






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


snake_case__      :	str       	=      logging.get_logger(__name__)

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

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""",
    },
}

snake_case__      :	Dict       	=      {
    """abeja/gpt-neox-japanese-2.7b""": 2_0_4_8,
}






def     _snake_case    (__lowercase    ,      __lowercase):
   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 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   VOCAB_FILES_NAMES
  A_						=   PRETRAINED_VOCAB_FILES_MAP
  A_						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_						=   ["""input_ids""", """attention_mask"""]




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase="<|endoftext|>"		,		_UpperCAmelCase="<|endoftext|>"		,		_UpperCAmelCase="<|startoftext|>"		,		_UpperCAmelCase="<|endoftext|>"		,		_UpperCAmelCase=False		,		**_UpperCAmelCase		,		)			-> Union[str, Any]:
     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)`' )
     UpperCamelCase_									=     do_clean_text
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     load_vocab_and_emoji(_UpperCAmelCase		,		_UpperCAmelCase )
     UpperCamelCase_									=     SubWordJapaneseTokenizer(
         vocab=self.vocab		,		ids_to_tokens=self.ids_to_tokens		,		emoji=self.emoji )




  @property
  def    _UpperCAmelCase     (     self )			-> Tuple:
     # 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 )			-> Any:
     return dict(self.raw_vocab		,		**self.added_tokens_encoder )




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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Dict:
     return self.vocab.get(_UpperCAmelCase		,		self.vocab.get(self.unk_token ) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Tuple:
     return self.subword_tokenizer.convert_id_to_token(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     ''.join(_UpperCAmelCase ).strip()
     return out_string




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[int]:
     UpperCamelCase_									=     []
     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:
        UpperCamelCase_									=     input_ids[-self.model_max_length :]
     return input_ids




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> Tuple[str]:
     UpperCamelCase_									=     0
     if os.path.isdir(_UpperCAmelCase ):
        UpperCamelCase_									=     os.path.join(
            _UpperCAmelCase		,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
        UpperCamelCase_									=     os.path.join(
            _UpperCAmelCase		,		(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(_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!' )
              UpperCamelCase_									=     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 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Union[str, Any]:
     UpperCamelCase_									=     vocab  # same as swe
     UpperCamelCase_									=     ids_to_tokens  # same as bpe
     UpperCamelCase_									=     emoji
     UpperCamelCase_									=     np.max([len(_UpperCAmelCase ) 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 )			-> Any:
     return len(self.ids_to_tokens )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Tuple:
     UpperCamelCase_									=     self.content_repattera.sub('<URL>'		,		_UpperCAmelCase )
     UpperCamelCase_									=     self.content_repattera.sub('<EMAIL>'		,		_UpperCAmelCase )
     UpperCamelCase_									=     self.content_repattera.sub('<TEL>'		,		_UpperCAmelCase )
     UpperCamelCase_									=     self.content_repattera.sub('<DATE>'		,		_UpperCAmelCase )
     UpperCamelCase_									=     self.content_repattera.sub('<DATE>'		,		_UpperCAmelCase )
     UpperCamelCase_									=     self.content_repattera.sub('<PRICE>'		,		_UpperCAmelCase )
     UpperCamelCase_									=     content.translate(self.content_transa )
     while "<BLOCK><BLOCK>" in content:
        UpperCamelCase_									=     content.replace('<BLOCK><BLOCK>'		,		'<BLOCK>' )
     return content




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=False )			-> Optional[Any]:
     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(_UpperCAmelCase		,		_UpperCAmelCase )
     if clean:
        UpperCamelCase_									=     self.clean_text(_UpperCAmelCase )

