
"""simple docstring"""

from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder






@dataclass
class 	_lowercase       (    snake_case_		):
 lowercase_				=     4_2






class 	_lowercase       (    snake_case_    ,	snake_case_		):
 lowercase_				=     True




 @register_to_config
 def __init__(			self		,			UpperCAmelCase_ = 3		,			UpperCAmelCase_ = 3		,			UpperCAmelCase_ = ("DownEncoderBlock2D",)		,			UpperCAmelCase_ = ("UpDecoderBlock2D",)		,			UpperCAmelCase_ = (64,)		,			UpperCAmelCase_ = 1		,			UpperCAmelCase_ = "silu"		,			UpperCAmelCase_ = 4		,			UpperCAmelCase_ = 32		,			UpperCAmelCase_ = 32		,			UpperCAmelCase_ = 0.18215		,			)		->							Optional[Any]:
   super().__init__()

   # pass init params to Encoder
   lowerCamelCase					:      str          =   Encoder(
       in_channels=UpperCAmelCase_		,			out_channels=UpperCAmelCase_		,			down_block_types=UpperCAmelCase_		,			block_out_channels=UpperCAmelCase_		,			layers_per_block=UpperCAmelCase_		,			act_fn=UpperCAmelCase_		,			norm_num_groups=UpperCAmelCase_		,			double_z=UpperCAmelCase_		,			)

   # pass init params to Decoder
   lowerCamelCase					:      Optional[int]          =   Decoder(
       in_channels=UpperCAmelCase_		,			out_channels=UpperCAmelCase_		,			up_block_types=UpperCAmelCase_		,			block_out_channels=UpperCAmelCase_		,			layers_per_block=UpperCAmelCase_		,			norm_num_groups=UpperCAmelCase_		,			act_fn=UpperCAmelCase_		,			)

   lowerCamelCase					:      int          =   nn.Convad(2 * latent_channels		,			2 * latent_channels		,			1 )
   lowerCamelCase					:      Tuple          =   nn.Convad(UpperCAmelCase_		,			UpperCAmelCase_		,			1 )

   lowerCamelCase					:      Optional[Any]          =   False
   lowerCamelCase					:      Optional[int]          =   False

   # only relevant if vae tiling is enabled
   lowerCamelCase					:      Optional[Any]          =   self.config.sample_size
   lowerCamelCase					:      Union[str, Any]          =   (
       self.config.sample_size[0]
       if isinstance(self.config.sample_size		,			(list, tuple) )
       else self.config.sample_size
   )
   lowerCamelCase					:      Tuple          =   int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
   lowerCamelCase					:      Optional[int]          =   0.25




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=False )		->							Tuple:
   if isinstance(UpperCAmelCase_		,			(Encoder, Decoder) ):
     lowerCamelCase					:      Dict          =   value




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_ = True )		->							List[str]:
   lowerCamelCase					:      Any          =   use_tiling




 def       _UpperCamelCase   (			self )		->							Dict:
   self.enable_tiling(UpperCAmelCase_ )




 def       _UpperCamelCase   (			self )		->							Tuple:
   lowerCamelCase					:      Optional[Any]          =   True




 def       _UpperCamelCase   (			self )		->							Union[str, Any]:
   lowerCamelCase					:      Optional[int]          =   False




 @property
 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
 def       _UpperCamelCase   (			self )		->							Dict[str, AttentionProcessor]:
   lowerCamelCase					:      str          =   {}

   def fn_recursive_add_processors(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ ):
     if hasattr(UpperCAmelCase_		,			'set_processor' ):
       lowerCamelCase					:      Any          =   module.processor

     for sub_name, child in module.named_children():
       fn_recursive_add_processors(F"""{name}.{sub_name}"""		,			UpperCAmelCase_		,			UpperCAmelCase_ )

     return processors

   for name, module in self.named_children():
     fn_recursive_add_processors(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )

   return processors




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
   lowerCamelCase					:      Tuple          =   len(self.attn_processors.keys() )

   if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) and len(UpperCAmelCase_ ) != count:
     raise ValueError(
         F"""A dict of processors was passed, but the number of processors {len(UpperCAmelCase_ )} does not match the"""
         F""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )

   def fn_recursive_attn_processor(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ ):
     if hasattr(UpperCAmelCase_		,			'set_processor' ):
       if not isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
         module.set_processor(UpperCAmelCase_ )
       else:
         module.set_processor(processor.pop(F"""{name}.processor""" ) )

     for sub_name, child in module.named_children():
       fn_recursive_attn_processor(F"""{name}.{sub_name}"""		,			UpperCAmelCase_		,			UpperCAmelCase_ )

   for name, module in self.named_children():
     fn_recursive_attn_processor(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )




 def       _UpperCamelCase   (			self )		->							Tuple:
   self.set_attn_processor(AttnProcessor() )




 @apply_forward_hook
 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = True )		->							AutoencoderKLOutput:
   if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
     return self.tiled_encode(UpperCAmelCase_		,			return_dict=UpperCAmelCase_ )

   if self.use_slicing and x.shape[0] > 1:
     lowerCamelCase					:      Optional[Any]          =   [self.encoder(UpperCAmelCase_ ) for x_slice in x.split(1 )]
     lowerCamelCase					:      List[str]          =   torch.cat(UpperCAmelCase_ )
   else:
     lowerCamelCase					:      Optional[int]          =   self.encoder(UpperCAmelCase_ )

   lowerCamelCase					:      List[str]          =   self.quant_conv(UpperCAmelCase_ )
   lowerCamelCase					:      List[str]          =   DiagonalGaussianDistribution(UpperCAmelCase_ )

   if not return_dict:
     return (posterior,)

   return AutoencoderKLOutput(latent_dist=UpperCAmelCase_ )




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = True )		->							Union[DecoderOutput, torch.FloatTensor]:
   if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
     return self.tiled_decode(UpperCAmelCase_		,			return_dict=UpperCAmelCase_ )

   lowerCamelCase					:      Optional[Any]          =   self.post_quant_conv(UpperCAmelCase_ )
   lowerCamelCase					:      Optional[int]          =   self.decoder(UpperCAmelCase_ )

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=UpperCAmelCase_ )




 @apply_forward_hook
 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = True )		->							Union[DecoderOutput, torch.FloatTensor]:
   if self.use_slicing and z.shape[0] > 1:
     lowerCamelCase					:      Optional[int]          =   [self._decode(UpperCAmelCase_ ).sample for z_slice in z.split(1 )]
     lowerCamelCase					:      int          =   torch.cat(UpperCAmelCase_ )
   else:
     lowerCamelCase					:      int          =   self._decode(UpperCAmelCase_ ).sample

   if not return_dict:
     return (decoded,)

   return DecoderOutput(sample=UpperCAmelCase_ )




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
   lowerCamelCase					:      int          =   min(a.shape[2]		,			b.shape[2]		,			UpperCAmelCase_ )
   for y in range(UpperCAmelCase_ ):
     lowerCamelCase					:      Any          =   a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
   return b




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							str:
   lowerCamelCase					:      Optional[int]          =   min(a.shape[3]		,			b.shape[3]		,			UpperCAmelCase_ )
   for x in range(UpperCAmelCase_ ):
     lowerCamelCase					:      str          =   a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
   return b




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = True )		->							AutoencoderKLOutput:
   lowerCamelCase					:      Optional[int]          =   int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
   lowerCamelCase					:      List[Any]          =   int(self.tile_latent_min_size * self.tile_overlap_factor )
   lowerCamelCase					:      Any          =   self.tile_latent_min_size - blend_extent

   # Split the image into 512x512 tiles and encode them separately.
   lowerCamelCase					:      Optional[Any]          =   []
   for i in range(0		,			x.shape[2]		,			UpperCAmelCase_ ):
     lowerCamelCase					:      Optional[Any]          =   []
     for j in range(0		,			x.shape[3]		,			UpperCAmelCase_ ):
       lowerCamelCase					:      str          =   x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
       lowerCamelCase					:      int          =   self.encoder(UpperCAmelCase_ )
       lowerCamelCase					:      str          =   self.quant_conv(UpperCAmelCase_ )
       row.append(UpperCAmelCase_ )
     rows.append(UpperCAmelCase_ )
   lowerCamelCase					:      Optional[Any]          =   []
   for i, row in enumerate(UpperCAmelCase_ ):
     lowerCamelCase					:      int          =   []
     for j, tile in enumerate(UpperCAmelCase_ ):
       # blend the above tile and the left tile
       # to the current tile and add the current tile to the result row
       if i > 0:
         lowerCamelCase					:      str          =   self.blend_v(rows[i - 1][j]		,			UpperCAmelCase_		,			UpperCAmelCase_ )
       if j > 0:
         lowerCamelCase					:      Dict          =   self.blend_h(row[j - 1]		,			UpperCAmelCase_		,			UpperCAmelCase_ )
       result_row.append(tile[:, :, :row_limit, :row_limit] )
     result_rows.append(torch.cat(UpperCAmelCase_		,			dim=3 ) )

   lowerCamelCase					:      List[Any]          =   torch.cat(UpperCAmelCase_		,			dim=2 )
   lowerCamelCase					:      List[str]          =   DiagonalGaussianDistribution(UpperCAmelCase_ )

   if not return_dict:
     return (posterior,)

   return AutoencoderKLOutput(latent_dist=UpperCAmelCase_ )




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = True )		->							Union[DecoderOutput, torch.FloatTensor]:
   lowerCamelCase					:      Union[str, Any]          =   int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
   lowerCamelCase					:      Dict          =   int(self.tile_sample_min_size * self.tile_overlap_factor )
   lowerCamelCase					:      Dict          =   self.tile_sample_min_size - blend_extent

   # Split z into overlapping 64x64 tiles and decode them separately.
   # The tiles have an overlap to avoid seams between tiles.
   lowerCamelCase					:      Optional[int]          =   []
   for i in range(0		,			z.shape[2]		,			UpperCAmelCase_ ):
     lowerCamelCase					:      Any          =   []
     for j in range(0		,			z.shape[3]		,			UpperCAmelCase_ ):
       lowerCamelCase					:      int          =   z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
       lowerCamelCase					:      Dict          =   self.post_quant_conv(UpperCAmelCase_ )
       lowerCamelCase					:      Any          =   self.decoder(UpperCAmelCase_ )
       row.append(UpperCAmelCase_ )
     rows.append(UpperCAmelCase_ )
   lowerCamelCase					:      List[str]          =   []
   for i, row in enumerate(UpperCAmelCase_ ):
     lowerCamelCase					:      Optional[int]          =   []
     for j, tile in enumerate(UpperCAmelCase_ ):
       # blend the above tile and the left tile
       # to the current tile and add the current tile to the result row
       if i > 0:
         lowerCamelCase					:      Union[str, Any]          =   self.blend_v(rows[i - 1][j]		,			UpperCAmelCase_		,			UpperCAmelCase_ )
       if j > 0:
         lowerCamelCase					:      Tuple          =   self.blend_h(row[j - 1]		,			UpperCAmelCase_		,			UpperCAmelCase_ )
       result_row.append(tile[:, :, :row_limit, :row_limit] )
     result_rows.append(torch.cat(UpperCAmelCase_		,			dim=3 ) )

   lowerCamelCase					:      List[Any]          =   torch.cat(UpperCAmelCase_		,			dim=2 )
   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=UpperCAmelCase_ )




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			)		->							Union[DecoderOutput, torch.FloatTensor]:
   lowerCamelCase					:      Optional[Any]          =   sample
   lowerCamelCase					:      Optional[Any]          =   self.encode(UpperCAmelCase_ ).latent_dist
   if sample_posterior:
     lowerCamelCase					:      List[str]          =   posterior.sample(generator=UpperCAmelCase_ )
   else:
     lowerCamelCase					:      int          =   posterior.mode()
   lowerCamelCase					:      List[Any]          =   self.decode(UpperCAmelCase_ ).sample

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=UpperCAmelCase_ )








"""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_ )






