




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
		from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						={
    """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/resolve/main/config.json""",
    """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/config.json""",
    """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json""",
    """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json""",
    """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/config.json""",
    """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json""",
}


class 	__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Union[str, Any]										="""bloom"""
				__a							: Any										=["""past_key_values"""]
				__a							: str										={
				    """num_hidden_layers""": """n_layer""",
				    """num_attention_heads""": """n_head""",
				}



				def __init__( self       , UpperCAmelCase_=25_08_80       , UpperCAmelCase_=64       , UpperCAmelCase_=2       , UpperCAmelCase_=8       , UpperCAmelCase_=1E-5       , UpperCAmelCase_=0.02       , UpperCAmelCase_=True       , UpperCAmelCase_=1       , UpperCAmelCase_=2       , UpperCAmelCase_=False       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.0       , UpperCAmelCase_=1       , UpperCAmelCase_=False       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		vocab_size
											# Backward compatibility with n_embed kwarg
											lowerCAmelCase   =		kwargs.pop('''n_embed'''       , UpperCAmelCase_						)
											lowerCAmelCase   =		hidden_size if n_embed is None else n_embed
											lowerCAmelCase   =		n_layer
											lowerCAmelCase   =		n_head
											lowerCAmelCase   =		layer_norm_epsilon
											lowerCAmelCase   =		initializer_range
											lowerCAmelCase   =		use_cache
											lowerCAmelCase   =		pretraining_tp
											lowerCAmelCase   =		apply_residual_connection_post_layernorm
											lowerCAmelCase   =		hidden_dropout
											lowerCAmelCase   =		attention_dropout

											lowerCAmelCase   =		bos_token_id
											lowerCAmelCase   =		eos_token_id
											lowerCAmelCase   =		slow_but_exact

											super().__init__(bos_token_id=UpperCAmelCase_       , eos_token_id=UpperCAmelCase_       , **UpperCAmelCase_						)




class 	__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: List[Any]										=version.parse("""1.12"""  )



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_ = "default"       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False       , ):
											super().__init__(UpperCAmelCase_       , task=UpperCAmelCase_       , patching_specs=UpperCAmelCase_       , use_past=UpperCAmelCase_						)
											if not getattr(self._config       , '''pad_token_id'''       , UpperCAmelCase_						):
																		# TODO: how to do that better?
																		lowerCAmelCase   =		0



				@property
				def       __snake_case		( self						):
											lowerCAmelCase   =		OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}						)
											if self.use_past:
																		# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
																		self.fill_with_past_key_values_(UpperCAmelCase_       , direction='''inputs'''       , inverted_values_shape=UpperCAmelCase_						)
																		lowerCAmelCase   =		{0: '''batch''', 1: '''past_sequence + sequence'''}
											else:
																		lowerCAmelCase   =		{0: '''batch''', 1: '''sequence'''}

											return common_inputs



				@property
				def       __snake_case		( self						):
											return self._config.n_layer



				@property
				def       __snake_case		( self						):
											return self._config.n_head



				@property
				def       __snake_case		( self						):
											return 1E-3



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = -1       , UpperCAmelCase_ = -1       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , ):
											lowerCAmelCase   =		super(UpperCAmelCase_       , self						).generate_dummy_inputs(
											    UpperCAmelCase_       , batch_size=UpperCAmelCase_       , seq_length=UpperCAmelCase_       , is_pair=UpperCAmelCase_       , framework=UpperCAmelCase_						)

											# We need to order the input in the way they appears in the forward()
											lowerCAmelCase   =		OrderedDict({'''input_ids''': common_inputs['''input_ids''']}						)

											# Need to add the past_keys
											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
																		else:
																									import torch

																									lowerCAmelCase   ,     lowerCAmelCase   =		common_inputs['''input_ids'''].shape
																									# Not using the same length for past_key_values
																									lowerCAmelCase   =		seqlen + 2
																									lowerCAmelCase   =		self._config.hidden_size // self.num_attention_heads
																									lowerCAmelCase   =		(
																									    batch * self.num_attention_heads,
																									    head_dim,
																									    past_key_values_length,
																									)
																									lowerCAmelCase   =		(
																									    batch * self.num_attention_heads,
																									    past_key_values_length,
																									    head_dim,
																									)
																									lowerCAmelCase   =		[
																									    (torch.zeros(UpperCAmelCase_						), torch.zeros(UpperCAmelCase_						)) for _ in range(self.num_layers						)
																									]

											lowerCAmelCase   =		common_inputs['''attention_mask''']
											if self.use_past:
																		lowerCAmelCase   =		ordered_inputs['''attention_mask'''].dtype
																		lowerCAmelCase   =		torch.cat(
																		    [ordered_inputs['''attention_mask'''], torch.ones(UpperCAmelCase_       , UpperCAmelCase_       , dtype=UpperCAmelCase_						)]       , dim=1						)

											return ordered_inputs






				@property
				def       __snake_case		( self						):
											return 13









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 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				def       __snake_case		( self						):
											lowerCAmelCase   =		tempfile.mkdtemp()
											lowerCAmelCase   =		8

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

											lowerCAmelCase   =		os.path.join(self.tmpdirname       , '''bart_tokenizer'''						)
											os.makedirs(UpperCAmelCase_       , exist_ok=UpperCAmelCase_						)
											lowerCAmelCase   =		os.path.join(UpperCAmelCase_       , BART_VOCAB_FILES_NAMES['''vocab_file''']						)
											lowerCAmelCase   =		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						):
											lowerCAmelCase   =		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						):
											lowerCAmelCase   =		self.get_dummy_dataset()
											lowerCAmelCase   =		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:
																		lowerCAmelCase   =		dataset
																		lowerCAmelCase   =		RagRetriever(
																		    UpperCAmelCase_       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , )
											return retriever



				def       __snake_case		( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		self.get_dummy_dataset()
											lowerCAmelCase   =		RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size       , question_encoder=DPRConfig().to_dict()       , generator=BartConfig().to_dict()       , index_name='''custom'''       , )
											if from_disk:
																		lowerCAmelCase   =		os.path.join(self.tmpdirname       , '''dataset'''						)
																		lowerCAmelCase   =		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
																		lowerCAmelCase   =		RagRetriever(
																		    UpperCAmelCase_       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , )
											else:
																		lowerCAmelCase   =		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						):
											lowerCAmelCase   =		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						)

											lowerCAmelCase   =		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'''						)						)

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

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



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



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



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



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



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

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

											lowerCAmelCase   =		retriever(
											    UpperCAmelCase_       , UpperCAmelCase_       , prefix=retriever.config.generator.prefix       , n_docs=UpperCAmelCase_       , return_tensors='''pt'''       , )
											lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		(  # 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						):
											lowerCAmelCase   =		self.get_dpr_ctx_encoder_tokenizer()
											lowerCAmelCase   =		1
											lowerCAmelCase   =		self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_						)
											retriever.set_ctx_encoder_tokenizer(UpperCAmelCase_						)

											lowerCAmelCase   =		[[5, 7], [10, 11]]
											lowerCAmelCase   =		np.array(
											    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]       , dtype=np.floataa						)
											lowerCAmelCase   =		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.



