
"""simple docstring"""



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

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
    import faiss





@require_faiss
class 				a			(					lowercase      ):



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					tempfile.mkdtemp()
          UpperCAmelCase__	:      Tuple     							=					8

          # DPR tok
          UpperCAmelCase__	:      Optional[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(UpperCamelCase_     ,	exist_ok=UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					os.path.join(UpperCamelCase_     ,	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__	:      List[str]     							=					[
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
          ]
          UpperCAmelCase__	:      Tuple     							=					dict(zip(UpperCamelCase_     ,	range(len(UpperCamelCase_      )      )      )      )
          UpperCAmelCase__	:      List[Any]     							=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          UpperCAmelCase__	:      Tuple     							=					{'unk_token': '<unk>'}

          UpperCAmelCase__	:      Optional[Any]     							=					os.path.join(self.tmpdirname     ,	'bart_tokenizer'      )
          os.makedirs(UpperCamelCase_     ,	exist_ok=UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					os.path.join(UpperCamelCase_     ,	BART_VOCAB_FILES_NAMES['vocab_file']      )
          UpperCAmelCase__	:      List[str]     							=					os.path.join(UpperCamelCase_     ,	BART_VOCAB_FILES_NAMES['merges_file']      )
          with open(self.vocab_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
              fp.write(json.dumps(UpperCamelCase_      ) + '\n'      )
          with open(self.merges_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
              fp.write('\n'.join(UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,	'dpr_tokenizer'      )      )



      def  __snake_case							(   self      ):
          return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,	'dpr_tokenizer'      )      )



      def  __snake_case							(   self      ):
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,	'bart_tokenizer'      )      )



      def  __snake_case							(   self      ):
          shutil.rmtree(self.tmpdirname      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[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      ):
          UpperCAmelCase__	:      Any     							=					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__	:      List[str]     							=					dataset
              UpperCAmelCase__	:      str     							=					RagRetriever(
                  UpperCamelCase_     ,	question_encoder_tokenizer=self.get_dpr_tokenizer()     ,	generator_tokenizer=self.get_bart_tokenizer()     ,	)
          return retriever



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					self.get_dummy_dataset()
          UpperCAmelCase__	:      List[Any]     							=					RagConfig(
              retrieval_vector_size=self.retrieval_vector_size     ,	question_encoder=DPRConfig().to_dict()     ,	generator=BartConfig().to_dict()     ,	index_name='custom'     ,	)
          if from_disk:
              UpperCAmelCase__	:      List[str]     							=					os.path.join(self.tmpdirname     ,	'dataset'      )
              UpperCAmelCase__	:      Dict     							=					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__	:      Tuple     							=					RagRetriever(
                  UpperCamelCase_     ,	question_encoder_tokenizer=self.get_dpr_tokenizer()     ,	generator_tokenizer=self.get_bart_tokenizer()     ,	)
          else:
              UpperCAmelCase__	:      Optional[Any]     							=					RagRetriever(
                  UpperCamelCase_     ,	question_encoder_tokenizer=self.get_dpr_tokenizer()     ,	generator_tokenizer=self.get_bart_tokenizer()     ,	index=CustomHFIndex(config.retrieval_vector_size     ,	UpperCamelCase_      )     ,	)
          return retriever



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					Dataset.from_dict(
              {
                  'id': ['0', '1'],
                  'text': ['foo', 'bar'],
                  'title': ['Foo', 'Bar'],
                  'embeddings': [np.ones(self.retrieval_vector_size + 1      ), 2 * np.ones(self.retrieval_vector_size + 1      )],
              }      )
          dataset.add_faiss_index('embeddings'     ,	string_factory='Flat'     ,	metric_type=faiss.METRIC_INNER_PRODUCT      )

          UpperCAmelCase__	:      Tuple     							=					os.path.join(self.tmpdirname     ,	'hf_bert_base.hnswSQ8_correct_phi_128.c_index'      )
          dataset.save_faiss_index('embeddings'     ,	index_file_name + '.index.dpr'      )
          pickle.dump(dataset['id']     ,	open(index_file_name + '.index_meta.dpr'     ,	'wb'      )      )

          UpperCAmelCase__	:      Dict     							=					os.path.join(self.tmpdirname     ,	'psgs_w100.tsv.pkl'      )
          UpperCAmelCase__	:      Dict     							=					{sample['id']: [sample['text'], sample['title']] for sample in dataset}
          pickle.dump(UpperCamelCase_     ,	open(UpperCamelCase_     ,	'wb'      )      )

