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 clipseg model import inspect import os import tempfile import unittest import numpy as np import requests import transformers from transformers import modelmapping clipsegconfig clipsegprocessor clipsegtextconfig clipsegvisionconfig from transformers models auto import getvalues from transformers testingutils import isflaxavailable isptflaxcrosstest requiretorch requirevision slow torchdevice from transformers utils import istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin configzeroinit floatstensor idstensor randomattentionmask from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from torch import nn from transformers import clipsegforimagesegmentation clipsegmodel clipsegtextmodel clipsegvisionmodel from transformers models clipseg modelingclipseg import clipsegpretrainedmodelarchivelist if isvisionavailable from pil import image if isflaxavailable import jax numpy as jnp from transformers modelingflaxpytorchutils import convertpytorchstatedicttoflax loadflaxweightsinpytorchmodel class clipsegvisionmodeltester def init self parent batchsize12 imagesize30 patchsize2 numchannels3 istrainingtrue hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 dropout0 1 attentiondropout0 1 initializerrange0 02 scopenone self parent parent self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels self istraining istraining self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self dropout dropout self attentiondropout attentiondropout self initializerrange initializerrange self scope scope in vit the seq length equals the number of patches 1 we add 1 for the cls token numpatches imagesize patchsize 2 self seqlength numpatches 1 def prepareconfigandinputsself pixelvalues floatstensorself batchsize self numchannels self imagesize self imagesize config self getconfig return config pixelvalues def getconfigself return clipsegvisionconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout initializerrangeself initializerrange def createandcheckmodelself config pixelvalues model clipsegvisionmodelconfigconfig model totorchdevice model eval with torch nograd result modelpixelvalues expected sequence length numpatches 1 we add 1 for the cls token imagesize self imagesize self imagesize patchsize self patchsize self patchsize numpatches imagesize1 patchsize1 imagesize0 patchsize0 self parent assertequalresult lasthiddenstate shape self batchsize numpatches 1 self hiddensize self parent assertequalresult pooleroutput shape self batchsize self hiddensize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config pixelvalues configandinputs inputsdict pixelvalues pixelvalues return config inputsdict requiretorch class clipsegvisionmodeltestmodeltestermixin unittest testcase allmodelclasses clipsegvisionmodel if istorchavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester clipsegvisionmodeltesterself self configtester configtester self configclassclipsegvisionconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasonclipseg does not use inputsembeds def testinputsembedsself pass def testmodelcommonattributesself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig self assertisinstancemodel getinputembeddings nn module x model getoutputembeddings self asserttruex is none or isinstancex nn linear def testforwardsignatureself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig signature inspect signaturemodel forward signature parameters is an ordereddict so argnames order is deterministic argnames signature parameters keys expectedargnames pixelvalues self assertlistequalargnames 1 expectedargnames def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testtrainingself pass def testtraininggradientcheckpointingself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantfalseself pass unittest skipreasonclipsegvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasonclipsegvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in clipsegpretrainedmodelarchivelist 1 model clipsegvisionmodel frompretrainedmodelname self assertisnotnonemodel class clipsegtextmodeltester def init self parent batchsize12 seqlength7 istrainingtrue useinputmasktrue uselabelstrue vocabsize99 hiddensize32 numhiddenlayers2 numattentionheads4 intermediatesize37 dropout0 1 attentiondropout0 1 maxpositionembeddings512 initializerrange0 02 scopenone self parent parent self batchsize batchsize self seqlength seqlength self istraining istraining self useinputmask useinputmask self uselabels uselabels self vocabsize vocabsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self dropout dropout self attentiondropout attentiondropout self maxpositionembeddings maxpositionembeddings self initializerrange initializerrange self scope scope def prepareconfigandinputsself inputids idstensorself batchsize self seqlength self vocabsize inputmask none if self useinputmask inputmask randomattentionmaskself batchsize self seqlength if inputmask is not none batchsize seqlength inputmask shape rndstartindices np random randint1 seqlength 1 sizebatchsize for batchidx startindex in enumeraterndstartindices inputmaskbatchidx startindex 1 inputmaskbatchidx startindex 0 