code: import json
  import os
  import shutil
  import sys
  import tempfile
  import unittest
  from unittest import TestCase
  from unittest.mock import patch
  
  import faiss
  import numpy as np
  from datasets import Dataset
  
  from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
  from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
  from transformers.integrations import is_ray_available
  from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
  from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
  from transformers.testing_utils import require_ray
  
  
  sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
  
  if is_torch_available():
   from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
  else:
   UpperCAmelCase					=				None
  
  if is_ray_available():
   import ray  # noqa: E402 # isort:skip
   from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
  else:
   UpperCAmelCase					=				None
   UpperCAmelCase					=				None
   UpperCAmelCase					=				None
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[Any]				):
         if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
                _UpperCamelCase     :       List[Any]  						=  unittest.skip('test requires Datasets, Faiss, psutil'				)(lowercase_				)
         return test_case
  
  
  
  
  
  @require_distributed_retrieval
  class    __lowerCAmelCase  (       a_						):
  
  
  
   '''simple docstring'''
  
   def 					__snake_case					(			self       :		str  ) ->							str:
          _UpperCamelCase     :       Tuple  						=  tempfile.mkdtemp()
          _UpperCamelCase     :       int  						=  8
  
          # DPR tok
          _UpperCamelCase     :       Any  						=  [
              '[UNK]',
              '[CLS]',
              '[SEP]',
              '[PAD]',
              '[MASK]',
              'want',
              '##want',
              '##ed',
              'wa',
              'un',
              'runn',
              '##ing',
              ',',
              'low',
              'lowest',
          ]
          _UpperCamelCase     :       List[Any]  						=  os.path.join(self.tmpdirname       ,			'dpr_tokenizer'  )
          os.makedirs(lowerCamelCase       ,			exist_ok=lowerCamelCase  )
          _UpperCamelCase     :       Dict  						=  os.path.join(lowerCamelCase       ,			DPR_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]  )  )
  
          # BART tok
          _UpperCamelCase     :       int  						=  [
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
          ]
          _UpperCamelCase     :       Tuple  						=  dict(zip(lowerCamelCase       ,			range(len(lowerCamelCase  )  )  )  )
          _UpperCamelCase     :       str  						=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          _UpperCamelCase     :       List[Any]  						=  {'unk_token': '<unk>'}
  
          _UpperCamelCase     :       str  						=  os.path.join(self.tmpdirname       ,			'bart_tokenizer'  )
          os.makedirs(lowerCamelCase       ,			exist_ok=lowerCamelCase  )
          _UpperCamelCase     :       Optional[int]  						=  os.path.join(lowerCamelCase       ,			BART_VOCAB_FILES_NAMES['vocab_file']  )
          _UpperCamelCase     :       List[Any]  						=  os.path.join(lowerCamelCase       ,			BART_VOCAB_FILES_NAMES['merges_file']  )
          with open(self.vocab_file       ,			'w'       ,			encoding='utf-8'  ) as fp:
                 fp.write(json.dumps(lowerCamelCase  ) + '\n'  )
          with open(self.merges_file       ,			'w'       ,			encoding='utf-8'  ) as fp:
                 fp.write('\n'.join(lowerCamelCase  )  )
  
   def 					__snake_case					(			self       :		List[str]  ) ->							DPRQuestionEncoderTokenizer:
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,			'dpr_tokenizer'  )  )
  
   def 					__snake_case					(			self       :		str  ) ->							BartTokenizer:
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,			'bart_tokenizer'  )  )
  
   def 					__snake_case					(			self       :		Union[str, Any]  ) ->							Tuple:
          shutil.rmtree(self.tmpdirname  )
  
   def 					__snake_case					(			self       :		int  ) ->							Tuple:
          _UpperCamelCase     :       Union[str, Any]  						=  Dataset.from_dict(
              {
                  'id': ['0', '1'],
                  'text': ['foo', 'bar'],
                  'title': ['Foo', 'Bar'],
                  'embeddings': [np.ones(self.retrieval_vector_size  ), 2 * np.ones(self.retrieval_vector_size  )],
              }  )
          dataset.add_faiss_index('embeddings'       ,			string_factory='Flat'       ,			metric_type=faiss.METRIC_INNER_PRODUCT  )
          return dataset
  
   def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		bool       ,			lowerCamelCase       :		Any=1_2_3_4_5  ) ->							RagPyTorchDistributedRetriever:
          _UpperCamelCase     :       Dict  						=  self.get_dummy_dataset()
          _UpperCamelCase     :       str  						=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size       ,			question_encoder=DPRConfig().to_dict()       ,			generator=BartConfig().to_dict()       ,			)
          with patch('transformers.models.rag.retrieval_rag.load_dataset'  ) as mock_load_dataset:
                 _UpperCamelCase     :       Any  						=  dataset
                 _UpperCamelCase     :       List[Any]  						=  RagPyTorchDistributedRetriever(
                     lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			)
                 if init_retrieval:
                        retriever.init_retrieval(lowerCamelCase  )
          return retriever
  
