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 instructblip model import inspect import tempfile import unittest import numpy as np import requests from transformers import configmapping instructblipconfig instructblipprocessor instructblipqformerconfig instructblipvisionconfig from transformers testingutils import requireaccelerate requirebitsandbytes requiretorch requirevision slow torchdevice from transformers utils import istorchavailable isvisionavailable from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor idstensor randomattentionmask if istorchavailable import torch from torch import nn from transformers import instructblipforconditionalgeneration instructblipvisionmodel from transformers models instructblip modelinginstructblip import instructblippretrainedmodelarchivelist if isvisionavailable from pil import image class instructblipvisionmodeltester 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 case of a vision transformer 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 instructblipvisionconfig 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 instructblipvisionmodelconfigconfig 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 instructblipvisionmodeltestmodeltestermixin unittest testcase allmodelclasses instructblipvisionmodel if istorchavailable else fxcompatible false testpruning false testresizeembeddings false testheadmasking false def setupself self modeltester instructblipvisionmodeltesterself self configtester configtester self configclassinstructblipvisionconfig hastextmodalityfalse hiddensize37 def testconfigself self configtester runcommontests unittest skipreasoninstructblip 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 unittest skipreasoninstructblipvisionmodel is an internal building block doesn t support standalone training def testtrainingself pass unittest skipreasoninstructblipvisionmodel is an internal building block doesn t support standalone training 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 skipreasoninstructblipvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinitfrombaseself pass unittest skipreasoninstructblipvisionmodel has no base class and is not available in modelmapping def testsaveloadfastinittobaseself pass slow def testmodelfrompretrainedself for modelname in instructblippretrainedmodelarchivelist 1 model instructblipvisionmodel frompretrainedmodelname self assertisnotnonemodel class instructblipqformermodeltester 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 qformerinputids idstensorself batchsize self seqlength self vocabsize inputmask none if self useinputmask inputmask randomattentionmaskself batchsize self seqlength qformerattentionmask idstensorself batchsize self seqlength vocabsize2 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 qformerinputids qformerattentionmask def getconfigself return instructblipqformerconfig 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 instructbliptextmodeldecoderonlytester 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 instructblipforconditionalgenerationdecoderonlymodeltester 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 instructblipvisionmodeltesterparent visionkwargs self qformermodeltester instructblipqformermodeltesterparent qformerkwargs self textmodeltester instructbliptextmodeldecoderonlytesterparent textkwargs self istraining istraining self numquerytokens numquerytokens def prepareconfigandinputsself pixelvalues self visionmodeltester prepareconfigandinputs qformerinputids qformerattentionmask self qformermodeltester prepareconfigandinputs inputids attentionmask self textmodeltester prepareconfigandinputs config self getconfig return config inputids attentionmask qformerinputids qformerattentionmask pixelvalues def getconfigself return instructblipconfig fromvisionqformertextconfigs visionconfigself visionmodeltester getconfig qformerconfigself qformermodeltester getconfig textconfigself textmodeltester getconfig numquerytokensself numquerytokens def createandcheckforconditionalgeneration self config inputids attentionmask qformerinputids qformerattentionmask pixelvalues model instructblipforconditionalgenerationconfig totorchdevice eval with torch nograd result model pixelvalues inputidsinputids attentionmaskattentionmask qformerinputidsqformerinputids qformerattentionmaskqformerattentionmask 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 qformerinputids qformerattentionmask pixelvalues configandinputs inputsdict pixelvalues pixelvalues inputids inputids attentionmask attentionmask qformerinputids qformerinputids qformerattentionmask qformerattentionmask labels inputids return config inputsdict requiretorch class instructblipforconditionalgenerationdecoderonlytestmodeltestermixin unittest testcase allmodelclasses instructblipforconditionalgeneration if istorchavailable else fxcompatible false testheadmasking false testpruning false testresizeembeddings false testattentionoutputs false testtorchscript false def setupself self modeltester instructblipforconditionalgenerationdecoderonlymodeltesterself def testforconditionalgenerationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforconditionalgenerationconfigandinputs unittest skipreasonhiddenstates is tested in individual model tests def testhiddenstatesoutputself pass unittest skipreasoninstructblipforconditionalgeneration doesn t support inputsembeds def testinputsembedsself pass unittest skipreasontied weights