



import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


UpperCAmelCase_		 =					re.compile(r"""\b(a|an|the)\b""", re.UNICODE)

UpperCAmelCase_		 =					None

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Dict:
   _A      =       argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''				)
   parser.add_argument('''data_file'''	,   metavar='''data.json'''	,   help='''Input data JSON file.'''				)
   parser.add_argument('''pred_file'''	,   metavar='''pred.json'''	,   help='''Model predictions.'''				)
   parser.add_argument(
       '''--out-file'''	,   '''-o'''	,   metavar='''eval.json'''	,   help='''Write accuracy metrics to file (default is stdout).'''				)
   parser.add_argument(
       '''--na-prob-file'''	,   '''-n'''	,   metavar='''na_prob.json'''	,   help='''Model estimates of probability of no answer.'''				)
   parser.add_argument(
       '''--na-prob-thresh'''	,   '''-t'''	,   type=lowercase__	,   default=1.0	,   help='''Predict \"\" if no-answer probability exceeds this (default = 1.0).'''	,   )
   parser.add_argument(
       '''--out-image-dir'''	,   '''-p'''	,   metavar='''out_images'''	,   default=lowercase__	,   help='''Save precision-recall curves to directory.'''				)
   parser.add_argument('''--verbose'''	,   '''-v'''	,   action='''store_true'''				)
   if len(sys.argv				) == 1:
      parser.print_help()
      sys.exit(1				)
   return parser.parse_args()

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     List[Any]:
   _A      =       {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            _A      =       bool(qa['''answers''']['''text''']				)
   return qid_to_has_ans

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     Tuple:

   def remove_articles(_snake_case	:Union[str, Any]				):
      return ARTICLES_REGEX.sub(''' '''	,   lowercase__				)

   def white_space_fix(_snake_case	:List[Any]				):
      return " ".join(text.split()				)

   def remove_punc(_snake_case	:Tuple				):
      _A      =       set(string.punctuation				)
      return "".join(ch for ch in text if ch not in exclude				)

   def lower(_snake_case	:Tuple				):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(lowercase__				)				)				)				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     Tuple:
   if not s:
      return []
   return normalize_answer(lowercase__				).split()

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:Union[str, Any]				)  ->     int:
   return int(normalize_answer(lowercase__				) == normalize_answer(lowercase__				)				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:Union[str, Any]				)  ->     Optional[Any]:
   _A      =       get_tokens(lowercase__				)
   _A      =       get_tokens(lowercase__				)
   _A      =       collections.Counter(lowercase__				) & collections.Counter(lowercase__				)
   _A      =       sum(common.values()				)
   if len(lowercase__				) == 0 or len(lowercase__				) == 0:
      # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
      return int(gold_toks == pred_toks				)
   if num_same == 0:
      return 0
   _A      =       1.0 * num_same / len(lowercase__				)
   _A      =       1.0 * num_same / len(lowercase__				)
   _A      =       (2 * precision * recall) / (precision + recall)
   return fa

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:Optional[int]				)  ->     int:
   _A      =       {}
   _A      =       {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            _A      =       qa["id"]
            _A      =       [t for t in qa["answers"]["text"] if normalize_answer(lowercase__				)]
            if not gold_answers:
               # For unanswerable questions, only correct answer is empty string
               _A      =       [""]
            if qid not in preds:
               print(F'''Missing prediction for {qid}'''				)
               continue
            _A      =       preds[qid]
            # Take max over all gold answers
            _A      =       max(compute_exact(lowercase__	,   lowercase__				) for a in gold_answers				)
            _A      =       max(compute_fa(lowercase__	,   lowercase__				) for a in gold_answers				)
   return exact_scores, fa_scores

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:Optional[int]	,   _snake_case	:List[Any]	,   _snake_case	:Any				)  ->     Optional[Any]:
   _A      =       {}
   for qid, s in scores.items():
      _A      =       na_probs[qid] > na_prob_thresh
      if pred_na:
         _A      =       float(not qid_to_has_ans[qid]				)
      else:
         _A      =       s
   return new_scores

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[Any]	,   _snake_case	:Optional[Any]	,   _snake_case	:List[Any]=None				)  ->     Dict:
   if not qid_list:
      _A      =       len(lowercase__				)
      return collections.OrderedDict(
          [
              ('''exact''', 100.0 * sum(exact_scores.values()				) / total),
              ('''f1''', 100.0 * sum(fa_scores.values()				) / total),
              ('''total''', total),
          ]				)
   else:
      _A      =       len(lowercase__				)
      return collections.OrderedDict(
          [
              ('''exact''', 100.0 * sum(exact_scores[k] for k in qid_list				) / total),
              ('''f1''', 100.0 * sum(fa_scores[k] for k in qid_list				) / total),
              ('''total''', total),
          ]				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:Tuple	,   _snake_case	:Any				)  ->     int:
   for k in new_eval:
      _A      =       new_eval[k]

