





import math







def  _SCREAMING_SNAKE_CASE				(   )      ->      None:
					__A	:						Tuple       =				input('Enter message: '  )
					__A	:						List[str]       =				int(input(F"""Enter key [2-{len(a  ) - 1}]: """  )  )
					__A	:						Any       =				input('Encryption/Decryption [e/d]: '  )

					if mode.lower().startswith('e'  ):
										__A	:						str       =				encrypt_message(a					,						a  )
					elif mode.lower().startswith('d'  ):
										__A	:						int       =				decrypt_message(a					,						a  )

					# Append pipe symbol (vertical bar) to identify spaces at the end.
					print(F"""Output:\n{text + "|"}"""  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      str:
					__A	:						Optional[int]       =				[''] * key
					for col in range(a  ):
										__A	:						Tuple       =				col
										while pointer < len(a  ):
															cipher_text[col] += message[pointer]
															pointer += key
					return "".join(a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      str:
					__A	:						Dict       =				math.ceil(len(a  ) / key  )
					__A	:						str       =				key
					__A	:						int       =				(num_cols * num_rows) - len(a  )
					__A	:						Dict       =				[''] * num_cols
					__A	:						str       =				0
					__A	:						Union[str, Any]       =				0

					for symbol in message:
										plain_text[col] += symbol
										col += 1

										if (
										    (col == num_cols)
										    or (col == num_cols - 1)
										    and (row >= num_rows - num_shaded_boxes)
										):
															__A	:						Optional[Any]       =				0
															row += 1

					return "".join(a  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()











import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				argparse.ArgumentParser()

					# Required parameters
					parser.add_argument(
					    '--src_path'					,						type=a					,						default='biencoder-nq-dev.json'					,						help='Path to raw DPR training data'					,						)
					parser.add_argument(
					    '--evaluation_set'					,						type=a					,						help='where to store parsed evaluation_set file'					,						)
					parser.add_argument(
					    '--gold_data_path'					,						type=a					,						help='where to store parsed gold_data_path file'					,						)
					__A	:						Optional[int]       =				parser.parse_args()

					with open(args.src_path					,						'r'  ) as src_file, open(args.evaluation_set					,						'w'  ) as eval_file, open(
					    args.gold_data_path					,						'w'  ) as gold_file:
										__A	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[context['title'] for context in dpr_record['positive_ctxs']]
															eval_file.write(question + '\n'  )
															gold_file.write('\t'.join(a  ) + '\n'  )


if __name__ == "__main__":
				main()




