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 groupvit model import inspect import os import random import tempfile import unittest import numpy as np import requests from transformers import groupvitconfig groupvittextconfig groupvitvisionconfig from transformers testingutils import ispttfcrosstest requiretorch 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 groupvitmodel groupvittextmodel groupvitvisionmodel from transformers models groupvit modelinggroupvit import groupvitpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import clipprocessor class groupvitvisionmodeltester def init self parent batchsize12 imagesize30 patchsize2 numchannels3 istrainingtrue hiddensize32 depths6 3 3 numgrouptokens64 8 0 numoutputgroups64 8 8 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 depths depths self numhiddenlayers sumdepths self expectednumhiddenlayers lendepths 1 self numgrouptokens numgrouptokens self numoutputgroups numoutputgroups self numattentionheads numattentionheads self intermediatesize intermediatesize self dropout dropout self attentiondropout attentiondropout self initializerrange initializerrange self scope scope numpatches imagesize patchsize 2 no cls token for groupvit self seqlength numpatches def prepareconfigandinputsself rng random random0 pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize rngrng config self getconfig return config pixelvalues def getconfigself return groupvitvisionconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize depthsself depths numgrouptokensself numgrouptokens numoutputgroupsself numoutputgroups numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout initializerrangeself initializerrange def createandcheckmodelself config pixelvalues model groupvitvisionmodelconfigconfig model totorchdevice model eval with torch nograd result modelpixelvalues self parent assertequal result lasthiddenstate shape self batchsize self numoutputgroups1 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 groupvitvisionmodeltestmodeltestermixin unittest testcase allmodelclasses groupvitvisionmodel if istorchavailable else testpruning false testtorchscript false testresizeembeddings false testheadmasking false def setupself self modeltester groupvitvisionmodeltesterself self configtester configtester self configclassgroupvitvisionconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasongroupvit does not use inputsembeds def testinputsembedsself pass ispttfcrosstest def testpttfmodelequivalenceself import tensorflow as tf seed 338 random seedseed np random seedseed torch manualseedseed torch cuda manualseedallseed tf random setseedseed return super testpttfmodelequivalence 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 def testattentionoutputsself config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true seqlen getattrself modeltester seqlength none expectednumattentionoutputs sumg 0 for g in self modeltester numgrouptokens for modelclass in self allmodelclasses inputsdictoutputattentions true inputsdictoutputhiddenstates false config returndict true model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass attentions outputs attentions groupvit returns attention grouping of each stage self assertequallenattentions sumg 0 for g in self modeltester numgrouptokens check that outputattentions also work using config del inputsdictoutputattentions config outputattentions true model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass attentions outputs attentions groupvit returns attention grouping of each stage self assertequallenattentions expectednumattentionoutputs outlen lenoutputs check attention is always last and order is fine inputsdictoutputattentions true inputsdictoutputhiddenstates true model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass addedhiddenstates 1 self assertequaloutlen addedhiddenstates lenoutputs selfattentions outputs attentions groupvit returns attention grouping of each stage self assertequallenselfattentions expectednumattentionoutputs for i selfattn in enumerateselfattentions if selfattn is none continue self assertlistequal listselfattentionsi shape2 self modeltester numoutputgroupsi self modeltester numoutputgroupsi 1 if i 0 else seqlen def testtrainingself pass 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 skipreasongroupvitvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasongroupvitvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass override since the attention mask from groupvit is not used to compute loss thus no grad def testretaingradhiddenstatesattentionsself config inputsdict self modeltester prepareconfigandinputsforcommon config outputhiddenstates true config outputattentions self hasattentions no need to test all models as different heads yield the same functionality modelclass self allmodelclasses0 model modelclassconfig model totorchdevice inputs self prepareforclassinputsdict modelclass outputs modelinputs output outputs0 if config isencoderdecoder seq2seq models encoderhiddenstates outputs encoderhiddenstates0 encoderhiddenstates retaingrad decoderhiddenstates outputs decoderhiddenstates0 decoderhiddenstates retaingrad if self hasattentions encoderattentions outputs encoderattentions0 encoderattentions retaingrad decoderattentions outputs decoderattentions0 decoderattentions retaingrad crossattentions outputs crossattentions0 crossattentions retaingrad output flatten0 backwardretaingraphtrue self assertisnotnoneencoderhiddenstates grad self assertisnotnonedecoderhiddenstates grad if self hasattentions self assertisnotnoneencoderattentions grad self assertisnotnonedecoderattentions grad self assertisnotnonecrossattentions grad else encoderdecoderonly models hiddenstates outputs hiddenstates0 hiddenstates retaingrad if self hasattentions attentions outputs attentions0 attentions retaingrad output flatten0 backwardretaingraphtrue self assertisnotnonehiddenstates grad if self hasattentions self assertisnoneattentions grad slow def testmodelfrompretrainedself for modelname in groupvitpretrainedmodelarchivelist 1 model groupvitvisionmodel frompretrainedmodelname self assertisnotnonemodel class groupvittextmodeltester def init self parent batchsize12 seqlength7 istrainingtrue useinputmasktrue uselabelstrue vocabsize99 hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 dropout0 1 attentiondropout0 1 maxpositionembeddings512 initializerrange0 02 scopenone self parent parent self batchsize batchsize 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 rng random random0 inputids idstensorself batchsize self seqlength self vocabsize rngrng inputmask none if self useinputmask inputmask randomattentionmaskself batchsize self seqlength if inputmask is not none batchsize seqlength inputmask shape rndstartindices np random randint1 seqlength 1 sizebatchsize for batchidx startindex in enumeraterndstartindices inputmaskbatchidx startindex 1 inputmaskbatchidx startindex 0 config self getconfig return