

'''simple docstring'''

import os
import pickle
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin





@custom_tokenizers
class 							_snake_case (       snake_case_	,  unittest.TestCase						):
   _A       :     List[Any]       =					BertJapaneseTokenizer
   _A       :     Union[str, Any]       =					False
   _A       :     str       =					True
   def     __UpperCamelCase						( self			:      Optional[int]							):
     super().setUp()

     SCREAMING_SNAKE_CASE: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",
         "、",
         "##、",
         "。",
         "##。",
     ]

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"]							)
     with open(self.vocab_file     ,"w"     ,encoding="utf-8"							) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)
   def     __UpperCamelCase						( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Any							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					"こんにちは、世界。 \nこんばんは、世界。"
     SCREAMING_SNAKE_CASE:Any						      =					"こんにちは 、 世界 。 こんばんは 、 世界 。"
     return input_text, output_text
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Tuple							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[int]						      =					self.get_input_output_texts(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str						      =					tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					tokenizer.decode(SCREAMING_SNAKE_CASE__     ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							)
     return text, ids
   def     __UpperCamelCase						( self			:      int							):
     pass  # TODO add if relevant
   def     __UpperCamelCase						( self			:      Any							):
     pass  # TODO add if relevant
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     pass  # TODO add if relevant
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.tokenizer_class(self.vocab_file							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					tokenizer.tokenize("こんにちは、世界。\nこんばんは、世界。"							)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"]							)
     self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)     ,[3, 12, 10, 14, 4, 9, 12, 10, 14]							)
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.tokenizer_class(self.vocab_file     ,word_tokenizer_type="mecab"							)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)

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

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

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

     SCREAMING_SNAKE_CASE:Dict						      =					tokenizer_new.tokenize(SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Dict						      =					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			:      Any							):
     try:
       SCREAMING_SNAKE_CASE: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     __UpperCamelCase						( self			:      str							):
     try:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					MecabTokenizer(mecab_dic="unidic"							)
     except ModuleNotFoundError:
       return

     self.assertListEqual(
         tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "							)     ,["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"]     ,)
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:int						      =					MecabTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__     ,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			:      Union[str, Any]							):
     try:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					MecabTokenizer(
           do_lower_case=SCREAMING_SNAKE_CASE__     ,normalize_text=SCREAMING_SNAKE_CASE__     ,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			:      str							):
     SCREAMING_SNAKE_CASE:Any						      =					MecabTokenizer(normalize_text=SCREAMING_SNAKE_CASE__     ,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							):
     SCREAMING_SNAKE_CASE:Any						      =					self.tokenizer_class(self.vocab_file     ,word_tokenizer_type="sudachi"							)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)

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

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

     with open(SCREAMING_SNAKE_CASE__     ,"rb"							) as handle:
       SCREAMING_SNAKE_CASE:Tuple						      =					pickle.load(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:int						      =					tokenizer_new.tokenize(SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   @require_sudachi
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					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			:      Dict							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					SudachiTokenizer(sudachi_dict_type="core"     ,sudachi_split_mode="A"							)

     self.assertListEqual(tokenizer.tokenize("外国人参政権"							)     ,["外国", "人", "参政", "権"]							)
   @require_sudachi
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:int						      =					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			:      str							):
     SCREAMING_SNAKE_CASE:Any						      =					SudachiTokenizer(sudachi_dict_type="core"     ,sudachi_split_mode="C"							)

     self.assertListEqual(tokenizer.tokenize("外国人参政権"							)     ,["外国人参政権"]							)
   @require_sudachi
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					SudachiTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__     ,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			:      Tuple							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					SudachiTokenizer(normalize_text=SCREAMING_SNAKE_CASE__     ,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			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					SudachiTokenizer(trim_whitespace=SCREAMING_SNAKE_CASE__     ,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			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					self.tokenizer_class(self.vocab_file     ,word_tokenizer_type="jumanpp"							)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)

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

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

     with open(SCREAMING_SNAKE_CASE__     ,"rb"							) as handle:
       SCREAMING_SNAKE_CASE:Dict						      =					pickle.load(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Tuple						      =					tokenizer_new.tokenize(SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   @require_jumanpp
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Any						      =					JumanppTokenizer()

     self.assertListEqual(
         tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "							)     ,["アップル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"]     ,)
   @require_jumanpp
   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE:int						      =					JumanppTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__							)

     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			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					JumanppTokenizer(normalize_text=SCREAMING_SNAKE_CASE__							)

     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			:      str							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					JumanppTokenizer(trim_whitespace=SCREAMING_SNAKE_CASE__							)

