


'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		)




class 		a__				:





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_=13					,    lowerCamelCase_=7					,    lowerCamelCase_=True					,    lowerCamelCase_=True					,    lowerCamelCase_=True					,    lowerCamelCase_=True					,    lowerCamelCase_=99					,    lowerCamelCase_=32					,    lowerCamelCase_=2					,    lowerCamelCase_=4					,    lowerCamelCase_=37					,    lowerCamelCase_="gelu"					,    lowerCamelCase_=0.1					,    lowerCamelCase_=0.1					,    lowerCamelCase_=5_12					,    lowerCamelCase_=16					,    lowerCamelCase_=2					,    lowerCamelCase_=0.02					,    lowerCamelCase_=3					,    lowerCamelCase_=4					,    lowerCamelCase_=None					,    )       ->							Union[str, Any]:
														lowerCAmelCase__          =	parent
														lowerCAmelCase__          =	13
														lowerCAmelCase__          =	7
														lowerCAmelCase__          =	True
														lowerCAmelCase__          =	True
														lowerCAmelCase__          =	True
														lowerCAmelCase__          =	True
														lowerCAmelCase__          =	99
														lowerCAmelCase__          =	32
														lowerCAmelCase__          =	2
														lowerCAmelCase__          =	4
														lowerCAmelCase__          =	37
														lowerCAmelCase__          =	'''gelu'''
														lowerCAmelCase__          =	0.1
														lowerCAmelCase__          =	0.1
														lowerCAmelCase__          =	5_12
														lowerCAmelCase__          =	16
														lowerCAmelCase__          =	False
														lowerCAmelCase__          =	-1
														lowerCAmelCase__          =	True
														lowerCAmelCase__          =	2
														lowerCAmelCase__          =	0.02
														lowerCAmelCase__          =	3
														lowerCAmelCase__          =	4
														lowerCAmelCase__          =	None







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size   )

														lowerCAmelCase__          =	None
														if self.use_input_mask:
																					lowerCAmelCase__          =	random_attention_mask([self.batch_size, self.seq_length]   )

														lowerCAmelCase__          =	None
														if self.use_token_type_ids:
																					lowerCAmelCase__          =	ids_tensor([self.batch_size, self.seq_length]					,    self.type_vocab_size   )

														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														if self.use_labels:
																					lowerCAmelCase__          =	ids_tensor([self.batch_size]					,    self.type_sequence_label_size   )
																					lowerCAmelCase__          =	ids_tensor([self.batch_size, self.seq_length]					,    self.num_labels   )

														lowerCAmelCase__          =	DebertaConfig(
														    vocab_size=self.vocab_size					,    hidden_size=self.hidden_size					,    num_hidden_layers=self.num_hidden_layers					,    num_attention_heads=self.num_attention_heads					,    intermediate_size=self.intermediate_size					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    attention_probs_dropout_prob=self.attention_probs_dropout_prob					,    max_position_embeddings=self.max_position_embeddings					,    type_vocab_size=self.type_vocab_size					,    relative_attention=self.relative_attention					,    max_relative_positions=self.max_relative_positions					,    position_biased_input=self.position_biased_input					,    initializer_range=self.initializer_range					,    return_dict=lowerCamelCase_					,    )

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[int]:
														lowerCAmelCase__          =	TFDebertaModel(config=lowerCamelCase_   )
														lowerCAmelCase__          =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

														lowerCAmelCase__          =	[input_ids, input_mask]
														lowerCAmelCase__          =	model(lowerCamelCase_   )

														lowerCAmelCase__          =	model(lowerCamelCase_   )

														self.parent.assertEqual(result.last_hidden_state.shape					,    (self.batch_size, self.seq_length, self.hidden_size)   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							List[str]:
														lowerCAmelCase__          =	TFDebertaForMaskedLM(config=lowerCamelCase_   )
														lowerCAmelCase__          =	{
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}
														lowerCAmelCase__          =	model(lowerCamelCase_   )
														self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.seq_length, self.vocab_size)   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							int:
														lowerCAmelCase__          =	self.num_labels
														lowerCAmelCase__          =	TFDebertaForSequenceClassification(config=lowerCamelCase_   )
														lowerCAmelCase__          =	{
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}

