





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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		"▁"

_SCREAMING_SNAKE_CASE      	=		{"vocab_file": "sentencepiece.bpe.model"}

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

_SCREAMING_SNAKE_CASE      	=		{
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
_SCREAMING_SNAKE_CASE      	=		["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 SCREAMING_SNAKE_CASE_						(					snake_case_					):
				__magic_name__:			Optional[Any]       =       VOCAB_FILES_NAMES
				__magic_name__:			int       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__:			Union[str, Any]       =       PRETRAINED_VOCAB_FILES_MAP
				__magic_name__:			Dict       =       ["input_ids", "attention_mask"]

				__magic_name__:			List[int]       =       []
				__magic_name__:			List[int]       =       []






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







					"""simple docstring"""


					snake_case_		:			List[str]											=	AddedToken(UpperCamelCase_		,   lstrip=UpperCamelCase_		,   rstrip=UpperCamelCase_	) if isinstance(UpperCamelCase_		,   UpperCamelCase_	) else mask_token

					snake_case_		:			Optional[int]											=	{} if sp_model_kwargs is None else sp_model_kwargs
					snake_case_		:			Any											=	legacy_behaviour

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

					snake_case_		:			List[str]											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
					self.sp_model.Load(str(UpperCamelCase_	)	)
					snake_case_		:			Optional[Any]											=	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_		:			List[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_		:			str											=	1

					snake_case_		:			Union[str, Any]											=	len(self.sp_model	)
					snake_case_		:			List[Any]											=	{
					    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase_	)
					}
					snake_case_		:			Optional[Any]											=	{v: k for k, v in self.lang_code_to_id.items()}
					snake_case_		:			Optional[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	)
					snake_case_		:			Union[str, Any]											=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
					snake_case_		:			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_		:			Optional[Any]											=	src_lang if src_lang is not None else "eng_Latn"
					snake_case_		:			Optional[int]											=	self.lang_code_to_id[self._src_lang]
					snake_case_		:			Tuple											=	tgt_lang
					self.set_src_lang_special_tokens(self._src_lang	)






				def __getstate__(      self      :      List[Any]	)							->    Optional[Any]:







					"""simple docstring"""


					snake_case_		:			int											=	self.__dict__.copy()
					snake_case_		:			str											=	None
					snake_case_		:			Any											=	self.sp_model.serialized_model_proto()
					return state






				def __setstate__(      self      :      str		,   _A      :      Optional[int]	)							->    Optional[Any]:







					"""simple docstring"""


					snake_case_		:			Optional[Any]											=	d

					# for backward compatibility
					if not hasattr(self		,   'sp_model_kwargs'	):
						snake_case_		:			Dict											=	{}

					snake_case_		:			List[Any]											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)






				@property
				def UpperCAmelCase_				(      self      :      Tuple	)							->    List[str]:







					"""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 UpperCAmelCase_				(      self      :      List[str]	)							->    Optional[int]:







					"""simple docstring"""


					return self._src_lang






				@src_lang.setter
				def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]	)							->    Tuple:







					"""simple docstring"""


					snake_case_		:			List[Any]											=	new_src_lang
					self.set_src_lang_special_tokens(self._src_lang	)






				def UpperCAmelCase_				(      self      :      str		,   _A      :      Dict		,   _A      :      Any = None		,   _A      :      int = False	)							->    List[Any]:







					"""simple docstring"""


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

					snake_case_		:			Optional[int]											=	[1] * len(self.prefix_tokens	)
					snake_case_		:			str											=	[1] * len(self.suffix_tokens	)
					if token_ids_a is None:
						return prefix_ones + ([0] * len(UpperCamelCase_	)) + suffix_ones
					return prefix_ones + ([0] * len(UpperCamelCase_	)) + ([0] * len(UpperCamelCase_	)) + suffix_ones






				def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Optional[int]		,   _A      :      Tuple = None	)							->    List[str]:







					"""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 UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Dict		,   _A      :      Tuple = None	)							->    Tuple:







					"""simple docstring"""


					snake_case_		:			Optional[Any]											=	[self.sep_token_id]
					snake_case_		:			Dict											=	[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      :      Dict		,   _A      :      str		,   _A      :      Any		,   _A      :      Optional[Any]		,   _A      :      List[Any]		,   **_A      :      Optional[int]	)							->    List[Any]:







					"""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_		:			Union[str, Any]											=	src_lang
					snake_case_		:			str											=	self(UpperCamelCase_		,   add_special_tokens=UpperCamelCase_		,   return_tensors=UpperCamelCase_		,   **UpperCamelCase_	)
					snake_case_		:			Union[str, Any]											=	self.convert_tokens_to_ids(UpperCamelCase_	)
					snake_case_		:			Optional[int]											=	tgt_lang_id
					return inputs






				def UpperCAmelCase_				(      self      :      List[str]	)							->    Optional[int]:







					"""simple docstring"""


					snake_case_		:			Optional[Any]											=	{self.convert_ids_to_tokens(UpperCamelCase_	): i for i in range(self.vocab_size	)}
					vocab.update(self.added_tokens_encoder	)
					return vocab






				def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Tuple	)							->    List[Any]:







					"""simple docstring"""


					return self.sp_model.encode(UpperCamelCase_		,   out_type=UpperCamelCase_	)






				def UpperCAmelCase_				(      self      :      Any		,   _A      :      Any	)							->    Union[str, Any]:







					"""simple docstring"""


					if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
					snake_case_		:			Union[str, Any]											=	self.sp_model.PieceToId(UpperCamelCase_	)

					# 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 UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    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 UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Union[str, Any]	)							->    int:







					"""simple docstring"""


					snake_case_		:			str											=	"".join(UpperCamelCase_	).replace(UpperCamelCase_		,   ' '	).strip()
					return out_string






				def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]		,   _A      :      List[str] = None	)							->    int:







					"""simple docstring"""


					if not os.path.isdir(UpperCamelCase_	):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
						return
					snake_case_		:			List[str]											=	os.path.join(
					    UpperCamelCase_		,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

					if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCamelCase_	) and os.path.isfile(self.vocab_file	):
						copyfile(self.vocab_file		,   UpperCamelCase_	)
					elif not os.path.isfile(self.vocab_file	):
						with open(UpperCamelCase_		,   'wb'	) as fi:
							snake_case_		:			Dict											=	self.sp_model.serialized_model_proto()
							fi.write(UpperCamelCase_	)

					return (out_vocab_file,)






				def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[int]		,   _A      :      str = "eng_Latn"		,   _A      :      List[Any] = None		,   _A      :      Optional[int] = "fra_Latn"		,   **_A      :      Union[str, Any]		,   )							->    Any:







					"""simple docstring"""


					snake_case_		:			Tuple											=	src_lang
					snake_case_		:			Dict											=	tgt_lang
					return super().prepare_seqaseq_batch(UpperCamelCase_		,   UpperCamelCase_		,   **UpperCamelCase_	)






				def UpperCAmelCase_				(      self      :      Dict	)							->    Any:







					"""simple docstring"""


					return self.set_src_lang_special_tokens(self.src_lang	)






				def UpperCAmelCase_				(      self      :      Dict	)							->    Any:







					"""simple docstring"""


					return self.set_tgt_lang_special_tokens(self.tgt_lang	)






				def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      int	)							->    Optional[int]:







					"""simple docstring"""


					snake_case_		:			int											=	self.lang_code_to_id[src_lang]
					if self.legacy_behaviour:
						snake_case_		:			Union[str, Any]											=	[]
						snake_case_		:			Optional[int]											=	[self.eos_token_id, self.cur_lang_code]
					else:
						snake_case_		:			Dict											=	[self.cur_lang_code]
						snake_case_		:			Any											=	[self.eos_token_id]






				def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[int]	)							->    List[str]:







					"""simple docstring"""


					snake_case_		:			int											=	self.lang_code_to_id[lang]
					if self.legacy_behaviour:
						snake_case_		:			Union[str, Any]											=	[]
						snake_case_		:			Optional[int]											=	[self.eos_token_id, self.cur_lang_code]
					else:
						snake_case_		:			Tuple											=	[self.cur_lang_code]
						snake_case_		:			Any											=	[self.eos_token_id]








def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ):
		raise ValueError('Input must be an integer'   )
	if input_num <= 0:
		raise ValueError('Input must be positive'   )
	return sum(
	    divisor for divisor in range(1   , input_num // 2 + 1   ) if input_num % divisor == 0   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

