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 swin model import collections import unittest from transformers import swinconfig from transformers testingutils import requiretorch 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 swinbackbone swinforimageclassification swinformaskedimagemodeling swinmodel from transformers models swin modelingswin import swinpretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import autoimageprocessor class swinmodeltester def init self parent batchsize13 imagesize32 patchsize2 numchannels3 embeddim16 depths1 2 1 numheads2 2 4 windowsize2 mlpratio2 0 qkvbiastrue hiddendropoutprob0 0 attentionprobsdropoutprob0 0 droppathrate0 1 hiddenactgelu useabsoluteembeddingsfalse patchnormtrue initializerrange0 02 layernormeps1e5 istrainingtrue scopenone uselabelstrue typesequencelabelsize10 encoderstride8 outfeaturesstage1 stage2 outindices1 2 self parent parent self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels self embeddim embeddim self depths depths self numheads numheads self windowsize windowsize self mlpratio mlpratio self qkvbias qkvbias self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self droppathrate droppathrate self hiddenact hiddenact self useabsoluteembeddings useabsoluteembeddings self patchnorm patchnorm self layernormeps layernormeps self initializerrange initializerrange self istraining istraining self scope scope self uselabels uselabels self typesequencelabelsize typesequencelabelsize self encoderstride encoderstride self outfeatures outfeatures self outindices outindices def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize labels none if self uselabels labels idstensorself batchsize self typesequencelabelsize config self getconfig return config pixelvalues labels def getconfigself return swinconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels embeddimself embeddim depthsself depths numheadsself numheads windowsizeself windowsize mlpratioself mlpratio qkvbiasself qkvbias hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob droppathrateself droppathrate hiddenactself hiddenact useabsoluteembeddingsself useabsoluteembeddings pathnormself patchnorm layernormepsself layernormeps initializerrangeself initializerrange encoderstrideself encoderstride outfeaturesself outfeatures outindicesself outindices def createandcheckmodelself config pixelvalues labels model swinmodelconfigconfig model totorchdevice model eval result modelpixelvalues expectedseqlen config imagesize config patchsize 2 4 lenconfig depths 1 expecteddim intconfig embeddim 2 lenconfig depths 1 self parent assertequalresult lasthiddenstate shape self batchsize expectedseqlen expecteddim def createandcheckbackboneself config pixelvalues labels model swinbackboneconfigconfig model totorchdevice model eval result modelpixelvalues verify hidden states self parent assertequallenresult featuremaps lenconfig outfeatures self parent assertlistequallistresult featuremaps0 shape self batchsize model channels0 16 16 verify channels self parent assertequallenmodel channels lenconfig outfeatures verify backbone works with outfeaturesnone config outfeatures none model swinbackboneconfigconfig model totorchdevice model eval result modelpixelvalues verify feature maps self parent assertequallenresult featuremaps 1 self parent assertlistequallistresult featuremaps0 shape self batchsize model channels1 4 4 verify channels self parent assertequallenmodel channels 1 def createandcheckformaskedimagemodelingself config pixelvalues labels model swinformaskedimagemodelingconfigconfig model totorchdevice model eval result modelpixelvalues self parent assertequal result logits shape self batchsize self numchannels self imagesize self imagesize test greyscale images config numchannels 1 model swinformaskedimagemodelingconfig model totorchdevice model eval pixelvalues floatstensorself batchsize 1 self imagesize self imagesize result modelpixelvalues self parent assertequalresult logits shape self batchsize 1 self imagesize self imagesize def createandcheckforimageclassificationself config pixelvalues labels config numlabels self typesequencelabelsize model swinforimageclassificationconfig model totorchdevice model eval result modelpixelvalues labelslabels self parent assertequalresult logits shape self batchsize self typesequencelabelsize test greyscale images config numchannels 1 model swinforimageclassificationconfig model totorchdevice model eval pixelvalues floatstensorself batchsize 1 self imagesize self imagesize result modelpixelvalues self parent assertequalresult logits shape self batchsize self typesequencelabelsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues labels configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class swinmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses swinmodel swinbackbone swinforimageclassification swinformaskedimagemodeling if istorchavailable else pipelinemodelmapping featureextraction swinmodel imageclassification swinforimageclassification if istorchavailable else fxcompatible true testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester swinmodeltesterself self configtester configtesterself configclassswinconfig embeddim37 def testconfigself self createandtestconfigcommonproperties self configtester createandtestconfigtojsonstring self configtester createandtestconfigtojsonfile self configtester createandtestconfigfromandsavepretrained self configtester createandtestconfigwithnumlabels self configtester checkconfigcanbeinitwithoutparams self configtester checkconfigargumentsinit def createandtestconfigcommonpropertiesself return def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs todo check if this works again for pytorch 2 x y unittest skipreasongot cuda error misaligned address with pytorch 2 0 0 def testmultigpudataparallelforwardself pass def testtraininggradientcheckpointingself super testtraininggradientcheckpointing def testbackboneself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckbackboneconfigandinputs def testformaskedimagemodelingself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckformaskedimagemodelingconfigandinputs def testforimageclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforimageclassificationconfigandinputs unittest skipreasonswin does not use inputsembeds def testinputsembedsself pass unittest skipreasonswin transformer does not use feedforward chunking 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 testattentionoutputsself config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true 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 expectednumattentions lenself modeltester depths self assertequallenattentions expectednumattentions check that outputattentions also work using config del inputsdictoutputattentions config outputattentions true windowsizesquared config windowsize2 model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass attentions outputs attentions self assertequallenattentions expectednumattentions self assertlistequal listattentions0 shape3 self modeltester numheads0 windowsizesquared windowsizesquared 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 also another 1 for reshapedhiddenstates addedhiddenstates 1 if modelclass name swinbackbone else 2 self assertequaloutlen addedhiddenstates lenoutputs selfattentions outputs attentions self assertequallenselfattentions expectednumattentions self assertlistequal listselfattentions0 shape3 self modeltester numheads0 windowsizesquared windowsizesquared def checkhiddenstatesoutputself inputsdict config modelclass imagesize model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumlayers getattr self modeltester expectednumhiddenlayers lenself modeltester depths 1 self assertequallenhiddenstates expectednumlayers swin has a different seqlength patchsize config patchsize if isinstanceconfig patchsize collections abc iterable else config patchsize config patchsize numpatches imagesize1 patchsize1 imagesize0 patchsize0 self assertlistequal listhiddenstates0 shape2 numpatches self modeltester embeddim if not modelclass name swinbackbone reshapedhiddenstates outputs reshapedhiddenstates self assertequallenreshapedhiddenstates expectednumlayers batchsize numchannels height width reshapedhiddenstates0 shape reshapedhiddenstates reshapedhiddenstates0 viewbatchsize numchannels height width permute0 2 1 self assertlistequal listreshapedhiddenstates shape2 numpatches self modeltester embeddim def testhiddenstatesoutputself config inputsdict self modeltester prepareconfigandinputsforcommon imagesize self modeltester imagesize if isinstanceself modeltester imagesize collections abc iterable else self modeltester imagesize self modeltester imagesize for modelclass in self allmodelclasses inputsdictoutputhiddenstates true self checkhiddenstatesoutputinputsdict config modelclass imagesize check that outputhiddenstates also work using config del inputsdictoutputhiddenstates config outputhiddenstates true self checkhiddenstatesoutputinputsdict config modelclass imagesize def testhiddenstatesoutputwithpaddingself