are tested in individual model tests def testtiedweightskeysself pass unittest skipreasonretaingrad is tested in individual model tests def testretaingradhiddenstatesattentionsself pass unittest skipreasoninstructblipmodel does not have inputoutput embeddings def testmodelcommonattributesself pass unittest skipreasonthere s no base instructblipmodel def testsaveloadfastinitfrombaseself pass unittest skipreasonthere s no base instructblipmodel 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 instructblipconfig and check if we can load instructblipvisionconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname visionconfig instructblipvisionconfig frompretrainedtmpdirname self assertdictequalconfig visionconfig todict visionconfig todict save instructblipconfig and check if we can load instructblipqformerconfig from it with tempfile temporarydirectory as tmpdirname config savepretrainedtmpdirname qformerconfig instructblipqformerconfig frompretrainedtmpdirname self assertdictequalconfig qformerconfig todict qformerconfig todict slow def testmodelfrompretrainedself for modelname in instructblippretrainedmodelarchivelist model instructblipforconditionalgeneration frompretrainedmodelname self assertisnotnonemodel 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 instructblipmodelintegrationtestunittest testcase requirebitsandbytes requireaccelerate def testinferencevicuna7bself processor instructblipprocessor frompretrainedsalesforceinstructblipvicuna7b model instructblipforconditionalgeneration frompretrained salesforceinstructblipvicuna7b loadin8bittrue lowcpumemusagetrue url https raw githubusercontent comsalesforcelavismaindocsstaticconfusingpictures jpg image image openrequests geturl streamtrue raw convertrgb prompt what is unusual about this image inputs processorimagesimage textprompt returntensorspt totorchdevice torch float16 verify logits with torch nograd logits modelinputs logits expectedslice torch tensor 3 4902 12 5078 8 4141 5 1211 12 1328 7 8281 4 0312 13 5938 9 1172 devicetorchdevice self asserttruetorch allcloselogits0 3 3 float expectedslice atol1e3 verify generation outputs model generateinputs maxnewtokens30 generatedtext processor batchdecodeoutputs skipspecialtokenstrue0 strip expectedoutputs 2 450 22910 9565 310 445 1967 338 393 263 767 338 13977 292 22095 373 278 1250 310 263 13328 20134 29963 1550 19500 1623 263 19587 4272 11952 29889 fmt skip self assertequaloutputs0 tolist expectedoutputs self assertequal generatedtext the unusual aspect of this image is that a man is ironing clothes on the back of a yellow suv while driving down a busy city street def testinferenceflant5xlself processor instructblipprocessor frompretrainedsalesforceinstructblipflant5xl model instructblipforconditionalgeneration frompretrained salesforceinstructblipflant5xl torchdtypetorch bfloat16 lowcpumemusagetrue totorchdevice url https raw githubusercontent comsalesforcelavismaindocsstaticconfusingpictures jpg image image openrequests geturl streamtrue raw convertrgb prompt what is unusual about this image inputs processorimagesimage textprompt returntensorspt totorchdevice for k v in inputs items if torch isfloatingpointv inputsk v totorch bfloat16 outputs model generate inputs dosamplefalse numbeams5 maxlength256 minlength1 topp0 9 repetitionpenalty1 5 lengthpenalty1 0 temperature1 generatedtext processor batchdecodeoutputs skipspecialtokenstrue0 expectedoutputs 0 37 1023 9850 7 3 9 388 3575 53 4954 30 8 223 13 3 9 4459 4049 16 8 2214 13 3 9 3164 690 2815 5 37 388 19 5119 3 9 4459 8677 28 3 9 2756 4459 6177 6 11 3 88 19 338 46 3575 53 1476 12 743 112 2491 5 37 1023 19 7225 788 12 8 685 24 34 1267 3 9 388 3575 53 4954 30 8 223 13 3 9 4049 16 8 2214 13 3 9 3164 690 2815 5 94 19 487 24 8 388 19 1119 12 1097 540 57 692 112 10428 30 8 223 13 8 4049 6 68 34 19 92 487 24 3 88 19 1119 12 1097 97 57 692 112 10428 30 8 223 13 8 4049 16 8 2214 13 3 9 3164 690 2815 5 3 13865 13 8 1053 21 8 388 31 7 2874 6 34 19 964 24 3 88 19 1119 12 1097 97 57 692 112 10428 30 8 223 13 8 4049 16 8 2214 13 3 9 3164 690 2815 5 1 fmt skip self assertequaloutputs0 tolist expectedoutputs self assertequal generatedtext the image depicts a man ironing clothes on the back of a yellow van in the middle of a busy city street the man is wearing a yellow shirt with a bright yellow tie and he is using an ironing board to complete his task the image is unusual due to the fact that it shows a man ironing clothes on the back of a van in the middle of a busy city street it is possible that the man is trying to save money by doing his laundry on the back of the van but it is also possible that he is trying to save time by doing his laundry on the back of the van in the middle of a busy city street regardless of the reason for the man s actions it is clear that he is trying to save time by doing his laundry on the back of the van in the middle of a busy city street 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 instructblip model in case of a vision transformer 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 instructblip 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 instructblipconfig and check if we can load instructblipvisionconfig from it save instructblipconfig and check if we can load instructblipqformerconfig from it we will verify our results on an image of cute cats verify logits verify generation fmt skip fmt skip
import inspect
import tempfile
import unittest

