[TensorFlow] Adding GroupViT (#18020)

* chore: initial commit

* chore: adding util methods

yet to work on the nn.functional.interpolate port with align_corener=True

* chore: refactor the utils

* used tf.compat.v1.image.resize to align the F.interpolate function
* added type hints to the method signatures
* added references to the gists where one 2 one alignment of torch and tf has been shown

* chore: adding the layers

* chore: porting all the layers from torch to tf

This is the initial draft, nothing is tested yet.

* chore: aligning the layers with reference to tf clip

* chore: aligning the modules

* added demaraction comments
* added copied and adapted from comments

* chore: aligning with CLIP

* chore: wrangling the layers to keep it tf compatible

* chore: aligning the names of the layers for porting

* chore: style changes

* chore: adding docs and inits

* chore: adding tfp dependencis

the code is taken from TAPAS

* chore: initial commit for testing

* chore: aligning the vision embeddings with the vit implementatino

* chore: changing model prefix

* chore: fixing the name of the model and the layer normalization test case

* chore: every test passes but the slow ones

* chore: fix style and integration test

* chore: moving comments below decorators

* chore: make fixup and fix-copies changes

* chore: adding the Vision and Text Model to check_repo

* chore: modifying the prefix name to align it with the torch implementation

* chore: fix typo in configuration

* choer: changing the name of the model variable

* chore: adding segmentation flag

* chore: gante's review

* chore: style refactor

* chore: amy review

* chore: adding shape_list to parts that have been copied from other snippets

* chore: init batchnorm with torch defaults

* chore: adding shape_list to pass the tests

* test fix: adding seed as 0

* set seed

* chore: changing the straight through trick to fix -ve dimensinos

* chore: adding a dimension to the loss

* chore: adding reviewers and contributors names to the docs

* chore: added changes after review

* chore: code quality fixup

* chore: fixing the segmentation snippet

* chore: adding  to the layer calls

* chore: changing int32 to int64 for inputs of serving

* chore: review changes

* chore: style changes

* chore: remove from_pt=True

* fix: repo consistency
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

docs/source/en/index.mdx

@@ -248,7 +248,7 @@ Flax), PyTorch, and/or TensorFlow.
 |          GPT NeoX           |       ❌       |       ✅       |       ✅        |         ❌         |      ❌      |
 |      GPT NeoX Japanese      |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            GPT-J            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
-|          GroupViT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          GroupViT           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/groupvit.mdx

@@ -26,7 +26,7 @@ Tips:
 - You may specify `output_segmentation=True` in the forward of `GroupViTModel` to get the segmentation logits of input texts. 
 - The quickest way to get started with GroupViT is by checking the [example notebooks](https://github.com/xvjiarui/GroupViT/blob/main/demo/GroupViT_hf_inference_notebook.ipynb) (which showcase zero-shot segmentation inference). One can also check out the [HuggingFace Spaces demo](https://huggingface.co/spaces/xvjiarui/GroupViT) to play with GroupViT. 
 
-This model was contributed by [xvjiarui](https://huggingface.co/xvjiarui).
+This model was contributed by [xvjiarui](https://huggingface.co/xvjiarui). The TensorFlow version was contributed by [ariG23498](https://huggingface.co/ariG23498) with the help of [Yih-Dar SHIEH](https://huggingface.co/ydshieh), [Amy Roberts](https://huggingface.co/amyeroberts), and [Joao Gante](https://huggingface.co/joaogante).
 The original code can be found [here](https://github.com/NVlabs/GroupViT).
 
 
@@ -59,3 +59,20 @@ The original code can be found [here](https://github.com/NVlabs/GroupViT).
 
 [[autodoc]] GroupViTVisionModel
     - forward
+
+## TFGroupViTModel
+
+[[autodoc]] TFGroupViTModel
+    - call
+    - get_text_features
+    - get_image_features
+
+## TFGroupViTTextModel
+
+[[autodoc]] TFGroupViTTextModel
+    - call
+
+## TFGroupViTVisionModel
+
+[[autodoc]] TFGroupViTVisionModel
+    - call
\ No newline at end of file

src/transformers/__init__.py

@@ -2417,6 +2417,15 @@ else:
             "TFGPTJPreTrainedModel",
         ]
     )
+    _import_structure["models.groupvit"].extend(
+        [
+            "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFGroupViTModel",
+            "TFGroupViTPreTrainedModel",
+            "TFGroupViTTextModel",
+            "TFGroupViTVisionModel",
+        ]
+    )
     _import_structure["models.hubert"].extend(
         [
             "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -4986,6 +4995,13 @@ if TYPE_CHECKING:
             TFGPTJModel,
             TFGPTJPreTrainedModel,
         )
+        from .models.groupvit import (
+            TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFGroupViTModel,
+            TFGroupViTPreTrainedModel,
+            TFGroupViTTextModel,
+            TFGroupViTVisionModel,
+        )
         from .models.hubert import (
             TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFHubertForCTC,

src/transformers/models/auto/modeling_tf_auto.py

@@ -50,6 +50,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("funnel", ("TFFunnelModel", "TFFunnelBaseModel")),
         ("gpt2", "TFGPT2Model"),
         ("gptj", "TFGPTJModel"),
+        ("groupvit", "TFGroupViTModel"),
         ("hubert", "TFHubertModel"),
         ("layoutlm", "TFLayoutLMModel"),
         ("layoutlmv3", "TFLayoutLMv3Model"),

src/transformers/models/groupvit/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
 
 
 _import_structure = {
@@ -44,6 +44,20 @@ else:
         "GroupViTVisionModel",
     ]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_groupvit"] = [
+        "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFGroupViTModel",
+        "TFGroupViTPreTrainedModel",
+        "TFGroupViTTextModel",
+        "TFGroupViTVisionModel",
+    ]
+
 if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -67,6 +81,20 @@ if TYPE_CHECKING:
             GroupViTVisionModel,
         )
 
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_groupvit import (
+            TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFGroupViTModel,
+            TFGroupViTPreTrainedModel,
+            TFGroupViTTextModel,
+            TFGroupViTVisionModel,
+        )
+
 else:
     import sys
 

src/transformers/models/groupvit/configuration_groupvit.py

@@ -162,7 +162,7 @@ class GroupViTVisionConfig(PretrainedConfig):
             The number of layers in each encoder block.
         num_group_tokens (`List[int]`, *optional*, defaults to [64, 8, 0]):
             The number of group tokens for each stage.
-        num_output_groups (`List[int]`, *optional*, defaults to [64, 8, 0]):
+        num_output_groups (`List[int]`, *optional*, defaults to [64, 8, 8]):
             The number of output groups for each stage, 0 means no group.
         num_attention_heads (`int`, *optional*, defaults to 6):
             Number of attention heads for each attention layer in the Transformer encoder.

src/transformers/models/groupvit/modeling_groupvit.py

@@ -1300,7 +1300,7 @@ class GroupViTVisionModel(GroupViTPreTrainedModel):
         >>> import requests
         >>> from transformers import AutoProcessor, GroupViTVisionModel
 
-        >>> processor = AutoPProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
+        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
         >>> model = GroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"

