codingutf8 2022 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license testing suite for the pytorch owlvit model import inspect import os import tempfile import unittest import numpy as np import requests from transformers import owlvitconfig owlvittextconfig owlvitvisionconfig from transformers testingutils import requiretorch requiretorchaccelerator requiretorchfp16 requirevision slow torchdevice from transformers utils import istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin configzeroinit floatstensor idstensor randomattentionmask from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from torch import nn from transformers import owlvitforobjectdetection owlvitmodel owlvittextmodel owlvitvisionmodel from transformers models owlvit modelingowlvit import owlvitpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import owlvitprocessor class owlvitvisionmodeltester def init self parent batchsize12 imagesize32 patchsize2 numchannels3 istrainingtrue hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 dropout0 1 attentiondropout0 1 initializerrange0 02 scopenone self parent parent self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels self istraining istraining self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self dropout dropout self attentiondropout attentiondropout self initializerrange initializerrange self scope scope in vit the seq length equals the number of patches 1 we add 1 for the cls token numpatches imagesize patchsize 2 self seqlength numpatches 1 def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize config self getconfig return config pixelvalues def getconfigself return owlvitvisionconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout initializerrangeself initializerrange def createandcheckmodelself config pixelvalues model owlvitvisionmodelconfigconfig totorchdevice model eval pixelvalues pixelvalues totorch float32 with torch nograd result modelpixelvalues expected sequence length numpatches 1 we add 1 for the cls token numpatches self imagesize self patchsize 2 self parent assertequalresult lasthiddenstate shape self batchsize numpatches 1 self hiddensize self parent assertequalresult pooleroutput shape self batchsize self hiddensize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class owlvitvisionmodeltestmodeltestermixin unittest testcase allmodelclasses owlvitvisionmodel if istorchavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester owlvitvisionmodeltesterself self configtester configtester self configclassowlvitvisionconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasonowlvit does not use inputsembeds def testinputsembedsself pass def testmodelcommonattributesself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig self assertisinstancemodel getinputembeddings nn module x model getoutputembeddings self asserttruex is none or isinstancex nn linear def testforwardsignatureself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig signature inspect signaturemodel forward signature parameters is an ordereddict so argnames order is deterministic argnames signature parameters keys expectedargnames pixelvalues self assertlistequalargnames 1 expectedargnames def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs unittest skipreasonowlvit does not support training yet def testtrainingself pass unittest skipreasonowlvit does not support training yet def testtraininggradientcheckpointingself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantfalseself pass unittest skipreasonowlvitvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasonowlvitvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in owlvitpretrainedmodelarchivelist 1 model owlvitvisionmodel frompretrainedmodelname self assertisnotnonemodel class owlvittextmodeltester def init self parent batchsize12 numqueries4 seqlength16 istrainingtrue useinputmasktrue uselabelstrue vocabsize99 hiddensize64 numhiddenlayers12 numattentionheads4 intermediatesize37 dropout0 1 attentiondropout0 1 maxpositionembeddings16 initializerrange0 02 scopenone self parent parent self batchsize batchsize self numqueries numqueries self seqlength seqlength self istraining istraining self useinputmask useinputmask self uselabels uselabels self vocabsize vocabsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self dropout dropout self attentiondropout attentiondropout self maxpositionembeddings maxpositionembeddings self initializerrange initializerrange self scope scope def prepareconfigandinputsself inputids idstensorself batchsize self numqueries self seqlength self vocabsize inputmask none if self useinputmask inputmask randomattentionmaskself batchsize self numqueries self seqlength if inputmask is not none numtext seqlength inputmask shape rndstartindices np random randint1 seqlength 1 sizenumtext for idx startindex in enumeraterndstartindices inputmaskidx startindex 1 inputmaskidx startindex 0 config self getconfig return config inputids inputmask def getconfigself return owlvittextconfig vocabsizeself vocabsize hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout maxpositionembeddingsself maxpositionembeddings initializerrangeself initializerrange def createandcheckmodelself config inputids inputmask model