     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			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					JumanppTokenizer()

     self.assertListEqual(
         tokenizer.tokenize("ありがとうございますm(_ _)ｍ見つけるのが大変です。"							)     ,["ありがとう", "ございます", "m(_ _)m", "見つける", "の", "が", "大変です", "。"]     ,)
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"]

     SCREAMING_SNAKE_CASE:Dict						      =					{}
     for i, token in enumerate(SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:List[Any]						      =					i
     SCREAMING_SNAKE_CASE:List[str]						      =					WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__     ,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[Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp"							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer.subword_tokenizer

     SCREAMING_SNAKE_CASE:Optional[int]						      =					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(SCREAMING_SNAKE_CASE__     ,["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"]							)

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







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

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

     SCREAMING_SNAKE_CASE:List[str]						      =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Dict						      =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

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





@custom_tokenizers
class 							_snake_case (       snake_case_	,  unittest.TestCase						):
   _A       :     Dict       =					BertJapaneseTokenizer
   _A       :     str       =					False
   def     __UpperCamelCase						( self			:      str							):
     super().setUp()

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

     SCREAMING_SNAKE_CASE:List[Any]						      =					os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"]							)
     with open(self.vocab_file     ,"w"     ,encoding="utf-8"							) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)
   def     __UpperCamelCase						( self			:      int     ,**SCREAMING_SNAKE_CASE__			:      str							):
     return BertJapaneseTokenizer.from_pretrained(self.tmpdirname     ,subword_tokenizer_type="character"     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					"こんにちは、世界。 \nこんばんは、世界。"
     SCREAMING_SNAKE_CASE:Dict						      =					"こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
     return input_text, output_text
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     pass  # TODO add if relevant
   def     __UpperCamelCase						( self			:      Dict							):
     pass  # TODO add if relevant
   def     __UpperCamelCase						( self			:      str							):
     pass  # TODO add if relevant
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.tokenizer_class(self.vocab_file     ,subword_tokenizer_type="character"							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					tokenizer.tokenize("こんにちは、世界。 \nこんばんは、世界。"							)
     self.assertListEqual(
         SCREAMING_SNAKE_CASE__     ,["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"]							)
     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)     ,[3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12]							)
   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE:Dict						      =					["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]

     SCREAMING_SNAKE_CASE:Dict						      =					{}
     for i, token in enumerate(SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					i
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					CharacterTokenizer(vocab=SCREAMING_SNAKE_CASE__     ,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			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char"							)

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

     SCREAMING_SNAKE_CASE:List[str]						      =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str						      =					tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

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





@custom_tokenizers
class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					"cl-tohoku/bert-base-japanese"
     SCREAMING_SNAKE_CASE:List[str]						      =					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							)
     self.assertIsInstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)





class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					"cl-tohoku/bert-base-japanese"
     with self.assertLogs("transformers"     ,level="WARNING"							) as cm:
       BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							)
       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."							)							)
     SCREAMING_SNAKE_CASE:int						      =					"bert-base-cased"
     with self.assertLogs("transformers"     ,level="WARNING"							) as cm:
       BertJapaneseTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							)
       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 random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel





def    A_  ( snake_case=32      ,       snake_case=10      ,       snake_case=100      ,       snake_case=1026      ,       snake_case=True      ,       snake_case="data/tokenized_stories_train_wikitext103.jbl"      ,       snake_case="igf_context_pairs.jbl"      ,       ):
  set_seed(3 )
  # generate train_data and objective_set
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[int]						      =					generate_datasets(
      snake_case      ,       snake_case      ,       number=snake_case      ,       min_len=1026      ,       trim=snake_case )
  # keeps model same across runs
  set_seed(4 )
  # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
  # can we train on GPU?
  SCREAMING_SNAKE_CASE:Tuple						      =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

  # load pretrained model
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					load_gpta("gpt2" ).to(snake_case )
  print("computing perplexity on objective set" )
  SCREAMING_SNAKE_CASE:str						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case ).item()
  print("perplexity on objective set:"      ,       snake_case )

  # collect igf pairs and save to file demo.jbl
  collect_objective_set(snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case )

  # clean up, delete model and data we don't need anymore
  del model, train_data, objective_set
  torch.cuda.empty_cache()





def    A_  ( snake_case      ,       snake_case=15      ,       snake_case=128      ,       snake_case=100      ,       snake_case="igf_model.pt"      ,       ):
  set_seed(42 )

  # Load pre-trained model
  SCREAMING_SNAKE_CASE:int						      =					GPTaLMHeadModel.from_pretrained("gpt2" )

  # Initialize secondary learner to use embedding weights of model
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					SecondaryLearner(snake_case )

