






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
    import tensorflow as tf

    from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class    _UpperCAmelCase			:




   """simple docstring"""

   snake_case    				=						PegasusConfig
   snake_case    				=						{}
   snake_case    				=						'''gelu'''

   def __init__(		self :	Tuple      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Optional[Any]=13      ,      __UpperCAmelCase :	int=7      ,      __UpperCAmelCase :	List[str]=True      ,      __UpperCAmelCase :	str=False      ,      __UpperCAmelCase :	Union[str, Any]=99      ,      __UpperCAmelCase :	Tuple=32      ,      __UpperCAmelCase :	Tuple=2      ,      __UpperCAmelCase :	int=4      ,      __UpperCAmelCase :	Tuple=37      ,      __UpperCAmelCase :	Optional[int]=0.1      ,      __UpperCAmelCase :	Optional[int]=0.1      ,      __UpperCAmelCase :	List[str]=40      ,      __UpperCAmelCase :	Tuple=2      ,      __UpperCAmelCase :	Optional[int]=1      ,      __UpperCAmelCase :	Any=0      ,      ):





         '''simple docstring'''





         _A			     =	parent
         _A			     =	batch_size
         _A			     =	seq_length
         _A			     =	is_training
         _A			     =	use_labels
         _A			     =	vocab_size
         _A			     =	hidden_size
         _A			     =	num_hidden_layers
         _A			     =	num_attention_heads
         _A			     =	intermediate_size

         _A			     =	hidden_dropout_prob
         _A			     =	attention_probs_dropout_prob
         _A			     =	max_position_embeddings
         _A			     =	eos_token_id
         _A			     =	pad_token_id
         _A			     =	bos_token_id

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         _A			     =	ids_tensor([self.batch_size, self.seq_length - 1]      ,      self.vocab_size		)
         _A			     =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)      ,      1		)
         _A			     =	tf.concat([input_ids, eos_tensor]      ,      axis=1		)

         _A			     =	ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size		)

         _A			     =	self.config_cls(
             vocab_size=self.vocab_size      ,      d_model=self.hidden_size      ,      encoder_layers=self.num_hidden_layers      ,      decoder_layers=self.num_hidden_layers      ,      encoder_attention_heads=self.num_attention_heads      ,      decoder_attention_heads=self.num_attention_heads      ,      encoder_ffn_dim=self.intermediate_size      ,      decoder_ffn_dim=self.intermediate_size      ,      dropout=self.hidden_dropout_prob      ,      attention_dropout=self.attention_probs_dropout_prob      ,      max_position_embeddings=self.max_position_embeddings      ,      eos_token_ids=[2]      ,      bos_token_id=self.bos_token_id      ,      pad_token_id=self.pad_token_id      ,      decoder_start_token_id=self.pad_token_id      ,      **self.config_updates      ,      )
         _A			     =	prepare_pegasus_inputs_dict(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase		)
         return config, inputs_dict

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	Optional[int]      ,      __UpperCAmelCase :	int		):





         '''simple docstring'''





         _A			     =	TFPegasusModel(config=__UpperCAmelCase		).get_decoder()
         _A			     =	inputs_dict["input_ids"]

         _A			     =	input_ids[:1, :]
         _A			     =	inputs_dict["attention_mask"][:1, :]
         _A			     =	inputs_dict["head_mask"]
         _A			     =	1

         # first forward pass
         _A			     =	model(__UpperCAmelCase      ,      attention_mask=__UpperCAmelCase      ,      head_mask=__UpperCAmelCase      ,      use_cache=__UpperCAmelCase		)

         _A     ,	_A			     =	outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         _A			     =	ids_tensor((self.batch_size, 3)      ,      config.vocab_size		)
         _A			     =	tf.cast(ids_tensor((self.batch_size, 3)      ,      2		)      ,      tf.inta		)

         # append to next input_ids and
         _A			     =	tf.concat([input_ids, next_tokens]      ,      axis=-1		)
         _A			     =	tf.concat([attention_mask, next_attn_mask]      ,      axis=-1		)

         _A			     =	model(__UpperCAmelCase      ,      attention_mask=__UpperCAmelCase		)[0]
         _A			     =	model(__UpperCAmelCase      ,      attention_mask=__UpperCAmelCase      ,      past_key_values=__UpperCAmelCase		)[0]

         self.parent.assertEqual(next_tokens.shape[1]      ,      output_from_past.shape[1]		)

