

'''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


lowerCamelCase :      str					 =     logging.get_logger(__name__)

lowerCamelCase :      str					 =     {"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase :      int					 =     {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

lowerCamelCase :      str					 =     {
    "camembert-base": 5_1_2,
}

lowerCamelCase :      List[Any]					 =     "▁"







class 							A__    (       A__  ):
  A__		 =				VOCAB_FILES_NAMES
  A__		 =				PRETRAINED_VOCAB_FILES_MAP
  A__		 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__		 =				['input_ids', 'attention_mask']





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =AddedToken(_a							,					lstrip=_a							,					rstrip=_a ) if isinstance(_a							,					_a ) else mask_token

       _SCREAMING_SNAKE_CASE							    ={} 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							,					additional_special_tokens=_a							,					sp_model_kwargs=self.sp_model_kwargs							,					**_a							,					)
       _SCREAMING_SNAKE_CASE							    =spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(str(_a ) )
       _SCREAMING_SNAKE_CASE							    =vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       _SCREAMING_SNAKE_CASE							    ={'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       _SCREAMING_SNAKE_CASE							    =len(self.fairseq_tokens_to_ids )
       _SCREAMING_SNAKE_CASE							    =len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
       _SCREAMING_SNAKE_CASE							    ={v: k for k, v in self.fairseq_tokens_to_ids.items()}





  def 						A  (		self      :	Tuple							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	Optional[Any]							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None							,					_a      :	bool = False )	->    List[int]:





       '''simple docstring'''






       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 						A  (		self      :	Dict							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.sep_token_id]
       _SCREAMING_SNAKE_CASE							    =[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 						A  (		self      :	Tuple )	->    Union[str, Any]:





       '''simple docstring'''






       return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )





  def 						A  (		self      :	Tuple )	->    List[Any]:





       '''simple docstring'''






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





  def 						A  (		self      :	Optional[int]							,					_a      :	str )	->    List[str]:





       '''simple docstring'''






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





  def 						A  (		self      :	Optional[int]							,					_a      :	Optional[Any] )	->    Optional[Any]:





       '''simple docstring'''






       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(_a ) == 0:
            # Convert sentence piece unk token to fairseq unk token index
            return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(_a )





  def 						A  (		self      :	Tuple							,					_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 						A  (		self      :	List[Any]							,					_a      :	List[Any] )	->    str:





       '''simple docstring'''






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





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





       '''simple docstring'''






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





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =d

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

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





  def 						A  (		self      :	Any							,					_a      :	str							,					_a      :	Optional[str] = None )	->    Tuple[str]:





       '''simple docstring'''






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

       return (out_vocab_file,)




'''simple docstring'''

a_  :				Any						= """0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich



