codingutf8 2023 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 clap model import inspect import os import tempfile import unittest import numpy as np from datasets import loaddataset from transformers import clapaudioconfig clapconfig clapprocessor claptextconfig from transformers testingutils import requiretorch slow torchdevice from transformers utils import istorchavailable 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 clapaudiomodel clapaudiomodelwithprojection clapmodel claptextmodel claptextmodelwithprojection from transformers models clap modelingclap import clappretrainedmodelarchivelist class clapaudiomodeltester def init self parent batchsize12 imagesize60 nummelbins16 windowsize4 specsize64 patchsize2 patchstride2 seqlength16 freqratio2 numchannels3 istrainingtrue hiddensize32 patchembedshiddensize16 projectiondim32 depths2 2 numhiddenlayers2 numheads2 2 intermediatesize37 dropout0 1 attentiondropout0 1 initializerrange0 02 scopenone self parent parent self batchsize batchsize self imagesize imagesize self nummelbins nummelbins self windowsize windowsize self patchsize patchsize self numchannels numchannels self istraining istraining self hiddensize hiddensize self projectiondim projectiondim self numhiddenlayers numhiddenlayers self depths depths self numheads numheads self numattentionheads numheads0 self seqlength seqlength self specsize specsize self freqratio freqratio self patchstride patchstride self patchembedshiddensize patchembedshiddensize self intermediatesize intermediatesize self dropout dropout self attentiondropout attentiondropout self initializerrange initializerrange self scope scope def prepareconfigandinputsself inputfeatures floatstensorself batchsize 1 self hiddensize self nummelbins config self getconfig return config inputfeatures def getconfigself return clapaudioconfig imagesizeself imagesize patchsizeself patchsize nummelbinsself nummelbins windowsizeself windowsize numchannelsself numchannels hiddensizeself hiddensize patchstrideself patchstride projectiondimself projectiondim depthsself depths numhiddenlayersself numhiddenlayers numattentionheadsself numheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout initializerrangeself initializerrange specsizeself specsize freqratioself freqratio patchembedshiddensizeself patchembedshiddensize def createandcheckmodelself config inputfeatures model clapaudiomodelconfigconfig model totorchdevice model eval with torch nograd result modelinputfeatures self parent assertequalresult pooleroutput shape self batchsize self hiddensize def createandcheckmodelwithprojectionself config inputfeatures model clapaudiomodelwithprojectionconfigconfig model totorchdevice model eval with torch nograd result modelinputfeatures self parent assertequalresult audioembeds shape self batchsize self projectiondim def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputfeatures configandinputs inputsdict inputfeatures inputfeatures return config inputsdict requiretorch class clapaudiomodeltestmodeltestermixin unittest testcase allmodelclasses clapaudiomodel clapaudiomodelwithprojection if istorchavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester clapaudiomodeltesterself self configtester configtesterself configclassclapaudioconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasonclapaudiomodel 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 testhiddenstatesoutputself def checkhiddenstatesoutputinputsdict config modelclass model modelclassconfig model totorchdevice model eval with torch nograd outputs modelself prepareforclassinputsdict modelclass hiddenstates outputs hiddenstates expectednumlayers getattr self modeltester expectednumhiddenlayers self modeltester numhiddenlayers 1 self assertequallenhiddenstates expectednumlayers self assertlistequal listhiddenstates0 shape2 2 self modeltester patchembedshiddensize 2 self modeltester patchembedshiddensize config inputsdict self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses inputsdictoutputhiddenstates true checkhiddenstatesoutputinputsdict config modelclass check that outputhiddenstates also work using config del inputsdictoutputhiddenstates config outputhiddenstates true checkhiddenstatesoutputinputsdict config modelclass unittest skipreasonclapaudiomodel does not output any loss term in the forward pass def testretaingradhiddenstatesattentionsself pass 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 inputfeatures self assertlistequalargnames 1 expectedargnames def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testmodelwithprojectionself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelwithprojectionconfigandinputs unittest skipreasonclapaudiomodel does not output any loss term in the forward pass def testtrainingself pass unittest skipreasonclapaudiomodel does not output any loss term in the forward 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 skipreasonclapaudiomodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasonclapaudiomodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in clappretrainedmodelarchivelist 1 model clapaudiomodel frompretrainedmodelname self assertisnotnonemodel slow def testmodelwithprojectionfrompretrainedself for modelname in clappretrainedmodelarchivelist 1 model clapaudiomodelwithprojection frompretrainedmodelname self assertisnotnonemodel self asserttruehasattrmodel audioprojection class claptextmodeltester def init self parent batchsize12 seqlength7 istrainingtrue useinputmasktrue uselabelstrue vocabsize99 hiddensize32 projectiondim32 numhiddenlayers2 numattentionheads4 intermediatesize37 dropout0 1 attentiondropout0 1 maxpositionembeddings512 initializerrange0 02 scopenone projectionhiddenactrelu 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 projectiondim projectiondim self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self dropout dropout self attentiondropout attentiondropout self maxpositionembeddings maxpositionembeddings self initializerrange initializerrange self scope scope self projectionhiddenact projectionhiddenact 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 claptextconfig vocabsizeself vocabsize hiddensizeself hiddensize projectiondimself projectiondim numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout maxpositionembeddingsself maxpositionembeddings initializerrangeself initializerrange projectionhiddenactself projectionhiddenact def createandcheckmodelself config inputids inputmask model claptextmodelconfigconfig 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 createandcheckmodelwithprojectionself config inputids inputmask model claptextmodelwithprojectionconfigconfig 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 textembeds shape self batchsize self projectiondim def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids inputmask configandinputs inputsdict inputids inputids attentionmask inputmask return config inputsdict requiretorch class claptextmodeltestmodeltestermixin unittest testcase allmodelclasses claptextmodel claptextmodelwithprojection if istorchavailable else fxcompatible false testpruning false testheadmasking false def setupself self modeltester claptextmodeltesterself self configtester configtesterself configclassclaptextconfig hiddensize37 def testconfigself self configtester runcommontests def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testmodelwithprojectionself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelwithprojectionconfigandinputs unittest skipreasonclaptextmodel does not output any loss term in the forward pass def testtrainingself pass unittest skipreasonclaptextmodel does not output any loss term in the forward 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 skipreasonclaptextmodel does not use inputsembeds def testinputsembedsself pass unittest skipreasonclaptextmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasonclaptextmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in clappretrainedmodelarchivelist 1 model claptextmodel frompretrainedmodelname self assertisnotnonemodel slow def testmodelwithprojectionfrompretrainedself for modelname in clappretrainedmodelarchivelist 1 model claptextmodelwithprojection frompretrainedmodelname self assertisnotnonemodel self asserttruehasattrmodel textprojection class clapmodeltester def initself parent textkwargsnone audiokwargsnone istrainingtrue if textkwargs is none textkwargs if audiokwargs is none audiokwargs self parent parent self textmodeltester claptextmodeltesterparent textkwargs self audiomodeltester clapaudiomodeltesterparent audiokwargs self istraining istraining def prepareconfigandinputsself inputids attentionmask self textmodeltester prepareconfigandinputs inputfeatures self audiomodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask inputfeatures def getconfigself return clapconfig fromtextaudioconfigs self textmodeltester getconfig self audiomodeltester getconfig projectiondim64 def createandcheckmodelself config inputids attentionmask inputfeatures model clapmodelconfig totorchdevice eval with torch nograd result modelinputids inputfeatures attentionmask self parent assertequal result logitsperaudio shape self audiomodeltester batchsize self textmodeltester batchsize self parent assertequal result logitspertext shape self textmodeltester batchsize self audiomodeltester batchsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask inputfeatures configandinputs inputsdict inputids inputids attentionmask attentionmask inputfeatures inputfeatures returnloss true return config inputsdict requiretorch class clapmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses clapmodel if istorchavailable else pipelinemodelmapping featureextraction clapmodel if istorchavailable else fxcompatible false testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false def setupself self modeltester clapmodeltesterself def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs 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 skipreasonclapmodel does not have inputoutput embeddings def testmodelcommonattributesself pass override as the logitscale parameter initilization is different for clap def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit for name param in model namedparameters if param requiresgrad check if logitscale is initilized as per the original implementation if name logitscale self assertalmostequal param data item np log1 0 07 delta1e3 msgfparameter