

"""simple docstring"""




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A_				:      Union[str, Any]           =			16
A_				:      Tuple           =			32

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = 16     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   AutoTokenizer.from_pretrained("""bert-base-cased"""     )
			SCREAMING_SNAKE_CASE__										=   DatasetDict(
			    {
			        """train""": dataset["""train"""].select(snake_case__     ),
			        """validation""": dataset["""train"""].select(snake_case__     ),
			        """test""": dataset["""validation"""],
			    }     )

			def tokenize_function(snake_case__     ):
						# max_length=None => use the model max length (it's actually the default)
						SCREAMING_SNAKE_CASE__										=   tokenizer(examples["""sentence1"""]		,					examples["""sentence2"""]		,					truncation=snake_case__		,					max_length=snake_case__     )
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			# starting with the main process first:
			with accelerator.main_process_first():
						SCREAMING_SNAKE_CASE__										=   datasets.map(
						    snake_case__		,					batched=snake_case__		,					remove_columns=["""idx""", """sentence1""", """sentence2"""]		,					)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			SCREAMING_SNAKE_CASE__										=   tokenized_datasets.rename_column("""label"""		,					"""labels"""     )

			def collate_fn(snake_case__     ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						SCREAMING_SNAKE_CASE__										=   1_28 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									SCREAMING_SNAKE_CASE__										=   16
						elif accelerator.mixed_precision != "no":
									SCREAMING_SNAKE_CASE__										=   8
						else:
									SCREAMING_SNAKE_CASE__										=   None

						return tokenizer.pad(
						    snake_case__		,					padding="""longest"""		,					max_length=snake_case__		,					pad_to_multiple_of=snake_case__		,					return_tensors="""pt"""		,					)

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE__										=   DataLoader(
			    tokenized_datasets["""train"""]		,					shuffle=snake_case__		,					collate_fn=snake_case__		,					batch_size=snake_case__     )
			SCREAMING_SNAKE_CASE__										=   DataLoader(
			    tokenized_datasets["""validation"""]		,					shuffle=snake_case__		,					collate_fn=snake_case__		,					batch_size=snake_case__     )

			SCREAMING_SNAKE_CASE__										=   DataLoader(
			    tokenized_datasets["""test"""]		,					shuffle=snake_case__		,					collate_fn=snake_case__		,					batch_size=snake_case__     )

			return train_dataloader, eval_dataloader, test_dataloader

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   []
			# Download the dataset
			SCREAMING_SNAKE_CASE__										=   load_dataset("""glue"""		,					"""mrpc"""     )
			# Create our splits
			SCREAMING_SNAKE_CASE__										=   StratifiedKFold(n_splits=int(args.num_folds     )     )
			# Initialize accelerator
			SCREAMING_SNAKE_CASE__										=   Accelerator(cpu=args.cpu		,					mixed_precision=args.mixed_precision     )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			SCREAMING_SNAKE_CASE__										=   config["""lr"""]
			SCREAMING_SNAKE_CASE__										=   int(config["""num_epochs"""]     )
			SCREAMING_SNAKE_CASE__										=   int(config["""seed"""]     )
			SCREAMING_SNAKE_CASE__										=   int(config["""batch_size"""]     )

			SCREAMING_SNAKE_CASE__										=   evaluate.load("""glue"""		,					"""mrpc"""     )

			# If the batch size is too big we use gradient accumulation
			SCREAMING_SNAKE_CASE__										=   1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						SCREAMING_SNAKE_CASE__										=   batch_size // MAX_GPU_BATCH_SIZE
						SCREAMING_SNAKE_CASE__										=   MAX_GPU_BATCH_SIZE

			set_seed(snake_case__     )

			# New Code #
			# Create our folds:
			SCREAMING_SNAKE_CASE__										=   kfold.split(np.zeros(datasets["""train"""].num_rows     )		,					datasets["""train"""]["""label"""]     )
			SCREAMING_SNAKE_CASE__										=   []
			# Iterate over them
			for i, (train_idxs, valid_idxs) in enumerate(snake_case__     ):
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   get_fold_dataloaders(
						    snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE__										=   AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,					return_dict=snake_case__     )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						SCREAMING_SNAKE_CASE__										=   model.to(accelerator.device     )

