



'''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


A__    :   Dict           =					logging.get_logger(__name__)

A__    :   Tuple           =					'''▁'''

A__    :   Any           =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

A__    :   List[str]           =					{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
        ),
    }
}

A__    :   Dict           =					{
    '''facebook/nllb-200-distilled-600M''': 1_0_2_4,
}

# fmt: off
A__    :   Union[str, 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__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= ['''input_ids''', '''attention_mask''']

 A__				= []
 A__				= []
 def __init__(   self						:							Optional[int]      ,     __a						:							List[str]      ,     __a						:							Optional[Any]="<s>"      ,     __a						:							Tuple="</s>"      ,     __a						:							Union[str, Any]="</s>"      ,     __a						:							int="<s>"      ,     __a						:							Dict="<unk>"      ,     __a						:							Optional[Any]="<pad>"      ,     __a						:							Optional[Any]="<mask>"      ,     __a						:							Dict=None      ,     __a						:							List[str]=None      ,     __a						:							List[Any]=None      ,     __a						:							Optional[Dict[str, Any]] = None      ,     __a						:							Optional[int]=None      ,     __a						:							Optional[Any]=False      ,     **__a						:							Union[str, Any]      ,     )      ->   str:







       '''simple docstring'''
       # Mask token behave like a normal word, i.e. include the space before it
       __snake_case :   Union[str, Any]													=    AddedToken(__a      ,     lstrip=__a      ,     rstrip=__a  ) if isinstance(__a      ,     __a  ) else mask_token

       __snake_case :   int													=    {} if sp_model_kwargs is None else sp_model_kwargs
       __snake_case :   str													=    legacy_behaviour

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

       __snake_case :   str													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(__a  )  )
       __snake_case :   Optional[int]													=    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
       __snake_case :   Optional[Any]													=    {'<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
       __snake_case :   Optional[Any]													=    1

       __snake_case :   str													=    len(self.sp_model  )
       __snake_case :   List[Any]													=    {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a  )
       }
       __snake_case :   Any													=    {v: k for k, v in self.lang_code_to_id.items()}
       __snake_case :   List[str]													=    len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
       __snake_case :   int													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       __snake_case :   Union[str, Any]													=    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]  )

       __snake_case :   Union[str, Any]													=    src_lang if src_lang is not None else 'eng_Latn'
       __snake_case :   Union[str, Any]													=    self.lang_code_to_id[self._src_lang]
       __snake_case :   List[Any]													=    tgt_lang
       self.set_src_lang_special_tokens(self._src_lang  )
 def __getstate__(   self						:							Tuple  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   int													=    self.__dict__.copy()
       __snake_case :   Any													=    None
       __snake_case :   Any													=    self.sp_model.serialized_model_proto()
       return state
 def __setstate__(   self						:							Tuple      ,     __a						:							Optional[int]  )      ->   int:







       '''simple docstring'''
       __snake_case :   Tuple													=    d

       # for backward compatibility
       if not hasattr(self      ,     'sp_model_kwargs'  ):
             __snake_case :   Optional[Any]													=    {}

       __snake_case :   str													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )
 @property
 def A_   (   self						:							str  )      ->   Tuple:







       '''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 A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       return self._src_lang
 @src_lang.setter
 def A_   (   self						:							List[str]      ,     __a						:							str  )      ->   None:







       '''simple docstring'''
       __snake_case :   Any													=    new_src_lang
       self.set_src_lang_special_tokens(self._src_lang  )
 def A_   (   self						:							Tuple      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None      ,     __a						:							bool = False  )      ->   List[int]:







       '''simple docstring'''

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

       __snake_case :   str													=    [1] * len(self.prefix_tokens  )
       __snake_case :   int													=    [1] * len(self.suffix_tokens  )
       if token_ids_a is None:
             return prefix_ones + ([0] * len(__a  )) + suffix_ones
       return prefix_ones + ([0] * len(__a  )) + ([0] * len(__a  )) + suffix_ones
 def A_   (   self						:							Dict      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''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 A_   (   self						:							int      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    [self.sep_token_id]
       __snake_case :   Tuple													=    [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 A_   (   self						:							int      ,     __a						:							Optional[Any]      ,     __a						:							str      ,     __a						:							Optional[str]      ,     __a						:							Optional[str]      ,     **__a						:							Any  )      ->   Tuple:







       '''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'  )
       __snake_case :   Optional[Any]													=    src_lang
       __snake_case :   Any													=    self(__a      ,     add_special_tokens=__a      ,     return_tensors=__a      ,     **__a  )
       __snake_case :   Optional[Any]													=    self.convert_tokens_to_ids(__a  )
       __snake_case :   Optional[int]													=    tgt_lang_id
       return inputs
 def A_   (   self						:							Optional[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Dict													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def A_   (   self						:							Any      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							Tuple      ,     __a						:							List[str]  )      ->   Tuple:







