






'''simple docstring'''
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OpenAIGPTConfig,
      OpenAIGPTDoubleHeadsModel,
      OpenAIGPTForSequenceClassification,
      OpenAIGPTLMHeadModel,
      OpenAIGPTModel,
  )






class    __SCREAMING_SNAKE_CASE					:




 def __init__(					self		:							List[str]    ,					__lowercase		:							int    ,					__lowercase		:							Optional[Any]=13    ,					__lowercase		:							str=7    ,					__lowercase		:							List[Any]=True    ,					__lowercase		:							Union[str, Any]=True    ,					__lowercase		:							Tuple=True    ,					__lowercase		:							Any=99    ,					__lowercase		:							Optional[Any]=32    ,					__lowercase		:							int=5    ,					__lowercase		:							Dict=4    ,					__lowercase		:							Optional[int]=37    ,					__lowercase		:							Dict="gelu"    ,					__lowercase		:							Union[str, Any]=0.1    ,					__lowercase		:							Optional[Any]=0.1    ,					__lowercase		:							str=5_12    ,					__lowercase		:							Optional[Any]=16    ,					__lowercase		:							Union[str, Any]=2    ,					__lowercase		:							List[Any]=0.02    ,					__lowercase		:							Tuple=3    ,					__lowercase		:							str=4    ,					__lowercase		:							Optional[Any]=None    ,					)		->  List[str]:
       SCREAMING_SNAKE_CASE__   : Optional[int]													=parent
       SCREAMING_SNAKE_CASE__   : str													=batch_size
       SCREAMING_SNAKE_CASE__   : int													=seq_length
       SCREAMING_SNAKE_CASE__   : Optional[int]													=is_training
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=use_token_type_ids
       SCREAMING_SNAKE_CASE__   : List[str]													=use_labels
       SCREAMING_SNAKE_CASE__   : Tuple													=vocab_size
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=hidden_size
       SCREAMING_SNAKE_CASE__   : Dict													=num_hidden_layers
       SCREAMING_SNAKE_CASE__   : str													=num_attention_heads
       SCREAMING_SNAKE_CASE__   : Tuple													=intermediate_size
       SCREAMING_SNAKE_CASE__   : Tuple													=hidden_act
       SCREAMING_SNAKE_CASE__   : Tuple													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   : int													=max_position_embeddings
       SCREAMING_SNAKE_CASE__   : Any													=type_vocab_size
       SCREAMING_SNAKE_CASE__   : Dict													=type_sequence_label_size
       SCREAMING_SNAKE_CASE__   : Tuple													=initializer_range
       SCREAMING_SNAKE_CASE__   : Tuple													=num_labels
       SCREAMING_SNAKE_CASE__   : Tuple													=num_choices
       SCREAMING_SNAKE_CASE__   : Any													=scope
       SCREAMING_SNAKE_CASE__   : int													=self.vocab_size - 1




 def    __magic_name__		(					self		:							Optional[int]					)		->  Any:
       SCREAMING_SNAKE_CASE__   : Any													=ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size					)

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

       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=None
       SCREAMING_SNAKE_CASE__   : Any													=None
       SCREAMING_SNAKE_CASE__   : List[str]													=None
       if self.use_labels:
             SCREAMING_SNAKE_CASE__   : Dict													=ids_tensor([self.batch_size]    ,					self.type_sequence_label_size					)
             SCREAMING_SNAKE_CASE__   : Dict													=ids_tensor([self.batch_size, self.seq_length]    ,					self.num_labels					)
             SCREAMING_SNAKE_CASE__   : List[Any]													=ids_tensor([self.batch_size]    ,					self.num_choices					)

       SCREAMING_SNAKE_CASE__   : Optional[int]													=OpenAIGPTConfig(
           vocab_size=self.vocab_size    ,					n_embd=self.hidden_size    ,					n_layer=self.num_hidden_layers    ,					n_head=self.num_attention_heads    ,					n_positions=self.max_position_embeddings    ,					pad_token_id=self.pad_token_id    ,					)

       SCREAMING_SNAKE_CASE__   : str													=ids_tensor([self.num_hidden_layers, self.num_attention_heads]    ,					2					)

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




 def    __magic_name__		(					self		:							List[Any]    ,					__lowercase		:							List[str]    ,					__lowercase		:							int    ,					__lowercase		:							List[Any]    ,					__lowercase		:							List[str]    ,					*__lowercase		:							Any					)		->  Dict:
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=OpenAIGPTModel(config=__lowercase					)
       model.to(__lowercase					)
       model.eval()

       SCREAMING_SNAKE_CASE__   : str													=model(__lowercase    ,					token_type_ids=__lowercase    ,					head_mask=__lowercase					)
       SCREAMING_SNAKE_CASE__   : List[Any]													=model(__lowercase    ,					token_type_ids=__lowercase					)
       SCREAMING_SNAKE_CASE__   : List[Any]													=model(__lowercase					)

