'''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, PreTrainedTokenizer
from ...utils import logging


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

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ='▁'

SCREAMING_SNAKE_CASE_:     List[str]  ={'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

SCREAMING_SNAKE_CASE_:     Any  ={
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

SCREAMING_SNAKE_CASE_:     Dict  ={'vinai/bartpho-syllable': 10_24}




class        __A      ( UpperCamelCase__	):
    a__       :						Dict          =  VOCAB_FILES_NAMES
    a__       :						Optional[Any]          =  PRETRAINED_VOCAB_FILES_MAP
    a__       :						str          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						Dict          =  ["""input_ids""", """attention_mask"""]



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

         UpperCAmelCase_           =   {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__a   ,			eos_token=__a   ,			unk_token=__a   ,			sep_token=__a   ,			cls_token=__a   ,			pad_token=__a   ,			mask_token=__a   ,			sp_model_kwargs=self.sp_model_kwargs   ,			**__a   ,			)

         UpperCAmelCase_           =   vocab_file
         UpperCAmelCase_           =   monolingual_vocab_file
         UpperCAmelCase_           =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(str(__a ) )

         # Load the reduced vocab

         # Keep order of special tokens for backward compatibility
         UpperCAmelCase_           =   {}
         UpperCAmelCase_           =   0
         for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
              if str(__a ) not in self.fairseq_tokens_to_ids:
                   UpperCAmelCase_           =   cnt
                   cnt += 1
         with open(__a   ,			"r"   ,			encoding="utf-8" ) as f:
              for line in f.readlines():
                   UpperCAmelCase_           =   line.strip().split()[0]
                   UpperCAmelCase_           =   len(self.fairseq_tokens_to_ids )
         if str(__a ) not in self.fairseq_tokens_to_ids:
              UpperCAmelCase_           =   len(self.fairseq_tokens_to_ids )

         UpperCAmelCase_           =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}



    def __getstate__(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.__dict__.copy()
         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   self.sp_model.serialized_model_proto()
         return state



    def __setstate__(self    :    Tuple   ,			__a    :    Optional[Any] ):
         UpperCAmelCase_           =   d

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

         UpperCAmelCase_           =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto )



    def 					_lowercase						(self    :    int   ,			__a    :    List[int]   ,			__a    :    Optional[List[int]] = None ):

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCAmelCase_           =   [self.cls_token_id]
         UpperCAmelCase_           =   [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep



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

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

         if token_ids_a is None:
              return [1] + ([0] * len(__a )) + [1]
         return [1] + ([0] * len(__a )) + [1, 1] + ([0] * len(__a )) + [1]



    def 					_lowercase						(self    :    List[str]   ,			__a    :    List[int]   ,			__a    :    Optional[List[int]] = None ):
         UpperCAmelCase_           =   [self.sep_token_id]
         UpperCAmelCase_           =   [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]



    @property
    def 					_lowercase						(self    :    Dict ):
         return len(self.fairseq_ids_to_tokens )



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   {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    :    Union[str, Any]   ,			__a    :    str ):
         return self.sp_model.encode(__a   ,			out_type=__a )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[str] ):
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         else:
              return self.unk_token_id



    def 					_lowercase						(self    :    Tuple   ,			__a    :    Optional[int] ):
         return self.fairseq_ids_to_tokens[index]



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Optional[int] ):
         UpperCAmelCase_           =   "".join(__a ).replace(__a   ,			" " ).strip()
         return out_string






    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    str   ,			__a    :    Optional[str] = None ):
         if not os.path.isdir(__a ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
              return
         UpperCAmelCase_           =   os.path.join(
             __a   ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
         UpperCAmelCase_           =   os.path.join(
             __a   ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_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:
                   UpperCAmelCase_           =   self.sp_model.serialized_model_proto()
                   fi.write(__a )

         if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
             __a ) and os.path.isfile(self.monolingual_vocab_file ):
              copyfile(self.monolingual_vocab_file   ,			__a )
         elif not os.path.isfile(self.monolingual_vocab_file ):
              with open(__a   ,			"w"   ,			encoding="utf-8" ) as fp:
                   for token in self.fairseq_tokens_to_ids:
                        if token not in self.all_special_tokens:
                             fp.write(f"""{str(__a )} \n""" )

         return out_vocab_file, out_monolingual_vocab_file





'''simple docstring'''





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


SCREAMING_SNAKE_CASE_:     Dict  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Tuple  ={}




class        __A      ( UpperCamelCase__	):
    a__       :						int          =  """llama"""
    a__       :						Any          =  ["""past_key_values"""]



    def __init__(self    :    List[str]   ,			__a    :    List[str]=32000   ,			__a    :    Tuple=4096   ,			__a    :    List[Any]=11008   ,			__a    :    Dict=32   ,			__a    :    Tuple=32   ,			__a    :    Any=None   ,			__a    :    Any="silu"   ,			__a    :    List[Any]=2048   ,			__a    :    List[Any]=0.02   ,			__a    :    str=1E-6   ,			__a    :    Optional[Any]=True   ,			__a    :    Union[str, Any]=0   ,			__a    :    Any=1   ,			__a    :    Dict=2   ,			__a    :    Dict=1   ,			__a    :    str=False   ,			__a    :    str=None   ,			**__a    :    Optional[Any]   ,			):
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
              UpperCAmelCase_           =   num_attention_heads

         UpperCAmelCase_           =   num_key_value_heads
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   rms_norm_eps
         UpperCAmelCase_           =   pretraining_tp
         UpperCAmelCase_           =   use_cache
         UpperCAmelCase_           =   rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=__a   ,			bos_token_id=__a   ,			eos_token_id=__a   ,			tie_word_embeddings=__a   ,			**__a   ,			)






    def 					_lowercase						(self    :    List[str] ):
         if self.rope_scaling is None:
              return

         if not isinstance(self.rope_scaling   ,			__a ) 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}""" )
         UpperCAmelCase_           =   self.rope_scaling.get("type"   ,			__a )
         UpperCAmelCase_           =   self.rope_scaling.get("factor"   ,			__a )
         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(__a   ,			__a ) or rope_scaling_factor <= 1.0:
              raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )




