


"""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 BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							"▁"

_snake_case          =							{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

_snake_case          =							{
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

_snake_case          =							{
    "facebook/m2m100_418M": 1024,
}

# fmt: off
_snake_case          =							{
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Optional[int]             =    VOCAB_FILES_NAMES
    a_    :				List[str]             =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a_    :				List[str]             =    PRETRAINED_VOCAB_FILES_MAP
    a_    :				Optional[Any]             =    ['input_ids', 'attention_mask']

    a_    :				List[int]             =    []
    a_    :				List[int]             =    []
    def __init__( self							:					str							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None							,					SCREAMING_SNAKE_CASE__							:					List[str]=None							,					SCREAMING_SNAKE_CASE__							:					Tuple="<s>"							,					SCREAMING_SNAKE_CASE__							:					Any="</s>"							,					SCREAMING_SNAKE_CASE__							:					List[str]="</s>"							,					SCREAMING_SNAKE_CASE__							:					str="<pad>"							,					SCREAMING_SNAKE_CASE__							:					List[str]="<unk>"							,					SCREAMING_SNAKE_CASE__							:					str="m2m100"							,					SCREAMING_SNAKE_CASE__							:					Optional[Dict[str, Any]] = None							,					SCREAMING_SNAKE_CASE__							:					int=8							,					**SCREAMING_SNAKE_CASE__							:					Optional[int]							,					):
          lowerCamelCase__           =		{} if sp_model_kwargs is None else sp_model_kwargs

          lowerCamelCase__           =		language_codes
          lowerCamelCase__           =		FAIRSEQ_LANGUAGE_CODES[language_codes]
          lowerCamelCase__           =		{lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

          lowerCamelCase__           =		kwargs.get('additional_special_tokens'							,					[]				)
          kwargs["additional_special_tokens"] += [
              self.get_lang_token(SCREAMING_SNAKE_CASE__				)
              for lang_code in fairseq_language_code
              if self.get_lang_token(SCREAMING_SNAKE_CASE__				) not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=SCREAMING_SNAKE_CASE__							,					tgt_lang=SCREAMING_SNAKE_CASE__							,					bos_token=SCREAMING_SNAKE_CASE__							,					eos_token=SCREAMING_SNAKE_CASE__							,					sep_token=SCREAMING_SNAKE_CASE__							,					unk_token=SCREAMING_SNAKE_CASE__							,					pad_token=SCREAMING_SNAKE_CASE__							,					language_codes=SCREAMING_SNAKE_CASE__							,					sp_model_kwargs=self.sp_model_kwargs							,					num_madeup_words=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__							,					)

          lowerCamelCase__           =		vocab_file
          lowerCamelCase__           =		load_json(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{v: k for k, v in self.encoder.items()}
          lowerCamelCase__           =		spm_file
          lowerCamelCase__           =		load_spm(SCREAMING_SNAKE_CASE__							,					self.sp_model_kwargs				)

          lowerCamelCase__           =		len(self.encoder				)

          lowerCamelCase__           =		{
              self.get_lang_token(SCREAMING_SNAKE_CASE__				): self.encoder_size + i for i, lang_code in enumerate(SCREAMING_SNAKE_CASE__				)
          }
          lowerCamelCase__           =		{lang_code: self.encoder_size + i for i, lang_code in enumerate(SCREAMING_SNAKE_CASE__				)}
          lowerCamelCase__           =		{v: k for k, v in self.lang_token_to_id.items()}

          lowerCamelCase__           =		src_lang if src_lang is not None else 'en'
          lowerCamelCase__           =		tgt_lang
          lowerCamelCase__           =		self.get_lang_id(self._src_lang				)
          self.set_src_lang_special_tokens(self._src_lang				)

          lowerCamelCase__           =		num_madeup_words
    @property
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          return len(self.encoder				) + len(self.lang_token_to_id				)
    @property
    def      _UpperCamelCase       ( self							:					List[str]				):
          return self._src_lang
    @src_lang.setter
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		new_src_lang
          self.set_src_lang_special_tokens(self._src_lang				)
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          return self.sp_model.encode(SCREAMING_SNAKE_CASE__							,					out_type=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[Any]				):
          if token in self.lang_token_to_id:
                return self.lang_token_to_id[token]
          return self.encoder.get(SCREAMING_SNAKE_CASE__							,					self.encoder[self.unk_token]				)
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int				):
          if index in self.id_to_lang_token:
                return self.id_to_lang_token[index]
          return self.decoder.get(SCREAMING_SNAKE_CASE__							,					self.unk_token				)
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          lowerCamelCase__           =		[]
          lowerCamelCase__           =		''
          for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                      out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__				) + token
                      lowerCamelCase__           =		[]
                else:
                      current_sub_tokens.append(SCREAMING_SNAKE_CASE__				)
          out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__				)
          return out_string.strip()
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[List[int]] = None							,					SCREAMING_SNAKE_CASE__							:					bool = False				):