config inputsdict self modeltester prepareconfigandinputsforcommon config patchsize 3 imagesize self modeltester imagesize if isinstanceself modeltester imagesize collections abc iterable else self modeltester imagesize self modeltester imagesize patchsize config patchsize if isinstanceconfig patchsize collections abc iterable else config patchsize config patchsize paddedheight imagesize0 patchsize0 imagesize0 patchsize0 paddedwidth imagesize1 patchsize1 imagesize1 patchsize1 for modelclass in self allmodelclasses inputsdictoutputhiddenstates true self checkhiddenstatesoutputinputsdict config modelclass paddedheight paddedwidth check that outputhiddenstates also work using config del inputsdictoutputhiddenstates config outputhiddenstates true self checkhiddenstatesoutputinputsdict config modelclass paddedheight paddedwidth slow def testmodelfrompretrainedself for modelname in swinpretrainedmodelarchivelist 1 model swinmodel frompretrainedmodelname self assertisnotnonemodel def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit for name param in model namedparameters if embeddings not in name and param requiresgrad self assertin param data mean 1e9 round 1e9 item 0 0 1 0 msgfparameter name of model modelclass seems not properly initialized requirevision requiretorch class swinmodelintegrationtestunittest testcase cachedproperty def defaultimageprocessorself return autoimageprocessor frompretrainedmicrosoftswintinypatch4window7224 if isvisionavailable else none slow def testinferenceimageclassificationheadself model swinforimageclassification frompretrainedmicrosoftswintinypatch4window7224 totorchdevice imageprocessor self defaultimageprocessor image image open testsfixturestestssamplescoco000000039769 png inputs imageprocessorimagesimage returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the logits expectedshape torch size1 1000 self assertequaloutputs logits shape expectedshape expectedslice torch tensor0 0948 0 6454 0 0921 totorchdevice self asserttruetorch allcloseoutputs logits0 3 expectedslice atol1e4 requiretorch class swinbackbonetestunittest testcase backbonetestermixin allmodelclasses swinbackbone if istorchavailable else configclass swinconfig def setupself self modeltester swinmodeltesterself 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 swin model verify hidden states verify channels verify backbone works with out_features none verify feature maps verify channels test greyscale images test greyscale images todo check if this works again for pytorch 2 x y check that output_attentions also work using config check attention is always last and order is fine also another 1 for reshaped_hidden_states swin has a different seq_length check that output_hidden_states also work using config check that output_hidden_states also work using config forward pass verify the logits
import collections
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, 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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
    from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class SwinModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=32,
        patch_size=2,
        num_channels=3,
        embed_dim=16,
        depths=[1, 2, 1],
        num_heads=[2, 2, 4],
        window_size=2,
        mlp_ratio=2.0,
        qkv_bias=True,
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        drop_path_rate=0.1,
        hidden_act="gelu",
        use_absolute_embeddings=False,
        patch_norm=True,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        is_training=True,
        scope=None,
        use_labels=True,
        type_sequence_label_size=10,
        encoder_stride=8,
        out_features=["stage1", "stage2"],
        out_indices=[1, 2],
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.embed_dim = embed_dim
        self.depths = depths
        self.num_heads = num_heads
        self.window_size = window_size
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.drop_path_rate = drop_path_rate
        self.hidden_act = hidden_act
        self.use_absolute_embeddings = use_absolute_embeddings
        self.patch_norm = patch_norm
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        self.is_training = is_training
        self.scope = scope
        self.use_labels = use_labels
        self.type_sequence_label_size = type_sequence_label_size
        self.encoder_stride = encoder_stride
        self.out_features = out_features
        self.out_indices = out_indices

