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 blip2 model import inspect import tempfile import unittest import numpy as np import requests from transformers import configmapping blip2config blip2qformerconfig blip2visionconfig from transformers testingutils import requiretorch requiretorchmultiaccelerator 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 blip2forconditionalgeneration blip2model blip2visionmodel from transformers models blip2 modelingblip2 import blip2pretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import blip2processor class blip2visionmodeltester def init self parent batchsize12 imagesize30 patchsize2 numchannels3 istrainingtrue hiddensize32 projectiondim32 numhiddenlayers2 numattentionheads4 intermediatesize37 dropout0 1 attentiondropout0 1 initializerrange1e10 scopenone self parent parent self batchsize batchsize self imagesize imagesize self patchsize patchsize self numchannels numchannels self istraining istraining self hiddensize hiddensize self projectiondim projectiondim 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 blip2visionconfig imagesizeself imagesize patchsizeself patchsize numchannelsself numchannels hiddensizeself hiddensize projectiondimself projectiondim numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout initializerrangeself initializerrange def createandcheckmodelself config pixelvalues model blip2visionmodelconfigconfig 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 blip2visionmodeltestmodeltestermixin unittest testcase allmodelclasses blip2visionmodel if istorchavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester blip2visionmodeltesterself self configtester configtester self configclassblip2visionconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasonblip2 s vision encoder 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 skipreasonblip2visionmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasonblip2visionmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in blip2pretrainedmodelarchivelist 1 model blip2visionmodel frompretrainedmodelname self assertisnotnonemodel class blip2qformermodeltester def init self parent batchsize12 seqlength7 istrainingtrue useinputmasktrue uselabelstrue vocabsize99 hiddensize32 projectiondim32 numhiddenlayers2 numattentionheads4 intermediatesize37 dropout0 1 attentiondropout0 1 maxpositionembeddings512 initializerrange0 02 bostokenid0 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 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 bostokenid bostokenid 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 blip2qformerconfig vocabsizeself vocabsize hiddensizeself hiddensize projectiondimself projectiondim numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout maxpositionembeddingsself maxpositionembeddings initializerrangeself initializerrange bostokenidself bostokenid this class is based on optmodeltester found in testsmodelsopttestmodelingopt py class blip2textmodeldecoderonlytester def init self parent batchsize12 seqlength7 istrainingtrue uselabelsfalse vocabsize99 hiddensize16 numhiddenlayers2 numattentionheads4 intermediatesize4 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings20 eostokenid2 padtokenid1 bostokenid0 embeddim16 numlabels3 wordembedprojdim16 typesequencelabelsize2 self parent parent self batchsize batchsize self seqlength seqlength self istraining istraining self uselabels uselabels self vocabsize vocabsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self eostokenid eostokenid self padtokenid padtokenid self bostokenid bostokenid self embeddim embeddim self numlabels numlabels self typesequencelabelsize typesequencelabelsize self wordembedprojdim wordembedprojdim self isencoderdecoder false def prepareconfigandinputsself config self getconfig inputids idstensorself batchsize self seqlength self vocabsize clamp3 inputids 1 self eostokenid eos token attentionmask inputids neself padtokenid return config inputids attentionmask def getconfigself return configmappingopt vocabsizeself vocabsize hiddensizeself hiddensize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads ffndimself intermediatesize dropoutself hiddendropoutprob attentiondropoutself attentionprobsdropoutprob maxpositionembeddingsself maxpositionembeddings eostokenidself eostokenid bostokenidself bostokenid padtokenidself padtokenid embeddimself embeddim isencoderdecoderfalse wordembedprojdimself wordembedprojdim this model tester uses a decoderonly language model opt class