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[str, Any]	,   _snake_case	:Optional[Any]	,   _snake_case	:Optional[int]	,   _snake_case	:Optional[int]				)  ->     List[Any]:
   plt.step(lowercase__	,   lowercase__	,   color='''b'''	,   alpha=0.2	,   where='''post'''				)
   plt.fill_between(lowercase__	,   lowercase__	,   step='''post'''	,   alpha=0.2	,   color='''b'''				)
   plt.xlabel('''Recall'''				)
   plt.ylabel('''Precision'''				)
   plt.xlim([0.0, 1.05]				)
   plt.ylim([0.0, 1.05]				)
   plt.title(lowercase__				)
   plt.savefig(lowercase__				)
   plt.clf()

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:Any	,   _snake_case	:Tuple	,   _snake_case	:str	,   _snake_case	:str=None	,   _snake_case	:Optional[int]=None				)  ->     Optional[Any]:
   _A      =       sorted(lowercase__	,   key=lambda _snake_case				: na_probs[k]				)
   _A      =       0.0
   _A      =       1.0
   _A      =       0.0
   _A      =       [1.0]
   _A      =       [0.0]
   _A      =       0.0
   for i, qid in enumerate(lowercase__				):
      if qid_to_has_ans[qid]:
         true_pos += scores[qid]
      _A      =       true_pos / float(i + 1				)
      _A      =       true_pos / float(lowercase__				)
      if i == len(lowercase__				) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
         # i.e., if we can put a threshold after this point
         avg_prec += cur_p * (cur_r - recalls[-1])
         precisions.append(lowercase__				)
         recalls.append(lowercase__				)
   if out_image:
      plot_pr_curve(lowercase__	,   lowercase__	,   lowercase__	,   lowercase__				)
   return {"ap": 100.0 * avg_prec}

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:Optional[int]	,   _snake_case	:int	,   _snake_case	:str	,   _snake_case	:Tuple	,   _snake_case	:Any				)  ->     str:
   if out_image_dir and not os.path.exists(lowercase__				):
      os.makedirs(lowercase__				)
   _A      =       sum(1 for v in qid_to_has_ans.values() if v				)
   if num_true_pos == 0:
      return
   _A      =       make_precision_recall_eval(
       lowercase__	,   lowercase__	,   lowercase__	,   lowercase__	,   out_image=os.path.join(lowercase__	,   '''pr_exact.png'''				)	,   title='''Precision-Recall curve for Exact Match score'''	,   )
   _A      =       make_precision_recall_eval(
       lowercase__	,   lowercase__	,   lowercase__	,   lowercase__	,   out_image=os.path.join(lowercase__	,   '''pr_f1.png'''				)	,   title='''Precision-Recall curve for F1 score'''	,   )
   _A      =       {k: float(lowercase__				) for k, v in qid_to_has_ans.items()}
   _A      =       make_precision_recall_eval(
       lowercase__	,   lowercase__	,   lowercase__	,   lowercase__	,   out_image=os.path.join(lowercase__	,   '''pr_oracle.png'''				)	,   title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''	,   )
   merge_eval(lowercase__	,   lowercase__	,   '''pr_exact'''				)
   merge_eval(lowercase__	,   lowercase__	,   '''pr_f1'''				)
   merge_eval(lowercase__	,   lowercase__	,   '''pr_oracle'''				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:Dict	,   _snake_case	:int	,   _snake_case	:int				)  ->     Dict:
   if not qid_list:
      return
   _A      =       [na_probs[k] for k in qid_list]
   _A      =       np.ones_like(lowercase__				) / float(len(lowercase__				)				)
   plt.hist(lowercase__	,   weights=lowercase__	,   bins=20	,   range=(0.0, 1.0)				)
   plt.xlabel('''Model probability of no-answer'''				)
   plt.ylabel('''Proportion of dataset'''				)
   plt.title(F'''Histogram of no-answer probability: {name}'''				)
   plt.savefig(os.path.join(lowercase__	,   F'''na_prob_hist_{name}.png'''				)				)
   plt.clf()