														lowerCAmelCase__          =	model(lowerCamelCase_   )
														self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.num_labels)   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[Any]:
														lowerCAmelCase__          =	self.num_labels
														lowerCAmelCase__          =	TFDebertaForTokenClassification(config=lowerCamelCase_   )
														lowerCAmelCase__          =	{
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}
														lowerCAmelCase__          =	model(lowerCamelCase_   )
														self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.seq_length, self.num_labels)   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Dict:
														lowerCAmelCase__          =	TFDebertaForQuestionAnswering(config=lowerCamelCase_   )
														lowerCAmelCase__          =	{
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}

														lowerCAmelCase__          =	model(lowerCamelCase_   )
														self.parent.assertEqual(result.start_logits.shape					,    (self.batch_size, self.seq_length)   )
														self.parent.assertEqual(result.end_logits.shape					,    (self.batch_size, self.seq_length)   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[Any]:
														lowerCAmelCase__          =	self.prepare_config_and_inputs()
														(
														    (
														    lowerCAmelCase__
														)   ,					(
														    lowerCAmelCase__
														)   ,					(
														    lowerCAmelCase__
														)   ,					(
														    lowerCAmelCase__
														)   ,					(
														    lowerCAmelCase__
														)   ,					(
														    lowerCAmelCase__
														)   ,					(
														    lowerCAmelCase__
														)   ,					
														)          =	config_and_inputs
														lowerCAmelCase__          =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
														return config, inputs_dict




@require_tf
class 		a__				(							a__				,				a__				,				unittest.TestCase  ):





							'''simple docstring'''






							lowercase__			:   Optional[Any]							=      (
							    (
							        TFDebertaModel,
							        TFDebertaForMaskedLM,
							        TFDebertaForQuestionAnswering,
							        TFDebertaForSequenceClassification,
							        TFDebertaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							lowercase__			:   Optional[Any]							=      (
							    {
							        "feature-extraction": TFDebertaModel,
							        "fill-mask": TFDebertaForMaskedLM,
							        "question-answering": TFDebertaForQuestionAnswering,
							        "text-classification": TFDebertaForSequenceClassification,
							        "token-classification": TFDebertaForTokenClassification,
							        "zero-shot": TFDebertaForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							lowercase__			:   List[Any]							=      False
							lowercase__			:   Optional[int]							=      False







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[str]:
														lowerCAmelCase__          =	TFDebertaModelTester(self   )
														lowerCAmelCase__          =	ConfigTester(self					,    config_class=lowerCamelCase_					,    hidden_size=37   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														self.config_tester.run_common_tests()







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[str]:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_   )







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														lowerCAmelCase__          =	TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''   )
														self.assertIsNotNone(lowerCamelCase_   )




@require_tf
class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							@unittest.skip(reason='''Model not available yet'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														pass







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[str]:
														lowerCAmelCase__          =	TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''   )
														lowerCAmelCase__          =	tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   )
														lowerCAmelCase__          =	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
														lowerCAmelCase__          =	model(lowerCamelCase_					,    attention_mask=lowerCamelCase_   )[0]

														lowerCAmelCase__          =	tf.constant(
														    [
														        [
														            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
														            [1.4_484_025, -0.93_483_794, -0.80_593_085],
														            [0.3_122_741, 0.00_316_059, -1.4_131_377],
														        ]
														    ]   )
														tf.debugging.assert_near(output[:, 1:4, 1:4]					,    lowerCamelCase_					,    atol=1e-4   )



'''simple docstring'''






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

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
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_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
		from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
		__UpperCAmelCase    				=							None

if is_ray_available():
		import ray  # noqa: E402 # isort:skip
		from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
		__UpperCAmelCase    				=							None
		__UpperCAmelCase    				=							None
		__UpperCAmelCase    				=							None


def 					_snake_case   (     A       )						->    Any:
							if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
														lowerCAmelCase__          =	unittest.skip('''test requires Datasets, Faiss, psutil'''       )(A       )
							return test_case