blip2forconditionalgenerationdecoderonlymodeltester def init self parent visionkwargsnone qformerkwargsnone textkwargsnone istrainingtrue numquerytokens10 if visionkwargs is none visionkwargs if qformerkwargs is none qformerkwargs if textkwargs is none textkwargs self parent parent self visionmodeltester blip2visionmodeltesterparent visionkwargs self qformermodeltester blip2qformermodeltesterparent qformerkwargs self textmodeltester blip2textmodeldecoderonlytesterparent textkwargs self istraining istraining self numquerytokens numquerytokens def prepareconfigandinputsself pixelvalues self visionmodeltester prepareconfigandinputs inputids attentionmask self textmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask pixelvalues def getconfigself return blip2config fromvisionqformertextconfigs visionconfigself visionmodeltester getconfig qformerconfigself qformermodeltester getconfig textconfigself textmodeltester getconfig numquerytokensself numquerytokens def createandcheckforconditionalgenerationself config inputids attentionmask pixelvalues model blip2forconditionalgenerationconfig totorchdevice eval with torch nograd result modelpixelvalues inputids attentionmask expectedseqlength self numquerytokens self textmodeltester seqlength self parent assertequal result logits shape self visionmodeltester batchsize expectedseqlength self textmodeltester vocabsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask pixelvalues configandinputs inputsdict pixelvalues pixelvalues inputids inputids attentionmask attentionmask labels inputids return config inputsdict requiretorch class blip2forconditionalgenerationdecoderonlytestmodeltestermixin unittest testcase allmodelclasses blip2forconditionalgeneration if istorchavailable else fxcompatible false testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false testtorchscript false def setupself self modeltester blip2forconditionalgenerationdecoderonlymodeltesterself def testforconditionalgenerationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforconditionalgenerationconfigandinputs 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 skipreasonblip2model does not have inputoutput embeddings def testmodelcommonattributesself pass unittest skipreasonthere s no base blip2model def testsaveloadfastinitfrombaseself pass unittest skipreasonthere s no base blip2model def testsaveloadfastinittobaseself 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 pixelvalues self assertlistequalargnames 1 expectedargnames def testloadvisionqformertextconfigself config inputsdict self modeltester prepareconfigandinputsforcommon save blip2config and check if we can load blip2visionconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname visionconfig blip2visionconfig frompretrainedtmpdirname self assertdictequalconfig visionconfig todict visionconfig todict save blip2config and check if we can load blip2qformerconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname qformerconfig blip2qformerconfig frompretrainedtmpdirname self assertdictequalconfig qformerconfig todict qformerconfig todict slow def testmodelfrompretrainedself for modelname in blip2pretrainedmodelarchivelist model blip2forconditionalgeneration frompretrainedmodelname self assertisnotnonemodel this class is based on t5modeltester found in testsmodelst5testmodelingt5 py class blip2textmodeltester def init self parent vocabsize99 batchsize12 encoderseqlength7 decoderseqlength9 for common tests istrainingtrue useattentionmasktrue uselabelstrue hiddensize32 numhiddenlayers2 numattentionheads4 dff37 relativeattentionnumbuckets8 dropoutrate0 1 initializerfactor0 002 eostokenid1 padtokenid0 decoderstarttokenid0 scopenone decoderlayersnone self parent parent self batchsize batchsize self encoderseqlength encoderseqlength self decoderseqlength decoderseqlength for common tests self seqlength self decoderseqlength self istraining istraining self useattentionmask useattentionmask self uselabels uselabels self vocabsize vocabsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self dff dff self relativeattentionnumbuckets relativeattentionnumbuckets self dropoutrate dropoutrate self initializerfactor initializerfactor self eostokenid eostokenid self padtokenid padtokenid self decoderstarttokenid decoderstarttokenid self scope none self decoderlayers decoderlayers def prepareconfigandinputsself inputids idstensorself batchsize self encoderseqlength self vocabsize decoderinputids idstensorself batchsize self decoderseqlength self vocabsize attentionmask