     def check_simbol(_UpperCAmelCase ):
        UpperCamelCase_									=     x.encode()
        if len(_UpperCAmelCase ) == 1 and len(_UpperCAmelCase ) == 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(_UpperCAmelCase ):
        UpperCamelCase_									=     x.encode()
        if len(_UpperCAmelCase ) == 1 and len(_UpperCAmelCase ) == 3:
           UpperCamelCase_									=     (int(e[0] ) << 16) + (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(_UpperCAmelCase ):
        UpperCamelCase_									=     min(len(_UpperCAmelCase )		,		pos + self.maxlen + 1 ) if text[pos] == '<' else pos + 3
        UpperCamelCase_									=     []  # (token_id, token, pos)
        for e in range(_UpperCAmelCase		,		_UpperCAmelCase		,		-1 ):
           UpperCamelCase_									=     text[pos:e]
           if wd in self.vocab:
              if wd[0] == "<" and len(_UpperCAmelCase ) > 2:
                 UpperCamelCase_									=     [(self.vocab[wd], wd, e)]
                 break
              else:
                 candidates.append((self.vocab[wd], wd, e) )
        if len(_UpperCAmelCase ) > 0:
           # the smallest token_id is adopted
           UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     sorted(_UpperCAmelCase		,		key=lambda _UpperCAmelCase : x[0] )[0]
           result.append(_UpperCAmelCase )
           UpperCamelCase_									=     e
        else:
           UpperCamelCase_									=     pos + 1
           UpperCamelCase_									=     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 )
           UpperCamelCase_									=     end
     return result




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase="\n" )			-> Dict:
     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(_UpperCAmelCase ) > 0:
           words.append(bytearray(_UpperCAmelCase ).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(_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' ) )
     UpperCamelCase_									=     ''.join(_UpperCAmelCase )
     return text













import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


snake_case__      :	str       	=      logging.getLogger(__name__)


@dataclass(frozen=UpperCAmelCase__    )
class 						_a       :






  """simple docstring"""





  A_						=   42
  A_						=   42
  A_						=   None
  A_						=   None
  A_						=   None




@dataclass(frozen=UpperCAmelCase__    )
class 						_a       :






  """simple docstring"""





  A_						=   42
  A_						=   None
  A_						=   None
  A_						=   None
  A_						=   None


if is_torch_available():
 import torch
 from torch.utils.data import Dataset


 class 						_a       (  UpperCAmelCase__    ):






   """simple docstring"""





   A_						=   42




   def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase=False		,		_UpperCAmelCase = False		,		)			-> Any:
      UpperCamelCase_									=     hans_processors[task]()

      UpperCamelCase_									=     os.path.join(
          _UpperCAmelCase		,		'cached_{}_{}_{}_{}'.format(
              'dev' if evaluate else 'train'		,		tokenizer.__class__.__name__		,		str(_UpperCAmelCase )		,		_UpperCAmelCase		,		)		,		)
      UpperCamelCase_									=     processor.get_labels()
      if tokenizer.__class__ in (
          RobertaTokenizer,
          RobertaTokenizerFast,
          XLMRobertaTokenizer,
          BartTokenizer,
          BartTokenizerFast,
      ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         UpperCamelCase_		,      UpperCamelCase_									=     label_list[2], label_list[1]
      UpperCamelCase_									=     label_list

      # Make sure only the first process in distributed training processes the dataset,
      # and the others will use the cache.
      UpperCamelCase_									=     cached_features_file + '.lock'
      with FileLock(_UpperCAmelCase ):
         if os.path.exists(_UpperCAmelCase ) and not overwrite_cache:
            logger.info(f"""Loading features from cached file {cached_features_file}""" )
            UpperCamelCase_									=     torch.load(_UpperCAmelCase )
         else:
            logger.info(f"""Creating features from dataset file at {data_dir}""" )

            UpperCamelCase_									=     (
                processor.get_dev_examples(_UpperCAmelCase ) if evaluate else processor.get_train_examples(_UpperCAmelCase )
            )

            logger.info('Training examples: %s'		,		len(_UpperCAmelCase ) )
            UpperCamelCase_									=     hans_convert_examples_to_features(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )
            logger.info('Saving features into cached file %s'		,		_UpperCAmelCase )
            torch.save(self.features		,		_UpperCAmelCase )




   def __len__(     self )			-> Dict:
      return len(self.features )




   def __getitem__(     self		,		_UpperCAmelCase )			-> InputFeatures:
      return self.features[i]




   def    _UpperCAmelCase     (     self )			-> Dict:
      return self.label_list


if is_tf_available():
 import tensorflow as tf


 class 						_a       :






   """simple docstring"""





   A_						=   42




   def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = 128		,		_UpperCAmelCase=False		,		_UpperCAmelCase = False		,		)			-> int:
      UpperCamelCase_									=     hans_processors[task]()
      UpperCamelCase_									=     processor.get_labels()
      if tokenizer.__class__ in (
          RobertaTokenizer,
          RobertaTokenizerFast,
          XLMRobertaTokenizer,
          BartTokenizer,
          BartTokenizerFast,
      ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         UpperCamelCase_		,      UpperCamelCase_									=     label_list[2], label_list[1]
      UpperCamelCase_									=     label_list

