





'''simple docstring'''


from sklearn.metrics import fa_score

import datasets


_SCREAMING_SNAKE_CASE									=				"\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results['f1'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


_SCREAMING_SNAKE_CASE									=				"\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,_KWARGS_DESCRIPTION		)
class  lowerCAmelCase_		(		datasets.Metric		):




 def 				_snake_case (						self		)     ->				Dict:
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
             {
                 "predictions": datasets.Sequence(datasets.Value("int32"		)		),
                 "references": datasets.Sequence(datasets.Value("int32"		)		),
             }
             if self.config_name == "multilabel"
             else {
                 "predictions": datasets.Value("int32"		),
                 "references": datasets.Value("int32"		),
             }		)      ,  reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]      ,  )





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase="binary"      ,  _lowerCAmelCase=None		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   fa_score(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  labels=_lowerCAmelCase      ,  pos_label=_lowerCAmelCase      ,  average=_lowerCAmelCase      ,  sample_weight=_lowerCAmelCase		)
     return {"f1": float(_lowerCAmelCase		) if score.size == 1 else score}








'''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      SCREAMING_SNAKE_CASE    (						snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Optional[Any]   							=					BertJapaneseTokenizer
      __magic_name__  :       int   							=					False
      __magic_name__  :       Tuple   							=					True






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           super().setUp()

           a_     :							List[Any]     						=  [
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """こんにちは""",
               """こん""",
               """にちは""",
               """ばんは""",
               """##こん""",
               """##にちは""",
               """##ばんは""",
               """世界""",
               """##世界""",
               """、""",
               """##、""",
               """。""",
               """##。""",
           ]

           a_     :							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 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  """こんにちは、世界。 \nこんばんは、世界。"""
           a_     :							Tuple     						=  """こんにちは 、 世界 。 こんばんは 、 世界 。"""
           return input_text, output_text






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


           '''simple docstring'''

           a_						,     a_     :							int     						=  self.get_input_output_texts(lowercase__       )
           a_     :							Tuple     						=  tokenizer.encode(lowercase__						,						add_special_tokens=lowercase__       )
           a_     :							Union[str, Any]     						=  tokenizer.decode(lowercase__						,						clean_up_tokenization_spaces=lowercase__       )
           return text, ids






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           pass  # TODO add if relevant






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           pass  # TODO add if relevant






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           pass  # TODO add if relevant






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Tuple     						=  self.tokenizer_class(self.vocab_file       )

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






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


           '''simple docstring'''

           a_     :							Tuple     						=  self.tokenizer_class(self.vocab_file						,						word_tokenizer_type="""mecab"""       )
           self.assertIsNotNone(lowercase__       )

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

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

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

           a_     :							Tuple     						=  tokenizer_new.tokenize(lowercase__       )

           self.assertListEqual(lowercase__						,						lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  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   :  Dict       ):


           '''simple docstring'''

           try:
                a_     :							Tuple     						=  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   :  str       ):


           '''simple docstring'''

           try:
                a_     :							Union[str, Any]     						=  MecabTokenizer(mecab_dic="""unidic"""       )
           except ModuleNotFoundError:
                return

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]						,						)






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  MecabTokenizer(do_lower_case=lowercase__						,						mecab_dic="""ipadic"""       )

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]						,						)






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

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

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""]						,						)






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							str     						=  MecabTokenizer(normalize_text=lowercase__						,						mecab_dic="""ipadic"""       )

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """　""", """。"""]						,						)






      @require_sudachi
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							int     						=  self.tokenizer_class(self.vocab_file						,						word_tokenizer_type="""sudachi"""       )
           self.assertIsNotNone(lowercase__       )

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

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

           with open(lowercase__						,						"""rb"""       ) as handle:
                a_     :							List[Any]     						=  pickle.load(lowercase__       )

           a_     :							Optional[int]     						=  tokenizer_new.tokenize(lowercase__       )

           self.assertListEqual(lowercase__						,						lowercase__       )






      @require_sudachi
      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  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   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  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   :  int       ):


           '''simple docstring'''

           a_     :							Dict     						=  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   :  Any       ):


