




'''simple docstring'''





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        __snake_case  (lowerCAmelCase__						,	unittest.TestCase		):
				lowerCAmelCase__				=     BertJapaneseTokenizer
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     True



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Optional[int]:





								'''simple docstring'''




								super().setUp()

								_lowerCAmelCase    :    Optional[int]										=      [
								    """[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""",
								    """、""",
								    """##、""",
								    """。""",
								    """##。""",
								]

								_lowerCAmelCase    :    Optional[int]										=      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       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """こんにちは、世界。 \nこんばんは、世界。"""
								_lowerCAmelCase    :    int										=      """こんにちは 、 世界 。 こんばんは 、 世界 。"""
								return input_text, output_text



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		str      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    List[Any]										=      self.get_input_output_texts(_lowerCamelCase      )
								_lowerCAmelCase    :    Any										=      tokenizer.encode(_lowerCamelCase		,	add_special_tokens=_lowerCamelCase      )
								_lowerCAmelCase    :    List[str]										=      tokenizer.decode(_lowerCamelCase		,	clean_up_tokenization_spaces=_lowerCamelCase      )
								return text, ids



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Dict:





								'''simple docstring'''




								pass  # TODO add if relevant



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    int:





								'''simple docstring'''




								pass  # TODO add if relevant



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    List[str]:





								'''simple docstring'''




								pass  # TODO add if relevant



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    int:





								'''simple docstring'''




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

								_lowerCAmelCase    :    List[str]										=      tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。"""      )
								self.assertListEqual(_lowerCamelCase		,	["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""]      )
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase      )		,	[3, 12, 10, 14, 4, 9, 12, 10, 14]      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      self.tokenizer_class(self.vocab_file		,	word_tokenizer_type="""mecab"""      )
								self.assertIsNotNone(_lowerCamelCase      )

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

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

								with open(_lowerCamelCase		,	"""rb"""      ) as handle:
												_lowerCAmelCase    :    int										=      pickle.load(_lowerCamelCase      )

								_lowerCAmelCase    :    Optional[Any]										=      tokenizer_new.tokenize(_lowerCamelCase      )

								self.assertListEqual(_lowerCamelCase		,	_lowerCamelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      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       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[str]:





								'''simple docstring'''




								try:
												_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    str:





								'''simple docstring'''




								try:
												_lowerCAmelCase    :    List[str]										=      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       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      MecabTokenizer(do_lower_case=_lowerCamelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Dict:





								'''simple docstring'''




								try:
												_lowerCAmelCase    :    List[str]										=      MecabTokenizer(
												    do_lower_case=_lowerCamelCase		,	normalize_text=_lowerCamelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      MecabTokenizer(normalize_text=_lowerCamelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.tokenizer_class(self.vocab_file		,	word_tokenizer_type="""sudachi"""      )
								self.assertIsNotNone(_lowerCamelCase      )

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

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

								with open(_lowerCamelCase		,	"""rb"""      ) as handle:
												_lowerCAmelCase    :    Optional[int]										=      pickle.load(_lowerCamelCase      )

								_lowerCAmelCase    :    Optional[int]										=      tokenizer_new.tokenize(_lowerCamelCase      )

								self.assertListEqual(_lowerCamelCase		,	_lowerCamelCase      )



				@require_sudachi
				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      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       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      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       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      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       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      SudachiTokenizer(do_lower_case=_lowerCamelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      SudachiTokenizer(normalize_text=_lowerCamelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      SudachiTokenizer(trim_whitespace=_lowerCamelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.tokenizer_class(self.vocab_file		,	word_tokenizer_type="""jumanpp"""      )
								self.assertIsNotNone(_lowerCamelCase      )

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

								_lowerCAmelCase    :    Tuple										=      os.path.join(self.tmpdirname		,	"""tokenizer.bin"""      )
								with open(_lowerCamelCase		,	"""wb"""      ) as handle:
												pickle.dump(_lowerCamelCase		,	_lowerCamelCase      )

								with open(_lowerCamelCase		,	"""rb"""      ) as handle:
												_lowerCAmelCase    :    Tuple										=      pickle.load(_lowerCamelCase      )

