Add TFViTModel (#13778)

* Start the work for TFViTModel

* Convert to TF code - need to check in the follow up commits

* Clean up model code

* Expose TFViTModel

* make style

* make quality

* Add test

* make style & quality

* Fix some imports

* fix wrong usage - *kwargs => ** kwargs

* Fix Conv2D weight loading (PT->TF) issue

* Add tests for images with different sizes + fix model

* Fix some common tests for TFViTModel

* Use inputs instead of input_ids in test_compile_tf_model

* Add a comment about transpose and Conv2D in convert_tf_weight_name_to_pt_weight_name

* Avoid transpose in TFViT call

* Fix Conv2D issue in load_tf2_weights_in_pytorch_model

* Use tf.keras.layers.Conv2D instead of tf.nn.conv2d

* Using simpler heuristic to detect Conv2D layer

* Change convert_tf_weight_name_to_pt_weight_name to return TransposeType

* Check tf_weight_shape is not None before using it

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

* fix missing comma

* fix input dtype
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/index.rst

@@ -503,7 +503,7 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             ViT             |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|             ViT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

docs/source/model_doc/auto.rst

@@ -223,6 +223,13 @@ TFAutoModelForCausalLM
     :members:
 
 
+TFAutoModelForImageClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFAutoModelForImageClassification
+    :members:
+
+
 TFAutoModelForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/vit.rst

@@ -120,6 +120,20 @@ ViTForImageClassification
     :members: forward
 
 
+TFViTModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFViTModel
+    :members: call
+
+
+TFViTForImageClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFViTForImageClassification
+    :members: call
+
+
 FlaxVitModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/__init__.py

@@ -1396,6 +1396,7 @@ if is_tf_available():
     _import_structure["models.auto"].extend(
         [
             "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
+            "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
             "TF_MODEL_FOR_MASKED_LM_MAPPING",
             "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
             "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
@@ -1408,6 +1409,7 @@ if is_tf_available():
             "TF_MODEL_WITH_LM_HEAD_MAPPING",
             "TFAutoModel",
             "TFAutoModelForCausalLM",
+            "TFAutoModelForImageClassification",
             "TFAutoModelForMaskedLM",
             "TFAutoModelForMultipleChoice",
             "TFAutoModelForPreTraining",
@@ -1734,6 +1736,13 @@ if is_tf_available():
             "TFTransfoXLPreTrainedModel",
         ]
     )
+    _import_structure["models.vit"].extend(
+        [
+            "TFViTForImageClassification",
+            "TFViTModel",
+            "TFViTPreTrainedModel",
+        ]
+    )
     _import_structure["models.wav2vec2"].extend(
         [
             "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -3133,6 +3142,7 @@ if TYPE_CHECKING:
         )
         from .models.auto import (
             TF_MODEL_FOR_CAUSAL_LM_MAPPING,
+            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
             TF_MODEL_FOR_MASKED_LM_MAPPING,
             TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
             TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
@@ -3145,6 +3155,7 @@ if TYPE_CHECKING:
             TF_MODEL_WITH_LM_HEAD_MAPPING,
             TFAutoModel,
             TFAutoModelForCausalLM,
+            TFAutoModelForImageClassification,
             TFAutoModelForMaskedLM,
             TFAutoModelForMultipleChoice,
             TFAutoModelForPreTraining,
@@ -3406,6 +3417,7 @@ if TYPE_CHECKING:
             TFTransfoXLModel,
             TFTransfoXLPreTrainedModel,
         )
+        from .models.vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
         from .models.wav2vec2 import (
             TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFWav2Vec2ForCTC,

src/transformers/modeling_tf_pytorch_utils.py

@@ -21,13 +21,24 @@ import re
 
 import numpy
 
+from .file_utils import ExplicitEnum
 from .utils import logging
 
 
 logger = logging.get_logger(__name__)
 
 
-def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=""):
+class TransposeType(ExplicitEnum):
+    """
+    Possible ...
+    """
+
+    NO = "no"
+    SIMPLE = "simple"
+    CONV2D = "conv2d"
+
+
+def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="", tf_weight_shape=None):
     """
     Convert a TF 2.0 model variable name in a pytorch model weight name.
 