src/transformers/models/groupvit/modeling_tf_groupvit.py

+# coding=utf-8
+# Copyright 2022 NVIDIA and 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.
+""" TF 2.0 GroupViT model."""
+
+
+import collections.abc
+import math
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Tuple, Union
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling
+from ...modeling_tf_utils import (
+    DUMMY_INPUTS,
+    TFModelInputType,
+    TFPreTrainedModel,
+    get_initializer,
+    keras_serializable,
+    unpack_inputs,
+)
+from ...tf_utils import shape_list, stable_softmax
+from ...utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_tensorflow_probability_available,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_groupvit import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
+
+
+logger = logging.get_logger(__name__)
+
+# soft dependency
+if is_tensorflow_probability_available():
+    try:
+        import tensorflow_probability as tfp
+
+        # On the first call, check whether a compatible version of TensorFlow is installed
+        # TensorFlow Probability depends on a recent stable release of TensorFlow
+        _ = tfp.distributions.Normal(loc=0.0, scale=1.0)
+    except ImportError:
+        logger.error(
+            "GroupViT models are not usable since `tensorflow_probability` can't be loaded."
+            "It seems you have `tensorflow_probability` installed with the wrong tensorflow version."
+            "Please try to reinstall it following the instructions here: https://github.com/tensorflow/probability."
+        )
+
+_CHECKPOINT_FOR_DOC = "nvidia/groupvit-gcc-yfcc"
+
+TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "nvidia/groupvit-gcc-yfcc",
+    # See all GroupViT models at https://huggingface.co/models?filter=groupvit
+]
+
+
+LARGE_NEGATIVE = -1e8
+
+
+# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
+def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    src_len = shape_list(mask)[1]
+    tgt_len = tgt_len if tgt_len is not None else src_len
+    one_cst = tf.constant(1.0)
+    mask = tf.cast(mask, dtype=one_cst.dtype)
+    expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
+
+    return (one_cst - expanded_mask) * LARGE_NEGATIVE
+
+
+# contrastive loss function, adapted from
+# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
+def contrastive_loss(logits: tf.Tensor) -> tf.Tensor:
+    return tf.math.reduce_mean(
+        tf.keras.metrics.sparse_categorical_crossentropy(
+            y_true=tf.range(shape_list(logits)[0]), y_pred=logits, from_logits=True
+        )
+    )
+
+
+# Copied from transformers.models.clip.modeling_tf_clip.clip_loss with clip->groupvit
+def groupvit_loss(similarity: tf.Tensor) -> tf.Tensor:
+    caption_loss = contrastive_loss(similarity)
+    image_loss = contrastive_loss(tf.transpose(similarity))
+    return (caption_loss + image_loss) / 2.0
+
+
+def hard_softmax(logits: tf.Tensor, dim: int) -> tf.Tensor:
+    y_soft = stable_softmax(logits, dim)
+    # Straight through.
+    index = tf.argmax(y_soft, dim)
+    y_hard = tf.one_hot(
+        index,
+        depth=shape_list(logits)[dim],
+        # TensorFlow expects axis to be -1 or between [0, 3).  But received: -2
+        # This is why the following code snippet is used.
+        axis=range(len(shape_list(logits)))[dim],
+        dtype=y_soft.dtype,
+    )
+    ret = y_hard - tf.stop_gradient(y_soft) + y_soft
+
+    return ret
+
+
+def gumbel_softmax(logits: tf.Tensor, tau: float = 1, hard: bool = False, dim: int = -1) -> tf.Tensor:
+    gumbel_dist = tfp.distributions.Gumbel(0.0, 1.0)
+    gumbels = gumbel_dist.sample(tf.shape(logits), dtype=logits.dtype)
+
+    gumbels = (logits + gumbels) / tau  # ~Gumbel(logits,tau)
+    y_soft = stable_softmax(gumbels, dim)
+
+    if hard:
+        # Straight through.
+        index = tf.argmax(y_soft, dim)
+        y_hard = tf.one_hot(
+            index,
+            depth=shape_list(logits)[dim],
+            # TensorFlow expects axis to be -1 or between [0, 3).  But received: -2
+            # This is why the following code snippet is used.
+            axis=range(len(shape_list(logits)))[dim],
+            dtype=y_soft.dtype,
+        )
+        ret = y_hard - tf.stop_gradient(y_soft) + y_soft
+    else:
+        # Reparametrization trick.
+        ret = y_soft
+    return ret
+
+
+def resize_attention_map(attentions: tf.Tensor, height: int, width: int, align_corners: bool = False) -> tf.Tensor:
+    """
+    Args:
+        attentions (`tf.Tensor`): attention map of shape [batch_size, groups, feat_height*feat_width]
+        height (`int`): height of the output attention map
+        width (`int`): width of the output attention map
+        align_corners (`bool`, *optional*): the `align_corner` argument for `nn.functional.interpolate`.
+
+    Returns:
+        `tf.Tensor`: resized attention map of shape [batch_size, groups, height, width]
+    """
+
+    scale = (height * width // attentions.shape[2]) ** 0.5
+    if height > width:
+        feat_width = int(np.round(width / scale))
+        feat_height = shape_list(attentions)[2] // feat_width
+    else:
+        feat_height = int(np.round(height / scale))
+        feat_width = shape_list(attentions)[2] // feat_height
+
+    batch_size = shape_list(attentions)[0]
+    groups = shape_list(attentions)[1]  # number of group token
+    # [batch_size, groups, height x width, groups] -> [batch_size, groups, height, width]
+    attentions = tf.reshape(attentions, (batch_size, groups, feat_height, feat_width))
+    attentions = tf.transpose(attentions, perm=(0, 2, 3, 1))
+    if align_corners:
+        attentions = tf.compat.v1.image.resize(
+            attentions,
+            size=(height, width),
+            method="bilinear",
+            align_corners=align_corners,
+        )
+    else:
+        attentions = tf.image.resize(attentions, size=(height, width), method="bilinear")
+    attentions = tf.transpose(attentions, perm=(0, 3, 1, 2))
+    return attentions
+
+
+def get_grouping_from_attentions(attentions: Tuple[tf.Tensor], hw_shape: Tuple[int]) -> tf.Tensor:
+    """
+    Args:
+        attentions (`tuple(tf.Tensor)`: tuple of attention maps returned by `TFGroupViTVisionTransformer`
+        hw_shape (`tuple(int)`): height and width of the output attention map
+    Returns:
+        `tf.Tensor`: the attention map of shape [batch_size, groups, height, width]
+    """
+
+    attn_maps = []
+    prev_attn_masks = None
+    for attn_masks in attentions:
+        # [batch_size, num_groups, height x width] -> [batch_size, height x width, num_groups]
+        attn_masks = tf.transpose(attn_masks, perm=(0, 2, 1))
+        if prev_attn_masks is None:
+            prev_attn_masks = attn_masks
+        else:
+            prev_attn_masks = tf.matmul(prev_attn_masks, attn_masks)
+        # [batch_size, height x width, num_groups] -> [batch_size, num_groups, height x width] -> [batch_size, num_groups, height, width]
+        cur_attn_map = resize_attention_map(tf.transpose(prev_attn_masks, perm=(0, 2, 1)), *hw_shape)
+        attn_maps.append(cur_attn_map)
+
+    # [batch_size, num_groups, height, width]
+    final_grouping = attn_maps[-1]
+
+    return tf.stop_gradient(final_grouping)
+
+
+@dataclass
+class TFGroupViTModelOutput(ModelOutput):
+    """
+    Args:
+        loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
+            Contrastive loss for image-text similarity.
+        logits_per_image (`tf.Tensor` of shape `(image_batch_size, text_batch_size)`):
+            The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
+            similarity scores.
+        logits_per_text (`tf.Tensor` of shape `(text_batch_size, image_batch_size)`):
+            The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
+            similarity scores.
+        segmentation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
+            Classification scores for each pixel.
+
+            <Tip warning={true}>
+
+            The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
+            to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
+            original image size as post-processing. You should always check your logits shape and resize as needed.
+
+            </Tip>
+
+        text_embeds (`tf.Tensor` of shape `(batch_size, output_dim`):
+            The text embeddings obtained by applying the projection layer to the pooled output of
+            [`TFGroupViTTextModel`].
+        image_embeds (`tf.Tensor` of shape `(batch_size, output_dim`):
+            The image embeddings obtained by applying the projection layer to the pooled output of
+            [`TFGroupViTVisionModel`].
+        text_model_output (`TFBaseModelOutputWithPooling`):
+            The output of the [`TFGroupViTTextModel`].
+        vision_model_output (`TFBaseModelOutputWithPooling`):
+            The output of the [`TFGroupViTVisionModel`].
+    """
+
+    loss: Optional[tf.Tensor] = None
+    logits_per_image: tf.Tensor = None
+    logits_per_text: tf.Tensor = None
+    segmentation_logits: tf.Tensor = None
+    text_embeds: tf.Tensor = None
+    image_embeds: tf.Tensor = None
+    text_model_output: TFBaseModelOutputWithPooling = None
+    vision_model_output: TFBaseModelOutputWithPooling = None
+
+    def to_tuple(self) -> Tuple[Any]:
+        return tuple(
+            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
+            for k in self.keys()
+        )
+
+
+class TFGroupViTCrossAttentionLayer(tf.keras.layers.Layer):
+    def __init__(self, config: GroupViTVisionConfig, **kwargs):
+        super().__init__(**kwargs)
+        self.attn = TFGroupViTAttention(config, name="attn")
+        self.norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm2")
+        self.mlp = TFGroupViTMLP(config, name="mlp")
+        self.norm_post = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_post")
+
+    def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False) -> tf.Tensor:
+        x = query
+        x = x + self.attn(query, encoder_hidden_states=key)[0]
+        x = x + self.mlp(self.norm2(x))
+        x = self.norm_post(x)
+        return x
+
+
+class TFGroupViTAssignAttention(tf.keras.layers.Layer):
+    def __init__(self, config: GroupViTVisionConfig, **kwargs):
+        super().__init__(**kwargs)
+        self.scale = config.hidden_size**-0.5
+
+        self.q_proj = tf.keras.layers.Dense(config.hidden_size, name="q_proj")
+        self.k_proj = tf.keras.layers.Dense(config.hidden_size, name="k_proj")
+        self.v_proj = tf.keras.layers.Dense(config.hidden_size, name="v_proj")
+        self.proj = tf.keras.layers.Dense(config.hidden_size, name="proj")
+        self.assign_eps = config.assign_eps
+
+    def get_attn(self, attn: tf.Tensor, gumbel: bool = True, hard: bool = True, training: bool = False) -> tf.Tensor:
+
+        if gumbel and training:
+            attn = gumbel_softmax(attn, dim=-2, hard=hard)
+        else:
+            if hard:
+                attn = hard_softmax(attn, dim=-2)
+            else:
+                attn = stable_softmax(attn, axis=-2)
+
+        return attn
+
+    def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False):
+        value = key
+        # [batch_size, query_length, channels]
+        query = self.q_proj(query)
+
+        # [batch_size, key_length, channels]
+        key = self.k_proj(key)
+
+        # [batch_size, key_length, channels]
+        value = self.v_proj(value)
+
+        # [batch_size, query_length, key_length]
+        raw_attn = tf.matmul(query, key, transpose_b=True) * self.scale
+
+        attn = self.get_attn(raw_attn, training=training)
+        soft_attn = self.get_attn(raw_attn, training=training, gumbel=False, hard=False)
+
+        attn = attn / (tf.math.reduce_sum(attn, axis=-1, keepdims=True) + self.assign_eps)
+
+        out = tf.matmul(attn, value)
+
+        out = self.proj(out)
+
+        return out, soft_attn
+
+
+class TFGroupViTTokenAssign(tf.keras.layers.Layer):
+    def __init__(self, config: GroupViTVisionConfig, num_group_token: int, num_output_group: int, **kwargs):
+        super().__init__(**kwargs)
+        self.num_output_group = num_output_group
+        # norm on group_tokens
+        self.norm_tokens = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_tokens")
+        assign_mlp_ratio = (
+            config.assign_mlp_ratio
+            if isinstance(config.assign_mlp_ratio, collections.abc.Iterable)
+            else (config.assign_mlp_ratio, config.assign_mlp_ratio)
+        )
+        tokens_dim, channels_dim = [int(x * config.hidden_size) for x in assign_mlp_ratio]
