




'''simple docstring'''

from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

a       :						str								=		(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a       :						list[int]								=		[ord(letter) for letter in string.ascii_lowercase]
a       :						set[int]								=		{ord(char) for char in VALID_CHARS}

a       :						list[str]								=		["the", "be", "to", "of", "and", "in", "that", "have"]


def __UpperCAmelCase	(   _UpperCAmelCase		:   list[int]		,				_UpperCAmelCase		:   tuple[int, ...]						)      ->      str | None:
			__snake_case			=		""
			__snake_case			=		42
			__snake_case			=		42
			__snake_case			=		42

			for keychar, cipherchar in zip(cycle(_UpperCAmelCase						)		,				_UpperCAmelCase						):
						__snake_case			=		cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(_UpperCAmelCase						)

			return decoded


def __UpperCAmelCase	(   _UpperCAmelCase		:   list[int]						)      ->      list[str]:
			__snake_case			=		[]
			for key in product(_UpperCAmelCase		,				repeat=3						):
						__snake_case			=		try_key(_UpperCAmelCase		,				_UpperCAmelCase						)
						if encoded is not None:
									possibles.append(_UpperCAmelCase						)
			return possibles


def __UpperCAmelCase	(   _UpperCAmelCase		:   list[str]		,				_UpperCAmelCase		:   str						)      ->      list[str]:
			return [possible for possible in possibles if common_word in possible.lower()]


def __UpperCAmelCase	(   _UpperCAmelCase		:   str = "p059_cipher.txt"						)      ->      int:
			__snake_case			=		42
			__snake_case			=		42
			__snake_case			=		42
			__snake_case			=		42
			__snake_case			=		Path(_UpperCAmelCase						).parent.joinpath(_UpperCAmelCase						).read_text(encoding="utf-8"						)

			__snake_case			=		[int(_UpperCAmelCase						) for number in data.strip().split(","						)]

			__snake_case			=		filter_valid_chars(_UpperCAmelCase						)
			for common_word in COMMON_WORDS:
						__snake_case			=		filter_common_word(_UpperCAmelCase		,				_UpperCAmelCase						)
						if len(_UpperCAmelCase						) == 1:
									break

			__snake_case			=		possibles[0]
			return sum(ord(_UpperCAmelCase						) for char in decoded_text						)


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







"""simple docstring"""


import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     BioGptTokenizer
						_lowerCamelCase:     Tuple   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							A     = [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    '<unk>',
							]
							A     = dict(zip(A_	,range(len(A_   )   )   )   )
							A     = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							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'   ) as fp:
								fp.write(json.dumps(A_   )   )
							with open(self.merges_file	,'w'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple   )  -> int:
							A     = 'lower newer'
							A     = 'lower newer'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = BioGptTokenizer(self.vocab_file	,self.merges_file   )

							A     = 'lower'
							A     = ['low', 'er</w>']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = tokens + ['<unk>']
							A     = [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Optional[Any]:
							A     = BioGptTokenizer.from_pretrained('microsoft/biogpt'   )

							A     = tokenizer.encode('sequence builders'	,add_special_tokens=A_   )
							A     = tokenizer.encode('multi-sequence build'	,add_special_tokens=A_   )

							A     = tokenizer.build_inputs_with_special_tokens(A_   )
							A     = tokenizer.build_inputs_with_special_tokens(A_	,A_   )

							self.assertTrue(encoded_sentence == [2] + text   )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a   )