

'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    str     ,					lowercase_						:    str     ,					lowercase_						:    PreTrainedTokenizer     ,					lowercase_						:    int     ,					lowercase_						:    Optional[int] = None     ,					)		->	int:




  '''simple docstring'''



  lowercase									={}

  if train_file is not None:
    lowercase									=[train_file]
  if eval_file is not None:
    lowercase									=[eval_file]
  if test_file is not None:
    lowercase									=[test_file]

  lowercase									=datasets.load_dataset('''csv'''     ,					data_files=lowercase_	)
  lowercase									=list(ds[list(files.keys()	)[0]].features.keys()	)
  lowercase									=features_name.pop(lowercase_	)
  lowercase									=list(set(ds[list(files.keys()	)[0]][label_name]	)	)
  lowercase									={label: i for i, label in enumerate(lowercase_	)}
  lowercase									=tokenizer.model_input_names
  lowercase									={}

  if len(lowercase_	) == 1:
    for k in files.keys():
      lowercase									=ds[k].map(
          lambda lowercase_	: tokenizer.batch_encode_plus(
              example[features_name[0]]     ,					truncation=lowercase_     ,					max_length=lowercase_     ,					padding='''max_length'''	)     ,					batched=lowercase_     ,					)
  elif len(lowercase_	) == 2:
    for k in files.keys():
      lowercase									=ds[k].map(
          lambda lowercase_	: tokenizer.batch_encode_plus(
              (example[features_name[0]], example[features_name[1]])     ,					truncation=lowercase_     ,					max_length=lowercase_     ,					padding='''max_length'''     ,					)     ,					batched=lowercase_     ,					)

  def gen_train():
    for ex in transformed_ds[datasets.Split.TRAIN]:
      lowercase									={k: v for k, v in ex.items() if k in input_names}
      lowercase									=labelaid[ex[label_name]]
      yield (d, label)

  def gen_val():
    for ex in transformed_ds[datasets.Split.VALIDATION]:
      lowercase									={k: v for k, v in ex.items() if k in input_names}
      lowercase									=labelaid[ex[label_name]]
      yield (d, label)

  def gen_test():
    for ex in transformed_ds[datasets.Split.TEST]:
      lowercase									={k: v for k, v in ex.items() if k in input_names}
      lowercase									=labelaid[ex[label_name]]
      yield (d, label)

  lowercase									=(
      tf.data.Dataset.from_generator(
          lowercase_     ,					({k: tf.intaa for k in input_names}, tf.intaa)     ,					({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))     ,					)
      if datasets.Split.TRAIN in transformed_ds
      else None
  )

  if train_ds is not None:
    lowercase									=train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

  lowercase									=(
      tf.data.Dataset.from_generator(
          lowercase_     ,					({k: tf.intaa for k in input_names}, tf.intaa)     ,					({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))     ,					)
      if datasets.Split.VALIDATION in transformed_ds
      else None
  )

  if val_ds is not None:
    lowercase									=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

  lowercase									=(
      tf.data.Dataset.from_generator(
          lowercase_     ,					({k: tf.intaa for k in input_names}, tf.intaa)     ,					({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))     ,					)
      if datasets.Split.TEST in transformed_ds
      else None
  )

  if test_ds is not None:
    lowercase									=test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

  return train_ds, val_ds, test_ds, labelaid


_UpperCAmelCase  :		List[Any]  =		logging.getLogger(__name__)

@dataclass
class 						__magic_name__   :
 UpperCamelCase__     =       field(metadata={'help': 'Which column contains the label'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'The path of the training file'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'The path of the development file'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'The path of the test file'} )
 UpperCamelCase__     =       field(
     default=1_28 ,				metadata={
         'help': (
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )
     } ,				)
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Overwrite the cached training and evaluation sets'} )

@dataclass
class 						__magic_name__   :
 UpperCamelCase__     =       field(
     metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Set this flag to use fast tokenization.'} )
 # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
 # or just modify its tokenizer_config.json.
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} ,				)




def 	UpperCamelCase   (       )		->	Optional[int]:




  '''simple docstring'''



  lowercase									=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
  lowercase							, lowercase							, lowercase									=parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir	)
      and os.listdir(training_args.output_dir	)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
        ''' --overwrite_output_dir to overcome.'''	)

  # Setup logging
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,					datefmt='''%m/%d/%Y %H:%M:%S'''     ,					level=logging.INFO     ,					)
  logger.info(
      f'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1	)}, '
      f'16-bits training: {training_args.fpaa}'	)
  logger.info(f'Training/evaluation parameters {training_args}'	)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  lowercase									=AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,					cache_dir=model_args.cache_dir     ,					)

  lowercase							, lowercase							, lowercase							, lowercase									=get_tfds(
      train_file=data_args.train_file     ,					eval_file=data_args.dev_file     ,					test_file=data_args.test_file     ,					tokenizer=lowercase_     ,					label_column_id=data_args.label_column_id     ,					max_seq_length=data_args.max_seq_length     ,					)

  lowercase									=AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,					num_labels=len(lowercase_	)     ,					labelaid=lowercase_     ,					idalabel={id: label for label, id in labelaid.items()}     ,					finetuning_task='''text-classification'''     ,					cache_dir=model_args.cache_dir     ,					)

  with training_args.strategy.scope():
    lowercase									=TFAutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path     ,					from_pt=bool('''.bin''' in model_args.model_name_or_path	)     ,					config=lowercase_     ,					cache_dir=model_args.cache_dir     ,					)

  def compute_metrics(lowercase_						:    EvalPrediction	) -> Dict:
    lowercase									=np.argmax(p.predictions     ,					axis=1	)

    return {"acc": (preds == p.label_ids).mean()}

  # Initialize our Trainer
  lowercase									=TFTrainer(
      model=lowercase_     ,					args=lowercase_     ,					train_dataset=lowercase_     ,					eval_dataset=lowercase_     ,					compute_metrics=lowercase_     ,					)

