





'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


a__							:							int =logging.get_logger(__name__)

a__							:							List[Any] ={'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

a__							:							int ={
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

a__							:							Dict ={
    '''abeja/gpt-neox-japanese-2.7b''': 2_048,
}
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






       with open(__lowercase				,  'r'				,  encoding='utf-8'     ) as f:
              __UpperCamelCase       =  json.loads(f.read()     )

       __UpperCamelCase       =  collections.OrderedDict()
       __UpperCamelCase       =  collections.OrderedDict()
       __UpperCamelCase       =  collections.OrderedDict()
       with open(__lowercase				,  'r'				,  encoding='utf-8'     ) as f:
              __UpperCamelCase       =  f.readlines()
       __UpperCamelCase       =  [[t.rstrip('\n'     )] if (t == ',' or ',' not in t) else t.rstrip('\n'     ).split(','     ) for t in token]
       for idx, b in enumerate(__lowercase     ):
              __UpperCamelCase       =  b
              __UpperCamelCase       =  idx
              for wd in b:
                     __UpperCamelCase       =  idx

       return vocab, raw_vocab, ids_to_tokens, emoji

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					List[str]					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					str					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_	:					Dict					=["input_ids", "attention_mask"]






 def __init__(       self       :							Dict							,	__A       :							List[Any]							,	__A       :							Union[str, Any]							,	__A       :							List[str]="<|endoftext|>"							,	__A       :							int="<|endoftext|>"							,	__A       :							Optional[Any]="<|startoftext|>"							,	__A       :							Dict="<|endoftext|>"							,	__A       :							Dict=False							,	**__A       :							Any							,	):
        super().__init__(
            unk_token=__A							,	pad_token=__A							,	bos_token=__A							,	eos_token=__A							,	do_clean_text=__A							,	**__A							,	)
        if not os.path.isfile(__A	):
               raise ValueError(
                   f'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'''
                   ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'	)
        if not os.path.isfile(__A	):
               raise ValueError(
                   f'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'''
                   ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'	)
        __UpperCamelCase       =  do_clean_text
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  load_vocab_and_emoji(__A							,	__A	)
        __UpperCamelCase       =  SubWordJapaneseTokenizer(
            vocab=self.vocab							,	ids_to_tokens=self.ids_to_tokens							,	emoji=self.emoji	)






 @property
 def        _lowerCamelCase					(       self       :							str	):
        # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
        return len(self.raw_vocab	)






 def        _lowerCamelCase					(       self       :							Any	):
        return dict(self.raw_vocab							,	**self.added_tokens_encoder	)






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							str	):
        return self.subword_tokenizer.tokenize(__A							,	clean=self.do_clean_text	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							int	):
        return self.vocab.get(__A							,	self.vocab.get(self.unk_token	)	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							List[Any]	):
        return self.subword_tokenizer.convert_id_to_token(__A	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							List[str]	):
        __UpperCamelCase       =  ''.join(__A	).strip()
        return out_string






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							"Conversation"	):
        __UpperCamelCase       =  []
        for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__A							,	add_special_tokens=__A	) + [self.eos_token_id]	)

        if len(__A	) > self.model_max_length:
               __UpperCamelCase       =  input_ids[-self.model_max_length :]
        return input_ids






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							str							,	__A       :							Optional[str] = None	):
        __UpperCamelCase       =  0
        if os.path.isdir(__A	):
               __UpperCamelCase       =  os.path.join(
                   __A							,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
               __UpperCamelCase       =  os.path.join(
                   __A							,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']	)
        else:
               __UpperCamelCase       =  (
                   (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
               )
               __UpperCamelCase       =  (
                   (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
               )
        with open(__A							,	'w'							,	encoding='utf-8'	) as writer:
               for token_index, token in self.ids_to_tokens.items():
                      if index != token_index:
                             logger.warning(
                                 f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                                 ' Please check that the vocabulary is not corrupted!'	)
                             __UpperCamelCase       =  token_index
                      writer.write(','.join(__A	) + '\n'	)
                      index += 1
        with open(__A							,	'w'							,	encoding='utf-8'	) as writer:
               json.dump(self.emoji							,	__A	)
        return vocab_file, emoji_file