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE__										=   AdamW(params=model.parameters()		,					lr=snake_case__     )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE__										=   get_linear_schedule_with_warmup(
						    optimizer=snake_case__		,					num_warmup_steps=1_00		,					num_training_steps=(len(snake_case__     ) * num_epochs) // gradient_accumulation_steps		,					)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   accelerator.prepare(
						    snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__     )

						# Now we train the model
						for epoch in range(snake_case__     ):
									model.train()
									for step, batch in enumerate(snake_case__     ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device     )
												SCREAMING_SNAKE_CASE__										=   model(**snake_case__     )
												SCREAMING_SNAKE_CASE__										=   outputs.loss
												SCREAMING_SNAKE_CASE__										=   loss / gradient_accumulation_steps
												accelerator.backward(snake_case__     )
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

									model.eval()
									for step, batch in enumerate(snake_case__     ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device     )
												with torch.no_grad():
															SCREAMING_SNAKE_CASE__										=   model(**snake_case__     )
												SCREAMING_SNAKE_CASE__										=   outputs.logits.argmax(dim=-1     )
												SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
												metric.add_batch(
												    predictions=snake_case__		,					references=snake_case__		,					)

									SCREAMING_SNAKE_CASE__										=   metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(f"""epoch {epoch}:"""		,					snake_case__     )

						# New Code #
						# We also run predictions on the test set at the very end
						SCREAMING_SNAKE_CASE__										=   []
						for step, batch in enumerate(snake_case__     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device     )
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__										=   model(**snake_case__     )
									SCREAMING_SNAKE_CASE__										=   outputs.logits
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
									fold_predictions.append(predictions.cpu()     )
									if i == 0:
												# We need all of the test predictions
												test_references.append(references.cpu()     )
        # Use accelerator.print to print only on the main process.
						test_predictions.append(torch.cat(snake_case__		,					dim=0     )     )
						# We now need to release all our memory and get rid of the current model, optimizer, etc
						accelerator.free_memory()
			# New Code #
			# Finally we check the accuracy of our folded results:
			SCREAMING_SNAKE_CASE__										=   torch.cat(snake_case__		,					dim=0     )
			SCREAMING_SNAKE_CASE__										=   torch.stack(snake_case__		,					dim=0     ).sum(dim=0     ).div(int(args.num_folds     )     ).argmax(dim=-1     )
			SCREAMING_SNAKE_CASE__										=   metric.compute(predictions=snake_case__		,					references=snake_case__     )
			accelerator.print("""Average test metrics from all folds:"""		,					snake_case__     )

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   argparse.ArgumentParser(description="""Simple example of training script."""     )
			parser.add_argument(
			    """--mixed_precision"""		,					type=snake_case__		,					default=snake_case__		,					choices=["""no""", """fp16""", """bf16""", """fp8"""]		,					help="""Whether to use mixed precision. Choose"""
			    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
			    """and an Nvidia Ampere GPU."""		,					)
			parser.add_argument("""--cpu"""		,					action="""store_true"""		,					help="""If passed, will train on the CPU."""     )
			# New Code #
			parser.add_argument("""--num_folds"""		,					type=snake_case__		,					default=3		,					help="""The number of splits to perform across the dataset"""     )
			SCREAMING_SNAKE_CASE__										=   parser.parse_args()
			SCREAMING_SNAKE_CASE__										=   {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(snake_case__		,					snake_case__     )


if __name__ == "__main__":
						main()





"""simple docstring"""




import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


A_				:      List[str]           =			pytest.mark.integration





@require_faiss
class  lowerCamelCase    (A__      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(__UpperCAmelCase							) for x in np.arange(3_0							).tolist()]}							)
						return dset

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Tuple:
						import faiss

						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						SCREAMING_SNAKE_CASE__										=   dset.map(
						    lambda __UpperCAmelCase     ,					__UpperCAmelCase							: {"vecs": i * np.ones(5     ,					dtype=np.floataa							)}     ,					with_indices=__UpperCAmelCase     ,					keep_in_memory=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   dset.add_faiss_index("""vecs"""     ,					batch_size=1_0_0     ,					metric_type=faiss.METRIC_INNER_PRODUCT							)
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   dset.get_nearest_examples("""vecs"""     ,					np.ones(5     ,					dtype=np.floataa							)							)
						self.assertEqual(examples["""filename"""][0]     ,					"""my_name-train_29"""							)
						dset.drop_index("""vecs"""							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						import faiss

