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_tf_vision_text_dual_encoder.py

+# coding=utf-8
+# Copyright 2023 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.
+"""TensorFlow VisionTextDualEncoder model."""
+
+
+import re
+from typing import Optional, Tuple, Union
+
+import tensorflow as tf
+from tensorflow.keras.layers import Dense
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_tf_utils import TFPreTrainedModel, unpack_inputs
+from ...tf_utils import shape_list
+from ...utils import (
+    DUMMY_INPUTS,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from ..auto.configuration_auto import AutoConfig
+from ..auto.modeling_tf_auto import TFAutoModel
+from ..clip.modeling_tf_clip import CLIPVisionConfig, TFCLIPOutput, TFCLIPVisionModel
+from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "VisionTextDualEncoderConfig"
+
+VISION_TEXT_DUAL_ENCODER_START_DOCSTRING = r"""
+    This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model
+    as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded
+    via the [`~TFAutoModel.from_pretrained`] method. The projection layers are automatically added to the model and
+    should be fine-tuned on a downstream task, like contrastive image-text modeling.
+
+    In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how
+    leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment
+    on new zero-shot vision tasks such as image classification or retrieval.
+
+    After such a Vision-Text-Dual-Encoder model has been trained/fine-tuned, it can be saved/loaded just like any other
+    models (see the examples for more information).
+
+    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a Keras [Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a
+    regular Keras Model and refer to the TF documentation for all matter related to general usage and behavior.
+
+    Parameters:
+        config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+VISION_TEXT_DUAL_ENCODER_TEXT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+VISION_TEXT_DUAL_ENCODER_VISION_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            an image processor (e.g. if you use ViT as the encoder, you should use [`AutoImageProcessor`]). See
+            [`ViTImageProcessor.__call__`] for details.
+        return_loss (`bool`, *optional*):
+            Whether or not to return the contrastive loss.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+# Copied from transformers.models.clip.modeling_tf_clip.contrastive_loss
+def contrastive_loss(logits: tf.Tensor) -> tf.Tensor:
+    return tf.math.reduce_mean(
+        tf.keras.metrics.sparse_categorical_crossentropy(
+            y_true=tf.range(shape_list(logits)[0]), y_pred=logits, from_logits=True
+        )
+    )
+
+
+# Copied from transformers.models.clip.modeling_tf_clip.clip_loss
+def clip_loss(similarity: tf.Tensor) -> tf.Tensor:
+    caption_loss = contrastive_loss(similarity)
+    image_loss = contrastive_loss(tf.transpose(similarity))
+    return (caption_loss + image_loss) / 2.0
+
+
+@add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING)
+class TFVisionTextDualEncoderModel(TFPreTrainedModel):
+    config_class = VisionTextDualEncoderConfig
+    base_model_prefix = "vision_text_dual_encoder"
+    load_weight_prefix = "tf_vision_text_dual_encoder_model"
+
+    def __init__(
+        self,
+        config: Optional[VisionTextDualEncoderConfig] = None,
+        vision_model: Optional[TFPreTrainedModel] = None,
+        text_model: Optional[TFPreTrainedModel] = None,
+    ):
+        if config is None and (vision_model is None or text_model is None):
+            raise ValueError("Either a configuration or an vision and a text model has to be provided")
+
+        if config is None:
+            config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config)
+        else:
+            if not isinstance(config, self.config_class):
+                raise ValueError(f"config: {config} has to be of type {self.config_class}")
+
+        # initialize with config
+        super().__init__(config)
+
+        if vision_model is None:
+            if isinstance(config.vision_config, CLIPVisionConfig):
+                vision_model = TFCLIPVisionModel.from_config(config.vision_config, name="vision_model")
+            else:
+                vision_model = TFAutoModel.from_config(config.vision_config, name="vision_model")
+
+        if text_model is None:
+            text_model = TFAutoModel.from_config(config.text_config, name="text_model")
+
+        self.vision_model = vision_model
+        self.text_model = text_model
+
+        # make sure that the individual model's config refers to the shared config
+        # so that the updates to the config will be synced
+        self.vision_model.config = self.config.vision_config
+        self.text_model.config = self.config.text_config
+
+        self.vision_embed_dim = config.vision_config.hidden_size
+        self.text_embed_dim = config.text_config.hidden_size
+        self.projection_dim = config.projection_dim
+
+        self.visual_projection = Dense(self.projection_dim, use_bias=False, name="visual_projection")
+        self.text_projection = Dense(self.projection_dim, use_bias=False, name="text_projection")
+        self.logit_scale = None
+
+    def build(self, input_shape=None):
+        # Build in the build() method to make sure the names are right
+        initializer = tf.keras.initializers.Constant(self.config.logit_scale_init_value)
+        self.logit_scale = self.add_weight(shape=(1,), initializer=initializer, name="logit_scale")
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        # Matt: The TF and PT weights don't align because our TF base classes have an extra layer compared to PT models
+        # (the main model stem is in the MainLayer class). If we remove that layer, then weight names sync up as normal.