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							Optional[Any]							,	__A       :							Optional[int]							,	__A       :							List[str]							,	__A       :							Dict	):
        __UpperCamelCase       =  vocab  # same as swe
        __UpperCamelCase       =  ids_to_tokens  # same as bpe
        __UpperCamelCase       =  emoji
        __UpperCamelCase       =  np.max([len(__A	) for w in self.vocab.keys()]	)
        __UpperCamelCase       =  re.compile(R'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'	)
        __UpperCamelCase       =  re.compile(R'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'	)
        __UpperCamelCase       =  re.compile(R'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'	)
        __UpperCamelCase       =  re.compile(
            R'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'	)
        __UpperCamelCase       =  re.compile(
            R'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'	)
        __UpperCamelCase       =  re.compile(
            R'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'	)
        __UpperCamelCase       =  '─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
        __UpperCamelCase       =  '▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
        __UpperCamelCase       =  str.maketrans({k: '<BLOCK>' for k in keisen + blocks}	)






 def __len__(       self       :							List[Any]	):
        return len(self.ids_to_tokens	)






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							List[str]	):
        __UpperCamelCase       =  self.content_repattera.sub('<URL>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<EMAIL>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<TEL>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<DATE>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<DATE>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<PRICE>'							,	__A	)
        __UpperCamelCase       =  content.translate(self.content_transa	)
        while "<BLOCK><BLOCK>" in content:
               __UpperCamelCase       =  content.replace('<BLOCK><BLOCK>'							,	'<BLOCK>'	)
        return content






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							Any							,	__A       :							Union[str, Any]=False	):
        __UpperCamelCase       =  text.replace(' '							,	'<SP>'	)
        __UpperCamelCase       =  text.replace('　'							,	'<SP>'	)
        __UpperCamelCase       =  text.replace('\r\n'							,	'<BR>'	)
        __UpperCamelCase       =  text.replace('\n'							,	'<BR>'	)
        __UpperCamelCase       =  text.replace('\r'							,	'<BR>'	)
        __UpperCamelCase       =  text.replace('\t'							,	'<TAB>'	)
        __UpperCamelCase       =  text.replace('—'							,	'ー'	)
        __UpperCamelCase       =  text.replace('−'							,	'ー'	)
        for k, v in self.emoji["emoji"].items():
               if k in text:
                      __UpperCamelCase       =  text.replace(__A							,	__A	)
        if clean:
               __UpperCamelCase       =  self.clean_text(__A	)

        def check_simbol(__A       :							str	):
               __UpperCamelCase       =  x.encode()
               if len(__A	) == 1 and len(__A	) == 2:
                      __UpperCamelCase       =  (int(e[0]	) << 8) + int(e[1]	)
                      if (
                          (c >= 0xc2a1 and c <= 0xc2bf)
                          or (c >= 0xc780 and c <= 0xc783)
                          or (c >= 0xcab9 and c <= 0xcbbf)
                          or (c >= 0xcc80 and c <= 0xcda2)
                      ):
                             return True
               return False

        def checkuae(__A       :							Tuple	):
               __UpperCamelCase       =  x.encode()
               if len(__A	) == 1 and len(__A	) == 3:
                      __UpperCamelCase       =  (int(e[0]	) << 1_6) + (int(e[1]	) << 8) + int(e[2]	)
                      if c >= 0xe2_8080 and c <= 0xe2_b07f:
                             return True
               return False

        __UpperCamelCase       =  0
        __UpperCamelCase       =  []
        while pos < len(__A	):
               __UpperCamelCase       =  min(len(__A	)							,	pos + self.maxlen + 1	) if text[pos] == '<' else pos + 3
               __UpperCamelCase       =  []  # (token_id, token, pos)
               for e in range(__A							,	__A							,	-1	):
                      __UpperCamelCase       =  text[pos:e]
                      if wd in self.vocab:
                             if wd[0] == "<" and len(__A	) > 2:
                                    __UpperCamelCase       =  [(self.vocab[wd], wd, e)]
                                    break
                             else:
                                    candidates.append((self.vocab[wd], wd, e)	)
               if len(__A	) > 0:
                      # the smallest token_id is adopted
                      __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  sorted(__A							,	key=lambda __A	: x[0]	)[0]
                      result.append(__A	)
                      __UpperCamelCase       =  e
               else:
                      __UpperCamelCase       =  pos + 1
                      __UpperCamelCase       =  text[pos:end]
                      if check_simbol(__A	):
                             result.append('<KIGOU>'	)
                      elif checkuae(__A	):
                             result.append('<U2000U2BFF>'	)
                      else:
                             for i in wd.encode('utf-8'	):
                                    result.append('<|byte%d|>' % i	)
                      __UpperCamelCase       =  end
        return result