@@ -39,8 +50,8 @@ def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="")
     return tuple with:
 
         - pytorch model weight name
-        - transpose: boolean indicating whether TF2.0 and PyTorch weights matrices are transposed with regards to each
-          other
+        - transpose: `TransposeType` member indicating whether and how TF2.0 and PyTorch weights matrices should be
+          transposed with regards to each other
     """
     tf_name = tf_name.replace(":0", "")  # device ids
     tf_name = re.sub(
@@ -56,11 +67,17 @@ def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="")
         tf_name = tf_name[1:]  # Remove level zero
 
     # When should we transpose the weights
-    transpose = bool(
+    if tf_name[-1] == "kernel" and tf_weight_shape is not None and tf_weight_shape.rank == 4:
+        # A simple heuristic to detect conv layer using weight array shape
+        transpose = TransposeType.CONV2D
+    elif bool(
         tf_name[-1] in ["kernel", "pointwise_kernel", "depthwise_kernel"]
         or "emb_projs" in tf_name
         or "out_projs" in tf_name
-    )
+    ):
+        transpose = TransposeType.SIMPLE
+    else:
+        transpose = TransposeType.NO
 
     # Convert standard TF2.0 names in PyTorch names
     if tf_name[-1] == "kernel" or tf_name[-1] == "embeddings" or tf_name[-1] == "gamma":
@@ -165,7 +182,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
     for symbolic_weight in symbolic_weights:
         sw_name = symbolic_weight.name
         name, transpose = convert_tf_weight_name_to_pt_weight_name(
-            sw_name, start_prefix_to_remove=start_prefix_to_remove
+            sw_name, start_prefix_to_remove=start_prefix_to_remove, tf_weight_shape=symbolic_weight.shape
         )
 
         # Find associated numpy array in pytorch model state dict
@@ -182,7 +199,12 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
 
         array = pt_state_dict[name].numpy()
 
-        if transpose:
+        if transpose is TransposeType.CONV2D:
+            # Conv2D weight:
+            #    PT: (num_out_channel, num_in_channel, kernel[0], kernel[1])
+            # -> TF: (kernel[0], kernel[1], num_in_channel, num_out_channel)
+            array = numpy.transpose(array, axes=(2, 3, 1, 0))
+        elif transpose is TransposeType.SIMPLE:
             array = numpy.transpose(array)
 
         if len(symbolic_weight.shape) < len(array.shape):
@@ -326,7 +348,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
     tf_weights_map = {}
     for tf_weight in tf_weights:
         pt_name, transpose = convert_tf_weight_name_to_pt_weight_name(
-            tf_weight.name, start_prefix_to_remove=start_prefix_to_remove
+            tf_weight.name, start_prefix_to_remove=start_prefix_to_remove, tf_weight_shape=tf_weight.shape
         )
         tf_weights_map[pt_name] = (tf_weight.numpy(), transpose)
 
@@ -350,7 +372,12 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
 
         array, transpose = tf_weights_map[pt_weight_name]
 
-        if transpose:
+        if transpose is TransposeType.CONV2D:
+            # Conv2D weight:
+            #    TF: (kernel[0], kernel[1], num_in_channel, num_out_channel)
+            # -> PT: (num_out_channel, num_in_channel, kernel[0], kernel[1])
+            array = numpy.transpose(array, axes=(3, 2, 0, 1))
+        elif transpose is TransposeType.SIMPLE:
             array = numpy.transpose(array)
 
         if len(pt_weight.shape) < len(array.shape):

src/transformers/models/auto/__init__.py

@@ -73,6 +73,7 @@ if is_torch_available():
 if is_tf_available():
     _import_structure["modeling_tf_auto"] = [
         "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
+        "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
         "TF_MODEL_FOR_MASKED_LM_MAPPING",
         "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
         "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
@@ -85,6 +86,7 @@ if is_tf_available():
         "TF_MODEL_WITH_LM_HEAD_MAPPING",
         "TFAutoModel",
         "TFAutoModelForCausalLM",
+        "TFAutoModelForImageClassification",
         "TFAutoModelForMaskedLM",
         "TFAutoModelForMultipleChoice",
         "TFAutoModelForPreTraining",
@@ -175,6 +177,7 @@ if TYPE_CHECKING:
     if is_tf_available():
         from .modeling_tf_auto import (
             TF_MODEL_FOR_CAUSAL_LM_MAPPING,
+            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
             TF_MODEL_FOR_MASKED_LM_MAPPING,
             TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
             TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
@@ -187,6 +190,7 @@ if TYPE_CHECKING:
             TF_MODEL_WITH_LM_HEAD_MAPPING,
             TFAutoModel,
             TFAutoModelForCausalLM,
+            TFAutoModelForImageClassification,
             TFAutoModelForMaskedLM,
             TFAutoModelForMultipleChoice,
             TFAutoModelForPreTraining,

src/transformers/models/auto/modeling_tf_auto.py

@@ -64,6 +64,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("pegasus", "TFPegasusModel"),
         ("blenderbot", "TFBlenderbotModel"),
         ("blenderbot-small", "TFBlenderbotSmallModel"),
+        ("vit", "TFViTModel"),
         ("wav2vec2", "TFWav2Vec2Model"),
         ("hubert", "TFHubertModel"),
     ]
@@ -144,6 +145,13 @@ TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
     ]
 )
 
+TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
+    [
+        # Model for Image-classsification
+        ("vit", "TFViTForImageClassification"),
+    ]
+)
+
 TF_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
     [
         # Model for Masked LM mapping
@@ -302,6 +310,9 @@ TF_MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_MAPPING_NAMES
 TF_MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
 TF_MODEL_WITH_LM_HEAD_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_WITH_LM_HEAD_MAPPING_NAMES)
 TF_MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
+TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
+    CONFIG_MAPPING_NAMES, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
+)
 TF_MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TF_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
 TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping(
     CONFIG_MAPPING_NAMES, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
@@ -352,6 +363,13 @@ class TFAutoModelForCausalLM(_BaseAutoModelClass):
 TFAutoModelForCausalLM = auto_class_update(TFAutoModelForCausalLM, head_doc="causal language modeling")
 
 
+class TFAutoModelForImageClassification(_BaseAutoModelClass):
+    _model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
+
+
+AutoModelForImageClassification = auto_class_update(TFAutoModelForImageClassification, head_doc="image classification")
+
+
 class TFAutoModelForMaskedLM(_BaseAutoModelClass):
     _model_mapping = TF_MODEL_FOR_MASKED_LM_MAPPING
 

src/transformers/models/vit/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...file_utils import _LazyModule, is_flax_available, is_torch_available, is_vision_available
+from ...file_utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available, is_vision_available
 
 
 _import_structure = {
@@ -35,6 +35,12 @@ if is_torch_available():
         "ViTPreTrainedModel",
     ]
 
+if is_tf_available():
+    _import_structure["modeling_tf_vit"] = [
+        "TFViTForImageClassification",
+        "TFViTModel",
+        "TFViTPreTrainedModel",
+    ]
 
 if is_flax_available():
     _import_structure["modeling_flax_vit"] = [
@@ -57,6 +63,9 @@ if TYPE_CHECKING:
             ViTPreTrainedModel,
         )
 
+    if is_tf_available():
+        from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
+
     if is_flax_available():
         from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
 

src/transformers/models/vit/modeling_tf_vit.py

+# coding=utf-8
+# Copyright 2021 Google AI, Ross Wightman, 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.
+""" TF 2.0 ViT model. """
+
+
+import collections.abc
+import math
+from typing import Dict, Optional, Tuple, Union
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
+from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput
+from ...modeling_tf_utils import (
+    TFModelInputType,
+    TFPreTrainedModel,
+    TFSequenceClassificationLoss,
+    get_initializer,
+    input_processing,
+    keras_serializable,
+    shape_list,
+)
+from ...utils import logging
+from .configuration_vit import ViTConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "ViTConfig"
+_CHECKPOINT_FOR_DOC = "google/vit-base-patch16-224"
+
+
+# Inspired by
+# https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py
+# From PyTorch internals
+def to_2tuple(x):
+    if isinstance(x, collections.abc.Iterable):
+        return x
+    return (x, x)
+
+
+# Based on timm implementation, which can be found here:
+# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+
+
+class TFViTEmbeddings(tf.keras.layers.Layer):
+    """
+    Construct the CLS token, position and patch embeddings.
+
+    """
+
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.patch_embeddings = TFPatchEmbeddings(config, name="patch_embeddings")
+        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
+
+    def build(self, input_shape: tf.TensorShape):
+
+        num_patches = self.patch_embeddings.num_patches
+        self.cls_token = self.add_weight(
+            shape=(1, 1, self.config.hidden_size), initializer="zeros", trainable=True, name="cls_token"
+        )
+        self.position_embeddings = self.add_weight(
+            shape=(1, num_patches + 1, self.config.hidden_size),
+            initializer="zeros",
+            trainable=True,
+            name="position_embeddings",
+        )
+
+        super().build(input_shape)
+
+    def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
+        resolution images.
+
+        Source:
+        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
+        """
+
+        batch_size, seq_len, dim = shape_list(embeddings)
+        npatch = seq_len - 1
+
+        _, N, _ = shape_list(self.position_embeddings)
+        N -= 1
+
+        if npatch == N and height == width:
+            return self.position_embeddings
+        class_pos_embed = self.position_embeddings[:, :1]
+        patch_pos_embed = self.position_embeddings[:, 1:]
+        h0 = height // self.config.patch_size
+        w0 = width // self.config.patch_size
+        patch_pos_embed = tf.image.resize(
+            images=tf.reshape(patch_pos_embed, shape=(1, int(math.sqrt(N)), int(math.sqrt(N)), dim)),
+            size=(h0, w0),
+            method="bicubic",
+        )
+
+        shape = shape_list(patch_pos_embed)
+        assert h0 == shape[-3] and w0 == shape[-2]
+        patch_pos_embed = tf.reshape(tensor=patch_pos_embed, shape=(1, -1, dim))
+        return tf.concat(values=(class_pos_embed, patch_pos_embed), axis=1)
+
+    def call(
+        self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
+    ) -> tf.Tensor:
+        batch_size, num_channels, height, width = shape_list(pixel_values)
+        embeddings = self.patch_embeddings(
+            pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, training=training
+        )
+
+        # add the [CLS] token to the embedded patch tokens
+        cls_tokens = tf.repeat(self.cls_token, repeats=batch_size, axis=0)
+        embeddings = tf.concat((cls_tokens, embeddings), axis=1)
+
+        # add positional encoding to each token
+        if interpolate_pos_encoding:
+            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+        else:
+            embeddings = embeddings + self.position_embeddings
+
+        embeddings = self.dropout(embeddings, training=training)
+
+        return embeddings
+
+
+# Based on timm implementation, which can be found here:
+# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+class TFPatchEmbeddings(tf.keras.layers.Layer):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+        image_size = to_2tuple(config.image_size)
+        patch_size = to_2tuple(config.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+        self.num_channels = config.num_channels
+        self.embed_dim = config.hidden_size
+        self.config = config
+
+        self.projection = tf.keras.layers.Conv2D(
+            filters=self.embed_dim,
+            kernel_size=patch_size,
+            strides=self.patch_size,
+            padding="valid",
+            data_format="channels_last",
+            use_bias=True,
+            kernel_initializer=get_initializer(self.config.initializer_range),
+            bias_initializer="zeros",
+            name="projection",
+        )
+
+    def call(
+        self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
+    ) -> tf.Tensor:
+        batch_size, num_channels, height, width = shape_list(pixel_values)
+        if not interpolate_pos_encoding:
+            if getattr(height, "numpy", None) and getattr(width, "numpy", None):
+                if height != self.image_size[0] or width != self.image_size[1]:
+                    raise ValueError(
+                        f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
+                    )
+
+        # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
+        # So change the input format from `NCHW` to `NHWC`.
+        # shape = (batch_size, in_height, in_width, in_channels=num_channels)
+        pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
+
+        projection = self.projection(pixel_values)
+
+        # Change the 2D spatial dimensions to a single temporal dimension.
+        # shape = (batch_size, num_patches, out_channels=embed_dim)
+        num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0])
+        x = tf.reshape(tensor=projection, shape=(batch_size, num_patches, -1))
+
+        return x
+
+
+class TFViTSelfAttention(tf.keras.layers.Layer):
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number "
+                f"of attention heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
+
+        self.query = tf.keras.layers.Dense(
+            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
+        )
+        self.key = tf.keras.layers.Dense(
+            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
+        )
+        self.value = tf.keras.layers.Dense(
+            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
+        )
+        self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
+        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
