






'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
  from test_module.custom_tokenization_fast import CustomTokenizerFast






class    __SCREAMING_SNAKE_CASE					(	unittest.TestCase ):




 def    __magic_name__		(					self		:							int					)		->  Union[str, Any]:
       # A mock response for an HTTP head request to emulate server down
       SCREAMING_SNAKE_CASE__   : Optional[int]													=mock.Mock()
       SCREAMING_SNAKE_CASE__   : Any													=5_00
       SCREAMING_SNAKE_CASE__   : Tuple													={}
       SCREAMING_SNAKE_CASE__   : Tuple													=HTTPError
       SCREAMING_SNAKE_CASE__   : Tuple													={}

       # Download this model to make sure it's in the cache.
       SCREAMING_SNAKE_CASE__   : Optional[int]													=BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''					)

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch('''requests.Session.request'''    ,					return_value=__lowercase					) as mock_head:
             SCREAMING_SNAKE_CASE__   : int													=BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''					)
             # This check we did call the fake head request
             mock_head.assert_called()




 @require_tokenizers
 def    __magic_name__		(					self		:							List[str]					)		->  Tuple:
       # A mock response for an HTTP head request to emulate server down
       SCREAMING_SNAKE_CASE__   : Any													=mock.Mock()
       SCREAMING_SNAKE_CASE__   : Dict													=5_00
       SCREAMING_SNAKE_CASE__   : Dict													={}
       SCREAMING_SNAKE_CASE__   : int													=HTTPError
       SCREAMING_SNAKE_CASE__   : int													={}

       # Download this model to make sure it's in the cache.
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=GPTaTokenizerFast.from_pretrained('''gpt2'''					)

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch('''requests.Session.request'''    ,					return_value=__lowercase					) as mock_head:
             SCREAMING_SNAKE_CASE__   : Dict													=GPTaTokenizerFast.from_pretrained('''gpt2'''					)
             # This check we did call the fake head request
             mock_head.assert_called()




 def    __magic_name__		(					self		:							Optional[Any]					)		->  Any:
       # This test is for deprecated behavior and can be removed in v5
       try:
             SCREAMING_SNAKE_CASE__   : Any													=tempfile.mktemp()
             with open(__lowercase    ,					'''wb'''					) as f:
                   http_get('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model'''    ,					__lowercase					)

             SCREAMING_SNAKE_CASE__   : Union[str, Any]													=AlbertTokenizer.from_pretrained(__lowercase					)
       finally:
             os.remove(__lowercase					)

       # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
       # the current folder and have the right name.
       if os.path.isfile('''tokenizer.json'''					):
             # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
             return
       try:
             with open('''tokenizer.json'''    ,					'''wb'''					) as f:
                   http_get('''https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'''    ,					__lowercase					)
             SCREAMING_SNAKE_CASE__   : List[Any]													=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
             # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
             self.assertEqual(tokenizer.vocab_size    ,					10_00					)
             # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

       finally:
             os.remove('''tokenizer.json'''					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  int:
       # This test is for deprecated behavior and can be removed in v5
       SCREAMING_SNAKE_CASE__   : Any													=AlbertTokenizer.from_pretrained('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model'''					)






@is_staging_test
class    __SCREAMING_SNAKE_CASE					(	unittest.TestCase ):
 snake_case_     							=							["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]




 @classmethod
 def    __magic_name__		(					cls		:							Any					)		->  Tuple:
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=TOKEN
       HfFolder.save_token(__lowercase					)




 @classmethod
 def    __magic_name__		(					cls		:							List[Any]					)		->  Dict:
       try:
             delete_repo(token=cls._token    ,					repo_id='''test-tokenizer'''					)
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token    ,					repo_id='''valid_org/test-tokenizer-org'''					)
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token    ,					repo_id='''test-dynamic-tokenizer'''					)
       except HTTPError:
             pass




 def    __magic_name__		(					self		:							List[Any]					)		->  List[str]:
       with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__   : Tuple													=os.path.join(__lowercase    ,					'''vocab.txt'''					)
             with open(__lowercase    ,					'''w'''    ,					encoding='''utf-8'''					) as vocab_writer:
                   vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]					)					)
             SCREAMING_SNAKE_CASE__   : Optional[Any]													=BertTokenizer(__lowercase					)

       tokenizer.push_to_hub('''test-tokenizer'''    ,					use_auth_token=self._token					)
       SCREAMING_SNAKE_CASE__   : int													=BertTokenizer.from_pretrained(F"{USER}/test-tokenizer"					)
       self.assertDictEqual(new_tokenizer.vocab    ,					tokenizer.vocab					)

       # Reset repo
       delete_repo(token=self._token    ,					repo_id='''test-tokenizer'''					)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(__lowercase    ,					repo_id='''test-tokenizer'''    ,					push_to_hub=__lowercase    ,					use_auth_token=self._token					)

       SCREAMING_SNAKE_CASE__   : Tuple													=BertTokenizer.from_pretrained(F"{USER}/test-tokenizer"					)
       self.assertDictEqual(new_tokenizer.vocab    ,					tokenizer.vocab					)




 def    __magic_name__		(					self		:							int					)		->  Union[str, Any]:
       with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__   : Optional[int]													=os.path.join(__lowercase    ,					'''vocab.txt'''					)
             with open(__lowercase    ,					'''w'''    ,					encoding='''utf-8'''					) as vocab_writer:
                   vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]					)					)
             SCREAMING_SNAKE_CASE__   : int													=BertTokenizer(__lowercase					)

       tokenizer.push_to_hub('''valid_org/test-tokenizer-org'''    ,					use_auth_token=self._token					)
       SCREAMING_SNAKE_CASE__   : List[Any]													=BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''					)
       self.assertDictEqual(new_tokenizer.vocab    ,					tokenizer.vocab					)

