
"""simple docstring"""

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFDeiTForImageClassification,
					    TFDeiTForImageClassificationWithTeacher,
					    TFDeiTForMaskedImageModeling,
					    TFDeiTModel,
					)
					from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor






class 	_lowercase       :




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_=13		,			UpperCAmelCase_=30		,			UpperCAmelCase_=2		,			UpperCAmelCase_=3		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			UpperCAmelCase_=32		,			UpperCAmelCase_=2		,			UpperCAmelCase_=4		,			UpperCAmelCase_=37		,			UpperCAmelCase_="gelu"		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=10		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=3		,			UpperCAmelCase_=None		,			UpperCAmelCase_=2		,			)		->							Union[str, Any]:
			lowerCamelCase					:      Tuple          =   parent
			lowerCamelCase					:      int          =   batch_size
			lowerCamelCase					:      Any          =   image_size
			lowerCamelCase					:      List[Any]          =   patch_size
			lowerCamelCase					:      Any          =   num_channels
			lowerCamelCase					:      List[Any]          =   is_training
			lowerCamelCase					:      List[str]          =   use_labels
			lowerCamelCase					:      Union[str, Any]          =   hidden_size
			lowerCamelCase					:      List[Any]          =   num_hidden_layers
			lowerCamelCase					:      Dict          =   num_attention_heads
			lowerCamelCase					:      Dict          =   intermediate_size
			lowerCamelCase					:      Optional[Any]          =   hidden_act
			lowerCamelCase					:      Tuple          =   hidden_dropout_prob
			lowerCamelCase					:      List[Any]          =   attention_probs_dropout_prob
			lowerCamelCase					:      List[str]          =   type_sequence_label_size
			lowerCamelCase					:      List[Any]          =   initializer_range
			lowerCamelCase					:      Dict          =   scope
			lowerCamelCase					:      Any          =   encoder_stride

			# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
			lowerCamelCase					:      Union[str, Any]          =   (image_size // patch_size) ** 2
			lowerCamelCase					:      Dict          =   num_patches + 2




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase					:      Optional[int]          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

			lowerCamelCase					:      str          =   None
			if self.use_labels:
					lowerCamelCase					:      Tuple          =   ids_tensor([self.batch_size]		,			self.type_sequence_label_size )

			lowerCamelCase					:      Union[str, Any]          =   self.get_config()

			return config, pixel_values, labels




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			return DeiTConfig(
			    image_size=self.image_size		,			patch_size=self.patch_size		,			num_channels=self.num_channels		,			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		,			is_decoder=UpperCAmelCase_		,			initializer_range=self.initializer_range		,			encoder_stride=self.encoder_stride		,			)




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			lowerCamelCase					:      Optional[Any]          =   TFDeiTModel(config=UpperCAmelCase_ )
			lowerCamelCase					:      Union[str, Any]          =   model(UpperCAmelCase_ )
			self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size) )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      List[Any]          =   TFDeiTForMaskedImageModeling(config=UpperCAmelCase_ )
			lowerCamelCase					:      Union[str, Any]          =   model(UpperCAmelCase_ )
			self.parent.assertEqual(
			    result.reconstruction.shape		,			(self.batch_size, self.num_channels, self.image_size, self.image_size) )

			# test greyscale images
			lowerCamelCase					:      Union[str, Any]          =   1
			lowerCamelCase					:      str          =   TFDeiTForMaskedImageModeling(UpperCAmelCase_ )

			lowerCamelCase					:      Dict          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
			lowerCamelCase					:      int          =   model(UpperCAmelCase_ )
			self.parent.assertEqual(result.reconstruction.shape		,			(self.batch_size, 1, self.image_size, self.image_size) )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      Optional[Any]          =   self.type_sequence_label_size
			lowerCamelCase					:      Tuple          =   TFDeiTForImageClassification(UpperCAmelCase_ )
			lowerCamelCase					:      Optional[Any]          =   model(UpperCAmelCase_		,			labels=UpperCAmelCase_ )
			self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size) )

			# test greyscale images
			lowerCamelCase					:      List[str]          =   1
			lowerCamelCase					:      List[str]          =   TFDeiTForImageClassification(UpperCAmelCase_ )

			lowerCamelCase					:      Optional[Any]          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
			lowerCamelCase					:      Union[str, Any]          =   model(UpperCAmelCase_		,			labels=UpperCAmelCase_ )
			self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size) )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      Optional[int]          =   self.prepare_config_and_inputs()
			lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   config_and_inputs
			lowerCamelCase					:      int          =   {'pixel_values': pixel_values}
			return config, inputs_dict






@require_tf
class 	_lowercase       (    __UpperCAmelCase    ,	__UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     (
	    (
	        TFDeiTModel,
	        TFDeiTForImageClassification,
	        TFDeiTForImageClassificationWithTeacher,
	        TFDeiTForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	lowercase_				=     (
	    {
	        'feature-extraction': TFDeiTModel,
	        'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
	    }
	    if is_tf_available()
	    else {}
	)

	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      Dict          =   TFDeiTModelTester(self )
			lowerCamelCase					:      Optional[int]          =   ConfigTester(self		,			config_class=UpperCAmelCase_		,			has_text_modality=UpperCAmelCase_		,			hidden_size=37 )




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			self.config_tester.run_common_tests()




	@unittest.skip(reason='DeiT does not use inputs_embeds' )
	def       _UpperCamelCase   (			self )		->							Tuple:
			pass




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCamelCase					:      Any          =   model_class(UpperCAmelCase_ )
					self.assertIsInstance(model.get_input_embeddings()		,			(tf.keras.layers.Layer) )
					lowerCamelCase					:      List[str]          =   model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(UpperCAmelCase_		,			tf.keras.layers.Dense ) )




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCamelCase					:      Optional[int]          =   model_class(UpperCAmelCase_ )
					lowerCamelCase					:      Optional[int]          =   inspect.signature(model.call )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCamelCase					:      Dict          =   [*signature.parameters.keys()]

					lowerCamelCase					:      List[str]          =   ['pixel_values']
					self.assertListEqual(arg_names[:1]		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase					:      List[str]          =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Dict:
			lowerCamelCase					:      Dict          =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=False )		->							List[Any]:
			lowerCamelCase					:      Union[str, Any]          =   super()._prepare_for_class(UpperCAmelCase_		,			UpperCAmelCase_		,			return_labels=UpperCAmelCase_ )

			if return_labels:
					if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
							del inputs_dict["labels"]

			return inputs_dict




	@slow
	def       _UpperCamelCase   (			self )		->							List[Any]:
			for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase					:      Dict          =   TFDeiTModel.from_pretrained(UpperCAmelCase_ )
					self.assertIsNotNone(UpperCAmelCase_ )


def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Union[str, Any]          =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
		return image






@require_tf
@require_vision
class 	_lowercase       (    unittest.TestCase		):




	@cached_property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			return (
			    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
			    if is_vision_available()
			    else None
			)




	@slow
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      Any          =   TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' )

			lowerCamelCase					:      List[str]          =   self.default_image_processor
			lowerCamelCase					:      Dict          =   prepare_img()
			lowerCamelCase					:      Any          =   image_processor(images=UpperCAmelCase_		,			return_tensors='tf' )

			# forward pass
			lowerCamelCase					:      List[str]          =   model(**UpperCAmelCase_ )

			# verify the logits
			lowerCamelCase					:      Dict          =   tf.TensorShape((1, 1000) )
			self.assertEqual(outputs.logits.shape		,			UpperCAmelCase_ )

			lowerCamelCase					:      str          =   tf.constant([-1.0266, 0.1912, -1.2861] )

			self.assertTrue(np.allclose(outputs.logits[0, :3]		,			UpperCAmelCase_		,			atol=1E-4 ) )








"""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						=			pytest.mark.integration






@require_faiss
class 	_lowercase       (    __UpperCAmelCase		):




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Any          =   Dataset.from_dict({'filename': ['my_name-train' + '_' + str(UpperCAmelCase_ ) for x in np.arange(30 ).tolist()]} )
			return dset




