



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


a_						:     Optional[Any]	     =		logging.get_logger(__name__)

a_						:     Optional[int]	     =		{
    'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			'gpt_neox_japanese'







     def __init__(self				,			__a=3_20_00				,			__a=25_60				,			__a=32				,			__a=32				,			__a=4				,			__a="gelu"				,			__a=1.00				,			__a=1_00_00				,			__a=20_48				,			__a=0.02				,			__a=1E-5				,			__a=True				,			__a=3_19_96				,			__a=3_19_99				,			__a=0.1				,			__a=0.0				,			**__a				,			):






           '''simple docstring'''



           super().__init__(bos_token_id=__a				,			eos_token_id=__a				,			**__a    )
           lowerCamelCase								=		vocab_size
           lowerCamelCase								=		max_position_embeddings
           lowerCamelCase								=		hidden_size
           lowerCamelCase								=		num_hidden_layers
           lowerCamelCase								=		num_attention_heads
           lowerCamelCase								=		intermediate_multiple_size
           lowerCamelCase								=		hidden_act
           lowerCamelCase								=		rotary_pct
           lowerCamelCase								=		rotary_emb_base
           lowerCamelCase								=		initializer_range
           lowerCamelCase								=		layer_norm_eps
           lowerCamelCase								=		use_cache
           lowerCamelCase								=		attention_dropout
           lowerCamelCase								=		hidden_dropout




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

a_						:     Any	     =		{'vocab_file': 'spiece.model'}

a_						:     List[Any]	     =		{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

a_						:     List[Any]	     =		{'bert_for_seq_generation': 5_1_2}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			VOCAB_FILES_NAMES
     _A       =			PRETRAINED_VOCAB_FILES_MAP
     _A       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A       =			[]
     _A       =			['input_ids', 'attention_mask']







     def __init__(self				,			__a				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<pad>"				,			__a="<::::>"				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		{} if sp_model_kwargs is None else sp_model_kwargs

           # Add extra_ids to the special token list
           super().__init__(
               bos_token=__a				,			eos_token=__a				,			unk_token=__a				,			pad_token=__a				,			sep_token=__a				,			sp_model_kwargs=self.sp_model_kwargs				,			**__a				,			)

           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(__a    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self.sp_model.get_piece_size()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		{self.convert_ids_to_tokens(__a    ): i for i in range(self.vocab_size    )}
           vocab.update(self.added_tokens_encoder    )
           return vocab







     def __getstate__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.__dict__.copy()
           lowerCamelCase								=		None
           return state







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

           # for backward compatibility
           if not hasattr(self				,			"sp_model_kwargs"    ):
                 lowerCamelCase								=		{}

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(self.vocab_file    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.piece_to_id(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.sp_model.IdToPiece(__a    )
           return token







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[]
           lowerCamelCase								=		""
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       out_string += self.sp_model.decode(__a    ) + token
                       lowerCamelCase								=		[]
                 else:
                       current_sub_tokens.append(__a    )
           out_string += self.sp_model.decode(__a    )
           return out_string.strip()







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not os.path.isdir(__a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		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:
                       lowerCamelCase								=		self.sp_model.serialized_model_proto()
                       fi.write(__a    )

           return (out_vocab_file,)