+        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
+
+        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
+        return tf.transpose(tensor, perm=[0, 2, 1, 3])
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        head_mask: tf.Tensor,
+        output_attentions: bool,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        batch_size = shape_list(hidden_states)[0]
+        mixed_query_layer = self.query(inputs=hidden_states)
+        mixed_key_layer = self.key(inputs=hidden_states)
+        mixed_value_layer = self.value(inputs=hidden_states)
+        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
+        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
+        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        # (batch size, num_heads, seq_len_q, seq_len_k)
+        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
+        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
+        attention_scores = tf.divide(attention_scores, dk)
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = tf.nn.softmax(logits=attention_scores, axis=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(inputs=attention_probs, training=training)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = tf.multiply(attention_probs, head_mask)
+
+        attention_output = tf.matmul(attention_probs, value_layer)
+        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
+
+        # (batch_size, seq_len_q, all_head_size)
+        attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
+        outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
+
+        return outputs
+
+
+class TFViTSelfOutput(tf.keras.layers.Layer):
+    """
+    The residual connection is defined in TFViTLayer instead of here (as is the case with other models), due to the
+    layernorm applied before each block.
+    """
+
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense = tf.keras.layers.Dense(
+            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+        )
+        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+
+    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+        hidden_states = self.dense(inputs=hidden_states)
+        hidden_states = self.dropout(inputs=hidden_states, training=training)
+
+        return hidden_states
+
+
+class TFViTAttention(tf.keras.layers.Layer):
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.self_attention = TFViTSelfAttention(config, name="attention")
+        self.dense_output = TFViTSelfOutput(config, name="output")
+
+    def prune_heads(self, heads):
+        raise NotImplementedError
+
+    def call(
+        self,
+        input_tensor: tf.Tensor,
+        head_mask: tf.Tensor,
+        output_attentions: bool,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        self_outputs = self.self_attention(
+            hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training
+        )
+        attention_output = self.dense_output(
+            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
+        )
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+
+        return outputs
+
+
+class TFViTIntermediate(tf.keras.layers.Layer):
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense = tf.keras.layers.Dense(
+            units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+        )
+
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        hidden_states = self.dense(inputs=hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+
+        return hidden_states
+
+
+class TFViTOutput(tf.keras.layers.Layer):
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense = tf.keras.layers.Dense(
+            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+        )
+        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+
+    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+        hidden_states = self.dense(inputs=hidden_states)
+        hidden_states = self.dropout(inputs=hidden_states, training=training)
+        hidden_states = hidden_states + input_tensor
+
+        return hidden_states
+
+
+class TFViTLayer(tf.keras.layers.Layer):
+    """This corresponds to the Block class in the timm implementation."""
+
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.attention = TFViTAttention(config, name="attention")
+        self.intermediate = TFViTIntermediate(config, name="intermediate")
+        self.vit_output = TFViTOutput(config, name="output")
+
+        self.layernorm_before = tf.keras.layers.LayerNormalization(
+            epsilon=config.layer_norm_eps, name="layernorm_before"
+        )
+        self.layernorm_after = tf.keras.layers.LayerNormalization(
+            epsilon=config.layer_norm_eps, name="layernorm_after"
+        )
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        head_mask: tf.Tensor,
+        output_attentions: bool,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        attention_outputs = self.attention(
+            # in ViT, layernorm is applied before self-attention
+            input_tensor=self.layernorm_before(inputs=hidden_states),
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            training=training,
+        )
+        attention_output = attention_outputs[0]
+
+        # first residual connection
+        hidden_states = attention_output + hidden_states
+
+        # in ViT, layernorm is also applied after self-attention
+        layer_output = self.layernorm_after(inputs=hidden_states)
+
+        intermediate_output = self.intermediate(hidden_states=layer_output)
+
+        # second residual connection is done here
+        layer_output = self.vit_output(
+            hidden_states=intermediate_output, input_tensor=hidden_states, training=training
+        )
+        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
+
+        return outputs
+
+
+class TFViTEncoder(tf.keras.layers.Layer):
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.layer = [TFViTLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        head_mask: tf.Tensor,
+        output_attentions: bool,
+        output_hidden_states: bool,
+        return_dict: bool,
+        training: bool = False,
+    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_outputs = layer_module(
+                hidden_states=hidden_states,
+                head_mask=head_mask[i],
+                output_attentions=output_attentions,
+                training=training,
+            )
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        # Add last layer
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+
+        return TFBaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+        )
+
+
+@keras_serializable
+class TFViTMainLayer(tf.keras.layers.Layer):
+    config_class = ViTConfig
+