       # Reset repo
       delete_repo(token=self._token    ,					repo_id='''valid_org/test-tokenizer-org'''					)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(
                 __lowercase    ,					repo_id='''valid_org/test-tokenizer-org'''    ,					push_to_hub=__lowercase    ,					use_auth_token=self._token					)

       SCREAMING_SNAKE_CASE__   : int													=BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''					)
       self.assertDictEqual(new_tokenizer.vocab    ,					tokenizer.vocab					)




 @require_tokenizers
 def    __magic_name__		(					self		:							Tuple					)		->  str:
       CustomTokenizer.register_for_auto_class()
       with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__   : Dict													=os.path.join(__lowercase    ,					'''vocab.txt'''					)
             with open(__lowercase    ,					'''w'''    ,					encoding='''utf-8'''					) as vocab_writer:
                   vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]					)					)
             SCREAMING_SNAKE_CASE__   : Union[str, Any]													=CustomTokenizer(__lowercase					)

       # No fast custom tokenizer
       tokenizer.push_to_hub('''test-dynamic-tokenizer'''    ,					use_auth_token=self._token					)

       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer"    ,					trust_remote_code=__lowercase					)
       # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
       self.assertEqual(tokenizer.__class__.__name__    ,					'''CustomTokenizer'''					)

       # Fast and slow custom tokenizer
       CustomTokenizerFast.register_for_auto_class()
       with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__   : List[str]													=os.path.join(__lowercase    ,					'''vocab.txt'''					)
             with open(__lowercase    ,					'''w'''    ,					encoding='''utf-8'''					) as vocab_writer:
                   vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]					)					)

             SCREAMING_SNAKE_CASE__   : int													=BertTokenizerFast.from_pretrained(__lowercase					)
             bert_tokenizer.save_pretrained(__lowercase					)
             SCREAMING_SNAKE_CASE__   : Any													=CustomTokenizerFast.from_pretrained(__lowercase					)

       tokenizer.push_to_hub('''test-dynamic-tokenizer'''    ,					use_auth_token=self._token					)

       SCREAMING_SNAKE_CASE__   : Any													=AutoTokenizer.from_pretrained(F"{USER}/test-dynamic-tokenizer"    ,					trust_remote_code=__lowercase					)
       # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
       self.assertEqual(tokenizer.__class__.__name__    ,					'''CustomTokenizerFast'''					)
       SCREAMING_SNAKE_CASE__   : List[str]													=AutoTokenizer.from_pretrained(
           F"{USER}/test-dynamic-tokenizer"    ,					use_fast=__lowercase    ,					trust_remote_code=__lowercase					)
       # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
       self.assertEqual(tokenizer.__class__.__name__    ,					'''CustomTokenizer'''					)






class    __SCREAMING_SNAKE_CASE					(	unittest.TestCase ):




 def    __magic_name__		(					self		:							Tuple					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : str													=Trie()
       trie.add('''Hello 友達'''					)
       self.assertEqual(trie.data    ,					{'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}					)
       trie.add('''Hello'''					)
       trie.data
       self.assertEqual(trie.data    ,					{'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {'''''': 1, ''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}					)




 def    __magic_name__		(					self		:							int					)		->  int:
       SCREAMING_SNAKE_CASE__   : Dict													=Trie()
       self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''					)    ,					['''[CLS] This is a extra_id_100''']					)
       trie.add('''[CLS]'''					)
       trie.add('''extra_id_1'''					)
       trie.add('''extra_id_100'''					)
       self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''					)    ,					['''[CLS]''', ''' This is a ''', '''extra_id_100''']					)




 def    __magic_name__		(					self		:							int					)		->  int:
       SCREAMING_SNAKE_CASE__   : Any													=Trie()
       trie.add('''A'''					)
       self.assertEqual(trie.split('''ABC'''					)    ,					['''A''', '''BC''']					)
       self.assertEqual(trie.split('''BCA'''					)    ,					['''BC''', '''A''']					)




 def    __magic_name__		(					self		:							Dict					)		->  Any:
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=Trie()
       trie.add('''TOKEN]'''					)
       trie.add('''[SPECIAL_TOKEN]'''					)
       self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''					)    ,					['''This is something ''', '''[SPECIAL_TOKEN]''']					)




 def    __magic_name__		(					self		:							str					)		->  str:
       SCREAMING_SNAKE_CASE__   : List[Any]													=Trie()
       trie.add('''A'''					)
       trie.add('''P'''					)
       trie.add('''[SPECIAL_TOKEN]'''					)
       self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''					)    ,					['''This is something ''', '''[SPECIAL_TOKEN]''']					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  List[str]:
       SCREAMING_SNAKE_CASE__   : Optional[int]													=Trie()
       trie.add('''AB'''					)
       trie.add('''B'''					)
       trie.add('''C'''					)
       self.assertEqual(trie.split('''ABC'''					)    ,					['''AB''', '''C''']					)




 def    __magic_name__		(					self		:							Optional[int]					)		->  str:
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=Trie()
       trie.add('''ABC'''					)
       trie.add('''B'''					)
       trie.add('''CD'''					)
       self.assertEqual(trie.split('''ABCD'''					)    ,					['''ABC''', '''D''']					)




 def    __magic_name__		(					self		:							Optional[int]					)		->  Dict:
       # Even if the offsets are wrong, we necessarily output correct string
       # parts.
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=Trie()
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=trie.cut_text('''ABC'''    ,					[0, 0, 2, 1, 2, 3]					)
       self.assertEqual(__lowercase    ,					['''AB''', '''C''']					)

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 )


