



'''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''': 1024,
}

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

_lowerCAmelCase      						=	{'''mustc''': MUSTC_LANGS}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Union[str, Any]				=      VOCAB_FILES_NAMES
  __lowercase							:      List[str]				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      Optional[Any]				=      MAX_MODEL_INPUT_SIZES
  __lowercase							:      Any				=      ['''input_ids''', '''attention_mask''']

  __lowercase							:      List[int]				=      []




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase="<s>"  ,__UpperCAmelCase="</s>"  ,__UpperCAmelCase="<pad>"  ,__UpperCAmelCase="<unk>"  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)    ->		None:
      lowerCAmelCase__ :  Optional[Any]							      =					{} if sp_model_kwargs is None else sp_model_kwargs

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

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

      if lang_codes is not None:
          lowerCAmelCase__ :  Union[str, Any]							      =					lang_codes
          lowerCAmelCase__ :  List[Any]							      =					LANGUAGES[lang_codes]
          lowerCAmelCase__ :  List[str]							      =					[F"""<lang:{lang}>""" for lang in self.langs]
          lowerCAmelCase__ :  Any							      =					{lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""      ) for lang in self.langs}

          lowerCAmelCase__ :  List[str]							      =					self.lang_tokens
          lowerCAmelCase__ :  Tuple							      =					tgt_lang if tgt_lang is not None else self.langs[0]

          self.set_tgt_lang_special_tokens(self._tgt_lang      )
      else:
          lowerCAmelCase__ :  List[Any]							      =					{}




  @property
  def 							UpperCAmelCase_	(    self      )    ->		int:
      return len(self.encoder      )




  @property
  def 							UpperCAmelCase_	(    self      )    ->		str:
      return self._tgt_lang




  @tgt_lang.setter
  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		None:
      lowerCAmelCase__ :  Union[str, Any]							      =					new_tgt_lang
      self.set_tgt_lang_special_tokens(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		None:
      lowerCAmelCase__ :  str							      =					self.lang_code_to_id[tgt_lang]
      lowerCAmelCase__ :  int							      =					[lang_code_id]




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      return self.sp_model.encode(__UpperCAmelCase  ,out_type=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      return self.encoder.get(__UpperCAmelCase  ,self.encoder[self.unk_token]      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      return self.decoder.get(__UpperCAmelCase  ,self.unk_token      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  List[Any]							      =					[]
      lowerCAmelCase__ :  Dict							      =					""""""
      for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
              lowerCAmelCase__ :  Any							      =					self.sp_model.decode(__UpperCAmelCase      )
              out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
              lowerCAmelCase__ :  Any							      =					[]
          else:
              current_sub_tokens.append(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					self.sp_model.decode(__UpperCAmelCase      )
      out_string += decoded.upper() if self.do_upper_case else decoded
      return out_string.strip()




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=None      )    ->		List[int]:
      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 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = False      )    ->		List[int]:

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

      lowerCAmelCase__ :  Any							      =					[1] * len(self.prefix_tokens      )
      lowerCAmelCase__ :  int							      =					[1]
      if token_ids_a is None:
          return prefix_ones + ([0] * len(__UpperCAmelCase      )) + suffix_ones
      return prefix_ones + ([0] * len(__UpperCAmelCase      )) + ([0] * len(__UpperCAmelCase      )) + suffix_ones




  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      lowerCAmelCase__ :  int							      =					self.encoder.copy()
      vocab.update(self.added_tokens_encoder      )
      return vocab




  def __getstate__(    self      )    ->		Dict:
      lowerCAmelCase__ :  Optional[Any]							      =					self.__dict__.copy()
      lowerCAmelCase__ :  str							      =					None
      return state




  def __setstate__(    self  ,__UpperCAmelCase      )    ->		None:
      lowerCAmelCase__ :  List[Any]							      =					d

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

      lowerCAmelCase__ :  Optional[Any]							      =					load_spm(self.spm_file  ,self.sp_model_kwargs      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      lowerCAmelCase__ :  Optional[int]							      =					Path(__UpperCAmelCase      )
      assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
      lowerCAmelCase__ :  Dict							      =					save_dir / (
          (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
      )
      lowerCAmelCase__ :  int							      =					save_dir / (
          (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
      )

      save_json(self.encoder  ,__UpperCAmelCase      )

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

      return (str(__UpperCAmelCase      ), str(__UpperCAmelCase      ))







def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					sentencepiece.SentencePieceProcessor(**UpperCamelCase			)
    spm.Load(str(UpperCamelCase			)			)
    return spm
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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




def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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







import argparse
import json

from tqdm import tqdm
def 		UpperCamelCase	(   )	->     Optional[int]:
   UpperCamelCase    :  List[Any]							=	argparse.ArgumentParser()

   # Required parameters
   parser.add_argument(
       '--src_path'       ,     type=snake_case__       ,     default='biencoder-nq-dev.json'       ,     help='Path to raw DPR training data'       ,     )
   parser.add_argument(
       '--evaluation_set'       ,     type=snake_case__       ,     help='where to store parsed evaluation_set file'       ,     )
   parser.add_argument(
       '--gold_data_path'       ,     type=snake_case__       ,     help='where to store parsed gold_data_path file'       ,     )
   UpperCamelCase    :  int							=	parser.parse_args()

   with open(args.src_path       ,     'r'	) as src_file, open(args.evaluation_set       ,     'w'	) as eval_file, open(
       args.gold_data_path       ,     'w'	) as gold_file:
      UpperCamelCase    :  int							=	json.load(snake_case__	)
      for dpr_record in tqdm(snake_case__	):
         UpperCamelCase    :  Union[str, Any]							=	dpr_record['question']
         UpperCamelCase    :  Dict							=	[context['title'] for context in dpr_record['positive_ctxs']]
         eval_file.write(question + '\n'	)
         gold_file.write('\t'.join(snake_case__	) + '\n'	)


if __name__ == "__main__":
    main()