          UpperCAmelCase__	:      int     							=					RagConfig(
              retrieval_vector_size=self.retrieval_vector_size     ,	question_encoder=DPRConfig().to_dict()     ,	generator=BartConfig().to_dict()     ,	index_name='legacy'     ,	index_path=self.tmpdirname     ,	)
          UpperCAmelCase__	:      Any     							=					RagRetriever(
              UpperCamelCase_     ,	question_encoder_tokenizer=self.get_dpr_tokenizer()     ,	generator_tokenizer=self.get_bart_tokenizer()      )
          return retriever



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[str]     							=					1
          UpperCAmelCase__	:      Optional[Any]     							=					self.get_dummy_canonical_hf_index_retriever()
          UpperCAmelCase__	:      List[Any]     							=					np.array(
              [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					retriever.retrieve(UpperCamelCase_     ,	n_docs=UpperCamelCase_      )
          self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
          self.assertEqual(len(UpperCamelCase_      )     ,	2      )
          self.assertEqual(sorted(doc_dicts[0]      )     ,	['embeddings', 'id', 'text', 'title']      )
          self.assertEqual(len(doc_dicts[0]['id']      )     ,	UpperCamelCase_      )
          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      ):
          UpperCAmelCase__	:      Dict     							=					self.get_dummy_canonical_hf_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
              with patch('transformers.models.rag.retrieval_rag.load_dataset'      ) as mock_load_dataset:
                  UpperCAmelCase__	:      Any     							=					self.get_dummy_dataset()
                  retriever.save_pretrained(UpperCamelCase_      )
                  UpperCAmelCase__	:      List[Any]     							=					RagRetriever.from_pretrained(UpperCamelCase_      )
                  self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_      )
                  UpperCAmelCase__	:      Dict     							=					np.array(
                      [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
                  UpperCAmelCase__	:      Union[str, Any]     							=					retriever.retrieve(UpperCamelCase_     ,	n_docs=1      )
                  self.assertTrue(out is not None      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[Any]     							=					1
          UpperCAmelCase__	:      str     							=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					np.array(
              [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					retriever.retrieve(UpperCamelCase_     ,	n_docs=UpperCamelCase_      )
          self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
          self.assertEqual(len(UpperCamelCase_      )     ,	2      )
          self.assertEqual(sorted(doc_dicts[0]      )     ,	['embeddings', 'id', 'text', 'title']      )
          self.assertEqual(len(doc_dicts[0]['id']      )     ,	UpperCamelCase_      )
          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      ):
          UpperCAmelCase__	:      int     							=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase_      )
          with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(UpperCamelCase_      )
              UpperCAmelCase__	:      str     							=					RagRetriever.from_pretrained(UpperCamelCase_      )
              self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_      )
              UpperCAmelCase__	:      Tuple     							=					np.array(
                  [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
              UpperCAmelCase__	:      Any     							=					retriever.retrieve(UpperCamelCase_     ,	n_docs=1      )
              self.assertTrue(out is not None      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					1
          UpperCAmelCase__	:      List[Any]     							=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					np.array(
              [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					retriever.retrieve(UpperCamelCase_     ,	n_docs=UpperCamelCase_      )
          self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
          self.assertEqual(len(UpperCamelCase_      )     ,	2      )
          self.assertEqual(sorted(doc_dicts[0]      )     ,	['embeddings', 'id', 'text', 'title']      )
          self.assertEqual(len(doc_dicts[0]['id']      )     ,	UpperCamelCase_      )
          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      ):
          UpperCAmelCase__	:      List[str]     							=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase_      )
          with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(UpperCamelCase_      )
              UpperCAmelCase__	:      List[str]     							=					RagRetriever.from_pretrained(UpperCamelCase_      )
              self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_      )
              UpperCAmelCase__	:      Optional[int]     							=					np.array(
                  [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
              UpperCAmelCase__	:      Any     							=					retriever.retrieve(UpperCamelCase_     ,	n_docs=1      )
              self.assertTrue(out is not None      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					1
          UpperCAmelCase__	:      Union[str, Any]     							=					self.get_dummy_legacy_index_retriever()
          UpperCAmelCase__	:      Dict     							=					np.array(
              [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Tuple     							=					retriever.retrieve(UpperCamelCase_     ,	n_docs=UpperCamelCase_      )
          self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
          self.assertEqual(len(UpperCamelCase_      )     ,	2      )
          self.assertEqual(sorted(doc_dicts[0]      )     ,	['text', 'title']      )
          self.assertEqual(len(doc_dicts[0]['text']      )     ,	UpperCamelCase_      )
          self.assertEqual(doc_dicts[0]['text'][0]     ,	'bar'      )  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]['text'][0]     ,	'foo'      )  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist()     ,	[[1], [0]]      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					self.get_dummy_legacy_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(UpperCamelCase_      )
              UpperCAmelCase__	:      Dict     							=					RagRetriever.from_pretrained(UpperCamelCase_      )
              self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_      )
              UpperCAmelCase__	:      int     							=					np.array(
                  [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
              UpperCAmelCase__	:      List[str]     							=					retriever.retrieve(UpperCamelCase_     ,	n_docs=1      )
              self.assertTrue(out is not None      )