  # Training
  if training_args.do_train:
    trainer.train()
    trainer.save_model()
    tokenizer.save_pretrained(training_args.output_dir	)

  # Evaluation
  lowercase									={}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''	)
    lowercase									=trainer.evaluate()
    lowercase									=os.path.join(training_args.output_dir     ,					'''eval_results.txt'''	)

    with open(lowercase_     ,					'''w'''	) as writer:
      logger.info('''***** Eval results *****'''	)

      for key, value in result.items():
        logger.info(f'  {key} = {value}'	)
        writer.write(f'{key} = {value}\n'	)

      results.update(lowercase_	)

  return results


if __name__ == "__main__":
     main()




'''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					:				List[str]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

__SCREAMING_SNAKE_CASE					:				str									=		{
    '''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					:				List[Any]									=		{
    '''abeja/gpt-neox-japanese-2.7b''': 2048,
}







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							with open(UpperCamelCase_						,  "r"						,  encoding="utf-8" ) as f:
														A_			    =		json.loads(f.read() )

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

							return vocab, raw_vocab, ids_to_tokens, emoji






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






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






						def __init__(  self							:      str ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      List[Any]="<|endoftext|>" ,  lowerCAmelCase							:      Optional[int]="<|endoftext|>" ,  lowerCAmelCase							:      Tuple="<|startoftext|>" ,  lowerCAmelCase							:      Union[str, Any]="<|endoftext|>" ,  lowerCAmelCase							:      List[Any]=False ,  **lowerCAmelCase							:      Optional[Any] ,  ):
													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)`"	)
													A_			    =		do_clean_text
													A_    ,					A_    ,					A_    ,					A_			    =		load_vocab_and_emoji(lowerCAmelCase ,  lowerCAmelCase	)
													A_			    =		SubWordJapaneseTokenizer(
													    vocab=self.vocab ,  ids_to_tokens=self.ids_to_tokens ,  emoji=self.emoji	)






						@property
						def 				_UpperCAmelCase       (  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 				_UpperCAmelCase       (  self							:      Optional[int]	):
													return dict(self.raw_vocab ,  **self.added_tokens_encoder	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      Tuple	):
													return self.subword_tokenizer.tokenize(lowerCAmelCase ,  clean=self.do_clean_text	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      int	):
													return self.vocab.get(lowerCAmelCase ,  self.vocab.get(self.unk_token	)	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      List[str]	):
													return self.subword_tokenizer.convert_id_to_token(lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      Optional[Any]	):
													A_			    =		"".join(lowerCAmelCase	).strip()
													return out_string






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      "Conversation"	):
													A_			    =		[]
													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:
																				A_			    =		input_ids[-self.model_max_length :]
													return input_ids






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Optional[str] = None	):
													A_			    =		0
													if os.path.isdir(lowerCAmelCase	):
																				A_			    =		os.path.join(
																				    lowerCAmelCase ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
																				A_			    =		os.path.join(
																				    lowerCAmelCase ,  (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(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!"	)
																																		A_			    =		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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      List[Any]	):
													A_			    =		vocab  # same as swe
													A_			    =		ids_to_tokens  # same as bpe
													A_			    =		emoji
													A_			    =		np.max([len(lowerCAmelCase	) 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							:      Union[str, Any]	):
													return len(self.ids_to_tokens	)






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      int	):
													A_			    =		self.content_repattera.sub("<URL>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<EMAIL>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<TEL>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<DATE>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<DATE>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<PRICE>" ,  lowerCAmelCase	)
													A_			    =		content.translate(self.content_transa	)
													while "<BLOCK><BLOCK>" in content:
																				A_			    =		content.replace("<BLOCK><BLOCK>" ,  "<BLOCK>"	)
													return content






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Tuple=False	):
													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(lowerCAmelCase ,  lowerCAmelCase	)
													if clean:
																				A_			    =		self.clean_text(lowerCAmelCase	)

													def check_simbol(lowerCAmelCase							:      Tuple	):
																				A_			    =		x.encode()
																				if len(lowerCAmelCase	) == 1 and len(lowerCAmelCase	) == 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(lowerCAmelCase							:      Tuple	):
																				A_			    =		x.encode()
																				if len(lowerCAmelCase	) == 1 and len(lowerCAmelCase	) == 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(lowerCAmelCase	):
																				A_			    =		min(len(lowerCAmelCase	) ,  pos + self.maxlen + 1	) if text[pos] == "<" else pos + 3
																				A_			    =		[]  # (token_id, token, pos)
																				for e in range(lowerCAmelCase ,  lowerCAmelCase ,  -1	):
																											A_			    =		text[pos:e]
																											if wd in self.vocab:
																																		if wd[0] == "<" and len(lowerCAmelCase	) > 2:
																																									A_			    =		[(self.vocab[wd], wd, e)]
																																									break
																																		else:
																																									candidates.append((self.vocab[wd], wd, e)	)
																				if len(lowerCAmelCase	) > 0:
																											# the smallest token_id is adopted
																											A_    ,					A_    ,					A_			    =		sorted(lowerCAmelCase ,  key=lambda lowerCAmelCase	: x[0]	)[0]
																											result.append(lowerCAmelCase	)
																											A_			    =		e
																				else:
																											A_			    =		pos + 1
																											A_			    =		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	)
																											A_			    =		end
													return result






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      Union[str, Any]="\n"	):
													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(lowerCAmelCase	) > 0:
																											words.append(bytearray(lowerCAmelCase	).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(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"	)	)
													A_			    =		"".join(lowerCAmelCase	)
													return text





