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 tensorflow blip model from future import annotations import inspect import tempfile import unittest import numpy as np import requests from transformers import blipconfig bliptextconfig blipvisionconfig from transformers testingutils import requiretf requirevision slow from transformers utils import istfavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingtfcommon import tfmodeltestermixin floatstensor idstensor randomattentionmask from testpipelinemixin import pipelinetestermixin if istfavailable import tensorflow as tf from transformers import tfblipforconditionalgeneration tfblipforimagetextretrieval tfblipforquestionanswering tfblipmodel tfbliptextmodel tfblipvisionmodel from transformers models blip modelingtfblip import tfblippretrainedmodelarchivelist if isvisionavailable from pil import image from transformers import blipprocessor class tfblipvisionmodeltester 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 blipvisionconfig 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 tfblipvisionmodelconfigconfig 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 requiretf class tfblipvisionmodeltesttfmodeltestermixin unittest testcase allmodelclasses tfblipvisionmodel if istfavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false testonnx false def setupself self modeltester tfblipvisionmodeltesterself self configtester configtesterself configclassblipvisionconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasonblip does not use inputsembeds def testinputsembedsself pass def testforwardsignatureself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig signature inspect signaturemodel call signature parameters is an ordereddict so argnames order is deterministic argnames signature parameters keys expectedargnames pixelvalues self assertlistequalargnames 1 expectedargnames def testmodelcommonattributesself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig self assertisinstancemodel getinputembeddings tf keras layers layer x model getoutputembeddings self asserttruex is none or isinstancex tf keras layers layer def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs unittest skipreasonblipvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasonblipvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in tfblippretrainedmodelarchivelist 1 model tfblipvisionmodel frompretrainedmodelname self assertisnotnonemodel class tfbliptextmodeltester 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 inputmask inputmask numpy batchsize seqlength inputmask shape rndstartindices np random randint1 seqlength 1 sizebatchsize for batchidx startindex in enumeraterndstartindices inputmaskbatchidx startindex 1 inputmaskbatchidx startindex 0 inputmask tf converttotensorinputmask config self getconfig return config inputids inputmask def getconfigself return bliptextconfig vocabsizeself vocabsize hiddensizeself hiddensize projectiondimself projectiondim numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize dropoutself dropout attentiondropoutself attentiondropout maxpositionembeddingsself maxpositionembeddings initializerrangeself initializerrange bostokenidself bostokenid def createandcheckmodelself config inputids inputmask model tfbliptextmodelconfigconfig result modelinputids attentionmaskinputmask trainingfalse result modelinputids trainingfalse self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize self parent assertequalresult pooleroutput shape self batchsize self hiddensize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids inputmask configandinputs inputsdict inputids inputids attentionmask inputmask return config inputsdict requiretf class tfbliptextmodeltesttfmodeltestermixin unittest testcase allmodelclasses tfbliptextmodel if istfavailable else fxcompatible false testpruning false testheadmasking false testonnx false def setupself self modeltester tfbliptextmodeltesterself self configtester configtesterself configclassbliptextconfig hiddensize37 def testconfigself self configtester runcommontests def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs unittest skipreasonblip does not use inputsembeds def testinputsembedsself pass unittest skipreasonbliptextmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasonbliptextmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in tfblippretrainedmodelarchivelist 1 model tfbliptextmodel frompretrainedmodelname self assertisnotnonemodel def testpttfmodelequivalenceself allowmissingkeystrue super testpttfmodelequivalenceallowmissingkeysallowmissingkeys class tfblipmodeltester def initself parent textkwargsnone visionkwargsnone istrainingtrue if textkwargs is none textkwargs if visionkwargs is none visionkwargs self parent parent self textmodeltester tfbliptextmodeltesterparent textkwargs self visionmodeltester tfblipvisionmodeltesterparent visionkwargs self istraining istraining def prepareconfigandinputsself textconfig inputids attentionmask self textmodeltester prepareconfigandinputs visionconfig pixelvalues self visionmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask pixelvalues def getconfigself return blipconfig fromtextvisionconfigs self textmodeltester getconfig self visionmodeltester getconfig projectiondim64 def createandcheckmodelself config inputids attentionmask pixelvalues model tfblipmodelconfig result modelinputids pixelvalues attentionmask trainingfalse self parent assertequal result logitsperimage shape self visionmodeltester batchsize self textmodeltester batchsize self parent assertequal result logitspertext shape self textmodeltester batchsize self visionmodeltester batchsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask pixelvalues configandinputs inputsdict inputids inputids attentionmask attentionmask pixelvalues pixelvalues returnloss true return config inputsdict requiretf class tfblipmodeltesttfmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses tfblipmodel if istfavailable else pipelinemodelmapping featureextraction tfblipmodel imagetotext tfblipforconditionalgeneration if istfavailable else testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false testonnx false def setupself self modeltester tfblipmodeltesterself 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 skipreasonblipmodel does not have inputoutput embeddings def testmodelcommonattributesself pass def testloadvisiontextconfigself config inputsdict self modeltester prepareconfigandinputsforcommon save blipconfig and check if we can load blipvisionconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname visionconfig blipvisionconfig frompretrainedtmpdirname self assertdictequalconfig visionconfig todict visionconfig todict save blipconfig and check if we can load bliptextconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname textconfig bliptextconfig frompretrainedtmpdirname self assertdictequalconfig textconfig todict textconfig todict slow def testmodelfrompretrainedself for modelname in tfblippretrainedmodelarchivelist 1 model tfblipmodel frompretrainedmodelname self assertisnotnonemodel def testpttfmodelequivalenceself allowmissingkeystrue super testpttfmodelequivalenceallowmissingkeysallowmissingkeys unittest skipmatt reenable this test when we have a proper export function for tf models def testsavedmodelcreationself this fails because the if returnloss conditional can return none or a tensor and tf hates that we could fix that by setting the bool to a constant when exporting but that requires a dedicated export function that we don t have yet pass class bliptextretrievalmodeltester def initself parent textkwargsnone visionkwargsnone istrainingtrue if textkwargs is none textkwargs if visionkwargs is none visionkwargs self parent parent self textmodeltester tfbliptextmodeltesterparent textkwargs self visionmodeltester tfblipvisionmodeltesterparent visionkwargs self istraining istraining def prepareconfigandinputsself textconfig inputids attentionmask self textmodeltester prepareconfigandinputs visionconfig pixelvalues self visionmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask pixelvalues def getconfigself return blipconfig fromtextvisionconfigs self textmodeltester getconfig self visionmodeltester getconfig projectiondim64 def createandcheckmodelself config inputids attentionmask pixelvalues model tfblipmodelconfig result modelinputids pixelvalues attentionmask trainingfalse self parent assertequal result logitsperimage shape self visionmodeltester batchsize self textmodeltester batchsize self parent assertequal result logitspertext shape self textmodeltester batchsize self visionmodeltester batchsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask pixelvalues configandinputs inputsdict inputids inputids attentionmask attentionmask pixelvalues pixelvalues return config inputsdict class bliptextimagemodelsmodeltester def initself parent textkwargsnone visionkwargsnone istrainingtrue if textkwargs is none textkwargs if visionkwargs is none visionkwargs self parent parent self textmodeltester tfbliptextmodeltesterparent textkwargs self visionmodeltester tfblipvisionmodeltesterparent visionkwargs self istraining istraining def prepareconfigandinputsself textconfig inputids attentionmask self textmodeltester prepareconfigandinputs visionconfig pixelvalues self visionmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask pixelvalues def getconfigself return blipconfig fromtextvisionconfigs self textmodeltester getconfig self visionmodeltester getconfig projectiondim64 def createandcheckmodelself config inputids attentionmask pixelvalues model tfblipmodelconfig result modelinputids pixelvalues attentionmask trainingfalse self parent assertequal result logitsperimage shape self visionmodeltester batchsize self textmodeltester batchsize self parent assertequal result logitspertext shape self textmodeltester batchsize self visionmodeltester batchsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask pixelvalues configandinputs inputsdict inputids inputids labels inputids attentionmask attentionmask pixelvalues pixelvalues return config inputsdict class blipvqamodelsmodeltester def initself parent textkwargsnone visionkwargsnone istrainingtrue if textkwargs is none textkwargs if visionkwargs is none visionkwargs self parent parent self textmodeltester tfbliptextmodeltesterparent textkwargs self visionmodeltester tfblipvisionmodeltesterparent visionkwargs self istraining istraining def prepareconfigandinputsself textconfig inputids attentionmask self textmodeltester prepareconfigandinputs visionconfig pixelvalues self visionmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask pixelvalues def getconfigself return blipconfig fromtextvisionconfigs self textmodeltester getconfig self visionmodeltester getconfig projectiondim64 def createandcheckmodelself config inputids attentionmask pixelvalues model tfblipmodelconfig result modelinputids pixelvalues attentionmask trainingfalse self parent assertequal result logitsperimage shape self visionmodeltester batchsize self textmodeltester batchsize self parent assertequal result logitspertext shape self textmodeltester batchsize self visionmodeltester batchsize def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids attentionmask pixelvalues configandinputs inputsdict inputids inputids decoderinputids inputids labels inputids attentionmask attentionmask pixelvalues pixelvalues return config inputsdict requiretf requirevision class tfblipvqamodeltesttfmodeltestermixin unittest testcase allmodelclasses tfblipforquestionanswering if istfavailable else testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false testonnx false def setupself self modeltester blipvqamodelsmodeltesterself def prepareinputsforvqaself inputsdict self modeltester prepareconfigandinputsforcommon inputsdictlabels inputsdictinputids inputsdictdecoderinputids inputsdictinputids inputsdict popreturnloss return inputsdict def testclassnameconsistencyself for modelclass in self allmodelclasses model modelclassself modeltester getconfig self asserttrue model class name endswithforquestionanswering fclass name should end with forvisualquestionanswering got model class name def testtrainingself for modelclass in self allmodelclasses model modelclassself modeltester getconfig loss modelself modeltester prepareconfigandinputsforcommon1 trainingtrue loss self assertisnotnoneloss loss should not be none 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 skipreasonblipmodel does not have inputoutput embeddings def testmodelcommonattributesself pass unittest skipreasontested in individual model tests def testcompiletfmodelself pass unittest skipmodel doesn t have a clean loss output def testkerasfitself pass requiretf class tfbliptextretrievalmodeltesttfmodeltestermixin unittest testcase allmodelclasses tfblipforimagetextretrieval if istfavailable else testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false testonnx false def setupself self modeltester bliptextretrievalmodeltesterself 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 skipreasonblipmodel does not have inputoutput embeddings def testmodelcommonattributesself pass def testtrainingself if not self modeltester istraining return for modelclass in self allmodelclasses 1 config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true model modelclassconfig inputs self prepareforclassinputsdict modelclass returnlabelstrue hardcode labels to be the same as inputids inputslabels inputsinputids loss modelinputs trainingtrue loss self asserttrueloss is not none def testloadvisiontextconfigself config inputsdict self modeltester prepareconfigandinputsforcommon save blipconfig and check if we can load blipvisionconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname visionconfig blipvisionconfig frompretrainedtmpdirname self assertdictequalconfig visionconfig todict visionconfig todict save blipconfig and check if we can load bliptextconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname textconfig bliptextconfig frompretrainedtmpdirname self assertdictequalconfig textconfig todict textconfig todict slow def testmodelfrompretrainedself for modelname in tfblippretrainedmodelarchivelist 1 model tfblipmodel frompretrainedmodelname self assertisnotnonemodel unittest skipreasontested in individual model tests def testcompiletfmodelself pass unittest skipmodel doesn t have a clean loss output def testkerasfitself pass requiretf class tfbliptextimagemodeltesttfmodeltestermixin unittest testcase allmodelclasses tfblipforconditionalgeneration if istfavailable else testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false testonnx false def setupself self modeltester bliptextimagemodelsmodeltesterself 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 def testforwardsignatureself config self modeltester prepareconfigandinputsforcommon for modelclass in self allmodelclasses model modelclassconfig signature inspect signaturemodel call signature parameters is an ordereddict so argnames order is deterministic argnames signature parameters keys if model config isencoderdecoder expectedargnames inputids attentionmask