Add TFVisionTextDualEncoder (#21873)

* Temporary commit to stash everything so far

* Temporary commit to stash everything so far

* stash commit

* Refactor from_pretrained

* Fix final test, make fixup

* Update dummies

* Add model to TEST_FILES_WITH_NO_COMMON_TESTS

* Update src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Add TFVisionTextDualEncoder to utils/documentation_tests.txt

* make fixup

---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

docs/source/en/index.mdx

@@ -397,7 +397,7 @@ Flax), PyTorch, and/or TensorFlow.
 |           VideoMAE            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             ViLT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |    Vision Encoder decoder     |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
-|     VisionTextDualEncoder     |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|     VisionTextDualEncoder     |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |          VisualBERT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |              ViT              |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |          ViT Hybrid           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/vision-text-dual-encoder.mdx

@@ -41,3 +41,8 @@ new zero-shot vision tasks such as image classification or retrieval.
 
 [[autodoc]] FlaxVisionTextDualEncoderModel
     - __call__
+
+## TFVisionTextDualEncoderModel
+
+[[autodoc]] TFVisionTextDualEncoderModel
+    - call

src/transformers/__init__.py

@@ -3275,6 +3275,7 @@ else:
         ]
     )
     _import_structure["models.vision_encoder_decoder"].extend(["TFVisionEncoderDecoderModel"])
+    _import_structure["models.vision_text_dual_encoder"].extend(["TFVisionTextDualEncoderModel"])
     _import_structure["models.vit"].extend(
         [
             "TFViTForImageClassification",
@@ -6335,6 +6336,7 @@ if TYPE_CHECKING:
             TFTransfoXLPreTrainedModel,
         )
         from .models.vision_encoder_decoder import TFVisionEncoderDecoderModel
+        from .models.vision_text_dual_encoder import TFVisionTextDualEncoderModel
         from .models.vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
         from .models.vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
         from .models.wav2vec2 import (

src/transformers/modeling_tf_utils.py

@@ -892,8 +892,6 @@ def load_tf_weights(model, resolved_archive_file, ignore_mismatched_sizes=False,
 
 
 def load_tf_weights_from_h5(model, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None):
-    missing_layers = []
-    unexpected_layers = []
     mismatched_layers = []
 
     # Read the H5 file

src/transformers/models/auto/modeling_tf_auto.py

@@ -81,6 +81,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("t5", "TFT5Model"),
         ("tapas", "TFTapasModel"),
         ("transfo-xl", "TFTransfoXLModel"),
+        ("vision-text-dual-encoder", "TFVisionTextDualEncoderModel"),
         ("vit", "TFViTModel"),
         ("vit_mae", "TFViTMAEModel"),
         ("wav2vec2", "TFWav2Vec2Model"),

src/transformers/models/clip/modeling_tf_clip.py

@@ -900,6 +900,8 @@ class TFCLIPPreTrainedModel(TFPreTrainedModel):
 
     config_class = CLIPConfig
     base_model_prefix = "clip"
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+    _keys_to_ignore_on_load_unexpected = [r"position_ids"]
 
 
 CLIP_START_DOCSTRING = r"""

src/transformers/models/vision_text_dual_encoder/__init__.py

@@ -13,7 +13,13 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -39,10 +45,18 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["modeling_flax_vision_text_dual_encoder"] = ["FlaxVisionTextDualEncoderModel"]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_vision_text_dual_encoder"] = ["TFVisionTextDualEncoderModel"]
+
 
 if TYPE_CHECKING:
     from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
-    from .processing_visiotn_text_dual_encoder import VisionTextDualEncoderProcessor
+    from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor
 
     try:
         if not is_torch_available():
@@ -58,7 +72,15 @@ if TYPE_CHECKING:
     except OptionalDependencyNotAvailable:
         pass
     else:
-        from .modeling_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
+        from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
+
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel
 
 
 else:

src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py

@@ -316,7 +316,7 @@ class VisionTextDualEncoderModel(PreTrainedModel):
         ...     VisionTextDualEncoderModel,
         ...     VisionTextDualEncoderProcessor,
         ...     AutoImageProcessor,
-        ...     Autookenizer,
+        ...     AutoTokenizer,
         ... )
 
         >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
@@ -428,7 +428,7 @@ class VisionTextDualEncoderModel(PreTrainedModel):
                       Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
                       user or organization name, like `dbmdz/bert-base-german-cased`.
                     - A path to a *directory* containing model weights saved using
-                      [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
+                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
                     - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt`
                       should be set to `True` and a configuration object should be provided as `config` argument. This
                       loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided
@@ -441,7 +441,7 @@ class VisionTextDualEncoderModel(PreTrainedModel):
                       Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
                       user or organization name, like `dbmdz/bert-base-german-cased`.
                     - A path to a *directory* containing model weights saved using
-                      [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
+                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
                     - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt`
                       should be set to `True` and a configuration object should be provided as `config` argument. This
                       loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided

src/transformers/utils/dummy_tf_objects.py

@@ -2469,6 +2469,13 @@ class TFVisionEncoderDecoderModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+class TFVisionTextDualEncoderModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 class TFViTForImageClassification(metaclass=DummyObject):
     _backends = ["tf"]
 

tests/models/vision_text_dual_encoder/test_modeling_tf_vision_text_dual_encoder.py

+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# 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 VisionTextDualEncoder model. """
+
+
+import collections
+import tempfile
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_tf, require_vision, slow
+from transformers.utils import is_tf_available, is_vision_available
+
+from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
+from ..bert.test_modeling_tf_bert import TFBertModelTester
+from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
+from ..deit.test_modeling_tf_deit import TFDeiTModelTester
+from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
+from ..vit.test_modeling_tf_vit import TFViTModelTester
+
+
+if is_tf_available():
+    from transformers import (
+        TFBertModel,
+        TFCLIPVisionModel,
+        TFDeiTModel,
+        TFRobertaModel,
+        TFVisionTextDualEncoderModel,
+        TFViTModel,
+        VisionTextDualEncoderConfig,
+    )
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import VisionTextDualEncoderProcessor
+
+
+# Inspired by
+# https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py
+# From PyTorch internals
+def to_2tuple(x):
+    if isinstance(x, collections.abc.Iterable):
+        return x
+    return (x, x)
+
+
+@require_tf
+class TFVisionTextDualEncoderMixin:
+    def get_vision_text_model(self, config, text_config):
+        pass
+
+    def prepare_config_and_inputs(self):
+        pass
+
+    def get_pretrained_model_and_inputs(self):
+        pass
+
+    def check_model_from_pretrained_configs(
+        self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
+    ):
+        config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
+
+        model = TFVisionTextDualEncoderModel(config)
+
+        output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
+
+        self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], config.projection_dim))
+        self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], config.projection_dim))
+
+    def check_vision_text_dual_encoder_model(
+        self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
+    ):
+        vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
+        model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
+
+        output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
+
+        self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim))
+        self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim))
+
+    def check_vision_text_dual_encoder_from_pretrained(
+        self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
+    ):
+        vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
+        kwargs = {"vision_model": vision_model, "text_model": text_model}
+        model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
+
+        output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
+
+        self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim))
+        self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim))
+
+    def check_save_load(self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs):
+        vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
+        model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
+
+        output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
+        out_1 = output[0].numpy()
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_pretrained(tmpdirname)
+            model = TFVisionTextDualEncoderModel.from_pretrained(tmpdirname)
+
+            after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
+            out_2 = after_output[0].numpy()
+            max_diff = np.amax(np.abs(out_2 - out_1))
+            self.assertLessEqual(max_diff, 1e-5)
+
+    def check_vision_text_output_attention(
+        self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
+    ):
+        vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
+        model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
+
+        output = model(
+            input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True
+        )
+
+        vision_attentions = output.vision_model_output.attentions
+        self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers)
+
+        # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
+        image_size = to_2tuple(vision_model.config.image_size)
+        patch_size = to_2tuple(vision_model.config.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        seq_len = num_patches + 1
+        self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len))
+
+        text_attentions = output.text_model_output.attentions
+        self.assertEqual(len(text_attentions), text_config.num_hidden_layers)
+
+        self.assertEqual(
+            text_attentions[0].shape[-3:],
+            (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
+        )
+
+    def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
+        diff = np.abs((a - b)).max()
+        self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
+
+    def test_vision_text_dual_encoder_model(self):
+        inputs_dict = self.prepare_config_and_inputs()
+        self.check_vision_text_dual_encoder_model(**inputs_dict)
+
+    def test_model_from_pretrained_configs(self):
+        inputs_dict = self.prepare_config_and_inputs()
+        self.check_model_from_pretrained_configs(**inputs_dict)
+
+    def test_vision_text_dual_encoder_from_pretrained(self):
+        inputs_dict = self.prepare_config_and_inputs()
+        self.check_vision_text_dual_encoder_from_pretrained(**inputs_dict)
+
+    def test_save_load(self):
+        inputs_dict = self.prepare_config_and_inputs()
+        self.check_save_load(**inputs_dict)
+
+    def test_vision_text_output_attention(self):
+        inputs_dict = self.prepare_config_and_inputs()
+        self.check_vision_text_output_attention(**inputs_dict)
+
+    @slow
+    def test_real_model_save_load_from_pretrained(self):
+        model_2, inputs = self.get_pretrained_model_and_inputs()
+
+        outputs = model_2(**inputs)
+        out_2 = outputs[0].numpy()
+
+        with tempfile.TemporaryDirectory() as tmp_dirname:
+            model_2.save_pretrained(tmp_dirname)
+            model_1 = TFVisionTextDualEncoderModel.from_pretrained(tmp_dirname)
+
+            after_outputs = model_1(**inputs)
+            out_1 = after_outputs[0].numpy()
+            max_diff = np.amax(np.abs(out_1 - out_2))
+            self.assertLessEqual(max_diff, 1e-5)
+
+
+@require_tf
+class TFViTBertModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase):
+    def get_pretrained_model_and_inputs(self):
+        model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
+            "hf-internal-testing/tiny-random-vit", "hf-internal-testing/tiny-random-bert"
+        )
+        batch_size = 13
+        pixel_values = floats_tensor(
+            [
+                batch_size,
+                model.vision_model.config.num_channels,
+                model.vision_model.config.image_size,
+                model.vision_model.config.image_size,
+            ]
+        )
+        input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
+        attention_mask = random_attention_mask([batch_size, 4])
+        inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
+
+        return model, inputs
+
+    def get_vision_text_model(self, vision_config, text_config):
+        vision_model = TFViTModel(vision_config, name="vision_model")
+        text_model = TFBertModel(text_config, name="text_model")
+        return vision_model, text_model
+
+    def prepare_config_and_inputs(self):
+        vit_model_tester = TFViTModelTester(self)
+        bert_model_tester = TFBertModelTester(self)
+        vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
+        text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
+
+        vision_config, pixel_values, _ = vision_config_and_inputs
+
+        (
+            text_config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = text_config_and_inputs
+
+        return {
+            "text_config": text_config,
+            "vision_config": vision_config,
+            "pixel_values": pixel_values,
+            "attention_mask": input_mask,
+            "input_ids": input_ids,
+            "text_token_type_ids": token_type_ids,
+            "text_sequence_labels": sequence_labels,
+            "text_token_labels": token_labels,
+            "text_choice_labels": choice_labels,
+        }
+
+
+@require_tf
+class TFDeiTRobertaModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase):
+    def get_pretrained_model_and_inputs(self):
+        # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
+        # just reinitialize it.
+        model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
+            "Rocketknight1/tiny-random-deit-tf", "hf-internal-testing/tiny-random-roberta"
+        )
+        batch_size = 13
+        pixel_values = floats_tensor(
+            [
+                batch_size,
+                model.vision_model.config.num_channels,
+                model.vision_model.config.image_size,
+                model.vision_model.config.image_size,
+            ]
+        )
+        input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
+        attention_mask = random_attention_mask([batch_size, 4])
+        inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
+
+        return model, inputs
+
+    def check_vision_text_output_attention(
+        self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
+    ):
+        vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
+        model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
+
+        output = model(
+            input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True
+        )
+
+        vision_attentions = output.vision_model_output.attentions
+        self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers)
+
+        # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
+        image_size = to_2tuple(vision_model.config.image_size)
+        patch_size = to_2tuple(vision_model.config.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        seq_len = num_patches + 2
+        self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len))
+
+        text_attentions = output.text_model_output.attentions
+        self.assertEqual(len(text_attentions), text_config.num_hidden_layers)
+
+        self.assertEqual(
+            text_attentions[0].shape[-3:],
+            (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
+        )
+
+    def get_vision_text_model(self, vision_config, text_config):
+        vision_model = TFDeiTModel(vision_config, name="vision_model")
+        text_model = TFRobertaModel(text_config, name="text_model")
+        return vision_model, text_model
+
+    def prepare_config_and_inputs(self):
+        vit_model_tester = TFDeiTModelTester(self)
+        bert_model_tester = TFRobertaModelTester(self)
+        vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
+        text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
+
+        vision_config, pixel_values, _ = vision_config_and_inputs
+
+        (
+            text_config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = text_config_and_inputs
+
+        return {
+            "text_config": text_config,
+            "vision_config": vision_config,
+            "pixel_values": pixel_values,
+            "attention_mask": input_mask,
+            "input_ids": input_ids,
+            "text_token_type_ids": token_type_ids,
+            "text_sequence_labels": sequence_labels,
+            "text_token_labels": token_labels,
+            "text_choice_labels": choice_labels,
+        }
+
+
+@require_tf
+class TFCLIPVisionBertModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase):
+    def get_pretrained_model_and_inputs(self):
+        model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
+            "Rocketknight1/tiny-random-clip-tf", "hf-internal-testing/tiny-random-bert"
+        )
+        batch_size = 13
+        pixel_values = floats_tensor(
+            [
+                batch_size,
+                model.vision_model.config.num_channels,
+                model.vision_model.config.image_size,
+                model.vision_model.config.image_size,
+            ]
+        )
+        input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
+        attention_mask = random_attention_mask([batch_size, 4])
+        inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
+
+        return model, inputs
+
+    def get_vision_text_model(self, vision_config, text_config):
+        vision_model = TFCLIPVisionModel(vision_config, name="vision_model")
+        text_model = TFBertModel(text_config, name="text_model")
+        return vision_model, text_model
+
+    def prepare_config_and_inputs(self):
+        clip_model_tester = TFCLIPVisionModelTester(self)
+        bert_model_tester = TFBertModelTester(self)
+        vision_config_and_inputs = clip_model_tester.prepare_config_and_inputs()
+        text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
+
+        vision_config, pixel_values = vision_config_and_inputs
+
+        (
+            text_config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = text_config_and_inputs
+
+        return {
+            "text_config": text_config,
+            "vision_config": vision_config,
+            "pixel_values": pixel_values,
+            "attention_mask": input_mask,
+            "input_ids": input_ids,
+            "text_token_type_ids": token_type_ids,
+            "text_sequence_labels": sequence_labels,
+            "text_token_labels": token_labels,
+            "text_choice_labels": choice_labels,
+        }
+
+
+@require_vision
+@require_tf
+class TFVisionTextDualEncoderIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model = TFVisionTextDualEncoderModel.from_pretrained(
+            "clip-italian/clip-italian", logit_scale_init_value=1, from_pt=True
+        )
+        processor = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")
+
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        inputs = processor(
+            text=["una foto di un gatto", "una foto di un cane"], images=image, padding=True, return_tensors="np"
+        )
+
+        outputs = model(**inputs)
+
+        # verify the logits
+        self.assertEqual(outputs.logits_per_image.shape, (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
+        self.assertEqual(
+            outputs.logits_per_text.shape,
+            (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),
+        )
+
+        expected_logits = np.array([[1.2284727, 0.3104122]])
+
+        self.assertTrue(np.allclose(outputs.logits_per_image.numpy(), expected_logits, atol=1e-3))

utils/check_repo.py

@@ -173,6 +173,7 @@ TEST_FILES_WITH_NO_COMMON_TESTS = [
     "models/xlm_prophetnet/test_modeling_xlm_prophetnet.py",
     "models/xlm_roberta/test_modeling_xlm_roberta.py",
     "models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py",
+    "models/vision_text_dual_encoder/test_modeling_tf_vision_text_dual_encoder.py",
     "models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py",
     "models/decision_transformer/test_modeling_decision_transformer.py",
 ]

utils/documentation_tests.txt

@@ -198,6 +198,7 @@ src/transformers/models/vilt/modeling_vilt.py
 src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py
 src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py
 src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py
+src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py
 src/transformers/models/vit/configuration_vit.py
 src/transformers/models/vit/modeling_vit.py
 src/transformers/models/vit/modeling_tf_vit.py