   def 					__snake_case					(			self       :		Tuple       ,			lowerCamelCase       :		bool  ) ->							RagRayDistributedRetriever:
          # Have to run in local mode because sys.path modifications at top of
          # file are not propogated to remote workers.
          # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
          ray.init(local_mode=lowerCamelCase  )
          _UpperCamelCase     :       Tuple  						=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size       ,			question_encoder=DPRConfig().to_dict()       ,			generator=BartConfig().to_dict()       ,			)
          _UpperCamelCase     :       Tuple  						=  ray.remote(lowerCamelCase  )
          _UpperCamelCase     :       Any  						=  [remote_cls.remote() for _ in range(1  )]
          with patch('transformers.models.rag.retrieval_rag.load_dataset'  ) as mock_load_dataset:
                 _UpperCamelCase     :       Any  						=  self.get_dummy_dataset()
                 _UpperCamelCase     :       List[Any]  						=  RagRayDistributedRetriever(
                     lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			retrieval_workers=lowerCamelCase       ,			)
                 if init_retrieval:
                        retriever.init_retrieval()
          return retriever
  
   def 					__snake_case					(			self       :		Dict       ,			lowerCamelCase       :		bool       ,			lowerCamelCase       :		bool       ,			lowerCamelCase       :		Union[str, Any]=1_2_3_4_5  ) ->							Optional[Any]:
          _UpperCamelCase     :       Tuple  						=  self.get_dummy_dataset()
          _UpperCamelCase     :       int  						=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size       ,			question_encoder=DPRConfig().to_dict()       ,			generator=BartConfig().to_dict()       ,			index_name='custom'       ,			)
          if from_disk:
                 _UpperCamelCase     :       Optional[Any]  						=  os.path.join(self.tmpdirname       ,			'dataset'  )
                 _UpperCamelCase     :       Any  						=  os.path.join(self.tmpdirname       ,			'index.faiss'  )
                 dataset.get_index('embeddings'  ).save(os.path.join(self.tmpdirname       ,			'index.faiss'  )  )
                 dataset.drop_index('embeddings'  )
                 dataset.save_to_disk(os.path.join(self.tmpdirname       ,			'dataset'  )  )
                 del dataset
                 _UpperCamelCase     :       Dict  						=  RagPyTorchDistributedRetriever(
                     lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			)
          else:
                 _UpperCamelCase     :       Optional[Any]  						=  RagPyTorchDistributedRetriever(
                     lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			index=CustomHFIndex(config.retrieval_vector_size       ,			lowerCamelCase  )       ,			)
          if init_retrieval:
                 retriever.init_retrieval(lowerCamelCase  )
          return retriever
  
   def 					__snake_case					(			self       :		List[Any]       ,			lowerCamelCase       :		bool       ,			lowerCamelCase       :		bool  ) ->							Any:
          # Have to run in local mode because sys.path modifications at top of
          # file are not propogated to remote workers.
          # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
          ray.init(local_mode=lowerCamelCase  )
          _UpperCamelCase     :       List[Any]  						=  self.get_dummy_dataset()
          _UpperCamelCase     :       int  						=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size       ,			question_encoder=DPRConfig().to_dict()       ,			generator=BartConfig().to_dict()       ,			index_name='custom'       ,			)
          _UpperCamelCase     :       Dict  						=  ray.remote(lowerCamelCase  )
          _UpperCamelCase     :       List[Any]  						=  [remote_cls.remote() for _ in range(1  )]
          if from_disk:
                 _UpperCamelCase     :       Union[str, Any]  						=  os.path.join(self.tmpdirname       ,			'dataset'  )
                 _UpperCamelCase     :       List[str]  						=  os.path.join(self.tmpdirname       ,			'index.faiss'  )
                 dataset.get_index('embeddings'  ).save(os.path.join(self.tmpdirname       ,			'index.faiss'  )  )
                 dataset.drop_index('embeddings'  )
                 dataset.save_to_disk(os.path.join(self.tmpdirname       ,			'dataset'  )  )
                 del dataset
                 _UpperCamelCase     :       Union[str, Any]  						=  RagRayDistributedRetriever(
                     lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			retrieval_workers=lowerCamelCase       ,			index=CustomHFIndex.load_from_disk(
                         vector_size=config.retrieval_vector_size       ,			dataset_path=config.passages_path       ,			index_path=config.index_path       ,			)       ,			)
          else:
                 _UpperCamelCase     :       List[str]  						=  RagRayDistributedRetriever(
                     lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			retrieval_workers=lowerCamelCase       ,			index=CustomHFIndex(config.retrieval_vector_size       ,			lowerCamelCase  )       ,			)
          if init_retrieval:
                 retriever.init_retrieval()
          return retriever
  
