VisionTextDualEncoder (#13511)

* init vision_text_dual_encoder

* fix merge

* remove extra heads

* fix tests

* remove VISION_TEXT_DUAL_ENCODER_PRETRAINED_CONFIG_ARCHIVE_MAP

* remove archive map

* fix imports

* fix more imports

* fix init

* delete tokenizers

* fix imports

* clean

* support clip's vision model

* handle None config

* begin tests

* more test and few fixes

* warn about newly init weights

* more tests

* add loss to model

* remove extra classes from doc

* add processor

* doc and small fixes

* add start docstr

* update flax model

* flax tests

* more flax tests

* doc

* quality

* doc and quality

* fix doc

* doc

* remove comments

* update warning

* quality

* fix docs

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* replace asserts, fix imports

* update imports

* fix import

* address some review comments

* fix check

* reduce tolerance

* fix test

* add flax integration test

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* address Sylvain's comments

* fix style

* add pt_flax_equivalence test in PT tests

* add pt integration test

* update test

* use pre-trained checkpoint in examples
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/index.rst

@@ -511,6 +511,8 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |   Vision Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|    VisionTextDualEncoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             ViT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
@@ -686,6 +688,7 @@ Flax), PyTorch, and/or TensorFlow.
     model_doc/unispeech
     model_doc/unispeech_sat
     model_doc/visionencoderdecoder
+    model_doc/vision_text_dual_encoder
     model_doc/vit
     model_doc/visual_bert
     model_doc/wav2vec2

docs/source/model_doc/vision_text_dual_encoder.rst

+.. 
+    Copyright 2021 The HuggingFace 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.
+
+VisionTextDualEncoder
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The :class:`~transformers.VisionTextDualEncoderModel` can be used to initialize a vision-text dual encoder model with
+any pretrained vision autoencoding model as the vision encoder (*e.g.* :doc:`ViT <vit>`, :doc:`BEiT <beit>`, :doc:`DeiT
+<deit>`) and any pretrained text autoencoding model as the text encoder (*e.g.* :doc:`RoBERTa <roberta>`, :doc:`BERT
+<bert>`). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
+to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
+downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
+training and then can be used for zero-shot vision tasks such image-classification or retrieval.
+
+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.
+
+VisionTextDualEncoderConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.VisionTextDualEncoderConfig
+    :members:
+
+
+VisionTextDualEncoderProcessor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.VisionTextDualEncoderProcessor
+    :members:
+
+
+VisionTextDualEncoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.VisionTextDualEncoderModel
+    :members: forward
+
+
+FlaxVisionTextDualEncoderModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxVisionTextDualEncoderModel
+    :members: __call__

src/transformers/__init__.py

@@ -297,6 +297,7 @@ _import_structure = {
         "UniSpeechSatConfig",
     ],
     "models.vision_encoder_decoder": ["VisionEncoderDecoderConfig"],
+    "models.vision_text_dual_encoder": ["VisionTextDualEncoderConfig", "VisionTextDualEncoderProcessor"],
     "models.visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"],
     "models.vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"],
     "models.wav2vec2": [
@@ -1307,6 +1308,7 @@ if is_torch_available():
         ]
     )
     _import_structure["models.vision_encoder_decoder"].extend(["VisionEncoderDecoderModel"])
+    _import_structure["models.vision_text_dual_encoder"].extend(["VisionTextDualEncoderModel"])
     _import_structure["models.visual_bert"].extend(
         [
             "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -1908,6 +1910,7 @@ if is_flax_available():
     )
 
     # Flax models structure
+
     _import_structure["models.bart"].extend(
         [
             "FlaxBartForConditionalGeneration",
@@ -2028,6 +2031,7 @@ if is_flax_available():
     )
     _import_structure["models.t5"].extend(["FlaxT5ForConditionalGeneration", "FlaxT5Model", "FlaxT5PreTrainedModel"])
     _import_structure["models.vision_encoder_decoder"].append("FlaxVisionEncoderDecoderModel")
+    _import_structure["models.vision_text_dual_encoder"].extend(["FlaxVisionTextDualEncoderModel"])
     _import_structure["models.vit"].extend(["FlaxViTForImageClassification", "FlaxViTModel", "FlaxViTPreTrainedModel"])
     _import_structure["models.wav2vec2"].extend(
         ["FlaxWav2Vec2ForCTC", "FlaxWav2Vec2ForPreTraining", "FlaxWav2Vec2Model", "FlaxWav2Vec2PreTrainedModel"]
@@ -2268,6 +2272,7 @@ if TYPE_CHECKING:
     from .models.unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
     from .models.unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig
     from .models.vision_encoder_decoder import VisionEncoderDecoderConfig
+    from .models.vision_text_dual_encoder import VisionTextDualEncoderConfig, VisionTextDualEncoderProcessor
     from .models.visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig
     from .models.vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig
     from .models.wav2vec2 import (
@@ -3111,6 +3116,7 @@ if TYPE_CHECKING:
             UniSpeechSatPreTrainedModel,
         )
         from .models.vision_encoder_decoder import VisionEncoderDecoderModel
+        from .models.vision_text_dual_encoder import VisionTextDualEncoderModel
         from .models.visual_bert import (
             VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             VisualBertForMultipleChoice,
@@ -3706,6 +3712,7 @@ if TYPE_CHECKING:
         )
         from .models.t5 import FlaxT5ForConditionalGeneration, FlaxT5Model, FlaxT5PreTrainedModel
         from .models.vision_encoder_decoder import FlaxVisionEncoderDecoderModel
+        from .models.vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
         from .models.vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
         from .models.wav2vec2 import (
             FlaxWav2Vec2ForCTC,

src/transformers/models/__init__.py

@@ -101,6 +101,7 @@ from . import (
     unispeech,
     unispeech_sat,
     vision_encoder_decoder,
+    vision_text_dual_encoder,
     visual_bert,
     vit,
     wav2vec2,

src/transformers/models/auto/configuration_auto.py

@@ -36,6 +36,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
         ("trocr", "TrOCRConfig"),
         ("fnet", "FNetConfig"),
         ("segformer", "SegformerConfig"),
+        ("vision-text-dual-encoder", "VisionTextDualEncoderConfig"),
         ("gptj", "GPTJConfig"),
         ("layoutlmv2", "LayoutLMv2Config"),
         ("beit", "BeitConfig"),
@@ -192,6 +193,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("trocr", "TrOCR"),
         ("fnet", "FNet"),
         ("segformer", "SegFormer"),
+        ("vision-text-dual-encoder", "VisionTextDualEncoder"),
         ("gptj", "GPT-J"),
         ("beit", "BEiT"),
         ("rembert", "RemBERT"),

src/transformers/models/auto/modeling_auto.py

@@ -32,6 +32,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("qdqbert", "QDQBertModel"),
         ("fnet", "FNetModel"),
         ("segformer", "SegformerModel"),
+        ("vision-text-dual-encoder", "VisionTextDualEncoderModel"),
         ("gptj", "GPTJModel"),
         ("layoutlmv2", "LayoutLMv2Model"),
         ("beit", "BeitModel"),

src/transformers/models/auto/modeling_flax_auto.py

@@ -29,6 +29,7 @@ FLAX_MODEL_MAPPING_NAMES = OrderedDict(
     [
         # Base model mapping
         ("pegasus", "FlaxPegasusModel"),
+        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
         ("distilbert", "FlaxDistilBertModel"),
         ("albert", "FlaxAlbertModel"),
         ("roberta", "FlaxRobertaModel"),

