Flax Regnet (#21867)

* initial commit

* review changes

* post model PR merge

* updating doc

docs/source/de/index.mdx

@@ -283,7 +283,7 @@ Flax), PyTorch, und/oder TensorFlow haben.
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            REALM            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RegNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|           RegNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           ResNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/en/index.mdx

@@ -377,7 +377,7 @@ Flax), PyTorch, and/or TensorFlow.
 |              RAG              |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |             REALM             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |           Reformer            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|            RegNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            RegNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |            RemBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            ResNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           RetriBERT           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/regnet.mdx

@@ -68,3 +68,15 @@ If you're interested in submitting a resource to be included here, please feel f
 
 [[autodoc]] TFRegNetForImageClassification
     - call
+
+
+## FlaxRegNetModel
+
+[[autodoc]] FlaxRegNetModel
+    - __call__
+
+
+## FlaxRegNetForImageClassification
+
+[[autodoc]] FlaxRegNetForImageClassification
+    - __call__

docs/source/es/index.mdx

@@ -235,7 +235,7 @@ Flax), PyTorch y/o TensorFlow.
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            Realm            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RegNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           RegNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           ResNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/fr/index.mdx

@@ -347,7 +347,7 @@ Le tableau ci-dessous représente la prise en charge actuelle dans la bibliothè
 |              RAG              |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |             REALM             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |           Reformer            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|            RegNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            RegNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |            RemBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            ResNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           RetriBERT           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/it/index.mdx

@@ -252,7 +252,7 @@ tokenizer (chiamato "slow"). Un tokenizer "fast" supportato dalla libreria 🤗
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            Realm            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RegNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           RegNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           ResNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/ja/index.mdx

@@ -337,7 +337,7 @@ specific language governing permissions and limitations under the License.
 |              RAG              |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |             REALM             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |           Reformer            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|            RegNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            RegNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |            RemBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            ResNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           RetriBERT           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/ko/index.mdx

@@ -306,7 +306,7 @@ specific language governing permissions and limitations under the License.
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            REALM            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RegNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|           RegNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           ResNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/pt/index.mdx

@@ -250,7 +250,7 @@ disso, são diferenciados pelo suporte em diferentes frameworks: JAX (por meio d
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            Realm            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RegNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           RegNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           ResNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/zh/index.mdx

@@ -336,7 +336,7 @@ Flax), PyTorch, 和/或者 TensorFlow.
 |              RAG              |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |             REALM             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |           Reformer            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|            RegNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            RegNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |            RemBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            ResNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           RetriBERT           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

src/transformers/__init__.py

@@ -3661,6 +3661,9 @@ else:
             "FlaxPegasusPreTrainedModel",
         ]
     )
+    _import_structure["models.regnet"].extend(
+        ["FlaxRegNetForImageClassification", "FlaxRegNetModel", "FlaxRegNetPreTrainedModel"]
+    )
     _import_structure["models.resnet"].extend(
         ["FlaxResNetForImageClassification", "FlaxResNetModel", "FlaxResNetPreTrainedModel"]
     )
@@ -6739,6 +6742,7 @@ if TYPE_CHECKING:
         from .models.mt5 import FlaxMT5EncoderModel, FlaxMT5ForConditionalGeneration, FlaxMT5Model
         from .models.opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
         from .models.pegasus import FlaxPegasusForConditionalGeneration, FlaxPegasusModel, FlaxPegasusPreTrainedModel
+        from .models.regnet import FlaxRegNetForImageClassification, FlaxRegNetModel, FlaxRegNetPreTrainedModel
         from .models.resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
         from .models.roberta import (
             FlaxRobertaForCausalLM,

src/transformers/models/auto/modeling_flax_auto.py

@@ -48,6 +48,7 @@ FLAX_MODEL_MAPPING_NAMES = OrderedDict(
         ("mt5", "FlaxMT5Model"),
         ("opt", "FlaxOPTModel"),
         ("pegasus", "FlaxPegasusModel"),
+        ("regnet", "FlaxRegNetModel"),
         ("resnet", "FlaxResNetModel"),
         ("roberta", "FlaxRobertaModel"),
         ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
@@ -120,6 +121,7 @@ FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Image-classsification
         ("beit", "FlaxBeitForImageClassification"),
+        ("regnet", "FlaxRegNetForImageClassification"),
         ("resnet", "FlaxResNetForImageClassification"),
         ("vit", "FlaxViTForImageClassification"),
     ]

src/transformers/models/regnet/__init__.py

@@ -13,7 +13,13 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_torch_available,
+)
 