import numpy as np
import requests

from transformers import (
    CONFIG_MAPPING,
    InstructBlipConfig,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
)
from transformers.testing_utils import (
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import InstructBlipForConditionalGeneration, InstructBlipVisionModel
    from transformers.models.instructblip.modeling_instructblip import INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image


class InstructBlipVisionModelTester:
    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 InstructBlipVisionConfig(
            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 = InstructBlipVisionModel(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 InstructBlipVisionModelTest(ModelTesterMixin, unittest.TestCase):
    

    all_model_classes = (InstructBlipVisionModel,) 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 = InstructBlipVisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=InstructBlipVisionConfig, has_text_modality=False, hidden_size=37
        )

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

    @unittest.skip(reason="InstructBLIP'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)

    @unittest.skip(reason="InstructBlipVisionModel is an internal building block, doesn't support standalone training")
    def test_training(self):
        pass

    @unittest.skip(reason="InstructBlipVisionModel is an internal building block, doesn't support standalone training")
    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="InstructBlipVisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

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


class InstructBlipQFormerModelTester:
    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)
        qformer_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])
            qformer_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

        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, qformer_input_ids, qformer_attention_mask

    def get_config(self):
        return InstructBlipQFormerConfig(
            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 InstructBlipTextModelDecoderOnlyTester:
    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 InstructBlipForConditionalGenerationDecoderOnlyModelTester:
    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 = InstructBlipVisionModelTester(parent, **vision_kwargs)
        self.qformer_model_tester = InstructBlipQFormerModelTester(parent, **qformer_kwargs)
        self.text_model_tester = InstructBlipTextModelDecoderOnlyTester(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()
        _, _, _, qformer_input_ids, qformer_attention_mask = self.qformer_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, qformer_input_ids, qformer_attention_mask, pixel_values

    def get_config(self):
        return InstructBlipConfig.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, qformer_input_ids, qformer_attention_mask, pixel_values
    ):
        model = InstructBlipForConditionalGeneration(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(
                pixel_values,
                input_ids=input_ids,
                attention_mask=attention_mask,
                qformer_input_ids=qformer_input_ids,
                qformer_attention_mask=qformer_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, qformer_input_ids, qformer_attention_mask, pixel_values = config_and_inputs
        inputs_dict = {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "qformer_input_ids": qformer_input_ids,
            "qformer_attention_mask": qformer_attention_mask,
            "labels": input_ids,
        }
        return config, inputs_dict


@require_torch
class InstructBlipForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (InstructBlipForConditionalGeneration,) 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 = InstructBlipForConditionalGenerationDecoderOnlyModelTester(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="InstructBlipForConditionalGeneration doesn't support inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Tied weights are tested in individual model tests")
    def test_tied_weights_keys(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="InstructBlipModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