+        self.mlp_inter = TFGroupViTMixerMLP(config, num_group_token, tokens_dim, num_output_group, name="mlp_inter")
+        self.norm_post_tokens = tf.keras.layers.LayerNormalization(
+            epsilon=config.layer_norm_eps, name="norm_post_tokens"
+        )
+        # norm on x
+        self.norm_x = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_x")
+        self.pre_assign_attn = TFGroupViTCrossAttentionLayer(config, name="pre_assign_attn")
+
+        self.assign = TFGroupViTAssignAttention(config, name="assign")
+        self.norm_new_x = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_new_x")
+        self.mlp_channels = TFGroupViTMLP(
+            config, config.hidden_size, channels_dim, config.hidden_size, name="mlp_channels"
+        )
+
+    def project_group_token(self, group_tokens: tf.Tensor) -> tf.Tensor:
+        """
+        Args:
+            group_tokens (tf.Tensor): group tokens, [batch_size, num_group_tokens, channels]
+
+        Returns:
+            projected_group_tokens (tf.Tensor): [batch_size, num_output_groups, channels]
+        """
+        # [B, num_output_groups, C] <- [B, num_group_tokens, C]
+        projected_group_tokens = self.mlp_inter(group_tokens)
+        projected_group_tokens = self.norm_post_tokens(projected_group_tokens)
+        return projected_group_tokens
+
+    def call(self, image_tokens: tf.Tensor, group_tokens: tf.Tensor, training: bool = False):
+        """
+        Args:
+            image_tokens (`tf.Tensor`): image tokens, of shape [batch_size, input_length, channels]
+            group_tokens (`tf.Tensor`): group tokens, [batch_size, num_group_tokens, channels]
+        """
+
+        group_tokens = self.norm_tokens(group_tokens)
+        image_tokens = self.norm_x(image_tokens)
+        # [batch_size, num_output_groups, channels]
+        projected_group_tokens = self.project_group_token(group_tokens)
+        projected_group_tokens = self.pre_assign_attn(projected_group_tokens, image_tokens)
+        new_image_tokens, attention = self.assign(projected_group_tokens, image_tokens)
+        new_image_tokens += projected_group_tokens
+
+        new_image_tokens = new_image_tokens + self.mlp_channels(self.norm_new_x(new_image_tokens))
+
+        return new_image_tokens, attention
+
+
+# Adapted from transformers.models.vit.modeling_tf_vit.TFViTPatchEmbeddings with ViT->GroupViT
+class TFGroupViTPatchEmbeddings(tf.keras.layers.Layer):
+    """
+    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
+    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
+    Transformer.
+    """
+
+    def __init__(self, config: GroupViTConfig, **kwargs):
+        super().__init__(**kwargs)
+        image_size, patch_size = config.image_size, config.patch_size
+        num_channels = config.num_channels
+        # hidden_size is a member as it will be required in the call method
+        self.hidden_size = config.hidden_size
+
+        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
+        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+        self.num_channels = num_channels
+        self.config = config
+
+        self.projection = tf.keras.layers.Conv2D(
+            filters=self.hidden_size,
+            kernel_size=patch_size,
+            strides=patch_size,
+            padding="valid",
+            data_format="channels_last",
+            use_bias=True,
+            kernel_initializer=get_initializer(self.config.initializer_range),
+            bias_initializer="zeros",
+            name="projection",
+        )
+
+    def call(
+        self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
+    ) -> tf.Tensor:
+        batch_size, num_channels, height, width = shape_list(pixel_values)
+        if tf.executing_eagerly() and num_channels != self.num_channels:
+            raise ValueError(
+                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
+            )
+        if (
+            not interpolate_pos_encoding
+            and tf.executing_eagerly()
+            and (height != self.image_size[0] or width != self.image_size[1])
+        ):
+            raise ValueError(
+                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
+            )
+
+        # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
+        # So change the input format from `NCHW` to `NHWC`.
+        # shape = (batch_size, in_height, in_width, in_channels=num_channels)
+        pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
+
+        projection = self.projection(pixel_values)
+
+        # Change the 2D spatial dimensions to a single temporal dimension.
+        # shape = (batch_size, num_patches, out_channels=embed_dim)
+        num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0])
+        # In the TFGroupViTVisionEmbeddings the embeddings from this layer will be layer normalized
+        # LayerNormalization layer needs to have static last dimension (otherwise the test_keras_save_load fails with symbolic tensors)
+        # This is why we have used the hidden_size in the reshape method
+        embeddings = tf.reshape(tensor=projection, shape=(batch_size, num_patches, self.hidden_size))
+
+        return embeddings
+
+
+# Adapted from transformers.vit.modeling_tf_vit.TFViTEmbeddings
+class TFGroupViTVisionEmbeddings(tf.keras.layers.Layer):
+    """
+    Construct the position and patch embeddings.
+
+    """
+
+    def __init__(self, config: GroupViTVisionConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.patch_embeddings = TFGroupViTPatchEmbeddings(config, name="patch_embeddings")
+        self.dropout = tf.keras.layers.Dropout(rate=config.dropout, name="dropout")
+        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
+        self.config = config
+
+    def build(self, input_shape: tf.TensorShape):
+
+        num_patches = self.patch_embeddings.num_patches
+        self.position_embeddings = self.add_weight(
+            shape=(1, num_patches, self.config.hidden_size),
+            initializer="zeros",
+            trainable=True,
+            name="position_embeddings",
+        )
+
+        super().build(input_shape)
+
+    def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
+        resolution images.
+
+        Source:
+        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
+        """
+
+        batch_size, num_patches, dim = shape_list(embeddings)
+        num_positions = shape_list(self.position_embeddings)[1]
+
+        if num_patches == num_positions and height == width:
+            return self.position_embeddings
+        patch_pos_embed = self.position_embeddings
+        h0 = height // self.config.patch_size
+        w0 = width // self.config.patch_size
+        patch_pos_embed = tf.image.resize(
+            images=tf.reshape(
+                patch_pos_embed, shape=(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
+            ),
+            size=(h0, w0),
+            method="bicubic",
+        )
+        patch_pos_embed = tf.reshape(tensor=patch_pos_embed, shape=(1, -1, dim))
+        return patch_pos_embed
+
+    def call(
+        self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
+    ) -> tf.Tensor:
+        _, _, height, width = shape_list(pixel_values)
+        embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
+        embeddings = self.layernorm(embeddings)
+
+        # add positional encoding to each token
+        if interpolate_pos_encoding:
+            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+        else:
+            embeddings = embeddings + self.position_embeddings
+
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+
+# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextEmbeddings with CLIP->GroupViT
+class TFGroupViTTextEmbeddings(tf.keras.layers.Layer):
+    def __init__(self, config: GroupViTTextConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.embed_dim = config.hidden_size
+        self.vocab_size = config.vocab_size
+
+        self.config = config
+
+    def build(self, input_shape: tf.TensorShape):
+
+        with tf.name_scope("token_embedding"):
+            self.weight = self.add_weight(
+                shape=(self.vocab_size, self.embed_dim),
+                initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
+                trainable=True,
+                name="weight",
+            )
+
+        with tf.name_scope("position_embedding"):
+            self.position_embedding = self.add_weight(
+                shape=(self.config.max_position_embeddings, self.embed_dim),
+                initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
+                trainable=True,
+                name="embeddings",
+            )
+
+        super().build(input_shape)
+
+    def call(
+        self,
+        input_ids: tf.Tensor = None,
+        position_ids: tf.Tensor = None,
+        inputs_embeds: tf.Tensor = None,
+    ) -> tf.Tensor:
+        """
+        Applies embedding based on inputs tensor.
+
+        Returns:
+            final_embeddings (`tf.Tensor`): output embedding tensor.
+        """
+        if input_ids is None and inputs_embeds is None:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound
+            # indices on GPU, returning zeros instead. This is a dangerous silent behavior.
+            tf.debugging.assert_less(
+                input_ids,
+                tf.cast(self.vocab_size, dtype=input_ids.dtype),
+                message=(
+                    "input_ids must be smaller than the embedding layer's input dimension (got"
+                    f" {tf.math.reduce_max(input_ids)} >= {self.vocab_size})"
+                ),
+            )
+            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
+
+        input_shape = shape_list(inputs_embeds)[:-1]
+
+        if position_ids is None:
+            position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
+
+        position_embeds = tf.gather(params=self.position_embedding, indices=position_ids)
+        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
+        final_embeddings = inputs_embeds + position_embeds
+
+        return final_embeddings
+
+
+class TFGroupViTStage(tf.keras.layers.Layer):
+    """This corresponds to the `GroupingLayer` class in the GroupViT implementation."""
+
+    def __init__(
+        self,
+        config: GroupViTVisionConfig,
+        depth: int,
+        num_prev_group_token: int,
+        num_group_token: int,
+        num_output_group: int,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.config = config
+        self.depth = depth
+        self.num_group_token = num_group_token
+        self.layers = [TFGroupViTEncoderLayer(config, name=f"layers_._{i}") for i in range(depth)]
+
+        if num_group_token > 0:
+            self.downsample = TFGroupViTTokenAssign(
+                config=config,
+                num_group_token=num_group_token,
+                num_output_group=num_output_group,
+                name="downsample",
+            )
+        else:
+            self.downsample = None
+
+        if num_prev_group_token > 0 and num_group_token > 0:
+            self.group_projector = [
+                tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="group_projector.0"),
+                TFGroupViTMixerMLP(
+                    config, num_prev_group_token, config.hidden_size // 2, num_group_token, name="group_projector.1"
+                ),
+            ]
+        else:
+            self.group_projector = None
+
+    def build(self, input_shape: tf.TensorShape):
+        if self.num_group_token > 0:
+            self.group_token = self.add_weight(
+                shape=(1, self.num_group_token, self.config.hidden_size),
+                initializer="zeros",
+                trainable=True,
+                name="group_token",
+            )
+        else:
+            self.group_token = None
+        super().build(input_shape)
+
+    @property
+    def with_group_token(self):
+        return self.group_token is not None
+
+    def split_x(self, x: tf.Tensor) -> tf.Tensor:
+        if self.with_group_token:
+            return x[:, : -self.num_group_token], x[:, -self.num_group_token :]
+        else:
+            return x, None
+
+    def concat_x(self, x: tf.Tensor, group_token: Optional[tf.Tensor] = None) -> tf.Tensor:
+        if group_token is None:
+            return x
+        return tf.concat([x, group_token], axis=1)
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        prev_group_token: Optional[tf.Tensor] = None,
+        output_attentions: bool = False,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        """
+        Args:
