






"""simple docstring"""




import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


lowerCAmelCase__							=				logging.getLogger(__name__)





class  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									super().__init__(
									    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=SCREAMING_SNAKE_CASE_       ,       generator_tokenizer=SCREAMING_SNAKE_CASE_       ,       index=SCREAMING_SNAKE_CASE_       ,       init_retrieval=SCREAMING_SNAKE_CASE_       ,       )
									SCREAMING_SNAKE_CASE_						 = None

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									logger.info('''initializing retrieval'''    )

									# initializing a separate process group for retrieval as the default
									# nccl backend doesn't support gather/scatter operations while gloo
									# is too slow to replace nccl for the core gpu communication
									if dist.is_initialized():
											logger.info('''dist initialized'''    )
											# needs to be set manually
											SCREAMING_SNAKE_CASE_						 = self._infer_socket_ifname()
											# avoid clash with the NCCL port
											SCREAMING_SNAKE_CASE_						 = str(distributed_port + 1    )
											SCREAMING_SNAKE_CASE_						 = dist.new_group(ranks=SCREAMING_SNAKE_CASE_       ,       backend='''gloo'''    )

									# initialize retriever only on the main worker
									if not dist.is_initialized() or self._is_main():
											logger.info('''dist not initialized / main'''    )
											self.index.init_index()

									# all processes wait untill the retriever is initialized by the main process
									if dist.is_initialized():
											torch.distributed.barrier(group=self.process_group    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return dist.get_rank(group=self.process_group    ) == 0

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=torch.floataa    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = torch.empty(SCREAMING_SNAKE_CASE_       ,       dtype=SCREAMING_SNAKE_CASE_    )
									dist.scatter(SCREAMING_SNAKE_CASE_       ,       src=0       ,       scatter_list=SCREAMING_SNAKE_CASE_       ,       group=self.process_group    )
									return target_tensor

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = psutil.net_if_addrs()
									# a hacky way to deal with varying network interface names
									SCREAMING_SNAKE_CASE_						 = next((addr for addr in addrs if addr.startswith('''e'''    ))       ,       SCREAMING_SNAKE_CASE_    )
									return ifname







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if not dist.is_initialized():
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._main_retrieve(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(SCREAMING_SNAKE_CASE_    )

									# distributed training
									SCREAMING_SNAKE_CASE_						 = dist.get_world_size(group=self.process_group    )

									# gather logic
									SCREAMING_SNAKE_CASE_						 = None
									if self._is_main():
											SCREAMING_SNAKE_CASE_						 = [torch.empty(question_hidden_states.shape       ,       dtype=torch.floataa    ) for _ in range(SCREAMING_SNAKE_CASE_    )]
									dist.gather(torch.tensor(SCREAMING_SNAKE_CASE_    )       ,       dst=0       ,       gather_list=SCREAMING_SNAKE_CASE_       ,       group=self.process_group    )

									# scatter logic
									SCREAMING_SNAKE_CASE_						 = question_hidden_states.shape[0]
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = []
									if self._is_main():
											assert len(SCREAMING_SNAKE_CASE_    ) == world_size
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._main_retrieve(torch.cat(SCREAMING_SNAKE_CASE_    ).numpy()       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = torch.tensor(SCREAMING_SNAKE_CASE_    ), torch.tensor(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = self._chunk_tensor(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = self._chunk_tensor(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = self._scattered(SCREAMING_SNAKE_CASE_       ,       [n_queries, n_docs]       ,       target_type=torch.intaa    )
									SCREAMING_SNAKE_CASE_						 = self._scattered(SCREAMING_SNAKE_CASE_       ,       [n_queries, n_docs, question_hidden_states.shape[1]]    )

									return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(SCREAMING_SNAKE_CASE_    )

"""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  snake_case     (							__lowercase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE_						 = 8

									# DPR tok
									SCREAMING_SNAKE_CASE_						 = [
									    '''[UNK]''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''[PAD]''',
									    '''[MASK]''',
									    '''want''',
									    '''##want''',
									    '''##ed''',
									    '''wa''',
									    '''un''',
									    '''runn''',
									    '''##ing''',
									    ''',''',
									    '''low''',
									    '''lowest''',
									]
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''dpr_tokenizer'''    )
									os.makedirs(SCREAMING_SNAKE_CASE_       ,       exist_ok=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       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
									SCREAMING_SNAKE_CASE_						 = [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									SCREAMING_SNAKE_CASE_						 = {'''unk_token''': '''<unk>'''}

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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									shutil.rmtree(self.tmpdirname    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE_						 = 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:
											SCREAMING_SNAKE_CASE_						 = dataset
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
									return retriever

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE_						 = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       index_name='''custom'''       ,       )
									if from_disk:
											SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''dataset'''    )
											SCREAMING_SNAKE_CASE_						 = 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
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       index=CustomHFIndex(config.retrieval_vector_size       ,       SCREAMING_SNAKE_CASE_    )       ,       )
									return retriever

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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    )

									SCREAMING_SNAKE_CASE_						 = 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'''    )    )

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

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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''embeddings''', '''id''', '''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''id''']    )       ,       SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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:
													SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
													retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
													self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = np.array(
													    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
													SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
													self.assertTrue(out is not None    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''embeddings''', '''id''', '''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''id''']    )       ,       SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
											self.assertTrue(out is not None    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''embeddings''', '''id''', '''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''id''']    )       ,       SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
											self.assertTrue(out is not None    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_legacy_index_retriever()
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(retrieved_doc_embeds.shape       ,       (2, n_docs, self.retrieval_vector_size)    )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       2    )
									self.assertEqual(sorted(doc_dicts[0]    )       ,       ['''text''', '''title''']    )
									self.assertEqual(len(doc_dicts[0]['''text''']    )       ,       SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_legacy_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
											self.assertTrue(out is not None    )

							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def _lowercase					(self    ):

									"""simple docstring"""







									import torch

									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE_						 = [[5, 7], [10, 11]]
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_						 = retriever(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (
									    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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       np.ndarray    )

									SCREAMING_SNAKE_CASE_						 = retriever(
									    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''       ,       )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (  # 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(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )







							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dpr_ctx_encoder_tokenizer()
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE_    )

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

									self.assertEqual(
									    len(SCREAMING_SNAKE_CASE_    )       ,       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''')    )       ,       SCREAMING_SNAKE_CASE_    )  # check for doc token related keys in dictionary.