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

    @unittest.skip(reason="There's no base InstructBlipModel")
    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 = InstructBlipVisionConfig.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 = InstructBlipQFormerConfig.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 INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST:
            model = InstructBlipForConditionalGeneration.from_pretrained(model_name)
            self.assertIsNotNone(model)



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 InstructBlipModelIntegrationTest(unittest.TestCase):
    @require_bitsandbytes
    @require_accelerate
    def test_inference_vicuna_7b(self):
        processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
        model = InstructBlipForConditionalGeneration.from_pretrained(
            "Salesforce/instructblip-vicuna-7b", load_in_8bit=True, low_cpu_mem_usage=True
        )

        url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
        image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
        prompt = "What is unusual about this image?"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, torch.float16)

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

        expected_slice = torch.tensor(
            [[-3.4902, -12.5078, 8.4141], [-5.1211, -12.1328, 7.8281], [-4.0312, -13.5938, 9.1172]],
            device=torch_device,
        )
        self.assertTrue(torch.allclose(logits[0, :3, :3].float(), expected_slice, atol=1e-3))

        
        outputs = model.generate(**inputs, max_new_tokens=30)
        generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()

        expected_outputs = [2, 450, 22910, 9565, 310, 445, 1967, 338, 393, 263, 767, 338, 13977, 292, 22095, 373, 278, 1250, 310, 263, 13328, 20134, 29963, 1550, 19500, 1623, 263, 19587, 4272, 11952, 29889]  
        self.assertEqual(outputs[0].tolist(), expected_outputs)
        self.assertEqual(
            generated_text,
            "The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV while driving down a busy city street.",
        )

    def test_inference_flant5_xl(self):
        processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl")
        model = InstructBlipForConditionalGeneration.from_pretrained(
            "Salesforce/instructblip-flan-t5-xl",
            torch_dtype=torch.bfloat16,
            low_cpu_mem_usage=True,
        ).to(torch_device)

        url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
        image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
        prompt = "What is unusual about this image?"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device)

        for k, v in inputs.items():
            if torch.is_floating_point(v):
                inputs[k] = v.to(torch.bfloat16)

        outputs = model.generate(
            **inputs,
            do_sample=False,
            num_beams=5,
            max_length=256,
            min_length=1,
            top_p=0.9,
            repetition_penalty=1.5,
            length_penalty=1.0,
            temperature=1,
        )
        generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]

        expected_outputs = [0, 37, 1023, 9850, 7, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4459, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 388, 19, 5119, 3, 9, 4459, 8677, 28, 3, 9, 2756, 4459, 6177, 6, 11, 3, 88, 19, 338, 46, 3575, 53, 1476, 12, 743, 112, 2491, 5, 37, 1023, 19, 7225, 788, 12, 8, 685, 24, 34, 1267, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 94, 19, 487, 24, 8, 388, 19, 1119, 12, 1097, 540, 57, 692, 112, 10428, 30, 8, 223, 13, 8, 4049, 6, 68, 34, 19, 92, 487, 24, 3, 88, 19, 1119, 12, 1097, 97, 57, 692, 112, 10428, 30, 8, 223, 13, 8, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 3, 13865, 13, 8, 1053, 21, 8, 388, 31, 7, 2874, 6, 34, 19, 964, 24, 3, 88, 19, 1119, 12, 1097, 97, 57, 692, 112, 10428, 30, 8, 223, 13, 8, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 1]  
        self.assertEqual(outputs[0].tolist(), expected_outputs)
        self.assertEqual(
            generated_text,
            "The image depicts a man ironing clothes on the back of a yellow van in the middle of a busy city street. The man is wearing a yellow shirt with a bright yellow tie, and he is using an ironing board to complete his task. The image is unusual due to the fact that it shows a man ironing clothes on the back of a van in the middle of a busy city street. It is possible that the man is trying to save money by doing his laundry on the back of the van, but it is also possible that he is trying to save time by doing his laundry on the back of the van in the middle of a busy city street. Regardless of the reason for the man's actions, it is clear that he is trying to save time by doing his laundry on the back of the van in the middle of a busy city street.",
        )