'''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:
 A_     :						Any    							=None


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


A_     :						Optional[Any]    							={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A_     :						int    							={
    '''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'''
        ),
    },
}

A_     :						List[str]    							={
    '''facebook/nllb-large-en-ro''': 10_24,
    '''facebook/nllb-200-distilled-600M''': 10_24,
}

# fmt: off
A_     :						Optional[int]    							=['''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     __UpperCAmelCase       ( __a		):
  __A :				List[Any]											= VOCAB_FILES_NAMES
  __A :				List[str]											= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __A :				List[str]											= PRETRAINED_VOCAB_FILES_MAP
  __A :				Any											= ['input_ids', 'attention_mask']
  __A :				int											= NllbTokenizer

  __A :				List[int]											= []
  __A :				List[int]											= []





  def __init__(	self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase="<s>"    ,     _lowerCamelCase="</s>"    ,     _lowerCamelCase="</s>"    ,     _lowerCamelCase="<s>"    ,     _lowerCamelCase="<unk>"    ,     _lowerCamelCase="<pad>"    ,     _lowerCamelCase="<mask>"    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=False    ,     **_lowerCamelCase    ,     ):
     # Mask token behave like a normal word, i.e. include the space before it
     lowerCAmelCase_			=      AddedToken(_lowerCamelCase    ,     lstrip=_lowerCamelCase    ,     rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase    ,     _lowerCamelCase ) else mask_token
     lowerCAmelCase_			=      legacy_behaviour
     super().__init__(
         vocab_file=_lowerCamelCase    ,     tokenizer_file=_lowerCamelCase    ,     bos_token=_lowerCamelCase    ,     eos_token=_lowerCamelCase    ,     sep_token=_lowerCamelCase    ,     cls_token=_lowerCamelCase    ,     unk_token=_lowerCamelCase    ,     pad_token=_lowerCamelCase    ,     mask_token=_lowerCamelCase    ,     src_lang=_lowerCamelCase    ,     tgt_lang=_lowerCamelCase    ,     additional_special_tokens=_lowerCamelCase    ,     legacy_behaviour=_lowerCamelCase    ,     **_lowerCamelCase    ,     )

     lowerCAmelCase_			=      vocab_file
     lowerCAmelCase_			=      False if not self.vocab_file else True

     lowerCAmelCase_			=      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_			=      {
         lang_code: self.convert_tokens_to_ids(_lowerCamelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     lowerCAmelCase_			=      src_lang if src_lang is not None else '''eng_Latn'''
     lowerCAmelCase_			=      self.convert_tokens_to_ids(self._src_lang )
     lowerCAmelCase_			=      tgt_lang
     self.set_src_lang_special_tokens(self._src_lang )





  @property
  def 						UpperCAmelCase_	(	self ):
     return self._src_lang





  @src_lang.setter
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      new_src_lang
     self.set_src_lang_special_tokens(self._src_lang )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = 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    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      [self.sep_token_id]
     lowerCAmelCase_			=      [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    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     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(_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     **_lowerCamelCase )
     lowerCAmelCase_			=      self.convert_tokens_to_ids(_lowerCamelCase )
     lowerCAmelCase_			=      tgt_lang_id
     return inputs





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = "eng_Latn"    ,     _lowerCamelCase = None    ,     _lowerCamelCase = "fra_Latn"    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      src_lang
     lowerCAmelCase_			=      tgt_lang
     return super().prepare_seqaseq_batch(_lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     return self.set_src_lang_special_tokens(self.src_lang )





  def 						UpperCAmelCase_	(	self ):
     return self.set_tgt_lang_special_tokens(self.tgt_lang )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.convert_tokens_to_ids(_lowerCamelCase )

     if self.legacy_behaviour:
        lowerCAmelCase_			=      []
        lowerCAmelCase_			=      [self.eos_token_id, self.cur_lang_code]
     else:
        lowerCAmelCase_			=      [self.cur_lang_code]
        lowerCAmelCase_			=      [self.eos_token_id]

     lowerCAmelCase_			=      self.convert_ids_to_tokens(self.prefix_tokens )
     lowerCAmelCase_			=      self.convert_ids_to_tokens(self.suffix_tokens )

     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.convert_tokens_to_ids(_lowerCamelCase )
     if self.legacy_behaviour:
        lowerCAmelCase_			=      []
        lowerCAmelCase_			=      [self.eos_token_id, self.cur_lang_code]
     else:
        lowerCAmelCase_			=      [self.cur_lang_code]
        lowerCAmelCase_			=      [self.eos_token_id]

     lowerCAmelCase_			=      self.convert_ids_to_tokens(self.prefix_tokens )
     lowerCAmelCase_			=      self.convert_ids_to_tokens(self.suffix_tokens )

     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     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(_lowerCamelCase ):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory.''' )
        return
     lowerCAmelCase_			=      os.path.join(
         _lowerCamelCase    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

     return (out_vocab_file,)






'''simple docstring'''

import gc
import threading
import time

import psutil
import torch


class     __UpperCAmelCase       :





  def __init__(	self ):
     lowerCAmelCase_			=      psutil.Process()
     lowerCAmelCase_			=      False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      -1

     while True:
        lowerCAmelCase_			=      max(self.process.memory_info().rss    ,     self.cpu_memory_peak )

        # can't sleep or will not catch the peak right (this comment is here on purpose)
        if not self.peak_monitoring:
           break





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      True
     lowerCAmelCase_			=      threading.Thread(target=self.peak_monitor )
     lowerCAmelCase_			=      True
     self.thread.start()







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      False
     self.thread.join()
     return self.cpu_memory_peak






A_     :						List[str]    							=PeakCPUMemory()



def snake_case_      (					)			->					Tuple:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time()}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      psutil.Process().memory_info().rss
   cpu_peak_tracker.start()

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      torch.cuda.memory_allocated(__snake_case)
   torch.cuda.reset_peak_memory_stats()

   return measures



def snake_case_      (					__snake_case  :							Any)			->					List[str]:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time() - start_measures['''time''']}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      (psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
   lowerCAmelCase_			=      (cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      (torch.cuda.memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20
      lowerCAmelCase_			=      (torch.cuda.max_memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20

   return measures






def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Optional[int])			->					Dict:
   print(F'''{description}:''')
   print(F'''- Time: {measures['time']:.2f}s''')
   for i in range(torch.cuda.device_count()):
      print(F'''- GPU {i} allocated: {measures[str(__snake_case)]:.2f}MiB''')
      lowerCAmelCase_			=      measures[F'''{i}-peak''']
      print(F'''- GPU {i} peak: {peak:.2f}MiB''')
   print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''')
   print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''')





