codingutf8 2021 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 beit model import unittest from datasets import loaddataset from packaging import version from transformers import beitconfig from transformers models auto import getvalues from transformers testingutils import requiretorch requiretorchmultigpu requirevision slow torchdevice from transformers utils import cachedproperty istorchavailable isvisionavailable from testbackbonecommon import backbonetestermixin from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin configzeroinit floatstensor idstensor from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from torch import nn from transformers import modelforbackbonemapping modelmapping beitbackbone beitforimageclassification beitformaskedimagemodeling beitforsemanticsegmentation beitmodel from transformers models beit modelingbeit import beitpretrainedmodelarchivelist if isvisionavailable import pil from pil import image from transformers import beitimageprocessor class beitmodeltester def init self parent vocabsize100 batchsize13 imagesize30 patchsize2 numchannels3 istrainingtrue uselabelstrue hiddensize32 numhiddenlayers4 numattentionheads4 intermediatesize37 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 typesequencelabelsize10 initializerrange0 02 numlabels3 scopenone outindices1 2 3 4 outfeaturesstage1 stage2 stage3 stage4 self parent parent self vocabsize vocabsize self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels self istraining istraining self uselabels uselabels self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self typesequencelabelsize typesequencelabelsize self initializerrange initializerrange self scope scope self outindices outindices self outfeatures outfeatures self numlabels numlabels in beit 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 labels none pixellabels none if self uselabels labels idstensorself batchsize self typesequencelabelsize pixellabels idstensorself batchsize self imagesize self imagesize self numlabels config self getconfig return config pixelvalues labels pixellabels def getconfigself return beitconfig vocabsizeself vocabsize imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob isdecoderfalse initializerrangeself initializerrange outindicesself outindices outfeaturesself outfeatures def createandcheckmodelself config pixelvalues labels pixellabels model beitmodelconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize def createandcheckbackboneself config pixelvalues labels pixellabels model beitbackboneconfigconfig model totorchdevice model eval result modelpixelvalues verify hidden states self parent assertequallenresult featuremaps lenconfig outfeatures expectedheight expectedwidth self imagesize config patchsize self parent assertlistequal listresult featuremaps0 shape self batchsize self hiddensize expectedheight expectedwidth verify channels self parent assertequallenmodel channels lenconfig outfeatures verify backbone works with outfeaturesnone config outfeatures none model beitbackboneconfigconfig model totorchdevice model eval result modelpixelvalues verify feature maps self parent assertequallenresult featuremaps 1 self parent assertlistequal listresult featuremaps0 shape self batchsize self hiddensize expectedheight expectedwidth verify channels self parent assertequallenmodel channels 1 def createandcheckformaskedlmself config pixelvalues labels pixellabels model beitformaskedimagemodelingconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequalresult logits shape self batchsize self seqlength 1 self vocabsize def createandcheckforimageclassificationself config pixelvalues labels pixellabels config numlabels self typesequencelabelsize model beitforimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self typesequencelabelsize test greyscale images config numchannels 1 model beitforimageclassificationconfig model totorchdevice model eval pixelvalues floatstensorself batchsize 1 self imagesize self imagesize result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self typesequencelabelsize def createandcheckforsemanticsegmentationself config pixelvalues labels pixellabels config numlabels self numlabels model beitforsemanticsegmentationconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result logits shape self batchsize self numlabels self imagesize 2 self imagesize 2 result modelpixelvalues labelspixellabels self parent assertequal result logits shape self batchsize self numlabels self imagesize 2 self imagesize 2 def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels pixellabels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class beitmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses beitmodel beitforimageclassification beitformaskedimagemodeling beitforsemanticsegmentation beitbackbone if istorchavailable else pipelinemodelmapping featureextraction beitmodel imageclassification beitforimageclassification imagesegmentation beitforsemanticsegmentation if istorchavailable else testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester beitmodeltesterself self configtester configtesterself configclassbeitconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasonbeit does not use inputsembeds def testinputsembedsself pass requiretorchmultigpu unittest skipreasonbeit has some layers using addmodule which doesn t work well with nn dataparallel def testmultigpudataparallelforwardself pass unittest skipreasonbeit does not support feedforward chunking yet def testfeedforwardchunkingself 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 testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testbackboneself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckbackboneconfigandinputs def testformaskedlmself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckformaskedlmconfigandinputs def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs def testforsemanticsegmentationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforsemanticsegmentationconfigandinputs def testtrainingself if not self modeltester istraining return config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true for modelclass in self allmodelclasses we don t test beitformaskedimagemodeling if modelclass in getvaluesmodelmapping getvaluesmodelforbackbonemapping beitformaskedimagemodeling continue model modelclassconfig model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward def testtraininggradientcheckpointingself config inputsdict self modeltester prepareconfigandinputsforcommon