none decoderattentionmask none if self useattentionmask attentionmask idstensorself batchsize self encoderseqlength vocabsize2 decoderattentionmask idstensorself batchsize self decoderseqlength vocabsize2 lmlabels none if self uselabels lmlabels idstensorself batchsize self decoderseqlength self vocabsize config self getconfig return config inputids decoderinputids attentionmask decoderattentionmask lmlabels def getconfigself return configmappingt5 vocabsizeself vocabsize dmodelself hiddensize dffself dff dkvself hiddensize self numattentionheads numlayersself numhiddenlayers numdecoderlayersself decoderlayers numheadsself numattentionheads relativeattentionnumbucketsself relativeattentionnumbuckets dropoutrateself dropoutrate initializerfactorself initializerfactor eostokenidself eostokenid bostokenidself padtokenid padtokenidself padtokenid decoderstarttokenidself decoderstarttokenid this model tester uses an encoderdecoder language model t5 class blip2modeltester def init self parent visionkwargsnone qformerkwargsnone textkwargsnone istrainingtrue numquerytokens10 if visionkwargs is none visionkwargs if qformerkwargs is none qformerkwargs if textkwargs is none textkwargs self parent parent self visionmodeltester blip2visionmodeltesterparent visionkwargs self qformermodeltester blip2qformermodeltesterparent qformerkwargs self textmodeltester blip2textmodeltesterparent textkwargs self istraining istraining self numquerytokens numquerytokens def prepareconfigandinputsself pixelvalues self visionmodeltester prepareconfigandinputs inputids decoderinputids attentionmask decoderattentionmask lmlabels self textmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask pixelvalues decoderinputids decoderattentionmask lmlabels def getconfigself return blip2config fromvisionqformertextconfigs visionconfigself visionmodeltester getconfig qformerconfigself qformermodeltester getconfig textconfigself textmodeltester getconfig numquerytokensself numquerytokens def createandcheckforconditionalgeneration self config inputids attentionmask pixelvalues decoderinputids decoderattentionmask labels model blip2forconditionalgenerationconfig totorchdevice eval with torch nograd result modelpixelvalues inputids attentionmask decoderinputids decoderattentionmask self parent assertequal result logits shape self visionmodeltester batchsize self textmodeltester seqlength self textmodeltester vocabsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask pixelvalues decoderinputids decoderattentionmask labels configandinputs inputsdict pixelvalues pixelvalues inputids inputids attentionmask attentionmask decoderinputids decoderinputids decoderattentionmask decoderattentionmask labels labels return config inputsdict requiretorch class blip2modeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses blip2forconditionalgeneration blip2model if istorchavailable else pipelinemodelmapping featureextraction blip2model imagetotext blip2forconditionalgeneration visualquestionanswering blip2forconditionalgeneration if istorchavailable else fxcompatible false testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false testtorchscript false def setupself self modeltester blip2modeltesterself def testforconditionalgenerationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforconditionalgenerationconfigandinputs 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 skipreasonblip2model does not have inputoutput embeddings def testmodelcommonattributesself pass unittest skipreasonthere s no base blip2model def testsaveloadfastinitfrombaseself pass unittest skipreasonthere s no base blip2model def testsaveloadfastinittobaseself pass unittest skipreasondoes not work on the tiny model as we keep hitting edge cases def testcpuoffloadself 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 pixelvalues self assertlistequalargnames 1 expectedargnames def testloadvisionqformertextconfigself config inputsdict self modeltester prepareconfigandinputsforcommon save blip2config and check if we can load blip2visionconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname visionconfig blip2visionconfig frompretrainedtmpdirname self assertdictequalconfig visionconfig todict visionconfig todict save blip2config and check if we can load blip2qformerconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname qformerconfig blip2qformerconfig frompretrainedtmpdirname self assertdictequalconfig qformerconfig todict qformerconfig todict slow def testmodelfrompretrainedself for