
import warnings

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


SCREAMING_SNAKE_CASE				:str									=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:Optional[int]									=							{
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}





class 				UpperCAmelCase (    A__    ):




	'''simple docstring'''





	snake_case_          =	"xlnet"
	snake_case_          =	["mems"]
	snake_case_          =	{
	    "n_token": "vocab_size",  # Backward compatibility
	    "hidden_size": "d_model",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}






	def __init__(			self   :					List[str]				,A   :					int=3_20_00				,A   :					Optional[Any]=10_24				,A   :					Optional[int]=24				,A   :					Optional[Any]=16				,A   :					List[str]=40_96				,A   :					int="gelu"				,A   :					int=True				,A   :					Union[str, Any]="bi"				,A   :					Union[str, Any]=0.02				,A   :					List[str]=1E-12				,A   :					int=0.1				,A   :					Tuple=5_12				,A   :					List[str]=None				,A   :					int=True				,A   :					List[str]=False				,A   :					Optional[Any]=False				,A   :					int=-1				,A   :					Optional[Any]=False				,A   :					int="last"				,A   :					List[Any]=True				,A   :					Any="tanh"				,A   :					Tuple=0.1				,A   :					Dict=5				,A   :					str=5				,A   :					Any=5				,A   :					Tuple=1				,A   :					Tuple=2				,**A   :					Union[str, Any]				,):
					__A       =					vocab_size
					__A       =					d_model
					__A       =					n_layer
					__A       =					n_head
					if d_model % n_head != 0:
									raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''					)
					if "d_head" in kwargs:
									if kwargs["d_head"] != d_model // n_head:
													raise ValueError(
													    f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''					)
					__A       =					d_model // n_head
					__A       =					ff_activation
					__A       =					d_inner
					__A       =					untie_r
					__A       =					attn_type

					__A       =					initializer_range
					__A       =					layer_norm_eps

					__A       =					dropout
					__A       =					mem_len
					__A       =					reuse_len
					__A       =					bi_data
					__A       =					clamp_len
					__A       =					same_length

					__A       =					summary_type
					__A       =					summary_use_proj
					__A       =					summary_activation
					__A       =					summary_last_dropout
					__A       =					start_n_top
					__A       =					end_n_top

					__A       =					bos_token_id
					__A       =					pad_token_id
					__A       =					eos_token_id

					if "use_cache" in kwargs:
									warnings.warn(
									    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
									    " instead."				,_lowerCamelCase				,)
									__A       =					kwargs['use_cache']

					__A       =					use_mems_eval
					__A       =					use_mems_train
					super().__init__(pad_token_id=_lowerCamelCase				,bos_token_id=_lowerCamelCase				,eos_token_id=_lowerCamelCase				,**_lowerCamelCase					)






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''					)
					return -1







	@max_position_embeddings.setter
	def 							UpperCamelCase_    (			self   :					Dict				,A   :					List[str]					):
					raise NotImplementedError(
					    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''					)


"""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							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {'vocab_file': 'sentencepiece.bpe.model'}

__SCREAMING_SNAKE_CASE							=     {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__SCREAMING_SNAKE_CASE							=     {
    'camembert-base': 512,
}

__SCREAMING_SNAKE_CASE							=     '▁'





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



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







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else mask_token

      UpperCamelCase_     :					List[str]   	={} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=_lowerCamelCase      ,  eos_token=_lowerCamelCase      ,  unk_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  additional_special_tokens=_lowerCamelCase      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **_lowerCamelCase      ,  )
      UpperCamelCase_     :					Optional[int]   	=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(_lowerCamelCase   )   )
      UpperCamelCase_     :					str   	=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>
      UpperCamelCase_     :					Tuple   	={'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
      UpperCamelCase_     :					List[str]   	=len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					List[str]   	=len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					Any   	={v: k for k, v in self.fairseq_tokens_to_ids.items()}



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



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



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







      '''simple docstring'''



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

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



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=[self.sep_token_id]
      UpperCamelCase_     :					Dict   	=[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  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	={self.convert_ids_to_tokens(_lowerCamelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[Any]   ):







      '''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  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=[]
      UpperCamelCase_     :					Optional[Any]   	=''
      UpperCamelCase_     :					Optional[Any]   	=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(_lowerCamelCase   ) + token
        UpperCamelCase_     :					Union[str, Any]   	=True
        UpperCamelCase_     :					Optional[Any]   	=[]
       else:
        current_sub_tokens.append(_lowerCamelCase   )
        UpperCamelCase_     :					Any   	=False
      out_string += self.sp_model.decode(_lowerCamelCase   )
      return out_string.strip()



     def __getstate__(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self.__dict__.copy()
      UpperCamelCase_     :					str   	=None
      return state



     def __setstate__(							self :Optional[Any]      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=d

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

      UpperCamelCase_     :					List[Any]   	=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      if not os.path.isdir(_lowerCamelCase   ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
       return
      UpperCamelCase_     :					Any   	=os.path.join(
          _lowerCamelCase      ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(_lowerCamelCase   ) and os.path.isfile(self.vocab_file   ):
       copyfile(self.vocab_file      ,  _lowerCamelCase   )
      elif not os.path.isfile(self.vocab_file   ):
       with open(_lowerCamelCase      ,  'wb'   ) as fi:
        UpperCamelCase_     :					int   	=self.sp_model.serialized_model_proto()
        fi.write(_lowerCamelCase   )

      return (out_vocab_file,)
