



"""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


lowercase__    :  Optional[int]							    =	logging.get_logger(__name__)

lowercase__    :  Any							    =	'''▁'''

lowercase__    :  List[str]							    =	{'''vocab_file''': '''sentencepiece.bpe.model'''}

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

lowercase__    :  List[Any]							    =	{
    '''facebook/nllb-200-distilled-600M''': 10_24,
}

# fmt: off
lowercase__    :  int							    =	['''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       (			lowerCAmelCase__):
 _lowerCAmelCase   :			str							=		VOCAB_FILES_NAMES
 _lowerCAmelCase   :			int							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase   :			List[Any]							=		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase   :			Any							=		["""input_ids""", """attention_mask"""]

 _lowerCAmelCase   :			List[int]							=		[]
 _lowerCAmelCase   :			List[int]							=		[]

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

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

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

     snake_case_ :     Optional[int]				=	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.Load(str(lowercase_				)				)
     snake_case_ :     str				=	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_ :     str				=	{'''<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[int]				=	1

     snake_case_ :     Union[str, Any]				=	len(self.sp_model				)
     snake_case_ :     str				=	{
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase_				)
     }
     snake_case_ :     int				=	{v: k for k, v in self.lang_code_to_id.items()}
     snake_case_ :     Any				=	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_ :     Any				=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     snake_case_ :     Optional[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_ :     List[str]				=	src_lang if src_lang is not None else '''eng_Latn'''
     snake_case_ :     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		:       List[Any]				):
     snake_case_ :     Dict				=	self.__dict__.copy()
     snake_case_ :     Dict				=	None
     snake_case_ :     Optional[int]				=	self.sp_model.serialized_model_proto()
     return state

 def __setstate__(     self		:       Dict  ,     lowercase_		:       Tuple				):
     snake_case_ :     Any				=	d

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

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

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

 @property
 def 						_snake_case					(     self		:       Optional[int]				):
     return self._src_lang

 @src_lang.setter
 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       str				):
     snake_case_ :     List[str]				=	new_src_lang
     self.set_src_lang_special_tokens(self._src_lang				)

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[int]  ,     lowercase_		:       Optional[List[int]] = None  ,     lowercase_		:       bool = False				):

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

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

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       List[int]  ,     lowercase_		:       Optional[List[int]] = None				):
     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 						_snake_case					(     self		:       str  ,     lowercase_		:       List[int]  ,     lowercase_		:       Optional[List[int]] = None				):
     snake_case_ :     str				=	[self.sep_token_id]
     snake_case_ :     str				=	[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 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       str  ,     lowercase_		:       Optional[str]  ,     lowercase_		:       Optional[str]  ,     **lowercase_		:       Tuple				):
     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[int]				=	src_lang
     snake_case_ :     Optional[int]				=	self(lowercase_  ,     add_special_tokens=lowercase_  ,     return_tensors=lowercase_  ,     **lowercase_				)
     snake_case_ :     int				=	self.convert_tokens_to_ids(lowercase_				)
     snake_case_ :     Tuple				=	tgt_lang_id
     return inputs

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

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       str				):
     return self.sp_model.encode(lowercase_  ,     out_type=lowercase_				)

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       List[Any]				):
     if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
     snake_case_ :     Tuple				=	self.sp_model.PieceToId(lowercase_				)

     # 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 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Optional[Any]				):
     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 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       Optional[int]				):
     snake_case_ :     List[str]				=	''''''.join(lowercase_				).replace(lowercase_  ,     ''' '''				).strip()
     return out_string

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       str  ,     lowercase_		:       Optional[str] = None				):
     if not os.path.isdir(lowercase_				):
         logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
         return
     snake_case_ :     List[str]				=	os.path.join(
         lowercase_  ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

     return (out_vocab_file,)