if not self modeltester istraining return config usecache false config returndict true for modelclass in self allmodelclasses we don t test beitformaskedimagemodeling if modelclass in getvaluesmodelmapping getvaluesmodelforbackbonemapping beitformaskedimagemodeling or not modelclass supportsgradientcheckpointing continue model modelclassconfig model gradientcheckpointingenable model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue loss modelinputs loss loss backward 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 testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit for name param in model namedparameters we skip lambda parameters as these require special initial values determined by config layerscaleinitvalue if lambda in name continue if param requiresgrad self assertin param data mean 1e9 round 1e9 item 0 0 1 0 msgfparameter name of model modelclass seems not properly initialized slow def testmodelfrompretrainedself for modelname in beitpretrainedmodelarchivelist 1 model beitmodel frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg image image open testsfixturestestssamplescoco000000039769 png return image requiretorch requirevision class beitmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return beitimageprocessor frompretrainedmicrosoftbeitbasepatch16224 if isvisionavailable else none slow def testinferencemaskedimagemodelingheadself model beitformaskedimagemodeling frompretrainedmicrosoftbeitbasepatch16224pt22k totorchdevice imageprocessor self defaultimageprocessor image prepareimg pixelvalues imageprocessorimagesimage returntensorspt pixelvalues totorchdevice prepare boolmaskedpos boolmaskedpos torch ones1 196 dtypetorch bool totorchdevice forward pass with torch nograd outputs modelpixelvaluespixelvalues boolmaskedposboolmaskedpos logits outputs logits verify the logits expectedshape torch size1 196 8192 self assertequallogits shape expectedshape expectedslice torch tensor 3 2437 0 5072 13 9174 3 2456 0 4948 13 9401 3 2033 0 5121 13 8550 totorchdevice self asserttruetorch allcloselogitsboolmaskedpos 3 3 expectedslice atol1e2 slow def testinferenceimageclassificationheadimagenet1kself model beitforimageclassification frompretrainedmicrosoftbeitbasepatch16224 totorchdevice imageprocessor self defaultimageprocessor image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs logits outputs logits verify the logits expectedshape torch size1 1000 self assertequallogits shape expectedshape expectedslice torch tensor1 2385 1 0987 1 0108 totorchdevice self asserttruetorch allcloselogits0 3 expectedslice atol1e4 expectedclassidx 281 self assertequallogits argmax1 item expectedclassidx slow def testinferenceimageclassificationheadimagenet22kself model beitforimageclassification frompretrainedmicrosoftbeitlargepatch16224pt22kft22k to torchdevice imageprocessor self defaultimageprocessor image prepareimg inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs logits outputs logits verify the logits expectedshape torch size1 21841 self assertequallogits shape expectedshape expectedslice torch tensor1 6881 0 2787 0 5901 totorchdevice self asserttruetorch allcloselogits0 3 expectedslice atol1e4 expectedclassidx 2396 self assertequallogits argmax1 item expectedclassidx slow def testinferencesemanticsegmentationself model beitforsemanticsegmentation frompretrainedmicrosoftbeitbasefinetunedade640640 model model totorchdevice imageprocessor beitimageprocessordoresizetrue size640 docentercropfalse ds loaddatasethfinternaltestingfixturesade20k splittest image image opends0file inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs logits outputs logits verify the logits expectedshape torch size1 150 160 160 self assertequallogits shape expectedshape ispillowlessthan9 version parsepil version version parse9 0 0 if ispillowlessthan9 expectedslice torch tensor 4 9225 2 3954 3 0522 2 8822 1 0046 1 7561 2 9549 1 3228 2 1347 5 8168 3 4129 4 0778 3 8651 2 2214 3 0277 3 8356 2 4643 3 3535 0 0078 3 9952 4 0754 2 9856 4 6944 5 0035 3 2413 4 7813 4 9969 devicetorchdevice else expectedslice torch tensor 4 8960 2 3688 3 0355 2 8478 0 9836 1 7418 2 9449 1 3332 2 1456 5 8081 3 4124 4 1006 3 8561 2 2081 3 0323 3 8365 2 4601 3 3669 0 0309 3 9868 4 0540 2 9640 4 6877 4 9976 3 2081 4 7690 4 9942 devicetorchdevice self asserttruetorch allcloselogits0 3 3 3 expectedslice atol1e4 slow def testpostprocessingsemanticsegmentationself model beitforsemanticsegmentation frompretrainedmicrosoftbeitbasefinetunedade640640 model model totorchdevice imageprocessor beitimageprocessordoresizetrue size640 docentercropfalse ds loaddatasethfinternaltestingfixturesade20k splittest image image opends0file inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs outputs logits outputs logits detach cpu segmentation imageprocessor postprocesssemanticsegmentationoutputsoutputs targetsizes500 300 expectedshape torch size500 300 self assertequalsegmentation0 shape expectedshape segmentation imageprocessor postprocesssemanticsegmentationoutputsoutputs expectedshape torch size160 160 self assertequalsegmentation0 shape expectedshape requiretorch class beitbackbonetestunittest testcase backbonetestermixin allmodelclasses beitbackbone if istorchavailable else configclass beitconfig def setupself self modeltester beitmodeltesterself coding utf 8 2021 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 beit model in beit the seq length equals the number of patches 1 we add 1 for the cls token verify hidden states verify channels verify backbone works with out_features none verify feature maps verify channels test greyscale images here we also overwrite some of the tests of test_modeling_common py as beit does not use input_ids inputs_embeds attention_mask and seq_length we don t test beitformaskedimagemodeling we don t test beitformaskedimagemodeling we skip lambda parameters as these require special initial values determined by config layer_scale_init_value we will verify our results on an image of cute cats prepare bool_masked_pos forward pass verify the logits forward pass verify the logits forward pass verify the logits forward pass verify the logits forward pass
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_FOR_BACKBONE_MAPPING,
        MODEL_MAPPING,
        BeitBackbone,
        BeitForImageClassification,
        BeitForMaskedImageModeling,
        BeitForSemanticSegmentation,
        BeitModel,
    )
    from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    import PIL
    from PIL import Image

    from transformers import BeitImageProcessor


class BeitModelTester:
    def __init__(
        self,
        parent,
        vocab_size=100,
        batch_size=13,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        use_labels=True,
        hidden_size=32,
        num_hidden_layers=4,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        type_sequence_label_size=10,
        initializer_range=0.02,
        num_labels=3,
        scope=None,
        out_indices=[1, 2, 3, 4],
        out_features=["stage1", "stage2", "stage3", "stage4"],
    ):
        self.parent = parent
        self.vocab_size = vocab_size
        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.use_labels = use_labels
        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.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.scope = scope
        self.out_indices = out_indices
        self.out_features = out_features
        self.num_labels = num_labels

        
        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])

        labels = None
        pixel_labels = None
        if self.use_labels:
            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            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 BeitConfig(
            vocab_size=self.vocab_size,
            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,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            is_decoder=False,
            initializer_range=self.initializer_range,
            out_indices=self.out_indices,
            out_features=self.out_features,
        )

    def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
        model = BeitModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_backbone(self, config, pixel_values, labels, pixel_labels):
        model = BeitBackbone(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        
        self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
        expected_height = expected_width = self.image_size // config.patch_size
        self.parent.assertListEqual(
            list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, expected_height, expected_width]
        )

        
        self.parent.assertEqual(len(model.channels), len(config.out_features))

        
        config.out_features = None
        model = BeitBackbone(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        
        self.parent.assertEqual(len(result.feature_maps), 1)
        self.parent.assertListEqual(
            list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, expected_height, expected_width]
        )

        
        self.parent.assertEqual(len(model.channels), 1)

    def create_and_check_for_masked_lm(self, config, pixel_values, labels, pixel_labels):
        model = BeitForMaskedImageModeling(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length - 1, self.vocab_size))

    def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
        config.num_labels = self.type_sequence_label_size
        model = BeitForImageClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, labels=labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

        
        config.num_channels = 1
        model = BeitForImageClassification(config)
        model.to(torch_device)
        model.eval()

        pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        result = model(pixel_values, labels=labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

    def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
        config.num_labels = self.num_labels
        model = BeitForSemanticSegmentation(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
        )
        result = model(pixel_values, labels=pixel_labels)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)
        )

    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_torch
class BeitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    

    all_model_classes = (
        (
            BeitModel,
            BeitForImageClassification,
            BeitForMaskedImageModeling,
            BeitForSemanticSegmentation,
            BeitBackbone,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": BeitModel,
            "image-classification": BeitForImageClassification,
            "image-segmentation": BeitForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False

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

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

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

    @require_torch_multi_gpu
    @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`")
    def test_multi_gpu_data_parallel_forward(self):
        pass

    @unittest.skip(reason="BEiT does not support feedforward chunking yet")
    def test_feed_forward_chunking(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_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_backbone(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*config_and_inputs)

    def test_for_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)

    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)

    def test_training(self):
        if not self.model_tester.is_training:
            return

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

        for model_class in self.all_model_classes:
            
            if model_class in [
                *get_values(MODEL_MAPPING),
                *get_values(MODEL_FOR_BACKBONE_MAPPING),
                BeitForMaskedImageModeling,
            ]:
                continue

            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

    def test_training_gradient_checkpointing(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
            return

        config.use_cache = False
        config.return_dict = True

        for model_class in self.all_model_classes:
            
            if (
                model_class
                in [*get_values(MODEL_MAPPING), *get_values(MODEL_FOR_BACKBONE_MAPPING), BeitForMaskedImageModeling]
                or not model_class.supports_gradient_checkpointing
            ):
                continue

            model = model_class(config)
            model.gradient_checkpointing_enable()
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

    @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 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 "lambda" in name:
                    continue
                if param.requires_grad:
                    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",
                    )

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



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


@require_torch
@require_vision
class BeitModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None

    @slow
    def test_inference_masked_image_modeling_head(self):
        model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k").to(torch_device)

        image_processor = self.default_image_processor
        image = prepare_img()
        pixel_values = image_processor(images=image, return_tensors="pt").pixel_values.to(torch_device)

        
        bool_masked_pos = torch.ones((1, 196), dtype=torch.bool).to(torch_device)

        
        with torch.no_grad():
            outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos)
        logits = outputs.logits

        
        expected_shape = torch.Size((1, 196, 8192))
        self.assertEqual(logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]
        ).to(torch_device)

        self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3], expected_slice, atol=1e-2))

    @slow
    def test_inference_image_classification_head_imagenet_1k(self):
        model = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224").to(torch_device)

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

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

        
        expected_shape = torch.Size((1, 1000))
        self.assertEqual(logits.shape, expected_shape)

        expected_slice = torch.tensor([-1.2385, -1.0987, -1.0108]).to(torch_device)

        self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))

        expected_class_idx = 281
        self.assertEqual(logits.argmax(-1).item(), expected_class_idx)

    @slow
    def test_inference_image_classification_head_imagenet_22k(self):
        model = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k").to(
            torch_device
        )

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

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

        
        expected_shape = torch.Size((1, 21841))
        self.assertEqual(logits.shape, expected_shape)

        expected_slice = torch.tensor([1.6881, -0.2787, 0.5901]).to(torch_device)

        self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))

        expected_class_idx = 2396
        self.assertEqual(logits.argmax(-1).item(), expected_class_idx)

    @slow
    def test_inference_semantic_segmentation(self):
        model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
        model = model.to(torch_device)

        image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)

        ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
        image = Image.open(ds[0]["file"])
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

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

        
        expected_shape = torch.Size((1, 150, 160, 160))
        self.assertEqual(logits.shape, expected_shape)

        is_pillow_less_than_9 = version.parse(PIL.__version__) < version.parse("9.0.0")

        if is_pillow_less_than_9:
            expected_slice = torch.tensor(
                [
                    [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                    [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                    [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
                ],
                device=torch_device,
            )
        else:
            expected_slice = torch.tensor(
                [
                    [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
                    [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
                    [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
                ],
                device=torch_device,
            )

        self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))

    @slow
    def test_post_processing_semantic_segmentation(self):
        model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
        model = model.to(torch_device)

        image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)

        ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
        image = Image.open(ds[0]["file"])
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

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

        outputs.logits = outputs.logits.detach().cpu()

        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)])
        expected_shape = torch.Size((500, 300))
        self.assertEqual(segmentation[0].shape, expected_shape)

        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
        expected_shape = torch.Size((160, 160))
        self.assertEqual(segmentation[0].shape, expected_shape)


@require_torch
class BeitBackboneTest(unittest.TestCase, BackboneTesterMixin):
    all_model_classes = (BeitBackbone,) if is_torch_available() else ()
    config_class = BeitConfig

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