config inputids inputmask def getconfigself return groupvittextconfig vocabsizeself vocabsize hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout maxpositionembeddingsself maxpositionembeddings initializerrangeself initializerrange def createandcheckmodelself config inputids inputmask model groupvittextmodelconfigconfig model totorchdevice model eval with torch nograd result modelinputids attentionmaskinputmask result modelinputids self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize self parent assertequalresult pooleroutput shape self batchsize self hiddensize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids inputmask configandinputs inputsdict inputids inputids attentionmask inputmask return config inputsdict requiretorch class groupvittextmodeltestmodeltestermixin unittest testcase allmodelclasses groupvittextmodel if istorchavailable else testpruning false testheadmasking false def setupself self modeltester groupvittextmodeltesterself self configtester configtesterself configclassgroupvittextconfig hiddensize37 def testconfigself self configtester runcommontests def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testtrainingself pass 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 skipreasongroupvittextmodel does not use inputsembeds def testinputsembedsself pass unittest skipreasongroupvittextmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasongroupvittextmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in groupvitpretrainedmodelarchivelist 1 model groupvittextmodel frompretrainedmodelname self assertisnotnonemodel class groupvitmodeltester def initself parent textkwargsnone visionkwargsnone istrainingtrue if textkwargs is none textkwargs if visionkwargs is none visionkwargs self parent parent self textmodeltester groupvittextmodeltesterparent textkwargs self visionmodeltester groupvitvisionmodeltesterparent visionkwargs self istraining istraining def prepareconfigandinputsself textconfig inputids attentionmask self textmodeltester prepareconfigandinputs visionconfig pixelvalues self visionmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask pixelvalues def getconfigself return groupvitconfig fromtextvisionconfigs self textmodeltester getconfig self visionmodeltester getconfig projectiondim64 def createandcheckmodelself config inputids attentionmask pixelvalues model groupvitmodelconfig totorchdevice eval with torch nograd result modelinputids pixelvalues attentionmask self parent assertequal result logitsperimage shape self visionmodeltester batchsize self textmodeltester batchsize self parent assertequal result logitspertext shape self textmodeltester batchsize self visionmodeltester batchsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask pixelvalues configandinputs inputsdict inputids inputids attentionmask attentionmask pixelvalues pixelvalues returnloss true return config inputsdict requiretorch class groupvitmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses groupvitmodel if istorchavailable else pipelinemodelmapping featureextraction groupvitmodel if istorchavailable else testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false def setupself self modeltester groupvitmodeltesterself def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs unittest skipreasonhiddenstates are tested in individual model tests def testhiddenstatesoutputself pass unittest skipreasoninputembeds are tested in individual model tests def testinputsembedsself pass unittest skipreasontested in individual model tests def testretaingradhiddenstatesattentionsself pass unittest skipreasongroupvitmodel does not have inputoutput embeddings def testmodelcommonattributesself pass overwritten from parent as this equivalent test needs a specific seed and hard to get a good one def checkpttfoutputsself tfoutputs ptoutputs modelclass tol2e5 nameoutputs attributesnone super checkpttfoutputstfoutputs ptoutputs modelclass toltol namename attributesattributes ispttfcrosstest def testpttfmodelequivalenceself import tensorflow as tf seed 163 random seedseed np random seedseed torch manualseedseed torch cuda manualseedallseed tf random setseedseed return super testpttfmodelequivalence override as the logitscale parameter initilization is different for groupvit 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 model totorchdevice model eval try inputids inputsdictinputids pixelvalues inputsdictpixelvalues groupvit 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 model totorchdevice model eval 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 groupvitconfig and check if we can load groupvitvisionconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname visionconfig groupvitvisionconfig frompretrainedtmpdirname self assertdictequalconfig visionconfig todict visionconfig todict save groupvitconfig and check if we can load groupvittextconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname textconfig groupvittextconfig frompretrainedtmpdirname self assertdictequalconfig textconfig todict textconfig todict slow def testmodelfrompretrainedself for modelname in groupvitpretrainedmodelarchivelist 1 model groupvitmodel 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 groupvitmodelintegrationtestunittest testcase slow def testinferenceself modelname nvidiagroupvitgccyfcc model groupvitmodel frompretrainedmodelname processor clipprocessor frompretrainedmodelname image prepareimg inputs processor texta photo of a cat a photo of a dog imagesimage paddingtrue returntensorspt 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 tensor13 3523 6 3629 self asserttruetorch allcloseoutputs logitsperimage expectedlogits atol1e3 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 groupvit model no cls token for groupvit here we also overwrite some of the tests of test_modeling_common py as groupvit does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic groupvit returns attention grouping of each stage check that output_attentions also work using config groupvit returns attention grouping of each stage check attention is always last and order is fine groupvit returns attention grouping of each stage override since the attention mask from groupvit is not used to compute loss thus no grad no need to test all models as different heads yield the same functionality seq2seq models encoder decoder only models overwritten from parent as this equivalent test needs a specific seed and hard to get a good one override as the logit_scale parameter initilization is different for groupvit check if logit_scale is initilized as per the original implementation to be sure we have no nan groupvit needs pixel_values save groupvitconfig and check if we can load groupvitvisionconfig from it save groupvitconfig and check if we can load groupvittextconfig from it we will verify our results on an image of cute cats forward pass verify the logits
import inspect
import os
import random
import tempfile
import unittest

import numpy as np
import requests

from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
from transformers.testing_utils import is_pt_tf_cross_test, require_torch, 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 GroupViTModel, GroupViTTextModel, GroupViTVisionModel
    from transformers.models.groupvit.modeling_groupvit import GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import CLIPProcessor


class GroupViTVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        depths=[6, 3, 3],
        num_group_tokens=[64, 8, 0],
        num_output_groups=[64, 8, 8],
        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.depths = depths
        self.num_hidden_layers = sum(depths)
        self.expected_num_hidden_layers = len(depths) + 1
        self.num_group_tokens = num_group_tokens
        self.num_output_groups = num_output_groups
        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

    def prepare_config_and_inputs(self):
        rng = random.Random(0)
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size], rng=rng)
        config = self.get_config()

        return config, pixel_values

    def get_config(self):
        return GroupViTVisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            depths=self.depths,
            num_group_tokens=self.num_group_tokens,
            num_output_groups=self.num_output_groups,
            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 = GroupViTVisionModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.num_output_groups[-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 GroupViTVisionModelTest(ModelTesterMixin, unittest.TestCase):
    

    all_model_classes = (GroupViTVisionModel,) if is_torch_available() else ()

    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False
    test_head_masking = False

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

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

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

    @is_pt_tf_cross_test
    def test_pt_tf_model_equivalence(self):
        import tensorflow as tf

        seed = 338
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
        tf.random.set_seed(seed)
        return super().test_pt_tf_model_equivalence()

    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)

    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        seq_len = getattr(self.model_tester, "seq_length", None)

        expected_num_attention_outputs = sum(g > 0 for g in self.model_tester.num_group_tokens)

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            config.return_dict = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions
            
            self.assertEqual(len(attentions), sum(g > 0 for g in self.model_tester.num_group_tokens))

            
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions
            
            self.assertEqual(len(attentions), expected_num_attention_outputs)

            out_len = len(outputs)

            
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            added_hidden_states = 1
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.attentions

            
            self.assertEqual(len(self_attentions), expected_num_attention_outputs)
            for i, self_attn in enumerate(self_attentions):
                if self_attn is None:
                    continue

                self.assertListEqual(
                    list(self_attentions[i].shape[-2:]),
                    [
                        self.model_tester.num_output_groups[i],
                        self.model_tester.num_output_groups[i - 1] if i > 0 else seq_len,
                    ],
                )

    def test_training(self):
        pass

    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="GroupViTVisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