name of model modelclass seems not properly initialized else self assertin param data mean 1e9 round 1e9 item 0 0 1 0 msgfparameter name of model modelclass seems not properly initialized def createandchecktorchscriptself config inputsdict if not self testtorchscript return configsnoinit configzeroinitconfig to be sure we have no nan configsnoinit torchscript true configsnoinit returndict false for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit model totorchdevice model eval try inputids inputsdictinputids inputfeatures inputsdictinputfeatures clap needs inputfeatures tracedmodel torch jit tracemodel inputids inputfeatures 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 testloadaudiotextconfigself config inputsdict self modeltester prepareconfigandinputsforcommon save clapconfig and check if we can load clapaudioconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname audioconfig clapaudioconfig frompretrainedtmpdirname self assertdictequalconfig audioconfig todict audioconfig todict save clapconfig and check if we can load claptextconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname textconfig claptextconfig frompretrainedtmpdirname self assertdictequalconfig textconfig todict textconfig todict slow def testmodelfrompretrainedself for modelname in clappretrainedmodelarchivelist 1 model clapmodel frompretrainedmodelname self assertisnotnonemodel slow requiretorch class clapmodelintegrationtestunittest testcase paddings repeatpad repeat pad def testintegrationunfusedself expectedmeansunfused repeatpad 0 0024 pad 0 0020 repeat 0 0023 librispeechdummy loaddatasethfinternaltestinglibrispeechasrdummy clean splitvalidation audiosample librispeechdummy1 modelid laionclaphtsatunfused model clapmodel frompretrainedmodelid totorchdevice processor clapprocessor frompretrainedmodelid for padding in self paddings inputs processoraudiosaudiosampleaudioarray returntensorspt paddingpadding to torchdevice audioembed model getaudiofeaturesinputs expectedmean expectedmeansunfusedpadding self asserttrue torch allcloseaudioembed cpu mean torch tensorexpectedmean atol1e3 rtol1e3 def testintegrationfusedself expectedmeansfused repeatpad 0 00069 repeat 0 00196 pad 0 000379 librispeechdummy loaddatasethfinternaltestinglibrispeechasrdummy clean splitvalidation audiosample librispeechdummy1 modelid laionclaphtsatfused model clapmodel frompretrainedmodelid totorchdevice processor clapprocessor frompretrainedmodelid for padding in self paddings inputs processor audiosaudiosampleaudioarray returntensorspt paddingpadding truncationfusion totorchdevice audioembed model getaudiofeaturesinputs expectedmean expectedmeansfusedpadding self asserttrue torch allcloseaudioembed cpu mean torch tensorexpectedmean atol1e3 rtol1e3 def testbatchedfusedself expectedmeansfused repeatpad 0 0010 repeat 0 0020 pad 0 0006 librispeechdummy loaddatasethfinternaltestinglibrispeechasrdummy clean splitvalidation audiosamples samplearray for sample in librispeechdummy0 4audio modelid laionclaphtsatfused model clapmodel frompretrainedmodelid totorchdevice processor clapprocessor frompretrainedmodelid for padding in self paddings inputs processoraudiosaudiosamples returntensorspt paddingpadding truncationfusion to torchdevice audioembed model getaudiofeaturesinputs expectedmean expectedmeansfusedpadding self asserttrue torch allcloseaudioembed cpu mean torch tensorexpectedmean atol1e3 rtol1e3 def testbatchedunfusedself expectedmeansfused repeatpad 0 0016 repeat 0 0019 pad 0 0019 librispeechdummy loaddatasethfinternaltestinglibrispeechasrdummy clean splitvalidation audiosamples samplearray for sample in librispeechdummy0 4audio modelid laionclaphtsatunfused model clapmodel frompretrainedmodelid totorchdevice processor clapprocessor frompretrainedmodelid for padding in self paddings inputs processoraudiosaudiosamples returntensorspt paddingpadding totorchdevice audioembed model getaudiofeaturesinputs expectedmean expectedmeansfusedpadding self asserttrue torch allcloseaudioembed cpu mean torch tensorexpectedmean atol1e3 rtol1e3 coding utf 8 2023 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 clap model here we also overwrite some of the tests of test_modeling_common py as clap does not use input_ids inputs_embeds attention_mask and seq_length check that output_hidden_states also work using config signature parameters is an ordereddict so arg_names order is deterministic override as the logit_scale parameter initilization is different for clap check if logit_scale is initilized as per the original implementation to be sure we have no nan clap needs input_features save clapconfig and check if we can load clapaudioconfig from it save clapconfig and check if we can load claptextconfig from it
import inspect
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import ClapAudioConfig, ClapConfig, ClapProcessor, ClapTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_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 (
        ClapAudioModel,
        ClapAudioModelWithProjection,
        ClapModel,
        ClapTextModel,
        ClapTextModelWithProjection,
    )
    from transformers.models.clap.modeling_clap import CLAP_PRETRAINED_MODEL_ARCHIVE_LIST


class ClapAudioModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=60,
        num_mel_bins=16,
        window_size=4,
        spec_size=64,
        patch_size=2,
        patch_stride=2,
        seq_length=16,
        freq_ratio=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        patch_embeds_hidden_size=16,
        projection_dim=32,
        depths=[2, 2],
        num_hidden_layers=2,
        num_heads=[2, 2],
        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.num_mel_bins = num_mel_bins
        self.window_size = window_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.projection_dim = projection_dim
        self.num_hidden_layers = num_hidden_layers
        self.depths = depths
        self.num_heads = num_heads
        self.num_attention_heads = num_heads[0]
        self.seq_length = seq_length
        self.spec_size = spec_size
        self.freq_ratio = freq_ratio
        self.patch_stride = patch_stride
        self.patch_embeds_hidden_size = patch_embeds_hidden_size
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_features = floats_tensor([self.batch_size, 1, self.hidden_size, self.num_mel_bins])
        config = self.get_config()

        return config, input_features

    def get_config(self):
        return ClapAudioConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_mel_bins=self.num_mel_bins,
            window_size=self.window_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            patch_stride=self.patch_stride,
            projection_dim=self.projection_dim,
            depths=self.depths,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            initializer_range=self.initializer_range,
            spec_size=self.spec_size,
            freq_ratio=self.freq_ratio,
            patch_embeds_hidden_size=self.patch_embeds_hidden_size,
        )

    def create_and_check_model(self, config, input_features):
        model = ClapAudioModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_features)
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def create_and_check_model_with_projection(self, config, input_features):
        model = ClapAudioModelWithProjection(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_features)
        self.parent.assertEqual(result.audio_embeds.shape, (self.batch_size, self.projection_dim))

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


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

    all_model_classes = (ClapAudioModel, ClapAudioModelWithProjection) 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 = ClapAudioModelTester(self)
        self.config_tester = ConfigTester(self, config_class=ClapAudioConfig, has_text_modality=False, hidden_size=37)

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

    @unittest.skip(reason="ClapAudioModel 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_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            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", self.model_tester.num_hidden_layers + 1
            )
            self.assertEqual(len(hidden_states), expected_num_layers)

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

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

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

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

            check_hidden_states_output(inputs_dict, config, model_class)

    @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass")
    def test_retain_grad_hidden_states_attentions(self):
        pass

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

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

            expected_arg_names = ["input_features"]
            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_model_with_projection(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_with_projection(*config_and_inputs)

    @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward pass")
    def test_training(self):
        pass

    @unittest.skip(reason="ClapAudioModel does not output any loss term in the forward 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="ClapAudioModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

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

    @slow
    def test_model_with_projection_from_pretrained(self):
        for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = ClapAudioModelWithProjection.from_pretrained(model_name)
            self.assertIsNotNone(model)
            self.assertTrue(hasattr(model, "audio_projection"))


class ClapTextModelTester:
    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,
        projection_dim=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,
        projection_hidden_act="relu",
    ):
        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.projection_dim = projection_dim
        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
        self.projection_hidden_act = projection_hidden_act

    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 ClapTextConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            projection_dim=self.projection_dim,
            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,
            projection_hidden_act=self.projection_hidden_act,
        )

    def create_and_check_model(self, config, input_ids, input_mask):
        model = ClapTextModel(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 create_and_check_model_with_projection(self, config, input_ids, input_mask):
        model = ClapTextModelWithProjection(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.text_embeds.shape, (self.batch_size, self.projection_dim))

    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 ClapTextModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (ClapTextModel, ClapTextModelWithProjection) if is_torch_available() else ()
    fx_compatible = False
    test_pruning = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = ClapTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=ClapTextConfig, 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_model_with_projection(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_with_projection(*config_and_inputs)

    @unittest.skip(reason="ClapTextModel does not output any loss term in the forward pass")
    def test_training(self):
        pass

    @unittest.skip(reason="ClapTextModel does not output any loss term in the forward 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="ClapTextModel does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

