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


SCREAMING_SNAKE_CASE__		:					Any						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					Any						=					{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE__		:					int						=					{
    "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",
    }
}

SCREAMING_SNAKE_CASE__		:					Optional[int]						=					{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

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

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     VOCAB_FILES_NAMES
  lowercase_			=     PRETRAINED_VOCAB_FILES_MAP
  lowercase_			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase_			=     'left'




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




       """simple docstring"""
       __A				:				List[str]			=		AddedToken(a_     , lstrip=a_     , rstrip=a_				) if isinstance(a_     , a_				) else mask_token

       __A				:				Dict			=		{} 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_     , )

       __A				:				Dict			=		3

       __A				:				int			=		do_lower_case
       __A				:				Optional[int]			=		remove_space
       __A				:				Tuple			=		keep_accents
       __A				:				Tuple			=		vocab_file

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




  @property
  def 	__lowercase(      self     :	Dict				)->       Union[str, Any]:




       """simple docstring"""
       return len(self.sp_model				)




  def 	__lowercase(      self     :	Optional[Any]				)->       Optional[Any]:




       """simple docstring"""
       __A				:				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     :	Dict				)->       Any:




       """simple docstring"""
       __A				:				Any			=		self.__dict__.copy()
       __A				:				int			=		None
       return state




  def __setstate__(      self     :	Dict     , a_     :	Tuple				)->       str:




       """simple docstring"""
       __A				:				Dict			=		d

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

       __A				:				Any			=		spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(self.vocab_file				)




  def 	__lowercase(      self     :	int     , a_     :	Tuple				)->       Union[str, Any]:




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

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

       return outputs




  def 	__lowercase(      self     :	Any     , a_     :	str				)->       List[str]:




       """simple docstring"""
       __A				:				Tuple			=		self.preprocess_text(a_				)
       __A				:				Tuple			=		self.sp_model.encode(a_     , out_type=a_				)
       __A				:				Any			=		[]
       for piece in pieces:
            if len(a_				) > 1 and piece[-1] == str(','				) and piece[-2].isdigit():
                 __A				:				Dict			=		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:
                           __A				:				Dict			=		cur_pieces[1:]
                      else:
                           __A				:				Dict			=		cur_pieces[0][1:]
                 cur_pieces.append(piece[-1]				)
                 new_pieces.extend(a_				)
            else:
                 new_pieces.append(a_				)

       return new_pieces




  def 	__lowercase(      self     :	List[str]     , a_     :	Union[str, Any]				)->       List[str]:




       """simple docstring"""
       return self.sp_model.PieceToId(a_				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Optional[Any]				)->       Optional[Any]:




       """simple docstring"""
       return self.sp_model.IdToPiece(a_				)




  def 	__lowercase(      self     :	str     , a_     :	Any				)->       int:




       """simple docstring"""
       __A				:				Dict			=		''.join(a_				).replace(a_     , ' '				).strip()
       return out_string




  def 	__lowercase(      self     :	Dict     , a_     :	List[int]     , a_     :	bool = False     , a_     :	bool = None     , a_     :	bool = True     , **a_     :	Dict     , )->       str:




       """simple docstring"""
       __A				:				int			=		kwargs.pop('use_source_tokenizer'     , a_				)

       __A				:				int			=		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
       __A				:				Dict			=		[]
       __A				:				List[Any]			=		[]
       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_				)				)
                      __A				:				Any			=		[]
                 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
       __A				:				Optional[int]			=		''.join(a_				)

       __A				:				str			=		(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
            __A				:				List[str]			=		self.clean_up_tokenization(a_				)
            return clean_text
       else:
            return text




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




       """simple docstring"""
       __A				:				List[Any]			=		[self.sep_token_id]
       __A				:				Dict			=		[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 	__lowercase(      self     :	str     , 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 not None:
            return ([0] * len(a_				)) + [1] + ([0] * len(a_				)) + [1, 1]
       return ([0] * len(a_				)) + [1, 1]




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[int]     , a_     :	Optional[List[int]] = None				)->       List[int]:




       """simple docstring"""
       __A				:				str			=		[self.sep_token_id]
       __A				:				Tuple			=		[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 	__lowercase(      self     :	Union[str, 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
       __A				:				Any			=		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:
                 __A				:				List[str]			=		self.sp_model.serialized_model_proto()
                 fi.write(a_				)

       return (out_vocab_file,)




import math







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     assert isinstance(lowercase__     ,  lowercase__ ) and (
         number >= 0
     ), "'number' must been an int and positive"

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or not number % 2:
          # Negatives, 0, 1 and all even numbers are not primes
          return False

     SCREAMING_SNAKE_CASE__				:				Tuple			=		range(3     ,  int(math.sqrt(lowercase__ ) + 1 )     ,  2 )
     return not any(not number % i for i in odd_numbers )







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Any=1     ,  **lowercase__  :			Any ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		factor * value
     SCREAMING_SNAKE_CASE__				:				Dict			=		value

     while not is_prime(lowercase__ ):
          value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

     if value == first_value_val:
          return next_prime(value + 1     ,  **lowercase__ )
     return value