owlvittextmodelconfigconfig totorchdevice model eval with torch nograd result modelinputidsinputids attentionmaskinputmask self parent assertequal result lasthiddenstate shape self batchsize self numqueries self seqlength self hiddensize self parent assertequalresult pooleroutput shape self batchsize self numqueries self hiddensize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids inputmask configandinputs inputsdict inputids inputids attentionmask inputmask return config inputsdict requiretorch class owlvittextmodeltestmodeltestermixin unittest testcase allmodelclasses owlvittextmodel if istorchavailable else fxcompatible false testpruning false testheadmasking false def setupself self modeltester owlvittextmodeltesterself self configtester configtesterself configclassowlvittextconfig hiddensize37 def testconfigself self configtester runcommontests def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs unittest skipreasonowlvit does not support training yet def testtrainingself pass unittest skipreasonowlvit does not support training yet def testtraininggradientcheckpointingself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantfalseself pass unittest skipreasonowlvit does not use inputsembeds def testinputsembedsself pass unittest skipreasonowlvittextmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasonowlvittextmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in owlvitpretrainedmodelarchivelist 1 model owlvittextmodel frompretrainedmodelname self assertisnotnonemodel class owlvitmodeltester def initself parent textkwargsnone visionkwargsnone istrainingtrue if textkwargs is none textkwargs if visionkwargs is none visionkwargs self parent parent self textmodeltester owlvittextmodeltesterparent textkwargs self visionmodeltester owlvitvisionmodeltesterparent visionkwargs self istraining istraining self textconfig self textmodeltester getconfig todict self visionconfig self visionmodeltester getconfig todict def prepareconfigandinputsself textconfig inputids attentionmask self textmodeltester prepareconfigandinputs visionconfig pixelvalues self visionmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask pixelvalues def getconfigself return owlvitconfig fromtextvisionconfigsself textconfig self visionconfig projectiondim64 def createandcheckmodelself config inputids attentionmask pixelvalues model owlvitmodelconfig totorchdevice eval with torch nograd result model inputidsinputids pixelvaluespixelvalues attentionmaskattentionmask imagelogitssize self visionmodeltester batchsize self textmodeltester batchsize self textmodeltester numqueries textlogitssize self textmodeltester batchsize self textmodeltester numqueries self visionmodeltester batchsize self parent assertequalresult logitsperimage shape imagelogitssize self parent assertequalresult logitspertext shape textlogitssize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask pixelvalues configandinputs inputsdict pixelvalues pixelvalues inputids inputids attentionmask attentionmask returnloss false return config inputsdict requiretorch class owlvitmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses owlvitmodel if istorchavailable else pipelinemodelmapping featureextraction owlvitmodel zeroshotobjectdetection owlvitforobjectdetection if istorchavailable else fxcompatible false testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false def setupself self modeltester owlvitmodeltesterself def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs unittest skipreasonhiddenstates is tested in individual model tests def testhiddenstatesoutputself pass unittest skipreasoninputsembeds is tested in individual model tests def testinputsembedsself pass unittest skipreasonretaingrad is tested in individual model tests def testretaingradhiddenstatesattentionsself pass unittest skipreasonowlvitmodel does not have inputoutput embeddings def testmodelcommonattributesself pass override as the logitscale parameter initilization is different for owlvit def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit for name param in model namedparameters if param requiresgrad check if logitscale is initilized as per the original implementation if name logitscale self assertalmostequal param data item np log1 0 07 delta1e3 msgfparameter name of model modelclass seems not properly initialized else self assertin param data mean 1e9 round 1e9 item 0 0 1 0 msgfparameter name of model modelclass seems not properly initialized def createandchecktorchscriptself config inputsdict if not self testtorchscript return configsnoinit configzeroinitconfig to be sure we have no nan configsnoinit torchscript true configsnoinit returndict false for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit totorchdevice model eval try inputids inputsdictinputids pixelvalues inputsdictpixelvalues owlvit needs pixelvalues tracedmodel torch jit tracemodel inputids pixelvalues except runtimeerror self failcouldn t trace module with tempfile temporarydirectory as tmpdirname ptfilename os path jointmpdirname tracedmodel pt try torch jit savetracedmodel ptfilename except exception self failcouldn t save module try loadedmodel torch jit loadptfilename except exception self failcouldn t load module loadedmodel loadedmodel