decoderinputids decoderattentionmask expectedargnames extend headmask decoderheadmask crossattnheadmask encoderoutputs if headmask and decoderheadmask and crossattnheadmask in argnames else encoderoutputs self assertlistequalargnames lenexpectedargnames expectedargnames else expectedargnames inputids if modelclass tfblipforconditionalgeneration else pixelvalues self assertlistequalargnames 1 expectedargnames unittest skipreasontested in individual model tests def testcompiletfmodelself pass unittest skiphas some odd input names def testkerasfitself pass unittest skipreasonretaingrad is tested in individual model tests def testretaingradhiddenstatesattentionsself pass unittest skipreasonblipmodel does not have inputoutput embeddings def testmodelcommonattributesself pass def testtrainingself if not self modeltester istraining return for modelclass in self allmodelclasses 1 config inputsdict self modeltester prepareconfigandinputsforcommon config returndict true model modelclassconfig inputs self prepareforclassinputsdict modelclass returnlabelstrue hardcode labels to be the same as inputids inputslabels inputsinputids loss modelinputs trainingtrue loss self assertisnotnoneloss def testloadvisiontextconfigself config inputsdict self modeltester prepareconfigandinputsforcommon save blipconfig and check if we can load blipvisionconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname visionconfig blipvisionconfig frompretrainedtmpdirname self assertdictequalconfig visionconfig todict visionconfig todict save blipconfig and check if we can load bliptextconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname textconfig bliptextconfig frompretrainedtmpdirname self assertdictequalconfig textconfig todict textconfig todict slow def testmodelfrompretrainedself for modelname in tfblippretrainedmodelarchivelist 1 model tfblipmodel frompretrainedmodelname self assertisnotnonemodel we will verify our results on an image of cute cats def prepareimg url https huggingface cohfinternaltestingbliptestimageresolvemaindemo jpg im image openrequests geturl streamtrue raw return im requirevision requiretf slow class tfblipmodelintegrationtestunittest testcase def testinferenceimagecaptioningself model tfblipforconditionalgeneration frompretrainedsalesforceblipimagecaptioningbase processor blipprocessor frompretrainedsalesforceblipimagecaptioningbase image prepareimg image only inputs processorimagesimage returntensorstf predictions model generateinputs test output self assertequal predictions0 numpy tolist 30522 1037 2450 3564 2006 1996 3509 2007 2014 3899 102 image and context context a picture of inputs processorimagesimage textcontext returntensorstf predictions model generateinputs test output self assertequal predictions0 numpy tolist 30522 1037 3861 1997 1037 2450 1998 2014 3899 2006 1996 3509 102 def testinferencevqaself model tfblipforquestionanswering frompretrainedsalesforceblipvqabase processor blipprocessor frompretrainedsalesforceblipvqabase image prepareimg text how many dogs are in the picture inputs processorimage texttext returntensorstf out model generateinputs test output self assertequalout0 numpy tolist 30522 1015 102 def testinferenceitmself model tfblipforimagetextretrieval frompretrainedsalesforceblipitmbasecoco processor blipprocessor frompretrainedsalesforceblipitmbasecoco image prepareimg text a woman and her dog sitting in a beach inputs processorimage text returntensorstf outitm modelinputs out modelinputs useitmheadfalse trainingfalse expectedscores tf converttotensor0 0029 0 9971 self asserttruenp allclosetf nn softmaxoutitm0 numpy expectedscores rtol1e3 atol1e3 self asserttruenp allcloseout0 tf converttotensor0 5162 rtol1e3 atol1e3 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 tensorflow blip 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 does not use input_ids inputs_embeds attention_mask and seq_length signature parameters is an ordereddict so arg_names order is deterministic save blipconfig and check if we can load blipvisionconfig from it save blipconfig and check if we can load bliptextconfig from it this fails because the if return_loss conditional can return none or a tensor and tf hates that we could fix that by setting the bool to a constant when exporting but that requires a dedicated export function that we don t have yet tests that all vqa models have a class name that ends with forquestionanswering tests that all vqa models can be trained on a single batch hardcode labels to be the same as input_ids save blipconfig and check if we can load blipvisionconfig from it save blipconfig and check if we can load bliptextconfig from it signature parameters is an ordereddict so arg_names order is deterministic hardcode labels to be the same as input_ids save blipconfig and check if we can load blipvisionconfig from it save blipconfig and check if we can load bliptextconfig from it we will verify our results on an image of cute cats image only test output image and context test output test output
from __future__ import annotations

import inspect
import tempfile
import unittest

import numpy as np
import requests

from transformers import BlipConfig, BlipTextConfig, BlipVisionConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFBlipForConditionalGeneration,
        TFBlipForImageTextRetrieval,
        TFBlipForQuestionAnswering,
        TFBlipModel,
        TFBlipTextModel,
        TFBlipVisionModel,
    )
    from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import BlipProcessor


class TFBlipVisionModelTester:
    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 BlipVisionConfig(
            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 = TFBlipVisionModel(config=config)
        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_tf
class TFBlipVisionModelTest(TFModelTesterMixin, unittest.TestCase):
    

    all_model_classes = (TFBlipVisionModel,) if is_tf_available() else ()
    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    test_onnx = False

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

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

    @unittest.skip(reason="Blip does not use inputs_embeds")
    def test_inputs_embeds(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.call)
            
            arg_names = [*signature.parameters.keys()]

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

    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(), (tf.keras.layers.Layer))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer))

    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="BlipVisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="BlipVisionModel 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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFBlipVisionModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


class TFBlipTextModelTester:
    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:
            input_mask = input_mask.numpy()
            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
            input_mask = tf.convert_to_tensor(input_mask)

        config = self.get_config()

        return config, input_ids, input_mask

    def get_config(self):
        return BlipTextConfig(
            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,
        )

    def create_and_check_model(self, config, input_ids, input_mask):
        model = TFBlipTextModel(config=config)
        result = model(input_ids, attention_mask=input_mask, training=False)
        result = model(input_ids, training=False)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

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


@require_tf
class TFBlipTextModelTest(TFModelTesterMixin, unittest.TestCase):
    all_model_classes = (TFBlipTextModel,) if is_tf_available() else ()
    fx_compatible = False
    test_pruning = False
    test_head_masking = False
    test_onnx = False

    def setUp(self):
        self.model_tester = TFBlipTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=BlipTextConfig, 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)

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

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

    @unittest.skip(reason="BlipTextModel 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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFBlipTextModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    def test_pt_tf_model_equivalence(self, allow_missing_keys=True):
        super().test_pt_tf_model_equivalence(allow_missing_keys=allow_missing_keys)


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

        self.parent = parent
        self.text_model_tester = TFBlipTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = TFBlipVisionModelTester(parent, **vision_kwargs)
        self.is_training = is_training

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

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return BlipConfig.from_text_vision_configs(
            self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
        )

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = TFBlipModel(config)
        result = model(input_ids, pixel_values, attention_mask, training=False)
        self.parent.assertEqual(
            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
        )
        self.parent.assertEqual(
            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
        )

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


@require_tf
class TFBlipModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (TFBlipModel,) if is_tf_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": TFBlipModel, "image-to-text": TFBlipForConditionalGeneration}
        if is_tf_available()
        else {}
    )
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    test_onnx = False

    def setUp(self):
        self.model_tester = TFBlipModelTester(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="BlipModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

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

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

        
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = BlipTextConfig.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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFBlipModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    def test_pt_tf_model_equivalence(self, allow_missing_keys=True):
        super().test_pt_tf_model_equivalence(allow_missing_keys=allow_missing_keys)

    @unittest.skip("Matt: Re-enable this test when we have a proper export function for TF models.")
    def test_saved_model_creation(self):
        
        
        
        pass


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

        self.parent = parent
        self.text_model_tester = TFBlipTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = TFBlipVisionModelTester(parent, **vision_kwargs)
        self.is_training = is_training

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

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return BlipConfig.from_text_vision_configs(
            self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
        )

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = TFBlipModel(config)
        result = model(input_ids, pixel_values, attention_mask, training=False)
        self.parent.assertEqual(
            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
        )
        self.parent.assertEqual(
            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
        )

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


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

        self.parent = parent
        self.text_model_tester = TFBlipTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = TFBlipVisionModelTester(parent, **vision_kwargs)
        self.is_training = is_training

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

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return BlipConfig.from_text_vision_configs(
            self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
        )

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = TFBlipModel(config)
        result = model(input_ids, pixel_values, attention_mask, training=False)
        self.parent.assertEqual(
            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
        )
        self.parent.assertEqual(
            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
        )

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


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

        self.parent = parent
        self.text_model_tester = TFBlipTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = TFBlipVisionModelTester(parent, **vision_kwargs)
        self.is_training = is_training

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

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return BlipConfig.from_text_vision_configs(
            self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
        )

    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = TFBlipModel(config)
        result = model(input_ids, pixel_values, attention_mask, training=False)
        self.parent.assertEqual(
            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
        )
        self.parent.assertEqual(
            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
        )

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


@require_tf
@require_vision
class TFBlipVQAModelTest(TFModelTesterMixin, unittest.TestCase):
    all_model_classes = (TFBlipForQuestionAnswering,) if is_tf_available() else ()
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    test_onnx = False

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

    def _prepare_inputs_for_vqa(self):
        _, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        inputs_dict["labels"] = inputs_dict["input_ids"]
        inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"]
        inputs_dict.pop("return_loss")
        return inputs_dict

    def test_class_name_consistency(self):
        
        for model_class in self.all_model_classes:
            model = model_class(self.model_tester.get_config())
            self.assertTrue(
                model.__class__.__name__.endswith("ForQuestionAnswering"),
                f"Class name should end with 'ForVisualQuestionAnswering' got {model.__class__.__name__}",
            )

    def test_training(self):
        
        for model_class in self.all_model_classes:
            model = model_class(self.model_tester.get_config())
            loss = model(**self.model_tester.prepare_config_and_inputs_for_common()[1], training=True).loss

            self.assertIsNotNone(loss, "Loss should not be None")

