





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


_lowercase							:      Union[str, Any]      							=logging.get_logger(__name__)

_lowercase							:      Union[str, Any]      							="▁"

_lowercase							:      str      							={"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

_lowercase							:      Tuple      							={
    "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",
    },
}

_lowercase							:      Optional[int]      							={"vinai/bartpho-syllable": 1_0_2_4}



class  SCREAMING_SNAKE_CASE_ (					_UpperCAmelCase			):
  '''simple docstring'''


  lowercase		:     Optional[Any]            =				VOCAB_FILES_NAMES
  lowercase		:     Dict            =				PRETRAINED_VOCAB_FILES_MAP
  lowercase		:     Any            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase		:     Tuple            =				["input_ids", "attention_mask"]







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

         A						:     Any         ={} if sp_model_kwargs is None else sp_model_kwargs

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

         A						:     Tuple         =vocab_file
         A						:     Any         =monolingual_vocab_file
         A						:     Optional[int]         =spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(str(lowercase_     )     )

         # Load the reduced vocab

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

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







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







  def __setstate__(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Any:
         A						:     int         =d

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

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None     )    ->				Optional[Any]:

         if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A						:     Optional[int]         =[self.cls_token_id]
         A						:     str         =[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None      ,	SCREAMING_SNAKE_CASE__    :					bool = False     )    ->				int:

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

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None     )    ->				List[str]:
         A						:     Any         =[self.sep_token_id]
         A						:     Tuple         =[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 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Any:
         return len(self.fairseq_ids_to_tokens     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Optional[Any]:
         A						:     Dict         ={self.convert_ids_to_tokens(lowercase_     ): i for i in range(self.vocab_size     )}
         vocab.update(self.added_tokens_encoder     )
         return vocab







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				Union[str, Any]:
         return self.sp_model.encode(lowercase_      ,	out_type=lowercase_     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Dict:
         if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
         else:
                return self.unk_token_id







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				Any:
         return self.fairseq_ids_to_tokens[index]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				Any:
         A						:     Union[str, Any]         ="""""".join(lowercase_     ).replace(lowercase_      ,	' '     ).strip()
         return out_string







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Optional[str] = None     )    ->				Optional[int]:
         if not os.path.isdir(lowercase_     ):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
                return
         A						:     Optional[int]         =os.path.join(
             lowercase_      ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
         A						:     str         =os.path.join(
             lowercase_      ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
                       A						:     Optional[Any]         =self.sp_model.serialized_model_proto()
                       fi.write(lowercase_     )

         if os.path.abspath(self.monolingual_vocab_file     ) != os.path.abspath(
             lowercase_     ) and os.path.isfile(self.monolingual_vocab_file     ):
                copyfile(self.monolingual_vocab_file      ,	lowercase_     )
         elif not os.path.isfile(self.monolingual_vocab_file     ):
                with open(lowercase_      ,	'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(lowercase_     )} \n'     )

         return out_vocab_file, out_monolingual_vocab_file







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


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