 
 _import_structure = {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}
@@ -44,6 +50,18 @@ else:
         "TFRegNetPreTrainedModel",
     ]
 
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_flax_regnet"] = [
+        "FlaxRegNetForImageClassification",
+        "FlaxRegNetModel",
+        "FlaxRegNetPreTrainedModel",
+    ]
+
 
 if TYPE_CHECKING:
     from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
@@ -74,6 +92,18 @@ if TYPE_CHECKING:
             TFRegNetPreTrainedModel,
         )
 
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_flax_regnet import (
+            FlaxRegNetForImageClassification,
+            FlaxRegNetModel,
+            FlaxRegNetPreTrainedModel,
+        )
+
 
 else:
     import sys

src/transformers/models/regnet/modeling_flax_regnet.py

@@ -0,0 +1,818 @@
+# coding=utf-8
+# Copyright 2023 The Google Flax Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from functools import partial
+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.traverse_util import flatten_dict, unflatten_dict
+
+from transformers import RegNetConfig
+from transformers.modeling_flax_outputs import (
+    FlaxBaseModelOutputWithNoAttention,
+    FlaxBaseModelOutputWithPooling,
+    FlaxBaseModelOutputWithPoolingAndNoAttention,
+    FlaxImageClassifierOutputWithNoAttention,
+)
+from transformers.modeling_flax_utils import (
+    ACT2FN,
+    FlaxPreTrainedModel,
+    append_replace_return_docstrings,
+    overwrite_call_docstring,
+)
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+)
+
+
+REGNET_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 [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
+    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
+    general usage and behavior.
+
+    Finally, this model supports inherent JAX features such as:
+
+    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
+    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
+    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
+    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
+
+    Parameters:
+        config ([`RegNetConfig`]): 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`].
+"""
+
+REGNET_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
+            [`RegNetImageProcessor.__call__`] for details.
+
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+# Copied from transformers.models.resnet.modeling_flax_resnet.Identity
+class Identity(nn.Module):
+    """Identity function."""
+
+    @nn.compact
+    def __call__(self, x, **kwargs):
+        return x
+
+
+class FlaxRegNetConvLayer(nn.Module):
+    out_channels: int
+    kernel_size: int = 3
+    stride: int = 1
+    groups: int = 1
+    activation: Optional[str] = "relu"
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.convolution = nn.Conv(
+            self.out_channels,
+            kernel_size=(self.kernel_size, self.kernel_size),
+            strides=self.stride,
+            padding=self.kernel_size // 2,
+            feature_group_count=self.groups,
+            use_bias=False,
+            kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
+            dtype=self.dtype,
+        )
+        self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
+        self.activation_func = ACT2FN[self.activation] if self.activation is not None else Identity()
+
+    def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+        hidden_state = self.convolution(hidden_state)
+        hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
+        hidden_state = self.activation_func(hidden_state)
+        return hidden_state
+
+
+class FlaxRegNetEmbeddings(nn.Module):
+    config: RegNetConfig
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.embedder = FlaxRegNetConvLayer(
+            self.config.embedding_size,
+            kernel_size=3,
+            stride=2,
+            activation=self.config.hidden_act,
+            dtype=self.dtype,
+        )
+
+    def __call__(self, pixel_values: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+        num_channels = pixel_values.shape[-1]
+        if num_channels != self.config.num_channels:
+            raise ValueError(
+                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
+            )
+        hidden_state = self.embedder(pixel_values, deterministic=deterministic)
+        return hidden_state
+
+
+# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetShortCut with ResNet->RegNet
+class FlaxRegNetShortCut(nn.Module):
+    """
+    RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
