
import os



def 		UpperCamelCase    (					)      -> Optional[int]:






						'''simple docstring'''

						_lowercase					:  Tuple    			=      0
						_lowercase					:  List[Any]    			=      0
						with open(os.path.dirname(_UpperCAmelCase     ) + "/p022_names.txt"     ) as file:
												_lowercase					:  Any    			=      str(file.readlines()[0]     )
												_lowercase					:  int    			=      name.replace("\""   , ""     ).split(","     )

						name.sort()
						for i in range(len(_UpperCAmelCase     )     ):
												for j in name[i]:
																		temp_sum += ord(_UpperCAmelCase     ) - ord("A"     ) + 1
												total_sum += (i + 1) * temp_sum
												_lowercase					:  Tuple    			=      0
						return total_sum


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






import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin




class      __lowercase	(	__snake_case      ,   unittest.TestCase					):
	_A   			=      BertweetTokenizer
	_A   			=      False






	def 			_a(self							:   Tuple						)   -> Any:
							super().setUp()

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

							_lowercase					:  int    			=      os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]						)
							_lowercase					:  Optional[int]    			=      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 			_a(self							:   List[Any]			,  **snake_case							:   Tuple						)   -> Union[str, Any]:
							kwargs.update(self.special_tokens_map						)
							return BertweetTokenizer.from_pretrained(self.tmpdirname			,  **snake_case						)






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






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

							_lowercase					:  str    			=      tokens + [tokenizer.unk_token]

							_lowercase					:  str    			=      [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						)