         # select random slice
         _A			     =	int(ids_tensor((1,)      ,      output_from_past.shape[-1]		)		)
         _A			     =	output_from_no_past[:, -3:, random_slice_idx]
         _A			     =	output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(__UpperCAmelCase      ,      __UpperCAmelCase      ,      rtol=1E-3		)





def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase=None   ,  __lowercase=None   ,  __lowercase=None   ,  __lowercase=None   ,  __lowercase=None   ,  )       ->						Union[str, Any]:

      '''simple docstring'''


      if attention_mask is None:
            _A			     =	tf.cast(tf.math.not_equal(__lowercase   ,  config.pad_token_id       )   ,  tf.inta       )
      if decoder_attention_mask is None:
            _A			     =	tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape   ,  dtype=tf.inta       ),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,  config.pad_token_id       )   ,  tf.inta       ),
                ]   ,  axis=-1   ,  )
      if head_mask is None:
            _A			     =	tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
      if decoder_head_mask is None:
            _A			     =	tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
      if cross_attn_head_mask is None:
            _A			     =	tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }

@require_tf
class    _UpperCAmelCase			(     snake_case_							,    snake_case_							,    unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
   snake_case    				=						(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
   snake_case    				=						(
       {
           '''conversational''': TFPegasusForConditionalGeneration,
           '''feature-extraction''': TFPegasusModel,
           '''summarization''': TFPegasusForConditionalGeneration,
           '''text2text-generation''': TFPegasusForConditionalGeneration,
           '''translation''': TFPegasusForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   snake_case    				=						True
   snake_case    				=						False
   snake_case    				=						False

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         _A			     =	TFPegasusModelTester(self		)
         _A			     =	ConfigTester(self      ,      config_class=__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         self.config_tester.run_common_tests()

   def 						lowerCAmelCase (		self :	Tuple		):





         '''simple docstring'''





         _A			     =	self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase		)





@require_sentencepiece
@require_tokenizers
@require_tf
class    _UpperCAmelCase			(     unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						[
       ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
       ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
   ]
   snake_case    				=						[
       '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
       ''' reduce the risk of wildfires.''',
       '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
   ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
   snake_case    				=						'''google/pegasus-xsum'''

   @cached_property
   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         return AutoTokenizer.from_pretrained(self.model_name		)

   @cached_property
   def 						lowerCAmelCase (		self :	Dict		):





         '''simple docstring'''





         _A			     =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
         return model

   def 						lowerCAmelCase (		self :	List[Any]      ,      **__UpperCAmelCase :	Union[str, Any]		):





         '''simple docstring'''





         _A			     =	self.translate_src_text(**__UpperCAmelCase		)
         assert self.expected_text == generated_words

   def 						lowerCAmelCase (		self :	Dict      ,      **__UpperCAmelCase :	Optional[int]		):





         '''simple docstring'''





         _A			     =	self.tokenizer(self.src_text      ,      **__UpperCAmelCase      ,      padding=__UpperCAmelCase      ,      return_tensors="tf"		)
         _A			     =	self.model.generate(
             model_inputs.input_ids      ,      attention_mask=model_inputs.attention_mask      ,      num_beams=2      ,      use_cache=__UpperCAmelCase      ,      )
         _A			     =	self.tokenizer.batch_decode(generated_ids.numpy()      ,      skip_special_tokens=__UpperCAmelCase		)
         return generated_words

   @slow
   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         self._assert_generated_batch_equal_expected()









"""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


lowerCAmelCase__      				=   pytest.mark.integration


@require_faiss
class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							Dataset.from_dict({"filename": ["my_name-train" + "_" + str(snake_case__						) for x in np.arange(30						).tolist()]}						)
         return dset

       def        lowercase__							(						self						):






         """simple docstring"""





         import faiss

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






         """simple docstring"""





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

       def        lowercase__							(						self						):






         """simple docstring"""





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

       def        lowercase__							(						self						):






         """simple docstring"""





         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(snake_case__		,     partial(dset.get_nearest_examples		,     "vecs2"		,     np.ones(5		,     dtype=np.floataa						)						)						)







       def        lowercase__							(						self						):






         """simple docstring"""