   def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		RagRetriever       ,			lowerCamelCase       :		np.array       ,			lowerCamelCase       :		int  ) ->							None:
          _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Any  						=  retriever.retrieve(lowerCamelCase       ,			n_docs=lowerCamelCase  )
          self.assertEqual(retrieved_doc_embeds.shape       ,			(2, n_docs, self.retrieval_vector_size)  )
          self.assertEqual(len(lowerCamelCase  )       ,			2  )
          self.assertEqual(sorted(doc_dicts[0]  )       ,			['embeddings', 'id', 'text', 'title']  )
          self.assertEqual(len(doc_dicts[0]['id']  )       ,			lowerCamelCase  )
          self.assertEqual(doc_dicts[0]['id'][0]       ,			'1'  )  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]['id'][0]       ,			'0'  )  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist()       ,			[[1], [0]]  )
  
   def 					__snake_case					(			self       :		str  ) ->							List[str]:
          _UpperCamelCase     :       str  						=  1
          _UpperCamelCase     :       Any  						=  np.array(
              [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )
  
          self.distributed_retriever_check(
              self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase  )
  
   def 					__snake_case					(			self       :		List[str]  ) ->							Optional[int]:
          _UpperCamelCase     :       Any  						=  1
          _UpperCamelCase     :       str  						=  np.array(
              [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )
  
          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase       ,			from_disk=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase       ,			)
  
   def 					__snake_case					(			self       :		Dict  ) ->							Tuple:
          _UpperCamelCase     :       Union[str, Any]  						=  1
          _UpperCamelCase     :       Dict  						=  np.array(
              [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )
  
          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase       ,			from_disk=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase       ,			)
  
   @require_ray
   def 					__snake_case					(			self       :		Dict  ) ->							List[Any]:
          _UpperCamelCase     :       Optional[Any]  						=  1
          _UpperCamelCase     :       Tuple  						=  np.array(
              [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )
  
          self.distributed_retriever_check(
              self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase  )
          ray.shutdown()
  
   @require_ray
   def 					__snake_case					(			self       :		int  ) ->							int:
          _UpperCamelCase     :       str  						=  1
          _UpperCamelCase     :       List[Any]  						=  np.array(
              [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )
          with self.assertRaises(lowerCamelCase  ):
                 self.distributed_retriever_check(
                     self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase       ,			from_disk=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase       ,			)
          ray.shutdown()
  
   @require_ray
   def 					__snake_case					(			self       :		List[str]  ) ->							Tuple:
          _UpperCamelCase     :       Tuple  						=  1
          _UpperCamelCase     :       List[Any]  						=  np.array(
              [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )
  
          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase       ,			from_disk=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase  )
          ray.shutdown()
  
  
  
  
style_context: UpperCAmelCase					=				[chr(i) for i in range(32, 1_26)]
  UpperCAmelCase					=				list(range(len(alphabets)))
  UpperCAmelCase					=				list(range(len(alphabets)))
  UpperCAmelCase					=				list(range(len(alphabets)))
  UpperCAmelCase					=				list(reversed(range(len(alphabets))))
  UpperCAmelCase					=				[]
  UpperCAmelCase					=				UpperCAmelCase					=				UpperCAmelCase					=				0
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         global gear_one_pos
         global gear_two_pos
         global gear_three_pos
         _UpperCamelCase     :       Any  						=  gear_one[0]
         gear_one.append(lowercase_				)
         del gear_one[0]
         gear_one_pos += 1
         if gear_one_pos % int(len(lowercase_				)				) == 0:
                _UpperCamelCase     :       str  						=  gear_two[0]
                gear_two.append(lowercase_				)
                del gear_two[0]
                gear_two_pos += 1
                if gear_two_pos % int(len(lowercase_				)				) == 0:
                       _UpperCamelCase     :       int  						=  gear_three[0]
                       gear_three.append(lowercase_				)
                       del gear_three[0]
                       gear_three_pos += 1
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
         _UpperCamelCase     :       Dict  						=  alphabets.index(lowercase_				)
         _UpperCamelCase     :       Union[str, Any]  						=  gear_one[target]
         _UpperCamelCase     :       Any  						=  gear_two[target]
         _UpperCamelCase     :       Tuple  						=  gear_three[target]
         _UpperCamelCase     :       Tuple  						=  reflector[target]
         _UpperCamelCase     :       Any  						=  gear_three.index(lowercase_				)
         _UpperCamelCase     :       Any  						=  gear_two.index(lowercase_				)
         _UpperCamelCase     :       str  						=  gear_one.index(lowercase_				)
         code.append(alphabets[target]				)
         rotator()
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				list(input('''Type your message:\n'''))
   while True:
    try:
     UpperCAmelCase					=				int(input('''Please set token:(must be only digits)\n'''))
     break
    except Exception as error:
     print(error)
   for _ in range(token):
    rotator()
   for j in decode:
    engine(j)
   print('''\n''' + ''''''.join(code))
   print(
       f'''\nYour Token is {token} please write it down.\nIf you want to decode '''
       '''this message again you should input same digits as token!'''
   )
  
  
  
  