modelname in blip2pretrainedmodelarchivelist model blip2forconditionalgeneration frompretrainedmodelname self assertisnotnonemodel def testgettextfeaturesself config self modeltester prepareconfigandinputsforcommon inputsdict inputids torch longtensor1 2 3 4 5 6 7 8 9 10 totorchdevice attentionmask torch longtensor1 1 1 1 1 1 1 1 1 1 totorchdevice decoderinputids torch longtensor1 2 3 4 5 6 7 8 9 10 totorchdevice model blip2modelconfig totorchdevice model eval textfeatures model gettextfeaturesinputsdict self assertequaltextfeatures0 shape 1 10 config textconfig vocabsize def testgetimagefeaturesself config inputsdict self modeltester prepareconfigandinputsforcommon keystopop inputids attentionmask decoderinputids decoderattentionmask labels for key in keystopop inputsdict popkey model blip2modelconfig totorchdevice model eval imagefeatures model getimagefeaturesinputsdict self assertequal imagefeatures0 shape self modeltester visionmodeltester batchsize self modeltester visionmodeltester seqlength config visionconfig hiddensize def testgetqformerfeaturesself config inputsdict self modeltester prepareconfigandinputsforcommon keystopop inputids attentionmask decoderinputids decoderattentionmask labels for key in keystopop inputsdict popkey model blip2modelconfig totorchdevice model eval qformerfeatures model getqformerfeaturesinputsdict self assertequal qformerfeatures0 shape self modeltester visionmodeltester batchsize 10 config visionconfig hiddensize override from common to deal with nested configurations visionconfig textconfig and qformerconfig def testinitializationself config inputsdict self modeltester prepareconfigandinputsforcommon configsnoinit configzeroinitconfig for key in visionconfig qformerconfig textconfig setattrconfigsnoinit key configzeroinitgetattrconfigsnoinit key for modelclass in self allmodelclasses model modelclassconfigconfigsnoinit for name param in model namedparameters if param requiresgrad self assertin param data mean 1e9 round 1e9 item 0 0 1 0 msgfparameter name of model modelclass seems not properly initialized we will verify our results on an image of cute cats def prepareimg url https huggingface cohfinternaltestingbliptestimageresolvemaindemo jpg image image openrequests geturl streamtrue raw return image requirevision requiretorch slow class blip2modelintegrationtestunittest testcase def testinferenceoptself processor blip2processor frompretrainedsalesforceblip2opt2 7b model blip2forconditionalgeneration frompretrained salesforceblip2opt2 7b torchdtypetorch float16 totorchdevice prepare image image prepareimg inputs processorimagesimage returntensorspt totorchdevice dtypetorch float16 predictions model generateinputs generatedtext processor batchdecodepredictions skipspecialtokenstrue0 strip test output self assertequalpredictions0 tolist 2 102 693 2828 15 5 4105 19 10 2335 50118 self assertequala woman sitting on the beach with a dog generatedtext image and context prompt question which city is this answer inputs processorimagesimage textprompt returntensorspt totorchdevice dtypetorch float16 predictions model generateinputs generatedtext processor batchdecodepredictions skipspecialtokenstrue0 strip test output self assertequal predictions0 tolist 2 24 18 45 10 343 6 24 18 10 4105 50118 self assertequalgeneratedtext it s not a city it s a beach def testinferenceoptbatchedbeamsearchself processor blip2processor frompretrainedsalesforceblip2opt2 7b model blip2forconditionalgeneration frompretrained salesforceblip2opt2 7b torchdtypetorch float16 totorchdevice prepare image image prepareimg inputs processorimagesimage image returntensorspt totorchdevice dtypetorch float16 predictions model generateinputs numbeams2 test output in this case slightly different from greedy search self assertequalpredictions0 tolist 2 102 693 2828 15 5 4105 19 69 2335 50118 self assertequalpredictions1 tolist 2 102 693 2828 15 5 4105 19 69 2335 50118 def testinferencet5self processor blip2processor frompretrainedsalesforceblip2flant5xl model blip2forconditionalgeneration frompretrained salesforceblip2flant5xl torchdtypetorch float16 totorchdevice prepare image image prepareimg inputs processorimagesimage returntensorspt totorchdevice dtypetorch float16 predictions model generateinputs generatedtext processor batchdecodepredictions skipspecialtokenstrue0 strip test output self assertequalpredictions0 tolist 0 2335 1556 28 1782 30 8 2608 1 self assertequalwoman playing with dog on the beach generatedtext image and context