






"""simple docstring"""




from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class  UpperCamelCase    :


 def __init__(  self	:		Dict							,		UpperCAmelCase__	:		int							)      -> None:
   _a						: str    =			vocab
   _a						: int    =			normalizers.BertNormalizer(
       clean_text=UpperCAmelCase__							,		handle_chinese_chars=UpperCAmelCase__							,		strip_accents=UpperCAmelCase__							,		lowercase=UpperCAmelCase__							,		)
   try:
     import rjieba
   except ImportError:
     raise ImportError(
         """You need to install rjieba to use RoFormerTokenizer. """
         """See https://pypi.org/project/rjieba/ for installation."""							)
   _a						: Any    =			rjieba


 def _lowercase			(  self	:		Optional[int]							,		UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		NormalizedString							)      -> List[NormalizedString]:
   _a						: Optional[int]    =			[]

   # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
   for token, start, end in self.jieba.tokenize(str(UpperCAmelCase__							)							,		hmm=UpperCAmelCase__							):
     if token in self.vocab:
       splits.append(normalized_string[start:end]							)
     else:
       _a						: Optional[Any]    =			self.normalizers.normalize_str(UpperCAmelCase__							).split()
       for token in token_list:
         if token:
           _a						: Any    =			start + len(UpperCAmelCase__							)
           splits.append(normalized_string[start:end]							)
           _a						: Any    =			end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

   return splits







 def _lowercase			(  self	:		str							,		UpperCAmelCase__	:		PreTokenizedString							)      -> int:
   pretok.split(self.jieba_split							)









"""simple docstring"""




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : Union[str, Any]    =      BertweetTokenizer
 UpperCamelCase      : Optional[Any]    =      False


 def _lowercase			(  self	:		Dict							)      -> List[Any]:
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   _a						: Tuple    =			["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
   _a						: str    =			dict(zip(UpperCAmelCase__							,		range(len(UpperCAmelCase__							)							)							)							)
   _a						: Dict    =			["""#version: 0.2""", """a m</w>"""]
   _a						: int    =			{"""unk_token""": """<unk>"""}

   _a						: Union[str, Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""]							)
   _a						: List[Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""merges_file"""]							)
   with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     for token in vocab_tokens:
       fp.write(f"""{token} {vocab_tokens[token]}\n"""							)
   with open(self.merges_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     fp.write("""\n""".join(UpperCAmelCase__							)							)


 def _lowercase			(  self	:		Any							,		**UpperCAmelCase__	:		Optional[Any]							)      -> Dict:
   kwargs.update(self.special_tokens_map							)
   return BertweetTokenizer.from_pretrained(self.tmpdirname							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		Optional[int]							)      -> Dict:
   _a						: List[str]    =			"""I am VinAI Research"""
   _a						: Optional[int]    =			"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
   return input_text, output_text







 def _lowercase			(  self	:		Tuple							)      -> Optional[int]:
   _a						: Optional[int]    =			BertweetTokenizer(self.vocab_file							,		self.merges_file							,		**self.special_tokens_map							)
   _a						: Tuple    =			"""I am VinAI Research"""
   _a						: Dict    =			"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
   _a						: str    =			tokenizer.tokenize(UpperCAmelCase__							)
   self.assertListEqual(UpperCAmelCase__							,		UpperCAmelCase__							)

   _a						: Tuple    =			tokens + [tokenizer.unk_token]

   _a						: Tuple    =			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__							)							,		UpperCAmelCase__							)

