

"""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     (						lowerCAmelCase__      ,       unittest.TestCase):
			__a	:    List[Any]			   =							BertweetTokenizer
			__a	:    int			   =							False
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				super().setUp()

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

				_UpperCAmelCase      :							Any       =   os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES["""vocab_file"""]       )
				_UpperCAmelCase      :							str       =   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(_A       )       )
			def 							__snake_case	(						self ,						**_A       )      ->      Optional[Any]:






				'''simple docstring'''







				kwargs.update(self.special_tokens_map       )
				return BertweetTokenizer.from_pretrained(self.tmpdirname ,						**_A       )
			def 							__snake_case	(						self ,						_A       )      ->      int:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[Any]       =   '''I am VinAI Research'''
				_UpperCAmelCase      :							List[str]       =   '''I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
				return input_text, output_text
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   BertweetTokenizer(self.vocab_file ,						self.merges_file ,						**self.special_tokens_map       )
				_UpperCAmelCase      :							Tuple       =   '''I am VinAI Research'''
				_UpperCAmelCase      :							Any       =   '''I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
				_UpperCAmelCase      :							List[Any]       =   tokenizer.tokenize(_A       )
				self.assertListEqual(_A ,						_A       )

				_UpperCAmelCase      :							int       =   tokens + [tokenizer.unk_token]

				_UpperCAmelCase      :							Dict       =   [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_A       ) ,						_A       )








"""simple docstring"""






from math import factorial
def  UpperCamelCase   (     _lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  List[str]	)			->     Dict:
	return factorial(_lowerCAmelCase	) / (factorial(_lowerCAmelCase	) * factorial(n - r	))
def  UpperCamelCase   (     )			->     Tuple:
	_UpperCAmelCase      :							Optional[int]       =   0

	for i in range(1,					101	):
		for j in range(1,					i + 1	):
			if combinations(_lowerCAmelCase,					_lowerCAmelCase	) > 1E6:
				total += 1
	return total


if __name__ == "__main__":
						print(solution())