prompt question which city is this answer inputs processorimagesimage textprompt returntensorspt totorchdevice dtypetorch float16 predictions model generateinputs generatedtext processor batchdecodepredictions skipspecialtokenstrue0 strip test output self assertequal predictions0 tolist 0 3 7 152 67 839 1 self assertequalgeneratedtext san diego def testinferencet5batchedbeamsearchself processor blip2processor frompretrainedsalesforceblip2flant5xl model blip2forconditionalgeneration frompretrained salesforceblip2flant5xl torchdtypetorch float16 totorchdevice prepare image image prepareimg inputs processorimagesimage image returntensorspt totorchdevice dtypetorch float16 predictions model generateinputs numbeams2 test output in this case slightly different from greedy search self assertequalpredictions0 tolist 0 2335 1556 28 1782 30 8 2608 1 self assertequalpredictions1 tolist 0 2335 1556 28 1782 30 8 2608 1 requiretorchmultiaccelerator def testinferenceoptmultiacceleratorself processor blip2processor frompretrainedsalesforceblip2opt2 7b model blip2forconditionalgeneration frompretrained salesforceblip2opt2 7b torchdtypetorch float16 devicemapbalanced prepare image image prepareimg inputs processorimagesimage returntensorspt to0 dtypetorch float16 predictions model generateinputs generatedtext processor batchdecodepredictions skipspecialtokenstrue0 strip test output self assertequalpredictions0 tolist 2 102 693 2828 15 5 4105 19 10 2335 50118 self assertequala woman sitting on the beach with a dog generatedtext image and context prompt question which city is this answer inputs processorimagesimage textprompt returntensorspt to0 dtypetorch float16 predictions model generateinputs generatedtext processor batchdecodepredictions skipspecialtokenstrue0 strip test output self assertequal predictions0 tolist 2 24 18 45 10 343 6 24 18 10 4105 50118 self assertequalgeneratedtext it s not a city it s a beach requiretorchmultiaccelerator def testinferencet5multiacceleratorself processor blip2processor frompretrainedsalesforceblip2flant5xl devicemap devicemap querytokens 0 visionmodel 0 languagemodel 1 languageprojection 0 qformer 0 model blip2forconditionalgeneration frompretrained salesforceblip2flant5xl torchdtypetorch float16 devicemapdevicemap prepare image image prepareimg inputs processorimagesimage returntensorspt to0 dtypetorch float16 predictions model generateinputs generatedtext processor batchdecodepredictions skipspecialtokenstrue0 strip test output self assertequalpredictions0 tolist 0 2335 1556 28 1782 30 8 2608 1 self assertequalwoman playing with dog on the beach generatedtext image and context prompt question which city is this answer inputs processorimagesimage textprompt returntensorspt to0 dtypetorch float16 predictions model generateinputs generatedtext processor batchdecodepredictions skipspecialtokenstrue0 strip test output self assertequal predictions0 tolist 0 3 7 152 67 839 1 self assertequalgeneratedtext san diego 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 blip 2 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 blip 2 s vision encoder does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic this class is based on optmodeltester found in tests models opt test_modeling_opt py eos token this model tester uses a decoder only language model opt signature parameters is an ordereddict so arg_names order is deterministic save blip2config and check if we can load blip2visionconfig from it save blip2config and check if we can load blip2qformerconfig from it this class is based on t5modeltester found in tests models t5 test_modeling_t5 py for common tests for common tests this model tester uses an encoder decoder language model t5 signature parameters is an ordereddict so arg_names order is deterministic save blip2config and check if we can load blip2visionconfig from it save blip2config and check if we can load blip2qformerconfig from it override from common to deal with nested configurations vision_config text_config and qformer_config we will verify our results on an image of cute cats prepare image test output image and context test output prepare image test output in this case slightly different from greedy search prepare image test output image and context test output prepare image test output in this case slightly different from greedy search prepare image test output image and context test output prepare image test output image and context test output
import inspect
import tempfile
import unittest