+        # However, the name of that extra layer is the name of the MainLayer in the base model.
+
+        if kwargs.get("from_pt", False):
+
+            def tf_to_pt_weight_rename(tf_weight):
+                if "vision_model" in tf_weight:
+                    if tf_weight.count("vision_model") == 1:
+                        return re.sub(r"vision_model\..*?\.", "vision_model.", tf_weight)
+                    elif tf_weight.count("vision_model") == 2:
+                        return re.sub(r"vision_model\..*?\.vision_model", "vision_model.vision_model", tf_weight)
+                    else:
+                        raise ValueError(
+                            f"Unexpected weight name {tf_weight}. Please file an issue on the"
+                            " Transformers repo to let us know about this error!"
+                        )
+                elif "text_model" in tf_weight:
+                    return re.sub(r"text_model\..*?\.", "text_model.", tf_weight)
+                else:
+                    return tf_weight
+
+            kwargs["tf_to_pt_weight_rename"] = tf_to_pt_weight_rename
+        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+
+    @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_TEXT_INPUTS_DOCSTRING)
+    def get_text_features(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        token_type_ids=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        Returns:
+            text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
+            the projection layer to the pooled output of [`TFCLIPTextModel`].
+
+        Examples:
+
+        ```python
+        >>> from transformers import TFVisionTextDualEncoderModel, AutoTokenizer
+
+        >>> model = TFVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian")
+        >>> tokenizer = AutoTokenizer.from_pretrained("clip-italian/clip-italian")
+
+        >>> inputs = tokenizer(["una foto di un gatto", "una foto di un cane"], padding=True, return_tensors="pt")
+        >>> text_features = model.get_text_features(**inputs)
+        ```"""
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = text_outputs[1]
+        text_features = self.text_projection(pooled_output)
+
+        return text_features
+
+    @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_VISION_INPUTS_DOCSTRING)
+    def get_image_features(
+        self,
+        pixel_values=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        Returns:
+            image_features (`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying
+            the projection layer to the pooled output of [`TFCLIPVisionModel`].
