





'''simple docstring'''

import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


a   	=    logging.get_logger(__name__)

a   	=    list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
a   	=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						field(
       default=_snake_case, metadata={'help': 'Model type selected in the list: ' + ', '.join(_snake_case      )}      )
   __UpperCamelCase :			str						   =						field(
       default=_snake_case, metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}      )
   __UpperCamelCase :			int						   =						field(
       default=128, metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }, )
   __UpperCamelCase :			int						   =						field(
       default=128, metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}, )
   __UpperCamelCase :			int						   =						field(
       default=64, metadata={
           'help': (
               'The maximum number of tokens for the question. Questions longer than this will '
               'be truncated to this length.'
           )
       }, )
   __UpperCamelCase :			int						   =						field(
       default=30, metadata={
           'help': (
               'The maximum length of an answer that can be generated. This is needed because the start '
               'and end predictions are not conditioned on one another.'
           )
       }, )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Overwrite the cached training and evaluation sets'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}      )
   __UpperCamelCase :			float						   =						field(
       default=0.0, metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}      )
   __UpperCamelCase :			int						   =						field(
       default=20, metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}      )
   __UpperCamelCase :			int						   =						field(
       default=0, metadata={
           'help': (
               'language id of input for language-specific xlm models (see'
               ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
           )
       }, )
   __UpperCamelCase :			int						   =						field(default=1, metadata={'help': 'multiple threads for converting example to features'}      )







class __a			( _snake_case      ):
   __UpperCamelCase :			Optional[Any]						   =						'train'
   __UpperCamelCase :			List[Any]						   =						'dev'







class __a			( _snake_case      ):
   __UpperCamelCase :			SquadDataTrainingArguments
   __UpperCamelCase :			List[SquadFeatures]
   __UpperCamelCase :			Split
   __UpperCamelCase :			bool





   def __init__(       self					:      List[str]					,lowerCamelCase					:      SquadDataTrainingArguments					,lowerCamelCase					:      PreTrainedTokenizer					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Union[str, Split] = Split.train					,lowerCamelCase					:      Optional[bool] = False					,lowerCamelCase					:      Optional[str] = None					,lowerCamelCase					:      Optional[str] = "pt"					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							args
      __SCREAMING_SNAKE_CASE   				=							is_language_sensitive
      __SCREAMING_SNAKE_CASE   				=							SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
      if isinstance(lowerCamelCase					,lowerCamelCase     ):
         try:
            __SCREAMING_SNAKE_CASE   				=							Split[mode]
         except KeyError:
            raise KeyError("""mode is not a valid split name"""     )
      __SCREAMING_SNAKE_CASE   				=							mode
      # Load data features from cache or dataset file
      __SCREAMING_SNAKE_CASE   				=							"""v2""" if args.version_2_with_negative else """v1"""
      __SCREAMING_SNAKE_CASE   				=							os.path.join(
          cache_dir if cache_dir is not None else args.data_dir					,f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""					,)

      # Make sure only the first process in distributed training processes the dataset,
      # and the others will use the cache.
      __SCREAMING_SNAKE_CASE   				=							cached_features_file + """.lock"""
      with FileLock(lowerCamelCase     ):
         if os.path.exists(lowerCamelCase     ) and not args.overwrite_cache:
            __SCREAMING_SNAKE_CASE   				=							time.time()
            __SCREAMING_SNAKE_CASE   				=							torch.load(lowerCamelCase     )

            # Legacy cache files have only features, while new cache files
            # will have dataset and examples also.
            __SCREAMING_SNAKE_CASE   				=							self.old_features["""features"""]
            __SCREAMING_SNAKE_CASE   				=							self.old_features.get("""dataset"""					,lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							self.old_features.get("""examples"""					,lowerCamelCase     )
            logger.info(
                f"""Loading features from cached file {cached_features_file} [took %.3f s]"""					,time.time() - start     )

            if self.dataset is None or self.examples is None:
               logger.warning(
                   f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
                   """ future run"""     )
         else:
            if mode == Split.dev:
               __SCREAMING_SNAKE_CASE   				=							self.processor.get_dev_examples(args.data_dir     )
            else:
               __SCREAMING_SNAKE_CASE   				=							self.processor.get_train_examples(args.data_dir     )

