


import os
import unicodedata
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 SPIECE_UNDERLINE, logging


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

__UpperCamelCase       :       List[str]						       =				{"vocab_file": "spiece.model"}

__UpperCamelCase       :       Union[str, Any]						       =				{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

__UpperCamelCase       :       str						       =				{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
__UpperCamelCase       :       Optional[Any]						       =				0
__UpperCamelCase       :       str						       =				1
__UpperCamelCase       :       List[str]						       =				2
__UpperCamelCase       :       Tuple						       =				3
__UpperCamelCase       :       Any						       =				4

class       __magic_name__	(     a__):
					A:		List[Any]			=      VOCAB_FILES_NAMES
					A:		Dict			=      PRETRAINED_VOCAB_FILES_MAP
					A:		Union[str, Any]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A:		List[str]			=      "left"


					def __init__(      self							:     str							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Tuple=False							,	lowerCamelCase__							:     Tuple=True							,	lowerCamelCase__							:     Any=False							,	lowerCamelCase__							:     int="<s>"							,	lowerCamelCase__							:     Tuple="</s>"							,	lowerCamelCase__							:     Tuple="<unk>"							,	lowerCamelCase__							:     Tuple="<sep>"							,	lowerCamelCase__							:     List[Any]="<pad>"							,	lowerCamelCase__							:     Tuple="<cls>"							,	lowerCamelCase__							:     Optional[int]="<mask>"							,	lowerCamelCase__							:     Tuple=["<eop>", "<eod>"]							,	lowerCamelCase__							:     Dict = None							,	**lowerCamelCase__							:     str							,	)  ->      int:





						'''simple docstring'''






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

						UpperCamelCase__					:    Union[str, Any]          =   {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    do_lower_case=_A							,	remove_space=_A							,	keep_accents=_A							,	bos_token=_A							,	eos_token=_A							,	unk_token=_A							,	sep_token=_A							,	pad_token=_A							,	cls_token=_A							,	mask_token=_A							,	additional_special_tokens=_A							,	sp_model_kwargs=self.sp_model_kwargs							,	**_A							,	)

						UpperCamelCase__					:    int          =   3

						UpperCamelCase__					:    Union[str, Any]          =   do_lower_case
						UpperCamelCase__					:    Dict          =   remove_space
						UpperCamelCase__					:    int          =   keep_accents
						UpperCamelCase__					:    List[str]          =   vocab_file

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


					@property
					def      UpperCAmelCase__	(      self							:     Dict			)  ->      Any:





						'''simple docstring'''






						return len(self.sp_model			)


					def      UpperCAmelCase__	(      self							:     Any			)  ->      str:





						'''simple docstring'''






						UpperCamelCase__					:    List[Any]          =   {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							:     Optional[int]			)  ->      Union[str, Any]:





						'''simple docstring'''






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


					def __setstate__(      self							:     str							,	lowerCamelCase__							:     Optional[Any]			)  ->      Optional[Any]:





						'''simple docstring'''






						UpperCamelCase__					:    Union[str, Any]          =   d

						# for backward compatibility
						if not hasattr(self							,	'''sp_model_kwargs'''			):
							UpperCamelCase__					:    Union[str, Any]          =   {}

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


					def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str			)  ->      List[Any]:





						'''simple docstring'''






						if self.remove_space:
							UpperCamelCase__					:    str          =   ' '.join(inputs.strip().split()			)
						else:
							UpperCamelCase__					:    Dict          =   inputs
						UpperCamelCase__					:    List[str]          =   outputs.replace('''``'''							,	'''"'''			).replace('''\'\''''							,	'''"'''			)

						if not self.keep_accents:
							UpperCamelCase__					:    Optional[Any]          =   unicodedata.normalize('''NFKD'''							,	_A			)
							UpperCamelCase__					:    Dict          =   ''.join([c for c in outputs if not unicodedata.combining(_A			)]			)
						if self.do_lower_case:
							UpperCamelCase__					:    Tuple          =   outputs.lower()

						return outputs


					def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     List[str]			)  ->      str:





						'''simple docstring'''