       '''simple docstring'''
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       __snake_case :   str													=    self.sp_model.PieceToId(__a  )

       # 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 A_   (   self						:							Tuple      ,     __a						:							Dict  )      ->   List[str]:







       '''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 A_   (   self						:							Dict      ,     __a						:							int  )      ->   int:







       '''simple docstring'''
       __snake_case :   List[Any]													=    ''.join(__a  ).replace(__a      ,     ' '  ).strip()
       return out_string
 def A_   (   self						:							List[str]      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







       '''simple docstring'''
       if not os.path.isdir(__a  ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
       __snake_case :   Tuple													=    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  ) and os.path.isfile(self.vocab_file  ):
             copyfile(self.vocab_file      ,     __a  )
       elif not os.path.isfile(self.vocab_file  ):
             with open(__a      ,     'wb'  ) as fi:
                   __snake_case :   Dict													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (out_vocab_file,)
 def A_   (   self						:							Tuple      ,     __a						:							List[str]      ,     __a						:							str = "eng_Latn"      ,     __a						:							Optional[List[str]] = None      ,     __a						:							str = "fra_Latn"      ,     **__a						:							Dict      ,     )      ->   BatchEncoding:







       '''simple docstring'''
       __snake_case :   List[Any]													=    src_lang
       __snake_case :   Tuple													=    tgt_lang
       return super().prepare_seqaseq_batch(__a      ,     __a      ,     **__a  )
 def A_   (   self						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       return self.set_src_lang_special_tokens(self.src_lang  )
 def A_   (   self						:							Optional[int]  )      ->   Dict:







       '''simple docstring'''
       return self.set_tgt_lang_special_tokens(self.tgt_lang  )
 def A_   (   self						:							Tuple      ,     __a						:							List[str]  )      ->   None:







       '''simple docstring'''
       __snake_case :   str													=    self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
             __snake_case :   List[str]													=    []
             __snake_case :   Optional[int]													=    [self.eos_token_id, self.cur_lang_code]
       else:
             __snake_case :   int													=    [self.cur_lang_code]
             __snake_case :   str													=    [self.eos_token_id]





 def A_   (   self						:							List[Any]      ,     __a						:							str  )      ->   None:







       '''simple docstring'''
       __snake_case :   List[Any]													=    self.lang_code_to_id[lang]
       if self.legacy_behaviour:
             __snake_case :   List[str]													=    []
             __snake_case :   str													=    [self.eos_token_id, self.cur_lang_code]
       else:
             __snake_case :   Optional[Any]													=    [self.cur_lang_code]
             __snake_case :   Union[str, Any]													=    [self.eos_token_id]








'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


A__    :   Union[str, Any]           =					logging.get_logger(__name__)



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''upernet'''
 def __init__(   self						:							Optional[int]      ,     __a						:							Dict=None      ,     __a						:							List[str]=512      ,     __a						:							int=0.0_2      ,     __a						:							Optional[Any]=[1, 2, 3, 6]      ,     __a						:							Union[str, Any]=True      ,     __a						:							Any=0.4      ,     __a						:							int=384      ,     __a						:							int=256      ,     __a						:							str=1      ,     __a						:							List[Any]=False      ,     __a						:							Dict=255      ,     **__a						:							Dict      ,     )      ->   Optional[int]:







       '''simple docstring'''
       super().__init__(**__a  )

       if backbone_config is None:
             logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'  )
             __snake_case :   Optional[int]													=    CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']  )
       elif isinstance(__a      ,     __a  ):
             __snake_case :   List[Any]													=    backbone_config.get('model_type'  )
             __snake_case :   List[str]													=    CONFIG_MAPPING[backbone_model_type]
             __snake_case :   int													=    config_class.from_dict(__a  )

       __snake_case :   Any													=    backbone_config
       __snake_case :   Tuple													=    hidden_size
       __snake_case :   str													=    initializer_range
       __snake_case :   Dict													=    pool_scales
       __snake_case :   Optional[Any]													=    use_auxiliary_head
       __snake_case :   Optional[Any]													=    auxiliary_loss_weight
       __snake_case :   Optional[int]													=    auxiliary_in_channels
       __snake_case :   Dict													=    auxiliary_channels
       __snake_case :   Any													=    auxiliary_num_convs
       __snake_case :   Optional[Any]													=    auxiliary_concat_input
       __snake_case :   List[str]													=    loss_ignore_index





 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    copy.deepcopy(self.__dict__  )
       __snake_case :   str													=    self.backbone_config.to_dict()
       __snake_case :   Optional[int]													=    self.__class__.model_type
       return output