         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     :     List[str]								=							{"acknowledged": True}
           mocked_bulk.return_value([(True, None)] * 30						)
           lowerCAmelCase     :     List[str]								=							{"hits": {"hits": [{"_score": 1, "_id": 29}]}}
           lowerCAmelCase     :     str								=							Elasticsearch()

           dset.add_elasticsearch_index("filename"		,     es_client=snake_case__						)
           lowerCAmelCase						,  lowerCAmelCase     :     int								=							dset.get_nearest_examples("filename"		,     "my_name-train_29"						)
           self.assertEqual(examples["filename"][0]		,     "my_name-train_29"						)


@require_faiss
class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         import faiss

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

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

       def        lowercase__							(						self						):






         """simple docstring"""





         import faiss

         lowerCAmelCase     :     Dict								=							FaissIndex(string_factory="Flat"						)
         index.add_vectors(np.eye(5		,     dtype=np.floataa						)						)
         self.assertIsInstance(index.faiss_index		,     faiss.IndexFlat						)
         lowerCAmelCase     :     Union[str, Any]								=							FaissIndex(string_factory="LSH"						)
         index.add_vectors(np.eye(5		,     dtype=np.floataa						)						)
         self.assertIsInstance(index.faiss_index		,     faiss.IndexLSH						)
         with self.assertRaises(snake_case__						):
           lowerCAmelCase     :     List[Any]								=							FaissIndex(string_factory="Flat"		,     custom_index=faiss.IndexFlat(5						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         import faiss

         lowerCAmelCase     :     Any								=							faiss.IndexFlat(5						)
         lowerCAmelCase     :     Union[str, Any]								=							FaissIndex(custom_index=snake_case__						)
         index.add_vectors(np.eye(5		,     dtype=np.floataa						)						)
         self.assertIsInstance(index.faiss_index		,     faiss.IndexFlat						)







       def        lowercase__							(						self						):






         """simple docstring"""





         import faiss

         lowerCAmelCase     :     str								=							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=snake_case__						) as tmp_file:
           index.save(tmp_file.name						)
           lowerCAmelCase     :     int								=							FaissIndex.load(tmp_file.name						)
         os.unlink(tmp_file.name						)

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

@require_faiss
def 					a__			(				SCREAMING_SNAKE_CASE    :    Any				):


  '''simple docstring'''







  import faiss

  lowerCAmelCase     :     List[Any]								=							FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT				)
  index.add_vectors(np.eye(5				,      dtype=np.floataa				)				)

  lowerCAmelCase     :     Union[str, Any]								=							"index.faiss"
  lowerCAmelCase     :     List[str]								=							f"""mock://{index_name}"""
  index.save(SCREAMING_SNAKE_CASE				,      storage_options=mockfs.storage_options				)
  lowerCAmelCase     :     Optional[Any]								=							FaissIndex.load(SCREAMING_SNAKE_CASE				,      storage_options=mockfs.storage_options				)

  lowerCAmelCase     :     Optional[int]								=							np.zeros(5				,      dtype=np.floataa				)
  lowerCAmelCase     :     Any								=							1
  lowerCAmelCase						,  lowerCAmelCase     :     Optional[int]								=							index.search(SCREAMING_SNAKE_CASE				)
  assert scores[0] > 0
  assert indices[0] == 1





@require_elasticsearch
class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         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     :     List[str]								=							Elasticsearch()
           lowerCAmelCase     :     Dict								=							{"acknowledged": True}
           lowerCAmelCase     :     Optional[int]								=							ElasticSearchIndex(es_client=snake_case__						)
           mocked_bulk.return_value([(True, None)] * 3						)
           index.add_documents(["foo", "bar", "foobar"]						)

           # single query
           lowerCAmelCase     :     List[str]								=							"foo"
           lowerCAmelCase     :     List[str]								=							{"hits": {"hits": [{"_score": 1, "_id": 0}]}}
           lowerCAmelCase						,  lowerCAmelCase     :     Optional[int]								=							index.search(snake_case__						)
           self.assertEqual(scores[0]		,     1						)
           self.assertEqual(indices[0]		,     0						)

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

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

           # batched queries with timeout
           lowerCAmelCase     :     Optional[Any]								=							["foo", "bar", "foobar"]
           lowerCAmelCase     :     List[Any]								=							{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
           lowerCAmelCase						,  lowerCAmelCase     :     Any								=							index.search_batch(snake_case__		,     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(snake_case__						)		,     0						)
           self.assertListEqual([1, 1, 1]		,     snake_case__						)





