'''simple docstring'''




import gc
import threading
import time

import psutil
import torch


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     List[Any]:
        lowercase_					:       str     				=					psutil.Process()
        lowercase_					:       str     				=					False
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Tuple     				=					-1

        while True:
             lowercase_					:       Tuple     				=					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 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       List[str]     				=					True
        lowercase_					:       List[str]     				=					threading.Thread(target=self.peak_monitor				)
        lowercase_					:       Tuple     				=					True
        self.thread.start()





   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Tuple     				=					False
        self.thread.join()
        return self.cpu_memory_peak



_A								=   PeakCPUMemory()

def _UpperCamelCase   (       ):
     # Time
     lowercase_					:       Dict     				=					{'time': time.time()}

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

     # CPU mem
     lowercase_					:       List[str]     				=					psutil.Process().memory_info().rss
     cpu_peak_tracker.start()

     # GPU mem
     for i in range(torch.cuda.device_count()							):
          lowercase_					:       Union[str, Any]     				=					torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_							)
     torch.cuda.reset_peak_memory_stats()

     return measures

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     # Time
     lowercase_					:       Any     				=					{'time': time.time() - start_measures['time']}

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

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

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

     return measures






def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     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(SCREAMING_SNAKE_CASE_							)]:.2f}MiB'''							)
          lowercase_					:       Union[str, Any]     				=					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'''							)





'''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								=   None


_A								=   logging.get_logger(__name__)


_A								=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

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

# fmt: off
_A								=   ['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__  (       _snake_case			):






   """simple docstring"""
   A							:       List[str]     =    VOCAB_FILES_NAMES
   A							:       Union[str, Any]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A							:       Dict     =    PRETRAINED_VOCAB_FILES_MAP
   A							:       str     =    ['''input_ids''', '''attention_mask''']
   A							:       str     =    NllbTokenizer

   A							:       List[int]     =    []
   A							:       List[int]     =    []
   def __init__(self       ,				_a=None       ,				_a=None       ,				_a="<s>"       ,				_a="</s>"       ,				_a="</s>"       ,				_a="<s>"       ,				_a="<unk>"       ,				_a="<pad>"       ,				_a="<mask>"       ,				_a=None       ,				_a=None       ,				_a=None       ,				_a=False       ,				**_a       ,				)    ->     Optional[int]:
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase_					:       Union[str, Any]     				=					AddedToken(_a       ,				lstrip=_a       ,				rstrip=_a				) if isinstance(_a       ,				_a				) else mask_token
        lowercase_					:       Any     				=					legacy_behaviour
        super().__init__(
            vocab_file=_a       ,				tokenizer_file=_a       ,				bos_token=_a       ,				eos_token=_a       ,				sep_token=_a       ,				cls_token=_a       ,				unk_token=_a       ,				pad_token=_a       ,				mask_token=_a       ,				src_lang=_a       ,				tgt_lang=_a       ,				additional_special_tokens=_a       ,				legacy_behaviour=_a       ,				**_a       ,				)

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

        lowercase_					:       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}				)
        lowercase_					:       Tuple     				=					{
            lang_code: self.convert_tokens_to_ids(_a				) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowercase_					:       Optional[Any]     				=					src_lang if src_lang is not None else 'eng_Latn'
        lowercase_					:       Tuple     				=					self.convert_tokens_to_ids(self._src_lang				)
        lowercase_					:       List[Any]     				=					tgt_lang
        self.set_src_lang_special_tokens(self._src_lang				)
   @property
   def 		_lowerCamelCase (self				)    ->     str:
        return self._src_lang
   @src_lang.setter
   def 		_lowerCamelCase (self       ,				_a				)    ->     None:
        lowercase_					:       Optional[int]     				=					new_src_lang
        self.set_src_lang_special_tokens(self._src_lang				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     List[int]:
        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 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     List[int]:
        lowercase_					:       Tuple     				=					[self.sep_token_id]
        lowercase_					:       int     				=					[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 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a       ,				**_a				)    ->     Optional[int]:
        if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'				)
        lowercase_					:       Dict     				=					src_lang
        lowercase_					:       Dict     				=					self(_a       ,				add_special_tokens=_a       ,				return_tensors=_a       ,				**_a				)
        lowercase_					:       Tuple     				=					self.convert_tokens_to_ids(_a				)
        lowercase_					:       Optional[Any]     				=					tgt_lang_id
        return inputs
   def 		_lowerCamelCase (self       ,				_a       ,				_a = "eng_Latn"       ,				_a = None       ,				_a = "fra_Latn"       ,				**_a       ,				)    ->     BatchEncoding:
        lowercase_					:       Dict     				=					src_lang
        lowercase_					:       Optional[int]     				=					tgt_lang
        return super().prepare_seqaseq_batch(_a       ,				_a       ,				**_a				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        return self.set_src_lang_special_tokens(self.src_lang				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        return self.set_tgt_lang_special_tokens(self.tgt_lang				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     None:
        lowercase_					:       Dict     				=					self.convert_tokens_to_ids(_a				)

        if self.legacy_behaviour:
             lowercase_					:       Tuple     				=					[]
             lowercase_					:       str     				=					[self.eos_token_id, self.cur_lang_code]
        else:
             lowercase_					:       Union[str, Any]     				=					[self.cur_lang_code]
             lowercase_					:       Union[str, Any]     				=					[self.eos_token_id]

        lowercase_					:       Optional[Any]     				=					self.convert_ids_to_tokens(self.prefix_tokens				)
        lowercase_					:       Tuple     				=					self.convert_ids_to_tokens(self.suffix_tokens				)

        lowercase_					:       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 		_lowerCamelCase (self       ,				_a				)    ->     None:
        lowercase_					:       str     				=					self.convert_tokens_to_ids(_a				)
        if self.legacy_behaviour:
             lowercase_					:       List[Any]     				=					[]
             lowercase_					:       Optional[Any]     				=					[self.eos_token_id, self.cur_lang_code]
        else:
             lowercase_					:       Optional[int]     				=					[self.cur_lang_code]
             lowercase_					:       Dict     				=					[self.eos_token_id]

        lowercase_					:       Dict     				=					self.convert_ids_to_tokens(self.prefix_tokens				)
        lowercase_					:       Optional[Any]     				=					self.convert_ids_to_tokens(self.suffix_tokens				)

        lowercase_					:       List[Any]     				=					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 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     Tuple[str]:
        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(_a				):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''				)
             return
        lowercase_					:       Any     				=					os.path.join(
            _a       ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

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

        return (out_vocab_file,)




