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 tensorflow mobilevit model from future import annotations import inspect import unittest from transformers import mobilevitconfig from transformers fileutils import istfavailable isvisionavailable from transformers testingutils import requiretf slow from testconfigurationcommon import configtester from testmodelingtfcommon import tfmodeltestermixin floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istfavailable import numpy as np import tensorflow as tf from transformers import tfmobilevitforimageclassification tfmobilevitforsemanticsegmentation tfmobilevitmodel from transformers models mobilevit modelingtfmobilevit import tfmobilevitpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import mobilevitimageprocessor class tfmobilevitconfigtesterconfigtester def createandtestconfigcommonpropertiesself config self configclassself inputsdict self parent asserttruehasattrconfig hiddensizes self parent asserttruehasattrconfig neckhiddensizes self parent asserttruehasattrconfig numattentionheads class tfmobilevitmodeltester def init self parent batchsize13 imagesize32 patchsize2 numchannels3 lasthiddensize32 numattentionheads4 hiddenactsilu convkernelsize3 outputstride32 hiddendropoutprob0 1 attentionprobsdropoutprob0 1 classifierdropoutprob0 1 initializerrange0 02 istrainingtrue uselabelstrue numlabels10 scopenone self parent parent self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels self lasthiddensize lasthiddensize self numattentionheads numattentionheads self hiddenact hiddenact self convkernelsize convkernelsize self outputstride outputstride self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self classifierdropoutprob classifierdropoutprob self uselabels uselabels self istraining istraining self numlabels numlabels self initializerrange initializerrange self scope scope def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none pixellabels none if self uselabels labels idstensorself batchsize self numlabels pixellabels idstensorself batchsize self imagesize self imagesize self numlabels config self getconfig return config pixelvalues labels pixellabels def getconfigself return mobilevitconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels numattentionheadsself numattentionheads hiddenactself hiddenact convkernelsizeself convkernelsize outputstrideself outputstride hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob classifierdropoutprobself classifierdropoutprob initializerrangeself initializerrange hiddensizes12 16 20 neckhiddensizes8 8 16 16 32 32 32 def createandcheckmodelself config pixelvalues labels pixellabels model tfmobilevitmodelconfigconfig result modelpixelvalues trainingfalse expectedheight expectedwidth self imagesize self outputstride self parent assertequal result lasthiddenstate shape self batchsize self lasthiddensize expectedheight expectedwidth def createandcheckforimageclassificationself config pixelvalues labels pixellabels config numlabels self numlabels model tfmobilevitforimageclassificationconfig result modelpixelvalues labelslabels trainingfalse self parent assertequalresult logits shape self batchsize self numlabels def createandcheckforsemanticsegmentationself config pixelvalues labels pixellabels config numlabels self numlabels model tfmobilevitforsemanticsegmentationconfig expectedheight expectedwidth self imagesize self outputstride result modelpixelvalues trainingfalse self parent assertequal result logits shape self batchsize self numlabels expectedheight expectedwidth result modelpixelvalues labelspixellabels trainingfalse self parent assertequal result logits shape self batchsize self numlabels expectedheight expectedwidth def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels pixellabels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretf class tfmobilevitmodeltesttfmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses tfmobilevitmodel tfmobilevitforimageclassification tfmobilevitforsemanticsegmentation if istfavailable else pipelinemodelmapping featureextraction tfmobilevitmodel imageclassification tfmobilevitforimageclassification if istfavailable else testpruning false testresizeembeddings false testheadmasking false hasattentions false testonnx false def setupself self modeltester tfmobilevitmodeltesterself self configtester tfmobilevitconfigtesterself configclassmobilevitconfig hastextmodalityfalse def testconfigself self configtester runcommontests unittest skipreasonmobilevit does not use inputsembeds def testinputsembedsself pass unittest skipreasonmobilevit does not support input and output embeddings def testmodelcommonattributesself pass unittest skipreasonmobilevit does not output attentions def testattentionoutputsself pass def testforwardsignatureself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig signature inspect signaturemodel call 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 testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumstages 5 self assertequallenhiddenstates expectednumstages mobilevit s feature maps are of shape batchsize numchannels height width with the width and height being successively divided by 2 divisor 2 for i in rangelenhiddenstates self assertlistequal listhiddenstatesi shape2 self modeltester imagesize divisor self modeltester imagesize divisor divisor 2 self assertequalself modeltester outputstride divisor 2 config inputsdict self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses inputsdictoutputhiddenstates true checkhiddenstatesoutputinputsdict config modelclass check that outputhiddenstates also work using config del inputsdictoutputhiddenstates config outputhiddenstates true checkhiddenstatesoutputinputsdict config modelclass def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs def testforsemanticsegmentationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforsemanticsegmentationconfigandinputs unittest skipif not istfavailable or lentf config listphysicaldevicesgpu 0 reasontf does not support backprop for grouped convolutions on cpu def testdatasetconversionself super testdatasetconversion def checkkerasfitresultsself valloss1 valloss2 atol2e1 rtol2e1 self asserttruenp allclosevalloss1 valloss2 atolatol rtolrtol unittest skipif not istfavailable or lentf config listphysicaldevicesgpu 0 reasontf does not support backprop for grouped convolutions on cpu slow def testkerasfitself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses since tfmobilevitmodel cannot operate with the default fit method if modelclass name tfmobilevitmodel model modelclassconfig if getattrmodel hfcomputeloss none super testkerasfit the default testlosscomputation uses 100 as a proxy ignoreindex to test masked losses overridding to avoid 100 since semantic segmentation models use semanticlossignoreindex from the config def testlosscomputationself config inputsdict self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses set an ignore index to correctly test the masked loss used in tfmobilevitforsemanticsegmentation if modelclass name tfmobilevitforsemanticsegmentation config semanticlossignoreindex 5 model modelclassconfig if getattrmodel hfcomputeloss none the number of elements in the loss should be the same as the number of elements in the label preparedforclass self prepareforclassinputsdict copy modelclass returnlabelstrue addedlabel preparedforclass sortedpreparedforclass keys inputsdict keys reversetrue0 expectedlosssize addedlabel shape aslist 1 test that model correctly compute the loss with kwargs preparedforclass self prepareforclassinputsdict copy modelclass returnlabelstrue possibleinputnames inputids pixelvalues inputfeatures inputname possibleinputnames intersectionsetpreparedforclass pop modelinput preparedforclass popinputname loss modelmodelinput preparedforclass0 self asserttrueloss shape aslist expectedlosssize or loss shape aslist 1 test that model correctly compute the loss when we mask some positions preparedforclass self prepareforclassinputsdict copy modelclass returnlabelstrue possibleinputnames inputids pixelvalues inputfeatures inputname possibleinputnames intersectionsetpreparedforclass pop modelinput preparedforclass popinputname if labels in preparedforclass labels preparedforclasslabels numpy if lenlabels shape 1 and labels shape1 1 labels0 100 preparedforclasslabels tf converttotensorlabels loss modelmodelinput preparedforclass0 self asserttrueloss shape aslist expectedlosssize or loss shape aslist 1 self asserttruenot np anynp isnanloss numpy test that model correctly compute the loss with a dict preparedforclass self prepareforclassinputsdict copy modelclass returnlabelstrue loss modelpreparedforclass0 self asserttrueloss shape aslist expectedlosssize or loss shape aslist 1 test that model correctly compute the loss with a tuple preparedforclass self prepareforclassinputsdict copy modelclass returnlabelstrue get keys that were added with the prepareforclass function labelkeys preparedforclass keys inputsdict keys signature inspect signaturemodel call parameters signaturenames listsignature keys create a dictionary holding the location of the tensors in the tuple tupleindexmapping 0 inputname for labelkey in labelkeys labelkeyindex signaturenames indexlabelkey tupleindexmappinglabelkeyindex labelkey sortedtupleindexmapping sortedtupleindexmapping items initialize a list with their default values update the values and convert to a tuple listinput for name in signaturenames if name kwargs listinput appendsignaturename default for index value in sortedtupleindexmapping listinputindex preparedforclassvalue tupleinput tuplelistinput send to model loss modeltupleinput 10 self asserttrueloss shape aslist expectedlosssize or loss shape aslist 1 slow def testmodelfrompretrainedself for modelname in tfmobilevitpretrainedmodelarchivelist 1 model tfmobilevitmodel frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg image image open testsfixturestestssamplescoco000000039769 png return image requiretf class tfmobilevitmodelintegrationtestunittest testcase slow def testinferenceimageclassificationheadself model tfmobilevitforimageclassification frompretrainedapplemobilevitxxsmall imageprocessor mobilevitimageprocessor frompretrainedapplemobilevitxxsmall image prepareimg inputs imageprocessorimagesimage returntensorstf forward pass outputs modelinputs trainingfalse verify the logits expectedshape tf tensorshape1 1000 self assertequaloutputs logits shape expectedshape expectedslice tf constant1 9364 1 2327 0 4653 tf debugging assertnearoutputs logits0 3 expectedslice atol1e4 rtol1e04 slow def testinferencesemanticsegmentationself frompt will be removed model tfmobilevitforsemanticsegmentation frompretrainedappledeeplabv3mobilevitxxsmall imageprocessor mobilevitimageprocessor frompretrainedappledeeplabv3mobilevitxxsmall image prepareimg inputs imageprocessorimagesimage returntensorstf forward pass outputs modelinputs pixelvalues trainingfalse logits outputs logits verify the logits expectedshape tf tensorshape1 21 32 32 self assertequallogits shape expectedshape expectedslice tf constant 6 9713 6 9786 7 2422 7 2893 7 2825 7 4446 7 6580 7 8797 7 9420 10 6869 10 3250 10 3471 10 4228 9 9868 9 7132 11 0405 11 0221 10 7318 3 3089 2 8539 2 6740 3 2706 2 5621 2 5108 3 2534 2 6615 2 6651 tf debugging assertnearlogits0 3 3 3 expectedslice rtol1e4 atol1e4 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 tensorflow mobilevit model here we also overwrite some of the tests of test_modeling_common py as mobilevit does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic mobilevit s feature maps are of shape batch_size num_channels height width with the width and height being successively divided by 2 check that output_hidden_states also work using config since tfmobilevitmodel cannot operate with the default fit method the default test_loss_computation uses 100 as a proxy ignore_index to test masked losses overridding to avoid 100 since semantic segmentation models use semantic_loss_ignore_index from the config set an ignore index to correctly test the masked loss used in tfmobilevitforsemanticsegmentation the number of elements in the loss should be the same as the number of elements in the label test that model correctly compute the loss with kwargs test that model correctly compute the loss when we mask some positions labels 0 100 test that model correctly compute the loss with a dict test that model correctly compute the loss with a tuple get keys that were added with the _prepare_for_class function create a dictionary holding the location of the tensors in the tuple initialize a list with their default values update the values and convert to a tuple send to model we will verify our results on an image of cute cats forward pass verify the logits from_pt will be removed forward pass verify the logits
from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
    from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor


class TFMobileViTConfigTester(ConfigTester):
    def create_and_test_config_common_properties(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "hidden_sizes"))
        self.parent.assertTrue(hasattr(config, "neck_hidden_sizes"))
        self.parent.assertTrue(hasattr(config, "num_attention_heads"))


class TFMobileViTModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=32,
        patch_size=2,
        num_channels=3,
        last_hidden_size=32,
        num_attention_heads=4,
        hidden_act="silu",
        conv_kernel_size=3,
        output_stride=32,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        classifier_dropout_prob=0.1,
        initializer_range=0.02,
        is_training=True,
        use_labels=True,
        num_labels=10,
        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.last_hidden_size = last_hidden_size
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.conv_kernel_size = conv_kernel_size
        self.output_stride = output_stride
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.classifier_dropout_prob = classifier_dropout_prob
        self.use_labels = use_labels
        self.is_training = is_training
        self.num_labels = num_labels
        self.initializer_range = initializer_range
        self.scope = scope

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

        labels = None
        pixel_labels = None
        if self.use_labels:
            labels = ids_tensor([self.batch_size], self.num_labels)
            pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)

        config = self.get_config()

        return config, pixel_values, labels, pixel_labels

    def get_config(self):
        return MobileViTConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            num_attention_heads=self.num_attention_heads,
            hidden_act=self.hidden_act,
            conv_kernel_size=self.conv_kernel_size,
            output_stride=self.output_stride,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            classifier_dropout_prob=self.classifier_dropout_prob,
            initializer_range=self.initializer_range,
            hidden_sizes=[12, 16, 20],
            neck_hidden_sizes=[8, 8, 16, 16, 32, 32, 32],
        )

    def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
        model = TFMobileViTModel(config=config)
        result = model(pixel_values, training=False)
        expected_height = expected_width = self.image_size // self.output_stride
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.last_hidden_size, expected_height, expected_width)
        )

    def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
        config.num_labels = self.num_labels
        model = TFMobileViTForImageClassification(config)
        result = model(pixel_values, labels=labels, training=False)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
        config.num_labels = self.num_labels
        model = TFMobileViTForSemanticSegmentation(config)
        expected_height = expected_width = self.image_size // self.output_stride

        result = model(pixel_values, training=False)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
        )

        result = model(pixel_values, labels=pixel_labels, training=False)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
        )