    @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="BlipModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="Tested in individual model tests")
    def test_compile_tf_model(self):
        pass

    @unittest.skip("Model doesn't have a clean loss output.")
    def test_keras_fit(self):
        pass


@require_tf
class TFBlipTextRetrievalModelTest(TFModelTesterMixin, unittest.TestCase):
    all_model_classes = (TFBlipForImageTextRetrieval,) if is_tf_available() else ()
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    test_onnx = False

    def setUp(self):
        self.model_tester = BlipTextRetrievalModelTester(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="BlipModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

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

        for model_class in self.all_model_classes[:-1]:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.return_dict = True

            model = model_class(config)
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)

            
            inputs["labels"] = inputs["input_ids"]

            loss = model(**inputs, training=True).loss
            self.assertTrue(loss is not None)

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

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

        
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = BlipTextConfig.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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFBlipModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    @unittest.skip(reason="Tested in individual model tests")
    def test_compile_tf_model(self):
        pass

    @unittest.skip("Model doesn't have a clean loss output.")
    def test_keras_fit(self):
        pass


@require_tf
class TFBlipTextImageModelTest(TFModelTesterMixin, unittest.TestCase):
    all_model_classes = (TFBlipForConditionalGeneration,) if is_tf_available() else ()
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    test_onnx = False

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

    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.call)
            
            arg_names = [*signature.parameters.keys()]

            if model.config.is_encoder_decoder:
                expected_arg_names = [
                    "input_ids",
                    "attention_mask",
                    "decoder_input_ids",
                    "decoder_attention_mask",
                ]
                expected_arg_names.extend(
                    ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
                    if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
                    else ["encoder_outputs"]
                )
                self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
            else:
                expected_arg_names = (
                    ["input_ids"] if model_class != TFBlipForConditionalGeneration else ["pixel_values"]
                )
                self.assertListEqual(arg_names[:1], expected_arg_names)

    @unittest.skip(reason="Tested in individual model tests")
    def test_compile_tf_model(self):
        pass

    @unittest.skip("Has some odd input names!")
    def test_keras_fit(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="BlipModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

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

        for model_class in self.all_model_classes[:-1]:
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            config.return_dict = True

            model = model_class(config)
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)

            
            inputs["labels"] = inputs["input_ids"]

            loss = model(**inputs, training=True).loss
            self.assertIsNotNone(loss)

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

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

        
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = BlipTextConfig.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 TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TFBlipModel.from_pretrained(model_name)
            self.assertIsNotNone(model)



def prepare_img():
    url = "https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im


@require_vision
@require_tf
@slow
class TFBlipModelIntegrationTest(unittest.TestCase):
    def test_inference_image_captioning(self):
        model = TFBlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
        processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
        image = prepare_img()

        
        inputs = processor(images=image, return_tensors="tf")

        predictions = model.generate(**inputs)

        
        self.assertEqual(
            predictions[0].numpy().tolist(), [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]
        )

        
        context = ["a picture of"]
        inputs = processor(images=image, text=context, return_tensors="tf")

        predictions = model.generate(**inputs)

        
        self.assertEqual(
            predictions[0].numpy().tolist(),
            [30522, 1037, 3861, 1997, 1037, 2450, 1998, 2014, 3899, 2006, 1996, 3509, 102],
        )

    def test_inference_vqa(self):
        model = TFBlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
        processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")

        image = prepare_img()
        text = "how many dogs are in the picture?"
        inputs = processor(image, text=text, return_tensors="tf")
        out = model.generate(**inputs)

        
        self.assertEqual(out[0].numpy().tolist(), [30522, 1015, 102])

    def test_inference_itm(self):
        model = TFBlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco")
        processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco")

        image = prepare_img()
        text = "A woman and her dog sitting in a beach"

        inputs = processor(image, text, return_tensors="tf")

        out_itm = model(**inputs)
        out = model(**inputs, use_itm_head=False, training=False)

        expected_scores = tf.convert_to_tensor([[0.0029, 0.9971]])
        self.assertTrue(np.allclose(tf.nn.softmax(out_itm[0]).numpy(), expected_scores, rtol=1e-3, atol=1e-3))
        self.assertTrue(np.allclose(out[0], tf.convert_to_tensor([[0.5162]]), rtol=1e-3, atol=1e-3))