

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__				=		logging.get_logger(__name__)

a__				=		'''▁'''

a__				=		{'''vocab_file''': '''sentencepiece.bpe.model'''}

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

a__				=		{
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# 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_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[str]	    =    VOCAB_FILES_NAMES
							UpperCAmelCase__       :						int	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__       :						str	    =    PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__       :						Any	    =    ["input_ids", "attention_mask"]

							UpperCAmelCase__       :						List[int]	    =    []
							UpperCAmelCase__       :						List[int]	    =    []


							def __init__(			self       ,							_a       ,							_a="<s>"       ,							_a="</s>"       ,							_a="</s>"       ,							_a="<s>"       ,							_a="<unk>"       ,							_a="<pad>"       ,							_a="<mask>"       ,							_a=None       ,							_a=None       ,							_a=None       ,							_a = None       ,							_a=None       ,							_a=False       ,							**_a       ,							)    ->    Dict:
												# Mask token behave like a normal word, i.e. include the space before it
												_a	:    Dict         =		AddedToken(_a       ,							lstrip=_a       ,							rstrip=_a       ) if isinstance(_a       ,							_a       ) else mask_token

												_a	:    Optional[int]         =		{} if sp_model_kwargs is None else sp_model_kwargs
												_a	:    Optional[Any]         =		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       ,							)

												_a	:    Union[str, Any]         =		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
												self.sp_model.Load(str(_a       )       )
												_a	:    Dict         =		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
												_a	:    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
												_a	:    Dict         =		1

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

												self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
												_a	:    List[Any]         =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
												_a	:    Tuple         =		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]       )

												_a	:    Any         =		src_lang if src_lang is not None else '''eng_Latn'''
												_a	:    Any         =		self.lang_code_to_id[self._src_lang]
												_a	:    Union[str, Any]         =		tgt_lang
												self.set_src_lang_special_tokens(self._src_lang       )


							def __getstate__(			self       )    ->    Optional[int]:
												_a	:    str         =		self.__dict__.copy()
												_a	:    int         =		None
												_a	:    str         =		self.sp_model.serialized_model_proto()
												return state


							def __setstate__(			self       ,							_a       )    ->    List[str]:
												_a	:    int         =		d

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

												_a	:    Optional[int]         =		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[int]:
												return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token


							@property
							def 				__lowercase				(			self       )    ->    str:
												return self._src_lang


							@src_lang.setter
							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    List[Any]         =		new_src_lang
												self.set_src_lang_special_tokens(self._src_lang       )


							def 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_a = False       )    ->    List[int]:

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

												_a	:    Optional[Any]         =		[1] * len(self.prefix_tokens       )
												_a	:    Any         =		[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 				__lowercase				(			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 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    List[int]:
												_a	:    Dict         =		[self.sep_token_id]
												_a	:    Any         =		[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 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							**_a       )    ->    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	:    str         =		src_lang
												_a	:    str         =		self(_a       ,							add_special_tokens=_a       ,							return_tensors=_a       ,							**_a       )
												_a	:    str         =		self.convert_tokens_to_ids(_a       )
												_a	:    List[Any]         =		tgt_lang_id
												return inputs


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    List[str]         =		{self.convert_ids_to_tokens(_a       ): i for i in range(self.vocab_size       )}
												vocab.update(self.added_tokens_encoder       )
												return vocab


							def 				__lowercase				(			self       ,							_a       )    ->    List[str]:
												return self.sp_model.encode(_a       ,							out_type=_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    int:
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												_a	:    Optional[int]         =		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 				__lowercase				(			self       ,							_a       )    ->    Tuple:
												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 				__lowercase				(			self       ,							_a       )    ->    str:
												_a	:    Dict         =		''''''.join(_a       ).replace(_a       ,							''' '''       ).strip()
												return out_string


							def 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    Tuple[str]:
												if not os.path.isdir(_a       ):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
																	return
												_a	:    int         =		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:
																						_a	:    Optional[Any]         =		self.sp_model.serialized_model_proto()
																						fi.write(_a       )

												return (out_vocab_file,)


							def 				__lowercase				(			self       ,							_a       ,							_a = "eng_Latn"       ,							_a = None       ,							_a = "fra_Latn"       ,							**_a       ,							)    ->    BatchEncoding:
												_a	:    Optional[int]         =		src_lang
												_a	:    Tuple         =		tgt_lang
												return super().prepare_seqaseq_batch(_a       ,							_a       ,							**_a       )


							def 				__lowercase				(			self       )    ->    str:
												return self.set_src_lang_special_tokens(self.src_lang       )


							def 				__lowercase				(			self       )    ->    Any:
												return self.set_tgt_lang_special_tokens(self.tgt_lang       )


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    str         =		self.lang_code_to_id[src_lang]
												if self.legacy_behaviour:
																	_a	:    Dict         =		[]
																	_a	:    List[Any]         =		[self.eos_token_id, self.cur_lang_code]
												else:
																	_a	:    Optional[int]         =		[self.cur_lang_code]
																	_a	:    List[Any]         =		[self.eos_token_id]




							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    Any         =		self.lang_code_to_id[lang]
												if self.legacy_behaviour:
																	_a	:    Dict         =		[]
																	_a	:    Dict         =		[self.eos_token_id, self.cur_lang_code]
												else:
																	_a	:    List[Any]         =		[self.cur_lang_code]
																	_a	:    Union[str, Any]         =		[self.eos_token_id]






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				=		{'''configuration_mmbt''': ['''MMBTConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__				=		['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
			from .configuration_mmbt import MMBTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
			import sys

			a__				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