totorchdevice loadedmodel eval modelstatedict model statedict loadedmodelstatedict loadedmodel statedict nonpersistentbuffers for key in loadedmodelstatedict keys if key not in modelstatedict keys nonpersistentbufferskey loadedmodelstatedictkey loadedmodelstatedict key value for key value in loadedmodelstatedict items if key not in nonpersistentbuffers self assertequalsetmodelstatedict keys setloadedmodelstatedict keys modelbuffers listmodel buffers for nonpersistentbuffer in nonpersistentbuffers values foundbuffer false for i modelbuffer in enumeratemodelbuffers if torch equalnonpersistentbuffer modelbuffer foundbuffer true break self asserttruefoundbuffer modelbuffers popi modelsequal true for layername p1 in modelstatedict items p2 loadedmodelstatedictlayername if p1 data nep2 data sum 0 modelsequal false self asserttruemodelsequal def testloadvisiontextconfigself config inputsdict self modeltester prepareconfigandinputsforcommon save owlvitconfig and check if we can load owlvitvisionconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname visionconfig owlvitvisionconfig frompretrainedtmpdirname self assertdictequalconfig visionconfig todict visionconfig todict save owlvitconfig and check if we can load owlvittextconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname textconfig owlvittextconfig frompretrainedtmpdirname self assertdictequalconfig textconfig todict textconfig todict slow def testmodelfrompretrainedself for modelname in owlvitpretrainedmodelarchivelist 1 model owlvitmodel frompretrainedmodelname self assertisnotnonemodel class owlvitforobjectdetectiontester def initself parent istrainingtrue self parent parent self textmodeltester owlvittextmodeltesterparent self visionmodeltester owlvitvisionmodeltesterparent self istraining istraining self textconfig self textmodeltester getconfig todict self visionconfig self visionmodeltester getconfig todict def prepareconfigandinputsself textconfig inputids attentionmask self textmodeltester prepareconfigandinputs visionconfig pixelvalues self visionmodeltester prepareconfigandinputs config self getconfig return config pixelvalues inputids attentionmask def getconfigself return owlvitconfig fromtextvisionconfigsself textconfig self visionconfig projectiondim64 def createandcheckmodelself config pixelvalues inputids attentionmask model owlvitforobjectdetectionconfig totorchdevice eval with torch nograd result model pixelvaluespixelvalues inputidsinputids attentionmaskattentionmask returndicttrue predboxessize self visionmodeltester batchsize self visionmodeltester imagesize self visionmodeltester patchsize 2 4 predlogitssize self visionmodeltester batchsize self visionmodeltester imagesize self visionmodeltester patchsize 2 4 predclassembedssize self visionmodeltester batchsize self visionmodeltester imagesize self visionmodeltester patchsize 2 self textmodeltester hiddensize self parent assertequalresult predboxes shape predboxessize self parent assertequalresult logits shape predlogitssize self parent assertequalresult classembeds shape predclassembedssize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues inputids attentionmask configandinputs inputsdict pixelvalues pixelvalues inputids inputids attentionmask attentionmask return config inputsdict requiretorch class owlvitforobjectdetectiontestmodeltestermixin unittest testcase allmodelclasses owlvitforobjectdetection if istorchavailable else fxcompatible false testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false def setupself self modeltester owlvitforobjectdetectiontesterself def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs unittest skipreasonhiddenstates is tested in individual model tests def testhiddenstatesoutputself pass unittest skipreasoninputsembeds is tested in individual model tests def testinputsembedsself pass unittest skipreasonretaingrad is tested in individual model tests def testretaingradhiddenstatesattentionsself pass unittest skipreasonowlvitmodel does not have inputoutput embeddings def testmodelcommonattributesself pass unittest skipreasontestinitialization is tested in individual model tests def testinitializationself pass unittest skipreasontestforwardsignature is tested in individual model tests def testforwardsignatureself pass unittest skipreasontestsaveloadfastinitfrombase is tested in individual model tests def testsaveloadfastinitfrombaseself pass unittest skipreasonowlvit does not support training yet def testtrainingself pass unittest skipreasonowlvit does not support training yet def testtraininggradientcheckpointingself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantfalseself pass def createandchecktorchscriptself config inputsdict if not self testtorchscript return configsnoinit configzeroinitconfig to be sure we have no nan configsnoinit torchscript true configsnoinit returndict false for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit totorchdevice model eval try inputids inputsdictinputids pixelvalues inputsdictpixelvalues owlvit needs pixelvalues tracedmodel torch jit tracemodel inputids pixelvalues except runtimeerror self failcouldn t trace module with tempfile temporarydirectory as tmpdirname ptfilename os path jointmpdirname tracedmodel pt try torch jit savetracedmodel ptfilename except exception self failcouldn t save module try loadedmodel torch jit loadptfilename except exception self failcouldn t load module loadedmodel loadedmodel totorchdevice loadedmodel eval modelstatedict model statedict loadedmodelstatedict loadedmodel statedict nonpersistentbuffers for key in loadedmodelstatedict keys if key not in modelstatedict keys nonpersistentbufferskey loadedmodelstatedictkey loadedmodelstatedict key value for key value in loadedmodelstatedict items if key not in nonpersistentbuffers self assertequalsetmodelstatedict keys setloadedmodelstatedict keys modelbuffers listmodel buffers for nonpersistentbuffer in nonpersistentbuffers values foundbuffer false for i modelbuffer in enumeratemodelbuffers if torch equalnonpersistentbuffer modelbuffer foundbuffer true break self asserttruefoundbuffer modelbuffers popi modelsequal true for layername p1 in modelstatedict items p2 loadedmodelstatedictlayername if p1 data nep2 data sum 0 modelsequal false self asserttruemodelsequal slow def testmodelfrompretrainedself for modelname in owlvitpretrainedmodelarchivelist 1 model owlvitforobjectdetection frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg url http images cocodataset orgval2017000000039769 jpg im image openrequests geturl streamtrue raw return im requirevision requiretorch class owlvitmodelintegrationtestunittest testcase slow def testinferenceself modelname googleowlvitbasepatch32 model owlvitmodel frompretrainedmodelname totorchdevice processor owlvitprocessor frompretrainedmodelname image prepareimg inputs processor texta photo of a cat a photo of a dog imagesimage maxlength16 paddingmaxlength returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits self assertequal outputs logitsperimage shape torch sizeinputs pixelvalues shape0 inputs inputids shape0 self assertequal outputs logitspertext shape torch sizeinputs inputids shape0 inputs pixelvalues shape0 expectedlogits torch tensor3 4613 0 9403 devicetorchdevice self asserttruetorch allcloseoutputs logitsperimage expectedlogits atol1e3 slow def testinferenceobjectdetectionself modelname googleowlvitbasepatch32 model owlvitforobjectdetection frompretrainedmodelname totorchdevice processor owlvitprocessor frompretrainedmodelname image prepareimg inputs processor texta photo of a cat a photo of a dog imagesimage maxlength16 paddingmaxlength returntensorspt totorchdevice with torch nograd outputs modelinputs numqueries intmodel config visionconfig imagesize model config visionconfig patchsize 2 self assertequaloutputs predboxes shape torch size1 numqueries 4 expectedsliceboxes torch tensor 0 0691 0 0445 0 1373 0 1592 0 0456 0 3192 0 1632 0 0423 0 2478 totorchdevice self asserttruetorch allcloseoutputs predboxes0 3 3 expectedsliceboxes atol1e4 slow def testinferenceoneshotobjectdetectionself modelname googleowlvitbasepatch32 model owlvitforobjectdetection frompretrainedmodelname totorchdevice processor owlvitprocessor frompretrainedmodelname image prepareimg queryimage prepareimg inputs processor imagesimage queryimagesqueryimage maxlength16 paddingmaxlength returntensorspt totorchdevice with torch nograd outputs model imageguideddetectioninputs numqueries intmodel config visionconfig imagesize model config visionconfig patchsize 2 self assertequaloutputs targetpredboxes shape torch size1 numqueries 4 expectedsliceboxes torch tensor 0 0691 0 0445 0 1373 0 1592 0 0456 0 3192 0 1632 0 0423 0 2478 totorchdevice self asserttruetorch allcloseoutputs targetpredboxes0 3 3 expectedsliceboxes atol1e4 slow requiretorchaccelerator requiretorchfp16 def testinferenceoneshotobjectdetectionfp16self modelname googleowlvitbasepatch32 model owlvitforobjectdetection frompretrainedmodelname torchdtypetorch float16 totorchdevice processor owlvitprocessor frompretrainedmodelname image prepareimg queryimage prepareimg inputs processor imagesimage queryimagesqueryimage maxlength16 paddingmaxlength returntensorspt totorchdevice with torch nograd outputs model imageguideddetectioninputs no need to check the logits we just check inference runs fine numqueries intmodel config visionconfig imagesize model config visionconfig patchsize 2 self assertequaloutputs targetpredboxes shape torch size1 numqueries 4 coding utf 8 2022 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license testing suite for the pytorch owlvit model in vit the seq length equals the number of patches 1 we add 1 for the cls token expected sequence length num_patches 1 we add 1 for the cls token here we also overwrite some of the tests of test_modeling_common py as owlvit does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic override as the logit_scale parameter initilization is different for owlvit check if logit_scale is initilized as per the original implementation to be sure we have no nan owlvit needs pixel_values save owlvitconfig and check if we can load owlvitvisionconfig from it save owlvitconfig and check if we can load owlvittextconfig from it to be sure we have no nan owlvit needs pixel_values we will verify our results on an image of cute cats forward pass verify the logits no need to check the logits we just check inference runs fine
import inspect
import os
import tempfile
import unittest

