






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   UpperCamelCase_ (       a__   ,					unittest.TestCase  ):
  _A       :					int				=	BertJapaneseTokenizer
  _A       :					str				=	False
  _A       :					Union[str, Any]				=	True



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



         """simple docstring"""





         super().setUp()

         UpperCAmelCase	    =			[
             """[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""",
             """、""",
             """##、""",
             """。""",
             """##。""",
         ]

         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			"""こんにちは、世界。 \nこんばんは、世界。"""
         UpperCAmelCase	    =			"""こんにちは 、 世界 。 こんばんは 、 世界 。"""
         return input_text, output_text



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_input_output_texts(lowerCamelCase_				)
         UpperCAmelCase	    =			tokenizer.encode(lowerCamelCase_ ,		add_special_tokens=lowerCamelCase_				)
         UpperCAmelCase	    =			tokenizer.decode(lowerCamelCase_ ,		clean_up_tokenization_spaces=lowerCamelCase_				)
         return text, ids



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



         """simple docstring"""





         pass  # TODO add if relevant



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



         """simple docstring"""





         pass  # TODO add if relevant



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



         """simple docstring"""





         pass  # TODO add if relevant



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.tokenizer_class(self.vocab_file				)

         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			self.tokenizer_class(self.vocab_file ,		word_tokenizer_type="""mecab"""				)
         self.assertIsNotNone(lowerCamelCase_				)

         UpperCAmelCase	    =			"""こんにちは、世界。\nこんばんは、世界。"""
         UpperCAmelCase	    =			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]				)

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

         with open(lowerCamelCase_ ,		"""rb"""				) as handle:
                UpperCAmelCase	    =			pickle.load(lowerCamelCase_				)

         UpperCAmelCase	    =			tokenizer_new.tokenize(lowerCamelCase_				)

         self.assertListEqual(lowerCamelCase_ ,		lowerCamelCase_				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         try:
                UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         try:
                UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         try:
                UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			self.tokenizer_class(self.vocab_file ,		word_tokenizer_type="""sudachi"""				)
         self.assertIsNotNone(lowerCamelCase_				)

         UpperCAmelCase	    =			"""こんにちは、世界。\nこんばんは、世界。"""
         UpperCAmelCase	    =			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]				)

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

         with open(lowerCamelCase_ ,		"""rb"""				) as handle:
                UpperCAmelCase	    =			pickle.load(lowerCamelCase_				)

         UpperCAmelCase	    =			tokenizer_new.tokenize(lowerCamelCase_				)

         self.assertListEqual(lowerCamelCase_ ,		lowerCamelCase_				)



  @require_sudachi
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			self.tokenizer_class(self.vocab_file ,		word_tokenizer_type="""jumanpp"""				)
         self.assertIsNotNone(lowerCamelCase_				)

         UpperCAmelCase	    =			"""こんにちは、世界。\nこんばんは、世界。"""
         UpperCAmelCase	    =			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]				)

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

         with open(lowerCamelCase_ ,		"""rb"""				) as handle:
                UpperCAmelCase	    =			pickle.load(lowerCamelCase_				)

         UpperCAmelCase	    =			tokenizer_new.tokenize(lowerCamelCase_				)

         self.assertListEqual(lowerCamelCase_ ,		lowerCamelCase_				)



  @require_jumanpp
  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]

         UpperCAmelCase	    =			{}
         for i, token in enumerate(lowerCamelCase_				):
                UpperCAmelCase	    =			i
         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp"""				)
         UpperCAmelCase	    =			tokenizer.subword_tokenizer

         UpperCAmelCase	    =			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""", """▁。"""]				)

         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese"""				)

         UpperCAmelCase	    =			tokenizer.encode("""ありがとう。""" ,		add_special_tokens=lowerCamelCase_				)
         UpperCAmelCase	    =			tokenizer.encode("""どういたしまして。""" ,		add_special_tokens=lowerCamelCase_				)

         UpperCAmelCase	    =			tokenizer.build_inputs_with_special_tokens(lowerCamelCase_				)
         UpperCAmelCase	    =			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   UpperCamelCase_ (       a__   ,					unittest.TestCase  ):
  _A       :					Union[str, Any]				=	BertJapaneseTokenizer
  _A       :					List[str]				=	False



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



         """simple docstring"""





         super().setUp()

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

         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self ,		**snake_case__				)			->			List[Any]:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			"""こんにちは、世界。 \nこんばんは、世界。"""
         UpperCAmelCase	    =			"""こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
         return input_text, output_text



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         pass  # TODO add if relevant



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



         """simple docstring"""





         pass  # TODO add if relevant



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



         """simple docstring"""





         pass  # TODO add if relevant



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



         """simple docstring"""





         UpperCAmelCase	    =			self.tokenizer_class(self.vocab_file ,		subword_tokenizer_type="""character"""				)

         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





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

         UpperCAmelCase	    =			{}
         for i, token in enumerate(lowerCamelCase_				):
                UpperCAmelCase	    =			i
         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





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

         UpperCAmelCase	    =			tokenizer.encode("""ありがとう。""" ,		add_special_tokens=lowerCamelCase_				)
         UpperCAmelCase	    =			tokenizer.encode("""どういたしまして。""" ,		add_special_tokens=lowerCamelCase_				)

         UpperCAmelCase	    =			tokenizer.build_inputs_with_special_tokens(lowerCamelCase_				)
         UpperCAmelCase	    =			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   UpperCamelCase_ (       unittest.TestCase  ):