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


@require_tf
class TFMobileViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (
        (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
        if is_tf_available()
        else ()
    )
    pipeline_model_mapping = (
        {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
        if is_tf_available()
        else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    has_attentions = False
    test_onnx = False

    def setUp(self):
        self.model_tester = TFMobileViTModelTester(self)
        self.config_tester = TFMobileViTConfigTester(self, config_class=MobileViTConfig, has_text_modality=False)

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

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

    @unittest.skip(reason="MobileViT does not support input and output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="MobileViT does not output attentions")
    def test_attention_outputs(self):
        pass

    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.call)
            
            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_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)

            outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.hidden_states

            expected_num_stages = 5
            self.assertEqual(len(hidden_states), expected_num_stages)

            
            
            divisor = 2
            for i in range(len(hidden_states)):
                self.assertListEqual(
                    list(hidden_states[i].shape[-2:]),
                    [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],
                )
                divisor *= 2

            self.assertEqual(self.model_tester.output_stride, divisor // 2)

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            
            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)

    def test_for_image_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)

    def test_for_semantic_segmentation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)

    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
        reason="TF does not support backprop for grouped convolutions on CPU.",
    )
    def test_dataset_conversion(self):
        super().test_dataset_conversion()

    def check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1):
        self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol))

    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
        reason="TF does not support backprop for grouped convolutions on CPU.",
    )
    @slow
    def test_keras_fit(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            
            if model_class.__name__ != "TFMobileViTModel":
                model = model_class(config)
                if getattr(model, "hf_compute_loss", None):
                    super().test_keras_fit()

    
    
    
    def test_loss_computation(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            
            
            if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
                config.semantic_loss_ignore_index = 5

            model = model_class(config)
            if getattr(model, "hf_compute_loss", None):
                
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                added_label = prepared_for_class[
                    sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
                ]
                expected_loss_size = added_label.shape.as_list()[:1]

                
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                possible_input_names = {"input_ids", "pixel_values", "input_features"}
                input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
                model_input = prepared_for_class.pop(input_name)

                loss = model(model_input, **prepared_for_class)[0]
                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                possible_input_names = {"input_ids", "pixel_values", "input_features"}
                input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
                model_input = prepared_for_class.pop(input_name)
                if "labels" in prepared_for_class:
                    labels = prepared_for_class["labels"].numpy()
                    if len(labels.shape) > 1 and labels.shape[1] != 1:
                        
                        prepared_for_class["labels"] = tf.convert_to_tensor(labels)
                        loss = model(model_input, **prepared_for_class)[0]
                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
                        self.assertTrue(not np.any(np.isnan(loss.numpy())))

                
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                loss = model(prepared_for_class)[0]
                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

                
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)

                
                label_keys = prepared_for_class.keys() - inputs_dict.keys()
                signature = inspect.signature(model.call).parameters
                signature_names = list(signature.keys())

                
                tuple_index_mapping = {0: input_name}
                for label_key in label_keys:
                    label_key_index = signature_names.index(label_key)
                    tuple_index_mapping[label_key_index] = label_key
                sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
                
                list_input = []

                for name in signature_names:
                    if name != "kwargs":
                        list_input.append(signature[name].default)

                for index, value in sorted_tuple_index_mapping:
                    list_input[index] = prepared_for_class[value]

                tuple_input = tuple(list_input)

                
                loss = model(tuple_input[:-1])[0]

                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])

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



def prepare_img():
    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image


@require_tf
class TFMobileViTModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_image_classification_head(self):
        model = TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small")

        image_processor = MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small")
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="tf")

        
        outputs = model(**inputs, training=False)

        
        expected_shape = tf.TensorShape((1, 1000))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = tf.constant([-1.9364, -1.2327, -0.4653])

        tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4, rtol=1e-04)

    @slow
    def test_inference_semantic_segmentation(self):
        
        model = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

        image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="tf")

        
        outputs = model(inputs.pixel_values, training=False)
        logits = outputs.logits

        
        expected_shape = tf.TensorShape((1, 21, 32, 32))
        self.assertEqual(logits.shape, expected_shape)

        expected_slice = tf.constant(
            [
                [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
            ]
        )

        tf.debugging.assert_near(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)