      @require_torch
      @require_tokenizers
      @require_sentencepiece
      def  __snake_case							(   self      ):
          import torch

          UpperCAmelCase__	:      List[str]     							=					1
          UpperCAmelCase__	:      Optional[int]     							=					self.get_dummy_canonical_hf_index_retriever()
          UpperCAmelCase__	:      Dict     							=					[[5, 7], [10, 11]]
          UpperCAmelCase__	:      Any     							=					np.array(
              [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
          UpperCAmelCase__	:      str     							=					retriever(UpperCamelCase_     ,	UpperCamelCase_     ,	prefix=retriever.config.generator.prefix     ,	n_docs=UpperCamelCase_      )
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					(
              out['context_input_ids'],
              out['context_attention_mask'],
              out['retrieved_doc_embeds'],
          )
          self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
          self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_      )
          self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_      )
          self.assertIsInstance(UpperCamelCase_     ,	np.ndarray      )

          UpperCAmelCase__	:      List[Any]     							=					retriever(
              UpperCamelCase_     ,	UpperCamelCase_     ,	prefix=retriever.config.generator.prefix     ,	n_docs=UpperCamelCase_     ,	return_tensors='pt'     ,	)
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					(  # noqa: F841
              out['context_input_ids'],
              out['context_attention_mask'],
              out['retrieved_doc_embeds'],
              out['doc_ids'],
          )
          self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
          self.assertIsInstance(UpperCamelCase_     ,	torch.Tensor      )
          self.assertIsInstance(UpperCamelCase_     ,	torch.Tensor      )
          self.assertIsInstance(UpperCamelCase_     ,	torch.Tensor      )



      @require_torch
      @require_tokenizers
      @require_sentencepiece
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[str]     							=					self.get_dpr_ctx_encoder_tokenizer()
          UpperCAmelCase__	:      str     							=					1
          UpperCAmelCase__	:      Optional[int]     							=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase_      )
          retriever.set_ctx_encoder_tokenizer(UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					[[5, 7], [10, 11]]
          UpperCAmelCase__	:      Any     							=					np.array(
              [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
          UpperCAmelCase__	:      int     							=					retriever(UpperCamelCase_     ,	UpperCamelCase_     ,	prefix=retriever.config.generator.prefix     ,	n_docs=UpperCamelCase_      )

          self.assertEqual(
              len(UpperCamelCase_      )     ,	6      )  # check whether the retriever output consist of 6 attributes including tokenized docs
          self.assertEqual(
              all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask')      )     ,	UpperCamelCase_      )  # check for doc token related keys in dictionary.



"""simple docstring"""



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 				a			:



      def __init__(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					False



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if not self.initialized:
              UpperCAmelCase__	:      Optional[Any]     							=					RagRetriever(
                  UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	index=UpperCamelCase_     ,	init_retrieval=UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Union[str, Any]     							=					True



      def  __snake_case							(   self      ):
          self.retriever.index.init_index()



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					self.retriever._main_retrieve(UpperCamelCase_     ,	UpperCamelCase_      )
          return doc_ids, retrieved_doc_embeds





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          if index is not None and index.is_initialized() and len(UpperCamelCase_      ) > 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__(
              UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	index=UpperCamelCase_     ,	init_retrieval=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      Union[str, Any]     							=					retrieval_workers
          if len(self.retrieval_workers      ) > 0:
              ray.get(
                  [
                      worker.create_rag_retriever.remote(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )
                      for worker in self.retrieval_workers
                  ]      )



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



      @classmethod
      def  __snake_case							(   cls     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          return super(UpperCamelCase_     ,	cls      ).get_tokenizers(UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )



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