						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)							) * np.arange(3_0							).reshape(-1     ,					1							)     ,					index_name="""vecs"""     ,					batch_size=1_0_0     ,					metric_type=faiss.METRIC_INNER_PRODUCT     ,					)
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   dset.get_nearest_examples("""vecs"""     ,					np.ones(5     ,					dtype=np.floataa							)							)
						self.assertEqual(examples["""filename"""][0]     ,					"""my_name-train_29"""							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Any:
						import faiss

						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)							) * np.arange(3_0							).reshape(-1     ,					1							)     ,					index_name="""vecs"""     ,					metric_type=faiss.METRIC_INNER_PRODUCT     ,					)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase							) as tmp_file:
									dset.save_faiss_index("""vecs"""     ,					tmp_file.name							)
									dset.load_faiss_index("""vecs2"""     ,					tmp_file.name							)
						os.unlink(tmp_file.name							)

						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   dset.get_nearest_examples("""vecs2"""     ,					np.ones(5     ,					dtype=np.floataa							)							)
						self.assertEqual(examples["""filename"""][0]     ,					"""my_name-train_29"""							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)							) * np.arange(3_0							).reshape(-1     ,					1							)     ,					index_name="""vecs"""							)
						dset.drop_index("""vecs"""							)
						self.assertRaises(__UpperCAmelCase     ,					partial(dset.get_nearest_examples     ,					"""vecs2"""     ,					np.ones(5     ,					dtype=np.floataa							)							)							)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						from elasticsearch import Elasticsearch

						SCREAMING_SNAKE_CASE__										=   self._create_dummy_dataset()
						with patch("""elasticsearch.Elasticsearch.search"""							) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""							) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""							) as mocked_bulk:
									SCREAMING_SNAKE_CASE__										=   {"""acknowledged""": True}
									mocked_bulk.return_value([(True, None)] * 3_0							)
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 2_9}]}}
									SCREAMING_SNAKE_CASE__										=   Elasticsearch()

									dset.add_elasticsearch_index("""filename"""     ,					es_client=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   dset.get_nearest_examples("""filename"""     ,					"""my_name-train_29"""							)
									self.assertEqual(examples["""filename"""][0]     ,					"""my_name-train_29"""							)





@require_faiss
class  lowerCamelCase    (A__      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     List[str]:
						import faiss

						SCREAMING_SNAKE_CASE__										=   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT							)

						# add vectors
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)
						self.assertIsNotNone(index.faiss_index							)
						self.assertEqual(index.faiss_index.ntotal     ,					5							)
						index.add_vectors(np.zeros((5, 5)     ,					dtype=np.floataa							)							)
						self.assertEqual(index.faiss_index.ntotal     ,					1_0							)

						# single query
						SCREAMING_SNAKE_CASE__										=   np.zeros(5     ,					dtype=np.floataa							)
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(__UpperCAmelCase							)
						self.assertRaises(__UpperCAmelCase     ,					index.search     ,					query.reshape(-1     ,					1							)							)
						self.assertGreater(scores[0]     ,					0							)
						self.assertEqual(indices[0]     ,					1							)

						# batched queries
						SCREAMING_SNAKE_CASE__										=   np.eye(5     ,					dtype=np.floataa							)[::-1]
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search_batch(__UpperCAmelCase							)
						self.assertRaises(__UpperCAmelCase     ,					index.search_batch     ,					queries[0]							)
						SCREAMING_SNAKE_CASE__										=   [scores[0] for scores in total_scores]
						SCREAMING_SNAKE_CASE__										=   [indices[0] for indices in total_indices]
						self.assertGreater(np.min(__UpperCAmelCase							)     ,					0							)
						self.assertListEqual([4, 3, 2, 1, 0]     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						import faiss

						SCREAMING_SNAKE_CASE__										=   FaissIndex(string_factory="""Flat"""							)
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)
						self.assertIsInstance(index.faiss_index     ,					faiss.IndexFlat							)
						SCREAMING_SNAKE_CASE__										=   FaissIndex(string_factory="""LSH"""							)
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)
						self.assertIsInstance(index.faiss_index     ,					faiss.IndexLSH							)
						with self.assertRaises(__UpperCAmelCase							):
									SCREAMING_SNAKE_CASE__										=   FaissIndex(string_factory="""Flat"""     ,					custom_index=faiss.IndexFlat(5							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						import faiss

