





'''simple docstring'''







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
					from PIL import Image


class        __a   (unittest.TestCase							,		lowerCamelCase  ):

							def      UpperCAmelCase__						(				self				:  int )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	load_tool('''image-question-answering''' )
													self.tool.setup()
													UpperCAmelCase_      :   Optional[int]      =	load_tool('''image-question-answering'''							,  remote=__magic_name__ )

							def      UpperCAmelCase__						(				self				:  int )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png''' )
													UpperCAmelCase_      :   Tuple      =	self.tool(__magic_name__							,  '''How many cats are sleeping on the couch?''' )
													self.assertEqual(__magic_name__							,  '''2''' )

							def      UpperCAmelCase__						(				self				:  int )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png''' )
													UpperCAmelCase_      :   Union[str, Any]      =	self.remote_tool(__magic_name__							,  '''How many cats are sleeping on the couch?''' )
													self.assertEqual(__magic_name__							,  '''2''' )

							def      UpperCAmelCase__						(				self				:  Union[str, Any] )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png''' )
													UpperCAmelCase_      :   Any      =	self.tool(image=__magic_name__							,  question='''How many cats are sleeping on the couch?''' )
													self.assertEqual(__magic_name__							,  '''2''' )



							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png''' )
													UpperCAmelCase_      :   Dict      =	self.remote_tool(image=__magic_name__							,  question='''How many cats are sleeping on the couch?''' )
													self.assertEqual(__magic_name__							,  '''2''' )











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
           ]       )