+    downsample the input using `stride=2`.
+    """
+
+    out_channels: int
+    stride: int = 2
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.convolution = nn.Conv(
+            self.out_channels,
+            kernel_size=(1, 1),
+            strides=self.stride,
+            use_bias=False,
+            kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
+            dtype=self.dtype,
+        )
+        self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
+
+    def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+        hidden_state = self.convolution(x)
+        hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
+        return hidden_state
+
+
+class FlaxRegNetSELayerCollection(nn.Module):
+    in_channels: int
+    reduced_channels: int
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.conv_1 = nn.Conv(
+            self.reduced_channels,
+            kernel_size=(1, 1),
+            kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
+            dtype=self.dtype,
+            name="0",
+        )  # 0 is the name used in corresponding pytorch implementation
+        self.conv_2 = nn.Conv(
+            self.in_channels,
+            kernel_size=(1, 1),
+            kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
+            dtype=self.dtype,
+            name="2",
+        )  # 2 is the name used in corresponding pytorch implementation
+
+    def __call__(self, hidden_state: jnp.ndarray) -> jnp.ndarray:
+        hidden_state = self.conv_1(hidden_state)
+        hidden_state = nn.relu(hidden_state)
+        hidden_state = self.conv_2(hidden_state)
+        attention = nn.sigmoid(hidden_state)
+
+        return attention
+
+
+class FlaxRegNetSELayer(nn.Module):
+    """
+    Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507).
+    """
+
+    in_channels: int
+    reduced_channels: int
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.pooler = partial(nn.avg_pool, padding=((0, 0), (0, 0)))
+        self.attention = FlaxRegNetSELayerCollection(self.in_channels, self.reduced_channels, dtype=self.dtype)
+
+    def __call__(self, hidden_state: jnp.ndarray) -> jnp.ndarray:
+        pooled = self.pooler(
+            hidden_state,
+            window_shape=(hidden_state.shape[1], hidden_state.shape[2]),
+            strides=(hidden_state.shape[1], hidden_state.shape[2]),
+        )
+        attention = self.attention(pooled)
+        hidden_state = hidden_state * attention
+        return hidden_state
+
+
+class FlaxRegNetXLayerCollection(nn.Module):
+    config: RegNetConfig
+    out_channels: int
+    stride: int = 1
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        groups = max(1, self.out_channels // self.config.groups_width)
+
+        self.layer = [
+            FlaxRegNetConvLayer(
+                self.out_channels,
+                kernel_size=1,
+                activation=self.config.hidden_act,
+                dtype=self.dtype,
+                name="0",
+            ),
+            FlaxRegNetConvLayer(
+                self.out_channels,
+                stride=self.stride,
+                groups=groups,
+                activation=self.config.hidden_act,
+                dtype=self.dtype,
+                name="1",
+            ),
+            FlaxRegNetConvLayer(
+                self.out_channels,
+                kernel_size=1,
+                activation=None,
+                dtype=self.dtype,
+                name="2",
+            ),
+        ]
+
+    def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+        for layer in self.layer:
+            hidden_state = layer(hidden_state, deterministic=deterministic)
+        return hidden_state
+
+
+class FlaxRegNetXLayer(nn.Module):
+    """
+    RegNet's layer composed by three `3x3` convolutions, same as a ResNet bottleneck layer with reduction = 1.
+    """
+
+    config: RegNetConfig
+    in_channels: int
+    out_channels: int
+    stride: int = 1
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
+        self.shortcut = (
+            FlaxRegNetShortCut(
+                self.out_channels,
+                stride=self.stride,
+                dtype=self.dtype,
+            )
+            if should_apply_shortcut
+            else Identity()
+        )
+        self.layer = FlaxRegNetXLayerCollection(
+            self.config,
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            stride=self.stride,
+            dtype=self.dtype,
+        )
+        self.activation_func = ACT2FN[self.config.hidden_act]
+
+    def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+        residual = hidden_state
+        hidden_state = self.layer(hidden_state)
+        residual = self.shortcut(residual, deterministic=deterministic)
+        hidden_state += residual
+        hidden_state = self.activation_func(hidden_state)
+        return hidden_state
+
+
+class FlaxRegNetYLayerCollection(nn.Module):
+    config: RegNetConfig
+    in_channels: int
+    out_channels: int
+    stride: int = 1
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        groups = max(1, self.out_channels // self.config.groups_width)
+
+        self.layer = [
+            FlaxRegNetConvLayer(
+                self.out_channels,
+                kernel_size=1,
+                activation=self.config.hidden_act,
+                dtype=self.dtype,
+                name="0",
+            ),
+            FlaxRegNetConvLayer(
+                self.out_channels,
+                stride=self.stride,
+                groups=groups,
+                activation=self.config.hidden_act,
+                dtype=self.dtype,
+                name="1",
+            ),
+            FlaxRegNetSELayer(
+                self.out_channels,
+                reduced_channels=int(round(self.in_channels / 4)),
+                dtype=self.dtype,
+                name="2",
+            ),
+            FlaxRegNetConvLayer(
+                self.out_channels,
+                kernel_size=1,
+                activation=None,
+                dtype=self.dtype,
+                name="3",
+            ),
+        ]
+
+    def __call__(self, hidden_state: jnp.ndarray) -> jnp.ndarray:
+        for layer in self.layer:
+            hidden_state = layer(hidden_state)
+        return hidden_state
+
+
+class FlaxRegNetYLayer(nn.Module):
+    """
+    RegNet's Y layer: an X layer with Squeeze and Excitation.
+    """
+
+    config: RegNetConfig
+    in_channels: int
+    out_channels: int
+    stride: int = 1
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
+
+        self.shortcut = (
+            FlaxRegNetShortCut(
+                self.out_channels,
+                stride=self.stride,
+                dtype=self.dtype,
+            )
+            if should_apply_shortcut
+            else Identity()
+        )
+        self.layer = FlaxRegNetYLayerCollection(
+            self.config,
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            stride=self.stride,
+            dtype=self.dtype,
+        )
+        self.activation_func = ACT2FN[self.config.hidden_act]
+
+    def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+        residual = hidden_state
+        hidden_state = self.layer(hidden_state)
+        residual = self.shortcut(residual, deterministic=deterministic)
+        hidden_state += residual
+        hidden_state = self.activation_func(hidden_state)
+        return hidden_state
+
+
+class FlaxRegNetStageLayersCollection(nn.Module):
+    """
+    A RegNet stage composed by stacked layers.
+    """
+
+    config: RegNetConfig
+    in_channels: int
+    out_channels: int
+    stride: int = 2
+    depth: int = 2
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        layer = FlaxRegNetXLayer if self.config.layer_type == "x" else FlaxRegNetYLayer
+
+        layers = [
+            # downsampling is done in the first layer with stride of 2
+            layer(
+                self.config,
+                self.in_channels,
+                self.out_channels,
+                stride=self.stride,
+                dtype=self.dtype,
+                name="0",
+            )
+        ]
+
+        for i in range(self.depth - 1):
+            layers.append(
+                layer(
+                    self.config,
+                    self.out_channels,
+                    self.out_channels,
+                    dtype=self.dtype,
+                    name=str(i + 1),
+                )
+            )
+
+        self.layers = layers
+
+    def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+        hidden_state = x
+        for layer in self.layers:
+            hidden_state = layer(hidden_state, deterministic=deterministic)
+        return hidden_state
+
+
+# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetStage with ResNet->RegNet
+class FlaxRegNetStage(nn.Module):
+    """
+    A RegNet stage composed by stacked layers.
+    """
+
+    config: RegNetConfig
+    in_channels: int
+    out_channels: int
+    stride: int = 2
+    depth: int = 2
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.layers = FlaxRegNetStageLayersCollection(
+            self.config,
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            stride=self.stride,
+            depth=self.depth,
+            dtype=self.dtype,
+        )
+
+    def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+        return self.layers(x, deterministic=deterministic)
+
+
+# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetStageCollection with ResNet->RegNet
+class FlaxRegNetStageCollection(nn.Module):
+    config: RegNetConfig
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        in_out_channels = zip(self.config.hidden_sizes, self.config.hidden_sizes[1:])
+        stages = [
+            FlaxRegNetStage(
+                self.config,
+                self.config.embedding_size,
+                self.config.hidden_sizes[0],
+                stride=2 if self.config.downsample_in_first_stage else 1,
+                depth=self.config.depths[0],
+                dtype=self.dtype,
+                name="0",
+            )
+        ]
+
+        for i, ((in_channels, out_channels), depth) in enumerate(zip(in_out_channels, self.config.depths[1:])):
+            stages.append(
+                FlaxRegNetStage(self.config, in_channels, out_channels, depth=depth, dtype=self.dtype, name=str(i + 1))
+            )
+
+        self.stages = stages
+
+    def __call__(
+        self,
+        hidden_state: jnp.ndarray,
+        output_hidden_states: bool = False,
+        deterministic: bool = True,
+    ) -> FlaxBaseModelOutputWithNoAttention:
+        hidden_states = () if output_hidden_states else None
+
+        for stage_module in self.stages:
+            if output_hidden_states:
+                hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
+
+            hidden_state = stage_module(hidden_state, deterministic=deterministic)
+
+        return hidden_state, hidden_states
+
+
+# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetEncoder with ResNet->RegNet
+class FlaxRegNetEncoder(nn.Module):
+    config: RegNetConfig
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.stages = FlaxRegNetStageCollection(self.config, dtype=self.dtype)
+
+    def __call__(
+        self,
+        hidden_state: jnp.ndarray,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+        deterministic: bool = True,
+    ) -> FlaxBaseModelOutputWithNoAttention:
+        hidden_state, hidden_states = self.stages(
+            hidden_state, output_hidden_states=output_hidden_states, deterministic=deterministic
+        )
+
+        if output_hidden_states:
+            hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_state, hidden_states] if v is not None)
+
+        return FlaxBaseModelOutputWithNoAttention(
+            last_hidden_state=hidden_state,
+            hidden_states=hidden_states,
+        )
+
+
+# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetPreTrainedModel with ResNet->RegNet,resnet->regnet,RESNET->REGNET
+class FlaxRegNetPreTrainedModel(FlaxPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = RegNetConfig
+    base_model_prefix = "regnet"
+    main_input_name = "pixel_values"
+    module_class: nn.Module = None
+
+    def __init__(
+        self,
+        config: RegNetConfig,
+        input_shape=(1, 224, 224, 3),
+        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)
+
+        rngs = {"params": rng}
+
+        random_params = self.module.init(rngs, pixel_values, return_dict=False)
+
+        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(REGNET_INPUTS_DOCSTRING)
+    def __call__(
+        self,
+        pixel_values,
+        params: dict = None,
+        train: bool = False,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        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 = {}
+
+        return self.module.apply(
+            {
+                "params": params["params"] if params is not None else self.params["params"],
+                "batch_stats": params["batch_stats"] if params is not None else self.params["batch_stats"],
+            },
+            jnp.array(pixel_values, dtype=jnp.float32),
+            not train,
+            output_hidden_states,
+            return_dict,
+            rngs=rngs,
+            mutable=["batch_stats"] if train else False,  # Returing tuple with batch_stats only when train is True
+        )
+
+
+# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetModule with ResNet->RegNet
+class FlaxRegNetModule(nn.Module):
+    config: RegNetConfig
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+
+    def setup(self):
+        self.embedder = FlaxRegNetEmbeddings(self.config, dtype=self.dtype)