+            hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`tf.Tensor`): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+                `(config.encoder_attention_heads,)`.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the grouping tensors of Grouping block.
+        """
+        if self.with_group_token:
+            group_token = tf.tile(self.group_token, multiples=(shape_list(hidden_states)[0], 1, 1))
+            if self.group_projector is not None:
+                for layer in self.group_projector:
+                    prev_group_token = layer(prev_group_token)
+                group_token = group_token + prev_group_token
+        else:
+            group_token = None
+
+        x = hidden_states
+
+        cat_x = self.concat_x(x, group_token)
+        for layer in self.layers:
+            layer_out = layer(
+                cat_x,
+                attention_mask=None,
+                causal_attention_mask=None,
+                output_attentions=None,
+            )
+            cat_x = layer_out[0]
+
+        x, group_token = self.split_x(cat_x)
+
+        attention = None
+        if self.downsample is not None:
+            x, attention = self.downsample(x, group_token)
+
+        outputs = (x, group_token)
+        if output_attentions:
+            outputs = outputs + (attention,)
+
+        return outputs
+
+
+class TFGroupViTMLP(tf.keras.layers.Layer):
+    def __init__(
+        self,
+        config: GroupViTVisionConfig,
+        hidden_size: Optional[int] = None,
+        intermediate_size: Optional[int] = None,
+        output_size: Optional[int] = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.config = config
+        self.activation_fn = get_tf_activation(config.hidden_act)
+        hidden_size = hidden_size if hidden_size is not None else config.hidden_size
+        intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
+        output_size = output_size if output_size is not None else hidden_size
+        self.fc1 = tf.keras.layers.Dense(intermediate_size, name="fc1")
+        self.fc2 = tf.keras.layers.Dense(output_size, name="fc2")
+
+    def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class TFGroupViTMixerMLP(TFGroupViTMLP):
+    def call(self, x, training: bool = False):
+        x = super().call(hidden_states=tf.transpose(x, perm=(0, 2, 1)))
+        return tf.transpose(x, perm=(0, 2, 1))
+
+
+# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPAttention
+class TFGroupViTAttention(tf.keras.layers.Layer):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: GroupViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.embed_dim = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = self.embed_dim // self.num_attention_heads
+        if self.attention_head_size * self.num_attention_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_attention_heads})."
+            )
+
+        factor = config.initializer_factor
+        in_proj_std = (self.embed_dim**-0.5) * ((2 * config.num_hidden_layers) ** -0.5) * factor
+        out_proj_std = (self.embed_dim**-0.5) * factor
+
+        self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
+
+        self.q_proj = tf.keras.layers.Dense(
+            units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="q_proj"
+        )
+        self.k_proj = tf.keras.layers.Dense(
+            units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="k_proj"
+        )
+        self.v_proj = tf.keras.layers.Dense(
+            units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="v_proj"
+        )
+
+        self.dropout = tf.keras.layers.Dropout(rate=config.attention_dropout)
+
+        self.out_proj = tf.keras.layers.Dense(
+            units=self.embed_dim, kernel_initializer=get_initializer(out_proj_std), name="out_proj"
+        )
+
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention.transpose_for_scores
+    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
+        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
+        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
+
+        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
+        return tf.transpose(tensor, perm=[0, 2, 1, 3])
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        attention_mask: tf.Tensor = None,
+        causal_attention_mask: tf.Tensor = None,
+        output_attentions: bool = None,
+        encoder_hidden_states: tf.Tensor = None,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        """Input shape: Batch x Time x Channel"""
+
+        batch_size = shape_list(hidden_states)[0]
+        is_cross_attention = encoder_hidden_states is not None
+
+        mixed_query_layer = self.q_proj(inputs=hidden_states)
+        if is_cross_attention:
+            mixed_key_layer = self.k_proj(inputs=encoder_hidden_states)
+            mixed_value_layer = self.v_proj(inputs=encoder_hidden_states)
+        else:
+            mixed_key_layer = self.k_proj(inputs=hidden_states)
+            mixed_value_layer = self.v_proj(inputs=hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
+        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
+        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        # (batch size, num_heads, seq_len_q, seq_len_k)
+        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
+        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
+        attention_scores = tf.divide(attention_scores, dk)
+
+        # apply the causal_attention_mask first
+        if causal_attention_mask is not None:
+            # Apply the causal attention mask (precomputed for all layers in TFCLIPModel call() function)
+            attention_scores = tf.add(attention_scores, causal_attention_mask)
+
+        if attention_mask is not None:
+            # Apply the attention mask (precomputed for all layers in TFCLIPModel call() function)
+            attention_scores = tf.add(attention_scores, attention_mask)
+
+        # Normalize the attention scores to probabilities.
+        _attention_probs = stable_softmax(logits=attention_scores, axis=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(inputs=_attention_probs)
+
+        attention_output = tf.matmul(attention_probs, value_layer)
+        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
+
+        # (batch_size, seq_len_q, embed_dim)
+        attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.embed_dim))
+
+        attention_output = self.out_proj(attention_output)
+        # In TFBert, attention weights are returned after dropout.
+        # However, in CLIP, they are returned before dropout.
+        outputs = (attention_output, _attention_probs) if output_attentions else (attention_output,)
+
+        return outputs
+
+
+# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPEncoderLayer with CLIP->GroupViT
+class TFGroupViTEncoderLayer(tf.keras.layers.Layer):
+    def __init__(self, config: GroupViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.embed_dim = config.hidden_size
+        self.self_attn = TFGroupViTAttention(config, name="self_attn")
+        self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1")
+        self.mlp = TFGroupViTMLP(config, name="mlp")
+        self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2")
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        attention_mask: tf.Tensor,
+        causal_attention_mask: tf.Tensor,
+        output_attentions: bool,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        """
+        Args:
+            hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`tf.Tensor`): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            causal_attention_mask (`tf.Tensor`): causal attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`):
+                Whether or not to return the attentions tensors of all attention layers. See `outputs` under returned
+                tensors for more detail.
+        """
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(inputs=hidden_states)
+        attention_outputs = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            causal_attention_mask=causal_attention_mask,
+            output_attentions=output_attentions,
+            training=training,
+        )
+        hidden_states = attention_outputs[0]
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(inputs=hidden_states)
+        hidden_states = self.mlp(hidden_states=hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,) + attention_outputs[1:]  # add attentions if we output them
+
+        return outputs
+
+
+# Adapted from transformers.models.clip.modeling_tf_clip.TFGroupViTTextEncoder
+class TFGroupViTTextEncoder(tf.keras.layers.Layer):
+    def __init__(self, config: GroupViTTextConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.layers = [TFGroupViTEncoderLayer(config, name=f"layers_._{i}") for i in range(config.num_hidden_layers)]
+
+    def call(
+        self,
+        hidden_states,
+        attention_mask: tf.Tensor,
+        causal_attention_mask: tf.Tensor,
+        output_attentions: bool,
+        output_hidden_states: bool,
+        return_dict: bool,
+        training: bool = False,
+    ) -> Union[Tuple, TFBaseModelOutput]:
+        encoder_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        for idx, encoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+
+            layer_outputs = encoder_layer(
+                hidden_states,
+                attention_mask,
+                causal_attention_mask,
+                output_attentions=output_attentions,
+            )
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+        return TFBaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+        )
+
+
+class TFGroupViTVisionEncoder(tf.keras.layers.Layer):
+    def __init__(self, config: GroupViTVisionConfig, **kwargs) -> None:
+        super().__init__(**kwargs)
+
+        self.stages = [
+            TFGroupViTStage(
+                config=config,
+                depth=config.depths[i],
+                num_group_token=config.num_group_tokens[i],
+                num_output_group=config.num_output_groups[i],
+                num_prev_group_token=config.num_output_groups[i - 1] if i > 0 else 0,
+                name=f"stages_._{i}",
+            )
+            for i in range(len(config.depths))
+        ]
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        output_hidden_states: bool,
+        output_attentions: bool,
+        return_dict: bool,
+        training: bool = False,
+    ) -> Union[tuple, TFBaseModelOutput]:
+        all_hidden_states = () if output_hidden_states else None
+        all_groupings = () if output_attentions else None
+
+        group_tokens = None
+
+        for stage in self.stages:
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_outputs = stage(hidden_states, group_tokens, output_attentions)
+
+            hidden_states = layer_outputs[0]
+            group_tokens = layer_outputs[1]
+
+            if output_attentions and layer_outputs[2] is not None:
+                all_groupings = all_groupings + (layer_outputs[2],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_groupings] if v is not None)
+        return TFBaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_groupings
+        )
+
+
+# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextTransformer with CLIPText->GroupViTText, CLIPEncoder->GroupViTTextEncoder
+class TFGroupViTTextTransformer(tf.keras.layers.Layer):
+    def __init__(self, config: GroupViTTextConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.embeddings = TFGroupViTTextEmbeddings(config, name="embeddings")
+        self.encoder = TFGroupViTTextEncoder(config, name="encoder")
+        self.final_layer_norm = tf.keras.layers.LayerNormalization(
+            epsilon=config.layer_norm_eps, name="final_layer_norm"
+        )
+
+    def call(
+        self,
+        input_ids: TFModelInputType,
+        attention_mask: tf.Tensor,
+        position_ids: tf.Tensor,
+        output_attentions: bool,
+        output_hidden_states: bool,
+        return_dict: bool,
+        training: bool = False,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        input_shape = shape_list(input_ids)
+
+        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids)
+
+        batch_size, seq_length = input_shape
+        # CLIP's text model uses causal mask, prepare it here.
+        # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
+        causal_attention_mask = self._build_causal_attention_mask(batch_size, seq_length, dtype=embedding_output.dtype)
+
+        # check attention mask and invert
