
import os
from collections import deque

import torch
from torch.utils.data import Dataset




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   Union[str, Any]      ,  lowercase     :   Optional[Any]=""      ,  lowercase     :   Dict="train"     ):


				'''simple docstring'''


				assert os.path.isdir(lowercase     )

				_snake_case          =      []
				_snake_case          =      os.listdir(lowercase     )
				for story_filename in story_filenames_list:
							if "summary" in story_filename:
										continue
							_snake_case          =      os.path.join(lowercase      ,  lowercase     )
							if not os.path.isfile(lowercase     ):
										continue
							self.documents.append(lowercase     )



	def __len__(  self     :   Optional[Any]     ):


				'''simple docstring'''


				return len(self.documents     )





	def __getitem__(  self     :   Optional[int]      ,  lowercase     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.documents[idx]
				_snake_case          =      document_path.split('/'     )[-1]
				with open(lowercase      ,  encoding='utf-8'     ) as source:
							_snake_case          =      source.read()
							_snake_case     , _snake_case          =      process_story(lowercase     )
				return document_name, story_lines, summary_lines






def 	a_		(	__lowercase       :     Any			)			->  Optional[int]:
			_snake_case          =      list(filter(lambda __lowercase			: len(__lowercase			) != 0	, [line.strip() for line in raw_story.split('\n'			)]			)			)

			# for some unknown reason some lines miss a period, add it
			_snake_case          =      [_add_missing_period(__lowercase			) for line in nonempty_lines]

			# gather article lines
			_snake_case          =      []
			_snake_case          =      deque(__lowercase			)
			while True:
						try:
									_snake_case          =      lines.popleft()
									if element.startswith('@highlight'			):
												break
									story_lines.append(__lowercase			)
						except IndexError:
									# if "@highlight" is absent from the file we pop
									# all elements until there is None, raising an exception.
									return story_lines, []

    # gather summary lines
			_snake_case          =      list(filter(lambda __lowercase			: not t.startswith('@highlight'			)	, __lowercase			)			)

			return story_lines, summary_lines






def 	a_		(	__lowercase       :     int			)			->  Optional[Any]:
			_snake_case          =      ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
			if line.startswith('@highlight'			):
						return line
			if line[-1] in END_TOKENS:
						return line
			return line + "."






def 	a_		(	__lowercase       :     Dict	, __lowercase       :     List[Any]	, __lowercase       :     Optional[Any]			)			->  Union[str, Any]:
			if len(__lowercase			) > block_size:
						return sequence[:block_size]
			else:
						sequence.extend([pad_token_id] * (block_size - len(__lowercase			))			)
						return sequence






def 	a_		(	__lowercase       :     List[str]	, __lowercase       :     List[str]			)			->  List[str]:
			_snake_case          =      torch.ones_like(__lowercase			)
			_snake_case          =      sequence == pad_token_id
			_snake_case          =      0
			return mask






def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     List[str]	, __lowercase       :     Optional[int]			)			->  Optional[int]:
			_snake_case          =      [tokenizer.encode(__lowercase			) for line in story_lines]
			_snake_case          =      [token for sentence in story_lines_token_ids for token in sentence]
			_snake_case          =      [tokenizer.encode(__lowercase			) for line in summary_lines]
			_snake_case          =      [token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids






def 	a_		(	__lowercase       :     Any	, __lowercase       :     Dict			)			->  Tuple:
			_snake_case          =      []
			for sequence in batch:
						_snake_case          =      -1
						_snake_case          =      []
						for s in sequence:
									if s == separator_token_id:
												sentence_num += 1
									embeddings.append(sentence_num % 2			)
						batch_embeddings.append(__lowercase			)
			return torch.tensor(__lowercase			)

import random




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	@staticmethod
	def 			A							(  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      [ord(lowercase     ) for i in text]
				_snake_case          =      []
				_snake_case          =      []
				for i in plain:
							_snake_case          =      random.randint(1      ,  300     )
							_snake_case          =      (i + k) * k
							cipher.append(lowercase     )
							key.append(lowercase     )
				return cipher, key





	@staticmethod
	def 			A							(  lowercase     :   list[int]      ,  lowercase     :   list[int]     ):


				'''simple docstring'''


				_snake_case          =      []
				for i in range(len(lowercase     )     ):
							_snake_case          =      int((cipher[i] - (key[i]) ** 2) / key[i]     )
							plain.append(chr(lowercase     )     )
				return "".join(lowercase     )


if __name__ == "__main__":
			_lowerCamelCase   ,					_lowerCamelCase	:    Optional[int]          =      Onepad().encrypt('''Hello''')
			print(c, k)
			print(Onepad().decrypt(c, k))