  # Train secondary learner
  SCREAMING_SNAKE_CASE:int						      =					train_secondary_learner(
      snake_case      ,       snake_case      ,       max_epochs=snake_case      ,       batch_size=snake_case      ,       eval_freq=100      ,       igf_model_path=snake_case      ,       )

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case=32      ,       snake_case=1000      ,       snake_case=16      ,       snake_case=1.0      ,       snake_case=recopy_gpta      ,       snake_case=None      ,       snake_case=10      ,       snake_case="gpt2_finetuned.pt"      ,       ):
  SCREAMING_SNAKE_CASE:str						      =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
  SCREAMING_SNAKE_CASE:Dict						      =					RandomSampler(snake_case )
  SCREAMING_SNAKE_CASE:str						      =					DataLoader(snake_case      ,       sampler=snake_case )

  SCREAMING_SNAKE_CASE:str						      =					max_steps // (len(snake_case )) + 1
  SCREAMING_SNAKE_CASE:Tuple						      =					0
  SCREAMING_SNAKE_CASE:List[Any]						      =					torch.zeros((1, context_len)      ,       dtype=torch.long      ,       device=snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					recopy_model(snake_case      ,       snake_case      ,       snake_case )

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(snake_case )
    secondary_learner.eval()
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  SCREAMING_SNAKE_CASE:Tuple						      =					0

  SCREAMING_SNAKE_CASE:Tuple						      =					[]
  SCREAMING_SNAKE_CASE:Tuple						      =					[]

  # Compute the performance of the transformer model at the beginning
  SCREAMING_SNAKE_CASE:int						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case )
  test_perps.append(snake_case )
  print("Test perplexity, step"      ,       snake_case      ,       ":"      ,       snake_case )
  for epoch in range(int(snake_case ) ):
    for step, example in enumerate(snake_case ):
      torch.cuda.empty_cache()
      SCREAMING_SNAKE_CASE:str						      =					random.randint(0      ,       example.size(2 ) - context_len - 1 )
      SCREAMING_SNAKE_CASE:List[str]						      =					example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      SCREAMING_SNAKE_CASE:Any						      =					model(snake_case      ,       labels=snake_case )
      SCREAMING_SNAKE_CASE:List[Any]						      =					True

      if secondary_learner is not None:
        SCREAMING_SNAKE_CASE:List[str]						      =					secondary_learner.forward(
            torch.tensor(snake_case      ,       dtype=torch.long      ,       device=snake_case ).unsqueeze(0 ) )[0].item()
        observed_qs.append(float(snake_case ) )

        # Here we implement the simple non-constant threshold for the predicted IG(X) value
        # We will decay the selectivity of our secondary learner filter from
        # 1 standard deviation above average to 1 below average after 10 batches.

        if global_step == 10:
          SCREAMING_SNAKE_CASE:int						      =					-1
        if predicted_q < threshold:
          SCREAMING_SNAKE_CASE:Optional[int]						      =					False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu() ) )
        SCREAMING_SNAKE_CASE:Union[str, Any]						      =					outputs[0]
        lm_loss.backward()
        examples += 1

      del outputs

      # Once the batch is filled with enough contexts, backprop on the batch.
      if examples == batch_size:
        torch.cuda.empty_cache()
        SCREAMING_SNAKE_CASE:int						      =					0
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters()      ,       3.0 )
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule
        global_step += 1
        # Compute the performance of the transformer model at this batch
        if global_step % eval_interval == 0:
          SCREAMING_SNAKE_CASE:Optional[Any]						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case )
          test_perps.append(snake_case )

          print("Test perplexity, step"      ,       snake_case      ,       ":"      ,       snake_case )
            # Break out of the loop after 60 batches
      if max_steps > 0 and global_step > 60:
        break
    if max_steps > 0 and global_step > 60:
      break

    # save finetuned transformer model
  torch.save(model.state_dict()      ,       snake_case )
  torch.cuda.empty_cache()
  # Do some cleaning up so we can reinitialize for the next run of this function
  del lm_optimizer
  del lm_scheduler
  return model





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )

  # Required parameters
  parser.add_argument(
      "--data_dir"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="The input data dir. Should contain data files for WikiText."      ,       )
  parser.add_argument(
      "--model_name_or_path"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="Path to pretrained model or model identifier from huggingface.co/models"      ,       )
  parser.add_argument(
      "--data_file"      ,       type=snake_case      ,       default=snake_case      ,       help=(
          "A jbl file containing tokenized data which can be split as objective dataset, "
          "train_dataset and test_dataset."
      )      ,       )

  parser.add_argument(
      "--igf_data_file"      ,       type=snake_case      ,       default=snake_case      ,       help="A jbl file containing the context and information gain pairs to train secondary learner."      ,       )

  parser.add_argument(
      "--output_dir"      ,       default=snake_case      ,       type=snake_case      ,       required=snake_case      ,       help="The output directory where the final fine-tuned model is stored."      ,       )

  parser.add_argument(
      "--tokenizer_name"      ,       default=snake_case      ,       type=snake_case      ,       help="Pretrained tokenizer name or path if not the same as model_name"      ,       )
  parser.add_argument("--seed"      ,       type=snake_case      ,       default=snake_case      ,       help="A seed for reproducible training." )

  parser.add_argument(
      "--context_len"      ,       default=32      ,       type=snake_case      ,       help=(
          "The maximum total input sequence length after tokenization. Sequences longer "
          "than this will be truncated, sequences shorter will be padded."
      )      ,       )

  parser.add_argument(
      "--size_objective_set"      ,       default=100      ,       type=snake_case      ,       help="number of articles that are long enough to be used as our objective set"      ,       )
  parser.add_argument(
      "--eval_freq"      ,       default=100      ,       type=snake_case      ,       help="secondary model evaluation is triggered at eval_freq" )

  parser.add_argument("--max_steps"      ,       default=1000      ,       type=snake_case      ,       help="To calculate training epochs" )

  parser.add_argument(
      "--secondary_learner_batch_size"      ,       default=128      ,       type=snake_case      ,       help="batch size of training data for secondary learner"      ,       )

  parser.add_argument(
      "--batch_size"      ,       default=16      ,       type=snake_case      ,       help="batch size of training data of language model(gpt2) " )

  parser.add_argument(
      "--eval_interval"      ,       default=10      ,       type=snake_case      ,       help=(
          "decay the selectivity of our secondary learner filter from"
          "1 standard deviation above average to 1 below average after 10 batches"
      )      ,       )

  parser.add_argument(
      "--number"      ,       default=100      ,       type=snake_case      ,       help="The number of examples split to be used as objective_set/test_data" )

  parser.add_argument(
      "--min_len"      ,       default=1026      ,       type=snake_case      ,       help="The minimum length of the article to be used as objective set" )

  parser.add_argument(
      "--secondary_learner_max_epochs"      ,       default=15      ,       type=snake_case      ,       help="number of epochs to train secondary learner" )

  parser.add_argument("--trim"      ,       default=snake_case      ,       type=snake_case      ,       help="truncate the example if it exceeds context length" )

  parser.add_argument(
      "--threshold"      ,       default=1.0      ,       type=snake_case      ,       help=(
          "The threshold value used by secondary learner to filter the train_data and allow only"
          " informative data as input to the model"
      )      ,       )

  parser.add_argument("--finetuned_model_name"      ,       default="gpt2_finetuned.pt"      ,       type=snake_case      ,       help="finetuned_model_name" )

  parser.add_argument(
      "--recopy_model"      ,       default=snake_case      ,       type=snake_case      ,       help="Reset the model to the original pretrained GPT-2 weights after each iteration"      ,       )

  # function calls
  # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
  generate_n_pairs(
      context_len=32      ,       max_steps=10      ,       size_objective_set=100      ,       min_len=1026      ,       trim=snake_case      ,       data_file="data/tokenized_stories_train_wikitext103.jbl"      ,       igf_data_file="igf_context_pairs.jbl"      ,       )

  # Load train data for secondary learner
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					joblib.load("data/IGF_values.jbl" )

  # Train secondary learner
  SCREAMING_SNAKE_CASE:Optional[int]						      =					training_secondary_learner(
      snake_case      ,       secondary_learner_max_epochs=15      ,       secondary_learner_batch_size=128      ,       eval_freq=100      ,       igf_model_path="igf_model.pt"      ,       )

  # load pretrained gpt2 model
  SCREAMING_SNAKE_CASE:Dict						      =					GPTaLMHeadModel.from_pretrained("gpt2" )
  set_seed(42 )

  # Generate train and test data to train and evaluate gpt2 model
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					generate_datasets(
      context_len=32      ,       file="data/tokenized_stories_train_wikitext103.jbl"      ,       number=100      ,       min_len=1026      ,       trim=snake_case )

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      snake_case      ,       snake_case      ,       snake_case      ,       context_len=32      ,       max_steps=1000      ,       batch_size=16      ,       threshold=1.0      ,       recopy_model=snake_case      ,       secondary_learner=snake_case      ,       eval_interval=10      ,       finetuned_model_name="gpt2_finetuned.pt"      ,       )


if __name__ == "__main__":
    main()






