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


UpperCamelCase			:       List[Any]									=				logging.get_logger(__name__)

UpperCamelCase			:       List[Any]									=				"▁"

UpperCamelCase			:       str									=				{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase			:       Dict									=				{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCamelCase			:       Tuple									=				{
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
UpperCamelCase			:       Any									=				["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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		VOCAB_FILES_NAMES
     lowercase		     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase		     =		PRETRAINED_VOCAB_FILES_MAP
     lowercase		     =		["input_ids", "attention_mask"]

     lowercase		     =		[]
     lowercase		     =		[]





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

      '''simple docstring'''




      __UpperCamelCase       						= AddedToken(__UpperCAmelCase			,  lstrip=__UpperCAmelCase			,  rstrip=__UpperCAmelCase   ) if isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ) else mask_token

      __UpperCamelCase       						= {} if sp_model_kwargs is None else sp_model_kwargs
      __UpperCamelCase       						= legacy_behaviour

      super().__init__(
          bos_token=__UpperCAmelCase			,  eos_token=__UpperCAmelCase			,  unk_token=__UpperCAmelCase			,  sep_token=__UpperCAmelCase			,  cls_token=__UpperCAmelCase			,  pad_token=__UpperCAmelCase			,  mask_token=__UpperCAmelCase			,  tokenizer_file=__UpperCAmelCase			,  src_lang=__UpperCAmelCase			,  tgt_lang=__UpperCAmelCase			,  additional_special_tokens=__UpperCAmelCase			,  sp_model_kwargs=self.sp_model_kwargs			,  legacy_behaviour=__UpperCAmelCase			,  **__UpperCAmelCase			,  )

      __UpperCamelCase       						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(__UpperCAmelCase   )   )
      __UpperCamelCase       						= vocab_file
      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
      # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
      # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

      # Mimic fairseq token-to-id alignment for the first 4 token
      __UpperCamelCase       						= {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      __UpperCamelCase       						= 1

      __UpperCamelCase       						= len(self.sp_model   )
      __UpperCamelCase       						= {
          code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__UpperCAmelCase   )
      }
      __UpperCamelCase       						= {v: k for k, v in self.lang_code_to_id.items()}
      __UpperCamelCase       						= len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

      self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
      __UpperCamelCase       						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
      __UpperCamelCase       						= list(self.lang_code_to_id.keys()   )

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

      __UpperCamelCase       						= src_lang if src_lang is not None else 'eng_Latn'
      __UpperCamelCase       						= self.lang_code_to_id[self._src_lang]
      __UpperCamelCase       						= tgt_lang
      self.set_src_lang_special_tokens(self._src_lang   )





     def __getstate__(			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.__dict__.copy()
      __UpperCamelCase       						= None
      __UpperCamelCase       						= self.sp_model.serialized_model_proto()
      return state





     def __setstate__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= d

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

      __UpperCamelCase       						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset + 1  # Plus 1 for the mask token





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._src_lang





     @src_lang.setter
     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= new_src_lang
      self.set_src_lang_special_tokens(self._src_lang   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False   ):

      '''simple docstring'''




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

      __UpperCamelCase       						= [1] * len(self.prefix_tokens   )
      __UpperCamelCase       						= [1] * len(self.suffix_tokens   )
      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			,  __UpperCAmelCase			,  __UpperCAmelCase = 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 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




      __UpperCamelCase       						= [self.sep_token_id]
      __UpperCamelCase       						= [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 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''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'   )
      __UpperCamelCase       						= src_lang
      __UpperCamelCase       						= self(__UpperCAmelCase			,  add_special_tokens=__UpperCAmelCase			,  return_tensors=__UpperCAmelCase			,  **__UpperCAmelCase   )
      __UpperCamelCase       						= self.convert_tokens_to_ids(__UpperCAmelCase   )
      __UpperCamelCase       						= tgt_lang_id
      return inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= {self.convert_ids_to_tokens(__UpperCAmelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      __UpperCamelCase       						= self.sp_model.PieceToId(__UpperCAmelCase   )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= ''.join(__UpperCAmelCase   ).replace(__UpperCAmelCase			,  ' '   ).strip()
      return out_string





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




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

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

      return (out_vocab_file,)





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = "eng_Latn"			,  __UpperCAmelCase = None			,  __UpperCAmelCase = "fra_Latn"			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= src_lang
      __UpperCamelCase       						= tgt_lang
      return super().prepare_seqaseq_batch(__UpperCAmelCase			,  __UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.set_src_lang_special_tokens(self.src_lang   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.set_tgt_lang_special_tokens(self.tgt_lang   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.lang_code_to_id[src_lang]
      if self.legacy_behaviour:
       __UpperCamelCase       						= []
       __UpperCamelCase       						= [self.eos_token_id, self.cur_lang_code]
      else:
       __UpperCamelCase       						= [self.cur_lang_code]
       __UpperCamelCase       						= [self.eos_token_id]







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.lang_code_to_id[lang]
      if self.legacy_behaviour:
       __UpperCamelCase       						= []
       __UpperCamelCase       						= [self.eos_token_id, self.cur_lang_code]
      else:
       __UpperCamelCase       						= [self.cur_lang_code]
       __UpperCamelCase       						= [self.eos_token_id]





"""simple docstring"""

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							A		(       )   ->			List[Any]:
 __UpperCamelCase       						= ArgumentParser(
     description=(
         'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
     )	)

 # Optional arguments for the launch helper
 parser.add_argument('--num_cores'       ,      type=snake_case       ,      default=1       ,      help='Number of TPU cores to use (1 or 8).'	)

 # positional
 parser.add_argument(
     'training_script'       ,      type=snake_case       ,      help=(
         'The full path to the single TPU training '
         'program/script to be launched in parallel, '
         'followed by all the arguments for the '
         'training script'
     )       ,      )

 # rest from the training program
 parser.add_argument('training_script_args'       ,      nargs=snake_case	)

 return parser.parse_args()






def 							A		(       )   ->			Optional[int]:
 __UpperCamelCase       						= parse_args()

 # Import training_script as a module.
 __UpperCamelCase       						= Path(args.training_script	)
 sys.path.append(str(script_fpath.parent.resolve()	)	)
 __UpperCamelCase       						= script_fpath.stem
 __UpperCamelCase       						= importlib.import_module(snake_case	)

 # Patch sys.argv
 __UpperCamelCase       						= [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores	)]

 xmp.spawn(mod._mp_fn       ,      args=()       ,      nprocs=args.num_cores	)


if __name__ == "__main__":
      main()