           '''simple docstring'''

           a_     :							Tuple     						=  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[Any]       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  SudachiTokenizer(do_lower_case=lowercase__						,						sudachi_dict_type="""core"""       )

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						[""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """]						,						)






      @require_sudachi
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Tuple     						=  SudachiTokenizer(normalize_text=lowercase__						,						sudachi_dict_type="""core"""       )

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						[""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """]						,						)






      @require_sudachi
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  SudachiTokenizer(trim_whitespace=lowercase__						,						sudachi_dict_type="""core"""       )

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]						,						)






      @require_jumanpp
      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.tokenizer_class(self.vocab_file						,						word_tokenizer_type="""jumanpp"""       )
           self.assertIsNotNone(lowercase__       )

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

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

           with open(lowercase__						,						"""rb"""       ) as handle:
                a_     :							Any     						=  pickle.load(lowercase__       )

           a_     :							List[Any]     						=  tokenizer_new.tokenize(lowercase__       )

           self.assertListEqual(lowercase__						,						lowercase__       )






      @require_jumanpp
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							int     						=  JumanppTokenizer()

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""]						,						)






      @require_jumanpp
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							str     						=  JumanppTokenizer(do_lower_case=lowercase__       )

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""]						,						)






      @require_jumanpp
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  JumanppTokenizer(normalize_text=lowercase__       )

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""]						,						)






      @require_jumanpp
      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  JumanppTokenizer(trim_whitespace=lowercase__       )

           self.assertListEqual(
               tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """       )						,						["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""]						,						)






      @require_jumanpp
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

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

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






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							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"""]

           a_     :							int     						=  {}
           for i, token in enumerate(lowercase__       ):
                a_     :							str     						=  i
           a_     :							int     						=  WordpieceTokenizer(vocab=lowercase__						,						unk_token="""[UNK]"""       )

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

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

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

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






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp"""       )
           a_     :							List[str]     						=  tokenizer.subword_tokenizer

           a_     :							Any     						=  subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。"""       )
           self.assertListEqual(lowercase__						,						["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""]       )

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






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese"""       )

           a_     :							Union[str, Any]     						=  tokenizer.encode("""ありがとう。"""						,						add_special_tokens=lowercase__       )
           a_     :							Any     						=  tokenizer.encode("""どういたしまして。"""						,						add_special_tokens=lowercase__       )

           a_     :							str     						=  tokenizer.build_inputs_with_special_tokens(lowercase__       )
           a_     :							Optional[int]     						=  tokenizer.build_inputs_with_special_tokens(lowercase__						,						lowercase__       )

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







@custom_tokenizers
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Optional[Any]   							=					BertJapaneseTokenizer
      __magic_name__  :       int   							=					False






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           super().setUp()

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

           a_     :							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 							lowercase_						( self   :  str						,						**lowercase__   :  Tuple       ):


           '''simple docstring'''

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






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


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           pass  # TODO add if relevant






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           pass  # TODO add if relevant






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           pass  # TODO add if relevant






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.tokenizer_class(self.vocab_file						,						subword_tokenizer_type="""character"""       )

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






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

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

           a_     :							Dict     						=  {}
           for i, token in enumerate(lowercase__       ):
                a_     :							List[Any]     						=  i
           a_     :							Dict     						=  CharacterTokenizer(vocab=lowercase__						,						unk_token="""[UNK]"""       )

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

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

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






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

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

           a_     :							List[Any]     						=  tokenizer.encode("""ありがとう。"""						,						add_special_tokens=lowercase__       )
           a_     :							List[Any]     						=  tokenizer.encode("""どういたしまして。"""						,						add_special_tokens=lowercase__       )

           a_     :							str     						=  tokenizer.build_inputs_with_special_tokens(lowercase__       )
           a_     :							Optional[Any]     						=  tokenizer.build_inputs_with_special_tokens(lowercase__						,						lowercase__       )

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







@custom_tokenizers
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  """cl-tohoku/bert-base-japanese"""
           a_     :							Union[str, Any]     						=  AutoTokenizer.from_pretrained(lowercase__       )
           self.assertIsInstance(lowercase__						,						lowercase__       )







class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

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