





import os
import pickle
import unittest

from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
    VOCAB_FILES_NAMES,
    BertJapaneseTokenizer,
    CharacterTokenizer,
    JumanppTokenizer,
    MecabTokenizer,
    SudachiTokenizer,
    WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi

from ...test_tokenization_common import TokenizerTesterMixin





@custom_tokenizers
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Optional[int]	  =  BertJapaneseTokenizer
   __A       :    List[str]	  =  False
   __A       :    List[str]	  =  True

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          super().setUp()

          a__						:     Dict			=    [
              '[UNK]',
              '[CLS]',
              '[SEP]',
              'こんにちは',
              'こん',
              'にちは',
              'ばんは',
              '##こん',
              '##にちは',
              '##ばんは',
              '世界',
              '##世界',
              '、',
              '##、',
              '。',
              '##。',
          ]

          a__						:     List[str]			=    os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES['vocab_file'])
          with open(self.vocab_file ,  'w' ,  encoding='utf-8') as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

   def 							__lowercase		( self ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          a__						:     List[Any]			=    'こんにちは、世界。 \nこんばんは、世界。'
          a__						:     List[Any]			=    'こんにちは 、 世界 。 こんばんは 、 世界 。'
          return input_text, output_text

   def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__			,							a__						:     Tuple			=    self.get_input_output_texts(lowercase)
          a__						:     int			=    tokenizer.encode(lowercase ,  add_special_tokens=lowercase)
          a__						:     Any			=    tokenizer.decode(lowercase ,  clean_up_tokenization_spaces=lowercase)
          return text, ids

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          pass  # TODO add if relevant

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          pass  # TODO add if relevant

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          pass  # TODO add if relevant

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     str			=    self.tokenizer_class(self.vocab_file)

          a__						:     Optional[int]			=    tokenizer.tokenize('こんにちは、世界。\nこんばんは、世界。')
          self.assertListEqual(lowercase ,  ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'])
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase) ,  [3, 12, 10, 14, 4, 9, 12, 10, 14])

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     List[Any]			=    self.tokenizer_class(self.vocab_file ,  word_tokenizer_type='mecab')
          self.assertIsNotNone(lowercase)

          a__						:     Tuple			=    'こんにちは、世界。\nこんばんは、世界。'
          a__						:     Optional[Any]			=    tokenizer.tokenize(lowercase)
          self.assertListEqual(lowercase ,  ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'])
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase) ,  [3, 12, 10, 14, 4, 9, 12, 10, 14])

          a__						:     Tuple			=    os.path.join(self.tmpdirname ,  'tokenizer.bin')
          with open(lowercase ,  'wb') as handle:
                 pickle.dump(lowercase ,  lowercase)

          with open(lowercase ,  'rb') as handle:
                 a__						:     Tuple			=    pickle.load(lowercase)

          a__						:     Any			=    tokenizer_new.tokenize(lowercase)

          self.assertListEqual(lowercase ,  lowercase)

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    MecabTokenizer(mecab_dic='ipadic')

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['アップルストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] ,  )

   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          try:
                 a__						:     List[str]			=    MecabTokenizer(mecab_dic='unidic_lite')
          except ModuleNotFoundError:
                 return

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] ,  )

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          try:
                 a__						:     Optional[int]			=    MecabTokenizer(mecab_dic='unidic')
          except ModuleNotFoundError:
                 return

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] ,  )

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     int			=    MecabTokenizer(do_lower_case=lowercase ,  mecab_dic='ipadic')

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['アップルストア', 'で', 'iphone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] ,  )

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          try:
                 a__						:     Optional[Any]			=    MecabTokenizer(
                     do_lower_case=lowercase ,  normalize_text=lowercase ,  mecab_option='-d /usr/local/lib/mecab/dic/jumandic')
          except RuntimeError:
                 # if dict doesn't exist in the system, previous code raises this error.
                 return

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れた', '\u3000', '。'] ,  )

