
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class     __A(				a						,     unittest.TestCase    ):
				snake_case_		     =					BertweetTokenizer
				snake_case_		     =					False




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''simple docstring'''




								super().setUp()

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

								__a    							=					os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES['''vocab_file''']       )
								__a    							=					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(_snake_case       )       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  **_snake_case       )					->   List[str]:


								'''simple docstring'''




								kwargs.update(self.special_tokens_map       )
								return BertweetTokenizer.from_pretrained(self.tmpdirname	,  **_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   List[Any]:


								'''simple docstring'''




								__a    							=					'''I am VinAI Research'''
								__a    							=					'''I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
								return input_text, output_text




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								__a    							=					BertweetTokenizer(self.vocab_file	,  self.merges_file	,  **self.special_tokens_map       )
								__a    							=					'''I am VinAI Research'''
								__a    							=					'''I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
								__a    							=					tokenizer.tokenize(_snake_case       )
								self.assertListEqual(_snake_case	,  _snake_case       )

								__a    							=					tokens + [tokenizer.unk_token]

								__a    							=					[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case       )	,  _snake_case       )

def 					__lowerCAmelCase    (    a__ = 99_8001						) ->						int:
				__a    							=					0
				for i in range(999		,					99		,					-1						):  # 3 digit numbers range from 999 down to 100
								for j in range(999		,					99		,					-1						):
												__a    							=					str(i * j						)
												if product_string == product_string[::-1] and i * j < n:
																__a    							=					max(a__		,					i * j						)
				return answer


if __name__ == "__main__":
					print(F"{solution() = }")