import numpy as np
import requests

from transformers import CONFIG_MAPPING, Blip2Config, Blip2QFormerConfig, Blip2VisionConfig
from transformers.testing_utils import (
    require_torch,
    require_torch_multi_accelerator,
    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 Blip2ForConditionalGeneration, Blip2Model, Blip2VisionModel
    from transformers.models.blip_2.modeling_blip_2 import BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import Blip2Processor


class Blip2VisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        projection_dim=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        initializer_range=1e-10,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        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.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 Blip2VisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            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,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values):
        model = Blip2VisionModel(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 Blip2VisionModelTest(ModelTesterMixin, unittest.TestCase):
    

    all_model_classes = (Blip2VisionModel,) 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 = Blip2VisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=Blip2VisionConfig, has_text_modality=False, hidden_size=37
        )

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

    @unittest.skip(reason="BLIP-2's vision encoder 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="Blip2VisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="Blip2VisionModel 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 BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = Blip2VisionModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


class Blip2QFormerModelTester:
    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,
        bos_token_id=0,
        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.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.bos_token_id = bos_token_id

    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 Blip2QFormerConfig(
            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,
            bos_token_id=self.bos_token_id,
        )



class Blip2TextModelDecoderOnlyTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_labels=False,
        vocab_size=99,
        hidden_size=16,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=4,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=20,
        eos_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
        embed_dim=16,
        num_labels=3,
        word_embed_proj_dim=16,
        type_sequence_label_size=2,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        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.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.embed_dim = embed_dim
        self.num_labels = num_labels
        self.type_sequence_label_size = type_sequence_label_size
        self.word_embed_proj_dim = word_embed_proj_dim
        self.is_encoder_decoder = False

    def prepare_config_and_inputs(self):
        config = self.get_config()

        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(3)
        input_ids[:, -1] = self.eos_token_id  

        attention_mask = input_ids.ne(self.pad_token_id)

        return config, input_ids, attention_mask

    def get_config(self):
        return CONFIG_MAPPING["opt"](
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            ffn_dim=self.intermediate_size,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            embed_dim=self.embed_dim,
            is_encoder_decoder=False,
            word_embed_proj_dim=self.word_embed_proj_dim,
        )



class Blip2ForConditionalGenerationDecoderOnlyModelTester:
    def __init__(
        self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10
    ):
        if vision_kwargs is None:
            vision_kwargs = {}
        if qformer_kwargs is None:
            qformer_kwargs = {}
        if text_kwargs is None:
            text_kwargs = {}

        self.parent = parent
        self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs)
        self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs)
        self.text_model_tester = Blip2TextModelDecoderOnlyTester(parent, **text_kwargs)
        self.is_training = is_training
        self.num_query_tokens = num_query_tokens

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

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return Blip2Config.from_vision_qformer_text_configs(
            vision_config=self.vision_model_tester.get_config(),
            qformer_config=self.qformer_model_tester.get_config(),
            text_config=self.text_model_tester.get_config(),
            num_query_tokens=self.num_query_tokens,
        )

    def create_and_check_for_conditional_generation(self, config, input_ids, attention_mask, pixel_values):
        model = Blip2ForConditionalGeneration(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(pixel_values, input_ids, attention_mask)

        expected_seq_length = self.num_query_tokens + self.text_model_tester.seq_length
        self.parent.assertEqual(
            result.logits.shape,
            (self.vision_model_tester.batch_size, expected_seq_length, self.text_model_tester.vocab_size),
        )

    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 = {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "labels": input_ids,
        }
        return config, inputs_dict


@require_torch
class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (Blip2ForConditionalGeneration,) if is_torch_available() else ()
    fx_compatible = False
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    test_torchscript = False

    def setUp(self):
        self.model_tester = Blip2ForConditionalGenerationDecoderOnlyModelTester(self)

    def test_for_conditional_generation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_conditional_generation(*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="Blip2Model does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="There's no base Blip2Model")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="There's no base Blip2Model")
    def test_save_load_fast_init_to_base(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 = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_load_vision_qformer_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 = Blip2VisionConfig.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)
            qformer_config = Blip2QFormerConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict())