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



         """simple docstring"""





         UpperCAmelCase	    =			"""cl-tohoku/bert-base-japanese"""
         UpperCAmelCase	    =			AutoTokenizer.from_pretrained(lowerCamelCase_				)
         self.assertIsInstance(lowerCamelCase_ ,		lowerCamelCase_				)





class   UpperCamelCase_ (       unittest.TestCase  ):



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			"""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."""				)				)
         UpperCAmelCase	    =			"""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."""				)				)








"""simple docstring"""







import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

lowerCAmelCase_	:    List[str]            =      {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=False	,      lowerCAmelCase=True		):







       '''simple docstring'''
       if model_type not in MODEL_CLASSES:
              raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       UpperCAmelCase	    =			config_class.from_json_file(lowerCAmelCase		)
       UpperCAmelCase	    =			True
       UpperCAmelCase	    =			True
       print(F'''Building TensorFlow model from configuration: {config}'''		)
       UpperCAmelCase	    =			model_class(lowerCAmelCase		)

       # Load weights from tf checkpoint
       if pytorch_checkpoint_path in aws_config_map.keys():
              UpperCAmelCase	    =			cached_file(
                  lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
       # Load PyTorch checkpoint in tf2 model:
       UpperCAmelCase	    =			load_pytorch_checkpoint_in_tfa_model(lowerCAmelCase	,      lowerCAmelCase		)

       if compare_with_pt_model:
              UpperCAmelCase	    =			tf_model(tf_model.dummy_inputs	,      training=lowerCAmelCase		)  # build the network

              UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)
              UpperCAmelCase	    =			pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=lowerCAmelCase	,      config=lowerCAmelCase	,      state_dict=lowerCAmelCase		)

              with torch.no_grad():
                     UpperCAmelCase	    =			pt_model(**pt_model.dummy_inputs		)

              UpperCAmelCase	    =			pto[0].numpy()
              UpperCAmelCase	    =			tfo[0].numpy()
              UpperCAmelCase	    =			np.amax(np.abs(np_pt - np_tf		)		)
              print(F'''Max absolute difference between models outputs {diff}'''		)
              assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

       # Save pytorch-model
       print(F'''Save TensorFlow model to {tf_dump_path}'''		)
       tf_model.save_weights(lowerCAmelCase	,      save_format="""h5"""		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      lowerCAmelCase=False	,      ):







       '''simple docstring'''
       if args_model_type is None:
              UpperCAmelCase	    =			list(MODEL_CLASSES.keys()		)
       else:
              UpperCAmelCase	    =			[args_model_type]

       for j, model_type in enumerate(lowerCAmelCase	,      start=1		):
              print("""=""" * 100		)
              print(F''' Converting model type {j}/{len(lowerCAmelCase		)}: {model_type}'''		)
              print("""=""" * 100		)
              if model_type not in MODEL_CLASSES:
                     raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

              UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			list(aws_model_maps.keys()		)
              if config_shortcut_names_or_path is None:
                     UpperCAmelCase	    =			model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(lowerCAmelCase	,      lowerCAmelCase		)	,      start=1		):
                     print("""-""" * 100		)
                     if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                            if not only_convert_finetuned_models:
                                   print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''		)
                                   continue
                            UpperCAmelCase	    =			model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''		)
                            continue
                     print(
                         F'''    Converting checkpoint {i}/{len(lowerCAmelCase		)}: {model_shortcut_name} - model_type {model_type}'''		)
                     print("""-""" * 100		)

                     if config_shortcut_name in aws_config_map:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            UpperCAmelCase	    =			cached_file(lowerCAmelCase	,      lowerCAmelCase	,      force_download=not use_cached_models		)
                     else:
                            UpperCAmelCase	    =			model_shortcut_name

                     if os.path.isfile(lowerCAmelCase		):
                            UpperCAmelCase	    =			"""converted_model"""

                     convert_pt_checkpoint_to_tf(
                         model_type=lowerCAmelCase	,      pytorch_checkpoint_path=lowerCAmelCase	,      config_file=lowerCAmelCase	,      tf_dump_path=os.path.join(lowerCAmelCase	,      model_shortcut_name + """-tf_model.h5"""		)	,      compare_with_pt_model=lowerCAmelCase	,      )
                     if remove_cached_files:
                            os.remove(lowerCAmelCase		)
                            os.remove(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    str            =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
 )
 parser.add_argument(
     '''--model_type''',
     default=None,
     type=str,
     help=(
         F'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
         '''convert all the models from AWS.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_checkpoint_path''',
     default=None,
     type=str,
     help=(
         '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
         '''If not given, will download and convert all the checkpoints from AWS.'''
     ),
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     help=(
         '''The config json file corresponding to the pre-trained model. \n'''
         '''This specifies the model architecture. If not given and '''
         '''--pytorch_checkpoint_path is not given or is a shortcut name '''
         '''use the configuration associated to the shortcut name on the AWS'''
     ),
 )
 parser.add_argument(
     '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
 )
 parser.add_argument(
     '''--use_cached_models''',
     action='''store_true''',
     help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
 )
 parser.add_argument(
     '''--remove_cached_files''',
     action='''store_true''',
     help='''Remove pytorch models after conversion (save memory when converting in batches).''',
 )
 parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
 lowerCAmelCase_	:    Optional[int]            =      parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )
