



"""simple docstring"""

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_						:    Optional[Any]				=      logging.get_logger(__name__)

A_						:    Optional[Any]				=      {'vocab_file': 'sentencepiece.bpe.model'}

A_						:    Union[str, Any]				=      {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

A_						:    Union[str, Any]				=      {
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

A_						:    Union[str, Any]				=      '▁'
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE	:		Dict  						= PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE	:		Dict  						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= ['''input_ids''', '''attention_mask''']





	def __init__(      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       int="<s>"     ,       _SCREAMING_SNAKE_CASE				:       Any="</s>"     ,       _SCREAMING_SNAKE_CASE				:       int="</s>"     ,       _SCREAMING_SNAKE_CASE				:       Any="<s>"     ,       _SCREAMING_SNAKE_CASE				:       List[Any]="<unk>"     ,       _SCREAMING_SNAKE_CASE				:       List[Any]="<pad>"     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]="<mask>"     ,       _SCREAMING_SNAKE_CASE				:       Optional[Dict[str, Any]] = None     ,       **_SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       )						->	None:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else mask_token

					SCREAMING_SNAKE_CASE						:							Dict							=     {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=_SCREAMING_SNAKE_CASE     ,       eos_token=_SCREAMING_SNAKE_CASE     ,       unk_token=_SCREAMING_SNAKE_CASE     ,       sep_token=_SCREAMING_SNAKE_CASE     ,       cls_token=_SCREAMING_SNAKE_CASE     ,       pad_token=_SCREAMING_SNAKE_CASE     ,       mask_token=_SCREAMING_SNAKE_CASE     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **_SCREAMING_SNAKE_CASE     ,       )

					SCREAMING_SNAKE_CASE						:							Any							=     vocab_file
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(str(_SCREAMING_SNAKE_CASE    )    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

					SCREAMING_SNAKE_CASE						:							Dict							=     len(self.sp_model    ) - 1
					SCREAMING_SNAKE_CASE						:							int							=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}





	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     [self.cls_token_id]
					SCREAMING_SNAKE_CASE						:							Dict							=     [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	List[int]:





					"""simple docstring"""


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

					if token_ids_a is None:
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					return [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							str							=     [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 + sep + token_ids_a + sep    ) * [0]





	@property
	def 			_lowerCAmelCase     (      self				:       Any    )						->	Optional[int]:





					"""simple docstring"""


					return len(self.sp_model    )





	def 			_lowerCAmelCase     (      self				:       int    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
					vocab.update(self.added_tokens_encoder    )
					return vocab





	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	List[str]:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	str:





					"""simple docstring"""


					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE    )

					return spm_id if spm_id else self.unk_token_id





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]    )						->	List[Any]:





					"""simple docstring"""


					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     []
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     ''
					SCREAMING_SNAKE_CASE						:							Tuple							=     False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE    ) + token
													SCREAMING_SNAKE_CASE						:							int							=     True
													SCREAMING_SNAKE_CASE						:							List[str]							=     []
									else:
													current_sub_tokens.append(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     False
					out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE    )
					return out_string.strip()





	def __getstate__(      self				:       Tuple    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     self.__dict__.copy()
					SCREAMING_SNAKE_CASE						:							List[str]							=     None
					return state





	def __setstate__(      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Any    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     d

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

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(self.vocab_file    )







	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	Tuple[str]:





					"""simple docstring"""


					if not os.path.isdir(_SCREAMING_SNAKE_CASE    ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
									return
					SCREAMING_SNAKE_CASE						:							Dict							=     os.path.join(
					    _SCREAMING_SNAKE_CASE     ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

					if os.path.abspath(self.vocab_file    ) != os.path.abspath(_SCREAMING_SNAKE_CASE    ) and os.path.isfile(self.vocab_file    ):
									copyfile(self.vocab_file     ,       _SCREAMING_SNAKE_CASE    )
					elif not os.path.isfile(self.vocab_file    ):
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as fi:
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.sp_model.serialized_model_proto()
													fi.write(_SCREAMING_SNAKE_CASE    )

					return (out_vocab_file,)










"""simple docstring"""

import argparse
import json

from tqdm import tqdm
def  __snake_case							(	)     ->       Any:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							str							=     argparse.ArgumentParser()

				# Required parameters
				parser.add_argument(
				    '--src_path'							,		type=__A							,		default='biencoder-nq-dev.json'							,		help='Path to raw DPR training data'							,		)
				parser.add_argument(
				    '--evaluation_set'							,		type=__A							,		help='where to store parsed evaluation_set file'							,		)
				parser.add_argument(
				    '--gold_data_path'							,		type=__A							,		help='where to store parsed gold_data_path file'							,		)
				SCREAMING_SNAKE_CASE						:							Tuple							=     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:
								SCREAMING_SNAKE_CASE						:							Tuple							=     json.load(__A     )
								for dpr_record in tqdm(__A     ):
												SCREAMING_SNAKE_CASE						:							Tuple							=     dpr_record['question']
												SCREAMING_SNAKE_CASE						:							Optional[int]							=     [context['title'] for context in dpr_record['positive_ctxs']]
												eval_file.write(question + '\n'     )
												gold_file.write('\t'.join(__A     ) + '\n'     )


if __name__ == "__main__":
							main()





