



"""simple docstring"""


import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


snake_case_    					=	logging.getLogger(__name__)







class 				A_      :



						"""simple docstring"""






						def __init__( self			:int )    ->      Dict:
												UpperCAmelCase					=			False






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Optional[int]       ,  lowercase_			:int       ,  lowercase_			:str       ,  lowercase_			:Any )    ->      Any:
												if not self.initialized:
																		UpperCAmelCase					=			RagRetriever(
																		    lowercase_       ,  question_encoder_tokenizer=lowercase_       ,  generator_tokenizer=lowercase_       ,  index=lowercase_       ,  init_retrieval=lowercase_       ,  )
																		UpperCAmelCase					=			True






						def      UpperCAmelCase__					( self			:Dict )    ->      Any:
												self.retriever.index.init_index()






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Optional[Any]       ,  lowercase_			:List[str] )    ->      Dict:
												UpperCAmelCase				,   UpperCAmelCase					=			self.retriever._main_retrieve(lowercase_       ,  lowercase_ )
												return doc_ids, retrieved_doc_embeds







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:List[str]       ,  lowercase_			:Any       ,  lowercase_			:Optional[Any]       ,  lowercase_			:int       ,  lowercase_			:Optional[int]       ,  lowercase_			:Optional[int]=None )    ->      Union[str, Any]:
												if index is not None and index.is_initialized() and len(lowercase_ ) > 0:
																		raise ValueError(
																		    'When using Ray for distributed fine-tuning, '
																		    'you\'ll need to provide the paths instead, '
																		    'as the dataset and the index are loaded '
																		    'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
												super().__init__(
												    lowercase_       ,  question_encoder_tokenizer=lowercase_       ,  generator_tokenizer=lowercase_       ,  index=lowercase_       ,  init_retrieval=lowercase_       ,  )
												UpperCAmelCase					=			retrieval_workers
												if len(self.retrieval_workers ) > 0:
																		ray.get(
																		    [
																		        worker.create_rag_retriever.remote(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_ )
																		        for worker in self.retrieval_workers
																		    ] )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Dict:
												logger.info('initializing retrieval' )

												if len(self.retrieval_workers ) > 0:
																		ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
												else:
																		# Non-distributed training. Load index into this same process.
																		self.index.init_index()






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Dict       ,  lowercase_			:str )    ->      str:
												if len(self.retrieval_workers ) > 0:
																		# Select a random retrieval actor.
																		UpperCAmelCase					=			self.retrieval_workers[random.randint(0       ,  len(self.retrieval_workers ) - 1 )]
																		UpperCAmelCase				,   UpperCAmelCase					=			ray.get(random_worker.retrieve.remote(lowercase_       ,  lowercase_ ) )
												else:
																		UpperCAmelCase				,   UpperCAmelCase					=			self._main_retrieve(lowercase_       ,  lowercase_ )
												return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase_ )






						@classmethod
						def      UpperCAmelCase__					( cls			:Optional[Any]       ,  lowercase_			:Dict       ,  lowercase_			:Any=None       ,  **lowercase_			:List[str] )    ->      int:
												return super(lowercase_       ,  cls ).get_tokenizers(lowercase_       ,  lowercase_       ,  **lowercase_ )






						@classmethod
						def      UpperCAmelCase__					( cls			:int       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Any       ,  lowercase_			:List[Any]=None       ,  **lowercase_			:Dict )    ->      Dict:
												UpperCAmelCase					=			kwargs.pop('config'       ,  lowercase_ ) or RagConfig.from_pretrained(lowercase_       ,  **lowercase_ )
												UpperCAmelCase					=			RagTokenizer.from_pretrained(lowercase_       ,  config=lowercase_ )
												UpperCAmelCase					=			rag_tokenizer.question_encoder
												UpperCAmelCase					=			rag_tokenizer.generator
												if indexed_dataset is not None:
																		UpperCAmelCase					=			'custom'
																		UpperCAmelCase					=			CustomHFIndex(config.retrieval_vector_size       ,  lowercase_ )
												else:
																		UpperCAmelCase					=			cls._build_index(lowercase_ )
												return cls(
												    lowercase_       ,  question_encoder_tokenizer=lowercase_       ,  generator_tokenizer=lowercase_       ,  retrieval_workers=lowercase_       ,  index=lowercase_       ,  )










"""simple docstring"""


import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

snake_case_    					=	3

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating primitive root of p'						)
						while True:
												UpperCAmelCase					=			random.randrange(3		,     lowercase_						)
												if pow(lowercase_		,     2		,     lowercase_						) == 1:
																		continue
												if pow(lowercase_		,     lowercase_		,     lowercase_						) == 1:
																		continue
												return g

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating prime p...'						)
						UpperCAmelCase					=			rabin_miller.generate_large_prime(lowercase_						)  # select large prime number.
						UpperCAmelCase					=			primitive_root(lowercase_						)  # one primitive root on modulo p.
						UpperCAmelCase					=			random.randrange(3		,     lowercase_						)  # private_key -> have to be greater than 2 for safety.
						UpperCAmelCase					=			cryptomath.find_mod_inverse(pow(lowercase_		,     lowercase_		,     lowercase_						)		,     lowercase_						)

						UpperCAmelCase					=			(key_size, e_a, e_a, p)
						UpperCAmelCase					=			(key_size, d)

						return public_key, private_key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						if os.path.exists(F"""{name}_pubkey.txt"""						) or os.path.exists(F"""{name}_privkey.txt"""						):
												print('\nWARNING:'						)
												print(
												    F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    'Use a different name or delete these files and re-run this program.'						)
												sys.exit()

						UpperCAmelCase				,   UpperCAmelCase					=			generate_key(lowercase_						)
						print(F"""\nWriting public key to file {name}_pubkey.txt..."""						)
						with open(F"""{name}_pubkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""						)

						print(F"""Writing private key to file {name}_privkey.txt..."""						)
						with open(F"""{name}_privkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{private_key[0]},{private_key[1]}"""						)

def 			_lowerCAmelCase				(       ):
						print('Making key files...'						)
						make_key_files('elgamal'		,     2048						)
						print('Key files generation successful'						)


if __name__ == "__main__":
				main()