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




 def    __magic_name__		(					self		:							Dict    ,					__lowercase		:							str    ,					__lowercase		:							Optional[Any]    ,					__lowercase		:							int    ,					__lowercase		:							Any    ,					*__lowercase		:							str					)		->  int:
       SCREAMING_SNAKE_CASE__   : List[str]													=OpenAIGPTLMHeadModel(__lowercase					)
       model.to(__lowercase					)
       model.eval()

       SCREAMING_SNAKE_CASE__   : Any													=model(__lowercase    ,					token_type_ids=__lowercase    ,					labels=__lowercase					)
       self.parent.assertEqual(result.loss.shape    ,					()					)
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.vocab_size)					)




 def    __magic_name__		(					self		:							List[str]    ,					__lowercase		:							Optional[int]    ,					__lowercase		:							List[Any]    ,					__lowercase		:							Union[str, Any]    ,					__lowercase		:							Optional[Any]    ,					*__lowercase		:							Optional[int]					)		->  Dict:
       SCREAMING_SNAKE_CASE__   : Any													=OpenAIGPTDoubleHeadsModel(__lowercase					)
       model.to(__lowercase					)
       model.eval()

       SCREAMING_SNAKE_CASE__   : List[Any]													=model(__lowercase    ,					token_type_ids=__lowercase    ,					labels=__lowercase					)
       self.parent.assertEqual(result.loss.shape    ,					()					)
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.vocab_size)					)




 def    __magic_name__		(					self		:							Union[str, Any]    ,					__lowercase		:							Union[str, Any]    ,					__lowercase		:							Union[str, Any]    ,					__lowercase		:							Optional[int]    ,					__lowercase		:							List[str]    ,					*__lowercase		:							Optional[Any]					)		->  int:
       SCREAMING_SNAKE_CASE__   : Optional[int]													=self.num_labels
       SCREAMING_SNAKE_CASE__   : Dict													=OpenAIGPTForSequenceClassification(__lowercase					)
       model.to(__lowercase					)
       model.eval()

       SCREAMING_SNAKE_CASE__   : Dict													=ids_tensor([self.batch_size]    ,					self.type_sequence_label_size					)
       SCREAMING_SNAKE_CASE__   : Any													=model(__lowercase    ,					token_type_ids=__lowercase    ,					labels=__lowercase					)
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)					)




 def    __magic_name__		(					self		:							Optional[int]					)		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__   : List[Any]													=self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__    
       )						,		(
           SCREAMING_SNAKE_CASE__    
       )						,		(
           SCREAMING_SNAKE_CASE__    
       )						,		(
           SCREAMING_SNAKE_CASE__    
       )						,		(
           SCREAMING_SNAKE_CASE__    
       )						,		(
           SCREAMING_SNAKE_CASE__    
       )						,		(
           SCREAMING_SNAKE_CASE__    
       )						,		    
       )   : List[str]													=config_and_inputs
       SCREAMING_SNAKE_CASE__   : Any													={
           '''input_ids''': input_ids,
           '''token_type_ids''': token_type_ids,
           '''head_mask''': head_mask,
       }

       return config, inputs_dict






@require_torch
class    __SCREAMING_SNAKE_CASE					(	snake_case_		, snake_case_		, snake_case_		, unittest.TestCase ):
 snake_case_     							=							(
     (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
     if is_torch_available()
     else ()
 )
 snake_case_     							=							(
     (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
 )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
 snake_case_     							=							(
     {
         """feature-extraction""": OpenAIGPTModel,
         """text-classification""": OpenAIGPTForSequenceClassification,
         """text-generation""": OpenAIGPTLMHeadModel,
         """zero-shot""": OpenAIGPTForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )




 def    __magic_name__		(					self		:							Tuple    ,					__lowercase		:							int    ,					__lowercase		:							Tuple    ,					__lowercase		:							Any    ,					__lowercase		:							List[str]    ,					__lowercase		:							List[str]					)		->  int:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
             # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
             # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
             # tiny config could not be created.
             return True

       return False




 def    __magic_name__		(					self		:							Optional[int]    ,					__lowercase		:							int    ,					__lowercase		:							List[Any]    ,					__lowercase		:							Tuple=False					)		->  Any:
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=super()._prepare_for_class(__lowercase    ,					__lowercase    ,					return_labels=__lowercase					)

       if return_labels:
             if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                   SCREAMING_SNAKE_CASE__   : List[Any]													=torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)    ,					dtype=torch.long    ,					device=__lowercase    ,					)
                   SCREAMING_SNAKE_CASE__   : List[str]													=inputs_dict['''labels''']
                   SCREAMING_SNAKE_CASE__   : Optional[Any]													=inputs_dict['''labels''']
                   SCREAMING_SNAKE_CASE__   : Optional[int]													=torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.num_choices)    ,					dtype=torch.long    ,					device=__lowercase    ,					)
                   SCREAMING_SNAKE_CASE__   : Optional[Any]													=torch.zeros(
                       self.model_tester.batch_size    ,					dtype=torch.long    ,					device=__lowercase					)
       return inputs_dict




 def    __magic_name__		(					self		:							Tuple					)		->  Dict:
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=OpenAIGPTModelTester(self					)
       SCREAMING_SNAKE_CASE__   : Optional[int]													=ConfigTester(self    ,					config_class=__lowercase    ,					n_embd=37					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Any:
       self.config_tester.run_common_tests()




 def    __magic_name__		(					self		:							Dict					)		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__   : List[Any]													=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_model(*__lowercase					)




 def    __magic_name__		(					self		:							Tuple					)		->  str:
       SCREAMING_SNAKE_CASE__   : Tuple													=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head_model(*__lowercase					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Tuple:
       SCREAMING_SNAKE_CASE__   : Any													=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_double_lm_head_model(*__lowercase					)




 def    __magic_name__		(					self		:							List[str]					)		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__   : Any													=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowercase					)




