Add Flax Dinov2 (#31960)

* tfmsenv restored in main

* installed flax

* forward pass done and all tests passed

* make fix-copies and cleaning the scripts

* fixup attempt 1

* fixup attempt 2

* fixup third attempt

* fixup attempt 4

* fixup attempt 5

* dinov2 doc fixed

* FlaxDinov2Model + ForImageClassification added to OBJECTS_TO_IGNORE

* external pos_encoding layer removed

* fixup attempt 6

* fixed integration test values

* fixup attempt 7

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* comments removed

* comment removed from the test

* fixup

* Update src/transformers/models/dinov2/modeling_flax_dinov2.py

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* new fixes 1

* interpolate_pos_encoding function removed

* droppath rng fixed, pretrained beit copied-from still not working

* modeling_flax_dinov2.py reformatted

* Update tests/models/dinov2/test_modeling_flax_dinov2.py

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* added Copied from, to the tests

* copied from statements removed from tests

* fixed copied from statements in the tests

* [run_slow] dinov2

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

docs/source/en/index.md

@@ -120,7 +120,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                          [DETR](model_doc/detr)                          |       ✅        |         ❌         |      ❌      |
 |                      [DialoGPT](model_doc/dialogpt)                      |       ✅        |         ✅         |      ✅      |
 |                         [DiNAT](model_doc/dinat)                         |       ✅        |         ❌         |      ❌      |
-|                        [DINOv2](model_doc/dinov2)                        |       ✅        |         ❌         |      ❌      |
+|                        [DINOv2](model_doc/dinov2)                        |       ✅        |         ❌         |      ✅      |
 |                    [DistilBERT](model_doc/distilbert)                    |       ✅        |         ✅         |      ✅      |
 |                           [DiT](model_doc/dit)                           |       ✅        |         ❌         |      ✅      |
 |                       [DonutSwin](model_doc/donut)                       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/dinov2.md

@@ -72,6 +72,9 @@ If you're interested in submitting a resource to be included here, please feel f
 
 [[autodoc]] Dinov2Config
 
+<frameworkcontent>
+<pt>
+
 ## Dinov2Model
 
 [[autodoc]] Dinov2Model
@@ -81,3 +84,20 @@ If you're interested in submitting a resource to be included here, please feel f
 
 [[autodoc]] Dinov2ForImageClassification
     - forward
+
+</pt>
+<jax>
+
+## FlaxDinov2Model
+
+[[autodoc]] FlaxDinov2Model
+    - __call__
+
+
+## FlaxDinov2ForImageClassification
+
+[[autodoc]] FlaxDinov2ForImageClassification
+    - __call__
+
+</jax>
+</frameworkcontent>

src/transformers/__init__.py

@@ -4587,6 +4587,13 @@ else:
             "FlaxCLIPVisionPreTrainedModel",
         ]
     )
+    _import_structure["models.dinov2"].extend(
+        [
+            "FlaxDinov2Model",
+            "FlaxDinov2ForImageClassification",
+            "FlaxDinov2PreTrainedModel",
+        ]
+    )
     _import_structure["models.distilbert"].extend(
         [
             "FlaxDistilBertForMaskedLM",
@@ -8706,6 +8713,11 @@ if TYPE_CHECKING:
             FlaxCLIPVisionModel,
             FlaxCLIPVisionPreTrainedModel,
         )
+        from .models.dinov2 import (
+            FlaxDinov2ForImageClassification,
+            FlaxDinov2Model,
+            FlaxDinov2PreTrainedModel,
+        )
         from .models.distilbert import (
             FlaxDistilBertForMaskedLM,
             FlaxDistilBertForMultipleChoice,

src/transformers/models/auto/modeling_flax_auto.py

@@ -36,6 +36,7 @@ FLAX_MODEL_MAPPING_NAMES = OrderedDict(
         ("blenderbot-small", "FlaxBlenderbotSmallModel"),
         ("bloom", "FlaxBloomModel"),
         ("clip", "FlaxCLIPModel"),
+        ("dinov2", "FlaxDinov2Model"),
         ("distilbert", "FlaxDistilBertModel"),
         ("electra", "FlaxElectraModel"),
         ("gemma", "FlaxGemmaModel"),
@@ -124,6 +125,7 @@ FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Image-classsification
         ("beit", "FlaxBeitForImageClassification"),
+        ("dinov2", "FlaxDinov2ForImageClassification"),
         ("regnet", "FlaxRegNetForImageClassification"),
         ("resnet", "FlaxResNetForImageClassification"),
         ("vit", "FlaxViTForImageClassification"),

src/transformers/models/dinov2/__init__.py

@@ -16,6 +16,7 @@ from typing import TYPE_CHECKING
 from ...utils import (
     OptionalDependencyNotAvailable,
     _LazyModule,
+    is_flax_available,
     is_torch_available,
 )
 
@@ -35,6 +36,18 @@ else:
         "Dinov2Backbone",
     ]
 
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_flax_dinov2"] = [
+        "FlaxDinov2ForImageClassification",
+        "FlaxDinov2Model",
+        "FlaxDinov2PreTrainedModel",
+    ]
+
 if TYPE_CHECKING:
     from .configuration_dinov2 import Dinov2Config, Dinov2OnnxConfig
 
@@ -51,6 +64,18 @@ if TYPE_CHECKING:
             Dinov2PreTrainedModel,
         )
 
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_flax_dinov2 import (
+            FlaxDinov2ForImageClassification,
+            FlaxDinov2Model,
+            FlaxDinov2PreTrainedModel,
+        )
+
 else:
     import sys
 

src/transformers/models/dinov2/modeling_flax_dinov2.py

@@ -0,0 +1,795 @@
+# coding=utf-8
+# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Flax DINOv2 model."""
+
+import collections.abc
+import math
+from typing import Optional, Tuple
+
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.linen.attention import dot_product_attention_weights
+from flax.traverse_util import flatten_dict, unflatten_dict
+
+from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxBaseModelOutputWithPooling, FlaxSequenceClassifierOutput
+from ...modeling_flax_utils import (
+    ACT2FN,
+    FlaxPreTrainedModel,
+    append_replace_return_docstrings,
+    overwrite_call_docstring,
+)
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward
+from .configuration_dinov2 import Dinov2Config
+
+
+DINOV2_START_DOCSTRING = r"""
+
+    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
+
+    This model is also a
+    [flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as
+    a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
+    behavior.
+
+    Finally, this model supports inherent JAX features such as:
+
+    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
+    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
+    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
+    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
+
+    Parameters:
+        config ([`Dinov2Config`]): 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 [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
+        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
+            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
+            `jax.numpy.bfloat16` (on TPUs).
+
+            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
+            specified all the computation will be performed with the given `dtype`.
+
+            **Note that this only specifies the dtype of the computation and does not influence the dtype of model
+            parameters.**
+
+            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
+            [`~FlaxPreTrainedModel.to_bf16`].
+"""
+
+DINOV2_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`Dinov2ImageProcessor.__call__`]
+            for details.
+
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+class FlaxDinov2PatchEmbeddings(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        image_size = self.config.image_size
+        patch_size = self.config.patch_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.num_patches = num_patches
+        self.num_channels = self.config.num_channels
+        self.projection = nn.Conv(
+            self.config.hidden_size,
+            kernel_size=patch_size,
+            strides=patch_size,
+            padding="VALID",
+            dtype=self.dtype,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, "fan_in", "truncated_normal"
+            ),
+        )
+
+    # Copied from transformers.models.vit.modeling_flax_vit.FlaxViTPatchEmbeddings.__call__
+    def __call__(self, pixel_values):
+        num_channels = pixel_values.shape[-1]
+        if 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."
+            )
+        embeddings = self.projection(pixel_values)
+        batch_size, _, _, channels = embeddings.shape
+        return jnp.reshape(embeddings, (batch_size, -1, channels))
+
+
+class FlaxDinov2Embeddings(nn.Module):
+    """Construct the CLS token, position and patch embeddings."""
+
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        self.cls_token = self.param(
+            "cls_token",
+            jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"),
+            (1, 1, self.config.hidden_size),
+        )
+        self.mask_token = self.param(
+            "mask_token",
+            jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"),
+            (1, self.config.hidden_size),
+        )
+        self.patch_embeddings = FlaxDinov2PatchEmbeddings(self.config, dtype=self.dtype)
+        num_patches = self.patch_embeddings.num_patches
+        self.position_embeddings = self.param(
+            "position_embeddings",
+            jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"),
+            (1, num_patches + 1, self.config.hidden_size),
+        )
+        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
+
+    def interpolate_pos_encoding(self, config, hidden_states, height, width, position_embeddings):
+        num_patches = hidden_states.shape[1] - 1
+        num_positions = position_embeddings.shape[1] - 1
+        if num_patches == num_positions and height == width:
+            return position_embeddings
+        class_pos_embed = position_embeddings[:, 0]
+        patch_pos_embed = position_embeddings[:, 1:]
+        dim = hidden_states.shape[-1]
+
+        h = height // config.patch_size
+        w = width // config.patch_size
+        height, width = h + 0.1, w + 0.1
+
+        patch_pos_embed = patch_pos_embed.reshape(
+            (1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
+        )
+        patch_pos_embed = jnp.transpose(patch_pos_embed, (0, 3, 1, 2))
+        target_dtype = patch_pos_embed.dtype
+        new_height_ratio = jnp.float32(height / math.sqrt(num_positions))
+        new_width_ratio = jnp.float32(width / math.sqrt(num_positions))
+
+        scale = jnp.array([new_height_ratio, new_width_ratio], dtype=jnp.float32)
+        translation = jnp.array([0.0, 0.0], dtype=jnp.float32)
+
+        patch_pos_embed = jax.image.scale_and_translate(
+            patch_pos_embed.astype(jnp.float32),
+            shape=(patch_pos_embed.shape[0], patch_pos_embed.shape[1], h, w),
+            spatial_dims=(2, 3),
+            scale=scale,
+            translation=translation,
+            method="bicubic",
+            antialias=False,
+        )
+        patch_pos_embed = patch_pos_embed.astype(target_dtype)
+        patch_pos_embed = jnp.transpose(patch_pos_embed, (0, 2, 3, 1)).reshape((hidden_states.shape[0], -1, dim))
+
+        return jnp.concatenate((class_pos_embed[jnp.newaxis, :], patch_pos_embed), axis=1)
+
+    def __call__(self, pixel_values, deterministic=True):
+        batch_size = pixel_values.shape[0]
+        target_dtype = self.patch_embeddings.projection.dtype
+        height, width = pixel_values.shape[1], pixel_values.shape[2]
+
+        embeddings = self.patch_embeddings(pixel_values.astype(target_dtype))
+
+        cls_tokens = jnp.broadcast_to(self.cls_token, (batch_size, 1, self.config.hidden_size))
+        embeddings = jnp.concatenate((cls_tokens, embeddings), axis=1)
+
+        embeddings = embeddings + self.interpolate_pos_encoding(
+            self.config, embeddings, height, width, self.position_embeddings
+        )
+
+        embeddings = self.dropout(embeddings, deterministic=deterministic)
+        return embeddings
+
+
+# Copied from transformers.models.vit.modeling_flax_vit.FlaxViTSelfAttention with ViT->Dinov2
+class FlaxDinov2SelfAttention(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        if self.config.hidden_size % self.config.num_attention_heads != 0:
+            raise ValueError(
+                "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads`:"
+                " {self.config.num_attention_heads}"
+            )
+
+        self.query = nn.Dense(
+            self.config.hidden_size,
+            dtype=self.dtype,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"
+            ),
+            use_bias=self.config.qkv_bias,
+        )
+        self.key = nn.Dense(
+            self.config.hidden_size,
+            dtype=self.dtype,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"
+            ),
+            use_bias=self.config.qkv_bias,
+        )
+        self.value = nn.Dense(
+            self.config.hidden_size,
+            dtype=self.dtype,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal"
+            ),
+            use_bias=self.config.qkv_bias,
+        )
+
+    def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False):
+        head_dim = self.config.hidden_size // self.config.num_attention_heads
+
+        query_states = self.query(hidden_states).reshape(
+            hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
+        )
+        value_states = self.value(hidden_states).reshape(
+            hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
+        )
+        key_states = self.key(hidden_states).reshape(
+            hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
+        )
+
+        dropout_rng = None
+        if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
+            dropout_rng = self.make_rng("dropout")
+
+        attn_weights = dot_product_attention_weights(
+            query_states,
+            key_states,
+            dropout_rng=dropout_rng,
+            dropout_rate=self.config.attention_probs_dropout_prob,
+            broadcast_dropout=True,
+            deterministic=deterministic,
+            dtype=self.dtype,
+            precision=None,
+        )
+
+        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))
+
+        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
+        return outputs
+
+
+# Copied from transformers.models.vit.modeling_flax_vit.FlaxViTSelfOutput with ViT->Dinov2
+class FlaxDinov2SelfOutput(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        self.dense = nn.Dense(
+            self.config.hidden_size,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, "fan_in", "truncated_normal"
+            ),
+            dtype=self.dtype,
+        )
+        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
+
+    def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
+        return hidden_states
+
+
+# Copied from transformers.models.vit.modeling_flax_vit.FlaxViTAttention with ViT->Dinov2
+class FlaxDinov2Attention(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.attention = FlaxDinov2SelfAttention(self.config, dtype=self.dtype)
+        self.output = FlaxDinov2SelfOutput(self.config, dtype=self.dtype)
+
+    def __call__(self, hidden_states, deterministic=True, output_attentions: bool = False):
+        attn_outputs = self.attention(hidden_states, deterministic=deterministic, output_attentions=output_attentions)
+        attn_output = attn_outputs[0]
+        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_outputs[1],)
+
+        return outputs
+
+
+def ones_with_scale(key, shape, scale, dtype=jnp.float32):
+    return jnp.ones(shape, dtype) * scale
+
+
+class FlaxDinov2LayerScale(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        self.lambda1 = self.config.layerscale_value * self.param(
+            "lambda1",
+            jax.nn.initializers.ones,
+            (self.config.hidden_size,),
+        )
+        self.lambda1 = self.lambda1 * self.config.layerscale_value
+
+    def __call__(self, hidden_states):
+        return self.lambda1 * hidden_states
+
+
+# Copied from transformers.models.beit.modeling_flax_beit.FlaxBeitDropPath with Beit -> Dinov2
+class FlaxDinov2DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    rate: float
+
+    @nn.module.compact
+    def __call__(self, inputs, deterministic: Optional[bool] = True):
+        if self.rate == 0.0:
+            return inputs
+        keep_prob = 1.0 - self.rate
+        if deterministic:
+            return inputs
+        else:
+            shape = (inputs.shape[0],) + (1,) * (inputs.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+            rng = self.make_rng("droppath")
+            random_tensor = keep_prob + jax.random.uniform(rng, shape=shape, dtype=inputs.dtype)
+            binary_tensor = jnp.floor(random_tensor)
+            output = inputs / keep_prob * binary_tensor
+            return output
+
+
+class FlaxDinov2MLP(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        self.fc1 = nn.Dense(
+            self.config.hidden_size * self.config.mlp_ratio,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, "fan_in", "truncated_normal"
+            ),
+            dtype=self.dtype,
+        )
+        self.fc2 = nn.Dense(
+            self.config.hidden_size,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, "fan_in", "truncated_normal"
+            ),
+            dtype=self.dtype,
+        )
+        if isinstance(self.config.hidden_act, str):
+            self.act = ACT2FN[self.config.hidden_act]
+        else:
+            self.act = self.config.hidden_act
+
+    def __call__(self, hidden_states):
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class FlaxDinov2SwiGLUFFN(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        hidden_features = int(self.config.hidden_size * self.config.mlp_ratio)
+        hidden_features = (int(self.hidden_features * 2 / 3) + 7) // 8 * 8
+
+        self.weights_in = nn.Dense(
+            2 * hidden_features,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, "fan_in", "truncated_normal"
+            ),
+            dtype=self.dtype,
+        )
+        self.weights_out = nn.Dense(
+            self.config.hidden_size,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, "fan_in", "truncated_normal"
+            ),
+            dtype=self.dtype,
+        )
+
+    def __call__(self, hidden_states):
+        hidden_states = self.weights_in(hidden_states)
+        x1, x2 = jnp.split(hidden_states, 2, axis=-1)
+        hidden = nn.silu(x1) * x2
+        return self.weights_out(hidden)
+
+
+class FlaxDinov2Layer(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        self.norm1 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
+        self.attention = FlaxDinov2Attention(self.config, dtype=self.dtype)
+        self.layer_scale1 = FlaxDinov2LayerScale(self.config, dtype=self.dtype)
+        self.drop_path = FlaxDinov2DropPath(self.config.drop_path_rate)
+        self.norm2 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
+
+        if self.config.use_swiglu_ffn:
+            self.mlp = FlaxDinov2SwiGLUFFN(self.config, dtype=self.dtype)
+        else:
+            self.mlp = FlaxDinov2MLP(self.config, dtype=self.dtype)
+
+        self.layer_scale2 = FlaxDinov2LayerScale(self.config, dtype=self.dtype)
+
+    def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False):
+        self_attention_outputs = self.attention(
+            self.norm1(hidden_states),  # in Dinov2, layernorm is applied before self-attention
+            deterministic=deterministic,
+            output_attentions=output_attentions,
+        )
+
+        attention_output = self_attention_outputs[0]
+
+        attention_output = self.layer_scale1(attention_output)
+
+        outputs = self_attention_outputs[1:]
+
+        # first residual connection
+        hidden_states = self.drop_path(attention_output) + hidden_states
+
+        # in Dinov2, layernorm is also applied after self-attention
+        layer_output = self.norm2(hidden_states)
+        layer_output = self.mlp(layer_output)
+        layer_output = self.layer_scale2(layer_output)
+
+        # second residual connection
+        layer_output = self.drop_path(layer_output) + hidden_states
+
+        outputs = (layer_output,) + outputs
+
+        return outputs
+
+
+# Copied from transformers.models.vit.modeling_flax_vit.FlaxViTLayerCollection with ViT->Dinov2
+class FlaxDinov2LayerCollection(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        self.layers = [
+            FlaxDinov2Layer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
+        ]
+
+    def __call__(
+        self,
+        hidden_states,
+        deterministic: bool = True,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ):
+        all_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+
+        for i, layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = layer(hidden_states, deterministic=deterministic, output_attentions=output_attentions)
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions += (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        outputs = (hidden_states,)
+        if not return_dict:
+            return tuple(v for v in outputs if v is not None)
+
+        return FlaxBaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+        )
+
+
+# Copied from transformers.models.vit.modeling_flax_vit.FlaxViTEncoder with ViT->Dinov2
+class FlaxDinov2Encoder(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        self.layer = FlaxDinov2LayerCollection(self.config, dtype=self.dtype)
+
+    def __call__(
+        self,
+        hidden_states,
+        deterministic: bool = True,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ):
+        return self.layer(
+            hidden_states,
+            deterministic=deterministic,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+
+class FlaxDinov2PreTrainedModel(FlaxPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = Dinov2Config
+    base_model_prefix = "dinov2"
+    main_input_name = "pixel_values"
+    module_class: nn.Module = None
+
+    def __init__(
+        self,
+        config: Dinov2Config,
+        input_shape=None,
+        seed: int = 0,
+        dtype: jnp.dtype = jnp.float32,
+        _do_init: bool = True,
+        **kwargs,
+    ):
+        module = self.module_class(config=config, dtype=dtype, **kwargs)
+        if input_shape is None:
+            input_shape = (1, config.image_size, config.image_size, config.num_channels)
+        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
+
+    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
+        # init input tensors
+        pixel_values = jnp.zeros(input_shape, dtype=self.dtype)
+
+        params_rng, dropout_rng = jax.random.split(rng)
+        dropout_rng, droppath_rng = jax.random.split(dropout_rng)
+        rngs = {"params": params_rng, "dropout": dropout_rng, "droppath": droppath_rng}
+
+        random_params = self.module.init(rngs, pixel_values, return_dict=False)["params"]
+
+        if params is not None:
+            random_params = flatten_dict(unfreeze(random_params))
+            params = flatten_dict(unfreeze(params))
+            for missing_key in self._missing_keys:
+                params[missing_key] = random_params[missing_key]
+            self._missing_keys = set()
+            return freeze(unflatten_dict(params))
+        else:
+            return random_params
+
+    @add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    def __call__(
+        self,
+        pixel_values,
+        params: dict = None,
+        dropout_rng: jax.random.PRNGKey = None,
+        train: bool = False,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
+        # Handle any PRNG if needed
+        rngs = {}
+        if dropout_rng is not None:
+            dropout_rng, droppath_rng = jax.random.split(dropout_rng)
+            rngs["dropout"] = dropout_rng
+            rngs["droppath"] = droppath_rng
+
+        return self.module.apply(
+            {"params": params or self.params},
+            jnp.array(pixel_values, dtype=jnp.float32),
+            not train,
+            output_attentions,
+            output_hidden_states,
+            return_dict,
+            rngs=rngs,
+        )
+
+
+class FlaxDinov2Module(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        self.embeddings = FlaxDinov2Embeddings(self.config, dtype=self.dtype)
+        self.encoder = FlaxDinov2Encoder(self.config, dtype=self.dtype)
+        self.layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
+
+    def __call__(
+        self,
+        pixel_values,
+        deterministic: bool = True,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ):
+        hidden_states = self.embeddings(pixel_values, deterministic=deterministic)
+
+        encoder_outputs = self.encoder(
+            hidden_states,
+            deterministic=deterministic,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.layernorm(sequence_output)
+        pooled_output = sequence_output[:, 0, :]
+
+        if not return_dict:
+            head_outputs = (sequence_output, pooled_output)
+            return head_outputs + encoder_outputs[1:]
+
+        return FlaxBaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    "The bare Dinov2 Model transformer outputting raw hidden-states without any specific head on top.",
+    DINOV2_START_DOCSTRING,
+)
+class FlaxDinov2Model(FlaxDinov2PreTrainedModel):
+    module_class = FlaxDinov2Module
+
+
+FLAX_VISION_MODEL_DOCSTRING = """
+    Returns:
+
+    Examples:
+
+    ```python
+    >>> from transformers import AutoImageProcessor, FlaxDinov2Model
+    >>> from PIL import Image
+    >>> import requests
+
+    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> image_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
+    >>> model = FlaxDinov2Model.from_pretrained("facebook/dinov2-base")
+
+    >>> inputs = image_processor(images=image, return_tensors="np")
+    >>> outputs = model(**inputs)
+    >>> last_hidden_states = outputs.last_hidden_state
+    ```
+"""
+
+overwrite_call_docstring(FlaxDinov2Model, FLAX_VISION_MODEL_DOCSTRING)
+append_replace_return_docstrings(
+    FlaxDinov2Model, output_type=FlaxBaseModelOutputWithPooling, config_class=Dinov2Config
+)
+
+
+class FlaxDinov2ForImageClassificationModule(nn.Module):
+    config: Dinov2Config
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.dinov2 = FlaxDinov2Module(config=self.config, dtype=self.dtype)
+        self.classifier = nn.Dense(
+            self.config.num_labels,
+            dtype=self.dtype,
+            kernel_init=jax.nn.initializers.variance_scaling(
+                self.config.initializer_range**2, "fan_in", "truncated_normal"
+            ),
+        )
+
+    def __call__(
+        self,
+        pixel_values=None,
+        deterministic: bool = True,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.dinov2(
+            pixel_values,
+            deterministic=deterministic,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+
+        cls_token = hidden_states[:, 0]
+        patch_tokens = hidden_states[:, 1:]
+        linear_input = jnp.concatenate([cls_token, patch_tokens.mean(axis=1)], axis=-1)
+
+        logits = self.classifier(linear_input)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return output
+
+        return FlaxSequenceClassifierOutput(
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Dinov2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
+    the [CLS] token) e.g. for ImageNet.
+    """,
+    DINOV2_START_DOCSTRING,
+)
+class FlaxDinov2ForImageClassification(FlaxDinov2PreTrainedModel):
+    module_class = FlaxDinov2ForImageClassificationModule
+
+
+FLAX_VISION_CLASSIFICATION_DOCSTRING = """
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoImageProcessor, FlaxDinov2ForImageClassification
+    >>> from PIL import Image
+    >>> import jax
+    >>> import requests
+
+    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> image_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base-imagenet1k-1-layer")
+    >>> model = FlaxDinov2ForImageClassification.from_pretrained("facebook/dinov2-base-imagenet1k-1-layer")
+
+    >>> inputs = image_processor(images=image, return_tensors="np")
+    >>> outputs = model(**inputs)
+    >>> logits = outputs.logits
+
+    >>> # model predicts one of the 1000 ImageNet classes
+    >>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1)
+    >>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()])
+    ```
+"""
+
+overwrite_call_docstring(FlaxDinov2ForImageClassification, FLAX_VISION_CLASSIFICATION_DOCSTRING)
+append_replace_return_docstrings(
+    FlaxDinov2ForImageClassification, output_type=FlaxSequenceClassifierOutput, config_class=Dinov2Config
+)

src/transformers/utils/dummy_flax_objects.py

@@ -618,6 +618,27 @@ class FlaxCLIPVisionPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["flax"])
 
 
+class FlaxDinov2ForImageClassification(metaclass=DummyObject):
+    _backends = ["flax"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxDinov2Model(metaclass=DummyObject):
+    _backends = ["flax"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxDinov2PreTrainedModel(metaclass=DummyObject):
+    _backends = ["flax"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
 class FlaxDistilBertForMaskedLM(metaclass=DummyObject):
     _backends = ["flax"]
 

tests/models/dinov2/test_modeling_flax_dinov2.py

@@ -0,0 +1,263 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the Flax Dinov2 model."""
+
+import inspect
+import unittest
+
+import numpy as np
+
+from transformers import Dinov2Config
+from transformers.testing_utils import require_flax, require_vision, slow
+from transformers.utils import cached_property, is_flax_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
+
+
+if is_flax_available():
+    import jax
+
+    from transformers.models.dinov2.modeling_flax_dinov2 import FlaxDinov2ForImageClassification, FlaxDinov2Model
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import AutoImageProcessor
+
+
+class FlaxDinov2ModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        use_labels=True,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        type_sequence_label_size=10,
+        initializer_range=0.02,
+    ):
+        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.use_labels = use_labels
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+
+        # in Dinov2, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches + 1
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        config = Dinov2Config(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+        )
+
+        return config, pixel_values
+
+    # Copied from transformers.models.vit.test_modeling_flax_vit.FlaxViTModelTester.prepare_config_and_inputs with ViT -> Dinov2
+    def create_and_check_model(self, config, pixel_values):
+        model = FlaxDinov2Model(config=config)
+        result = model(pixel_values)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        image_size = (self.image_size, self.image_size)