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__						:     List[str]			=    MecabTokenizer(normalize_text=lowercase ,  mecab_dic='ipadic')

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れ', 'た', '　', '。'] ,  )

   @require_sudachi
   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     str			=    self.tokenizer_class(self.vocab_file ,  word_tokenizer_type='sudachi')
          self.assertIsNotNone(lowercase)

          a__						:     Optional[int]			=    'こんにちは、世界。\nこんばんは、世界。'
          a__						:     List[Any]			=    tokenizer.tokenize(lowercase)
          self.assertListEqual(lowercase ,  ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'])
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase) ,  [3, 12, 10, 14, 4, 9, 12, 10, 14])

          a__						:     List[str]			=    os.path.join(self.tmpdirname ,  'tokenizer.bin')
          with open(lowercase ,  'wb') as handle:
                 pickle.dump(lowercase ,  lowercase)

          with open(lowercase ,  'rb') as handle:
                 a__						:     List[Any]			=    pickle.load(lowercase)

          a__						:     List[str]			=    tokenizer_new.tokenize(lowercase)

          self.assertListEqual(lowercase ,  lowercase)

   @require_sudachi
   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     str			=    SudachiTokenizer(sudachi_dict_type='core')

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  [' ', '\t', 'アップル', 'ストア', 'で', 'iPhone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] ,  )

   @require_sudachi
   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     List[str]			=    SudachiTokenizer(sudachi_dict_type='core' ,  sudachi_split_mode='A')

          self.assertListEqual(tokenizer.tokenize('外国人参政権') ,  ['外国', '人', '参政', '権'])

   @require_sudachi
   def 							__lowercase		( self)      ->						Dict:







          '''simple docstring'''

          a__						:     Any			=    SudachiTokenizer(sudachi_dict_type='core' ,  sudachi_split_mode='B')

          self.assertListEqual(tokenizer.tokenize('外国人参政権') ,  ['外国人', '参政権'])

   @require_sudachi
   def 							__lowercase		( self)      ->						Dict:







          '''simple docstring'''

          a__						:     List[Any]			=    SudachiTokenizer(sudachi_dict_type='core' ,  sudachi_split_mode='C')

          self.assertListEqual(tokenizer.tokenize('外国人参政権') ,  ['外国人参政権'])

   @require_sudachi
   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          a__						:     Tuple			=    SudachiTokenizer(do_lower_case=lowercase ,  sudachi_dict_type='core')

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  [' ', '\t', 'アップル', 'ストア', 'で', 'iphone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] ,  )

   @require_sudachi
   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    SudachiTokenizer(normalize_text=lowercase ,  sudachi_dict_type='core')

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  [' ', '\t', 'ｱｯﾌﾟﾙ', 'ストア', 'で', 'iPhone', '８', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', '\u3000', '。', ' ', ' '] ,  )

   @require_sudachi
   def 							__lowercase		( self)      ->						Dict:







          '''simple docstring'''

          a__						:     Any			=    SudachiTokenizer(trim_whitespace=lowercase ,  sudachi_dict_type='core')

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] ,  )

   @require_jumanpp
   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.tokenizer_class(self.vocab_file ,  word_tokenizer_type='jumanpp')
          self.assertIsNotNone(lowercase)

          a__						:     Union[str, Any]			=    'こんにちは、世界。\nこんばんは、世界。'
          a__						:     Optional[Any]			=    tokenizer.tokenize(lowercase)
          self.assertListEqual(lowercase ,  ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'])
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase) ,  [3, 12, 10, 14, 4, 9, 12, 10, 14])

          a__						:     Optional[Any]			=    os.path.join(self.tmpdirname ,  'tokenizer.bin')
          with open(lowercase ,  'wb') as handle:
                 pickle.dump(lowercase ,  lowercase)

          with open(lowercase ,  'rb') as handle:
                 a__						:     List[Any]			=    pickle.load(lowercase)

          a__						:     int			=    tokenizer_new.tokenize(lowercase)

          self.assertListEqual(lowercase ,  lowercase)