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

        labels = None
        if self.use_labels:
            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        return SwinConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            embed_dim=self.embed_dim,
            depths=self.depths,
            num_heads=self.num_heads,
            window_size=self.window_size,
            mlp_ratio=self.mlp_ratio,
            qkv_bias=self.qkv_bias,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            drop_path_rate=self.drop_path_rate,
            hidden_act=self.hidden_act,
            use_absolute_embeddings=self.use_absolute_embeddings,
            path_norm=self.patch_norm,
            layer_norm_eps=self.layer_norm_eps,
            initializer_range=self.initializer_range,
            encoder_stride=self.encoder_stride,
            out_features=self.out_features,
            out_indices=self.out_indices,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = SwinModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)

        expected_seq_len = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
        expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1))

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim))

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

        
        self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], 16, 16])

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

        
        config.out_features = None
        model = SwinBackbone(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, model.channels[-1], 4, 4])

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

    def create_and_check_for_masked_image_modeling(self, config, pixel_values, labels):
        model = SwinForMaskedImageModeling(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size)
        )

        
        config.num_channels = 1
        model = SwinForMaskedImageModeling(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)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size))

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.type_sequence_label_size
        model = SwinForImageClassification(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 = SwinForImageClassification(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)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

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


@require_torch
class SwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            SwinModel,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
        if is_torch_available()
        else {}
    )
    fx_compatible = True

    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = SwinModelTester(self)
        self.config_tester = ConfigTester(self, config_class=SwinConfig, embed_dim=37)

    def test_config(self):
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

    def create_and_test_config_common_properties(self):
        return

    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="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
    def test_multi_gpu_data_parallel_forward(self):
        pass

    def test_training_gradient_checkpointing(self):
        super().test_training_gradient_checkpointing()

    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_image_modeling(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*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)

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

    @unittest.skip(reason="Swin Transformer does not use feedforward chunking")
    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_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        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
            expected_num_attentions = len(self.model_tester.depths)
            self.assertEqual(len(attentions), expected_num_attentions)

            
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            window_size_squared = config.window_size**2
            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_attentions)

            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [self.model_tester.num_heads[0], window_size_squared, window_size_squared],
            )
            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 if model_class.__name__ == "SwinBackbone" else 2
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.attentions

            self.assertEqual(len(self_attentions), expected_num_attentions)

            self.assertListEqual(
                list(self_attentions[0].shape[-3:]),
                [self.model_tester.num_heads[0], window_size_squared, window_size_squared],
            )

    def check_hidden_states_output(self, inputs_dict, config, model_class, image_size):
        model = model_class(config)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            outputs = model(**self._prepare_for_class(inputs_dict, model_class))

        hidden_states = outputs.hidden_states

        expected_num_layers = getattr(
            self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1
        )
        self.assertEqual(len(hidden_states), expected_num_layers)

        
        patch_size = (
            config.patch_size
            if isinstance(config.patch_size, collections.abc.Iterable)
            else (config.patch_size, config.patch_size)
        )

        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]),
            [num_patches, self.model_tester.embed_dim],
        )

        if not model_class.__name__ == "SwinBackbone":
            reshaped_hidden_states = outputs.reshaped_hidden_states
            self.assertEqual(len(reshaped_hidden_states), expected_num_layers)

            batch_size, num_channels, height, width = reshaped_hidden_states[0].shape
            reshaped_hidden_states = (
                reshaped_hidden_states[0].view(batch_size, num_channels, height * width).permute(0, 2, 1)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]),
                [num_patches, self.model_tester.embed_dim],
            )

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

        image_size = (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size, collections.abc.Iterable)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

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

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

            self.check_hidden_states_output(inputs_dict, config, model_class, image_size)

    def test_hidden_states_output_with_padding(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.patch_size = 3

        image_size = (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size, collections.abc.Iterable)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        patch_size = (
            config.patch_size
            if isinstance(config.patch_size, collections.abc.Iterable)
            else (config.patch_size, config.patch_size)
        )

        padded_height = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        padded_width = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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

            
            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True
            self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width))

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

    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 "embeddings" not in name and 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",
                    )


@require_vision
@require_torch
class SwinModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
            if is_vision_available()
            else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224").to(torch_device)
        image_processor = self.default_image_processor

        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

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

        
        expected_shape = torch.Size((1, 1000))
        self.assertEqual(outputs.logits.shape, expected_shape)
        expected_slice = torch.tensor([-0.0948, -0.6454, -0.0921]).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))


@require_torch
class SwinBackboneTest(unittest.TestCase, BackboneTesterMixin):
    all_model_classes = (SwinBackbone,) if is_torch_available() else ()
    config_class = SwinConfig

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