



"""simple docstring"""

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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							A_						:    List[Any]				=      None

A_						:    List[str]				=      logging.get_logger(__name__)
A_						:    Optional[int]				=      {'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

A_						:    Tuple				=      {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

A_						:    List[str]				=      {
    'google/rembert': 256,
}

A_						:    List[Any]				=      '▁'
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE	:		Tuple  						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE	:		int  						= RemBertTokenizer





	def __init__(      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=None     ,       _SCREAMING_SNAKE_CASE				:       int=None     ,       _SCREAMING_SNAKE_CASE				:       str=True     ,       _SCREAMING_SNAKE_CASE				:       Dict=True     ,       _SCREAMING_SNAKE_CASE				:       int=False     ,       _SCREAMING_SNAKE_CASE				:       Dict="[CLS]"     ,       _SCREAMING_SNAKE_CASE				:       Tuple="[SEP]"     ,       _SCREAMING_SNAKE_CASE				:       Any="<unk>"     ,       _SCREAMING_SNAKE_CASE				:       str="[SEP]"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]="<pad>"     ,       _SCREAMING_SNAKE_CASE				:       Any="[CLS]"     ,       _SCREAMING_SNAKE_CASE				:       int="[MASK]"     ,       **_SCREAMING_SNAKE_CASE				:       str     ,       )						->	Union[str, Any]:





					"""simple docstring"""


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

					super().__init__(
					    _SCREAMING_SNAKE_CASE     ,       tokenizer_file=_SCREAMING_SNAKE_CASE     ,       do_lower_case=_SCREAMING_SNAKE_CASE     ,       remove_space=_SCREAMING_SNAKE_CASE     ,       keep_accents=_SCREAMING_SNAKE_CASE     ,       bos_token=_SCREAMING_SNAKE_CASE     ,       eos_token=_SCREAMING_SNAKE_CASE     ,       unk_token=_SCREAMING_SNAKE_CASE     ,       sep_token=_SCREAMING_SNAKE_CASE     ,       pad_token=_SCREAMING_SNAKE_CASE     ,       cls_token=_SCREAMING_SNAKE_CASE     ,       mask_token=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     do_lower_case
					SCREAMING_SNAKE_CASE						:							List[str]							=     remove_space
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     keep_accents
					SCREAMING_SNAKE_CASE						:							str							=     vocab_file
					SCREAMING_SNAKE_CASE						:							Tuple							=     False if not self.vocab_file else True





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							List[Any]							=     [self.cls_token_id]
					if token_ids_a is None:
									return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep





	def 			_lowerCAmelCase     (      self				:       Optional[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:
									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(_SCREAMING_SNAKE_CASE    )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					return [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     [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				:       str     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	Tuple[str]:





					"""simple docstring"""


					if not os.path.isdir(_SCREAMING_SNAKE_CASE    ):
									logger.error('Vocabulary path ({}) should be a directory'.format(_SCREAMING_SNAKE_CASE    )    )
									return
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     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    ):
									copyfile(self.vocab_file     ,       _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()