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import TFVisionTextDualEncoderModel, AutoImageProcessor
+
+        >>> model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian")
+        >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = image_processor(images=image, return_tensors="np")
+
+        >>> image_features = model.get_image_features(**inputs)
+        ```"""
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = vision_outputs[1]  # pooled_output
+        image_features = self.visual_projection(pooled_output)
+
+        return image_features
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TFCLIPOutput, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        input_ids: Optional[tf.Tensor] = None,
+        pixel_values: Optional[tf.Tensor] = None,
+        attention_mask: Optional[tf.Tensor] = None,
+        position_ids: Optional[tf.Tensor] = None,
+        return_loss: Optional[bool] = None,
+        token_type_ids: Optional[tf.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> Union[Tuple[tf.Tensor], TFCLIPOutput]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import (
+        ...     TFVisionTextDualEncoderModel,
+        ...     VisionTextDualEncoderProcessor,
+        ...     AutoImageProcessor,
+        ...     AutoTokenizer,
+        ... )
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+        >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
+        >>> processor = VisionTextDualEncoderProcessor(image_processor, tokenizer)
+        >>> model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
+        ...     "google/vit-base-patch16-224", "bert-base-uncased"
+        ... )
+
+        >>> # contrastive training
+        >>> urls = [
+        ...     "http://images.cocodataset.org/val2017/000000039769.jpg",
+        ...     "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg",
+        ... ]
+        >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls]
+        >>> inputs = processor(
+        ...     text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="pt", padding=True
+        ... )
+        >>> outputs = model(
+        ...     input_ids=inputs.input_ids,
+        ...     attention_mask=inputs.attention_mask,
+        ...     pixel_values=inputs.pixel_values,
+        ...     return_loss=True,
+        ... )
+        >>> loss, logits_per_image = outputs.loss, outputs.logits_per_image  # this is the image-text similarity score
+
+        >>> # save and load from pretrained
+        >>> model.save_pretrained("vit-bert")
+        >>> model = TFVisionTextDualEncoderModel.from_pretrained("vit-bert")
+
+        >>> # inference
+        >>> outputs = model(**inputs)
+        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
+        >>> probs = tf.nn.softmax(logits_per_image, axis=1)  # we can take the softmax to get the label probabilities
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        image_embeds = vision_outputs[1]  # pooler_output
+        image_embeds = self.visual_projection(image_embeds)
+
+        text_embeds = text_outputs[1]  # pooler_output
+        text_embeds = self.text_projection(text_embeds)
+
+        # normalized features
+        image_embeds = image_embeds / tf.norm(image_embeds, axis=-1, keepdims=True)
+        text_embeds = text_embeds / tf.norm(text_embeds, axis=-1, keepdims=True)
+
+        # cosine similarity as logits
+        logit_scale = tf.math.exp(self.logit_scale)
+        logits_per_text = tf.matmul(text_embeds, image_embeds, transpose_b=True) * logit_scale
+        logits_per_image = tf.transpose(logits_per_text)
+
+        loss = None
+        if return_loss:
+            loss = clip_loss(logits_per_text)
+
+        if not return_dict:
+            output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
+            return ((loss,) + output) if loss is not None else output
+
+        return TFCLIPOutput(
+            loss=loss,
+            logits_per_image=logits_per_image,
+            logits_per_text=logits_per_text,
+            text_embeds=text_embeds,
+            image_embeds=image_embeds,
+            text_model_output=text_outputs,
+            vision_model_output=vision_outputs,
+        )
+
+    @classmethod
+    def from_vision_text_pretrained(
+        cls,
+        vision_model_name_or_path: str = None,
+        text_model_name_or_path: str = None,
+        *model_args,
+        **kwargs,
+    ) -> TFPreTrainedModel:
+        """
+        Params:
+            vision_model_name_or_path (`str`, *optional*, defaults to `None`):
+                Information necessary to initiate the vision model. Can be either:
+
+                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+                      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
+                      [`~TFPreTrainedModel.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.
+
+            text_model_name_or_path (`str`, *optional*):
+                Information necessary to initiate the text model. Can be either:
+
+                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+                      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
+                      [`~TFPreTrainedModel.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.
+
+            model_args (remaining positional arguments, *optional*):
+                All remaning positional arguments will be passed to the underlying model's `__init__` method.
+
+            kwargs (remaining dictionary of keyword arguments, *optional*):
+                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
+                `output_attentions=True`).
+
+                - To update the text configuration, use the prefix *text_* for each configuration parameter.
+                - To update the vision configuration, use the prefix *vision_* for each configuration parameter.
+                - To update the parent model configuration, do not use a prefix for each configuration parameter.
+
+                Behaves differently depending on whether a `config` is provided or automatically loaded.
+
+        Example:
+
+        ```python
+        >>> from transformers import TFVisionTextDualEncoderModel
+
+        >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized.
+        >>> model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
+        ...     "google/vit-base-patch16-224", "bert-base-uncased"
+        ... )
+        >>> # saving model after fine-tuning
+        >>> model.save_pretrained("./vit-bert")
+        >>> # load fine-tuned model
+        >>> model = TFVisionTextDualEncoderModel.from_pretrained("./vit-bert")
+        ```"""
+        kwargs_vision = {
+            argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_")
+        }
+
+        kwargs_text = {
+            argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_")
+        }
+
+        # remove vision, text kwargs from kwargs
+        for key in kwargs_vision.keys():
+            del kwargs["vision_" + key]
+        for key in kwargs_text.keys():
+            del kwargs["text_" + key]
+
+        # Load and initialize the vision and text model
+        vision_model = kwargs_vision.pop("model", None)
+        if vision_model is None:
+            if vision_model_name_or_path is None:
+                raise ValueError(
+                    "If `vision_model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
+                )
+            kwargs_vision["name"] = "vision_model"
+            kwargs_vision["load_weight_prefix"] = cls.load_weight_prefix
+
+            vision_config_dict, unused_args = PretrainedConfig.get_config_dict(vision_model_name_or_path, **kwargs)
+            if vision_config_dict.get("model_type", None) == "clip_vision_model":
+                vision_config = CLIPVisionConfig.from_dict(vision_config_dict)
+            else:
+                vision_config = AutoConfig.from_pretrained(vision_model_name_or_path)
+
+            if vision_config.model_type == "clip_vision_model":
+                kwargs_vision["config"] = vision_config
+                vision_class = TFCLIPVisionModel
+            elif vision_config.model_type == "clip":
+                kwargs_vision["config"] = vision_config.vision_config
+                vision_class = TFCLIPVisionModel
+            else:
+                kwargs_vision["config"] = vision_config
+                vision_class = TFAutoModel
+            vision_model = vision_class.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision)
+
+        text_model = kwargs_text.pop("model", None)
+        if text_model is None:
+            if text_model_name_or_path is None:
+                raise ValueError(
+                    "If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
+                )
+            kwargs_text["name"] = "text_model"
+            kwargs_text["load_weight_prefix"] = cls.load_weight_prefix
+
+            if "config" not in kwargs_text:
+                text_config = AutoConfig.from_pretrained(text_model_name_or_path)
+                kwargs_text["config"] = text_config
+
+            text_model = TFAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text)
+
+        # instantiate config with corresponding kwargs
+        config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs)
+
+        # init model
+        model = cls(config=config, vision_model=vision_model, text_model=text_model)
+
+        # the projection layers are always newly initialized when loading the model
+        # using pre-trained vision and text model.
+        logger.warning(
+            "The projection layer and logit scale weights `['visual_projection.weight', 'text_projection.weight',"
+            " 'logit_scale']` are newly initialized. You should probably TRAIN this model on a down-stream task to be"
+            " able to use it for predictions and inference."
+        )
+
+        if vision_model.name != "vision_model":
+            raise ValueError("vision model must be created with the name `vision_model`.")
+        if text_model.name != "text_model":
+            raise ValueError("text model must be created with the name `text_model`.")
+
+        return model
+
+    @property
+    def dummy_inputs(self):
+        """
+        Dummy inputs to build the network.
+
+        Returns:
+            `Dict[str, tf.Tensor]`: The dummy inputs.
+        """
+        input_ids = tf.constant(DUMMY_INPUTS, dtype=tf.int32)
+        batch_size, seq_len = input_ids.shape
+
+        VISION_DUMMY_INPUTS = tf.random.uniform(
+            shape=(
+                batch_size,
+                self.config.vision_config.num_channels,
+                self.config.vision_config.image_size,
+                self.config.vision_config.image_size,
+            ),
+            dtype=tf.float32,
+        )
+        pixel_values = tf.constant(VISION_DUMMY_INPUTS)
+        dummy = {"pixel_values": pixel_values, "input_ids": input_ids}
+        return dummy

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