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

    
    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = self.has_attentions

        
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        inputs = self._prepare_for_class(inputs_dict, model_class)

        outputs = model(**inputs)

        output = outputs[0]

        if config.is_encoder_decoder:
            
            encoder_hidden_states = outputs.encoder_hidden_states[0]
            encoder_hidden_states.retain_grad()

            decoder_hidden_states = outputs.decoder_hidden_states[0]
            decoder_hidden_states.retain_grad()

            if self.has_attentions:
                encoder_attentions = outputs.encoder_attentions[0]
                encoder_attentions.retain_grad()

                decoder_attentions = outputs.decoder_attentions[0]
                decoder_attentions.retain_grad()

                cross_attentions = outputs.cross_attentions[0]
                cross_attentions.retain_grad()

            output.flatten()[0].backward(retain_graph=True)

            self.assertIsNotNone(encoder_hidden_states.grad)
            self.assertIsNotNone(decoder_hidden_states.grad)

            if self.has_attentions:
                self.assertIsNotNone(encoder_attentions.grad)
                self.assertIsNotNone(decoder_attentions.grad)
                self.assertIsNotNone(cross_attentions.grad)
        else:
            
            hidden_states = outputs.hidden_states[0]
            hidden_states.retain_grad()

            if self.has_attentions:
                attentions = outputs.attentions[0]
                attentions.retain_grad()

            output.flatten()[0].backward(retain_graph=True)

            self.assertIsNotNone(hidden_states.grad)

            if self.has_attentions:
                self.assertIsNone(attentions.grad)

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


class GroupViTTextModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        max_position_embeddings=512,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        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):
        rng = random.Random(0)
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, rng=rng)

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])

        if input_mask is not None:
            batch_size, seq_length = input_mask.shape
            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
            for batch_idx, start_index in enumerate(rnd_start_indices):
                input_mask[batch_idx, :start_index] = 1
                input_mask[batch_idx, start_index:] = 0

        config = self.get_config()

        return config, input_ids, input_mask

    def get_config(self):
        return GroupViTTextConfig(
            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 = GroupViTTextModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_ids, attention_mask=input_mask)
            result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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, input_ids, input_mask = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class GroupViTTextModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (GroupViTTextModel,) if is_torch_available() else ()
    test_pruning = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = GroupViTTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=GroupViTTextConfig, 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)

    def test_training(self):
        pass

    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="GroupViTTextModel does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

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

    @unittest.skip(reason="GroupViTTextModel 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 GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = GroupViTTextModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


class GroupViTModelTester:
    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 = GroupViTTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = GroupViTVisionModelTester(parent, **vision_kwargs)
        self.is_training = is_training

    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 GroupViTConfig.from_text_vision_configs(
            self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
        )

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = GroupViTModel(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(input_ids, pixel_values, attention_mask)
        self.parent.assertEqual(
            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
        )
        self.parent.assertEqual(
            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_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 = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "pixel_values": pixel_values,
            "return_loss": True,
        }
        return config, inputs_dict


@require_torch
class GroupViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (GroupViTModel,) if is_torch_available() else ()
    pipeline_model_mapping = {"feature-extraction": GroupViTModel} if is_torch_available() else {}
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False

    def setUp(self):
        self.model_tester = GroupViTModelTester(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 are tested in individual model tests")
    def test_hidden_states_output(self):
        pass

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

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

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

    
    def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-5, name="outputs", attributes=None):
        super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol=tol, name=name, attributes=attributes)

    @is_pt_tf_cross_test
    def test_pt_tf_model_equivalence(self):
        import tensorflow as tf

        seed = 163
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
        tf.random.set_seed(seed)
        return super().test_pt_tf_model_equivalence()

    
    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)
            model.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.")

            model.to(torch_device)
            model.eval()

            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 = GroupViTVisionConfig.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 = GroupViTTextConfig.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 GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = GroupViTModel.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 GroupViTModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "nvidia/groupvit-gcc-yfcc"
        model = GroupViTModel.from_pretrained(model_name)
        processor = CLIPProcessor.from_pretrained(model_name)

        image = prepare_img()
        inputs = processor(
            text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="pt"
        )

        
        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([[13.3523, 6.3629]])

        self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))