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

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

    @slow
    def test_model_with_projection_from_pretrained(self):
        for model_name in CLAP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = ClapTextModelWithProjection.from_pretrained(model_name)
            self.assertIsNotNone(model)
            self.assertTrue(hasattr(model, "text_projection"))


class ClapModelTester:
    def __init__(self, parent, text_kwargs=None, audio_kwargs=None, is_training=True):
        if text_kwargs is None:
            text_kwargs = {}
        if audio_kwargs is None:
            audio_kwargs = {}

        self.parent = parent
        self.text_model_tester = ClapTextModelTester(parent, **text_kwargs)
        self.audio_model_tester = ClapAudioModelTester(parent, **audio_kwargs)
        self.is_training = is_training

    def prepare_config_and_inputs(self):
        _, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
        _, input_features = self.audio_model_tester.prepare_config_and_inputs()

        config = self.get_config()

        return config, input_ids, attention_mask, input_features

    def get_config(self):
        return ClapConfig.from_text_audio_configs(
            self.text_model_tester.get_config(), self.audio_model_tester.get_config(), projection_dim=64
        )

    def create_and_check_model(self, config, input_ids, attention_mask, input_features):
        model = ClapModel(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(input_ids, input_features, attention_mask)
        self.parent.assertEqual(
            result.logits_per_audio.shape, (self.audio_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.audio_model_tester.batch_size)
        )

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


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

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

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

    @unittest.skip(reason="Hidden_states 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="ClapModel does not have input/output embeddings")
    def test_model_common_attributes(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 name == "logit_scale":
                        self.assertAlmostEqual(
                            param.data.item(),
                            np.log(1 / 0.07),
                            delta=1e-3,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

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

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

            try:
                input_ids = inputs_dict["input_ids"]
                input_features = inputs_dict["input_features"]  
                traced_model = torch.jit.trace(model, (input_ids, input_features))
            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_audio_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)
            audio_config = ClapAudioConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.audio_config.to_dict(), audio_config.to_dict())

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

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


@slow
@require_torch
class ClapModelIntegrationTest(unittest.TestCase):
    paddings = ["repeatpad", "repeat", "pad"]

    def test_integration_unfused(self):
        EXPECTED_MEANS_UNFUSED = {
            "repeatpad": 0.0024,
            "pad": 0.0020,
            "repeat": 0.0023,
        }

        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        audio_sample = librispeech_dummy[-1]

        model_id = "laion/clap-htsat-unfused"

        model = ClapModel.from_pretrained(model_id).to(torch_device)
        processor = ClapProcessor.from_pretrained(model_id)

        for padding in self.paddings:
            inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt", padding=padding).to(
                torch_device
            )

            audio_embed = model.get_audio_features(**inputs)
            expected_mean = EXPECTED_MEANS_UNFUSED[padding]

            self.assertTrue(
                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
            )

    def test_integration_fused(self):
        EXPECTED_MEANS_FUSED = {
            "repeatpad": 0.00069,
            "repeat": 0.00196,
            "pad": -0.000379,
        }

        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        audio_sample = librispeech_dummy[-1]

        model_id = "laion/clap-htsat-fused"

        model = ClapModel.from_pretrained(model_id).to(torch_device)
        processor = ClapProcessor.from_pretrained(model_id)

        for padding in self.paddings:
            inputs = processor(
                audios=audio_sample["audio"]["array"], return_tensors="pt", padding=padding, truncation="fusion"
            ).to(torch_device)

            audio_embed = model.get_audio_features(**inputs)
            expected_mean = EXPECTED_MEANS_FUSED[padding]

            self.assertTrue(
                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
            )

    def test_batched_fused(self):
        EXPECTED_MEANS_FUSED = {
            "repeatpad": 0.0010,
            "repeat": 0.0020,
            "pad": 0.0006,
        }

        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]]

        model_id = "laion/clap-htsat-fused"

        model = ClapModel.from_pretrained(model_id).to(torch_device)
        processor = ClapProcessor.from_pretrained(model_id)

        for padding in self.paddings:
            inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding, truncation="fusion").to(
                torch_device
            )

            audio_embed = model.get_audio_features(**inputs)
            expected_mean = EXPECTED_MEANS_FUSED[padding]

            self.assertTrue(
                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
            )

    def test_batched_unfused(self):
        EXPECTED_MEANS_FUSED = {
            "repeatpad": 0.0016,
            "repeat": 0.0019,
            "pad": 0.0019,
        }

        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]]

        model_id = "laion/clap-htsat-unfused"

        model = ClapModel.from_pretrained(model_id).to(torch_device)
        processor = ClapProcessor.from_pretrained(model_id)

        for padding in self.paddings:
            inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding).to(torch_device)

            audio_embed = model.get_audio_features(**inputs)
            expected_mean = EXPECTED_MEANS_FUSED[padding]

            self.assertTrue(
                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
            )