src/transformers/models/auto/processing_auto.py

@@ -41,6 +41,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
         ("speech_to_text_2", "Speech2Text2Processor"),
         ("trocr", "TrOCRProcessor"),
         ("wav2vec2", "Wav2Vec2Processor"),
+        ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
     ]
 )
 

src/transformers/models/vision_text_dual_encoder/__init__.py

+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+# Copyright 2021 The HuggingFace 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.
+from typing import TYPE_CHECKING
+
+# rely on isort to merge the imports
+from ...file_utils import _LazyModule, is_flax_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
+    "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
+}
+
+
+if is_torch_available():
+    _import_structure["modeling_vision_text_dual_encoder"] = ["VisionTextDualEncoderModel"]
+
+
+if is_flax_available():
+    _import_structure["modeling_flax_vision_text_dual_encoder"] = ["FlaxVisionTextDualEncoderModel"]
+
+
+if TYPE_CHECKING:
+    from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
+    from .processing_visiotn_text_dual_encoder import VisionTextDualEncoderProcessor
+
+    if is_torch_available():
+        from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel
+
+    if is_flax_available():
+        from .modeling_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py

+# coding=utf-8
+# Copyright 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.
+""" VisionTextDualEncoder model configuration """
+
+import copy
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ..auto.configuration_auto import AutoConfig
+from ..clip.configuration_clip import CLIPVisionConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class VisionTextDualEncoderConfig(PretrainedConfig):
+    r"""
+    :class:`~transformers.VisionTextDualEncoderConfig` is the configuration class to store the configuration of a
+    :class:`~transformers.VisionTextDualEncoderModel`. It is used to instantiate
+    :class:`~transformers.VisionTextDualEncoderModel` model according to the specified arguments, defining the text
+    model and vision model configs.
+
+    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
+    outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
+
+    Args:
+        text_config_dict (:obj:`dict`):
+            Dictionary of configuration options that defines text model config.
+        vision_config_dict (:obj:`dict`):
+            Dictionary of configuration options that defines vison model config.
+        projection_dim (:obj:`int`, `optional`, defaults to 512):
+            Dimentionality of text and vision projection layers.
+        logit_scale_init_value (:obj:`float`, `optional`, defaults to 2.6592):
+            The inital value of the `logit_scale` paramter. Default is used as per the original CLIP implementation.
+        kwargs (`optional`):
+            Dictionary of keyword arguments.
+
+    Examples::
+
+        >>> from transformers import ViTConfig, BertConfig, VisionTextDualEncoderConfig, VisionTextDualEncoderModel
+
+        >>> # Initializing a BERT and ViT configuration
+        >>> config_vision = ViTConfig()
+        >>> config_text = BertConfig()
+
+        >>> config = VisionTextDualEncoderConfig.from_vision_text_configs(config_vision, config_text, projection_dim=512)
+
+        >>> # Initializing a BERT and ViT model
+        >>> model = VisionTextDualEncoderModel(config=config)
+
+        >>> # Accessing the model configuration
+        >>> config_vision  = model.config.vision_config
+        >>> config_text = model.config.text_config
+
+        >>> # Saving the model, including its configuration
+        >>> model.save_pretrained('my-model')
+
+        >>> # loading model and config from pretrained folder
+        >>> vision_text_config = VisionTextDualEncoderConfig.from_pretrained('vit-bert')
+        >>> model = VisionTextDualEncoderModel.from_pretrained('vit-bert', config=vision_text_config)
+    """
+
+    model_type = "vision-text-dual-encoder"
+    is_composition = True
+
+    def __init__(self, projection_dim=512, logit_scale_init_value=2.6592, **kwargs):
+        super().__init__(**kwargs)
+
+        if "vision_config" not in kwargs:
+            raise ValueError("`vision_config` can not be `None`.")
+
+        if "text_config" not in kwargs:
+            raise ValueError("`text_config` can not be `None`.")
+
+        vision_config = kwargs.pop("vision_config")
+        text_config = kwargs.pop("text_config")
+
+        vision_model_type = vision_config.pop("model_type")
+        text_model_type = text_config.pop("model_type")
+
+        if vision_model_type == "clip":
+            self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config).vision_config
+        elif vision_model_type == "clip_vision_model":
+            self.vision_config = CLIPVisionConfig(**vision_config)
+        else:
+            self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config)
+
+        self.text_config = AutoConfig.for_model(text_model_type, **text_config)
+
+        self.projection_dim = projection_dim
+        self.logit_scale_init_value = logit_scale_init_value
+
+    @classmethod
+    def from_vision_text_configs(cls, vision_config: PretrainedConfig, text_config: PretrainedConfig, **kwargs):
+        r"""
+        Instantiate a :class:`VisionTextDualEncoderConfig` (or a derived class) from text model configuration and
+        vision model configuration.
+
+        Returns:
+            :class:`VisionTextDualEncoderConfig`: An instance of a configuration object
+        """
+
+        return cls(vision_config=vision_config.to_dict(), text_config=text_config.to_dict(), **kwargs)
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default
+        :meth:`~transformers.PretrainedConfig.to_dict`.
+
+        Returns:
+            :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+        output["vision_config"] = self.vision_config.to_dict()
+        output["text_config"] = self.text_config.to_dict()
+        output["model_type"] = self.__class__.model_type
+        return output