          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__				)

          lowerCamelCase__           =		[1] * len(self.prefix_tokens				)
          lowerCamelCase__           =		[1] * len(self.suffix_tokens				)
          if token_ids_a is None:
                return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE__				)) + suffix_ones
          return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE__				)) + ([0] * len(SCREAMING_SNAKE_CASE__				)) + suffix_ones
    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[List[int]] = None				):
          if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # 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.suffix_tokens
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		{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 __getstate__( self							:					int				):
          lowerCamelCase__           =		self.__dict__.copy()
          lowerCamelCase__           =		None
          return state
    def __setstate__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		d

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

          lowerCamelCase__           =		load_spm(self.spm_file							,					self.sp_model_kwargs				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = None				):
          lowerCamelCase__           =		Path(SCREAMING_SNAKE_CASE__				)
          if not save_dir.is_dir():
                raise OSError(F'{save_directory} should be a directory'				)
          lowerCamelCase__           =		save_dir / (
              (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
          )
          lowerCamelCase__           =		save_dir / (
              (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
          )

          save_json(self.encoder							,					SCREAMING_SNAKE_CASE__				)

          if os.path.abspath(self.spm_file				) != os.path.abspath(SCREAMING_SNAKE_CASE__				) and os.path.isfile(self.spm_file				):
                copyfile(self.spm_file							,					SCREAMING_SNAKE_CASE__				)
          elif not os.path.isfile(self.spm_file				):
                with open(SCREAMING_SNAKE_CASE__							,					'wb'				) as fi:
                      lowerCamelCase__           =		self.sp_model.serialized_model_proto()
                      fi.write(SCREAMING_SNAKE_CASE__				)

          return (str(SCREAMING_SNAKE_CASE__				), str(SCREAMING_SNAKE_CASE__				))
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					str = "en"							,					SCREAMING_SNAKE_CASE__							:					Optional[List[str]] = None							,					SCREAMING_SNAKE_CASE__							:					str = "ro"							,					**SCREAMING_SNAKE_CASE__							:					str							,					):
          lowerCamelCase__           =		src_lang
          lowerCamelCase__           =		tgt_lang
          self.set_src_lang_special_tokens(self.src_lang				)
          return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Optional[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[str]							,					**SCREAMING_SNAKE_CASE__							:					List[str]				):
          if src_lang is None or tgt_lang is None:
                raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'				)
          lowerCamelCase__           =		src_lang
          lowerCamelCase__           =		self(SCREAMING_SNAKE_CASE__							,					add_special_tokens=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.get_lang_id(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		tgt_lang_id
          return inputs
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          self.set_src_lang_special_tokens(self.src_lang				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          self.set_tgt_lang_special_tokens(self.tgt_lang				)
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		self.get_lang_token(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.lang_token_to_id[lang_token]
          lowerCamelCase__           =		[self.cur_lang_id]
          lowerCamelCase__           =		[self.eos_token_id]
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		self.get_lang_token(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.lang_token_to_id[lang_token]
          lowerCamelCase__           =		[self.cur_lang_id]
          lowerCamelCase__           =		[self.eos_token_id]
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          return self.lang_code_to_token[lang]


    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		self.get_lang_token(SCREAMING_SNAKE_CASE__				)
          return self.lang_token_to_id[lang_token]





def   snake_case						(				_a:     str		,   _a:     Dict[str, Any]							)->		sentencepiece.SentencePieceProcessor:
      '''simple docstring'''


      lowerCamelCase__           =		sentencepiece.SentencePieceProcessor(**_a							)
      spm.Load(str(_a							)							)
      return spm



def   snake_case						(				_a:     str							)->		Union[Dict, List]:
      '''simple docstring'''


      with open(_a		,   'r'							) as f:
            return json.load(_a							)






def   snake_case						(				_a:     str		,   _a:     str							)->		None:
      '''simple docstring'''


      with open(_a		,   'w'							) as f:
            json.dump(_a		,   _a		,   indent=2							)







"""simple docstring"""


import argparse
import json

from tqdm import tqdm



def   snake_case						(				)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		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'		,   )
      lowerCamelCase__           =		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:
            lowerCamelCase__           =		json.load(_a							)
            for dpr_record in tqdm(_a							):
                  lowerCamelCase__           =		dpr_record['question']
                  lowerCamelCase__           =		[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()



