chore: added rest of the files.

docs/source/en/index.mdx

@@ -249,7 +249,7 @@ Flax), PyTorch, and/or TensorFlow.
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            Realm            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RegNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           RegNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           ResNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/regnet.mdx

@@ -27,7 +27,8 @@ Tips:
 - One can use [`AutoFeatureExtractor`] to prepare images for the model.
 - The huge 10B model from [Self-supervised Pretraining of Visual Features in the Wild](https://arxiv.org/abs/2103.01988), trained on one billion Instagram images, is available on the [hub](https://huggingface.co/facebook/regnet-y-10b-seer)
 
-This model was contributed by [Francesco](https://huggingface.co/Francesco).
+This model was contributed by [Francesco](https://huggingface.co/Francesco). TensorFlow version of the model
+was contributed by [sayakpaul](https://github.com/sayakpaul) and [ariG23498](https://github.com/ariG23498).
 The original code can be found [here](https://github.com/facebookresearch/pycls).
 
 
@@ -45,4 +46,15 @@ The original code can be found [here](https://github.com/facebookresearch/pycls)
 ## RegNetForImageClassification
 
 [[autodoc]] RegNetForImageClassification
-    - forward
+    - forward
+
+## TFRegNetModel
+
+[[autodoc]] TFRegNetModel
+    - call
+
+
+## TFRegNetForImageClassification
+
+[[autodoc]] TFRegNetForImageClassification
+    - call

src/transformers/__init__.py

@@ -2224,6 +2224,14 @@
             "TFRagTokenForGeneration",
         ]
     )
+    _import_structure["models.regnet"].extend(
+        [
+            "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFRegNetForImageClassification",
+            "TFRegNetModel",
+            "TFRegNetPreTrainedModel",
+        ]
+    )
     _import_structure["models.rembert"].extend(
         [
             "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -4450,6 +4458,12 @@
         )
         from .models.pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel
         from .models.rag import TFRagModel, TFRagPreTrainedModel, TFRagSequenceForGeneration, TFRagTokenForGeneration
+        from .models.regnet import (
+            TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFRegNetForImageClassification,
+            TFRegNetModel,
+            TFRegNetPreTrainedModel,
+        )
         from .models.rembert import (
             TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFRemBertForCausalLM,

src/transformers/models/auto/modeling_tf_auto.py

@@ -61,6 +61,7 @@
         ("mt5", "TFMT5Model"),
         ("openai-gpt", "TFOpenAIGPTModel"),
         ("pegasus", "TFPegasusModel"),
+        ("regnet", "TFRegNetModel"),
         ("rembert", "TFRemBertModel"),
         ("roberta", "TFRobertaModel"),
         ("roformer", "TFRoFormerModel"),
@@ -171,6 +172,7 @@
         # Model for Image-classsification
         ("convnext", "TFConvNextForImageClassification"),
         ("data2vec-vision", "TFData2VecVisionForImageClassification"),
+        ("regnet", "TFRegNetForImageClassification"),
         ("swin", "TFSwinForImageClassification"),
         ("vit", "TFViTForImageClassification"),
     ]

src/transformers/models/regnet/__init__.py

@@ -18,8 +18,7 @@
 from typing import TYPE_CHECKING
 
 # rely on isort to merge the imports
-from ...file_utils import _LazyModule, is_torch_available
-from ...utils import OptionalDependencyNotAvailable
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
 
 
 _import_structure = {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}
@@ -37,6 +36,19 @@
         "RegNetPreTrainedModel",
     ]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_regnet"] = [
+        "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFRegNetForImageClassification",
+        "TFRegNetModel",
+        "TFRegNetPreTrainedModel",
+    ]
+
 
 if TYPE_CHECKING:
     from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
@@ -54,6 +66,19 @@
             RegNetPreTrainedModel,
         )
 
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_regnet import (
+            TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFRegNetForImageClassification,
+            TFRegNetModel,
+            TFRegNetPreTrainedModel,
+        )
+
 
 else:
     import sys

src/transformers/models/regnet/configuration_regnet.py

@@ -37,7 +37,7 @@ class RegNetConfig(PretrainedConfig):
 
     Args:
         image_size (`int`, *optional*, defaults to 224):
-            Size of the input images.
+            Size (resolution) of the input images.
         num_channels (`int`, *optional*, defaults to 3):
             The number of input channels.
         embedding_size (`int`, *optional*, defaults to 64):

src/transformers/utils/dummy_tf_objects.py

@@ -255,6 +255,30 @@ def __init__(self, *args, **kwargs):
         requires_backends(self, ["tf"])
 
 
+class TFRegNetForImageClassification(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRegNetModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRegNetPreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
 TF_MODEL_FOR_CAUSAL_LM_MAPPING = None