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]	,   _snake_case	:Dict	,   _snake_case	:Tuple	,   _snake_case	:Optional[Any]				)  ->     Optional[Any]:
   _A      =       sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]				)
   _A      =       num_no_ans
   _A      =       cur_score
   _A      =       0.0
   _A      =       sorted(lowercase__	,   key=lambda _snake_case				: na_probs[k]				)
   for i, qid in enumerate(lowercase__				):
      if qid not in scores:
         continue
      if qid_to_has_ans[qid]:
         _A      =       scores[qid]
      else:
         if preds[qid]:
            _A      =       -1
         else:
            _A      =       0
      cur_score += diff
      if cur_score > best_score:
         _A      =       cur_score
         _A      =       na_probs[qid]
   return 100.0 * best_score / len(lowercase__				), best_thresh

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:List[Any]	,   _snake_case	:str	,   _snake_case	:Dict	,   _snake_case	:List[str]	,   _snake_case	:str				)  ->     Any:
   _A      =       find_best_thresh(lowercase__	,   lowercase__	,   lowercase__	,   lowercase__				)
   _A      =       find_best_thresh(lowercase__	,   lowercase__	,   lowercase__	,   lowercase__				)
   _A      =       best_exact
   _A      =       exact_thresh
   _A      =       best_fa
   _A      =       fa_thresh

def 	SCREAMING_SNAKE_CASE_							(	)  ->     List[str]:
   with open(OPTS.data_file				) as f:
      _A      =       json.load(lowercase__				)
      _A      =       dataset_json["data"]
   with open(OPTS.pred_file				) as f:
      _A      =       json.load(lowercase__				)
   if OPTS.na_prob_file:
      with open(OPTS.na_prob_file				) as f:
         _A      =       json.load(lowercase__				)
   else:
      _A      =       {k: 0.0 for k in preds}
   _A      =       make_qid_to_has_ans(lowercase__				)  # maps qid to True/False
   _A      =       [k for k, v in qid_to_has_ans.items() if v]
   _A      =       [k for k, v in qid_to_has_ans.items() if not v]
   _A      =       get_raw_scores(lowercase__	,   lowercase__				)
   _A      =       apply_no_ans_threshold(lowercase__	,   lowercase__	,   lowercase__	,   OPTS.na_prob_thresh				)
   _A      =       apply_no_ans_threshold(lowercase__	,   lowercase__	,   lowercase__	,   OPTS.na_prob_thresh				)
   _A      =       make_eval_dict(lowercase__	,   lowercase__				)
   if has_ans_qids:
      _A      =       make_eval_dict(lowercase__	,   lowercase__	,   qid_list=lowercase__				)
      merge_eval(lowercase__	,   lowercase__	,   '''HasAns'''				)
   if no_ans_qids:
      _A      =       make_eval_dict(lowercase__	,   lowercase__	,   qid_list=lowercase__				)
      merge_eval(lowercase__	,   lowercase__	,   '''NoAns'''				)
   if OPTS.na_prob_file:
      find_all_best_thresh(lowercase__	,   lowercase__	,   lowercase__	,   lowercase__	,   lowercase__	,   lowercase__				)
   if OPTS.na_prob_file and OPTS.out_image_dir:
      run_precision_recall_analysis(lowercase__	,   lowercase__	,   lowercase__	,   lowercase__	,   lowercase__	,   OPTS.out_image_dir				)
      histogram_na_prob(lowercase__	,   lowercase__	,   OPTS.out_image_dir	,   '''hasAns'''				)
      histogram_na_prob(lowercase__	,   lowercase__	,   OPTS.out_image_dir	,   '''noAns'''				)
   if OPTS.out_file:
      with open(OPTS.out_file	,   '''w'''				) as f:
         json.dump(lowercase__	,   lowercase__				)
   else:
      print(json.dumps(lowercase__	,   indent=2				)				)


if __name__ == "__main__":
   UpperCAmelCase_		 =					parse_args()
   if OPTS.out_image_dir:
      import matplotlib

      matplotlib.use("""Agg""")
      import matplotlib.pyplot as plt
   main()






"""simple docstring"""

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

from tokenizers import processors

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


if is_sentencepiece_available():
    from .tokenization_nllb import NllbTokenizer
else:
    _UpperCamelCase		     =    None


_UpperCamelCase		     =    logging.get_logger(__name__)


_UpperCamelCase		     =    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase		     =    {
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCamelCase		     =    {
    """facebook/nllb-large-en-ro""": 1_0_2_4,
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
_UpperCamelCase		     =    ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[Any]    =						VOCAB_FILES_NAMES
       __UpperCamelCase					:      Dict    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase					:      Dict    =						PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase					:      int    =						['input_ids', 'attention_mask']
       __UpperCamelCase					:      Union[str, Any]    =						NllbTokenizer

