




'''simple docstring'''



import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
      import tensorflow as tf
      from tensorflow.python.framework.errors_impl import ResourceExhaustedError

      from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
      import pyanvml.pyanvml as nvml

UpperCamelCase_    	=				logging.get_logger(__name__)




def   lowercase__(			__UpperCamelCase:					bool      ,__UpperCamelCase:					bool    ):

      """simple docstring"""






      def run_func(__UpperCamelCase:					Dict    ):
            @wraps(__UpperCamelCase    )
            def run_in_eager_mode(*__UpperCamelCase:					List[str]      ,**__UpperCamelCase:					Union[str, Any]    ):
                  return func(*__UpperCamelCase      ,**__UpperCamelCase    )

            @wraps(__UpperCamelCase    )
            @tf.function(experimental_compile=__UpperCamelCase    )
            def run_in_graph_mode(*__UpperCamelCase:					str      ,**__UpperCamelCase:					Union[str, Any]    ):
                  return func(*__UpperCamelCase      ,**__UpperCamelCase    )

            if do_eager_mode is True:
                  if use_xla is not False:
                        raise ValueError(
                            'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'    )
                  return run_in_eager_mode
            else:
                  return run_in_graph_mode

      return run_func




def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					int      ,__UpperCamelCase:					int    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						random.Random()
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[rng.randint(0      ,vocab_size - 1    ) for i in range(batch_size * sequence_length    )]
      return tf.constant(__UpperCamelCase      ,shape=(batch_size, sequence_length)      ,dtype=tf.intaa    )






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				TensorFlowBenchmarkArguments
       A						:				PretrainedConfig
       A						:				str        =						"TensorFlow"







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return tf.__version__







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.args.strategy
             if strategy is None:
                   raise ValueError('A device strategy has to be initialized before using TensorFlow.'  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self._prepare_inference_func(A,							A,							A  )
             return self._measure_speed(_inference  )







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.args.strategy
             if strategy is None:
                   raise ValueError('A device strategy has to be initialized before using TensorFlow.'  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self._prepare_train_func(A,							A,							A  )
             return self._measure_speed(_train  )







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             if self.args.is_gpu:
                   tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx],							A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.args.strategy
             if strategy is None:
                   raise ValueError('A device strategy has to be initialized before using TensorFlow.'  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						self._prepare_inference_func(A,							A,							A  )
             return self._measure_memory(_inference  )







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             if self.args.is_gpu:
                   tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx],							A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.args.strategy
             if strategy is None:
                   raise ValueError('A device strategy has to be initialized before using TensorFlow.'  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self._prepare_train_func(A,							A,							A  )
             return self._measure_memory(_train  )







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.config_dict[model_name]

             if self.args.fpaa:
                   raise NotImplementedError('Mixed precision is currently not supported.'  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						(
                 hasattr(A,							'architectures'  )
                 and isinstance(config.architectures,							A  )
                 and len(config.architectures  ) > 0
             )
             if not self.args.only_pretrain_model and has_model_class_in_config:
                   try:
                         SCREAMING_SNAKE_CASE   :      int    					=						'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                         SCREAMING_SNAKE_CASE   :      int    					=						__import__('transformers',							fromlist=[model_class]  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						getattr(A,							A  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						model_cls(A  )
                   except ImportError:
                         raise ImportError(
                             F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                             ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'  )
             else:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						TF_MODEL_MAPPING[config.__class__](A  )

             # encoder-decoder has vocab size saved differently
             SCREAMING_SNAKE_CASE   :      str    					=						config.vocab_size if hasattr(A,							'vocab_size'  ) else config.encoder.vocab_size
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						random_input_ids(A,							A,							A  )

             @run_with_tf_optimizations(self.args.eager_mode,							self.args.use_xla  )
             def encoder_decoder_forward():
                   return model(A,							decoder_input_ids=A,							training=A  )

             @run_with_tf_optimizations(self.args.eager_mode,							self.args.use_xla  )
             def encoder_forward():
                   return model(A,							training=A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

             return _inference







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.config_dict[model_name]

             if self.args.eager_mode is not False:
                   raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'  )

             if self.args.fpaa:
                   raise NotImplementedError('Mixed precision is currently not supported.'  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						(
                 hasattr(A,							'architectures'  )
                 and isinstance(config.architectures,							A  )
                 and len(config.architectures  ) > 0
             )
             if not self.args.only_pretrain_model and has_model_class_in_config:
                   try:
                         SCREAMING_SNAKE_CASE   :      str    					=						'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						__import__('transformers',							fromlist=[model_class]  )
                         SCREAMING_SNAKE_CASE   :      Dict    					=						getattr(A,							A  )
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model_cls(A  )
                   except ImportError:
                         raise ImportError(
                             F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                             ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A  )

             # encoder-decoder has vocab size saved differently
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						config.vocab_size if hasattr(A,							'vocab_size'  ) else config.encoder.vocab_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						random_input_ids(A,							A,							A  )

             @run_with_tf_optimizations(self.args.eager_mode,							self.args.use_xla  )
             def encoder_decoder_train():
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						model(A,							decoder_input_ids=A,							labels=A,							training=A  )[0]
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tf.gradients(A,							model.trainable_variables  )
                   return gradients

             @run_with_tf_optimizations(self.args.eager_mode,							self.args.use_xla  )
             def encoder_train():
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model(A,							labels=A,							training=A  )[0]
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tf.gradients(A,							model.trainable_variables  )
                   return gradients

             SCREAMING_SNAKE_CASE   :      int    					=						encoder_decoder_train if config.is_encoder_decoder else encoder_train

             return _train







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             with self.args.strategy.scope():
                   try:
                         if self.args.is_tpu or self.args.use_xla:
                               # run additional 10 times to stabilize compilation for tpu
                               logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'  )
                               timeit.repeat(A,							repeat=1,							number=5  )

                         # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						timeit.repeat(
                             A,							repeat=self.args.repeat,							number=10,							)

                         return min(A  ) / 10.0
                   except ResourceExhaustedError as e:
                         self.print_fn(F"Doesn't fit on GPU. {e}"  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             logger.info(
                 'Note that TensorFlow allocates more memory than '
                 'it might need to speed up computation. '
                 'The memory reported here corresponds to the memory '
                 'reported by `nvidia-smi`, which can vary depending '
                 'on total available memory on the GPU that is used.'  )
             with self.args.strategy.scope():
                   try:
                         if self.args.trace_memory_line_by_line:
                               if not self.args.eager_mode:
                                     raise ValueError(
                                         '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
                                         ' consumption line by line.'  )
                               SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						start_memory_tracing('transformers'  )

                         if self.args.is_tpu:
                               # tpu
                               raise NotImplementedError(
                                   'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
                                   ' with `args.memory=False`'  )
                         elif self.args.is_gpu:
                               # gpu
                               if not is_pyanvml_available():
                                     logger.warning(
                                         'py3nvml not installed, we won\'t log GPU memory usage. '
                                         'Install py3nvml (pip install py3nvml) to log information about GPU.'  )
                                     SCREAMING_SNAKE_CASE   :      str    					=						'N/A'
                               else:
                                     logger.info(
                                         'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
                                         ' running on the same GPU.'  )
                                     # init nvml
                                     nvml.nvmlInit()
                                     func()
                                     SCREAMING_SNAKE_CASE   :      Any    					=						nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx  )
                                     SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						nvml.nvmlDeviceGetMemoryInfo(A  )
                                     SCREAMING_SNAKE_CASE   :      List[str]    					=						meminfo.used
                                     SCREAMING_SNAKE_CASE   :      List[str]    					=						Memory(A  )
                                     # shutdown nvml
                                     nvml.nvmlShutdown()
                         else:
                               # cpu
                               if self.args.trace_memory_line_by_line:
                                     logger.info(
                                         'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
                                         ' TensorFlow.'  )
                                     SCREAMING_SNAKE_CASE   :      str    					=						None
                               else:
                                     SCREAMING_SNAKE_CASE   :      Tuple    					=						measure_peak_memory_cpu(A  )
                                     SCREAMING_SNAKE_CASE   :      Any    					=						Memory(A  ) if isinstance(A,							A  ) else memory_bytes
                         if self.args.trace_memory_line_by_line:
                               SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						stop_memory_tracing(A  )
                               if memory is None:
                                     SCREAMING_SNAKE_CASE   :      int    					=						summary.total
                         else:
                               SCREAMING_SNAKE_CASE   :      int    					=						None

                         return memory, summary
                   except ResourceExhaustedError as e:
                         self.print_fn(F"Doesn't fit on GPU. {e}"  )
                         return "N/A", None










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


_UpperCamelCase			:	Optional[Any]								=pytest.mark.integration






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						_A	:				Any   =      Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(A__		) for x in np.arange(30		).tolist()]}		)
						return dset







		def  A__      (					self		):
						import faiss

						_A	:				Dataset   =      self._create_dummy_dataset()
						_A	:				List[str]   =      dset.map(
						    lambda A__       ,A__		: {"vecs": i * np.ones(5       ,dtype=np.floataa		)}       ,with_indices=A__       ,keep_in_memory=A__		)
						_A	:				Tuple   =      dset.add_faiss_index('''vecs'''       ,batch_size=100       ,metric_type=faiss.METRIC_INNER_PRODUCT		)
						_A  ,				_A	:				List[Any]   =      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  A__      (					self		):
						import faiss

						_A	:				Dataset   =      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1       ,1		)       ,index_name='''vecs'''       ,batch_size=100       ,metric_type=faiss.METRIC_INNER_PRODUCT       ,)
						_A  ,				_A	:				Tuple   =      dset.get_nearest_examples('''vecs'''       ,np.ones(5       ,dtype=np.floataa		)		)
						self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)







		def  A__      (					self		):
						import faiss

						_A	:				Dataset   =      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)		) * np.arange(30		).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=A__		) as tmp_file:
										dset.save_faiss_index('''vecs'''       ,tmp_file.name		)
										dset.load_faiss_index('''vecs2'''       ,tmp_file.name		)
						os.unlink(tmp_file.name		)

						_A  ,				_A	:				Optional[int]   =      dset.get_nearest_examples('''vecs2'''       ,np.ones(5       ,dtype=np.floataa		)		)
						self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)







		def  A__      (					self		):
						_A	:				Dataset   =      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1       ,1		)       ,index_name='''vecs'''		)
						dset.drop_index('''vecs'''		)
						self.assertRaises(A__       ,partial(dset.get_nearest_examples       ,'''vecs2'''       ,np.ones(5       ,dtype=np.floataa		)		)		)







		def  A__      (					self		):
						from elasticsearch import Elasticsearch

						_A	:				Dataset   =      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:
										_A	:				str   =      {'''acknowledged''': True}
										mocked_bulk.return_value([(True, None)] * 30		)
										_A	:				Optional[Any]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
										_A	:				Optional[Any]   =      Elasticsearch()

										dset.add_elasticsearch_index('''filename'''       ,es_client=A__		)
										_A  ,				_A	:				Tuple   =      dset.get_nearest_examples('''filename'''       ,'''my_name-train_29'''		)
										self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						import faiss

						_A	:				Union[str, Any]   =      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       ,10		)

						# single query
						_A	:				str   =      np.zeros(5       ,dtype=np.floataa		)
						_A	:				Dict   =      1
						_A  ,				_A	:				Optional[int]   =      index.search(A__		)
						self.assertRaises(A__       ,index.search       ,query.reshape(-1       ,1		)		)
						self.assertGreater(scores[0]       ,0		)
						self.assertEqual(indices[0]       ,1		)

						# batched queries
						_A	:				Optional[Any]   =      np.eye(5       ,dtype=np.floataa		)[::-1]
						_A  ,				_A	:				Dict   =      index.search_batch(A__		)
						self.assertRaises(A__       ,index.search_batch       ,queries[0]		)
						_A	:				Tuple   =      [scores[0] for scores in total_scores]
						_A	:				List[str]   =      [indices[0] for indices in total_indices]
						self.assertGreater(np.min(A__		)       ,0		)
						self.assertListEqual([4, 3, 2, 1, 0]       ,A__		)







		def  A__      (					self		):
						import faiss

						_A	:				List[Any]   =      FaissIndex(string_factory='''Flat'''		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsInstance(index.faiss_index       ,faiss.IndexFlat		)
						_A	:				str   =      FaissIndex(string_factory='''LSH'''		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsInstance(index.faiss_index       ,faiss.IndexLSH		)
						with self.assertRaises(A__		):
										_A	:				List[str]   =      FaissIndex(string_factory='''Flat'''       ,custom_index=faiss.IndexFlat(5		)		)







		def  A__      (					self		):
						import faiss

						_A	:				Any   =      faiss.IndexFlat(5		)
						_A	:				Any   =      FaissIndex(custom_index=A__		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsInstance(index.faiss_index       ,faiss.IndexFlat		)







		def  A__      (					self		):
						import faiss

						_A	:				Optional[int]   =      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=A__		) as tmp_file:
										index.save(tmp_file.name		)
										_A	:				str   =      FaissIndex.load(tmp_file.name		)
						os.unlink(tmp_file.name		)

						_A	:				Tuple   =      np.zeros(5       ,dtype=np.floataa		)
						_A	:				Tuple   =      1
						_A  ,				_A	:				Union[str, Any]   =      index.search(A__		)
						self.assertGreater(scores[0]       ,0		)
						self.assertEqual(indices[0]       ,1		)



@require_faiss
def  a__ (__lowercase							:List[Any]   )		-> int:
				import faiss

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

				_A	:				List[Any]   =      '''index.faiss'''
				_A	:				Any   =      f"""mock://{index_name}"""
				index.save(__lowercase					,  storage_options=mockfs.storage_options   )
				_A	:				Dict   =      FaissIndex.load(__lowercase					,  storage_options=mockfs.storage_options   )

				_A	:				Tuple   =      np.zeros(5					,  dtype=np.floataa   )
				_A	:				Union[str, Any]   =      1
				_A  ,				_A	:				Optional[int]   =      index.search(__lowercase   )
				assert scores[0] > 0
				assert indices[0] == 1






@require_elasticsearch
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						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:
										_A	:				Tuple   =      Elasticsearch()
										_A	:				Tuple   =      {'''acknowledged''': True}
										_A	:				int   =      ElasticSearchIndex(es_client=A__		)
										mocked_bulk.return_value([(True, None)] * 3		)
										index.add_documents(['''foo''', '''bar''', '''foobar''']		)

										# single query
										_A	:				Dict   =      '''foo'''
										_A	:				Optional[Any]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
										_A  ,				_A	:				Optional[Any]   =      index.search(A__		)
										self.assertEqual(scores[0]       ,1		)
										self.assertEqual(indices[0]       ,0		)

										# single query with timeout
										_A	:				Union[str, Any]   =      '''foo'''
										_A	:				Optional[int]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
										_A  ,				_A	:				Optional[int]   =      index.search(A__       ,request_timeout=30		)
										self.assertEqual(scores[0]       ,1		)
										self.assertEqual(indices[0]       ,0		)

										# batched queries
										_A	:				Dict   =      ['''foo''', '''bar''', '''foobar''']
										_A	:				Dict   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
										_A  ,				_A	:				Dict   =      index.search_batch(A__		)
										_A	:				Union[str, Any]   =      [scores[0] for scores in total_scores]
										_A	:				Tuple   =      [indices[0] for indices in total_indices]
										self.assertGreater(np.min(A__		)       ,0		)
										self.assertListEqual([1, 1, 1]       ,A__		)

										# batched queries with timeout
										_A	:				Union[str, Any]   =      ['''foo''', '''bar''', '''foobar''']
										_A	:				int   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
										_A  ,				_A	:				str   =      index.search_batch(A__       ,request_timeout=30		)
										_A	:				Dict   =      [scores[0] for scores in total_scores]
										_A	:				List[str]   =      [indices[0] for indices in total_indices]
										self.assertGreater(np.min(A__		)       ,0		)
										self.assertListEqual([1, 1, 1]       ,A__		)



