


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:
						snake_case							: Optional[int]      =   None


snake_case							: Any      =   logging.get_logger(__name__)


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

snake_case							: Any      =   {
    '''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'''
        ),
    },
}

snake_case							: Any      =   {
    '''facebook/nllb-large-en-ro''': 10_24,
    '''facebook/nllb-200-distilled-600M''': 10_24,
}

# fmt: off
snake_case							: List[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  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					str					   =    VOCAB_FILES_NAMES
					UpperCAmelCase__      :					Tuple					   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__      :					Union[str, Any]					   =    PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__      :					str					   =    ['''input_ids''', '''attention_mask''']
					UpperCAmelCase__      :					str					   =    NllbTokenizer

					UpperCAmelCase__      :					List[int]					   =    []
					UpperCAmelCase__      :					List[int]					   =    []
					def __init__(   self     :List[str]    ,__snake_case     :List[str]=None    ,__snake_case     :Any=None    ,__snake_case     :Dict="<s>"    ,__snake_case     :List[Any]="</s>"    ,__snake_case     :List[Any]="</s>"    ,__snake_case     :Any="<s>"    ,__snake_case     :str="<unk>"    ,__snake_case     :Union[str, Any]="<pad>"    ,__snake_case     :Dict="<mask>"    ,__snake_case     :Optional[Any]=None    ,__snake_case     :int=None    ,__snake_case     :Any=None    ,__snake_case     :str=False    ,**__snake_case     :Optional[int]    ,)      ->					str:
												# Mask token behave like a normal word, i.e. include the space before it
												a__    =  AddedToken(__snake_case    ,lstrip=__snake_case    ,rstrip=__snake_case   ) if isinstance(__snake_case    ,__snake_case   ) else mask_token
												a__    =  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    ,)

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

												a__    =  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}   )
												a__    =  {
												    lang_code: self.convert_tokens_to_ids(__snake_case   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
												}

												a__    =  src_lang if src_lang is not None else 'eng_Latn'
												a__    =  self.convert_tokens_to_ids(self._src_lang   )
												a__    =  tgt_lang
												self.set_src_lang_special_tokens(self._src_lang   )
					@property
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					str:
												return self._src_lang
					@src_lang.setter
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :str   )      ->					None:
												a__    =  new_src_lang
												self.set_src_lang_special_tokens(self._src_lang   )
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :List[int]    ,__snake_case     :Optional[List[int]] = 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     :int    ,__snake_case     :List[int]    ,__snake_case     :Optional[List[int]] = None   )      ->					List[int]:
												a__    =  [self.sep_token_id]
												a__    =  [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     :Union[str, Any]    ,__snake_case     :Any    ,__snake_case     :str    ,__snake_case     :Optional[str]    ,__snake_case     :Optional[str]    ,**__snake_case     :Dict   )      ->					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'   )
												a__    =  src_lang
												a__    =  self(__snake_case    ,add_special_tokens=__snake_case    ,return_tensors=__snake_case    ,**__snake_case   )
												a__    =  self.convert_tokens_to_ids(__snake_case   )
												a__    =  tgt_lang_id
												return inputs
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :List[str]    ,__snake_case     :str = "eng_Latn"    ,__snake_case     :Optional[List[str]] = None    ,__snake_case     :str = "fra_Latn"    ,**__snake_case     :Tuple    ,)      ->					BatchEncoding:
												a__    =  src_lang
												a__    =  tgt_lang
												return super().prepare_seqaseq_batch(__snake_case    ,__snake_case    ,**__snake_case   )
					def 				lowerCamelCase__(   self     :int   )      ->					Tuple:
												return self.set_src_lang_special_tokens(self.src_lang   )
					def 				lowerCamelCase__(   self     :Dict   )      ->					Optional[Any]:
												return self.set_tgt_lang_special_tokens(self.tgt_lang   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :List[Any]   )      ->					None:
												a__    =  self.convert_tokens_to_ids(__snake_case   )

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

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

												a__    =  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     :int    ,__snake_case     :str   )      ->					None:
												a__    =  self.convert_tokens_to_ids(__snake_case   )
												if self.legacy_behaviour:
																			a__    =  []
																			a__    =  [self.eos_token_id, self.cur_lang_code]
												else:
																			a__    =  [self.cur_lang_code]
																			a__    =  [self.eos_token_id]

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

												a__    =  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     :List[Any]    ,__snake_case     :str    ,__snake_case     :Optional[str] = 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(__snake_case   ):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory.'   )
																			return
												a__    =  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,)








import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					Optional[Any]					   =    1
					@register_to_config
					def __init__(   self     :Optional[int]    ,__snake_case     :int = 10_00    ,__snake_case     :Optional[Union[np.ndarray, List[float]]] = None   )      ->					int:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__snake_case   )

												# standard deviation of the initial noise distribution
												a__    =  1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												a__    =  4

												# running values
												a__    =  []
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :int    ,__snake_case     :Union[str, torch.device] = None   )      ->					Union[str, Any]:
												a__    =  num_inference_steps
												a__    =  torch.linspace(1    ,0    ,num_inference_steps + 1   )[:-1]
												a__    =  torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																			a__    =  torch.tensor(self.config.trained_betas    ,dtype=torch.floataa   )
												else:
																			a__    =  torch.sin(steps * math.pi / 2   ) ** 2

												a__    =  (1.0 - self.betas**2) ** 0.5

												a__    =  (torch.atana(self.betas    ,self.alphas   ) / math.pi * 2)[:-1]
												a__    =  timesteps.to(__snake_case   )

												a__    =  []
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :torch.FloatTensor    ,__snake_case     :int    ,__snake_case     :torch.FloatTensor    ,__snake_case     :bool = True    ,)      ->					Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																			raise ValueError(
																			    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'   )

												a__    =  (self.timesteps == timestep).nonzero().item()
												a__    =  timestep_index + 1

												a__    =  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__snake_case   )

												if len(self.ets   ) == 1:
																			a__    =  self.ets[-1]
												elif len(self.ets   ) == 2:
																			a__    =  (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																			a__    =  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																			a__    =  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												a__    =  self._get_prev_sample(__snake_case    ,__snake_case    ,__snake_case    ,__snake_case   )

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :torch.FloatTensor    ,*__snake_case     :int    ,**__snake_case     :Optional[int]   )      ->					torch.FloatTensor:
												return sample
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Any   )      ->					Optional[Any]:
												a__    =  self.alphas[timestep_index]
												a__    =  self.betas[timestep_index]

												a__    =  self.alphas[prev_timestep_index]
												a__    =  self.betas[prev_timestep_index]

												a__    =  (sample - sigma * ets) / max(__snake_case    ,1E-8   )
												a__    =  next_alpha * pred + ets * next_sigma

												return prev_sample



					def __len__(   self     :Any   )      ->					Union[str, Any]:
												return self.config.num_train_timesteps