       __UpperCamelCase					:      List[int]    =						[]
       __UpperCamelCase					:      List[int]    =						[]






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





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				AddedToken(snake_case			,					lstrip=snake_case			,					rstrip=snake_case   ) if isinstance(snake_case			,					snake_case   ) else mask_token
        lowerCAmelCase__   :				Tuple 			=				legacy_behaviour
        super().__init__(
            vocab_file=snake_case			,					tokenizer_file=snake_case			,					bos_token=snake_case			,					eos_token=snake_case			,					sep_token=snake_case			,					cls_token=snake_case			,					unk_token=snake_case			,					pad_token=snake_case			,					mask_token=snake_case			,					src_lang=snake_case			,					tgt_lang=snake_case			,					additional_special_tokens=snake_case			,					legacy_behaviour=snake_case			,					**snake_case			,					)

        lowerCAmelCase__   :				Optional[int] 			=				vocab_file
        lowerCAmelCase__   :				int 			=				False if not self.vocab_file else True

        lowerCAmelCase__   :				Union[str, Any] 			=				FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
         # Only add those special tokens if they are not already there.
         _additional_special_tokens.extend(
             [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

        self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}   )
        lowerCAmelCase__   :				Tuple 			=				{
            lang_code: self.convert_tokens_to_ids(snake_case   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowerCAmelCase__   :				Union[str, Any] 			=				src_lang if src_lang is not None else "eng_Latn"
        lowerCAmelCase__   :				List[str] 			=				self.convert_tokens_to_ids(self._src_lang   )
        lowerCAmelCase__   :				Any 			=				tgt_lang
        self.set_src_lang_special_tokens(self._src_lang   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self._src_lang






       @src_lang.setter
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				new_src_lang
        self.set_src_lang_special_tokens(self._src_lang   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				[self.sep_token_id]
        lowerCAmelCase__   :				Dict 			=				[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]






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					**snake_case   ):





        """simple docstring"""

        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__   :				Any 			=				src_lang
        lowerCAmelCase__   :				Optional[Any] 			=				self(snake_case			,					add_special_tokens=snake_case			,					return_tensors=snake_case			,					**snake_case   )
        lowerCAmelCase__   :				str 			=				self.convert_tokens_to_ids(snake_case   )
        lowerCAmelCase__   :				Any 			=				tgt_lang_id
        return inputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = "eng_Latn"			,					snake_case = None			,					snake_case = "fra_Latn"			,					**snake_case			,					):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				src_lang
        lowerCAmelCase__   :				str 			=				tgt_lang
        return super().prepare_seqaseq_batch(snake_case			,					snake_case			,					**snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.set_src_lang_special_tokens(self.src_lang   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.set_tgt_lang_special_tokens(self.tgt_lang   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				self.convert_tokens_to_ids(snake_case   )

        if self.legacy_behaviour:
         lowerCAmelCase__   :				Optional[int] 			=				[]
         lowerCAmelCase__   :				Optional[int] 			=				[self.eos_token_id, self.cur_lang_code]
        else:
         lowerCAmelCase__   :				int 			=				[self.cur_lang_code]
         lowerCAmelCase__   :				List[str] 			=				[self.eos_token_id]

        lowerCAmelCase__   :				Any 			=				self.convert_ids_to_tokens(self.prefix_tokens   )
        lowerCAmelCase__   :				str 			=				self.convert_ids_to_tokens(self.suffix_tokens   )

        lowerCAmelCase__   :				Tuple 			=				processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str			,					pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str			,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,					self.prefix_tokens + self.suffix_tokens   )   )			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.convert_tokens_to_ids(snake_case   )
        if self.legacy_behaviour:
         lowerCAmelCase__   :				Dict 			=				[]
         lowerCAmelCase__   :				Dict 			=				[self.eos_token_id, self.cur_lang_code]
        else:
         lowerCAmelCase__   :				str 			=				[self.cur_lang_code]
         lowerCAmelCase__   :				Tuple 			=				[self.eos_token_id]

        lowerCAmelCase__   :				Any 			=				self.convert_ids_to_tokens(self.prefix_tokens   )
        lowerCAmelCase__   :				List[Any] 			=				self.convert_ids_to_tokens(self.suffix_tokens   )

        lowerCAmelCase__   :				Optional[int] 			=				processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str			,					pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str			,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,					self.prefix_tokens + self.suffix_tokens   )   )			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        if not self.can_save_slow_tokenizer:
         raise ValueError(
             "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
             "tokenizer."   )

        if not os.path.isdir(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""   )
         return
        lowerCAmelCase__   :				str 			=				os.path.join(
            snake_case			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

        if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case   ):
         copyfile(self.vocab_file			,					snake_case   )

        return (out_vocab_file,)