src/transformers/models/vision_text_dual_encoder/modeling_flax_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.
+""" Flax VisionTextDualEncoder model. """
+
+
+from typing import Optional, Tuple
+
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+from flax.core.frozen_dict import FrozenDict
+
+from ...file_utils import add_start_docstrings
+from ...modeling_flax_utils import FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring
+from ...utils import logging
+from ..auto.configuration_auto import AutoConfig
+from ..auto.modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel
+from ..clip.modeling_flax_clip import FlaxCLIPOutput, FlaxCLIPVisionModel
+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 :meth:`~transformers.FlaxAutoModel.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 :class:`~transformers.PreTrainedModel`. 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 Flax Linen `flax.linen.Module
+     <https://flax.readthedocs.io/en/latest/flax.linen.html#module>`__ subclass. Use it as a regular Flax linen Module
+     and refer to the Flax documentation for all matter related to general usage and behavior.
+
+    Finally, this model supports inherent JAX features such as:
+
+    - `Just-In-Time (JIT) compilation <https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit>`__
+    - `Automatic Differentiation <https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation>`__
+    - `Vectorization <https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap>`__
+    - `Parallelization <https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap>`__
+
+    Parameters:
+        config (:class:`~transformers.VisionTextDualEncoderConfig`): 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 :meth:`~transformers.FlaxPreTrainedModel.from_pretrained` method to load the
+            model weights.
+        dtype (:obj:`jax.numpy.dtype`, `optional`, defaults to :obj:`jax.numpy.float32`):
+            The data type of the computation. Can be one of :obj:`jax.numpy.float32`, :obj:`jax.numpy.float16` (on
+            GPUs) and :obj:`jax.numpy.bfloat16` (on TPUs).
+
+            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
+            specified all the computation will be performed with the given ``dtype``.
+
+            **Note that this only specifies the dtype of the computation and does not influence the dtype of model
+            parameters.**
+
+            If you wish to change the dtype of the model parameters, see
+            :meth:`~transformers.FlaxPreTrainedModel.to_fp16` and :meth:`~transformers.FlaxPreTrainedModel.to_bf16`.
+"""
+
+
+VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (:obj:`numpy.ndarray` of shape :obj:`(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 :class:`~transformers.PreTrainedTokenizer`. See
+            :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
+            details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+        attention_mask (:obj:`torch.Tensor` of shape :obj:`(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.html#attention-mask>`__
+        position_ids (:obj:`numpy.ndarray` of shape :obj:`(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.html#position-ids>`_
+        pixel_values (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            a feature extractor (e.g. if you use ViT as the encoder, you should use
+            :class:`~transformers.ViTFeatureExtractor`). See :meth:`transformers.ViTFeatureExtractor.__call__` for
+            details.
+        output_attentions (:obj:`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 (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
+"""
+
+
+class FlaxVisionTextDualEncoderModule(nn.Module):
+    config: VisionTextDualEncoderConfig
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        vision_config = self.config.vision_config
+        text_config = self.config.text_config
+
+        self.vision_embed_dim = vision_config.hidden_size
+        self.text_embed_dim = text_config.hidden_size
+        self.projection_dim = self.config.projection_dim
+
+        vision_module = FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__, FlaxCLIPVisionModel).module_class
+        text_module = FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
+
+        self.vision_model = vision_module(vision_config, dtype=self.dtype)
+        self.text_model = text_module(text_config, dtype=self.dtype)
+
+        self.visual_projection = nn.Dense(
+            self.projection_dim,
+            dtype=self.dtype,
+            kernel_init=jax.nn.initializers.normal(0.02),
+            use_bias=False,
+        )
+        self.text_projection = nn.Dense(
+            self.projection_dim,
+            dtype=self.dtype,
+            kernel_init=jax.nn.initializers.normal(0.02),
+            use_bias=False,
+        )
+
+        self.logit_scale = self.param(
+            "logit_scale", lambda _, shape: jnp.ones(shape) * self.config.logit_scale_init_value, []
+        )
+
+    def __call__(
+        self,
+        input_ids=None,
+        pixel_values=None,
+        attention_mask=None,
+        position_ids=None,
+        token_type_ids=None,
+        deterministic: bool = True,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            deterministic=deterministic,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            deterministic=deterministic,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        image_embeds = vision_outputs[1]
+        image_embeds = self.visual_projection(image_embeds)
+
+        text_embeds = text_outputs[1]
+        text_embeds = self.text_projection(text_embeds)
+
+        # normalized features
+        image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True)
+        text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True)
+
+        # cosine similarity as logits
+        logit_scale = jnp.exp(self.logit_scale)
+        logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale
+        logits_per_image = logits_per_text.T
+
+        if not return_dict:
+            return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
+
+        return FlaxCLIPOutput(
+            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,
+        )
+
+
+@add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING)
+class FlaxVisionTextDualEncoderModel(FlaxPreTrainedModel):
+    config_class = VisionTextDualEncoderConfig
+    module_class = FlaxVisionTextDualEncoderModule
+
+    def __init__(
+        self,
+        config: VisionTextDualEncoderConfig,
+        input_shape: Optional[Tuple] = None,
+        seed: int = 0,
+        dtype: jnp.dtype = jnp.float32,
+        **kwargs
+    ):
+        if input_shape is None:
+            input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))
+
+        module = self.module_class(config=config, dtype=dtype, **kwargs)
+        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)
+
+    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
+        # init input tensor
+        input_ids = jnp.zeros(input_shape[0], dtype="i4")
+        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0])
+        token_type_ids = jnp.ones_like(input_ids)
+        attention_mask = jnp.ones_like(input_ids)
+
+        pixel_values = jax.random.normal(rng, input_shape[1])
+
+        params_rng, dropout_rng = jax.random.split(rng)
+        rngs = {"params": params_rng, "dropout": dropout_rng}
+
+        return self.module.init(rngs, input_ids, pixel_values, attention_mask, position_ids, token_type_ids)["params"]
+
+    def __call__(
+        self,
+        input_ids,
+        pixel_values,
+        attention_mask=None,
+        position_ids=None,
+        token_type_ids=None,
+        params: dict = None,
+        dropout_rng: jax.random.PRNGKey = None,
+        train: bool = False,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
+
+        if position_ids is None:
+            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
+
+        if token_type_ids is None:
+            token_type_ids = jnp.zeros_like(input_ids)
+
+        if attention_mask is None:
+            attention_mask = jnp.ones_like(input_ids)
+
+        # Handle any PRNG if needed
+        rngs = {}
+        if dropout_rng is not None:
+            rngs["dropout"] = dropout_rng
+
+        return self.module.apply(
+            {"params": params or self.params},
+            jnp.array(input_ids, dtype="i4"),
+            jnp.array(pixel_values, dtype=jnp.float32),
+            jnp.array(attention_mask, dtype="i4"),
+            jnp.array(position_ids, dtype="i4"),
+            jnp.array(token_type_ids, dtype="i4"),
+            not train,
+            output_attentions,
+            output_hidden_states,
+            return_dict,
+            rngs=rngs,
+        )
+
+    def get_text_features(
+        self,
+        input_ids,
+        attention_mask=None,
+        position_ids=None,
+        token_type_ids=None,
+        params: dict = None,
+        dropout_rng: jax.random.PRNGKey = None,
+        train=False,
+    ):
+        r"""
+        Args:
+            input_ids (:obj:`numpy.ndarray` of shape :obj:`(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 :class:`~transformers.PreTrainedTokenizer`. See
+                :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
+                for details.
+
+                `What are input IDs? <../glossary.html#input-ids>`__
+
+        Returns:
+            text_features (:obj:`jnp.ndarray` of shape :obj:`(batch_size, output_dim`): The text embeddings obtained by
+            applying the projection layer to the pooled output of text model.
+        """
+        if position_ids is None:
+            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
+
+        if token_type_ids is None:
+            token_type_ids = jnp.zeros_like(input_ids)
+
+        if attention_mask is None:
+            attention_mask = jnp.ones_like(input_ids)
+
+        # Handle any PRNG if needed
+        rngs = {}
+        if dropout_rng is not None:
+            rngs["dropout"] = dropout_rng
+
+        def _get_features(module, input_ids, attention_mask, position_ids, token_type_ids, deterministic):
+            text_outputs = module.text_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                token_type_ids=token_type_ids,
+                deterministic=deterministic,
+            )
+            pooled_output = text_outputs[1]
+            text_features = module.text_projection(pooled_output)
+            return text_features
+
+        return self.module.apply(
+            {"params": params or self.params},
+            jnp.array(input_ids, dtype="i4"),
+            jnp.array(attention_mask, dtype="i4"),
+            jnp.array(position_ids, dtype="i4"),
+            jnp.array(token_type_ids, dtype="i4"),
+            not train,
+            method=_get_features,
+            rngs=rngs,
+        )
+
+    def get_image_features(
+        self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False
+    ):
+        r"""
+        Args:
+            pixel_values (:obj:`numpy.ndarray` of shape :obj:`(batch_size, num_channels, height, width)`):
+                Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained
+                using :class:`~transformers.ImageFeatureExtractionMixin`. See
+                :meth:`transformers.ImageFeatureExtractionMixin.__call__` for details.
+
+        Returns:
+            image_features (:obj:`jnp.ndarray` of shape :obj:`(batch_size, output_dim`): The image embeddings obtained
+            by applying the projection layer to the pooled output of vision model.
+        """
+
+        # Handle any PRNG if needed
+        rngs = {}
+        if dropout_rng is not None:
+            rngs["dropout"] = dropout_rng
+
+        def _get_features(module, pixel_values, deterministic):
+            vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic)
+            pooled_output = vision_outputs[1]  # pooled_output
+            image_features = module.visual_projection(pooled_output)
+            return image_features
+
+        return self.module.apply(
+            {"params": params or self.params},
+            jnp.array(pixel_values, dtype=jnp.float32),
+            not train,
+            method=_get_features,
+            rngs=rngs,
+        )
+
+    @classmethod
+    def from_vision_text_pretrained(
+        cls,
+        vision_model_name_or_path: str = None,
+        text_model_name_or_path: str = None,
+        *model_args,
+        **kwargs,
+    ) -> FlaxPreTrainedModel:
+        """
+        Params:
+            vision_model_name_or_path (:obj: `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
+                      :func:`~transformers.FlaxPreTrainedModel.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 :obj:`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 conversion scripts and loading the Flax model afterwards.
+
+            text_model_name_or_path (:obj: `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
+                      :func:`~transformers.FlaxPreTrainedModel.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 :obj:`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 conversion scripts and loading the Flax model afterwards.
+
+            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.,
+                :obj:`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 :obj:`config` is provided or automatically loaded.
+
+        Example::
+
+            >>> from transformers import FlaxVisionTextDualEncoderModel
+            >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized.
+            >>> model =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained('bert-base-uncased', 'google/vit-base-patch16-224')
+            >>> # saving model after fine-tuning
+            >>> model.save_pretrained("./vit-bert")
+            >>> # load fine-tuned model
+            >>> model =  FlaxVisionTextDualEncoderModel.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 text, vision 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 text and vision 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"
+                )
+
+            if "config" not in kwargs_vision:
+                vision_config = AutoConfig.from_pretrained(vision_model_name_or_path)
+
+            if vision_config.model_type == "clip":
+                kwargs_vision["config"] = vision_config.vision_config
+                vision_model = FlaxCLIPVisionModel.from_pretrained(
+                    vision_model_name_or_path, *model_args, **kwargs_vision
+                )
+            else:
+                kwargs_vision["config"] = vision_config
+                vision_model = FlaxAutoModel.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"
+                )
+
+            if "config" not in kwargs_text:
+                text_config = AutoConfig.from_pretrained(text_model_name_or_path)
+                kwargs_text["config"] = text_config
+
+            text_model = FlaxAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text)
+
+        # instantiate config with corresponding kwargs
+        dtype = kwargs.pop("dtype", jnp.float32)
+        config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs)
+
+        # init model
+        model = cls(config, *model_args, dtype=dtype, **kwargs)
+
+        model.params["vision_model"] = vision_model.params
+        model.params["text_model"] = text_model.params
+
+        # 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', 'kernel'), ('text_projection', 'kernel'), ('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."
+        )
+
+        return model
+
+
+VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING = r"""
+    Returns:
+
+    Examples::
+
+        >>> from PIL import Image
+        >>> import requests
+        >>> import jax
+        >>> from transformers import FlaxVisionTextDualEncoderModel, VisionTextDualEncoderProcessor, ViTFeatureExtractor, BertTokenizer
+
+        >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+        >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
+        >>> processor = VisionTextDualEncoderProcessor(feature_extractor, tokenizer)
+        >>> model = FlaxVisionTextDualEncoderModel.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="np", 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_imag # this is the image-text similarity score
+
+        >>> # save and load from pretrained
+        >>> model.save_pretrained("vit-bert")
+        >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("vit-bert")
+
+        >>> # inference
+        >>> outputs = model(**inputs)
+        >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+        >>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities
+
+"""
+
+overwrite_call_docstring(
+    FlaxVisionTextDualEncoderModel,
+    VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING + VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING,
+)
+append_replace_return_docstrings(
+    FlaxVisionTextDualEncoderModel, output_type=FlaxCLIPOutput, config_class=_CONFIG_FOR_DOC
+)