import numpy as np
import requests

from transformers import OwlViTConfig, OwlViTTextConfig, OwlViTVisionConfig
from transformers.testing_utils import (
    require_torch,
    require_torch_accelerator,
    require_torch_fp16,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    ModelTesterMixin,
    _config_zero_init,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import OwlViTForObjectDetection, OwlViTModel, OwlViTTextModel, OwlViTVisionModel
    from transformers.models.owlvit.modeling_owlvit import OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import OwlViTProcessor


class OwlViTVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=32,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.scope = scope

        
        num_patches = (image_size // patch_size) ** 2
        self.seq_length = num_patches + 1

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        config = self.get_config()

        return config, pixel_values

    def get_config(self):
        return OwlViTVisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values):
        model = OwlViTVisionModel(config=config).to(torch_device)
        model.eval()

        pixel_values = pixel_values.to(torch.float32)

        with torch.no_grad():
            result = model(pixel_values)
        
        num_patches = (self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class OwlViTVisionModelTest(ModelTesterMixin, unittest.TestCase):
    

    all_model_classes = (OwlViTVisionModel,) if is_torch_available() else ()
    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = OwlViTVisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=OwlViTVisionConfig, has_text_modality=False, hidden_size=37
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    @unittest.skip(reason="OWLVIT does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    def test_model_common_attributes(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training(self):
        pass

    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @unittest.skip(reason="OwlViTVisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="OwlViTVisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_to_base(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = OwlViTVisionModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


class OwlViTTextModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        num_queries=4,
        seq_length=16,
        is_training=True,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=64,
        num_hidden_layers=12,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        max_position_embeddings=16,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_queries = num_queries
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size * self.num_queries, self.seq_length], self.vocab_size)
        input_mask = None

        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size * self.num_queries, self.seq_length])

        if input_mask is not None:
            num_text, seq_length = input_mask.shape

            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(num_text,))
            for idx, start_index in enumerate(rnd_start_indices):
                input_mask[idx, :start_index] = 1
                input_mask[idx, start_index:] = 0

        config = self.get_config()

        return config, input_ids, input_mask

    def get_config(self):
        return OwlViTTextConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, input_ids, input_mask):
        model = OwlViTTextModel(config=config).to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_ids=input_ids, attention_mask=input_mask)

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

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, input_mask = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class OwlViTTextModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (OwlViTTextModel,) if is_torch_available() else ()
    fx_compatible = False
    test_pruning = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = OwlViTTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=OwlViTTextConfig, hidden_size=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training(self):
        pass

    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @unittest.skip(reason="OWLVIT does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="OwlViTTextModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="OwlViTTextModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_to_base(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = OwlViTTextModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


class OwlViTModelTester:
    def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
        if text_kwargs is None:
            text_kwargs = {}
        if vision_kwargs is None:
            vision_kwargs = {}

        self.parent = parent
        self.text_model_tester = OwlViTTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = OwlViTVisionModelTester(parent, **vision_kwargs)
        self.is_training = is_training
        self.text_config = self.text_model_tester.get_config().to_dict()
        self.vision_config = self.vision_model_tester.get_config().to_dict()

    def prepare_config_and_inputs(self):
        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
        config = self.get_config()
        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return OwlViTConfig.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64)

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = OwlViTModel(config).to(torch_device).eval()

        with torch.no_grad():
            result = model(
                input_ids=input_ids,
                pixel_values=pixel_values,
                attention_mask=attention_mask,
            )

        image_logits_size = (
            self.vision_model_tester.batch_size,
            self.text_model_tester.batch_size * self.text_model_tester.num_queries,
        )
        text_logits_size = (
            self.text_model_tester.batch_size * self.text_model_tester.num_queries,
            self.vision_model_tester.batch_size,
        )
        self.parent.assertEqual(result.logits_per_image.shape, image_logits_size)
        self.parent.assertEqual(result.logits_per_text.shape, text_logits_size)

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, attention_mask, pixel_values = config_and_inputs
        inputs_dict = {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "return_loss": False,
        }
        return config, inputs_dict


@require_torch
class OwlViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (OwlViTModel,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": OwlViTModel, "zero-shot-object-detection": OwlViTForObjectDetection}
        if is_torch_available()
        else {}
    )
    fx_compatible = False
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False

    def setUp(self):
        self.model_tester = OwlViTModelTester(self)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="Hidden_states is tested in individual model tests")
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Retain_grad is tested in individual model tests")
    def test_retain_grad_hidden_states_attentions(self):
        pass

    @unittest.skip(reason="OwlViTModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

    
    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if param.requires_grad:
                    
                    if name == "logit_scale":
                        self.assertAlmostEqual(
                            param.data.item(),
                            np.log(1 / 0.07),
                            delta=1e-3,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    def _create_and_check_torchscript(self, config, inputs_dict):
        if not self.test_torchscript:
            return

        configs_no_init = _config_zero_init(config)  
        configs_no_init.torchscript = True
        configs_no_init.return_dict = False
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init).to(torch_device)
            model.eval()

            try:
                input_ids = inputs_dict["input_ids"]
                pixel_values = inputs_dict["pixel_values"]  
                traced_model = torch.jit.trace(model, (input_ids, pixel_values))
            except RuntimeError:
                self.fail("Couldn't trace module.")