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Any							,	__A       :							List[Any]="\n"	):
        __UpperCamelCase       =  []
        __UpperCamelCase       =  []
        __UpperCamelCase       =  self.ids_to_tokens[index][0]
        if word[:6] == "<|byte" and word[-2:] == "|>":
               byte_tokens.append(int(word[6:-2]	)	)
        else:
               if len(__A	) > 0:
                      words.append(bytearray(__A	).decode('utf-8'							,	errors='replace'	)	)
                      __UpperCamelCase       =  []
               if word[:7] == "<|emoji" and word[-2:] == "|>":
                      words.append(self.emoji['emoji_inv'][word]	)
               elif word == "<SP>":
                      words.append(' '	)
               elif word == "<BR>":
                      words.append(__A	)
               elif word == "<TAB>":
                      words.append('\t'	)
               elif word == "<BLOCK>":
                      words.append('▀'	)
               elif word == "<KIGOU>":
                      words.append('ǀ'	)
               elif word == "<U2000U2BFF>":
                      words.append('‖'	)
               else:
                      words.append(__A	)
        if len(__A	) > 0:
               words.append(bytearray(__A	).decode('utf-8'							,	errors='replace'	)	)
        __UpperCamelCase       =  ''.join(__A	)
        return text


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase  :					int													=       logging.get_logger(__name__)


lowercase  :					Union[str, Any]													=       '▁'

lowercase  :					Tuple													=       {'vocab_file': 'spiece.model'}

lowercase  :					Dict													=       {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

lowercase  :					Any													=       {
    'google/reformer-crime-and-punishment': 524288,
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     VOCAB_FILES_NAMES
	_A								=     PRETRAINED_VOCAB_FILES_MAP
	_A								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=     ['input_ids', 'attention_mask']







	def __init__(					self		:int     ,   a		:List[Any]     ,   a		:Tuple="</s>"     ,   a		:str="<unk>"     ,   a		:Dict=[]     ,   a		:Optional[Dict[str, Any]] = None     ,   **a		:Union[str, Any]     ,   )		-> None:
								__UpperCamelCase	:   str			   =					{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=a     ,   unk_token=a     ,   additional_special_tokens=a     ,   sp_model_kwargs=self.sp_model_kwargs     ,   **a     ,   )

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







	@property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Any:
								return self.sp_model.get_piece_size()







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Dict[str, int]:
								__UpperCamelCase	:   str			   =					{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		:str						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.__dict__.copy()
								__UpperCamelCase	:   Optional[Any]			   =					None
								return state







	def __setstate__(					self		:int     ,   a		:List[str]						)		-> int:
								__UpperCamelCase	:   Union[str, Any]			   =					d

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

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







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str						)		-> List[str]:
								return self.sp_model.encode(a     ,   out_type=a						)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Optional[Any]						)		-> str:
								return self.sp_model.piece_to_id(a						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Union[str, Any]						)		-> Optional[int]:
								if index < self.sp_model.get_piece_size():
															__UpperCamelCase	:   Optional[int]			   =					self.sp_model.IdToPiece(a						)
								return token







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[int]			   =					[]
								__UpperCamelCase	:   str			   =					""
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(a						) + token
																						__UpperCamelCase	:   List[Any]			   =					[]
															else:
																						current_sub_tokens.append(a						)
								out_string += self.sp_model.decode(a						)
								return out_string.strip()







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:Optional[str] = None						)		-> Tuple[str]:
								if not os.path.isdir(a						):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
															return
								__UpperCamelCase	:   List[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:
																						__UpperCamelCase	:   int			   =					self.sp_model.serialized_model_proto()
																						fi.write(a						)

								return (out_vocab_file,)