config self getconfig return config inputids inputmask def getconfigself return clipsegtextconfig vocabsizeself vocabsize hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout maxpositionembeddingsself maxpositionembeddings initializerrangeself initializerrange def createandcheckmodelself config inputids inputmask model clipsegtextmodelconfigconfig model totorchdevice model eval with torch nograd result modelinputids attentionmaskinputmask result modelinputids self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize self parent assertequalresult pooleroutput shape self batchsize self hiddensize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids inputmask configandinputs inputsdict inputids inputids attentionmask inputmask return config inputsdict requiretorch class clipsegtextmodeltestmodeltestermixin unittest testcase allmodelclasses clipsegtextmodel if istorchavailable else fxcompatible false testpruning false testheadmasking false modelsplitpercents 0 5 0 8 0 9 def setupself self modeltester clipsegtextmodeltesterself self configtester configtesterself configclassclipsegtextconfig hiddensize37 def testconfigself self configtester runcommontests def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testtrainingself pass def testtraininggradientcheckpointingself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantfalseself pass unittest skipreasonclipseg does not use inputsembeds def testinputsembedsself pass unittest skipreasonclipsegtextmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasonclipsegtextmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in clipsegpretrainedmodelarchivelist 1 model clipsegtextmodel frompretrainedmodelname self assertisnotnonemodel class clipsegmodeltester def init self parent textkwargsnone visionkwargsnone istrainingtrue this should respect the numhiddenlayers in clipsegvisionmodeltester extractlayers1 if textkwargs is none textkwargs if visionkwargs is none visionkwargs self parent parent self textmodeltester clipsegtextmodeltesterparent textkwargs self visionmodeltester clipsegvisionmodeltesterparent visionkwargs self istraining istraining self extractlayers extractlayers def prepareconfigandinputsself textconfig inputids attentionmask self textmodeltester prepareconfigandinputs visionconfig pixelvalues self visionmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask pixelvalues def getconfigself return clipsegconfig fromtextvisionconfigs self textmodeltester getconfig self visionmodeltester getconfig projectiondim64 reducedim32 extractlayersself extractlayers def createandcheckmodelself config inputids attentionmask pixelvalues model clipsegmodelconfig totorchdevice eval with torch nograd result modelinputids pixelvalues attentionmask self parent assertequal result logitsperimage shape self visionmodeltester batchsize self textmodeltester batchsize self parent assertequal result logitspertext shape self textmodeltester batchsize self visionmodeltester batchsize def createandcheckmodelforimagesegmentationself config inputids attentionmaks pixelvalues model clipsegforimagesegmentationconfig totorchdevice eval with torch nograd result modelinputids pixelvalues self parent assertequal result logits shape self visionmodeltester batchsize self visionmodeltester imagesize self visionmodeltester imagesize self parent assertequal result conditionalembeddings shape self textmodeltester batchsize config projectiondim def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask pixelvalues configandinputs inputsdict inputids inputids attentionmask attentionmask pixelvalues pixelvalues return config inputsdict requiretorch class clipsegmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses clipsegmodel clipsegforimagesegmentation if istorchavailable else pipelinemodelmapping featureextraction clipsegmodel if istorchavailable else fxcompatible false testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false def prepareforclassself inputsdict modelclass returnlabelsfalse clipsegforimagesegmentation requires special treatment if returnlabels if modelclass name clipsegforimagesegmentation batchsize height width inputsdictpixelvalues shape inputsdictlabels torch zeros batchsize height width devicetorchdevice dtypetorch float return inputsdict def setupself self modeltester clipsegmodeltesterself def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testmodelforimagesegmentationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelforimagesegmentationconfigandinputs unittest skipreasonhiddenstates is tested in individual model tests def testhiddenstatesoutputself pass unittest skipreasoninputsembeds is tested in individual model tests def testinputsembedsself pass unittest skipreasonretaingrad is tested in individual model tests def testretaingradhiddenstatesattentionsself pass unittest skipreasonclipsegmodel does not have inputoutput embeddings def testmodelcommonattributesself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantself pass unittest skip reasonthis architecure seem to not compute gradients properly when using gc check https github comhuggingfacetransformerspull27124 def testtraininggradientcheckpointingusereentrantfalseself pass override as the some parameters require custom initialization def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit for name param in model namedparameters if param requiresgrad check if logitscale is initilized as per the original implementation if logitscale in name self assertalmostequal param data item np log1 0 07 delta1e3 msgfparameter name of model modelclass seems not properly initialized elif film in name or transposedconv in name or reduce in name those parameters use pytorch default nn linear initialization scheme pass else self assertin param data mean 1e9 round 1e9 item 0 0 1 0 msgfparameter name of model modelclass seems not properly initialized def createandchecktorchscriptself config inputsdict if not self testtorchscript return configsnoinit configzeroinitconfig to be sure we have no nan configsnoinit torchscript true configsnoinit returndict false for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit model totorchdevice model eval try inputids inputsdictinputids pixelvalues inputsdictpixelvalues clipseg needs pixelvalues tracedmodel torch jit tracemodel inputids pixelvalues except runtimeerror self failcouldn t trace module with tempfile temporarydirectory as tmpdirname ptfilename os path jointmpdirname tracedmodel pt try torch jit savetracedmodel ptfilename except exception self failcouldn t save module try loadedmodel torch jit loadptfilename except exception self failcouldn t load module model totorchdevice model eval loadedmodel totorchdevice loadedmodel eval modelstatedict model statedict loadedmodelstatedict loadedmodel statedict nonpersistentbuffers for key in loadedmodelstatedict keys if key not in modelstatedict keys nonpersistentbufferskey loadedmodelstatedictkey loadedmodelstatedict key value for key value in loadedmodelstatedict items if key not in nonpersistentbuffers self assertequalsetmodelstatedict keys setloadedmodelstatedict keys modelbuffers listmodel buffers for nonpersistentbuffer in nonpersistentbuffers values foundbuffer false for i modelbuffer in enumeratemodelbuffers if torch equalnonpersistentbuffer modelbuffer foundbuffer true break self asserttruefoundbuffer modelbuffers popi modelsequal true for layername p1 in modelstatedict items p2 loadedmodelstatedictlayername if p1 data nep2 data sum 0 modelsequal false self asserttruemodelsequal def testloadvisiontextconfigself config inputsdict self modeltester prepareconfigandinputsforcommon save clipsegconfig and check if we can load clipsegvisionconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname visionconfig clipsegvisionconfig frompretrainedtmpdirname self assertdictequalconfig visionconfig todict visionconfig todict save clipsegconfig and check if we can load clipsegtextconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname textconfig clipsegtextconfig frompretrainedtmpdirname self assertdictequalconfig textconfig todict textconfig todict overwrite from common since flaxclipsegmodel returns nested output which is not supported in the common test isptflaxcrosstest def testequivalencepttoflaxself config inputsdict self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses with self subtestmodelclass name load pytorch class ptmodel modelclassconfig eval flax models don t use the usecache option and cache is not returned as a default so we disable usecache here for pytorch model ptmodel config usecache false fxmodelclassname flax modelclass name if not hasattrtransformers fxmodelclassname return fxmodelclass getattrtransformers fxmodelclassname load flax class fxmodel fxmodelclassconfig dtypejnp float32 make sure only flax inputs are forward that actually exist in function args fxinputkeys inspect signaturefxmodel call parameters keys prepare inputs ptinputs self prepareforclassinputsdict modelclass remove function args that don t exist in flax ptinputs k v for k v in ptinputs items if k in fxinputkeys fxstate convertpytorchstatedicttoflaxptmodel statedict fxmodel fxmodel params fxstate with torch nograd ptoutputs ptmodelptinputs totuple convert inputs to flax fxinputs k np arrayv tocpu for k v in ptinputs items if torch istensorv fxoutputs fxmodelfxinputs totuple self assertequallenfxoutputs lenptoutputs output lengths differ between flax and pytorch for fxoutput ptoutput in zipfxoutputs 4 ptoutputs 4 self assertalmostequalsfxoutput ptoutput numpy 4e2 with tempfile temporarydirectory as tmpdirname ptmodel savepretrainedtmpdirname fxmodelloaded fxmodelclass frompretrainedtmpdirname frompttrue fxoutputsloaded fxmodelloadedfxinputs totuple self assertequal lenfxoutputsloaded lenptoutputs output lengths differ between flax and pytorch for fxoutputloaded ptoutput in zipfxoutputsloaded 4 ptoutputs 4 self assertalmostequalsfxoutputloaded ptoutput numpy 4e2 overwrite from common since flaxclipsegmodel returns nested output which is not supported in the common test isptflaxcrosstest def testequivalenceflaxtoptself config inputsdict self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses with self subtestmodelclass name load corresponding pytorch class ptmodel modelclassconfig eval so we disable usecache here for pytorch model ptmodel config usecache false fxmodelclassname flax modelclass name if not hasattrtransformers fxmodelclassname no flax model exists for this class return fxmodelclass getattrtransformers fxmodelclassname load flax class fxmodel fxmodelclassconfig dtypejnp float32 make sure only flax inputs are forward that actually exist in function args fxinputkeys inspect signaturefxmodel call parameters keys ptmodel loadflaxweightsinpytorchmodelptmodel fxmodel params make sure weights are tied in pytorch ptmodel tieweights prepare inputs ptinputs self prepareforclassinputsdict modelclass remove function args that don t exist in flax ptinputs k v for k v in ptinputs items if k in fxinputkeys with torch nograd ptoutputs ptmodelptinputs totuple fxinputs k np arrayv tocpu for k v in ptinputs items if torch istensorv fxoutputs fxmodelfxinputs totuple self assertequallenfxoutputs lenptoutputs output lengths differ between flax and pytorch for fxoutput ptoutput in zipfxoutputs 4 ptoutputs 4 self assertalmostequalsfxoutput ptoutput numpy 4e2 with tempfile temporarydirectory as tmpdirname fxmodel savepretrainedtmpdirname ptmodelloaded modelclass frompretrainedtmpdirname fromflaxtrue with torch nograd ptoutputsloaded ptmodelloadedptinputs totuple self assertequal lenfxoutputs lenptoutputsloaded output lengths differ between flax and pytorch for fxoutput ptoutput in zipfxoutputs 4 ptoutputsloaded 4 self assertalmostequalsfxoutput ptoutput numpy 4e2 def testtrainingself if not self modeltester istraining return for modelclass in self allmodelclasses config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true if modelclass in getvaluesmodelmapping continue printmodel class modelclass model modelclassconfig model totorchdevice model train inputs self prepareforclassinputsdict modelclass returnlabelstrue for k v in inputs items printk v shape loss modelinputs loss loss backward slow def testmodelfrompretrainedself for modelname in clipsegpretrainedmodelarchivelist 1 model clipsegmodel frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg url http images cocodataset orgval2017000000039769 jpg image image openrequests geturl streamtrue raw return image requirevision requiretorch class clipsegmodelintegrationtestunittest testcase slow def testinferenceimagesegmentationself modelname cidasclipsegrd64refined processor clipsegprocessor frompretrainedmodelname model clipsegforimagesegmentation frompretrainedmodelname totorchdevice image prepareimg texts a cat a remote a blanket inputs processortexttexts imagesimage lentexts paddingtrue returntensorspt totorchdevice forward pass with torch nograd outputs modelinputs verify the predicted masks self assertequal outputs logits shape torch size3 352 352 expectedmasksslice torch tensor 7 4613 7 4785 7 3628 7 3268 7 0899 7 1333 6 9838 6 7900 6 8913 totorchdevice self asserttruetorch allcloseoutputs logits0 3 3 expectedmasksslice atol1e3 verify conditional and pooled output expectedconditional torch tensor0 5601 0 0314 0 1980 totorchdevice expectedpooledoutput torch tensor0 5036 0 2681 0 2644 totorchdevice self asserttruetorch allcloseoutputs conditionalembeddings0 3 expectedconditional atol1e3 self asserttruetorch allcloseoutputs pooledoutput0 3 expectedpooledoutput atol1e3 coding utf 8 2022 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license testing suite for the pytorch clipseg model in vit the seq length equals the number of patches 1 we add 1 for the cls token expected sequence length num_patches 1 we add 1 for the cls token here we also overwrite some of the tests of test_modeling_common py as clipseg does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic this should respect the num_hidden_layers in clipsegvisionmodeltester clipsegforimagesegmentation requires special treatment override as the some parameters require custom initialization check if logit_scale is initilized as per the original implementation those parameters use pytorch default nn linear initialization scheme to be sure we have no nan clipseg needs pixel_values save clipsegconfig and check if we can load clipsegvisionconfig from it save clipsegconfig and check if we can load clipsegtextconfig from it overwrite from common since flaxclipsegmodel returns nested output which is not supported in the common test load pytorch class flax models don t use the use_cache option and cache is not returned as a default so we disable use_cache here for pytorch model load flax class make sure only flax inputs are forward that actually exist in function args prepare inputs remove function args that don t exist in flax convert inputs to flax overwrite from common since flaxclipsegmodel returns nested output which is not supported in the common test load corresponding pytorch class so we disable use_cache here for pytorch model no flax model exists for this class load flax class make sure only flax inputs are forward that actually exist in function args make sure weights are tied in pytorch prepare inputs remove function args that don t exist in flax we will verify our results on an image of cute cats forward pass verify the predicted masks verify conditional and pooled output
import inspect
import os
import tempfile
import unittest

import numpy as np
import requests

import transformers
from transformers import MODEL_MAPPING, CLIPSegConfig, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    is_flax_available,
    is_pt_flax_cross_test,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegTextModel, CLIPSegVisionModel
    from transformers.models.clipseg.modeling_clipseg import CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image


if is_flax_available():
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )


class CLIPSegVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.scope = scope

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

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

        return config, pixel_values

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

    def create_and_check_model(self, config, pixel_values):
        model = CLIPSegVisionModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        
        image_size = (self.image_size, self.image_size)
        patch_size = (self.patch_size, self.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

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


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

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

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

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

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

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

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

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

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

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

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

    def test_training(self):
        pass

    def test_training_gradient_checkpointing(self):
        pass

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

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

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

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

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


class CLIPSegTextModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        max_position_embeddings=512,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

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

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

        config = self.get_config()

        return config, input_ids, input_mask

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

    def create_and_check_model(self, config, input_ids, input_mask):
        model = CLIPSegTextModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_ids, attention_mask=input_mask)
            result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

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


@require_torch
class CLIPSegTextModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (CLIPSegTextModel,) if is_torch_available() else ()
    fx_compatible = False
    test_pruning = False
    test_head_masking = False
    model_split_percents = [0.5, 0.8, 0.9]

    def setUp(self):
        self.model_tester = CLIPSegTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=CLIPSegTextConfig, hidden_size=37)

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

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

    def test_training(self):
        pass

    def test_training_gradient_checkpointing(self):
        pass

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

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

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

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

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

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


class CLIPSegModelTester:
    def __init__(
        self,
        parent,
        text_kwargs=None,
        vision_kwargs=None,
        is_training=True,
        
        extract_layers=(1,),
    ):
        if text_kwargs is None:
            text_kwargs = {}
        if vision_kwargs is None:
            vision_kwargs = {}

        self.parent = parent
        self.text_model_tester = CLIPSegTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = CLIPSegVisionModelTester(parent, **vision_kwargs)
        self.is_training = is_training
        self.extract_layers = extract_layers

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

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return CLIPSegConfig.from_text_vision_configs(
            self.text_model_tester.get_config(),
            self.vision_model_tester.get_config(),
            projection_dim=64,
            reduce_dim=32,
            extract_layers=self.extract_layers,
        )

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = CLIPSegModel(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(input_ids, pixel_values, attention_mask)
        self.parent.assertEqual(
            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
        )
        self.parent.assertEqual(
            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
        )

    def create_and_check_model_for_image_segmentation(self, config, input_ids, attention_maks, pixel_values):
        model = CLIPSegForImageSegmentation(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(input_ids, pixel_values)
        self.parent.assertEqual(
            result.logits.shape,
            (
                self.vision_model_tester.batch_size,
                self.vision_model_tester.image_size,
                self.vision_model_tester.image_size,
            ),
        )
        self.parent.assertEqual(
            result.conditional_embeddings.shape, (self.text_model_tester.batch_size, config.projection_dim)
        )