            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")

                try:
                    torch.jit.save(traced_model, pt_file_name)
                except Exception:
                    self.fail("Couldn't save module.")

                try:
                    loaded_model = torch.jit.load(pt_file_name)
                except Exception:
                    self.fail("Couldn't load module.")

            loaded_model = loaded_model.to(torch_device)
            loaded_model.eval()

            model_state_dict = model.state_dict()
            loaded_model_state_dict = loaded_model.state_dict()

            non_persistent_buffers = {}
            for key in loaded_model_state_dict.keys():
                if key not in model_state_dict.keys():
                    non_persistent_buffers[key] = loaded_model_state_dict[key]

            loaded_model_state_dict = {
                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
            }

            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))

            model_buffers = list(model.buffers())
            for non_persistent_buffer in non_persistent_buffers.values():
                found_buffer = False
                for i, model_buffer in enumerate(model_buffers):
                    if torch.equal(non_persistent_buffer, model_buffer):
                        found_buffer = True
                        break

                self.assertTrue(found_buffer)
                model_buffers.pop(i)

            models_equal = True
            for layer_name, p1 in model_state_dict.items():
                p2 = loaded_model_state_dict[layer_name]
                if p1.data.ne(p2.data).sum() > 0:
                    models_equal = False

            self.assertTrue(models_equal)

    def test_load_vision_text_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            vision_config = OwlViTVisionConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())

        
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = OwlViTTextConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())

    @slow
    def test_model_from_pretrained(self):
        for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = OwlViTModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


class OwlViTForObjectDetectionTester:
    def __init__(self, parent, is_training=True):
        self.parent = parent
        self.text_model_tester = OwlViTTextModelTester(parent)
        self.vision_model_tester = OwlViTVisionModelTester(parent)
        self.is_training = is_training
        self.text_config = self.text_model_tester.get_config().to_dict()
        self.vision_config = self.vision_model_tester.get_config().to_dict()

    def prepare_config_and_inputs(self):
        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
        config = self.get_config()
        return config, pixel_values, input_ids, attention_mask

    def get_config(self):
        return OwlViTConfig.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64)

    def create_and_check_model(self, config, pixel_values, input_ids, attention_mask):
        model = OwlViTForObjectDetection(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(
                pixel_values=pixel_values,
                input_ids=input_ids,
                attention_mask=attention_mask,
                return_dict=True,
            )

        pred_boxes_size = (
            self.vision_model_tester.batch_size,
            (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
            4,
        )
        pred_logits_size = (
            self.vision_model_tester.batch_size,
            (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
            4,
        )
        pred_class_embeds_size = (
            self.vision_model_tester.batch_size,
            (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
            self.text_model_tester.hidden_size,
        )
        self.parent.assertEqual(result.pred_boxes.shape, pred_boxes_size)
        self.parent.assertEqual(result.logits.shape, pred_logits_size)
        self.parent.assertEqual(result.class_embeds.shape, pred_class_embeds_size)

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values, input_ids, attention_mask = config_and_inputs
        inputs_dict = {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict


@require_torch
class OwlViTForObjectDetectionTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (OwlViTForObjectDetection,) if is_torch_available() else ()
    fx_compatible = False
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False

    def setUp(self):
        self.model_tester = OwlViTForObjectDetectionTester(self)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="Hidden_states is tested in individual model tests")
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Retain_grad is tested in individual model tests")
    def test_retain_grad_hidden_states_attentions(self):
        pass

    @unittest.skip(reason="OwlViTModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="Test_initialization is tested in individual model tests")
    def test_initialization(self):
        pass

    @unittest.skip(reason="Test_forward_signature is tested in individual model tests")
    def test_forward_signature(self):
        pass

    @unittest.skip(reason="Test_save_load_fast_init_from_base is tested in individual model tests")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training(self):
        pass

    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    def _create_and_check_torchscript(self, config, inputs_dict):
        if not self.test_torchscript:
            return

        configs_no_init = _config_zero_init(config)  
        configs_no_init.torchscript = True
        configs_no_init.return_dict = False
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init).to(torch_device)
            model.eval()

            try:
                input_ids = inputs_dict["input_ids"]
                pixel_values = inputs_dict["pixel_values"]  
                traced_model = torch.jit.trace(model, (input_ids, pixel_values))
            except RuntimeError:
                self.fail("Couldn't trace module.")

            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")

                try:
                    torch.jit.save(traced_model, pt_file_name)
                except Exception:
                    self.fail("Couldn't save module.")

                try:
                    loaded_model = torch.jit.load(pt_file_name)
                except Exception:
                    self.fail("Couldn't load module.")

            loaded_model = loaded_model.to(torch_device)
            loaded_model.eval()

            model_state_dict = model.state_dict()
            loaded_model_state_dict = loaded_model.state_dict()

            non_persistent_buffers = {}
            for key in loaded_model_state_dict.keys():
                if key not in model_state_dict.keys():
                    non_persistent_buffers[key] = loaded_model_state_dict[key]

            loaded_model_state_dict = {
                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
            }

            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))

            model_buffers = list(model.buffers())
            for non_persistent_buffer in non_persistent_buffers.values():
                found_buffer = False
                for i, model_buffer in enumerate(model_buffers):
                    if torch.equal(non_persistent_buffer, model_buffer):
                        found_buffer = True
                        break

                self.assertTrue(found_buffer)
                model_buffers.pop(i)

            models_equal = True
            for layer_name, p1 in model_state_dict.items():
                p2 = loaded_model_state_dict[layer_name]
                if p1.data.ne(p2.data).sum() > 0:
                    models_equal = False

            self.assertTrue(models_equal)

    @slow
    def test_model_from_pretrained(self):
        for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = OwlViTForObjectDetection.from_pretrained(model_name)
            self.assertIsNotNone(model)



def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im


@require_vision
@require_torch
class OwlViTModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "google/owlvit-base-patch32"
        model = OwlViTModel.from_pretrained(model_name).to(torch_device)
        processor = OwlViTProcessor.from_pretrained(model_name)

        image = prepare_img()
        inputs = processor(
            text=[["a photo of a cat", "a photo of a dog"]],
            images=image,
            max_length=16,
            padding="max_length",
            return_tensors="pt",
        ).to(torch_device)

        
        with torch.no_grad():
            outputs = model(**inputs)

        
        self.assertEqual(
            outputs.logits_per_image.shape,
            torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
        )
        self.assertEqual(
            outputs.logits_per_text.shape,
            torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
        )
        expected_logits = torch.tensor([[3.4613, 0.9403]], device=torch_device)
        self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))

    @slow
    def test_inference_object_detection(self):
        model_name = "google/owlvit-base-patch32"
        model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)

        processor = OwlViTProcessor.from_pretrained(model_name)

        image = prepare_img()
        inputs = processor(
            text=[["a photo of a cat", "a photo of a dog"]],
            images=image,
            max_length=16,
            padding="max_length",
            return_tensors="pt",
        ).to(torch_device)

        with torch.no_grad():
            outputs = model(**inputs)

        num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
        self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4)))

        expected_slice_boxes = torch.tensor(
            [[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]]
        ).to(torch_device)
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))

    @slow
    def test_inference_one_shot_object_detection(self):
        model_name = "google/owlvit-base-patch32"
        model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)

        processor = OwlViTProcessor.from_pretrained(model_name)

        image = prepare_img()
        query_image = prepare_img()
        inputs = processor(
            images=image,
            query_images=query_image,
            max_length=16,
            padding="max_length",
            return_tensors="pt",
        ).to(torch_device)

        with torch.no_grad():
            outputs = model.image_guided_detection(**inputs)

        num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
        self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))

        expected_slice_boxes = torch.tensor(
            [[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]]
        ).to(torch_device)
        self.assertTrue(torch.allclose(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))

    @slow
    @require_torch_accelerator
    @require_torch_fp16
    def test_inference_one_shot_object_detection_fp16(self):
        model_name = "google/owlvit-base-patch32"
        model = OwlViTForObjectDetection.from_pretrained(model_name, torch_dtype=torch.float16).to(torch_device)

        processor = OwlViTProcessor.from_pretrained(model_name)

        image = prepare_img()
        query_image = prepare_img()
        inputs = processor(
            images=image,
            query_images=query_image,
            max_length=16,
            padding="max_length",
            return_tensors="pt",
        ).to(torch_device)

        with torch.no_grad():
            outputs = model.image_guided_detection(**inputs)

        
        num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
        self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))