								_lowerCAmelCase    :    Union[str, Any]										=      tokenizer_new.tokenize(_lowerCamelCase      )

								self.assertListEqual(_lowerCamelCase		,	_lowerCamelCase      )



				@require_jumanpp
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      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       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      JumanppTokenizer(do_lower_case=_lowerCamelCase      )

								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       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      JumanppTokenizer(normalize_text=_lowerCamelCase      )

								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       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      JumanppTokenizer(trim_whitespace=_lowerCamelCase      )

								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       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      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       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      ["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]

								_lowerCAmelCase    :    Union[str, Any]										=      {}
								for i, token in enumerate(_lowerCamelCase      ):
												_lowerCAmelCase    :    Optional[int]										=      i
								_lowerCAmelCase    :    int										=      WordpieceTokenizer(vocab=_lowerCamelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp"""      )
								_lowerCAmelCase    :    Union[str, Any]										=      tokenizer.subword_tokenizer

								_lowerCAmelCase    :    str										=      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(_lowerCamelCase		,	["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""]      )

								_lowerCAmelCase    :    Any										=      subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは"""      )
								self.assertListEqual(_lowerCamelCase		,	["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""]      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese"""      )

								_lowerCAmelCase    :    str										=      tokenizer.encode("""ありがとう。"""		,	add_special_tokens=_lowerCamelCase      )
								_lowerCAmelCase    :    Any										=      tokenizer.encode("""どういたしまして。"""		,	add_special_tokens=_lowerCamelCase      )

								_lowerCAmelCase    :    str										=      tokenizer.build_inputs_with_special_tokens(_lowerCamelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      tokenizer.build_inputs_with_special_tokens(_lowerCamelCase		,	_lowerCamelCase      )

								# 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        __snake_case  (lowerCAmelCase__						,	unittest.TestCase		):
				lowerCAmelCase__				=     BertJapaneseTokenizer
				lowerCAmelCase__				=     False



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								super().setUp()

								_lowerCAmelCase    :    str										=      ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]

								_lowerCAmelCase    :    Tuple										=      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       SCREAMING_SNAKE_CASE       (      self      :		int		,	**_UpperCAmelCase      :		Dict      )  ->    Dict:





								'''simple docstring'''




								return BertJapaneseTokenizer.from_pretrained(self.tmpdirname		,	subword_tokenizer_type="""character"""		,	**_lowerCamelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		str      )  ->    Optional[int]:





								'''simple docstring'''




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



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





								'''simple docstring'''




								pass  # TODO add if relevant



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





								'''simple docstring'''




								pass  # TODO add if relevant



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Dict:





								'''simple docstring'''




								pass  # TODO add if relevant



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      self.tokenizer_class(self.vocab_file		,	subword_tokenizer_type="""character"""      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]

								_lowerCAmelCase    :    Optional[int]										=      {}
								for i, token in enumerate(_lowerCamelCase      ):
												_lowerCAmelCase    :    str										=      i
								_lowerCAmelCase    :    Optional[Any]										=      CharacterTokenizer(vocab=_lowerCamelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char"""      )

								_lowerCAmelCase    :    Any										=      tokenizer.encode("""ありがとう。"""		,	add_special_tokens=_lowerCamelCase      )
								_lowerCAmelCase    :    Any										=      tokenizer.encode("""どういたしまして。"""		,	add_special_tokens=_lowerCamelCase      )

								_lowerCAmelCase    :    int										=      tokenizer.build_inputs_with_special_tokens(_lowerCamelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      tokenizer.build_inputs_with_special_tokens(_lowerCamelCase		,	_lowerCamelCase      )