    @slow
    def test_model_from_pretrained(self):
        for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST:
            model = Blip2ForConditionalGeneration.from_pretrained(model_name)
            self.assertIsNotNone(model)



class Blip2TextModelTester:
    def __init__(
        self,
        parent,
        vocab_size=99,
        batch_size=12,
        encoder_seq_length=7,
        decoder_seq_length=9,
        
        is_training=True,
        use_attention_mask=True,
        use_labels=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        d_ff=37,
        relative_attention_num_buckets=8,
        dropout_rate=0.1,
        initializer_factor=0.002,
        eos_token_id=1,
        pad_token_id=0,
        decoder_start_token_id=0,
        scope=None,
        decoder_layers=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.encoder_seq_length = encoder_seq_length
        self.decoder_seq_length = decoder_seq_length
        
        self.seq_length = self.decoder_seq_length
        self.is_training = is_training
        self.use_attention_mask = use_attention_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.d_ff = d_ff
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.dropout_rate = dropout_rate
        self.initializer_factor = initializer_factor
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.decoder_start_token_id = decoder_start_token_id
        self.scope = None
        self.decoder_layers = decoder_layers

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
        decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        attention_mask = None
        decoder_attention_mask = None
        if self.use_attention_mask:
            attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
            decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)

        lm_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        config = self.get_config()

        return (
            config,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        )

    def get_config(self):
        return CONFIG_MAPPING["t5"](
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            d_ff=self.d_ff,
            d_kv=self.hidden_size // self.num_attention_heads,
            num_layers=self.num_hidden_layers,
            num_decoder_layers=self.decoder_layers,
            num_heads=self.num_attention_heads,
            relative_attention_num_buckets=self.relative_attention_num_buckets,
            dropout_rate=self.dropout_rate,
            initializer_factor=self.initializer_factor,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.pad_token_id,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.decoder_start_token_id,
        )



class Blip2ModelTester:
    def __init__(
        self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10
    ):
        if vision_kwargs is None:
            vision_kwargs = {}
        if qformer_kwargs is None:
            qformer_kwargs = {}
        if text_kwargs is None:
            text_kwargs = {}

        self.parent = parent
        self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs)
        self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs)
        self.text_model_tester = Blip2TextModelTester(parent, **text_kwargs)
        self.is_training = is_training
        self.num_query_tokens = num_query_tokens

    def prepare_config_and_inputs(self):
        _, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
        (
            _,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        ) = self.text_model_tester.prepare_config_and_inputs()

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values, decoder_input_ids, decoder_attention_mask, lm_labels

    def get_config(self):
        return Blip2Config.from_vision_qformer_text_configs(
            vision_config=self.vision_model_tester.get_config(),
            qformer_config=self.qformer_model_tester.get_config(),
            text_config=self.text_model_tester.get_config(),
            num_query_tokens=self.num_query_tokens,
        )

    def create_and_check_for_conditional_generation(
        self, config, input_ids, attention_mask, pixel_values, decoder_input_ids, decoder_attention_mask, labels
    ):
        model = Blip2ForConditionalGeneration(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(pixel_values, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)

        self.parent.assertEqual(
            result.logits.shape,
            (
                self.vision_model_tester.batch_size,
                self.text_model_tester.seq_length,
                self.text_model_tester.vocab_size,
            ),
        )

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


@require_torch
class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (Blip2ForConditionalGeneration, Blip2Model) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": Blip2Model,
            "image-to-text": Blip2ForConditionalGeneration,
            "visual-question-answering": Blip2ForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = False
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    test_torchscript = False

    def setUp(self):
        self.model_tester = Blip2ModelTester(self)

    def test_for_conditional_generation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_conditional_generation(*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="Blip2Model does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="There's no base Blip2Model")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="There's no base Blip2Model")
    def test_save_load_fast_init_to_base(self):
        pass

    @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
    def test_cpu_offload(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 = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_load_vision_qformer_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 = Blip2VisionConfig.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)
            qformer_config = Blip2QFormerConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict())

    @slow
    def test_model_from_pretrained(self):
        for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST:
            model = Blip2ForConditionalGeneration.from_pretrained(model_name)
            self.assertIsNotNone(model)