						UpperCamelCase__					:    Union[str, Any]          =   self.preprocess_text(_A			)
						UpperCamelCase__					:    int          =   self.sp_model.encode(_A							,	out_type=_A			)
						UpperCamelCase__					:    int          =   []
						for piece in pieces:
							if len(_A			) > 1 and piece[-1] == str(''','''			) and piece[-2].isdigit():
								UpperCamelCase__					:    Union[str, Any]          =   self.sp_model.EncodeAsPieces(piece[:-1].replace(_A							,	''''''			)			)
								if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
									if len(cur_pieces[0]			) == 1:
										UpperCamelCase__					:    int          =   cur_pieces[1:]
									else:
										UpperCamelCase__					:    Any          =   cur_pieces[0][1:]
								cur_pieces.append(piece[-1]			)
								new_pieces.extend(_A			)
							else:
								new_pieces.append(_A			)

						return new_pieces


					def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     Any			)  ->      str:





						'''simple docstring'''






						return self.sp_model.PieceToId(_A			)


					def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     int			)  ->      Dict:





						'''simple docstring'''






						return self.sp_model.IdToPiece(_A			)


					def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     str			)  ->      List[str]:





						'''simple docstring'''






						UpperCamelCase__					:    Optional[Any]          =   ''.join(_A			).replace(_A							,	''' '''			).strip()
						return out_string


					def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     Any = False							,	lowerCamelCase__							:     Dict = None							,	lowerCamelCase__							:     Dict = True							,	**lowerCamelCase__							:     int							,	)  ->      Optional[int]:





						'''simple docstring'''






						UpperCamelCase__					:    Dict          =   kwargs.pop('''use_source_tokenizer'''							,	_A			)

						UpperCamelCase__					:    Dict          =   self.convert_ids_to_tokens(_A							,	skip_special_tokens=_A			)

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						UpperCamelCase__					:    Optional[Any]          =   []
						UpperCamelCase__					:    int          =   []
						for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
								continue
							if token in self.added_tokens_encoder:
								if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(_A			)			)
									UpperCamelCase__					:    Tuple          =   []
								sub_texts.append(_A			)
							else:
								current_sub_text.append(_A			)
						if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(_A			)			)

						# Mimic the behavior of the Rust tokenizer:
						# By default, there are no spaces between special tokens
						UpperCamelCase__					:    List[Any]          =   ''.join(_A			)

						UpperCamelCase__					:    Tuple          =   (
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
							UpperCamelCase__					:    int          =   self.clean_up_tokenization(_A			)
							return clean_text
						else:
							return text


					def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Dict = None			)  ->      str:





						'''simple docstring'''






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


					def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     Dict = None							,	lowerCamelCase__							:     Union[str, Any] = False			)  ->      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 not None:
							return ([0] * len(_A			)) + [1] + ([0] * len(_A			)) + [1, 1]
						return ([0] * len(_A			)) + [1, 1]


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





						'''simple docstring'''






						UpperCamelCase__					:    str          =   [self.sep_token_id]
						UpperCamelCase__					:    Any          =   [2]

						if token_ids_a is None:
							return len(token_ids_a + sep			) * [0] + cls_segment_id
						return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id


					def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     int = None			)  ->      List[str]:





						'''simple docstring'''






						if not os.path.isdir(_A			):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
							return
						UpperCamelCase__					:    str          =   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:
								UpperCamelCase__					:    Optional[Any]          =   self.sp_model.serialized_model_proto()
								fi.write(_A			)

						return (out_vocab_file,)






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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_camembert import CamembertTokenizer
else:
       __UpperCamelCase       :       Tuple						       =				None


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

__UpperCamelCase       :       Any						       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Tuple						       =				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


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





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      super().__init__(
          lowerCamelCase__							,	tokenizer_file=lowerCamelCase__							,	bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	additional_special_tokens=lowerCamelCase__							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     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]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    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]


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''			)

      if not os.path.isdir(lowerCamelCase__			):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
       return
      UpperCamelCase__					:    List[str]          =   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__			):
       copyfile(self.vocab_file							,	lowerCamelCase__			)

      return (out_vocab_file,)