   @require_jumanpp
   def 							__lowercase		( self)      ->						Dict:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    JumanppTokenizer()

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['アップル', 'ストア', 'で', 'iPhone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] ,  )

   @require_jumanpp
   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Any			=    JumanppTokenizer(do_lower_case=lowercase)

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['アップル', 'ストア', 'で', 'iphone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] ,  )

   @require_jumanpp
   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__						:     int			=    JumanppTokenizer(normalize_text=lowercase)

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['ｱ', 'ｯ', 'ﾌ', 'ﾟ', 'ﾙ', 'ストア', 'で', 'iPhone', '８', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] ,  )

   @require_jumanpp
   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     str			=    JumanppTokenizer(trim_whitespace=lowercase)

          self.assertListEqual(
              tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  ') ,  ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れた', '。'] ,  )

   @require_jumanpp
   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__						:     List[Any]			=    JumanppTokenizer()

          self.assertListEqual(
              tokenizer.tokenize('ありがとうございますm(_ _)ｍ見つけるのが大変です。') ,  ['ありがとう', 'ございます', 'm(_ _)m', '見つける', 'の', 'が', '大変です', '。'] ,  )

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    ['[UNK]', '[CLS]', '[SEP]', 'こんにちは', 'こん', 'にちは', 'ばんは', '##こん', '##にちは', '##ばんは']

          a__						:     List[str]			=    {}
          for i, token in enumerate(lowercase):
                 a__						:     Optional[int]			=    i
          a__						:     Any			=    WordpieceTokenizer(vocab=lowercase ,  unk_token='[UNK]')

          self.assertListEqual(tokenizer.tokenize('') ,  [])

          self.assertListEqual(tokenizer.tokenize('こんにちは') ,  ['こんにちは'])

          self.assertListEqual(tokenizer.tokenize('こんばんは') ,  ['こん', '##ばんは'])

          self.assertListEqual(tokenizer.tokenize('こんばんは こんばんにちは こんにちは') ,  ['こん', '##ばんは', '[UNK]', 'こんにちは'])

   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Optional[int]			=    BertJapaneseTokenizer.from_pretrained('nlp-waseda/roberta-base-japanese-with-auto-jumanpp')
          a__						:     Any			=    tokenizer.subword_tokenizer

          a__						:     Tuple			=    subword_tokenizer.tokenize('国境 の 長い トンネル を 抜ける と 雪国 であった 。')
          self.assertListEqual(lowercase ,  ['▁国境', '▁の', '▁長い', '▁トンネル', '▁を', '▁抜ける', '▁と', '▁雪', '国', '▁であった', '▁。'])

          a__						:     List[str]			=    subword_tokenizer.tokenize('こんばんは こんばん にち は こんにちは')
          self.assertListEqual(lowercase ,  ['▁こん', 'ばん', 'は', '▁こん', 'ばん', '▁に', 'ち', '▁は', '▁こんにちは'])

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     Dict			=    self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese')

          a__						:     int			=    tokenizer.encode('ありがとう。' ,  add_special_tokens=lowercase)
          a__						:     str			=    tokenizer.encode('どういたしまして。' ,  add_special_tokens=lowercase)

          a__						:     str			=    tokenizer.build_inputs_with_special_tokens(lowercase)
          a__						:     int			=    tokenizer.build_inputs_with_special_tokens(lowercase ,  lowercase)

          # 2 is for "[CLS]", 3 is for "[SEP]"
          assert encoded_sentence == [2] + text + [3]
          assert encoded_pair == [2] + text + [3] + text_a + [3]





@custom_tokenizers
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Any	  =  BertJapaneseTokenizer
   __A       :    List[str]	  =  False

   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          super().setUp()

          a__						:     Any			=    ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']

          a__						:     Any			=    os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES['vocab_file'])
          with open(self.vocab_file ,  'w' ,  encoding='utf-8') as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

   def 							__lowercase		( self ,  **lowercase)      ->						List[Any]:







          '''simple docstring'''

          return BertJapaneseTokenizer.from_pretrained(self.tmpdirname ,  subword_tokenizer_type='character' ,  **lowercase)

   def 							__lowercase		( self ,  lowercase)      ->						Tuple:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    'こんにちは、世界。 \nこんばんは、世界。'
          a__						:     Dict			=    'こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'
          return input_text, output_text

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          pass  # TODO add if relevant

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          pass  # TODO add if relevant

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          pass  # TODO add if relevant

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.tokenizer_class(self.vocab_file ,  subword_tokenizer_type='character')

          a__						:     Tuple			=    tokenizer.tokenize('こんにちは、世界。 \nこんばんは、世界。')
          self.assertListEqual(
              lowercase ,  ['こ', 'ん', 'に', 'ち', 'は', '、', '世', '界', '。', 'こ', 'ん', 'ば', 'ん', 'は', '、', '世', '界', '。'])
          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(lowercase) ,  [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12])

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     List[str]			=    ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']

          a__						:     int			=    {}
          for i, token in enumerate(lowercase):
                 a__						:     Any			=    i
          a__						:     List[Any]			=    CharacterTokenizer(vocab=lowercase ,  unk_token='[UNK]')

          self.assertListEqual(tokenizer.tokenize('') ,  [])

          self.assertListEqual(tokenizer.tokenize('こんにちは') ,  ['こ', 'ん', 'に', 'ち', 'は'])

          self.assertListEqual(tokenizer.tokenize('こんにちほ') ,  ['こ', 'ん', 'に', 'ち', '[UNK]'])

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     List[str]			=    self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese-char')

          a__						:     Optional[int]			=    tokenizer.encode('ありがとう。' ,  add_special_tokens=lowercase)
          a__						:     Optional[int]			=    tokenizer.encode('どういたしまして。' ,  add_special_tokens=lowercase)

          a__						:     Tuple			=    tokenizer.build_inputs_with_special_tokens(lowercase)
          a__						:     Tuple			=    tokenizer.build_inputs_with_special_tokens(lowercase ,  lowercase)

          # 2 is for "[CLS]", 3 is for "[SEP]"
          assert encoded_sentence == [2] + text + [3]
          assert encoded_pair == [2] + text + [3] + text_a + [3]





@custom_tokenizers
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     List[Any]			=    'cl-tohoku/bert-base-japanese'
          a__						:     Union[str, Any]			=    AutoTokenizer.from_pretrained(lowercase)
          self.assertIsInstance(lowercase ,  lowercase)





class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     List[Any]			=    'cl-tohoku/bert-base-japanese'
          with self.assertLogs('transformers' ,  level='WARNING') as cm:
                 BertTokenizer.from_pretrained(lowercase)
                 self.assertTrue(
                     cm.records[0].message.startswith(
                         'The tokenizer class you load from this checkpoint is not the same type as the class this function'
                         ' is called from.'))
          a__						:     Any			=    'bert-base-cased'
          with self.assertLogs('transformers' ,  level='WARNING') as cm:
                 BertJapaneseTokenizer.from_pretrained(lowercase)
                 self.assertTrue(
                     cm.records[0].message.startswith(
                         'The tokenizer class you load from this checkpoint is not the same type as the class this function'
                         ' is called from.'))









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


lowercase				:	str      						=  logging.get_logger(__name__)

lowercase				:	Optional[int]      						=  list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowercase				:	str      						=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 			A__   :




   """simple docstring"""



