






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			):

				"""simple docstring"""




				lowercase__       							= BertJapaneseTokenizer
				lowercase__       							= False
				lowercase__       							= True




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								super().setUp()

								lowercase__     				=    [
								    '''[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''',
								    '''、''',
								    '''##、''',
								    '''。''',
								    '''##。''',
								]

								lowercase__     				=    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		:					Any,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    '''こんにちは、世界。 \nこんばんは、世界。'''
								lowercase__     				=    '''こんにちは 、 世界 。 こんばんは 、 世界 。'''
								return input_text, output_text




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.get_input_output_texts(lowerCamelCase						)
								lowercase__     				=    tokenizer.encode(lowerCamelCase,						add_special_tokens=lowerCamelCase						)
								lowercase__     				=    tokenizer.decode(lowerCamelCase,						clean_up_tokenization_spaces=lowerCamelCase						)
								return text, ids




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								pass  # TODO add if relevant




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass  # TODO add if relevant




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								pass  # TODO add if relevant




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer_class(self.vocab_file						)

								lowercase__     				=    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        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer_class(self.vocab_file,						word_tokenizer_type='''mecab'''						)
								self.assertIsNotNone(lowerCamelCase						)

								lowercase__     				=    '''こんにちは、世界。\nこんばんは、世界。'''
								lowercase__     				=    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]						)

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

								with open(lowerCamelCase,						'''rb'''						) as handle:
												lowercase__     				=    pickle.load(lowerCamelCase						)

								lowercase__     				=    tokenizer_new.tokenize(lowerCamelCase						)

								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    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		:					List[str]						):

								'''simple docstring'''
								try:
												lowercase__     				=    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		:					Optional[Any]						):

								'''simple docstring'''
								try:
												lowercase__     				=    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		:					Any						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								try:
												lowercase__     				=    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        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer_class(self.vocab_file,						word_tokenizer_type='''sudachi'''						)
								self.assertIsNotNone(lowerCamelCase						)

								lowercase__     				=    '''こんにちは、世界。\nこんばんは、世界。'''
								lowercase__     				=    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]						)

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

								with open(lowerCamelCase,						'''rb'''						) as handle:
												lowercase__     				=    pickle.load(lowerCamelCase						)

								lowercase__     				=    tokenizer_new.tokenize(lowerCamelCase						)

								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				@require_sudachi
				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    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		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    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		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    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		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    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		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer_class(self.vocab_file,						word_tokenizer_type='''jumanpp'''						)
								self.assertIsNotNone(lowerCamelCase						)

								lowercase__     				=    '''こんにちは、世界。\nこんばんは、世界。'''
								lowercase__     				=    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]						)

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

								with open(lowerCamelCase,						'''rb'''						) as handle:
												lowercase__     				=    pickle.load(lowerCamelCase						)

								lowercase__     				=    tokenizer_new.tokenize(lowerCamelCase						)

								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				@require_jumanpp
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    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		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    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		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こんにちは''', '''こん''', '''にちは''', '''ばんは''', '''##こん''', '''##にちは''', '''##ばんは''']

								lowercase__     				=    {}
								for i, token in enumerate(lowerCamelCase						):
												lowercase__     				=    i
								lowercase__     				=    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        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    BertJapaneseTokenizer.from_pretrained('''nlp-waseda/roberta-base-japanese-with-auto-jumanpp'''						)
								lowercase__     				=    tokenizer.subword_tokenizer

								lowercase__     				=    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''', '''▁。''']						)

								lowercase__     				=    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        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese'''						)

								lowercase__     				=    tokenizer.encode('''ありがとう。''',						add_special_tokens=lowerCamelCase						)
								lowercase__     				=    tokenizer.encode('''どういたしまして。''',						add_special_tokens=lowerCamelCase						)

								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase						)
								lowercase__     				=    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			):

				"""simple docstring"""




				lowercase__       							= BertJapaneseTokenizer
				lowercase__       							= False




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								super().setUp()

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

								lowercase__     				=    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		:					str,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								return BertJapaneseTokenizer.from_pretrained(self.tmpdirname,						subword_tokenizer_type='''character''',						**lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    '''こんにちは、世界。 \nこんばんは、世界。'''
								lowercase__     				=    '''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		:					Dict						):

								'''simple docstring'''
								pass  # TODO add if relevant




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer_class(self.vocab_file,						subword_tokenizer_type='''character'''						)

								lowercase__     				=    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        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''こ''', '''ん''', '''に''', '''ち''', '''は''', '''ば''', '''世''', '''界''', '''、''', '''。''']

								lowercase__     				=    {}
								for i, token in enumerate(lowerCamelCase						):
												lowercase__     				=    i
								lowercase__     				=    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        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese-char'''						)

								lowercase__     				=    tokenizer.encode('''ありがとう。''',						add_special_tokens=lowerCamelCase						)
								lowercase__     				=    tokenizer.encode('''どういたしまして。''',						add_special_tokens=lowerCamelCase						)

								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase						)
								lowercase__     				=    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			):

				"""simple docstring"""




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    '''cl-tohoku/bert-base-japanese'''
								lowercase__     				=    AutoTokenizer.from_pretrained(lowerCamelCase						)
								self.assertIsInstance(lowerCamelCase,						lowerCamelCase						)



class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    '''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.'''						)						)
								lowercase__     				=    '''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.'''						)						)












# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return torch.where(
								    timesteps == 0,						torch.zeros_like(t.to(timesteps.device						)						),						self.discrete_sigmas[timesteps - 1].to(timesteps.device						),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (timestep * (len(self.timesteps						) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								lowercase__     				=    randn_tensor(
								    sample.shape,						layout=sample.layout,						generator=lowerCamelCase,						device=sample.device,						dtype=sample.dtype						)
								lowercase__     				=    sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								lowercase__     				=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
												return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowerCamelCase,						prev_sample_mean=lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								lowercase__     				=    randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCamelCase						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								lowercase__     				=    torch.norm(model_output.reshape(model_output.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    torch.norm(noise.reshape(noise.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								lowercase__     				=    step_size * torch.ones(sample.shape[0]						).to(sample.device						)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								lowercase__     				=    step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
												lowercase__     				=    step_size.unsqueeze(-1						)
								lowercase__     				=    sample + step_size * model_output
								lowercase__     				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




				def __len__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.config.num_train_timesteps