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        attention_mask = _expand_mask(attention_mask)
+
+        encoder_outputs = self.encoder(
+            hidden_states=embedding_output,
+            attention_mask=attention_mask,
+            causal_attention_mask=causal_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.final_layer_norm(inputs=sequence_output)
+
+        # text_embeds.shape = [batch_size, n_ctx, transformer.width]
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        pooled_output = tf.gather_nd(
+            params=sequence_output,
+            indices=tf.stack(
+                values=(tf.range(input_shape[0], dtype=tf.int64), tf.math.argmax(input_ids, axis=-1)), axis=1
+            ),
+        )
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+    def _build_causal_attention_mask(self, batch_size, seq_length, dtype=tf.float32):
+        # It is possible with an unspecified sequence length for seq_length to be
+        # a runtime value, which is unsupported by tf.constant. Per the TensorFlow
+        # docs, tf.fill can handle runtime dynamic shapes:
+        # https://www.tensorflow.org/api_docs/python/tf/fill
+        diag = tf.cast(tf.fill((seq_length,), 0.0), dtype)
+
+        # set an additive 2D attention mask with all places being masked
+        to_mask = tf.cast(tf.fill((seq_length, seq_length), -10000.0), dtype)
+
+        # set diagonal & lower triangular parts to 0 (i.e. the places not to be masked)
+        # TIP: think the 2D matrix as the space of (query_seq, key_seq)
+        to_mask = tf.linalg.band_part(to_mask, 0, -1)
+        # to_mask = tf.linalg.band_part(to_mask, -1, 0)
+        to_mask = tf.linalg.set_diag(to_mask, diagonal=diag)
+
+        return tf.broadcast_to(input=to_mask, shape=(batch_size, 1, seq_length, seq_length))
+
+
+# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPVisionTransformer
+class TFGroupViTVisionTransformer(tf.keras.layers.Layer):
+    def __init__(self, config: GroupViTVisionConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.embeddings = TFGroupViTVisionEmbeddings(config, name="embeddings")
+        self.encoder = TFGroupViTVisionEncoder(config, name="encoder")
+        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
+
+    def call(
+        self,
+        pixel_values: TFModelInputType,
+        output_attentions: bool,
+        output_hidden_states: bool,
+        return_dict: bool,
+        training: bool = False,
+    ) -> Union[Tuple, TFBaseModelOutputWithPooling]:
+
+        embedding_output = self.embeddings(pixel_values)
+
+        encoder_outputs = self.encoder(
+            hidden_states=embedding_output,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+            return_dict=return_dict,
+        )
+
+        last_hidden_state = encoder_outputs[0]
+
+        # normalize the last hidden state
+        last_hidden_state = self.layernorm(last_hidden_state)
+        pooled_output = tf.math.reduce_mean(last_hidden_state, axis=1)
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+@keras_serializable
+# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextMainLayer with CLIP->GroupViT
+class TFGroupViTTextMainLayer(tf.keras.layers.Layer):
+    config_class = GroupViTTextConfig
+
+    def __init__(self, config: GroupViTTextConfig, **kwargs):
+        super().__init__(**kwargs)
+        self.config = config
+        self.text_model = TFGroupViTTextTransformer(config, name="text_model")
+
+    def get_input_embeddings(self) -> tf.keras.layers.Layer:
+        return self.text_model.embeddings
+
+    def set_input_embeddings(self, value: tf.Variable):
+        self.text_model.embeddings.weight = value
+        self.text_model.embeddings.vocab_size = shape_list(value)[0]
+
+    @unpack_inputs
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        if input_ids is None:
+            raise ValueError("You have to specify input_ids")
+
+        input_shape = shape_list(input_ids)
+
+        if attention_mask is None:
+            attention_mask = tf.fill(dims=input_shape, value=1)
+
+        text_model_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,
+            training=training,
+        )
+
+        return text_model_outputs
+
+
+@keras_serializable
+# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPVisionMainLayer with CLIP->GroupViT
+class TFGroupViTVisionMainLayer(tf.keras.layers.Layer):
+    config_class = GroupViTVisionConfig
+
+    def __init__(self, config: GroupViTVisionConfig, **kwargs):
+        super().__init__(**kwargs)
+        self.config = config
+        self.vision_model = TFGroupViTVisionTransformer(config, name="vision_model")
+
+    def get_input_embeddings(self) -> tf.keras.layers.Layer:
+        return self.vision_model.embeddings
+
+    @unpack_inputs
+    def call(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values")
+
+        vision_model_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        return vision_model_outputs
+
+
+@keras_serializable
+# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPMainLayer
+class TFGroupViTMainLayer(tf.keras.layers.Layer):
+    config_class = GroupViTConfig
+
+    def __init__(self, config: GroupViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        if not isinstance(config.text_config, GroupViTTextConfig):
+            raise ValueError(
+                "config.text_config is expected to be of type GroupViTTextConfig but is of type"
+                f" {type(config.text_config)}."
+            )
+
+        if not isinstance(config.vision_config, GroupViTVisionConfig):
+            raise ValueError(
+                "config.vision_config is expected to be of type GroupViTVisionConfig but is of type"
+                f" {type(config.vision_config)}."
+            )
+
+        self.config = config
+
+        text_config = config.text_config
+        vision_config = config.vision_config
+
+        self.projection_dim = config.projection_dim
+        self.projection_intermediate_dim = config.projection_intermediate_dim
+        self.text_embed_dim = text_config.hidden_size
+        self.vision_embed_dim = vision_config.hidden_size
+
+        self.text_model = TFGroupViTTextTransformer(text_config, name="text_model")
+        self.vision_model = TFGroupViTVisionTransformer(vision_config, name="vision_model")
+
+        self.visual_projection = [
+            tf.keras.layers.Dense(self.projection_intermediate_dim, name="visual_projection.0"),
+            tf.keras.layers.BatchNormalization(name="visual_projection.1", momentum=0.1, epsilon=1e-5),
+            tf.keras.layers.ReLU(name="visual_projection.2"),
+            tf.keras.layers.Dense(self.projection_dim, name="visual_projection.3"),
+        ]
+        self.text_projection = [
+            tf.keras.layers.Dense(self.projection_intermediate_dim, name="text_projection.0"),
+            tf.keras.layers.BatchNormalization(name="text_projection.1", momentum=0.1, epsilon=1e-5),
+            tf.keras.layers.ReLU(name="text_projection.2"),
+            tf.keras.layers.Dense(self.projection_dim, name="text_projection.3"),
+        ]
+
+    def build(self, input_shape: tf.TensorShape):
+
+        self.logit_scale = self.add_weight(
+            shape=(1,),
+            initializer=tf.keras.initializers.Constant(self.config.logit_scale_init_value),
+            trainable=True,
+            name="logit_scale",
+        )
+
+        super().build(input_shape)
+
+    @unpack_inputs
+    def get_text_features(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> tf.Tensor:
+
+        if input_ids is None:
+            raise ValueError("You have to specify either input_ids")
+
+        input_shape = shape_list(input_ids)
+
+        if attention_mask is None:
+            attention_mask = tf.fill(dims=input_shape, value=1)
+
+        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,
+            training=training,
+        )
+
+        pooled_output = text_outputs[1]
+        for layer in self.text_projection:
+            pooled_output = layer(pooled_output)
+
+        text_features = pooled_output
+        return text_features
+
+    @unpack_inputs
+    def get_image_features(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> tf.Tensor:
+
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values")
+
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        pooled_output = vision_outputs[1]
+        for layer in self.visual_projection:
+            pooled_output = layer(pooled_output)
+
+        image_features = pooled_output
+        return image_features
+
+    @unpack_inputs
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        pixel_values: Optional[TFModelInputType] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        return_loss: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_segmentation: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> Union[TFGroupViTModelOutput, Tuple[tf.Tensor]]:
+
+        if input_ids is None:
+            raise ValueError("You have to specify either input_ids")
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values")
+
+        input_shape = shape_list(input_ids)
+
+        if attention_mask is None:
+            attention_mask = tf.fill(dims=input_shape, value=1)
+        if output_segmentation:
+            output_attentions = True
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        image_embeds = vision_outputs[1]
+        for layer in self.visual_projection:
+            image_embeds = layer(image_embeds)
+
+        text_embeds = text_outputs[1]
+        for layer in self.text_projection:
+            text_embeds = layer(text_embeds)
+
+        # normalized features
+        image_embeds = image_embeds / tf.norm(image_embeds, axis=-1, keepdims=True)
+        text_embeds = text_embeds / tf.norm(text_embeds, axis=-1, keepdims=True)
+
+        # cosine similarity as logits
+        logit_scale = tf.math.exp(self.logit_scale)
+        logits_per_text = tf.matmul(text_embeds, image_embeds, transpose_b=True) * logit_scale
+        logits_per_image = tf.transpose(logits_per_text)
+
+        seg_logits = None
+        if output_segmentation:
+            # grouped features
+            # [batch_size_image, num_group, hidden_size]
+            image_group_embeds = vision_outputs[0]
+            # [batch_size_image*num_group, hidden_size]
+            image_group_embeds = tf.reshape(image_group_embeds, shape=(-1, shape_list(image_group_embeds)[-1]))
+            for layer in self.visual_projection:
+                image_group_embeds = layer(image_group_embeds)
+            if output_hidden_states:
+                attentions = vision_outputs[3]
+            else:
+                attentions = vision_outputs[2]
+            # [batch_size_image, num_group, height, width]
+            grouping = get_grouping_from_attentions(attentions, pixel_values.shape[2:])
+
+            # normalized features
+            image_group_embeds = image_group_embeds / tf.norm(
+                tensor=image_group_embeds, ord="euclidean", axis=-1, keepdims=True
+            )
+            # [batch_size_image x num_group, batch_size_text]
+            logits_per_image_group = tf.matmul(image_group_embeds, text_embeds, transpose_b=True) * logit_scale
+            # [batch_size_image, batch_size_text, num_group]
+            logits_per_image_group = tf.reshape(
+                logits_per_image_group, shape=(image_embeds.shape[0], -1, text_embeds.shape[0])
+            )
+            logits_per_image_group = tf.transpose(logits_per_image_group, perm=(0, 2, 1))
+
+            # [batch_size_image, batch_size_text, height x width]
+            flatten_grouping = tf.reshape(grouping, shape=(shape_list(grouping)[0], shape_list(grouping)[1], -1))
+
+            # [batch_size_image, batch_size_text, height, width]
+            seg_logits = tf.matmul(logits_per_image_group, flatten_grouping) * logit_scale
+            seg_logits = tf.reshape(
+                seg_logits, shape=(seg_logits.shape[0], seg_logits.shape[1], grouping.shape[2], grouping.shape[3])
+            )
+
+        loss = None
+        if return_loss:
+            loss = groupvit_loss(logits_per_text)[None, ...]
+
+        if not return_dict:
+            if seg_logits is not None:
+                output = (
+                    logits_per_image,
+                    logits_per_text,
+                    seg_logits,
+                    text_embeds,
+                    image_embeds,
+                    text_outputs,
+                    vision_outputs,
+                )
+            else:
+                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 TFGroupViTModelOutput(
+            loss=loss,
+            logits_per_image=logits_per_image,
+            logits_per_text=logits_per_text,
+            segmentation_logits=seg_logits,
+            text_embeds=text_embeds,
+            image_embeds=image_embeds,
+            text_model_output=text_outputs,
+            vision_model_output=vision_outputs,
+        )
+
+
+class TFGroupViTPreTrainedModel(TFPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = GroupViTConfig
+    base_model_prefix = "groupvit"
+
+
+GROUPVIT_START_DOCSTRING = r"""
+    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+    behavior.
+
+    <Tip>
+
+    TF 2.0 models accepts two formats as inputs:
+
+    - having all inputs as keyword arguments (like PyTorch models), or
+    - having all inputs as a list, tuple or dict in the first positional arguments.
+
+    This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
+    tensors in the first argument of the model call function: `model(inputs)`.
+
+    If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
+    first positional argument :
+
+    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+      `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+      `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+    </Tip>
+
+    Args:
+        config ([`GroupViTConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+GROUPVIT_TEXT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+            [`PreTrainedTokenizer.encode`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+            config will be used instead.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+            used instead.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
+        training (`bool`, *optional*, defaults to `False``):
+            Whether or not to use the model in training mode (some modules like dropout modules have different
+            behaviors between training and evaluation).
+"""
+
+GROUPVIT_VISION_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]`, `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See
+            [`CLIPFeatureExtractor.__call__`] for details.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+            config will be used instead.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+            used instead.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
+        training (`bool`, *optional*, defaults to `False``):
+            Whether or not to use the model in training mode (some modules like dropout modules have different
+            behaviors between training and evaluation).
+"""
+
+GROUPVIT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+            [`PreTrainedTokenizer.encode`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See
+            [`CLIPFeatureExtractor.__call__`] for details.
+        attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        return_loss (`bool`, *optional*):
+            Whether or not to return the contrastive loss.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+            config will be used instead.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+            used instead.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+            eager mode, in graph mode the value will always be set to True.
+        training (`bool`, *optional*, defaults to `False``):
+            Whether or not to use the model in training mode (some modules like dropout modules have different
+            behaviors between training and evaluation).
+"""
+
+
+class TFGroupViTTextModel(TFGroupViTPreTrainedModel):
+    config_class = GroupViTTextConfig
+    main_input_name = "input_ids"
+
+    def __init__(self, config: GroupViTTextConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.groupvit = TFGroupViTTextMainLayer(config, name="groupvit")
+
+    @property
+    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
+        """
+        Dummy inputs to build the network.
+
+        Returns:
+            `Dict[str, tf.Tensor]`: The dummy inputs.
+        """
+        return {
+            "input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32),
+        }
+
+    @tf.function(
+        input_signature=[
+            {
+                "input_ids": tf.TensorSpec((None, None), tf.int64, name="input_ids"),
+                "attention_mask": tf.TensorSpec((None, None), tf.int64, name="attention_mask"),
+            }
+        ]
+    )
+    def serving(self, inputs: Dict[str, tf.Tensor]) -> TFBaseModelOutputWithPooling:
+        output = self.call(inputs)
+        return self.serving_output(output)
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=GroupViTTextConfig)
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from transformers import CLIPTokenizer, TFGroupViTTextModel
+
+        >>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
+        >>> model = TFGroupViTTextModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
+
+        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
+
+        >>> outputs = model(**inputs)
+        >>> last_hidden_state = outputs.last_hidden_state
+        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
+        ```"""
+
+        outputs = self.groupvit(
+            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,
+            training=training,
+        )
+
+        return outputs
+
+    def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=output.last_hidden_state,
+            pooler_output=output.pooler_output,
+            hidden_states=hs,
+            attentions=attns,
+        )
+
+
+class TFGroupViTVisionModel(TFGroupViTPreTrainedModel):
+    config_class = GroupViTVisionConfig
+    main_input_name = "pixel_values"
+
+    def __init__(self, config: GroupViTVisionConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.groupvit = TFGroupViTVisionMainLayer(config, name="groupvit")
+
+    @property
+    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
+        """
+        Dummy inputs to build the network.
+
+        Returns:
+            `Dict[str, tf.Tensor]`: The dummy inputs.
+        """
+        VISION_DUMMY_INPUTS = tf.random.uniform(
+            shape=(len(DUMMY_INPUTS), 3, self.config.image_size, self.config.image_size), dtype=tf.float32
+        )
+        return {"pixel_values": VISION_DUMMY_INPUTS}
+
+    @tf.function(
+        input_signature=[
+            {
+                "pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
+            }
+        ]
+    )
+    def serving(self, inputs: Dict[str, tf.Tensor]) -> TFBaseModelOutputWithPooling:
+        """
+        Method used for serving the model.
+
+        Args:
+            inputs (`Dict[str, tf.Tensor]`):
+                The input of the saved model as a dictionary of tensors.
+        """
+        output = self.call(inputs)
+
+        return self.serving_output(output)
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
+    def call(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, TFGroupViTVisionModel
+
+        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
+        >>> model = TFGroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(images=image, return_tensors="tf")
+
+        >>> outputs = model(**inputs)
+        >>> last_hidden_state = outputs.last_hidden_state
+        >>> pooled_output = outputs.pooler_output  # pooled CLS states
+        ```"""
+
+        outputs = self.groupvit(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        return outputs
+
+    def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
+        # hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=output.last_hidden_state,
+            pooler_output=output.pooler_output,
+            hidden_states=output.hidden_states,
+            attentions=output.attentions,
+        )
+
+
+@add_start_docstrings(GROUPVIT_START_DOCSTRING)
+class TFGroupViTModel(TFGroupViTPreTrainedModel):
+    config_class = GroupViTConfig
+
+    def __init__(self, config: GroupViTConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.groupvit = TFGroupViTMainLayer(config, name="groupvit")
+
+    @property
+    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
+        """
+        Dummy inputs to build the network.
+
+        Returns:
+            `Dict[str, tf.Tensor]`: The dummy inputs.
+        """
+        VISION_DUMMY_INPUTS = tf.random.uniform(
+            shape=(len(DUMMY_INPUTS), 3, self.config.vision_config.image_size, self.config.vision_config.image_size),
+            dtype=tf.float32,
+        )
+        return {
+            "input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32),
+            "pixel_values": VISION_DUMMY_INPUTS,
+        }
+
+    @tf.function(
+        input_signature=[
+            {
+                "input_ids": tf.TensorSpec((None, None), tf.int64, name="input_ids"),
+                "pixel_values": tf.TensorSpec((None, None, None, None), tf.float64, name="pixel_values"),
+                "attention_mask": tf.TensorSpec((None, None), tf.int64, name="attention_mask"),
+            }
+        ]
+    )
+    def serving(self, inputs: Dict[str, tf.Tensor]) -> TFGroupViTModelOutput:
+        """
+        Method used for serving the model.
+
+        Args:
+            inputs (`Dict[str, tf.Tensor]`):
+                The input of the saved model as a dictionary of tensors.
+        """
+        output = self.call(inputs)
+
+        return self.serving_output(output)
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    def get_text_features(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> tf.Tensor:
+        r"""
+        Returns:
+            text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
+            the projection layer to the pooled output of [`TFGroupViTTextModel`].
+
+        Examples:
+
+        ```python
+        >>> from transformers import CLIPTokenizer, TFGroupViTModel
+
+        >>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
+        >>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
+
+        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
+        >>> text_features = model.get_text_features(**inputs)
+        ```"""
+
+        text_features = self.groupvit.get_text_features(
+            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,
+            training=training,
+        )
+
+        return text_features
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
+    def get_image_features(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> tf.Tensor:
+        r"""
+        Returns:
+            image_features (`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying
+            the projection layer to the pooled output of [`TFGroupViTVisionModel`].
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, TFGroupViTModel
+
+        >>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
+        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(images=image, return_tensors="tf")
+
+        >>> image_features = model.get_image_features(**inputs)
+        ```"""
+
+        image_features = self.groupvit.get_image_features(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        return image_features
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GROUPVIT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=TFGroupViTModelOutput, config_class=GroupViTConfig)
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        pixel_values: Optional[TFModelInputType] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        return_loss: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_segmentation: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+    ) -> Union[TFGroupViTModelOutput, Tuple[tf.Tensor]]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, TFGroupViTModel
+
+        >>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
+        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(
+        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True
+        ... )
+
+        >>> 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
+        ```"""
+
+        outputs = self.groupvit(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            return_loss=return_loss,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_segmentation=output_segmentation,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        return outputs
+
+    def serving_output(self, output: TFGroupViTModelOutput) -> TFGroupViTModelOutput:
+        # TODO: As is this currently fails with saved_model=True, because
+        # TensorFlow cannot trace through nested dataclasses. Reference:
+        # https://github.com/huggingface/transformers/pull/16886
+        return output

src/transformers/utils/dummy_tf_objects.py

@@ -1309,6 +1309,37 @@ class TFGPTJPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFGroupViTModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGroupViTPreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGroupViTTextModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFGroupViTVisionModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/models/groupvit/test_modeling_groupvit.py

@@ -17,6 +17,7 @@
 
 import inspect
 import os
+import random
 import tempfile
 import unittest
 
@@ -24,7 +25,7 @@ import numpy as np
 
 import requests
 from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
-from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+from transformers.testing_utils import is_pt_tf_cross_test, require_torch, require_vision, slow, torch_device
 from transformers.utils import is_torch_available, is_vision_available
 
 from ...test_configuration_common import ConfigTester
@@ -95,7 +96,8 @@ class GroupViTVisionModelTester:
         self.seq_length = num_patches
 
     def prepare_config_and_inputs(self):
-        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        rng = random.Random(0)
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size], rng=rng)
         config = self.get_config()
 
         return config, pixel_values
@@ -161,6 +163,18 @@ class GroupViTVisionModelTest(ModelTesterMixin, unittest.TestCase):
     def test_inputs_embeds(self):
         pass
 
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        import tensorflow as tf
+
+        seed = 338
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        tf.random.set_seed(seed)
+        return super().test_pt_tf_model_equivalence()
+
     def test_model_common_attributes(self):
         config, _ = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -368,7 +382,8 @@ class GroupViTTextModelTester:
         self.scope = scope
 
     def prepare_config_and_inputs(self):
-        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        rng = random.Random(0)
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, rng=rng)
 
         input_mask = None
         if self.use_input_mask:
@@ -532,6 +547,18 @@ class GroupViTModelTest(ModelTesterMixin, unittest.TestCase):
     def test_model_common_attributes(self):
         pass
 
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        import tensorflow as tf
+
+        seed = 163
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        tf.random.set_seed(seed)
+        return super().test_pt_tf_model_equivalence()
+
     # override as the `logit_scale` parameter initilization is different for GROUPVIT
     def test_initialization(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

tests/models/groupvit/test_modeling_tf_groupvit.py

+# coding=utf-8
+# Copyright 2022 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 TensorFlow GroupViT model. """
+
+
+import inspect
+import os
+import random
+import tempfile
+import unittest
+from importlib import import_module
+
+import numpy as np
+
+import requests
+from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
+from transformers.testing_utils import is_pt_tf_cross_test, require_tf, require_vision, slow
+from transformers.utils import is_tf_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import TFGroupViTModel, TFGroupViTTextModel, TFGroupViTVisionModel, TFSharedEmbeddings
+    from transformers.models.groupvit.modeling_tf_groupvit import TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import CLIPProcessor
+
+
+class TFGroupViTVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=32,
+        depths=[6, 3, 3],
+        num_group_tokens=[64, 8, 0],
+        num_output_groups=[64, 8, 8],
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.hidden_size = hidden_size
+        self.depths = depths
+        self.num_hidden_layers = sum(depths)
+        self.expected_num_hidden_layers = len(depths) + 1
+        self.num_group_tokens = num_group_tokens
+        self.num_output_groups = num_output_groups
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+        num_patches = (image_size // patch_size) ** 2
+        # no [CLS] token for GroupViT
+        self.seq_length = num_patches
+
+    def prepare_config_and_inputs(self):
+
+        rng = random.Random(0)
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size], rng=rng)
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self):
+        return GroupViTVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            depths=self.depths,
+            num_group_tokens=self.num_group_tokens,
+            num_output_groups=self.num_output_groups,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = TFGroupViTVisionModel(config=config)
+        result = model(pixel_values, training=False)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.num_output_groups[-1], self.hidden_size)
+        )
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_tf
+class TFGroupViTVisionModelTest(TFModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as GroupViT does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (TFGroupViTVisionModel,) if is_tf_available() else ()
+
+    test_pruning = False
+    test_resize_embeddings = False
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFGroupViTVisionModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=GroupViTVisionConfig, has_text_modality=False, hidden_size=37
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="GroupViT does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="GroupViT does not use inputs_embeds")
+    def test_graph_mode_with_inputs_embeds(self):
+        pass
+
+    def test_model_common_attributes(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer))
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.call)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        seq_len = getattr(self.model_tester, "seq_length", None)
+
+        expected_num_attention_outputs = sum(g > 0 for g in self.model_tester.num_group_tokens)
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class(config)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+            attentions = outputs.attentions
+            # GroupViT returns attention grouping of each stage
+            self.assertEqual(len(attentions), sum(g > 0 for g in self.model_tester.num_group_tokens))
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+            attentions = outputs.attentions
+            # GroupViT returns attention grouping of each stage
+            self.assertEqual(len(attentions), expected_num_attention_outputs)
+
+            out_len = len(outputs)
+
+            # Check attention is always last and order is fine
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+
+            added_hidden_states = 1
+            self.assertEqual(out_len + added_hidden_states, len(outputs))
+
+            self_attentions = outputs.attentions
+
+            # GroupViT returns attention grouping of each stage
+            self.assertEqual(len(self_attentions), expected_num_attention_outputs)
+            for i, self_attn in enumerate(self_attentions):
+                if self_attn is None:
+                    continue
+
+                self.assertListEqual(
+                    list(self_attentions[i].shape[-2:]),
+                    [
+                        self.model_tester.num_output_groups[i],
+                        self.model_tester.num_output_groups[i - 1] if i > 0 else seq_len,
+                    ],
+                )
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+
+            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            seq_length = getattr(self.model_tester, "seq_length", None)
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [seq_length, self.model_tester.hidden_size],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        # `GroupViT` computes some indices using argmax, uses them as
+        # one-hot encoding for further computation. The problem is
+        # while PT/TF have very small difference in `y_soft` (~ 1e-9),
+        # the argmax could be totally different, if there are at least
+        # 2 indices with almost identical values. This leads to very
+        # large difference in the outputs. We need specific seeds to
+        # avoid almost identical values happening in `y_soft`.
+        import torch
+
+        seed = 338
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        tf.random.set_seed(seed)
+        return super().test_pt_tf_model_equivalence()
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFGroupViTVisionModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+    @slow
+    def test_saved_model_creation_extended(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = True
+
+        if hasattr(config, "use_cache"):
+            config.use_cache = True
+
+        seq_len = getattr(self.model_tester, "seq_length", None)
+
+        for model_class in self.all_model_classes:
+            class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+            model = model_class(config)
+            num_out = len(model(class_inputs_dict))
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname, saved_model=True)
+                saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
+                model = tf.keras.models.load_model(saved_model_dir)
+                outputs = model(class_inputs_dict)
+                output_hidden_states = outputs["hidden_states"]
+                output_attentions = outputs["attentions"]
+
+                # Check num outputs
+                self.assertEqual(len(outputs), num_out)
+
+                # Check num layers
+                expected_num_layers = getattr(
+                    self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+                )
+
+                self.assertEqual(len(output_hidden_states), expected_num_layers)
+                self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
+
+                # Check attention outputs
+                image_size = (self.model_tester.image_size, self.model_tester.image_size)
+                patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
+                num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+                seq_len = num_patches + 1
+
+                self.assertListEqual(
+                    list(output_attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_len, seq_len],
+                )
+
+                # Check hidden states
+                self.assertListEqual(
+                    list(output_hidden_states[0].shape[-2:]),
+                    [seq_len, self.model_tester.hidden_size],
+                )
+
+
+class TFGroupViTTextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        rng = random.Random(0)
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, rng=rng)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+            # make sure the first token has attention mask `1` to ensure that, after combining the causal mask, there
+            # is still at least one token being attended to for each batch.
+            # TODO: Change `random_attention_mask` in PT/TF/Flax common test file, after a discussion with the team.
+            input_mask = tf.concat(
+                [tf.ones_like(input_mask[:, :1], dtype=input_mask.dtype), input_mask[:, 1:]], axis=-1
+            )
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask
+
+    def get_config(self):
+        return GroupViTTextConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            max_position_embeddings=self.max_position_embeddings,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask):
+        model = TFGroupViTTextModel(config=config)
+        result = model(input_ids, attention_mask=input_mask, training=False)
+        result = model(input_ids, training=False)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, input_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_tf
+class TFGroupViTTextModelTest(TFModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (TFGroupViTTextModel,) if is_tf_available() else ()
+    test_pruning = False
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFGroupViTTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=GroupViTTextConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="GroupViTTextModel does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFGroupViTTextModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+    @slow
+    def test_saved_model_creation_extended(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = True
+
+        if hasattr(config, "use_cache"):
+            config.use_cache = True
+
+        for model_class in self.all_model_classes:
+            class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+            model = model_class(config)
+            num_out = len(model(class_inputs_dict))
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname, saved_model=True)
+                saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
+                model = tf.keras.models.load_model(saved_model_dir)
+                outputs = model(class_inputs_dict)
+                output_hidden_states = outputs["hidden_states"]
+                output_attentions = outputs["attentions"]
+
+                # Check number of outputs
+                self.assertEqual(len(outputs), num_out)
+
+                # Check number of layers
+                expected_num_layers = getattr(
+                    self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+                )
+
+                # Check hidden states
+                self.assertEqual(len(output_hidden_states), expected_num_layers)
+                self.assertListEqual(
+                    list(output_hidden_states[0].shape[-2:]),
+                    [self.model_tester.seq_length, self.model_tester.hidden_size],
+                )
+
+                # Check attention outputs
+                self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
+
+                seq_length = self.model_tester.seq_length
+                key_length = getattr(self.model_tester, "key_length", seq_length)
+
+                self.assertListEqual(
+                    list(output_attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_length, key_length],
+                )
+
+
+class TFGroupViTModelTester:
+    def __init__(self, parent, is_training=True):
+        self.parent = parent
+        self.text_model_tester = TFGroupViTTextModelTester(parent)
+        self.vision_model_tester = TFGroupViTVisionModelTester(parent)
+        self.is_training = is_training
+
+    def prepare_config_and_inputs(self):
+        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+
+        config = self.get_config()
+
+        return config, input_ids, attention_mask, pixel_values
+
+    def get_config(self):
+        return GroupViTConfig.from_text_vision_configs(
+            self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
+        )
+
+    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
+        model = TFGroupViTModel(config)
+        result = model(input_ids, pixel_values, attention_mask, training=False)
+        self.parent.assertEqual(
+            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
+        )
+        self.parent.assertEqual(
+            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, attention_mask, pixel_values = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "pixel_values": pixel_values,
+            "return_loss": True,
+        }
+        return config, inputs_dict
+
+
+@require_tf
+class TFGroupViTModelTest(TFModelTesterMixin, unittest.TestCase):
+    all_model_classes = (TFGroupViTModel,) if is_tf_available() else ()
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFGroupViTModelTester(self)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="hidden_states are tested in individual model tests")
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="input_embeds are tested in individual model tests")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="CLIPModel does not have input/output embeddings")
+    def test_model_common_attributes(self):
+        pass
+
+    @is_pt_tf_cross_test
+    def test_pt_tf_model_equivalence(self):
+        # `GroupViT` computes some indices using argmax, uses them as
+        # one-hot encoding for further computation. The problem is
+        # while PT/TF have very small difference in `y_soft` (~ 1e-9),
+        # the argmax could be totally different, if there are at least
+        # 2 indices with almost identical values. This leads to very
+        # large difference in the outputs. We need specific seeds to
+        # avoid almost identical values happening in `y_soft`.
+        import torch
+
+        seed = 158
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        tf.random.set_seed(seed)
+        return super().test_pt_tf_model_equivalence()
+
+    # overwrite from common since `TFGroupViTModelTester` set `return_loss` to `True` and causes the preparation of
+    # `symbolic_inputs` failed.
+    def test_keras_save_load(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # remove `return_loss` to make code work
+        if self.__class__.__name__ == "TFGroupViTModelTest":
+            inputs_dict.pop("return_loss", None)
+
+        tf_main_layer_classes = set(
+            module_member
+            for model_class in self.all_model_classes
+            for module in (import_module(model_class.__module__),)
+            for module_member_name in dir(module)
+            if module_member_name.endswith("MainLayer")
+            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
+            and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
+            for module_member in (getattr(module, module_member_name),)
+            if isinstance(module_member, type)
+            and tf.keras.layers.Layer in module_member.__bases__
+            and getattr(module_member, "_keras_serializable", False)
+        )
+        for main_layer_class in tf_main_layer_classes:
+            # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
+            if "T5" in main_layer_class.__name__:
+                # Take the same values than in TFT5ModelTester for this shared layer
+                shared = TFSharedEmbeddings(99, 32, name="shared")
+                config.use_cache = inputs_dict.pop("use_cache", None)
+                main_layer = main_layer_class(config, embed_tokens=shared)
+            else:
+                main_layer = main_layer_class(config)
+
+            symbolic_inputs = {
+                name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
+            }
+
+            model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
+            outputs = model(inputs_dict)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                filepath = os.path.join(tmpdirname, "keras_model.h5")
+                model.save(filepath)
+                if "T5" in main_layer_class.__name__:
+                    model = tf.keras.models.load_model(
+                        filepath,
+                        custom_objects={
+                            main_layer_class.__name__: main_layer_class,
+                            "TFSharedEmbeddings": TFSharedEmbeddings,
+                        },
+                    )
+                else:
+                    model = tf.keras.models.load_model(
+                        filepath, custom_objects={main_layer_class.__name__: main_layer_class}
+                    )
+                assert isinstance(model, tf.keras.Model)
+                after_outputs = model(inputs_dict)
+                self.assert_outputs_same(after_outputs, outputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFGroupViTModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+    @unittest.skip(reason="Currently `saved_model` doesn't work with nested outputs.")
+    @slow
+    def test_saved_model_creation(self):
+        pass
+
+    @unittest.skip(reason="Currently `saved_model` doesn't work with nested outputs.")
+    @slow
+    def test_saved_model_creation_extended(self):
+        pass
+
+    @unittest.skip(reason="`saved_model` doesn't work with nested outputs so no preparation happens.")
+    @slow
+    def test_prepare_serving_output(self):
+        pass
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@require_vision
+@require_tf
+class TFGroupViTModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model_name = "nvidia/groupvit-gcc-yfcc"
+        model = TFGroupViTModel.from_pretrained(model_name)
+        processor = CLIPProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        inputs = processor(
+            text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="tf"
+        )
+
+        outputs = model(**inputs, training=False)
+
+        # verify the logits
+        self.assertEqual(
+            outputs.logits_per_image.shape,
+            tf.TensorShape((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
+        )
+        self.assertEqual(
+            outputs.logits_per_text.shape,
+            tf.TensorShape((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
+        )
+
+        expected_logits = tf.constant([[13.3523, 6.3629]])
+
+        tf.debugging.assert_near(outputs.logits_per_image, expected_logits, atol=1e-3)

tests/test_modeling_tf_common.py

@@ -757,7 +757,7 @@ class TFModelTesterMixin:
                     name="pixel_values",
                     dtype="float32",
                 )
-            elif model_class.__name__ in ["TFCLIPModel"]:
+            elif model_class.__name__ in ["TFCLIPModel", "TFGroupViTModel"]:
                 inputs = {
                     "input_ids": tf.keras.Input(batch_shape=(3, max_input), name="input_ids", dtype="int32"),
                     "pixel_values": tf.keras.Input(

utils/check_repo.py

@@ -163,6 +163,8 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "GroupViTVisionModel",
     "TFCLIPTextModel",
     "TFCLIPVisionModel",
+    "TFGroupViTTextModel",
+    "TFGroupViTVisionModel",
     "FlaxCLIPTextModel",
     "FlaxCLIPVisionModel",
     "FlaxWav2Vec2ForCTC",

utils/documentation_tests.txt

@@ -39,6 +39,8 @@ src/transformers/models/electra/modeling_tf_electra.py
 src/transformers/models/glpn/modeling_glpn.py
 src/transformers/models/gpt2/modeling_gpt2.py
 src/transformers/models/gptj/modeling_gptj.py
+src/transformers/models/groupvit/modeling_groupvit.py
+src/transformers/models/groupvit/modeling_tf_groupvit.py
 src/transformers/models/hubert/modeling_hubert.py
 src/transformers/models/layoutlm/modeling_layoutlm.py
 src/transformers/models/layoutlm/modeling_tf_layoutlm.py