+        self.encoder = FlaxRegNetEncoder(self.config, dtype=self.dtype)
+
+        # Adaptive average pooling used in resnet
+        self.pooler = partial(
+            nn.avg_pool,
+            padding=((0, 0), (0, 0)),
+        )
+
+    def __call__(
+        self,
+        pixel_values,
+        deterministic: bool = True,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ) -> FlaxBaseModelOutputWithPoolingAndNoAttention:
+        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.use_return_dict
+
+        embedding_output = self.embedder(pixel_values, deterministic=deterministic)
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            deterministic=deterministic,
+        )
+
+        last_hidden_state = encoder_outputs[0]
+
+        pooled_output = self.pooler(
+            last_hidden_state,
+            window_shape=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
+            strides=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
+        ).transpose(0, 3, 1, 2)
+
+        last_hidden_state = last_hidden_state.transpose(0, 3, 1, 2)
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return FlaxBaseModelOutputWithPoolingAndNoAttention(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+        )
+
+
+@add_start_docstrings(
+    "The bare RegNet model outputting raw features without any specific head on top.",
+    REGNET_START_DOCSTRING,
+)
+class FlaxRegNetModel(FlaxRegNetPreTrainedModel):
+    module_class = FlaxRegNetModule
+
+
+FLAX_VISION_MODEL_DOCSTRING = """
+    Returns:
+
+    Examples:
+
+    ```python
+    >>> from transformers import AutoImageProcessor, FlaxRegNetModel
+    >>> 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/regnet-y-040")
+    >>> model = FlaxRegNetModel.from_pretrained("facebook/regnet-y-040")
+
+    >>> inputs = image_processor(images=image, return_tensors="np")
+    >>> outputs = model(**inputs)
+    >>> last_hidden_states = outputs.last_hidden_state
+    ```
+"""
+
+overwrite_call_docstring(FlaxRegNetModel, FLAX_VISION_MODEL_DOCSTRING)
+append_replace_return_docstrings(
+    FlaxRegNetModel,
+    output_type=FlaxBaseModelOutputWithPooling,
+    config_class=RegNetConfig,
+)
+
+
+# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetClassifierCollection with ResNet->RegNet
+class FlaxRegNetClassifierCollection(nn.Module):
+    config: RegNetConfig
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype, name="1")
+
+    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
+        return self.classifier(x)
+
+
+# Copied from transformers.models.resnet.modeling_flax_resnet.FlaxResNetForImageClassificationModule with ResNet->RegNet,resnet->regnet,RESNET->REGNET
+class FlaxRegNetForImageClassificationModule(nn.Module):
+    config: RegNetConfig
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.regnet = FlaxRegNetModule(config=self.config, dtype=self.dtype)
+
+        if self.config.num_labels > 0:
+            self.classifier = FlaxRegNetClassifierCollection(self.config, dtype=self.dtype)
+        else:
+            self.classifier = Identity()
+
+    def __call__(
+        self,
+        pixel_values=None,
+        deterministic: bool = True,
+        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.regnet(
+            pixel_values,
+            deterministic=deterministic,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs.pooler_output if return_dict else outputs[1]
+
+        logits = self.classifier(pooled_output[:, :, 0, 0])
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return output
+
+        return FlaxImageClassifierOutputWithNoAttention(logits=logits, hidden_states=outputs.hidden_states)
+
+
+@add_start_docstrings(
+    """
+    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
+    ImageNet.
+    """,
+    REGNET_START_DOCSTRING,
+)
+class FlaxRegNetForImageClassification(FlaxRegNetPreTrainedModel):
+    module_class = FlaxRegNetForImageClassificationModule
+
+
+FLAX_VISION_CLASSIF_DOCSTRING = """
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoImageProcessor, FlaxRegNetForImageClassification
+    >>> 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/regnet-y-040")
+    >>> model = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
+
+    >>> 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(FlaxRegNetForImageClassification, FLAX_VISION_CLASSIF_DOCSTRING)
+append_replace_return_docstrings(
+    FlaxRegNetForImageClassification,
+    output_type=FlaxImageClassifierOutputWithNoAttention,
+    config_class=RegNetConfig,
+)

