoder[self.unk_token]		)

       def 			lowercase__				(		self						,	snake_case__		):






         """simple docstring"""





         return self.decoder.get(__a						,	self.unk_token		)

       def 			lowercase__				(		self						,	snake_case__		):






         """simple docstring"""





         lowerCAmelCase	:	Optional[Any]								=				[]
         lowerCAmelCase	:	Tuple								=				""
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             lowerCAmelCase	:	Dict								=				self.sp_model.decode(__a		)
             out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
             lowerCAmelCase	:	Any								=				[]
           else:
             current_sub_tokens.append(__a		)
         lowerCAmelCase	:	List[str]								=				self.sp_model.decode(__a		)
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()

       def 			lowercase__				(		self						,	snake_case__						,	snake_case__=None		):






         """simple docstring"""





         if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]

       def 			lowercase__				(		self						,	snake_case__						,	snake_case__ = None						,	snake_case__ = False		):






         """simple docstring"""





         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__a						,	token_ids_a=__a						,	already_has_special_tokens=__a		)

         lowerCAmelCase	:	Tuple								=				[1] * len(self.prefix_tokens		)
         lowerCAmelCase	:	Any								=				[1]
         if token_ids_a is None:
           return prefix_ones + ([0] * len(__a		)) + suffix_ones
         return prefix_ones + ([0] * len(__a		)) + ([0] * len(__a		)) + suffix_ones

       def 			lowercase__				(		self		):






         """simple docstring"""





         lowerCAmelCase	:	Any								=				self.encoder.copy()
         vocab.update(self.added_tokens_encoder		)
         return vocab

       def __getstate__(		self		):






         """simple docstring"""





         lowerCAmelCase	:	Any								=				self.__dict__.copy()
         lowerCAmelCase	:	List[Any]								=				None
         return state

       def __setstate__(		self						,	snake_case__		):






         """simple docstring"""





         lowerCAmelCase	:	List[Any]								=				d

         # for backward compatibility
         if not hasattr(self						,	"sp_model_kwargs"		):
           lowerCAmelCase	:	Optional[int]								=				{}

         lowerCAmelCase	:	str								=				load_spm(self.spm_file						,	self.sp_model_kwargs		)







       def 			lowercase__				(		self						,	snake_case__						,	snake_case__ = None		):






         """simple docstring"""





         lowerCAmelCase	:	List[str]								=				Path(__a		)
         assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
         lowerCAmelCase	:	Optional[int]								=				save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
         )
         lowerCAmelCase	:	List[str]								=				save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
         )

         save_json(self.encoder						,	__a		)

         if os.path.abspath(self.spm_file		) != os.path.abspath(__a		) and os.path.isfile(self.spm_file		):
           copyfile(self.spm_file						,	__a		)
         elif not os.path.isfile(self.spm_file		):
           with open(__a						,	"wb"		) as fi:
             lowerCAmelCase	:	List[Any]								=				self.sp_model.serialized_model_proto()
             fi.write(__a		)

         return (str(__a		), str(__a		))

def    a__     (							SCREAMING_SNAKE_CASE       :       str							, SCREAMING_SNAKE_CASE       :       Union[str, Any]							):


  '''simple docstring'''







  lowerCAmelCase	:	Tuple								=				sentencepiece.SentencePieceProcessor(**lowercase_							)
  spm.Load(str(lowercase_							)							)
  return spm
def    a__     (							SCREAMING_SNAKE_CASE       :       List[Any]							):


  '''simple docstring'''







  with open(lowercase_							, "r"							) as f:
    return json.load(lowercase_							)





def    a__     (							SCREAMING_SNAKE_CASE       :       List[Any]							, SCREAMING_SNAKE_CASE       :       Tuple							):


  '''simple docstring'''







  with open(lowercase_							, "w"							) as f:
    json.dump(lowercase_							, lowercase_							, indent=2							)












"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

      assert masked_input.count("<mask>"      ) == 1
      _UpperCamelCase   :						str    =							torch.tensor(tokenizer.encode(lowercase_		,add_special_tokens=lowercase_      )      ).unsqueeze(0      )  # Batch size 1
      _UpperCamelCase   :						Union[str, Any]    =							model(lowercase_      )[0]  # The last hidden-state is the first element of the output tuple
      _UpperCamelCase   :						List[Any]    =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      _UpperCamelCase   :						Tuple    =							logits[0, masked_index, :]
      _UpperCamelCase   :						List[str]    =							logits.softmax(dim=0      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							prob.topk(k=lowercase_		,dim=0      )
      _UpperCamelCase   :						str    =							" ".join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(lowercase_      )      )]      )
      _UpperCamelCase   :						Any    =							tokenizer.mask_token
      _UpperCamelCase   :						List[Any]    =							[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "      )      ):
            _UpperCamelCase   :						Optional[int]    =							predicted_token_bpe.replace("\u2581"		," "      )
            if " {0}".format(lowercase_      ) in masked_input:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(" {0}".format(lowercase_      )		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
            else:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(lowercase_		,lowercase_      ),
                          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))