								# 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        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      """cl-tohoku/bert-base-japanese"""
								_lowerCAmelCase    :    Any										=      AutoTokenizer.from_pretrained(_lowerCamelCase      )
								self.assertIsInstance(_lowerCamelCase		,	_lowerCamelCase      )






class        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """cl-tohoku/bert-base-japanese"""
								with self.assertLogs("""transformers"""		,	level="""WARNING"""      ) as cm:
												BertTokenizer.from_pretrained(_lowerCamelCase      )
												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."""      )      )
								_lowerCAmelCase    :    Dict										=      """bert-base-cased"""
								with self.assertLogs("""transformers"""		,	level="""WARNING"""      ) as cm:
												BertJapaneseTokenizer.from_pretrained(_lowerCamelCase      )
												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 argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCamelCase      :  Any      						=						1_6
_lowerCamelCase      :  Tuple      						=						3_2




def 							_UpperCAmelCase  (UpperCamelCase_ : Accelerator      ,    UpperCamelCase_ : DatasetDict      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : int = 16       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      AutoTokenizer.from_pretrained("""bert-base-cased"""       )
				_lowerCAmelCase    :    int										=      DatasetDict(
				    {
				        """train""": dataset["""train"""].select(UpperCamelCase_       ),
				        """validation""": dataset["""train"""].select(UpperCamelCase_       ),
				        """test""": dataset["""validation"""],
				    }       )

				def tokenize_function(UpperCamelCase_ : str       ):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCAmelCase    :    List[Any]										=      tokenizer(examples["""sentence1"""]      ,    examples["""sentence2"""]      ,    truncation=UpperCamelCase_      ,    max_length=UpperCamelCase_       )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								_lowerCAmelCase    :    List[str]										=      datasets.map(
								    UpperCamelCase_      ,    batched=UpperCamelCase_      ,    remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,    )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_lowerCAmelCase    :    Optional[Any]										=      tokenized_datasets.rename_column("""label"""      ,    """labels"""       )

				def collate_fn(UpperCamelCase_ : Optional[Any]       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_lowerCAmelCase    :    List[str]										=      128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												_lowerCAmelCase    :    Optional[Any]										=      16
								elif accelerator.mixed_precision != "no":
												_lowerCAmelCase    :    int										=      8
								else:
												_lowerCAmelCase    :    Dict										=      None

								return tokenizer.pad(
								    UpperCamelCase_      ,    padding="""longest"""      ,    max_length=UpperCamelCase_      ,    pad_to_multiple_of=UpperCamelCase_      ,    return_tensors="""pt"""      ,    )

				# Instantiate dataloaders.
				_lowerCAmelCase    :    Optional[Any]										=      DataLoader(
				    tokenized_datasets["""train"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )
				_lowerCAmelCase    :    Tuple										=      DataLoader(
				    tokenized_datasets["""validation"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				_lowerCAmelCase    :    List[str]										=      DataLoader(
				    tokenized_datasets["""test"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				return train_dataloader, eval_dataloader, test_dataloader




def 							_UpperCAmelCase  (UpperCamelCase_ : Any      ,    UpperCamelCase_ : Optional[int]       ):
				'''simple docstring'''
				# New Code #
				_lowerCAmelCase    :    Dict										=      []
				# Download the dataset
				_lowerCAmelCase    :    Optional[int]										=      load_dataset("""glue"""      ,    """mrpc"""       )
				# Create our splits
				_lowerCAmelCase    :    Any										=      StratifiedKFold(n_splits=int(args.num_folds       )       )
				# Initialize accelerator
				_lowerCAmelCase    :    int										=      Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_lowerCAmelCase    :    List[str]										=      config["""lr"""]
				_lowerCAmelCase    :    Any										=      int(config["""num_epochs"""]       )
				_lowerCAmelCase    :    Dict										=      int(config["""seed"""]       )
				_lowerCAmelCase    :    Union[str, Any]										=      int(config["""batch_size"""]       )

				_lowerCAmelCase    :    Optional[Any]										=      evaluate.load("""glue"""      ,    """mrpc"""       )

				# If the batch size is too big we use gradient accumulation
				_lowerCAmelCase    :    Optional[Any]										=      1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								_lowerCAmelCase    :    Optional[Any]										=      batch_size // MAX_GPU_BATCH_SIZE
								_lowerCAmelCase    :    int										=      MAX_GPU_BATCH_SIZE

				set_seed(UpperCamelCase_       )

