

'''simple docstring'''
import math




def 	UpperCamelCase   (       )		->	None:




  '''simple docstring'''



  lowercase									=input('''Enter message: '''	)
  lowercase									=int(input(f'Enter key [2-{len(lowercase_	) - 1}]: '	)	)
  lowercase									=input('''Encryption/Decryption [e/d]: '''	)

  if mode.lower().startswith('''e'''	):
    lowercase									=encrypt_message(lowercase_     ,					lowercase_	)
  elif mode.lower().startswith('''d'''	):
    lowercase									=decrypt_message(lowercase_     ,					lowercase_	)

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




def 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    str	)		->	str:




  '''simple docstring'''



  lowercase									=[''''''] * key
  for col in range(lowercase_	):
    lowercase									=col
    while pointer < len(lowercase_	):
      cipher_text[col] += message[pointer]
      pointer += key
  return "".join(lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    str	)		->	str:




  '''simple docstring'''



  lowercase									=math.ceil(len(lowercase_	) / key	)
  lowercase									=key
  lowercase									=(num_cols * num_rows) - len(lowercase_	)
  lowercase									=[''''''] * num_cols
  lowercase									=0
  lowercase									=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)
    ):
      lowercase									=0
      row += 1

  return "".join(lowercase_	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




'''simple docstring'''







import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							tempfile.mkdtemp()
            _lowerCAmelCase     :				List[str]       =							5

            # Realm tok
            _lowerCAmelCase     :				str       =							[
                '[UNK]',
                '[CLS]',
                '[SEP]',
                '[PAD]',
                '[MASK]',
                'test',
                'question',
                'this',
                'is',
                'the',
                'first',
                'second',
                'third',
                'fourth',
                'fifth',
                'record',
                'want',
                '##want',
                '##ed',
                'wa',
                'un',
                'runn',
                '##ing',
                ',',
                'low',
                'lowest',
            ]
            _lowerCAmelCase     :				Union[str, Any]       =							os.path.join(self.tmpdirname   ,						'realm_tokenizer' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )
            _lowerCAmelCase     :				Dict       =							os.path.join(snake_case__   ,						VOCAB_FILES_NAMES['vocab_file'] )
            with open(self.vocab_file   ,						'w'   ,						encoding='utf-8' ) as vocab_writer:
                   vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

            _lowerCAmelCase     :				List[str]       =							os.path.join(self.tmpdirname   ,						'realm_block_records' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,						'realm_tokenizer' ) )







     def a						(			self ):

            '''simple docstring'''

            shutil.rmtree(self.tmpdirname )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							RealmConfig(num_block_records=self.num_block_records )
            return config







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							Dataset.from_dict(
                {
                    'id': ['0', '1'],
                    'question': ['foo', 'bar'],
                    'answers': [['Foo', 'Bar'], ['Bar']],
                } )
            return dataset







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							np.array(
                [
                    b'This is the first record',
                    b'This is the second record',
                    b'This is the third record',
                    b'This is the fourth record',
                    b'This is the fifth record',
                    b'This is a longer longer longer record',
                ]   ,						dtype=snake_case__   ,						)
            return block_records







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							RealmRetriever(
                block_records=self.get_dummy_block_records()   ,						tokenizer=self.get_tokenizer()   ,						)
            return retriever







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_config()
            _lowerCAmelCase     :				List[Any]       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				Any       =							retriever.tokenizer

            _lowerCAmelCase     :				Tuple       =							np.array([0, 3]   ,						dtype='long' )
            _lowerCAmelCase     :				Dict       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				int       =							tokenizer(
                ['the fourth']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Tuple       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(concat_inputs.input_ids.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.attention_mask.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.token_type_ids.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.special_tokens_mask.shape   ,						(2, 10) )
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )   ,						['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']   ,						)
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )   ,						['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']   ,						)







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.get_config()
            _lowerCAmelCase     :				int       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				List[str]       =							retriever.tokenizer

            _lowerCAmelCase     :				List[Any]       =							np.array([0, 3, 5]   ,						dtype='long' )
            _lowerCAmelCase     :				str       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				Union[str, Any]       =							tokenizer(
                ['the fourth', 'longer longer']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Optional[int]       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

            self.assertEqual([False, True, True]   ,						snake_case__ )
            self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,						snake_case__ )
            self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.get_dummy_retriever()
            retriever.save_pretrained(os.path.join(self.tmpdirname   ,						'realm_block_records' ) )

            # Test local path
            _lowerCAmelCase     :				Union[str, Any]       =							retriever.from_pretrained(os.path.join(self.tmpdirname   ,						'realm_block_records' ) )
            self.assertEqual(retriever.block_records[0]   ,						b'This is the first record' )

            # Test mocked remote path
            with patch('transformers.models.realm.retrieval_realm.hf_hub_download' ) as mock_hf_hub_download:
                   _lowerCAmelCase     :				Any       =							os.path.join(
                       os.path.join(self.tmpdirname   ,						'realm_block_records' )   ,						_REALM_BLOCK_RECORDS_FILENAME )
                   _lowerCAmelCase     :				str       =							RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa' )

            self.assertEqual(retriever.block_records[0]   ,						b'This is the first record' )