 @slow
 def    __magic_name__		(					self		:							List[str]					)		->  Any:
       for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE__   : List[Any]													=OpenAIGPTModel.from_pretrained(__lowercase					)
             self.assertIsNotNone(__lowercase					)






@require_torch
class    __SCREAMING_SNAKE_CASE					(	unittest.TestCase ):




 @slow
 def    __magic_name__		(					self		:							Union[str, Any]					)		->  str:
       SCREAMING_SNAKE_CASE__   : Dict													=OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''					)
       model.to(__lowercase					)
       SCREAMING_SNAKE_CASE__   : Any													=torch.tensor([[4_81, 47_35, 5_44]]    ,					dtype=torch.long    ,					device=__lowercase					)  # the president is
       SCREAMING_SNAKE_CASE__   : int													=[
           4_81,
           47_35,
           5_44,
           2_46,
           9_63,
           8_70,
           7_62,
           2_39,
           2_44,
           4_04_77,
           2_44,
           2_49,
           7_19,
           8_81,
           4_87,
           5_44,
           2_40,
           2_44,
           6_03,
           4_81,
       ]  # the president is a very good man. " \n " i\'m sure he is, " said the

       SCREAMING_SNAKE_CASE__   : List[Any]													=model.generate(__lowercase    ,					do_sample=__lowercase					)
       self.assertListEqual(output_ids[0].tolist()    ,					__lowercase					)

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     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			Dataset.from_dict({"filename": ["my_name-train" + "_" + str(lowercase ) for x in np.arange(30 ).tolist()]} )
					return dset



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

					lowerCamelCase_						=			self._create_dummy_dataset()
					lowerCamelCase_						=			dset.map(
					    lambda lowercase						,  lowercase : {"vecs": i * np.ones(5						,  dtype=np.floataa )}						,  with_indices=lowercase						,  keep_in_memory=lowercase )
					lowerCamelCase_						=			dset.add_faiss_index("vecs"						,  batch_size=100						,  metric_type=faiss.METRIC_INNER_PRODUCT )
					lowerCamelCase_	,    lowerCamelCase_						=			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 )				->       Dict:
					import faiss

					lowerCamelCase_						=			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_						=			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 )				->       Optional[int]:
					import faiss

					lowerCamelCase_						=			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=lowercase ) 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_						=			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 )				->       Union[str, Any]:
					lowerCamelCase_						=			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(lowercase						,  partial(dset.get_nearest_examples						,  "vecs2"						,  np.ones(5						,  dtype=np.floataa ) ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					from elasticsearch import Elasticsearch

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

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




@require_faiss
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					import faiss

					lowerCamelCase_						=			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_						=			np.zeros(5						,  dtype=np.floataa )
					lowerCamelCase_						=			1
					lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowercase )
					self.assertRaises(lowercase						,  index.search						,  query.reshape(-1						,  1 ) )
					self.assertGreater(scores[0]						,  0 )
					self.assertEqual(indices[0]						,  1 )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					import faiss

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					import faiss

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



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

					lowerCamelCase_						=			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=lowercase ) as tmp_file:
									index.save(tmp_file.name )
									lowerCamelCase_						=			FaissIndex.load(tmp_file.name )
					os.unlink(tmp_file.name )

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





@require_faiss
def 			lowerCamelCase_ (				lowerCamelCase__    ):
				import faiss

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

				lowerCamelCase_						=			"index.faiss"
				lowerCamelCase_						=			F'mock://{index_name}'
				index.save(lowerCamelCase__	, storage_options=mockfs.storage_options    )
				lowerCamelCase_						=			FaissIndex.load(lowerCamelCase__	, storage_options=mockfs.storage_options    )

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





@require_elasticsearch
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					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_						=			Elasticsearch()
									lowerCamelCase_						=			{"acknowledged": True}
									lowerCamelCase_						=			ElasticSearchIndex(es_client=lowercase )
									mocked_bulk.return_value([(True, None)] * 3 )
									index.add_documents(["foo", "bar", "foobar"] )

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

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

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

									# batched queries with timeout
									lowerCamelCase_						=			["foo", "bar", "foobar"]
									lowerCamelCase_						=			{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
									lowerCamelCase_	,    lowerCamelCase_						=			index.search_batch(lowercase						,  request_timeout=30 )
									lowerCamelCase_						=			[scores[0] for scores in total_scores]
									lowerCamelCase_						=			[indices[0] for indices in total_indices]
									self.assertGreater(np.min(lowercase )						,  0 )
									self.assertListEqual([1, 1, 1]						,  lowercase )