src/transformers/models/vision_text_dual_encoder/modeling_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.
+""" PyTorch VisionTextDualEncoder model. """
+
+
+from typing import Optional
+
+import torch
+from torch import nn
+
+from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
+from ...modeling_utils import PreTrainedModel
+from ...utils import logging
+from ..auto.configuration_auto import AutoConfig
+from ..auto.modeling_auto import AutoModel
+from ..clip.modeling_clip import CLIPOutput, CLIPVisionConfig, CLIPVisionModel
+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 :meth:`~transformers.AutoModel.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 :class:`~transformers.PreTrainedModel`. 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 PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
+    subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
+    general usage and behavior.
+
+    Parameters:
+        config (:class:`~transformers.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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
+            weights.
+"""
+
+
+VISION_TEXT_DUAL_ENCODER_TEXT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (:obj:`torch.LongTensor` of shape :obj:`(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 :class:`~transformers.PreTrainedTokenizer`. See
+            :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
+            details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+        attention_mask (:obj:`torch.Tensor` of shape :obj:`(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.html#attention-mask>`__
+        position_ids (:obj:`torch.LongTensor` of shape :obj:`(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.html#position-ids>`_
+        output_attentions (:obj:`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 (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
+"""
+
+VISION_TEXT_DUAL_ENCODER_VISION_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            :class:`~transformers.CLIPFeatureExtractor`. See :meth:`transformers.CLIPFeatureExtractor.__call__` for
+            details.
+        output_attentions (:obj:`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 (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
+"""
+
+VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (:obj:`torch.LongTensor` of shape :obj:`(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 :class:`~transformers.CLIPTokenizer`. See
+            :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
+            details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+        attention_mask (:obj:`torch.Tensor` of shape :obj:`(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.html#attention-mask>`__
+        position_ids (:obj:`torch.LongTensor` of shape :obj:`(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.html#position-ids>`_
+        pixel_values (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            a feature extractor (e.g. if you use ViT as the encoder, you should use
+            :class:`~transformers.ViTFeatureExtractor`). See :meth:`transformers.ViTFeatureExtractor.__call__` for
+            details.
+        return_loss (:obj:`bool`, `optional`):
+            Whether or not to return the contrastive loss.
+        output_attentions (:obj:`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 (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
+"""
+
+
+# Copied from transformers.models.clip.modeling_clip.contrastive_loss
+def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
+    return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
+
+
+# Copied from transformers.models.clip.modeling_clip.clip_loss
+def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
+    caption_loss = contrastive_loss(similarity)
+    image_loss = contrastive_loss(similarity.T)
+    return (caption_loss + image_loss) / 2.0
+
+
+@add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING)
+class VisionTextDualEncoderModel(PreTrainedModel):
+    config_class = VisionTextDualEncoderConfig
+    base_model_prefix = "vision_text_dual_encoder"
+
+    def __init__(
+        self,
+        config: Optional[VisionTextDualEncoderConfig] = None,
+        vision_model: Optional[PreTrainedModel] = None,
+        text_model: Optional[PreTrainedModel] = 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 = CLIPVisionModel(config.vision_config)
+            else:
+                vision_model = AutoModel.from_config(config.vision_config)
+
+        if text_model is None:
+            text_model = AutoModel.from_config(config.text_config)
+
+        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 = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
+        self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
+        self.logit_scale = nn.Parameter(torch.ones([]) * self.config.logit_scale_init_value)
+
+    @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,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        Returns:
+            text_features (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, output_dim`): The text embeddings
+            obtained by applying the projection layer to the pooled output of :class:`~transformers.CLIPTextModel`.
+
+        Examples::
+
+            >>> from transformers import VisionTextDualEncoderModel, AutoTokenizer
+
+            >>> model = VisionTextDualEncoderModel.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,
+            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 (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, output_dim`): The image embeddings
+            obtained by applying the projection layer to the pooled output of :class:`~transformers.CLIPVisionModel`.
+
+        Examples::
+
+            >>> from PIL import Image
+            >>> import requests
+            >>> from transformers import VisionTextDualEncoderModel, AutoFeatureExtractor
+
+            >>> model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian")
+            >>> feature_extractor = AutoFeatureExtractor.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 = feature_extractor(images=image, return_tensors="pt")
+
+            >>> 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
+
+    @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CLIPOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids=None,
+        pixel_values=None,
+        attention_mask=None,
+        position_ids=None,
+        return_loss=None,
+        token_type_ids=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        Returns:
+
+        Examples::
+
+            >>> from PIL import Image
+            >>> import requests
+            >>> from transformers import VisionTextDualEncoderModel, VisionTextDualEncoderProcessor, ViTFeatureExtractor, BertTokenizer
+
+            >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+            >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
+            >>> processor = VisionTextDualEncoderProcessor(feature_extractor, tokenizer)
+            >>> model = VisionTextDualEncoderModel.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_imag # this is the image-text similarity score
+
+            >>> # save and load from pretrained
+            >>> model.save_pretrained("vit-bert")
+            >>> model = VisionTextDualEncoderModel.from_pretrained("vit-bert")
+
+            >>> # inference
+            >>> outputs = model(**inputs)
+            >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+            >>> probs = logits_per_image.softmax(dim=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,
+        )
+
+        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,
+        )
+
+        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 / image_embeds.norm(dim=-1, keepdim=True)
+        text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)
+
+        # cosine similarity as logits
+        logit_scale = self.logit_scale.exp()
+        logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
+        logits_per_image = logits_per_text.T
+
+        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 CLIPOutput(
+            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_pretrained(cls, *args, **kwargs):
+        # At the moment fast initialization is not supported
+        # for composite models
+        kwargs["_fast_init"] = False
+        return super().from_pretrained(*args, **kwargs)
+
+    @classmethod
+    def from_vision_text_pretrained(
+        cls,
+        vision_model_name_or_path: str = None,
+        text_model_name_or_path: str = None,
+        *model_args,
+        **kwargs,
+    ) -> PreTrainedModel:
+        """
+        Params:
+            vision_model_name_or_path (:obj: `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
+                      :func:`~transformers.FlaxPreTrainedModel.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 :obj:`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 conversion scripts and loading the Flax model afterwards.
+
+            text_model_name_or_path (:obj: `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
+                      :func:`~transformers.FlaxPreTrainedModel.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 :obj:`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 conversion scripts and loading the Flax model afterwards.
+
+            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.,
+                :obj:`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 :obj:`config` is provided or automatically loaded.
+
+        Example::
+
+            >>> from transformers import VisionTextDualEncoderModel
+            >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized.
+            >>> model = VisionTextDualEncoderModel.from_vision_text_pretrained('bert-base-uncased', 'google/vit-base-patch16-224')
+            >>> # saving model after fine-tuning
+            >>> model.save_pretrained("./vit-bert")
+            >>> # load fine-tuned model
+            >>> model = VisionTextDualEncoderModel.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"
+                )
+
+            if "config" not in kwargs_vision:
+                vision_config = AutoConfig.from_pretrained(vision_model_name_or_path)
+
+            if vision_config.model_type == "clip":
+                kwargs_vision["config"] = vision_config.vision_config
+                vision_model = CLIPVisionModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision)
+                # TODO: Should we use the pre-trained projection as well ?
+            else:
+                kwargs_vision["config"] = vision_config
+                vision_model = AutoModel.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"
+                )
+
+            if "config" not in kwargs_text:
+                text_config = AutoConfig.from_pretrained(text_model_name_or_path)
+                kwargs_text["config"] = text_config
+
+            text_model = AutoModel.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."
+        )
+
+        return model

src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py

+# coding=utf-8
+# Copyright 2021 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.
+"""
+Processor class for VisionTextDualEncoder
+"""
+from typing import Union
+
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
+from transformers.feature_extraction_utils import FeatureExtractionMixin
+
+from ...tokenization_utils_base import BatchEncoding
+from ..auto.feature_extraction_auto import AutoFeatureExtractor
+from ..auto.tokenization_auto import AutoTokenizer
+
+
+class VisionTextDualEncoderProcessor:
+    r"""
+    Constructs a VisionTextDualEncoder processor which wraps a vision feature extractor and a tokenizer into a single
+    processor.
+
+    :class:`~transformers.VisionTextDualEncoderProcessor` offers all the functionalities of
+    :class:`~transformers.AutoFeatureExtractor` and :class:`~transformers.AutoTokenizer`. See the
+    :meth:`~transformers.VisionTextDualEncoderProcessor.__call__` and
+    :meth:`~transformers.VisionTextDualEncoderProcessor.decode` for more information.
+
+    Args:
+        feature_extractor (:class:`~transformers.AutoFeatureExtractor`):
+            The feature extractor is a required input.
+        tokenizer (:class:`~transformers.PreTrainedTokenizer`):
+            The tokenizer is a required input.
+    """
+
+    def __init__(
+        self, feature_extractor: FeatureExtractionMixin, tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
+    ):
+        if not isinstance(feature_extractor, FeatureExtractionMixin):
+            raise ValueError(
+                f"`feature_extractor` has to be of type {FeatureExtractionMixin.__class__}, but is {type(feature_extractor)}"
+            )
+        if not isinstance(tokenizer, (PreTrainedTokenizer, PreTrainedTokenizerFast)):
+            raise ValueError(
+                f"`tokenizer` has to be of type `PreTrainedTokenizer` or `PreTrainedTokenizerFast`, but is {type(tokenizer)}"
+            )
+
+        self.feature_extractor = feature_extractor
+        self.tokenizer = tokenizer
+        self.current_processor = self.feature_extractor
+
+    def save_pretrained(self, save_directory):
+        """
+        Save a VisionTextDualEncoder feature extractor object and VisionTextDualEncoder tokenizer object to the
+        directory ``save_directory``, so that it can be re-loaded using the
+        :func:`~transformers.VisionTextDualEncoderProcessor.from_pretrained` class method.
+
+        .. note::
+
+            This class method is simply calling :meth:`~transformers.PreTrainedFeatureExtractor.save_pretrained` and
+            :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.save_pretrained`. Please refer to the
+            docstrings of the methods above for more information.
+
+        Args:
+            save_directory (:obj:`str` or :obj:`os.PathLike`):
+                Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
+                be created if it does not exist).
+        """
+
+        self.feature_extractor.save_pretrained(save_directory)
+        self.tokenizer.save_pretrained(save_directory)
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        r"""
+        Instantiate a :class:`~transformers.VisionTextDualEncoderProcessor` from a pretrained VisionTextDualEncoder
+        processor.
+
+        .. note::
+
+            This class method is simply calling AutoFeatureExtractor's
+            :meth:`~transformers.PreTrainedFeatureExtractor.from_pretrained` and AutoTokenizer's
+            :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.from_pretrained`. Please refer to the
+            docstrings of the methods above for more information.
+
+        Args:
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+                This can be either:
+
+                - a string, the `model id` of a pretrained feature_extractor 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 a feature extractor file saved using the
+                  :meth:`~transformers.PreTrainedFeatureExtractor.save_pretrained` method, e.g.,
+                  ``./my_model_directory/``.
+                - a path or url to a saved feature extractor JSON `file`, e.g.,
+                  ``./my_model_directory/preprocessor_config.json``.
+
+            **kwargs
+                Additional keyword arguments passed along to both :class:`~transformers.PreTrainedFeatureExtractor` and
+                :class:`~transformers.PreTrainedTokenizer`
+        """
+        feature_extractor = AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+        return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
+
+    def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the
+        :obj:`text` and :obj:`kwargs` arguments to VisionTextDualEncoderTokenizer's
+        :meth:`~transformers.PreTrainedTokenizer.__call__` if :obj:`text` is not :obj:`None` to encode the text. To
+        prepare the image(s), this method forwards the :obj:`images` and :obj:`kwrags` arguments to
+        AutoFeatureExtractor's :meth:`~transformers.AutoFeatureExtractor.__call__` if :obj:`images` is not :obj:`None`.
+        Please refer to the doctsring of the above two methods for more information.
+
+        Args:
+            text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                :obj:`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            images (:obj:`PIL.Image.Image`, :obj:`np.ndarray`, :obj:`torch.Tensor`, :obj:`List[PIL.Image.Image]`, :obj:`List[np.ndarray]`, :obj:`List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
+                number of channels, H and W are image height and width.
+
+            return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
+                If set, will return tensors of a particular framework. Acceptable values are:
+
+                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
+                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
+                * :obj:`'np'`: Return NumPy :obj:`np.ndarray` objects.
+                * :obj:`'jax'`: Return JAX :obj:`jnp.ndarray` objects.
+
+        Returns:
+            :class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when :obj:`text` is not :obj:`None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              :obj:`return_attention_mask=True` or if `"attention_mask"` is in :obj:`self.model_input_names` and if
+              :obj:`text` is not :obj:`None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when :obj:`images` is not :obj:`None`.
+        """
+
+        if text is None and images is None:
+            raise ValueError("You have to specify either text or images. Both cannot be none.")
+
+        if text is not None:
+            encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
+
+        if images is not None:
+            image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)
+
+        if text is not None and images is not None:
+            encoding["pixel_values"] = image_features.pixel_values
+            return encoding
+        elif text is not None:
+            return encoding
+        else:
+            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to VisionTextDualEncoderTokenizer's
+        :meth:`~transformers.PreTrainedTokenizer.batch_decode`. Please refer to the docstring of this method for more
+        information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to VisionTextDualEncoderTokenizer's
+        :meth:`~transformers.PreTrainedTokenizer.decode`. Please refer to the docstring of this method for more
+        information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)

src/transformers/utils/dummy_flax_objects.py

@@ -1292,6 +1292,18 @@ class FlaxVisionEncoderDecoderModel:
         requires_backends(self, ["flax"])
 
 
+class FlaxVisionTextDualEncoderModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    def __call__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
 class FlaxViTForImageClassification:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])

src/transformers/utils/dummy_pt_objects.py

@@ -4849,6 +4849,18 @@ class VisionEncoderDecoderModel:
         requires_backends(self, ["torch"])
 
 
+class VisionTextDualEncoderModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    def forward(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/test_modeling_flax_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.file_utils import is_flax_available, is_torch_available, is_vision_available
+from transformers.testing_utils import (
+    is_pt_flax_cross_test,
+    require_flax,
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+from .test_modeling_flax_bert import FlaxBertModelTester
+from .test_modeling_flax_clip import FlaxCLIPVisionModelTester
+from .test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
+from .test_modeling_flax_vit import FlaxViTModelTester
+
+
+if is_flax_available():
+    from transformers import (
+        FlaxBertModel,
+        FlaxCLIPVisionModel,
+        FlaxVisionTextDualEncoderModel,
+        FlaxViTModel,
+        VisionTextDualEncoderConfig,
+        VisionTextDualEncoderProcessor,
+    )
+    from transformers.modeling_flax_pytorch_utils import (
+        convert_pytorch_state_dict_to_flax,
+        load_flax_weights_in_pytorch_model,
+    )
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import VisionTextDualEncoderModel
+
+if is_vision_available():
+    from PIL import Image
+
+
+# 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_flax
+class VisionTextDualEncoderMixin:
+    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 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 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 = FlaxVisionTextDualEncoderModel(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_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 = FlaxVisionTextDualEncoderModel.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)
+        kwargs = {"vision_model": vision_model, "text_model": text_model}
+        model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
+
+        output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
+        out_1 = output[0]
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_pretrained(tmpdirname)
+            model = FlaxVisionTextDualEncoderModel.from_pretrained(tmpdirname)
+
+            after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
+            out_2 = after_output[0]
+            max_diff = np.amax(np.abs(out_2 - out_1))
+            self.assertLessEqual(max_diff, 1e-3)
+
+    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)
+        kwargs = {"vision_model": vision_model, "text_model": text_model}
+        model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
+
+        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 check_pt_flax_equivalence(self, pt_model, fx_model, inputs_dict):
+
+        pt_model.to(torch_device)
+        pt_model.eval()
+
+        # prepare inputs
+        flax_inputs = inputs_dict
+        pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}
+
+        with torch.no_grad():
+            pt_outputs = pt_model(**pt_inputs).to_tuple()
+
+        fx_outputs = fx_model(**inputs_dict).to_tuple()
+        self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
+        for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
+            self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
+
+        # PT -> Flax
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            pt_model.save_pretrained(tmpdirname)
+            fx_model_loaded = FlaxVisionTextDualEncoderModel.from_pretrained(tmpdirname, from_pt=True)
+
+        fx_outputs_loaded = fx_model_loaded(**inputs_dict).to_tuple()
+        self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
+        for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]):
+            self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)
+
+        # Flax -> PT
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            fx_model.save_pretrained(tmpdirname)
+            pt_model_loaded = VisionTextDualEncoderModel.from_pretrained(tmpdirname, from_flax=True)
+
+        pt_model_loaded.to(torch_device)
+        pt_model_loaded.eval()
+
+        with torch.no_grad():
+            pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
+
+        self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
+        for fx_output, pt_output_loaded in zip(fx_outputs[:4], pt_outputs_loaded[:4]):
+            self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 4e-2)
+
+    def check_equivalence_pt_to_flax(self, vision_config, text_config, inputs_dict):
+
+        config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
+
+        pt_model = VisionTextDualEncoderModel(config)
+        fx_model = FlaxVisionTextDualEncoderModel(config)
+
+        fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
+        fx_model.params = fx_state
+
+        self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict)
+
+    def check_equivalence_flax_to_pt(self, vision_config, text_config, inputs_dict):
+
+        config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
+
+        pt_model = VisionTextDualEncoderModel(config)
+        fx_model = FlaxVisionTextDualEncoderModel(config)
+
+        pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
+
+        self.check_pt_flax_equivalence(pt_model, fx_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)
+
+    @is_pt_flax_cross_test
+    def test_pt_flax_equivalence(self):
+
+        config_inputs_dict = self.prepare_config_and_inputs()
+        vision_config = config_inputs_dict.pop("vision_config")
+        text_config = config_inputs_dict.pop("text_config")
+
+        inputs_dict = config_inputs_dict
+
+        self.check_equivalence_pt_to_flax(vision_config, text_config, inputs_dict)
+        self.check_equivalence_flax_to_pt(vision_config, text_config, 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]
+
+        with tempfile.TemporaryDirectory() as tmp_dirname:
+            model_2.save_pretrained(tmp_dirname)
+            model_1 = FlaxVisionTextDualEncoderModel.from_pretrained(tmp_dirname)
+
+            after_outputs = model_1(**inputs)
+            out_1 = after_outputs[0]
+            max_diff = np.amax(np.abs(out_1 - out_2))
+            self.assertLessEqual(max_diff, 1e-5)
+
+
+@require_flax
+class FlaxViTBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
+    def get_pretrained_model_and_inputs(self):
+        model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
+            "hf-internal-testing/tiny-random-vit",
+            "hf-internal-testing/tiny-bert",
+            vision_from_pt=True,
+            text_from_pt=True,
+        )
+        batch_size = 13
+        pixel_values = floats_tensor(
+            [
+                batch_size,
+                model.config.vision_config.num_channels,
+                model.config.vision_config.image_size,
+                model.config.vision_config.image_size,
+            ]
+        )
+        input_ids = ids_tensor([batch_size, 4], model.config.text_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 = FlaxViTModel(vision_config)
+        text_model = FlaxBertModel(text_config)
+        return vision_model, text_model
+
+    def prepare_config_and_inputs(self):
+        vit_model_tester = FlaxViTModelTester(self)
+        bert_model_tester = FlaxBertModelTester(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, attention_mask = text_config_and_inputs
+
+        # make sure that cross attention layers are added
+        return {
+            "text_config": text_config,
+            "vision_config": vision_config,
+            "pixel_values": pixel_values,
+            "attention_mask": attention_mask,
+            "text_config": text_config,
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+        }
+
+
+@require_torch
+class FlaxCLIPVisionBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
+    def get_pretrained_model_and_inputs(self):
+        model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
+            "hf-internal-testing/tiny-random-clip",
+            "hf-internal-testing/tiny-bert",
+            vision_from_pt=True,
+            text_from_pt=True,
+        )
+        batch_size = 13
+        pixel_values = floats_tensor(
+            [
+                batch_size,
+                model.config.vision_config.num_channels,
+                model.config.vision_config.image_size,
+                model.config.vision_config.image_size,
+            ]
+        )
+        input_ids = ids_tensor([batch_size, 4], model.config.text_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 = FlaxCLIPVisionModel(vision_config)
+        text_model = FlaxBertModel(text_config)
+        return vision_model, text_model
+
+    def prepare_config_and_inputs(self):
+        clip_model_tester = FlaxCLIPVisionModelTester(self)
+        bert_model_tester = FlaxBertModelTester(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, attention_mask = text_config_and_inputs
+
+        # make sure that cross attention layers are added
+        return {
+            "text_config": text_config,
+            "vision_config": vision_config,
+            "pixel_values": pixel_values,
+            "attention_mask": attention_mask,
+            "text_config": text_config,
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+        }
+
+
+@require_flax
+@require_vision
+class FlaxVisionTextDualEncoderIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model = FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", logit_scale_init_value=1)
+        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, expected_logits, atol=1e-3))

tests/test_modeling_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.file_utils import is_flax_available, is_torch_available, is_vision_available
+from transformers.testing_utils import is_pt_flax_cross_test, require_torch, require_vision, slow, torch_device
+
+from .test_modeling_bert import BertModelTester
+from .test_modeling_clip import CLIPVisionModelTester
+from .test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
+from .test_modeling_deit import DeiTModelTester
+from .test_modeling_roberta import RobertaModelTester
+from .test_modeling_vit import ViTModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        BertModel,
+        CLIPVisionModel,
+        DeiTModel,
+        RobertaModel,
+        VisionTextDualEncoderConfig,
+        VisionTextDualEncoderModel,
+        ViTModel,
+    )
+
+if is_flax_available():
+    from transformers import FlaxVisionTextDualEncoderModel
+    from transformers.modeling_flax_pytorch_utils import (
+        convert_pytorch_state_dict_to_flax,
+        load_flax_weights_in_pytorch_model,
+    )
+
+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_torch
+class VisionTextDualEncoderMixin:
+    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 = VisionTextDualEncoderModel(config)
+        model.to(torch_device)
+        model.eval()
+
+        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 = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
+        model.to(torch_device)
+        model.eval()
+
+        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 = VisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
+        model.to(torch_device)
+        model.eval()
+
+        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 = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
+        model.to(torch_device)
+        model.eval()
+
+        with torch.no_grad():
+            output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
+            out_1 = output[0].cpu().numpy()
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+                model = VisionTextDualEncoderModel.from_pretrained(tmpdirname).eval()
+                model.to(torch_device)
+
+                after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
+                out_2 = after_output[0].cpu().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 = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
+        model.to(torch_device)
+        model.eval()
+
+        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 check_pt_flax_equivalence(self, pt_model, fx_model, input_ids, attention_mask, pixel_values, **kwargs):
+
+        pt_model.to(torch_device)
+        pt_model.eval()
+
+        # prepare inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values}
+        pt_inputs = inputs_dict
+        flax_inputs = {k: v.numpy() for k, v in pt_inputs.items()}
+
+        with torch.no_grad():
+            pt_outputs = pt_model(**pt_inputs).to_tuple()
+
+        fx_outputs = fx_model(**flax_inputs).to_tuple()
+        self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
+        for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
+            self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
+
+        # PT -> Flax
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            pt_model.save_pretrained(tmpdirname)
+            fx_model_loaded = FlaxVisionTextDualEncoderModel.from_pretrained(tmpdirname, from_pt=True)
+
+        fx_outputs_loaded = fx_model_loaded(**flax_inputs).to_tuple()
+        self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
+        for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]):
+            self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)
+
+        # Flax -> PT
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            fx_model.save_pretrained(tmpdirname)
+            pt_model_loaded = VisionTextDualEncoderModel.from_pretrained(tmpdirname, from_flax=True)
+
+        pt_model_loaded.to(torch_device)
+        pt_model_loaded.eval()
+
+        with torch.no_grad():
+            pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
+
+        self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
+        for fx_output, pt_output_loaded in zip(fx_outputs[:4], pt_outputs_loaded[:4]):
+            self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 4e-2)
+
+    def check_equivalence_pt_to_flax(self, vision_config, text_config, inputs_dict):
+
+        config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
+
+        pt_model = VisionTextDualEncoderModel(config)
+        fx_model = FlaxVisionTextDualEncoderModel(config)
+
+        fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
+        fx_model.params = fx_state
+
+        self.check_pt_flax_equivalence(pt_model, fx_model, **inputs_dict)
+
+    def check_equivalence_flax_to_pt(self, vision_config, text_config, inputs_dict):
+
+        config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
+
+        pt_model = VisionTextDualEncoderModel(config)
+        fx_model = FlaxVisionTextDualEncoderModel(config)
+
+        pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
+
+        self.check_pt_flax_equivalence(pt_model, fx_model, **inputs_dict)
+
+    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)
+
+    @is_pt_flax_cross_test
+    def test_pt_flax_equivalence(self):
+
+        config_inputs_dict = self.prepare_config_and_inputs()
+        vision_config = config_inputs_dict.pop("vision_config")
+        text_config = config_inputs_dict.pop("text_config")
+
+        inputs_dict = config_inputs_dict
+
+        self.check_equivalence_pt_to_flax(vision_config, text_config, inputs_dict)
+        self.check_equivalence_flax_to_pt(vision_config, text_config, inputs_dict)
+
+    @slow
+    def test_real_model_save_load_from_pretrained(self):
+        model_2, inputs = self.get_pretrained_model_and_inputs()
+        model_2.to(torch_device)
+
+        with torch.no_grad():
+            outputs = model_2(**inputs)
+            out_2 = outputs[0].cpu().numpy()
+
+            with tempfile.TemporaryDirectory() as tmp_dirname:
+                model_2.save_pretrained(tmp_dirname)
+                model_1 = VisionTextDualEncoderModel.from_pretrained(tmp_dirname)
+                model_1.to(torch_device)
+
+                after_outputs = model_1(**inputs)
+                out_1 = after_outputs[0].cpu().numpy()
+                max_diff = np.amax(np.abs(out_1 - out_2))
+                self.assertLessEqual(max_diff, 1e-5)
+
+
+@require_torch
+class ViTBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
+    def get_pretrained_model_and_inputs(self):
+        model = VisionTextDualEncoderModel.from_vision_text_pretrained(
+            "hf-internal-testing/tiny-random-vit", "hf-internal-testing/tiny-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 = ViTModel(vision_config).eval()
+        text_model = BertModel(text_config).eval()
+        return vision_model, text_model
+
+    def prepare_config_and_inputs(self):
+        vit_model_tester = ViTModelTester(self)
+        bert_model_tester = BertModelTester(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,
+            "text_config": text_config,
+            "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_torch
+class DeiTRobertaModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
+    def get_pretrained_model_and_inputs(self):
+        model = VisionTextDualEncoderModel.from_vision_text_pretrained(
+            "hf-internal-testing/tiny-random-deit", "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 = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
+        model.to(torch_device)
+        model.eval()
+
+        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 = DeiTModel(vision_config).eval()
+        text_model = RobertaModel(text_config).eval()
+        return vision_model, text_model
+
+    def prepare_config_and_inputs(self):
+        vit_model_tester = DeiTModelTester(self)
+        bert_model_tester = RobertaModelTester(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,
+            "text_config": text_config,
+            "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,
+        }
+
+    # skip as DeiT is not available in Flax
+    def test_pt_flax_equivalence(self):
+        pass
+
+
+@require_torch
+class CLIPVisionBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase):
+    def get_pretrained_model_and_inputs(self):
+        model = VisionTextDualEncoderModel.from_vision_text_pretrained(
+            "hf-internal-testing/tiny-random-clip", "hf-internal-testing/tiny-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 = CLIPVisionModel(vision_config).eval()
+        text_model = BertModel(text_config).eval()
+        return vision_model, text_model
+
+    def prepare_config_and_inputs(self):
+        clip_model_tester = CLIPVisionModelTester(self)
+        bert_model_tester = BertModelTester(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,
+            "text_config": text_config,
+            "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_torch
+class VisionTextDualEncoderIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", logit_scale_init_value=1)
+        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="pt"
+        )
+
+        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 = torch.tensor([[1.2284727, 0.3104122]])
+
+        self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))

