




'''simple docstring'''

import requests

try:
		from django.contrib.auth import authenticate, login
		from django.shortcuts import redirect, render
except ImportError:
		a__         =     a__         =     a__         =     a__         =     print




def    snake_case__     (  a	)			->       Optional[Any]:





			'''simple docstring'''






			snake_case__          =						"""secretKey"""
			snake_case__          =						"""https://www.google.com/recaptcha/api/siteverify"""

			# when method is not POST, direct user to login page
			if request.method != "POST":
						return render(_A       ,      """login.html"""	)

			# from the frontend, get username, password, and client_key
			snake_case__          =						request.POST.get("""username"""	)
			snake_case__          =						request.POST.get("""password"""	)
			snake_case__          =						request.POST.get("""g-recaptcha-response"""	)

			# post recaptcha response to Google's recaptcha api
			snake_case__          =						requests.post(_A       ,      data={"""secret""": secret_key, """response""": client_key}	)
			# if the recaptcha api verified our keys
			if response.json().get("""success"""       ,      _A	):
						# authenticate the user
						snake_case__          =						authenticate(_A       ,      username=_A       ,      password=_A	)
						if user_in_database:
									login(_A       ,      _A	)
									return redirect("""/your-webpage"""	)
			return render(_A       ,      """login.html"""	)





import logging
import random

import ray

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


UpperCamelCase__       				=	logging.getLogger(__name__)
class 							__lowercase   :



     def __init__(  self   :							List[str]       ):
       a_		    =  False



     def 				__magic_name__		(  self   :							Union[str, Any]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							Optional[Any]      ,		lowercase__   :							Union[str, Any]       ):
       if not self.initialized:
         a_		    =  RagRetriever(
             lowercase__      ,		question_encoder_tokenizer=lowercase__      ,		generator_tokenizer=lowercase__      ,		index=lowercase__      ,		init_retrieval=lowercase__      ,		)
         a_		    =  True



     def 				__magic_name__		(  self   :							List[Any]       ):
       self.retriever.index.init_index()



     def 				__magic_name__		(  self   :							List[Any]       ):
       # delete the old self.retriever object before assigning the new index
       del self.retriever
       a_		    =  False



     def 				__magic_name__		(  self   :							Optional[Any]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Dict       ):
       a_    , a_		    =  self.retriever._main_retrieve(lowercase__      ,		lowercase__       )
       a_		    =  self.retriever.index.get_doc_dicts(lowercase__       )
       return doc_ids, retrieved_doc_embeds, doc_dicts



class 							__lowercase   (			a__    ):



     def __init__(  self   :							List[Any]      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Tuple      ,		lowercase__   :							Dict      ,		lowercase__   :							Dict=None       ):
       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__      ,		)

       a_		    =  retrieval_workers
       a_		    =  question_encoder_tokenizer
       a_		    =  generator_tokenizer
       if len(self.retrieval_workers       ) > 0:
         ray.get(
             [
                 worker.create_rag_retriever.remote(lowercase__      ,		lowercase__      ,		lowercase__      ,		lowercase__       )
                 for worker in self.retrieval_workers
             ]       )



     def 				__magic_name__		(  self   :							int       ):
       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 				__magic_name__		(  self   :							str      ,		lowercase__   :							List[str]      ,		lowercase__   :							Any       ):
       if len(self.retrieval_workers       ) > 0:
         # Select a random retrieval actor.
         a_		    =  self.retrieval_workers[random.randint(0      ,		len(self.retrieval_workers       ) - 1       )]
         a_    , a_    , a_		    =  ray.get(
             random_worker.retrieve.remote(lowercase__      ,		lowercase__       )       )
       else:
         a_    , a_		    =  self._main_retrieve(lowercase__      ,		lowercase__       )
         a_		    =  self.index.get_doc_dicts(lowercase__       )
       return retrieved_doc_embeds, doc_ids, doc_dicts



     @classmethod
     def 				__magic_name__		(  cls   :							Dict      ,		lowercase__   :							Optional[Any]      ,		lowercase__   :							List[str]=None      ,		**lowercase__   :							int       ):
       return super(lowercase__      ,		cls       ).get_tokenizers(lowercase__      ,		lowercase__      ,		**lowercase__       )



     @classmethod
     def 				__magic_name__		(  cls   :							Tuple      ,		lowercase__   :							Any      ,		lowercase__   :							str      ,		lowercase__   :							List[Any]=None      ,		**lowercase__   :							Union[str, Any]       ):
       a_		    =  kwargs.pop('''config'''      ,		lowercase__       ) or RagConfig.from_pretrained(lowercase__      ,		**lowercase__       )
       a_		    =  RagTokenizer.from_pretrained(lowercase__      ,		config=lowercase__       )
       a_		    =  rag_tokenizer.question_encoder
       a_		    =  rag_tokenizer.generator

       if indexed_dataset is not None:
         a_		    =  '''custom'''
         a_		    =  CustomHFIndex(config.retrieval_vector_size      ,		lowercase__       )
       else:
         a_		    =  cls._build_index(lowercase__       )

       return cls(
           lowercase__      ,		question_encoder_tokenizer=lowercase__      ,		generator_tokenizer=lowercase__      ,		retrieval_workers=lowercase__      ,		index=lowercase__      ,		)



     def 				__magic_name__		(  self   :							Optional[Any]       ):
       logger.info('''re-loading the new dataset with embeddings'''       )
       # access from the training loop

       ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]       )

       # build the index object again
       a_		    =  self._build_index(self.config       )

       ray.get(
           [
               worker.create_rag_retriever.remote(
                   self.config      ,		self.question_encoder_tokenizer      ,		self.generator_tokenizer      ,		lowercase__       )
               for worker in self.retrieval_workers
           ]       )
