'''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


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

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

SCREAMING_SNAKE_CASE_:	List[Any]    ={
    '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',
    },
}

SCREAMING_SNAKE_CASE_:	Optional[Any]    ={
    'abeja/gpt-neox-japanese-2.7b': 20_48,
}





def       lowerCAmelCase_							(   snake_case_			:					Any   ,       snake_case_			:					Tuple     )       -> int:


     '''simple docstring'''




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

     UpperCAmelCase_			       =  collections.OrderedDict()
     UpperCAmelCase_			       =  collections.OrderedDict()
     UpperCAmelCase_			       =  collections.OrderedDict()
     with open(snake_case_   ,       "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(snake_case_     ):
          UpperCAmelCase_			       =  b
          UpperCAmelCase_			       =  idx
          for wd in b:
               UpperCAmelCase_			       =  idx

     return vocab, raw_vocab, ids_to_tokens, emoji




class 			__A (					UpperCamelCase__      ):
    a__			:		int			  =    VOCAB_FILES_NAMES
    a__			:		List[Any]			  =    PRETRAINED_VOCAB_FILES_MAP
    a__			:		Optional[int]			  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__			:		Tuple			  =    ["""input_ids""", """attention_mask"""]



    def __init__(self       :       int  ,     __a       :       Any  ,     __a       :       int  ,     __a       :       Optional[Any]="<|endoftext|>"  ,     __a       :       Optional[Any]="<|endoftext|>"  ,     __a       :       Optional[Any]="<|startoftext|>"  ,     __a       :       int="<|endoftext|>"  ,     __a       :       Tuple=False  ,     **__a       :       Tuple  ,     ):
         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    _lowercase		(self       :       int					):
         # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
         return len(self.raw_vocab					)



    def    _lowercase		(self       :       Union[str, Any]					):
         return dict(self.raw_vocab  ,     **self.added_tokens_encoder					)



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       List[str]					):
         return self.subword_tokenizer.tokenize(__a  ,     clean=self.do_clean_text					)



    def    _lowercase		(self       :       Any  ,     __a       :       List[Any]					):
         return self.vocab.get(__a  ,     self.vocab.get(self.unk_token					)					)



    def    _lowercase		(self       :       Tuple  ,     __a       :       Tuple					):
         return self.subword_tokenizer.convert_id_to_token(__a					)



    def    _lowercase		(self       :       Optional[Any]  ,     __a       :       Optional[int]					):
         UpperCAmelCase_			       =  "".join(__a					).strip()
         return out_string



    def    _lowercase		(self       :       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    _lowercase		(self       :       Optional[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 			__A (					UpperCamelCase__      ):



    def __init__(self       :       Optional[Any]  ,     __a       :       str  ,     __a       :       Tuple  ,     __a       :       Optional[int]					):
         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       :       Dict					):
         return len(self.ids_to_tokens					)



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       Optional[int]					):
         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    _lowercase		(self       :       List[str]  ,     __a       :       Union[str, Any]  ,     __a       :       str=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       :       Union[str, Any]					):
              UpperCAmelCase_			       =  x.encode()
              if len(__a					) == 1 and len(__a					) == 2:
                   UpperCAmelCase_			       =  (int(e[0]					) << 8) + int(e[1]					)
                   if (
                       (c >= 0Xc2_a1 and c <= 0Xc2_bf)
                       or (c >= 0Xc7_80 and c <= 0Xc7_83)
                       or (c >= 0Xca_b9 and c <= 0Xcb_bf)
                       or (c >= 0Xcc_80 and c <= 0Xcd_a2)
                   ):
                        return True
              return False

         def checkuae(__a       :       Dict					):
              UpperCAmelCase_			       =  x.encode()
              if len(__a					) == 1 and len(__a					) == 3:
                   UpperCAmelCase_			       =  (int(e[0]					) << 16) + (int(e[1]					) << 8) + int(e[2]					)
                   if c >= 0Xe2_80_80 and c <= 0Xe2_b0_7f:
                        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    _lowercase		(self       :       Dict  ,     __a       :       Tuple  ,     __a       :       Optional[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








'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'ChineseCLIPImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




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

							'''simple docstring'''


							UpperCamelCase__  :List[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.'''       ,  UpperCamelCase_       ,  )
												UpperCamelCase__  :List[Any]				   =					kwargs.pop('''feature_extractor'''				)

							UpperCamelCase__  :List[str]				   =					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__(UpperCamelCase_       ,  UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

							if text is not None:
												UpperCamelCase__  :Tuple				   =					self.tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if images is not None:
												UpperCamelCase__  :Any				   =					self.image_processor(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if text is not None and images is not None:
												UpperCamelCase__  :Optional[int]				   =					image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**UpperCamelCase_				)       ,  tensor_type=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :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.'''       ,  UpperCamelCase_       ,  )
							return self.image_processor_class