            __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							squad_convert_examples_to_features(
                examples=self.examples					,tokenizer=lowerCamelCase					,max_seq_length=args.max_seq_length					,doc_stride=args.doc_stride					,max_query_length=args.max_query_length					,is_training=mode == Split.train					,threads=args.threads					,return_dataset=lowerCamelCase					,)

            __SCREAMING_SNAKE_CASE   				=							time.time()
            torch.save(
                {"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples}					,lowerCamelCase					,)
            # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
            logger.info(
                f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""     )





   def __len__(       self					:      Optional[Any]     ):

      '''simple docstring'''

      return len(self.features     )





   def __getitem__(       self					:      Optional[Any]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.features[i]

      __SCREAMING_SNAKE_CASE   				=							torch.tensor(feature.input_ids					,dtype=torch.long     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(feature.attention_mask					,dtype=torch.long     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(feature.token_type_ids					,dtype=torch.long     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(feature.cls_index					,dtype=torch.long     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(feature.p_mask					,dtype=torch.float     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(feature.is_impossible					,dtype=torch.float     )

      __SCREAMING_SNAKE_CASE   				=							{
          """input_ids""": input_ids,
          """attention_mask""": attention_mask,
          """token_type_ids""": token_type_ids,
      }

      if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
         del inputs["token_type_ids"]

      if self.args.model_type in ["xlnet", "xlm"]:
         inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask}     )
         if self.args.version_2_with_negative:
            inputs.update({"""is_impossible""": is_impossible}     )
         if self.is_language_sensitive:
            inputs.update({"""langs""": (torch.ones(input_ids.shape					,dtype=torch.intaa     ) * self.args.lang_id)}     )

      if self.mode == Split.train:
         __SCREAMING_SNAKE_CASE   				=							torch.tensor(feature.start_position					,dtype=torch.long     )
         __SCREAMING_SNAKE_CASE   				=							torch.tensor(feature.end_position					,dtype=torch.long     )
         inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions}     )

      return inputs












'''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   	=    logging.get_logger(__name__)

a   	=    {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

a   	=    {
    "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   	=    {
    "abeja/gpt-neox-japanese-2.7b": 2048,
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







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

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

   return vocab, raw_vocab, ids_to_tokens, emoji







class __a			( _snake_case      ):
   __UpperCamelCase :			Tuple						   =						VOCAB_FILES_NAMES
   __UpperCamelCase :			Tuple						   =						PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase :			Any						   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase :			Union[str, Any]						   =						['input_ids', 'attention_mask']





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

      '''simple docstring'''

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





   @property
   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return len(self.raw_vocab     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      str     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      return self.subword_tokenizer.convert_id_to_token(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""""".join(lowerCamelCase     ).strip()
      return out_string





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      "Conversation"     ):

      '''simple docstring'''

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

      if len(lowerCamelCase     ) > self.model_max_length:
         __SCREAMING_SNAKE_CASE   				=							input_ids[-self.model_max_length :]
      return input_ids





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      Optional[str] = None     ):

      '''simple docstring'''

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







class __a			( _snake_case      ):





   def __init__(       self					:      Tuple					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

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





   def __len__(       self					:      List[str]     ):

      '''simple docstring'''

      return len(self.ids_to_tokens     )





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[str]=False     ):

      '''simple docstring'''

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

      def check_simbol(lowerCamelCase					:      List[str]     ):
         __SCREAMING_SNAKE_CASE   				=							x.encode()
         if len(lowerCamelCase     ) == 1 and len(lowerCamelCase     ) == 2:
            __SCREAMING_SNAKE_CASE   				=							(int(e[0]     ) << 8) + int(e[1]     )
            if (
                (c >= 0xC_2A1 and c <= 0xC_2BF)
                or (c >= 0xC_780 and c <= 0xC_783)
                or (c >= 0xC_AB9 and c <= 0xC_BBF)
                or (c >= 0xC_C80 and c <= 0xC_DA2)
            ):
               return True
         return False

      def checkuae(lowerCamelCase					:      Union[str, Any]     ):
         __SCREAMING_SNAKE_CASE   				=							x.encode()
         if len(lowerCamelCase     ) == 1 and len(lowerCamelCase     ) == 3:
            __SCREAMING_SNAKE_CASE   				=							(int(e[0]     ) << 16) + (int(e[1]     ) << 8) + int(e[2]     )
            if c >= 0xE28_080 and c <= 0xE2B_07F:
               return True
         return False

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





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]="\n"     ):

      '''simple docstring'''

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