				# New Code #
				# Create our folds:
				_lowerCAmelCase    :    Optional[int]										=      kfold.split(np.zeros(datasets["""train"""].num_rows       )      ,    datasets["""train"""]["""label"""]       )
				_lowerCAmelCase    :    Any										=      []
				# Iterate over them
				for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase_       ):
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      get_fold_dataloaders(
								    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    )
								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								_lowerCAmelCase    :    int										=      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,    return_dict=UpperCamelCase_       )

								# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
								# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
								# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
								_lowerCAmelCase    :    Union[str, Any]										=      model.to(accelerator.device       )

								# Instantiate optimizer
								_lowerCAmelCase    :    Any										=      AdamW(params=model.parameters()      ,    lr=UpperCamelCase_       )

								# Instantiate scheduler
								_lowerCAmelCase    :    Tuple										=      get_linear_schedule_with_warmup(
								    optimizer=UpperCamelCase_      ,    num_warmup_steps=100      ,    num_training_steps=(len(UpperCamelCase_       ) * num_epochs) // gradient_accumulation_steps      ,    )

								# Prepare everything
								# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
								# prepare method.
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      accelerator.prepare(
								    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_       )

								# Now we train the model
								for epoch in range(UpperCamelCase_       ):
												model.train()
												for step, batch in enumerate(UpperCamelCase_       ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device       )
																_lowerCAmelCase    :    Tuple										=      model(**UpperCamelCase_       )
																_lowerCAmelCase    :    Optional[int]										=      outputs.loss
																_lowerCAmelCase    :    Optional[int]										=      loss / gradient_accumulation_steps
																accelerator.backward(UpperCamelCase_       )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(UpperCamelCase_       ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device       )
																with torch.no_grad():
																				_lowerCAmelCase    :    List[Any]										=      model(**UpperCamelCase_       )
																_lowerCAmelCase    :    List[Any]										=      outputs.logits.argmax(dim=-1       )
																_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
																metric.add_batch(
																    predictions=UpperCamelCase_      ,    references=UpperCamelCase_      ,    )

												_lowerCAmelCase    :    List[Any]										=      metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"epoch {epoch}:"      ,    UpperCamelCase_       )

								# New Code #
								# We also run predictions on the test set at the very end
								_lowerCAmelCase    :    Dict										=      []
								for step, batch in enumerate(UpperCamelCase_       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												with torch.no_grad():
																_lowerCAmelCase    :    Dict										=      model(**UpperCamelCase_       )
												_lowerCAmelCase    :    List[Any]										=      outputs.logits
												_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[int]										=      accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
												fold_predictions.append(predictions.cpu()       )
												if i == 0:
																# We need all of the test predictions
																test_references.append(references.cpu()       )
        # Use accelerator.print to print only on the main process.
								test_predictions.append(torch.cat(UpperCamelCase_      ,    dim=0       )       )
								# We now need to release all our memory and get rid of the current model, optimizer, etc
								accelerator.free_memory()
				# New Code #
				# Finally we check the accuracy of our folded results:
				_lowerCAmelCase    :    Tuple										=      torch.cat(UpperCamelCase_      ,    dim=0       )
				_lowerCAmelCase    :    Tuple										=      torch.stack(UpperCamelCase_      ,    dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
				_lowerCAmelCase    :    Union[str, Any]										=      metric.compute(predictions=UpperCamelCase_      ,    references=UpperCamelCase_       )
				accelerator.print("""Average test metrics from all folds:"""      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      argparse.ArgumentParser(description="""Simple example of training script."""       )
				parser.add_argument(
				    """--mixed_precision"""      ,    type=UpperCamelCase_      ,    default=UpperCamelCase_      ,    choices=["""no""", """fp16""", """bf16""", """fp8"""]      ,    help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU."""      ,    )
				parser.add_argument("""--cpu"""      ,    action="""store_true"""      ,    help="""If passed, will train on the CPU."""       )
				# New Code #
				parser.add_argument("""--num_folds"""      ,    type=UpperCamelCase_      ,    default=3      ,    help="""The number of splits to perform across the dataset"""       )
				_lowerCAmelCase    :    Optional[Any]										=      parser.parse_args()
				_lowerCAmelCase    :    List[Any]										=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(UpperCamelCase_      ,    UpperCamelCase_       )


if __name__ == "__main__":
	main()