   __A       :    str	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(__UpperCAmelCase     )}     )
   __A       :    str	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''}     )
   __A       :    int	  =  field(
       default=1_2_8			,  metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }			,  )
   __A       :    int	  =  field(
       default=1_2_8			,  metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''}			,  )
   __A       :    int	  =  field(
       default=6_4			,  metadata={
           '''help''': (
               '''The maximum number of tokens for the question. Questions longer than this will '''
               '''be truncated to this length.'''
           )
       }			,  )
   __A       :    int	  =  field(
       default=3_0			,  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.'''
           )
       }			,  )
   __A       :    bool	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )
   __A       :    bool	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''}     )
   __A       :    float	  =  field(
       default=0.0			,  metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}     )
   __A       :    int	  =  field(
       default=2_0			,  metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}     )
   __A       :    int	  =  field(
       default=0			,  metadata={
           '''help''': (
               '''language id of input for language-specific xlm models (see'''
               ''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
           )
       }			,  )
   __A       :    int	  =  field(default=1			,  metadata={'''help''': '''multiple threads for converting example to features'''}     )





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Dict	  =  '''train'''
   __A       :    Tuple	  =  '''dev'''





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    SquadDataTrainingArguments
   __A       :    List[SquadFeatures]
   __A       :    Split
   __A       :    bool

   def __init__( self ,  lowercase ,  lowercase ,  lowercase = None ,  lowercase = Split.train ,  lowercase = False ,  lowercase = None ,  lowercase = "pt" ,  )      ->						str:







          '''simple docstring'''

          a__						:     List[str]			=    args
          a__						:     Any			=    is_language_sensitive
          a__						:     Optional[Any]			=    SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
          if isinstance(lowercase ,  lowercase):
                 try:
                        a__						:     Optional[int]			=    Split[mode]
                 except KeyError:
                        raise KeyError('mode is not a valid split name')
          a__						:     List[Any]			=    mode
          # Load data features from cache or dataset file
          a__						:     List[str]			=    'v2' if args.version_2_with_negative else 'v1'
          a__						:     Optional[Any]			=    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.
          a__						:     List[Any]			=    cached_features_file + '.lock'
          with FileLock(lowercase):
                 if os.path.exists(lowercase) and not args.overwrite_cache:
                        a__						:     List[Any]			=    time.time()
                        a__						:     Optional[Any]			=    torch.load(lowercase)

                        # Legacy cache files have only features, while new cache files
                        # will have dataset and examples also.
                        a__						:     List[str]			=    self.old_features['features']
                        a__						:     int			=    self.old_features.get('dataset' ,  lowercase)
                        a__						:     Dict			=    self.old_features.get('examples' ,  lowercase)
                        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:
                               a__						:     Dict			=    self.processor.get_dev_examples(args.data_dir)
                        else:
                               a__						:     int			=    self.processor.get_train_examples(args.data_dir)

                        a__			,							a__						:     Any			=    squad_convert_examples_to_features(
                            examples=self.examples ,  tokenizer=lowercase ,  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=lowercase ,  )

                        a__						:     Tuple			=    time.time()
                        torch.save(
                            {'features': self.features, 'dataset': self.dataset, 'examples': self.examples} ,  lowercase ,  )
                        # ^ 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)      ->						Tuple:







          '''simple docstring'''

          return len(self.features)

   def __getitem__( self ,  lowercase)      ->						Dict[str, torch.Tensor]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.features[i]

          a__						:     Optional[int]			=    torch.tensor(feature.input_ids ,  dtype=torch.long)
          a__						:     Tuple			=    torch.tensor(feature.attention_mask ,  dtype=torch.long)
          a__						:     Optional[Any]			=    torch.tensor(feature.token_type_ids ,  dtype=torch.long)
          a__						:     str			=    torch.tensor(feature.cls_index ,  dtype=torch.long)
          a__						:     Optional[Any]			=    torch.tensor(feature.p_mask ,  dtype=torch.float)
          a__						:     Tuple			=    torch.tensor(feature.is_impossible ,  dtype=torch.float)

          a__						:     str			=    {
              '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:
                 a__						:     Dict			=    torch.tensor(feature.start_position ,  dtype=torch.long)
                 a__						:     str			=    torch.tensor(feature.end_position ,  dtype=torch.long)
                 inputs.update({'start_positions': start_positions, 'end_positions': end_positions})

          return inputs