tests/test_processor_vision_text_dual_encoder.py

+# Copyright 2021 The HuggingFace 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.
+
+import json
+import os
+import shutil
+import tempfile
+import unittest
+
+import numpy as np
+
+from transformers import BertTokenizerFast
+from transformers.file_utils import FEATURE_EXTRACTOR_NAME, is_vision_available
+from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
+from transformers.testing_utils import require_tokenizers, require_vision
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import VisionTextDualEncoderProcessor, ViTFeatureExtractor
+
+
+@require_tokenizers
+@require_vision
+class VisionTextDualEncoderProcessorTest(unittest.TestCase):
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        # fmt: off
+        vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
+        # fmt: on
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+
+        feature_extractor_map = {
+            "do_resize": True,
+            "size": 18,
+            "do_normalize": True,
+            "image_mean": [0.5, 0.5, 0.5],
+            "image_std": [0.5, 0.5, 0.5],
+        }
+        self.feature_extractor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
+        with open(self.feature_extractor_file, "w", encoding="utf-8") as fp:
+            json.dump(feature_extractor_map, fp)
+
+    def get_tokenizer(self, **kwargs):
+        return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_feature_extractor(self, **kwargs):
+        return ViTFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def prepare_image_inputs(self):
+        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
+        or a list of PyTorch tensors if one specifies torchify=True.
+        """
+
+        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
+
+        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
+
+        return image_inputs
+
+    def test_save_load_pretrained_default(self):
+        tokenizer = self.get_tokenizer()
+        feature_extractor = self.get_feature_extractor()
+
+        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        processor.save_pretrained(self.tmpdirname)
+        processor = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname)
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
+        self.assertIsInstance(processor.tokenizer, (BertTokenizer, BertTokenizerFast))
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, ViTFeatureExtractor)
+
+    def test_save_load_pretrained_additional_features(self):
+        processor = VisionTextDualEncoderProcessor(
+            tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
+        )
+        processor.save_pretrained(self.tmpdirname)
+
+        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
+        feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
+
+        processor = VisionTextDualEncoderProcessor.from_pretrained(
+            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
+        )
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
+        self.assertIsInstance(processor.tokenizer, (BertTokenizer, BertTokenizerFast))
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, ViTFeatureExtractor)
+
+    def test_feature_extractor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        image_input = self.prepare_image_inputs()
+
+        input_feat_extract = feature_extractor(image_input, return_tensors="np")
+        input_processor = processor(images=image_input, return_tensors="np")
+
+        for key in input_feat_extract.keys():
+            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+    def test_tokenizer(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        input_str = "lower newer"
+
+        encoded_processor = processor(text=input_str)
+
+        encoded_tok = tokenizer(input_str)
+
+        for key in encoded_tok.keys():
+            self.assertListEqual(encoded_tok[key], encoded_processor[key])
+
+    def test_processor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        input_str = "lower newer"
+        image_input = self.prepare_image_inputs()
+
+        inputs = processor(text=input_str, images=image_input)
+
+        self.assertListEqual(list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"])
+
+        # test if it raises when no input is passed
+        with self.assertRaises(ValueError):
+            processor()
+
+    def test_tokenizer_decode(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
+
+        decoded_processor = processor.batch_decode(predicted_ids)
+        decoded_tok = tokenizer.batch_decode(predicted_ids)
+
+        self.assertListEqual(decoded_tok, decoded_processor)

utils/check_repo.py

@@ -94,6 +94,8 @@ TEST_FILES_WITH_NO_COMMON_TESTS = [
     "test_modeling_tf_xlm_roberta.py",
     "test_modeling_xlm_prophetnet.py",
     "test_modeling_xlm_roberta.py",
+    "test_modeling_vision_text_dual_encoder.py",
+    "test_modeling_flax_vision_text_dual_encoder.py",
 ]
 
 # Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and