      UpperCamelCase_									=     processor.get_dev_examples(_UpperCAmelCase ) if evaluate else processor.get_train_examples(_UpperCAmelCase )
      UpperCamelCase_									=     hans_convert_examples_to_features(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

      def gen():
         for ex_index, ex in tqdm.tqdm(enumerate(self.features )		,		desc='convert examples to features' ):
            if ex_index % 10000 == 0:
               logger.info('Writing example %d of %d' % (ex_index, len(_UpperCAmelCase )) )

            yield (
                {
                    "example_id": 0,
                    "input_ids": ex.input_ids,
                    "attention_mask": ex.attention_mask,
                    "token_type_ids": ex.token_type_ids,
                },
                ex.label,
            )

      UpperCamelCase_									=     tf.data.Dataset.from_generator(
          _UpperCAmelCase		,		(
              {
                  'example_id': tf.intaa,
                  'input_ids': tf.intaa,
                  'attention_mask': tf.intaa,
                  'token_type_ids': tf.intaa,
              },
              tf.intaa,
          )		,		(
              {
                  'example_id': tf.TensorShape([] ),
                  'input_ids': tf.TensorShape([None, None] ),
                  'attention_mask': tf.TensorShape([None, None] ),
                  'token_type_ids': tf.TensorShape([None, None] ),
              },
              tf.TensorShape([] ),
          )		,		)




   def    _UpperCAmelCase     (     self )			-> List[str]:
      return self.dataset




   def __len__(     self )			-> str:
      return len(self.features )




   def __getitem__(     self		,		_UpperCAmelCase )			-> InputFeatures:
      return self.features[i]




   def    _UpperCAmelCase     (     self )			-> int:
      return self.label_list


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Tuple:
     return self._create_examples(self._read_tsv(os.path.join(_UpperCAmelCase		,		'heuristics_train_set.txt' ) )		,		'train' )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Dict:
     return self._create_examples(self._read_tsv(os.path.join(_UpperCAmelCase		,		'heuristics_evaluation_set.txt' ) )		,		'dev' )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     return ["contradiction", "entailment", "neutral"]




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     []
     for i, line in enumerate(_UpperCAmelCase ):
        if i == 0:
           continue
        UpperCamelCase_									=     '%s-%s' % (set_type, line[0])
        UpperCamelCase_									=     line[5]
        UpperCamelCase_									=     line[6]
        UpperCamelCase_									=     line[7][2:] if line[7].startswith('ex' ) else line[7]
        UpperCamelCase_									=     line[0]
        examples.append(InputExample(guid=_UpperCAmelCase		,		text_a=_UpperCAmelCase		,		text_b=_UpperCAmelCase		,		label=_UpperCAmelCase		,		pairID=_UpperCAmelCase ) )
     return examples






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   UpperCamelCase_									=     {label: i for i, label in enumerate(__lowercase)}

   UpperCamelCase_									=     []
   for ex_index, example in tqdm.tqdm(enumerate(__lowercase)    ,      desc='convert examples to features'):
      if ex_index % 10000 == 0:
         logger.info('Writing example %d' % (ex_index))

      UpperCamelCase_									=     tokenizer(
          example.text_a    ,      example.text_b    ,      add_special_tokens=__lowercase    ,      max_length=__lowercase    ,      padding='max_length'    ,      truncation=__lowercase    ,      return_overflowing_tokens=__lowercase    ,      )

      UpperCamelCase_									=     label_map[example.label] if example.label in label_map else 0

      UpperCamelCase_									=     int(example.pairID)

      features.append(InputFeatures(**__lowercase    ,      label=__lowercase    ,      pairID=__lowercase))

   for i, example in enumerate(examples[:5]):
      logger.info('*** Example ***')
      logger.info(f"""guid: {example}""")
      logger.info(f"""features: {features[i]}""")

   return features


snake_case__      :	List[str]       	=      {
    """hans""": 3,
}

snake_case__      :	Union[str, Any]       	=      {
    """hans""": HansProcessor,
}