+        patch_size = (self.patch_size, self.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
+
+    # Copied from transformers.models.vit.test_modeling_flax_vit.FlaxViTModelTester.create_and_check_for_image_classification with ViT -> Dinov2
+    def create_and_check_for_image_classification(self, config, pixel_values):
+        config.num_labels = self.type_sequence_label_size
+        model = FlaxDinov2ForImageClassification(config=config)
+        result = model(pixel_values)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+
+        # test greyscale images
+        config.num_channels = 1
+        model = FlaxDinov2ForImageClassification(config)
+
+        pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
+        result = model(pixel_values)
+
+    # Copied from transformers.models.vit.test_modeling_flax_vit.FlaxViTModelTester.prepare_config_and_inputs_for_common
+    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_flax
+# Copied from transformers.models.vit.test_modeling_flax_vit.FlaxViTModelTest with google/vit-base-patch16-224 -> facebook/dinov2-base
+class FlaxDionv2ModelTest(FlaxModelTesterMixin, unittest.TestCase):
+    all_model_classes = (FlaxDinov2Model, FlaxDinov2ForImageClassification) if is_flax_available() else ()
+
+    def setUp(self) -> None:
+        self.model_tester = FlaxDinov2ModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Dinov2Config, has_text_modality=False, 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)
+
+    def test_for_image_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
+
+    # We need to override this test because Dinov2's forward signature is different than text models.
+    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)
+
+    # We need to override this test because Dinov2 expects pixel_values instead of input_ids
+    def test_jit_compilation(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            with self.subTest(model_class.__name__):
+                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+                model = model_class(config)
+
+                @jax.jit
+                def model_jitted(pixel_values, **kwargs):
+                    return model(pixel_values=pixel_values, **kwargs)
+
+                with self.subTest("JIT Enabled"):
+                    jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
+
+                with self.subTest("JIT Disabled"):
+                    with jax.disable_jit():
+                        outputs = model_jitted(**prepared_inputs_dict).to_tuple()
+
+                self.assertEqual(len(outputs), len(jitted_outputs))
+                for jitted_output, output in zip(jitted_outputs, outputs):
+                    self.assertEqual(jitted_output.shape, output.shape)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_class_name in self.all_model_classes:
+            model = model_class_name.from_pretrained("facebook/dinov2-base")
+            outputs = model(np.ones((1, 3, 224, 224)))
+            self.assertIsNotNone(outputs)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_vision
+@require_flax
+class FlaxDinov2ModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return AutoImageProcessor.from_pretrained("facebook/dinov2-base") if is_vision_available() else None
+
+    @slow
+    def test_inference_no_head(self):
+        model = FlaxDinov2Model.from_pretrained("facebook/dinov2-base")
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        pixel_values = image_processor(images=image, return_tensors="np").pixel_values
+
+        # forward pass
+        outputs = model(pixel_values=pixel_values)
+
+        # verify the logits
+        expected_shape = (1, 257, 768)
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
+
+        expected_slice = np.array(
+            [
+                [-2.1629121, -0.46566057, 1.0925977],
+                [-3.5971704, -1.0283585, -1.1780515],
+                [-2.900407, 1.1334689, -0.74357724],
+            ]
+        )
+
+        self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
+
+    @slow
+    def test_inference_image_classification_head_imagenet_1k(self):
+        model = FlaxDinov2ForImageClassification.from_pretrained(
+            "facebook/dinov2-base-imagenet1k-1-layer", from_pt=True
+        )
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="np")
+
+        # forward pass
+        outputs = model(**inputs)
+        logits = outputs.logits
+
+        # verify the logits
+        expected_shape = (1, 1000)
+        self.assertEqual(logits.shape, expected_shape)
+
+        expected_slice = np.array([-2.1776447, 0.36716992, 0.13870952])
+
+        self.assertTrue(np.allclose(logits[0, :3], expected_slice, atol=1e-4))
+
+        expected_class_idx = 281
+        self.assertEqual(logits.argmax(-1).item(), expected_class_idx)

utils/check_docstrings.py

@@ -214,6 +214,8 @@ OBJECTS_TO_IGNORE = [
     "FlaxBloomForCausalLM",
     "FlaxBloomModel",
     "FlaxCLIPModel",
+    "FlaxDinov2ForImageClassification",
+    "FlaxDinov2Model",
     "FlaxDistilBertForMaskedLM",
     "FlaxDistilBertForMultipleChoice",
     "FlaxDistilBertForQuestionAnswering",