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

			lowerCamelCase					:      Dataset          =   self._create_dummy_dataset()
			lowerCamelCase					:      Optional[int]          =   dset.map(
			    lambda UpperCAmelCase_		,			UpperCAmelCase_ : {"vecs": i * np.ones(5		,			dtype=np.floataa )}		,			with_indices=UpperCAmelCase_		,			keep_in_memory=UpperCAmelCase_ )
			lowerCamelCase					:      Dict          =   dset.add_faiss_index('vecs'		,			batch_size=100		,			metric_type=faiss.METRIC_INNER_PRODUCT )
			lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   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       _UpperCamelCase   (			self )		->							Tuple:
			import faiss

			lowerCamelCase					:      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		,			)
			lowerCamelCase     ,   lowerCamelCase					:      str          =   dset.get_nearest_examples('vecs'		,			np.ones(5		,			dtype=np.floataa ) )
			self.assertEqual(examples['filename'][0]		,			'my_name-train_29' )




	def       _UpperCamelCase   (			self )		->							int:
			import faiss

			lowerCamelCase					:      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=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 )

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




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      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(UpperCAmelCase_		,			partial(dset.get_nearest_examples		,			'vecs2'		,			np.ones(5		,			dtype=np.floataa ) ) )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			from elasticsearch import Elasticsearch

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

					dset.add_elasticsearch_index('filename'		,			es_client=UpperCAmelCase_ )
					lowerCamelCase     ,   lowerCamelCase					:      str          =   dset.get_nearest_examples('filename'		,			'my_name-train_29' )
					self.assertEqual(examples['filename'][0]		,			'my_name-train_29' )






@require_faiss
class 	_lowercase       (    __UpperCAmelCase		):




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

			lowerCamelCase					:      str          =   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
			lowerCamelCase					:      Optional[int]          =   np.zeros(5		,			dtype=np.floataa )
			lowerCamelCase					:      List[str]          =   1
			lowerCamelCase     ,   lowerCamelCase					:      int          =   index.search(UpperCAmelCase_ )
			self.assertRaises(UpperCAmelCase_		,			index.search		,			query.reshape(-1		,			1 ) )
			self.assertGreater(scores[0]		,			0 )
			self.assertEqual(indices[0]		,			1 )

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




	def       _UpperCamelCase   (			self )		->							Dict:
			import faiss

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




	def       _UpperCamelCase   (			self )		->							Any:
			import faiss

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




	def       _UpperCamelCase   (			self )		->							Any:
			import faiss

			lowerCamelCase					:      Any          =   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 )
					lowerCamelCase					:      List[str]          =   FaissIndex.load(tmp_file.name )
			os.unlink(tmp_file.name )

			lowerCamelCase					:      Dict          =   np.zeros(5		,			dtype=np.floataa )
			lowerCamelCase					:      Optional[Any]          =   1
			lowerCamelCase     ,   lowerCamelCase					:      str          =   index.search(UpperCAmelCase_ )
			self.assertGreater(scores[0]		,			0 )
			self.assertEqual(indices[0]		,			1 )


@require_faiss
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		import faiss

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

		lowerCamelCase					:      Union[str, Any]          =   'index.faiss'
		lowerCamelCase					:      List[Any]          =   F"""mock://{index_name}"""
		index.save(a_,						storage_options=mockfs.storage_options      )
		lowerCamelCase					:      Optional[int]          =   FaissIndex.load(a_,						storage_options=mockfs.storage_options      )

		lowerCamelCase					:      str          =   np.zeros(5,						dtype=np.floataa      )
		lowerCamelCase					:      str          =   1
		lowerCamelCase     ,   lowerCamelCase					:      int          =   index.search(a_      )
		assert scores[0] > 0
		assert indices[0] == 1






@require_elasticsearch
class 	_lowercase       (    __UpperCAmelCase		):




	def       _UpperCamelCase   (			self )		->							int:
			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:
					lowerCamelCase					:      Union[str, Any]          =   Elasticsearch()
					lowerCamelCase					:      Optional[Any]          =   {'acknowledged': True}
					lowerCamelCase					:      str          =   ElasticSearchIndex(es_client=UpperCAmelCase_ )
					mocked_bulk.return_value([(True, None)] * 3 )
					index.add_documents(['foo', 'bar', 'foobar'] )

					# single query
					lowerCamelCase					:      Tuple          =   'foo'
					lowerCamelCase					:      List[str]          =   {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
					lowerCamelCase     ,   lowerCamelCase					:      Any          =   index.search(UpperCAmelCase_ )
					self.assertEqual(scores[0]		,			1 )
					self.assertEqual(indices[0]		,			0 )

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

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

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






