






'''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


lowerCamelCase_												=					logging.get_logger(__name__)

lowerCamelCase_												=					{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

lowerCamelCase_												=					{
    '''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''',
    },
}

lowerCamelCase_												=					{
    '''abeja/gpt-neox-japanese-2.7b''': 20_48,
}

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						str:

      '''simple docstring'''


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

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

      return vocab, raw_vocab, ids_to_tokens, emoji

class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

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

   def __init__(		self :	Tuple      ,      __UpperCAmelCase :	int      ,      __UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	List[str]="<|endoftext|>"      ,      __UpperCAmelCase :	Optional[Any]="<|endoftext|>"      ,      __UpperCAmelCase :	Optional[Any]="<|startoftext|>"      ,      __UpperCAmelCase :	Optional[Any]="<|endoftext|>"      ,      __UpperCAmelCase :	Dict=False      ,      **__UpperCAmelCase :	Tuple      ,      ):





         '''simple docstring'''





         super().__init__(
             unk_token=__UpperCAmelCase      ,      pad_token=__UpperCAmelCase      ,      bos_token=__UpperCAmelCase      ,      eos_token=__UpperCAmelCase      ,      do_clean_text=__UpperCAmelCase      ,      **__UpperCAmelCase      ,      )
         if not os.path.isfile(__UpperCAmelCase		):
               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(__UpperCAmelCase		):
               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)`"		)
         _A			     =	do_clean_text
         _A     ,	_A     ,	_A     ,	_A			     =	load_vocab_and_emoji(__UpperCAmelCase      ,      __UpperCAmelCase		)
         _A			     =	SubWordJapaneseTokenizer(
             vocab=self.vocab      ,      ids_to_tokens=self.ids_to_tokens      ,      emoji=self.emoji		)

   @property
   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         return len(self.raw_vocab		)

   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         return dict(self.raw_vocab      ,      **self.added_tokens_encoder		)

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	str		):





         '''simple docstring'''





         return self.subword_tokenizer.tokenize(__UpperCAmelCase      ,      clean=self.do_clean_text		)

   def 						lowerCAmelCase (		self :	Union[str, Any]      ,      __UpperCAmelCase :	Dict		):





         '''simple docstring'''





         return self.vocab.get(__UpperCAmelCase      ,      self.vocab.get(self.unk_token		)		)

   def 						lowerCAmelCase (		self :	Union[str, Any]      ,      __UpperCAmelCase :	Optional[Any]		):





         '''simple docstring'''





         return self.subword_tokenizer.convert_id_to_token(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Optional[Any]      ,      __UpperCAmelCase :	Tuple		):





         '''simple docstring'''





         _A			     =	"".join(__UpperCAmelCase		).strip()
         return out_string

   def 						lowerCAmelCase (		self :	List[Any]      ,      __UpperCAmelCase :	"Conversation"		):





         '''simple docstring'''





         _A			     =	[]
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__UpperCAmelCase      ,      add_special_tokens=__UpperCAmelCase		) + [self.eos_token_id]		)

         if len(__UpperCAmelCase		) > self.model_max_length:
               _A			     =	input_ids[-self.model_max_length :]
         return input_ids

   def 						lowerCAmelCase (		self :	Any      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	Optional[str] = None		):





         '''simple docstring'''





         _A			     =	0
         if os.path.isdir(__UpperCAmelCase		):
               _A			     =	os.path.join(
                   __UpperCAmelCase      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
               _A			     =	os.path.join(
                   __UpperCAmelCase      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]		)
         else:
               _A			     =	(
                   (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
               )
               _A			     =	(
                   (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
               )
         with open(__UpperCAmelCase      ,      "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!"		)
                           _A			     =	token_index
                     writer.write(",".join(__UpperCAmelCase		) + "\n"		)
                     index += 1
         with open(__UpperCAmelCase      ,      "w"      ,      encoding="utf-8"		) as writer:
               json.dump(self.emoji      ,      __UpperCAmelCase		)
         return vocab_file, emoji_file





class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   def __init__(		self :	Optional[Any]      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	Dict		):





         '''simple docstring'''





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

   def __len__(		self :	Optional[int]		):





         '''simple docstring'''





         return len(self.ids_to_tokens		)

   def 						lowerCAmelCase (		self :	Any      ,      __UpperCAmelCase :	List[Any]		):





         '''simple docstring'''





         _A			     =	self.content_repattera.sub("<URL>"      ,      __UpperCAmelCase		)
         _A			     =	self.content_repattera.sub("<EMAIL>"      ,      __UpperCAmelCase		)
         _A			     =	self.content_repattera.sub("<TEL>"      ,      __UpperCAmelCase		)
         _A			     =	self.content_repattera.sub("<DATE>"      ,      __UpperCAmelCase		)
         _A			     =	self.content_repattera.sub("<DATE>"      ,      __UpperCAmelCase		)
         _A			     =	self.content_repattera.sub("<PRICE>"      ,      __UpperCAmelCase		)
         _A			     =	content.translate(self.content_transa		)
         while "<BLOCK><BLOCK>" in content:
               _A			     =	content.replace("<BLOCK><BLOCK>"      ,      "<BLOCK>"		)
         return content

   def 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	Optional[Any]=False		):