src/transformers/models/resnet/modeling_flax_resnet.py

@@ -89,7 +89,7 @@ class Identity(nn.Module):
     """Identity function."""
 
     @nn.compact
-    def __call__(self, x):
+    def __call__(self, x, **kwargs):
         return x
 
 

src/transformers/utils/dummy_flax_objects.py

@@ -881,6 +881,27 @@ class FlaxPegasusPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["flax"])
 
 
+class FlaxRegNetForImageClassification(metaclass=DummyObject):
+    _backends = ["flax"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxRegNetModel(metaclass=DummyObject):
+    _backends = ["flax"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
+class FlaxRegNetPreTrainedModel(metaclass=DummyObject):
+    _backends = ["flax"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
 class FlaxResNetForImageClassification(metaclass=DummyObject):
     _backends = ["flax"]
 

tests/models/regnet/test_modeling_flax_regnet.py

@@ -0,0 +1,237 @@
+# 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.
+
+import inspect
+import unittest
+
+from transformers import RegNetConfig, is_flax_available
+from transformers.testing_utils import require_flax, slow
+from transformers.utils import cached_property, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
+
+
+if is_flax_available():
+    import jax
+    import jax.numpy as jnp
+
+    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import AutoFeatureExtractor
+
+
+class FlaxRegNetModelTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        image_size=32,
+        num_channels=3,
+        embeddings_size=10,
+        hidden_sizes=[10, 20, 30, 40],
+        depths=[1, 1, 2, 1],
+        is_training=True,
+        use_labels=True,
+        hidden_act="relu",
+        num_labels=3,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.num_channels = num_channels
+        self.embeddings_size = embeddings_size
+        self.hidden_sizes = hidden_sizes
+        self.depths = depths
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.hidden_act = hidden_act
+        self.num_labels = num_labels
+        self.scope = scope
+        self.num_stages = len(hidden_sizes)
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self):
+        return RegNetConfig(
+            num_channels=self.num_channels,
+            embeddings_size=self.embeddings_size,
+            hidden_sizes=self.hidden_sizes,
+            depths=self.depths,
+            hidden_act=self.hidden_act,
+            num_labels=self.num_labels,
+            image_size=self.image_size,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = FlaxRegNetModel(config=config)
+        result = model(pixel_values)
+
+        # Output shape (b, c, h, w)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape,
+            (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
+        )
+
+    def create_and_check_for_image_classification(self, config, pixel_values):
+        config.num_labels = self.num_labels
+        model = FlaxRegNetForImageClassification(config=config)
+        result = model(pixel_values)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    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
+class FlaxResNetModelTest(FlaxModelTesterMixin, unittest.TestCase):
+    all_model_classes = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
+
+    is_encoder_decoder = False
+    test_head_masking = False
+    has_attentions = False
+
+    def setUp(self) -> None:
+        self.model_tester = FlaxRegNetModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=RegNetConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.create_and_test_config_common_properties()
+        self.config_tester.create_and_test_config_to_json_string()
+        self.config_tester.create_and_test_config_to_json_file()
+        self.config_tester.create_and_test_config_from_and_save_pretrained()
+        self.config_tester.create_and_test_config_with_num_labels()
+        self.config_tester.check_config_can_be_init_without_params()
+        self.config_tester.check_config_arguments_init()
+
+    def create_and_test_config_common_properties(self):
+        return
+
+    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)
+
+    @unittest.skip(reason="RegNet does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="RegNet does not support input and output embeddings")
+    def test_model_common_attributes(self):
+        pass
+
+    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_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))
+
+            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+
+            expected_num_stages = self.model_tester.num_stages
+            self.assertEqual(len(hidden_states), expected_num_stages + 1)
+
+        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)
+
+    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)
+
+
+# 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_flax
+class FlaxRegNetModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_feature_extractor(self):
+        return AutoFeatureExtractor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None
+
+    @slow
+    def test_inference_image_classification_head(self):
+        model = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
+
+        feature_extractor = self.default_feature_extractor
+        image = prepare_img()
+        inputs = feature_extractor(images=image, return_tensors="np")
+
+        outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = (1, 1000)
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = jnp.array([-0.4180, -1.5051, -3.4836])
+
+        self.assertTrue(jnp.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))