						SCREAMING_SNAKE_CASE__										=   faiss.IndexFlat(5							)
						SCREAMING_SNAKE_CASE__										=   FaissIndex(custom_index=__UpperCAmelCase							)
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)
						self.assertIsInstance(index.faiss_index     ,					faiss.IndexFlat							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						import faiss

						SCREAMING_SNAKE_CASE__										=   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT							)
						index.add_vectors(np.eye(5     ,					dtype=np.floataa							)							)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase							) as tmp_file:
									index.save(tmp_file.name							)
									SCREAMING_SNAKE_CASE__										=   FaissIndex.load(tmp_file.name							)
						os.unlink(tmp_file.name							)

						SCREAMING_SNAKE_CASE__										=   np.zeros(5     ,					dtype=np.floataa							)
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(__UpperCAmelCase							)
						self.assertGreater(scores[0]     ,					0							)
						self.assertEqual(indices[0]     ,					1							)

@require_faiss
def 						A							(  snake_case__     ):




			'''simple docstring'''


			import faiss

			SCREAMING_SNAKE_CASE__										=   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )
			index.add_vectors(np.eye(5		,					dtype=np.floataa     )     )

			SCREAMING_SNAKE_CASE__										=   """index.faiss"""
			SCREAMING_SNAKE_CASE__										=   f"""mock://{index_name}"""
			index.save(snake_case__		,					storage_options=mockfs.storage_options     )
			SCREAMING_SNAKE_CASE__										=   FaissIndex.load(snake_case__		,					storage_options=mockfs.storage_options     )

			SCREAMING_SNAKE_CASE__										=   np.zeros(5		,					dtype=np.floataa     )
			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(snake_case__     )
			assert scores[0] > 0
			assert indices[0] == 1





@require_elasticsearch
class  lowerCamelCase    (A__      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Dict:
						from elasticsearch import Elasticsearch

						with patch("""elasticsearch.Elasticsearch.search"""							) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""							) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""							) as mocked_bulk:
									SCREAMING_SNAKE_CASE__										=   Elasticsearch()
									SCREAMING_SNAKE_CASE__										=   {"""acknowledged""": True}
									SCREAMING_SNAKE_CASE__										=   ElasticSearchIndex(es_client=__UpperCAmelCase							)
									mocked_bulk.return_value([(True, None)] * 3							)
									index.add_documents(["""foo""", """bar""", """foobar"""]							)

									# single query
									SCREAMING_SNAKE_CASE__										=   """foo"""
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(__UpperCAmelCase							)
									self.assertEqual(scores[0]     ,					1							)
									self.assertEqual(indices[0]     ,					0							)

									# single query with timeout
									SCREAMING_SNAKE_CASE__										=   """foo"""
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search(__UpperCAmelCase     ,					request_timeout=3_0							)
									self.assertEqual(scores[0]     ,					1							)
									self.assertEqual(indices[0]     ,					0							)

									# batched queries
									SCREAMING_SNAKE_CASE__										=   ["""foo""", """bar""", """foobar"""]
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search_batch(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   [scores[0] for scores in total_scores]
									SCREAMING_SNAKE_CASE__										=   [indices[0] for indices in total_indices]
									self.assertGreater(np.min(__UpperCAmelCase							)     ,					0							)
									self.assertListEqual([1, 1, 1]     ,					__UpperCAmelCase							)

									# batched queries with timeout
									SCREAMING_SNAKE_CASE__										=   ["""foo""", """bar""", """foobar"""]
									SCREAMING_SNAKE_CASE__										=   {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   index.search_batch(__UpperCAmelCase     ,					request_timeout=3_0							)
									SCREAMING_SNAKE_CASE__										=   [scores[0] for scores in total_scores]
									SCREAMING_SNAKE_CASE__										=   [indices[0] for indices in total_indices]
									self.assertGreater(np.min(__UpperCAmelCase							)     ,					0							)
									self.assertListEqual([1, 1, 1]     ,					__UpperCAmelCase							)