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

        inputs_dict = {
            "input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device),
            "attention_mask": torch.LongTensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]).to(torch_device),
            "decoder_input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device),
        }

        model = Blip2Model(config).to(torch_device)
        model.eval()
        text_features = model.get_text_features(**inputs_dict)
        self.assertEqual(text_features[0].shape, (1, 10, config.text_config.vocab_size))

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

        keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]

        for key in keys_to_pop:
            inputs_dict.pop(key)

        model = Blip2Model(config).to(torch_device)
        model.eval()
        image_features = model.get_image_features(**inputs_dict)
        self.assertEqual(
            image_features[0].shape,
            (
                self.model_tester.vision_model_tester.batch_size,
                self.model_tester.vision_model_tester.seq_length,
                config.vision_config.hidden_size,
            ),
        )

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

        keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]

        for key in keys_to_pop:
            inputs_dict.pop(key)

        model = Blip2Model(config).to(torch_device)
        model.eval()
        qformer_features = model.get_qformer_features(**inputs_dict)
        self.assertEqual(
            qformer_features[0].shape,
            (self.model_tester.vision_model_tester.batch_size, 10, config.vision_config.hidden_size),
        )

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

        configs_no_init = _config_zero_init(config)
        for key in ["vision_config", "qformer_config", "text_config"]:
            setattr(configs_no_init, key, _config_zero_init(getattr(configs_no_init, key)))
        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:
                    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 prepare_img():
    url = "https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    return image


@require_vision
@require_torch
@slow
class Blip2ModelIntegrationTest(unittest.TestCase):
    def test_inference_opt(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
        ).to(torch_device)

        
        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        
        self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
        self.assertEqual("a woman sitting on the beach with a dog", generated_text)

        
        prompt = "Question: which city is this? Answer:"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        
        self.assertEqual(
            predictions[0].tolist(),
            [2, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
        )
        self.assertEqual(generated_text, "it's not a city, it's a beach")

    def test_inference_opt_batched_beam_search(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
        ).to(torch_device)

        
        image = prepare_img()
        inputs = processor(images=[image, image], return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs, num_beams=2)

        
        self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 69, 2335, 50118])
        self.assertEqual(predictions[1].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 69, 2335, 50118])

    def test_inference_t5(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16
        ).to(torch_device)

        
        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        
        self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
        self.assertEqual("woman playing with dog on the beach", generated_text)

        
        prompt = "Question: which city is this? Answer:"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        
        self.assertEqual(
            predictions[0].tolist(),
            [0, 3, 7, 152, 67, 839, 1],
        )
        self.assertEqual(generated_text, "san diego")

    def test_inference_t5_batched_beam_search(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16
        ).to(torch_device)

        
        image = prepare_img()
        inputs = processor(images=[image, image], return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs, num_beams=2)

        
        self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
        self.assertEqual(predictions[1].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])

    @require_torch_multi_accelerator
    def test_inference_opt_multi_accelerator(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16, device_map="balanced"
        )

        
        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(0, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        
        self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
        self.assertEqual("a woman sitting on the beach with a dog", generated_text)

        
        prompt = "Question: which city is this? Answer:"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(0, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        
        self.assertEqual(
            predictions[0].tolist(),
            [2, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
        )
        self.assertEqual(generated_text, "it's not a city, it's a beach")

    @require_torch_multi_accelerator
    def test_inference_t5_multi_accelerator(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
        device_map = device_map = {
            "query_tokens": 0,
            "vision_model": 0,
            "language_model": 1,
            "language_projection": 0,
            "qformer": 0,
        }

        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16, device_map=device_map
        )

        
        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(0, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        
        self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
        self.assertEqual("woman playing with dog on the beach", generated_text)

        
        prompt = "Question: which city is this? Answer:"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(0, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        
        self.assertEqual(
            predictions[0].tolist(),
            [0, 3, 7, 152, 67, 839, 1],
        )
        self.assertEqual(generated_text, "san diego")