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[str]  ,     lowercase_		:       str = "eng_Latn"  ,     lowercase_		:       Optional[List[str]] = None  ,     lowercase_		:       str = "fra_Latn"  ,     **lowercase_		:       Dict  ,     ):
     snake_case_ :     List[Any]				=	src_lang
     snake_case_ :     Any				=	tgt_lang
     return super().prepare_seqaseq_batch(lowercase_  ,     lowercase_  ,     **lowercase_				)

 def 						_snake_case					(     self		:       str				):
     return self.set_src_lang_special_tokens(self.src_lang				)

 def 						_snake_case					(     self		:       List[str]				):
     return self.set_tgt_lang_special_tokens(self.tgt_lang				)

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






 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       str				):
     snake_case_ :     Any				=	self.lang_code_to_id[lang]
     if self.legacy_behaviour:
         snake_case_ :     List[Any]				=	[]
         snake_case_ :     Union[str, Any]				=	[self.eos_token_id, self.cur_lang_code]
     else:
         snake_case_ :     str				=	[self.cur_lang_code]
         snake_case_ :     Any				=	[self.eos_token_id]










"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__    :  int							    =	logging.get_logger(__name__)

lowercase__    :  List[Any]							    =	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[Any]							=		"""gpt_neox"""

 def __init__(     self		:       List[str]  ,     lowercase_		:       str=50432  ,     lowercase_		:       List[Any]=6144  ,     lowercase_		:       List[Any]=44  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       List[str]=24576  ,     lowercase_		:       List[Any]="gelu"  ,     lowercase_		:       str=0.25  ,     lowercase_		:       Optional[int]=10000  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       int=0.1  ,     lowercase_		:       Tuple=2048  ,     lowercase_		:       Union[str, Any]=0.02  ,     lowercase_		:       List[str]=1E-5  ,     lowercase_		:       str=True  ,     lowercase_		:       str=0  ,     lowercase_		:       Union[str, Any]=2  ,     lowercase_		:       List[str]=False  ,     lowercase_		:       Optional[int]=True  ,     lowercase_		:       List[Any]=None  ,     **lowercase_		:       Optional[int]  ,     ):
     super().__init__(bos_token_id=lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)
     snake_case_ :     List[str]				=	vocab_size
     snake_case_ :     Optional[Any]				=	max_position_embeddings
     snake_case_ :     str				=	hidden_size
     snake_case_ :     Dict				=	num_hidden_layers
     snake_case_ :     Dict				=	num_attention_heads
     snake_case_ :     List[Any]				=	intermediate_size
     snake_case_ :     List[Any]				=	hidden_act
     snake_case_ :     str				=	rotary_pct
     snake_case_ :     Dict				=	rotary_emb_base
     snake_case_ :     Optional[int]				=	attention_dropout
     snake_case_ :     Tuple				=	hidden_dropout
     snake_case_ :     Tuple				=	classifier_dropout
     snake_case_ :     List[str]				=	initializer_range
     snake_case_ :     Union[str, Any]				=	layer_norm_eps
     snake_case_ :     Any				=	use_cache
     snake_case_ :     Optional[int]				=	tie_word_embeddings
     snake_case_ :     Any				=	use_parallel_residual
     snake_case_ :     Union[str, Any]				=	rope_scaling
     self._rope_scaling_validation()

     if self.hidden_size % self.num_attention_heads != 0:
         raise ValueError(
             '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''				)






 def 						_snake_case					(     self		:       Optional[int]				):
     if self.rope_scaling is None:
         return

     if not isinstance(self.rope_scaling  ,     lowercase_				) or len(self.rope_scaling				) != 2:
         raise ValueError(
             '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
             f"got {self.rope_scaling}"				)
     snake_case_ :     Any				=	self.rope_scaling.get('''type'''  ,     lowercase_				)
     snake_case_ :     Union[str, Any]				=	self.rope_scaling.get('''factor'''  ,     lowercase_				)
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
         raise ValueError(
             f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"				)
     if rope_scaling_factor is None or not isinstance(lowercase_  ,     lowercase_				) or rope_scaling_factor <= 1.0:
         raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"				)