         '''simple docstring'''





         _A			     =	text.replace(" "      ,      "<SP>"		)
         _A			     =	text.replace("　"      ,      "<SP>"		)
         _A			     =	text.replace("\r\n"      ,      "<BR>"		)
         _A			     =	text.replace("\n"      ,      "<BR>"		)
         _A			     =	text.replace("\r"      ,      "<BR>"		)
         _A			     =	text.replace("\t"      ,      "<TAB>"		)
         _A			     =	text.replace("—"      ,      "ー"		)
         _A			     =	text.replace("−"      ,      "ー"		)
         for k, v in self.emoji["emoji"].items():
               if k in text:
                     _A			     =	text.replace(__UpperCAmelCase      ,      __UpperCAmelCase		)
         if clean:
               _A			     =	self.clean_text(__UpperCAmelCase		)

         def check_simbol(__UpperCAmelCase :	Union[str, Any]		):
               _A			     =	x.encode()
               if len(__UpperCAmelCase		) == 1 and len(__UpperCAmelCase		) == 2:
                     _A			     =	(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(__UpperCAmelCase :	Any		):
               _A			     =	x.encode()
               if len(__UpperCAmelCase		) == 1 and len(__UpperCAmelCase		) == 3:
                     _A			     =	(int(e[0]		) << 16) + (int(e[1]		) << 8) + int(e[2]		)
                     if c >= 0xE2_8080 and c <= 0xE2_B07F:
                           return True
               return False

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

   def 						lowerCAmelCase (		self :	List[Any]      ,      __UpperCAmelCase :	List[Any]      ,      __UpperCAmelCase :	List[str]="\n"		):





         '''simple docstring'''





         _A			     =	[]
         _A			     =	[]
         _A			     =	self.ids_to_tokens[index][0]
         if word[:6] == "<|byte" and word[-2:] == "|>":
               byte_tokens.append(int(word[6:-2]		)		)
         else:
               if len(__UpperCAmelCase		) > 0:
                     words.append(bytearray(__UpperCAmelCase		).decode("utf-8"      ,      errors="replace"		)		)
                     _A			     =	[]
               if word[:7] == "<|emoji" and word[-2:] == "|>":
                     words.append(self.emoji["emoji_inv"][word]		)
               elif word == "<SP>":
                     words.append(" "		)
               elif word == "<BR>":
                     words.append(__UpperCAmelCase		)
               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(__UpperCAmelCase		)
         if len(__UpperCAmelCase		) > 0:
               words.append(bytearray(__UpperCAmelCase		).decode("utf-8"      ,      errors="replace"		)		)
         _A			     =	"".join(__UpperCAmelCase		)
         return text












"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Dict	    =   ["""image_processor""", """tokenizer"""]
      lowercase_      : Union[str, Any]	    =   """ViltImageProcessor"""
      lowercase_      : Any	    =   ("""BertTokenizer""", """BertTokenizerFast""")

      def __init__(     self					,      snake_case_=None					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  None
        if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'					,      snake_case_					,      )
          A_  :       Dict										=  kwargs.pop('feature_extractor'  )

        A_  :       Dict										=  image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
          raise ValueError('You need to specify an `image_processor`.'  )
        if tokenizer is None:
          raise ValueError('You need to specify a `tokenizer`.'  )

        super().__init__(snake_case_					,      snake_case_  )
        A_  :       List[str]										=  self.image_processor

      def __call__(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = True					,      snake_case_ = False					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = 0					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = True					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       str										=  self.tokenizer(
            text=snake_case_					,      add_special_tokens=snake_case_					,      padding=snake_case_					,      truncation=snake_case_					,      max_length=snake_case_					,      stride=snake_case_					,      pad_to_multiple_of=snake_case_					,      return_token_type_ids=snake_case_					,      return_attention_mask=snake_case_					,      return_overflowing_tokens=snake_case_					,      return_special_tokens_mask=snake_case_					,      return_offsets_mapping=snake_case_					,      return_length=snake_case_					,      verbose=snake_case_					,      return_tensors=snake_case_					,      **snake_case_					,      )
        # add pixel_values + pixel_mask
        A_  :       Optional[int]										=  self.image_processor(snake_case_					,      return_tensors=snake_case_  )
        encoding.update(snake_case_  )

        return encoding

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return self.tokenizer.batch_decode(*snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return self.tokenizer.decode(*snake_case_					,      **snake_case_  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.tokenizer.model_input_names
        A_  :       Any										=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,      snake_case_					,      )
        return self.image_processor_class

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,      snake_case_					,      )
        return self.image_processor