@require_distributed_retrieval
class 		a__				(							a__  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														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(lowerCamelCase_					,    exist_ok=lowerCamelCase_   )
														lowerCAmelCase__          =	os.path.join(lowerCamelCase_					,    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(lowerCamelCase_					,    range(len(lowerCamelCase_   )   )   )   )
														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(lowerCamelCase_					,    exist_ok=lowerCamelCase_   )
														lowerCAmelCase__          =	os.path.join(lowerCamelCase_					,    BART_VOCAB_FILES_NAMES['''vocab_file''']   )
														lowerCAmelCase__          =	os.path.join(lowerCamelCase_					,    BART_VOCAB_FILES_NAMES['''merges_file''']   )
														with open(self.vocab_file					,    '''w'''					,    encoding='''utf-8'''   ) as fp:
																					fp.write(json.dumps(lowerCamelCase_   ) + '''\n'''   )
														with open(self.merges_file					,    '''w'''					,    encoding='''utf-8'''   ) as fp:
																					fp.write('''\n'''.join(lowerCamelCase_   )   )







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







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







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														shutil.rmtree(self.tmpdirname   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_=1_23_45   )       ->							RagPyTorchDistributedRetriever:
														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__          =	RagPyTorchDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    )
																					if init_retrieval:
																												retriever.init_retrieval(lowerCamelCase_   )
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							RagRayDistributedRetriever:
														# Have to run in local mode because sys.path modifications at top of
														# file are not propogated to remote workers.
														# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
														ray.init(local_mode=lowerCamelCase_   )
														lowerCAmelCase__          =	RagConfig(
														    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    )
														lowerCAmelCase__          =	ray.remote(lowerCamelCase_   )
														lowerCAmelCase__          =	[remote_cls.remote() for _ in range(1   )]
														with patch('''transformers.models.rag.retrieval_rag.load_dataset'''   ) as mock_load_dataset:
																					lowerCAmelCase__          =	self.get_dummy_dataset()
																					lowerCAmelCase__          =	RagRayDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCamelCase_					,    )
																					if init_retrieval:
																												retriever.init_retrieval()
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_=1_23_45   )       ->							Union[str, Any]:
														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__          =	RagPyTorchDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    )
														else:
																					lowerCAmelCase__          =	RagPyTorchDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    index=CustomHFIndex(config.retrieval_vector_size					,    lowerCamelCase_   )					,    )
														if init_retrieval:
																					retriever.init_retrieval(lowerCamelCase_   )
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Union[str, Any]:
														# Have to run in local mode because sys.path modifications at top of
														# file are not propogated to remote workers.
														# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
														ray.init(local_mode=lowerCamelCase_   )
														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'''					,    )
														lowerCAmelCase__          =	ray.remote(lowerCamelCase_   )
														lowerCAmelCase__          =	[remote_cls.remote() for _ in range(1   )]
														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__          =	RagRayDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCamelCase_					,    index=CustomHFIndex.load_from_disk(
																					        vector_size=config.retrieval_vector_size					,    dataset_path=config.passages_path					,    index_path=config.index_path					,    )					,    )
														else:
																					lowerCAmelCase__          =	RagRayDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCamelCase_					,    index=CustomHFIndex(config.retrieval_vector_size					,    lowerCamelCase_   )					,    )
														if init_retrieval:
																					retriever.init_retrieval()
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							None:
														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	retriever.retrieve(lowerCamelCase_					,    n_docs=lowerCamelCase_   )
														self.assertEqual(retrieved_doc_embeds.shape					,    (2, n_docs, self.retrieval_vector_size)   )
														self.assertEqual(len(lowerCamelCase_   )					,    2   )
														self.assertEqual(sorted(doc_dicts[0]   )					,    ['''embeddings''', '''id''', '''text''', '''title''']   )
														self.assertEqual(len(doc_dicts[0]['''id''']   )					,    lowerCamelCase_   )
														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   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[Any]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	np.array(
														    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]					,    dtype=np.floataa   )

														self.distributed_retriever_check(
														    self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	np.array(
														    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]					,    dtype=np.floataa   )

														self.distributed_retriever_check(
														    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_					,    )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	np.array(
														    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]					,    dtype=np.floataa   )

														self.distributed_retriever_check(
														    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_					,    )







							@require_ray
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	np.array(
														    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]					,    dtype=np.floataa   )

														self.distributed_retriever_check(
														    self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_   )
														ray.shutdown()







							@require_ray
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	np.array(
														    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]					,    dtype=np.floataa   )
														with self.assertRaises(lowerCamelCase_   ):
																					self.distributed_retriever_check(
																					    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_					,    )
														ray.shutdown()







							@require_ray
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[str]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	np.array(
														    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]					,    dtype=np.floataa   )

														self.distributed_retriever_check(
														    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_   )
														ray.shutdown()