
"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import CTRLConfig, 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.models.ctrl.modeling_tf_ctrl import (
			    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFCTRLForSequenceClassification,
			    TFCTRLLMHeadModel,
			    TFCTRLModel,
			)




class    _snake_case  (						_lowercase       ):






							def __init__(					self						:			List[Any]						, snake_case__						:			str						, ):






												"""simple docstring"""



												__a:						Union[str, Any]		    =					parent
												__a:						Dict		    =					1_3
												__a:						Union[str, Any]		    =					7
												__a:						List[str]		    =					True
												__a:						Tuple		    =					True
												__a:						Tuple		    =					True
												__a:						str		    =					True
												__a:						Tuple		    =					True
												__a:						Optional[int]		    =					9_9
												__a:						Optional[Any]		    =					3_2
												__a:						List[str]		    =					2
												__a:						str		    =					4
												__a:						List[str]		    =					3_7
												__a:						Dict		    =					"""gelu"""
												__a:						Dict		    =					0.1
												__a:						List[str]		    =					0.1
												__a:						Optional[int]		    =					5_1_2
												__a:						Tuple		    =					1_6
												__a:						List[str]		    =					2
												__a:						Union[str, Any]		    =					0.02
												__a:						Optional[Any]		    =					3
												__a:						int		    =					4
												__a:						Any		    =					None
												__a:						Dict		    =					self.vocab_size - 1






							def 							__lowercase  (					self						:			Optional[Any]    ):






												"""simple docstring"""



												__a:						Dict		    =					ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size    )

												__a:						int		    =					None
												if self.use_input_mask:
																	__a:						Tuple		    =					random_attention_mask([self.batch_size, self.seq_length]    )

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

												__a:						Tuple		    =					None
												if self.use_mc_token_ids:
																	__a:						Tuple		    =					ids_tensor([self.batch_size, self.num_choices]						, self.seq_length    )

												__a:						Tuple		    =					None
												__a:						Optional[int]		    =					None
												__a:						Union[str, Any]		    =					None
												if self.use_labels:
																	__a:						Tuple		    =					ids_tensor([self.batch_size]						, self.type_sequence_label_size    )
																	__a:						List[str]		    =					ids_tensor([self.batch_size, self.seq_length]						, self.num_labels    )
																	__a:						Any		    =					ids_tensor([self.batch_size]						, self.num_choices    )

												__a:						Dict		    =					CTRLConfig(
												    vocab_size=self.vocab_size						, n_embd=self.hidden_size						, n_layer=self.num_hidden_layers						, n_head=self.num_attention_heads						, n_positions=self.max_position_embeddings						, pad_token_id=self.pad_token_id						, )

												__a:						Optional[int]		    =					ids_tensor([self.num_hidden_layers, self.num_attention_heads]						, 2    )

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												    token_type_ids,
												    mc_token_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)






							def 							__lowercase  (					self						:			Any						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			Optional[Any]						, snake_case__						:			Optional[Any]						, snake_case__						:			Optional[int]						, *snake_case__						:			List[Any]    ):






												"""simple docstring"""



												__a:						Tuple		    =					TFCTRLModel(config=snake_case__    )
												__a:						Optional[Any]		    =					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
												__a:						Tuple		    =					model(snake_case__    )

												__a:						List[str]		    =					[input_ids, None, input_mask]  # None is the input for 'past'
												__a:						Optional[int]		    =					model(snake_case__    )

												__a:						Dict		    =					model(snake_case__    )

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






							def 							__lowercase  (					self						:			List[str]						, snake_case__						:			Union[str, Any]						, snake_case__						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			str						, snake_case__						:			int						, *snake_case__						:			int    ):






												"""simple docstring"""



												__a:						Any		    =					TFCTRLLMHeadModel(config=snake_case__    )
												__a:						Dict		    =					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
												__a:						str		    =					model(snake_case__    )
												self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size)    )






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			List[str]						, snake_case__						:			Optional[Any]						, snake_case__						:			List[str]						, snake_case__						:			Tuple						, snake_case__						:			str						, *snake_case__						:			Dict    ):






												"""simple docstring"""



												__a:						Any		    =					self.num_labels
												__a:						str		    =					ids_tensor([self.batch_size]						, self.type_sequence_label_size    )
												__a:						List[Any]		    =					{
												    """input_ids""": input_ids,
												    """token_type_ids""": token_type_ids,
												    """labels""": sequence_labels,
												}
												__a:						Any		    =					TFCTRLForSequenceClassification(snake_case__    )
												__a:						Optional[Any]		    =					model(snake_case__    )
												self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_labels)    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						Tuple		    =					self.prepare_config_and_inputs()

												(
												    (
												    __a
												)    ,				(
												    __a
												)    ,				(
												    __a
												)    ,				(
												    __a
												)    ,				(
												    __a
												)    ,				(
												    __a
												)    ,				(
												    __a
												)    ,				(
												    __a
												)    ,				(
												    __a
												)    ,				
												):						str		    =					config_and_inputs

												__a:						Dict		    =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
												return config, inputs_dict






@require_tf
class    _snake_case  (						_lowercase ,				_lowercase ,				unittest.TestCase       ):
							SCREAMING_SNAKE_CASE		     =						(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
							SCREAMING_SNAKE_CASE		     =						(TFCTRLLMHeadModel,) if is_tf_available() else ()
							SCREAMING_SNAKE_CASE		     =						(
							    {
							        '''feature-extraction''': TFCTRLModel,
							        '''text-classification''': TFCTRLForSequenceClassification,
							        '''text-generation''': TFCTRLLMHeadModel,
							        '''zero-shot''': TFCTRLForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							SCREAMING_SNAKE_CASE		     =						False
							SCREAMING_SNAKE_CASE		     =						False






							def 							__lowercase  (					self						:			List[Any]						, snake_case__						:			Optional[int]						, snake_case__						:			List[str]						, snake_case__						:			Optional[int]						, snake_case__						:			Optional[Any]						, snake_case__						:			Tuple    ):






												"""simple docstring"""



												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																	# config could not be created.
																	return True

												return False






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						List[Any]		    =					TFCTRLModelTester(self    )
												__a:						Any		    =					ConfigTester(self						, config_class=snake_case__						, n_embd=3_7    )






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												self.config_tester.run_common_tests()






							def 							__lowercase  (					self						:			Optional[Any]    ):






												"""simple docstring"""



												__a:						Dict		    =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_model(*snake_case__    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						Any		    =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_lm_head(*snake_case__    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						str		    =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_for_sequence_classification(*snake_case__    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a    ,				__a:						List[Any]		    =					self.model_tester.prepare_config_and_inputs_for_common()
												__a:						Union[str, Any]		    =					[TFCTRLLMHeadModel]
												__a:						Dict		    =					[TFCTRLForSequenceClassification]

												for model_class in self.all_model_classes:
																	__a:						List[str]		    =					model_class(snake_case__    )
																	model.build()  # may be needed for the get_bias() call below
																	assert isinstance(model.get_input_embeddings()						, tf.keras.layers.Layer    )

																	if model_class in list_lm_models:
																						__a:						str		    =					model.get_output_embeddings()
																						assert isinstance(snake_case__						, tf.keras.layers.Layer    )
																						__a:						List[Any]		    =					model.get_bias()
																						assert isinstance(snake_case__						, snake_case__    )
																						for k, v in name.items():
																											assert isinstance(snake_case__						, tf.Variable    )
																	elif model_class in list_other_models_with_output_ebd:
																						__a:						Tuple		    =					model.get_output_embeddings()
																						assert isinstance(snake_case__						, tf.keras.layers.Layer    )
																						__a:						int		    =					model.get_bias()
																						assert name is None
																	else:
																						__a:						Any		    =					model.get_output_embeddings()
																						assert x is None
																						__a:						Union[str, Any]		    =					model.get_bias()
																						assert name is None






							@slow
							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__a:						int		    =					TFCTRLModel.from_pretrained(snake_case__    )
																	self.assertIsNotNone(snake_case__    )






@require_tf
class    _snake_case  (						unittest.TestCase       ):






							@slow
							def 							__lowercase  (					self						:			Union[str, Any]    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					TFCTRLLMHeadModel.from_pretrained("""ctrl"""    )
												__a:						int		    =					tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]						, dtype=tf.intaa    )  # Legal the president is
												__a:						List[Any]		    =					[
												    1_1_8_5_9,
												    0,
												    1_6_1_1,
												    8,
												    5,
												    1_5_0,
												    2_6_4_4_9,
												    2,
												    1_9,
												    3_4_8,
												    4_6_9,
												    3,
												    2_5_9_5,
												    4_8,
												    2_0_7_4_0,
												    2_4_6_5_3_3,
												    2_4_6_5_3_3,
												    1_9,
												    3_0,
												    5,
												]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

												__a:						Dict		    =					model.generate(snake_case__						, do_sample=snake_case__    )
												self.assertListEqual(output_ids[0].numpy().tolist()						, snake_case__    )


"""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:
			lowercase		:						str =					None

if is_ray_available():
			import ray  # noqa: E402 # isort:skip
			from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
			lowercase		:						Optional[int] =					None
			lowercase		:						Union[str, Any] =					None
			lowercase		:						List[str] =					None


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


					'''simple docstring'''
					if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
										__a:						int		    =					unittest.skip("""test requires Datasets, Faiss, psutil""" )(lowerCamelCase_ )
					return test_case




@require_distributed_retrieval
class    _snake_case  (						_lowercase       ):






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					tempfile.mkdtemp()
												__a:						int		    =					8

												# DPR tok
												__a:						Any		    =					[
												    """[UNK]""",
												    """[CLS]""",
												    """[SEP]""",
												    """[PAD]""",
												    """[MASK]""",
												    """want""",
												    """##want""",
												    """##ed""",
												    """wa""",
												    """un""",
												    """runn""",
												    """##ing""",
												    """,""",
												    """low""",
												    """lowest""",
												]
												__a:						str		    =					os.path.join(self.tmpdirname						, """dpr_tokenizer"""    )
												os.makedirs(snake_case__						, exist_ok=snake_case__    )
												__a:						Tuple		    =					os.path.join(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
												__a:						Any		    =					[
												    """l""",
												    """o""",
												    """w""",
												    """e""",
												    """r""",
												    """s""",
												    """t""",
												    """i""",
												    """d""",
												    """n""",
												    """\u0120""",
												    """\u0120l""",
												    """\u0120n""",
												    """\u0120lo""",
												    """\u0120low""",
												    """er""",
												    """\u0120lowest""",
												    """\u0120newer""",
												    """\u0120wider""",
												    """<unk>""",
												]
												__a:						List[str]		    =					dict(zip(snake_case__						, range(len(snake_case__    )    )    )    )
												__a:						List[str]		    =					["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
												__a:						List[str]		    =					{"""unk_token""": """<unk>"""}

												__a:						Optional[int]		    =					os.path.join(self.tmpdirname						, """bart_tokenizer"""    )
												os.makedirs(snake_case__						, exist_ok=snake_case__    )
												__a:						Tuple		    =					os.path.join(snake_case__						, BART_VOCAB_FILES_NAMES["""vocab_file"""]    )
												__a:						str		    =					os.path.join(snake_case__						, BART_VOCAB_FILES_NAMES["""merges_file"""]    )
												with open(self.vocab_file						, """w"""						, encoding="""utf-8"""    ) as fp:
																	fp.write(json.dumps(snake_case__    ) + """\n"""    )
												with open(self.merges_file						, """w"""						, encoding="""utf-8"""    ) as fp:
																	fp.write("""\n""".join(snake_case__    )    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												shutil.rmtree(self.tmpdirname    )






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												__a:						int		    =					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						:			Any						, snake_case__						:			bool						, snake_case__						:			Dict=1_2_3_4_5    ):






												"""simple docstring"""



												__a:						str		    =					self.get_dummy_dataset()
												__a:						int		    =					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:
																	__a:						List[str]		    =					dataset
																	__a:						List[Any]		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, )
																	if init_retrieval:
																						retriever.init_retrieval(snake_case__    )
												return retriever






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			bool    ):






												"""simple docstring"""



												ray.init(local_mode=snake_case__    )
												__a:						int		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, )
												__a:						List[str]		    =					ray.remote(snake_case__    )
												__a:						Tuple		    =					[remote_cls.remote() for _ in range(1    )]
												with patch("""transformers.models.rag.retrieval_rag.load_dataset"""    ) as mock_load_dataset:
																	__a:						int		    =					self.get_dummy_dataset()
																	__a:						List[Any]		    =					RagRayDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, retrieval_workers=snake_case__						, )
																	if init_retrieval:
																						retriever.init_retrieval()
												return retriever






							def 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool						, snake_case__						:			Union[str, Any]=1_2_3_4_5    ):






												"""simple docstring"""



												__a:						List[Any]		    =					self.get_dummy_dataset()
												__a:						Optional[Any]		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, index_name="""custom"""						, )
												if from_disk:
																	__a:						int		    =					os.path.join(self.tmpdirname						, """dataset"""    )
																	__a:						int		    =					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
																	__a:						str		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, )
												else:
																	__a:						List[Any]		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, index=CustomHFIndex(config.retrieval_vector_size						, snake_case__    )						, )
												if init_retrieval:
																	retriever.init_retrieval(snake_case__    )
												return retriever






							def 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool    ):






												"""simple docstring"""



												ray.init(local_mode=snake_case__    )
												__a:						Tuple		    =					self.get_dummy_dataset()
												__a:						Dict		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, index_name="""custom"""						, )
												__a:						Optional[Any]		    =					ray.remote(snake_case__    )
												__a:						Any		    =					[remote_cls.remote() for _ in range(1    )]
												if from_disk:
																	__a:						List[Any]		    =					os.path.join(self.tmpdirname						, """dataset"""    )
																	__a:						Any		    =					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
																	__a:						Any		    =					RagRayDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, retrieval_workers=snake_case__						, index=CustomHFIndex.load_from_disk(
																	        vector_size=config.retrieval_vector_size						, dataset_path=config.passages_path						, index_path=config.index_path						, )						, )
												else:
																	__a:						Any		    =					RagRayDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, retrieval_workers=snake_case__						, index=CustomHFIndex(config.retrieval_vector_size						, snake_case__    )						, )
												if init_retrieval:
																	retriever.init_retrieval()
												return retriever






							def 							__lowercase  (					self						:			str						, snake_case__						:			RagRetriever						, snake_case__						:			np.array						, snake_case__						:			int    ):






												"""simple docstring"""



												__a    ,				__a    ,				__a:						Optional[Any]		    =					retriever.retrieve(snake_case__						, n_docs=snake_case__    )
												self.assertEqual(retrieved_doc_embeds.shape						, (2, n_docs, self.retrieval_vector_size)    )
												self.assertEqual(len(snake_case__    )						, 2    )
												self.assertEqual(sorted(doc_dicts[0]    )						, ["""embeddings""", """id""", """text""", """title"""]    )
												self.assertEqual(len(doc_dicts[0]["""id"""]    )						, 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						:			List[str]    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						Tuple		    =					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=snake_case__    )						, snake_case__						, snake_case__    )






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						Tuple		    =					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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						int		    =					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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							@require_ray
							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						str		    =					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=snake_case__    )						, snake_case__						, snake_case__    )
												ray.shutdown()






							@require_ray
							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						List[str]		    =					np.array(
												    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]						, dtype=np.floataa    )
												with self.assertRaises(snake_case__    ):
																	self.distributed_retriever_check(
																	    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )
												ray.shutdown()






							@require_ray
							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						str		    =					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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__    )
												ray.shutdown()
