

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a:     Optional[int]							=      {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:     List[Any]							=      [
				    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'Blip2Model',
				    'Blip2QFormerModel',
				    'Blip2PreTrainedModel',
				    'Blip2ForConditionalGeneration',
				    'Blip2VisionModel',
				]

if TYPE_CHECKING:
				from .configuration_blip_a import (
				    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BlipaConfig,
				    BlipaQFormerConfig,
				    BlipaVisionConfig,
				)
				from .processing_blip_a import BlipaProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_blip_a import (
								    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BlipaForConditionalGeneration,
								    BlipaModel,
								    BlipaPreTrainedModel,
								    BlipaQFormerModel,
								    BlipaVisionModel,
								)

else:
				import sys

				_a:     Optional[Any]							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


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






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







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







		def  A__      (					self		):
						import faiss

						_A	:				Dataset   =      self._create_dummy_dataset()
						_A	:				List[str]   =      dset.map(
						    lambda A__       ,A__		: {"vecs": i * np.ones(5       ,dtype=np.floataa		)}       ,with_indices=A__       ,keep_in_memory=A__		)
						_A	:				Tuple   =      dset.add_faiss_index('''vecs'''       ,batch_size=100       ,metric_type=faiss.METRIC_INNER_PRODUCT		)
						_A  ,				_A	:				List[Any]   =      dset.get_nearest_examples('''vecs'''       ,np.ones(5       ,dtype=np.floataa		)		)
						self.assertEqual(examples['''filename'''][0]       ,'''my_name-train_29'''		)
						dset.drop_index('''vecs'''		)







		def  A__      (					self		):
						import faiss

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







		def  A__      (					self		):
						import faiss

						_A	:				Dataset   =      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1       ,1		)       ,index_name='''vecs'''       ,metric_type=faiss.METRIC_INNER_PRODUCT       ,)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=A__		) as tmp_file:
										dset.save_faiss_index('''vecs'''       ,tmp_file.name		)
										dset.load_faiss_index('''vecs2'''       ,tmp_file.name		)
						os.unlink(tmp_file.name		)

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







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







		def  A__      (					self		):
						from elasticsearch import Elasticsearch

						_A	:				Dataset   =      self._create_dummy_dataset()
						with patch('''elasticsearch.Elasticsearch.search'''		) as mocked_search, patch(
						    '''elasticsearch.client.IndicesClient.create'''		) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''		) as mocked_bulk:
										_A	:				str   =      {'''acknowledged''': True}
										mocked_bulk.return_value([(True, None)] * 30		)
										_A	:				Optional[Any]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
										_A	:				Optional[Any]   =      Elasticsearch()

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






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						import faiss

						_A	:				Union[str, Any]   =      FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)

						# add vectors
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsNotNone(index.faiss_index		)
						self.assertEqual(index.faiss_index.ntotal       ,5		)
						index.add_vectors(np.zeros((5, 5)       ,dtype=np.floataa		)		)
						self.assertEqual(index.faiss_index.ntotal       ,10		)

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

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







		def  A__      (					self		):
						import faiss

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







		def  A__      (					self		):
						import faiss

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







		def  A__      (					self		):
						import faiss

						_A	:				Optional[int]   =      FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=A__		) as tmp_file:
										index.save(tmp_file.name		)
										_A	:				str   =      FaissIndex.load(tmp_file.name		)
						os.unlink(tmp_file.name		)

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



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

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

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

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






@require_elasticsearch
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						from elasticsearch import Elasticsearch

						with patch('''elasticsearch.Elasticsearch.search'''		) as mocked_search, patch(
						    '''elasticsearch.client.IndicesClient.create'''		) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''		) as mocked_bulk:
										_A	:				Tuple   =      Elasticsearch()
										_A	:				Tuple   =      {'''acknowledged''': True}
										_A	:				int   =      ElasticSearchIndex(es_client=A__		)
										mocked_bulk.return_value([(True, None)] * 3		)
										index.add_documents(['''foo''', '''bar''', '''foobar''']		)

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

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

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

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