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


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

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        
        if return_labels:
            if model_class.__name__ == "CLIPSegForImageSegmentation":
                batch_size, _, height, width = inputs_dict["pixel_values"].shape
                inputs_dict["labels"] = torch.zeros(
                    [batch_size, height, width], device=torch_device, dtype=torch.float
                )

        return inputs_dict

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

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

    def test_model_for_image_segmentation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_for_image_segmentation(*config_and_inputs)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

            model.to(torch_device)
            model.eval()

            loaded_model.to(torch_device)
            loaded_model.eval()

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

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

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

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

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

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

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

            self.assertTrue(models_equal)

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

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

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

    
    
    @is_pt_flax_cross_test
    def test_equivalence_pt_to_flax(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
                
                pt_model = model_class(config).eval()
                
                
                pt_model.config.use_cache = False

                fx_model_class_name = "Flax" + model_class.__name__

                if not hasattr(transformers, fx_model_class_name):
                    return

                fx_model_class = getattr(transformers, fx_model_class_name)

                
                fx_model = fx_model_class(config, dtype=jnp.float32)
                
                fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()

                
                pt_inputs = self._prepare_for_class(inputs_dict, model_class)

                
                pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}

                fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
                fx_model.params = fx_state

                with torch.no_grad():
                    pt_outputs = pt_model(**pt_inputs).to_tuple()

                
                fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
                fx_outputs = fx_model(**fx_inputs).to_tuple()
                self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
                for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
                    self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)

                with tempfile.TemporaryDirectory() as tmpdirname:
                    pt_model.save_pretrained(tmpdirname)
                    fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, from_pt=True)

                fx_outputs_loaded = fx_model_loaded(**fx_inputs).to_tuple()
                self.assertEqual(
                    len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
                )
                for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]):
                    self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)

    
    
    @is_pt_flax_cross_test
    def test_equivalence_flax_to_pt(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
                
                pt_model = model_class(config).eval()

                
                pt_model.config.use_cache = False

                fx_model_class_name = "Flax" + model_class.__name__

                if not hasattr(transformers, fx_model_class_name):
                    
                    return

                fx_model_class = getattr(transformers, fx_model_class_name)

                
                fx_model = fx_model_class(config, dtype=jnp.float32)
                
                fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()

                pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)

                
                pt_model.tie_weights()

                
                pt_inputs = self._prepare_for_class(inputs_dict, model_class)

                
                pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}

                with torch.no_grad():
                    pt_outputs = pt_model(**pt_inputs).to_tuple()

                fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}

                fx_outputs = fx_model(**fx_inputs).to_tuple()
                self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")

                for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
                    self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)

                with tempfile.TemporaryDirectory() as tmpdirname:
                    fx_model.save_pretrained(tmpdirname)
                    pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)

                with torch.no_grad():
                    pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()

                self.assertEqual(
                    len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
                )
                for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs_loaded[:4]):
                    self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)

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

        for model_class in self.all_model_classes:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.return_dict = True

            if model_class in get_values(MODEL_MAPPING):
                continue

            print("Model class:", model_class)

            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            for k, v in inputs.items():
                print(k, v.shape)
            loss = model(**inputs).loss
            loss.backward()

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



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


@require_vision
@require_torch
class CLIPSegModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_image_segmentation(self):
        model_name = "CIDAS/clipseg-rd64-refined"
        processor = CLIPSegProcessor.from_pretrained(model_name)
        model = CLIPSegForImageSegmentation.from_pretrained(model_name).to(torch_device)

        image = prepare_img()
        texts = ["a cat", "a remote", "a blanket"]
        inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to(torch_device)

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

        
        self.assertEqual(
            outputs.logits.shape,
            torch.Size((3, 352, 352)),
        )
        expected_masks_slice = torch.tensor(
            [[-7.4613, -7.4785, -7.3628], [-7.3268, -7.0899, -7.1333], [-6.9838, -6.7900, -6.8913]]
        ).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-3))

        
        expected_conditional = torch.tensor([0.5601, -0.0314, 0.1980]).to(torch_device)
        expected_pooled_output = torch.tensor([0.5036, -0.2681, -0.2644]).to(torch_device)
        self.assertTrue(torch.allclose(outputs.conditional_embeddings[0, :3], expected_conditional, atol=1e-3))
        self.assertTrue(torch.allclose(outputs.pooled_output[0, :3], expected_pooled_output, atol=1e-3))