+    def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
+
+        self.embeddings = TFViTEmbeddings(config, name="embeddings")
+        self.encoder = TFViTEncoder(config, name="encoder")
+        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
+        self.pooler = TFViTPooler(config, name="pooler") if add_pooling_layer else None
+
+    def get_input_embeddings(self) -> tf.keras.layers.Layer:
+        return self.embeddings.patch_embeddings
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        raise NotImplementedError
+
+    def call(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+        **kwargs,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=pixel_values,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        if "input_ids" in inputs:
+            inputs["pixel_values"] = inputs.pop("input_ids")
+
+        if inputs["pixel_values"] is None:
+            raise ValueError("You have to specify pixel_values")
+
+        embedding_output = self.embeddings(
+            pixel_values=inputs["pixel_values"],
+            interpolate_pos_encoding=inputs["interpolate_pos_encoding"],
+            training=inputs["training"],
+        )
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        if inputs["head_mask"] is not None:
+            raise NotImplementedError
+        else:
+            inputs["head_mask"] = [None] * self.config.num_hidden_layers
+
+        encoder_outputs = self.encoder(
+            hidden_states=embedding_output,
+            head_mask=inputs["head_mask"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.layernorm(inputs=sequence_output)
+        pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None
+
+        if not inputs["return_dict"]:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class TFViTPreTrainedModel(TFPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = ViTConfig
+    base_model_prefix = "vit"
+
+    @property
+    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
+        """
+        Dummy inputs to build the network.
+
+        Returns:
+            :obj:`Dict[str, tf.Tensor]`: The dummy inputs.
+        """
+        VISION_DUMMY_INPUTS = tf.random.uniform(
+            shape=(3, self.config.num_channels, self.config.image_size, self.config.image_size), dtype=tf.float32
+        )
+        return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
+
+    @tf.function(
+        input_signature=[
+            {
+                "pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
+            }
+        ]
+    )
+    def serving(self, inputs):
+        """
+        Method used for serving the model.
+
+        Args:
+            inputs (:obj:`Dict[str, tf.Tensor]`):
+                The input of the saved model as a dictionary of tensors.
+        """
+        output = self.call(inputs)
+
+        return self.serving_output(output)
+
+
+VIT_START_DOCSTRING = r"""
+
+    This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
+    generic methods the library implements for all its model (such as downloading or saving, resizing the input
+    embeddings, pruning heads etc.)
+
+    This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
+    it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
+    and behavior.
+
+    .. note::
+
+        TF 2.0 models accepts two formats as inputs:
+
+        - having all inputs as keyword arguments (like PyTorch models), or
+        - having all inputs as a list, tuple or dict in the first positional arguments.
+
+        This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
+        the tensors in the first argument of the model call function: :obj:`model(inputs)`.
+
+    Args:
+        config (:class:`~transformers.ViTConfig`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the :meth:`~transformers.TFPreTrainedModel.from_pretrained` method to load the
+            model weights.
+"""
+
+VIT_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (:obj:`np.ndarray`, :obj:`tf.Tensor`, :obj:`List[tf.Tensor]` :obj:`Dict[str, tf.Tensor]` or :obj:`Dict[str, np.ndarray]` and each example must have the shape :obj:`(batch_size, num_channels, height, width)`):
+            Pixel values. Pixel values can be obtained using :class:`~transformers.ViTFeatureExtractor`. See
+            :meth:`transformers.ViTFeatureExtractor.__call__` for details.
+
+        head_mask (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        output_attentions (:obj:`bool`, `optional`):
+            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
+            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+            config will be used instead.
+        output_hidden_states (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+            used instead.
+        interpolate_pos_encoding (:obj:`bool`, `optional`):
+            Whether to interpolate the pre-trained position encodings.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
+            argument can be used in eager mode, in graph mode the value will always be set to True.
+        training (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not to use the model in training mode (some modules like dropout modules have different
+            behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+    "The bare ViT Model transformer outputting raw hidden-states without any specific head on top.",
+    VIT_START_DOCSTRING,
+)
+class TFViTModel(TFViTPreTrainedModel):
+    def __init__(self, config: ViTConfig, *inputs, add_pooling_layer=True, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.vit = TFViTMainLayer(config, add_pooling_layer=add_pooling_layer, name="vit")
+
+    @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+        **kwargs,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        r"""
+        Returns:
+
+        Examples::
+
+            >>> from transformers import ViTFeatureExtractor, TFViTModel
+            >>> from PIL import Image
+            >>> import requests
+
+            >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+            >>> image = Image.open(requests.get(url, stream=True).raw)
+
+            >>> feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
+            >>> model = TFViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
+
+            >>> inputs = feature_extractor(images=image, return_tensors="tf")
+            >>> outputs = model(**inputs)
+            >>> last_hidden_states = outputs.last_hidden_state
+        """
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=pixel_values,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        if "input_ids" in inputs:
+            inputs["pixel_values"] = inputs.pop("input_ids")
+
+        outputs = self.vit(
+            pixel_values=inputs["pixel_values"],
+            head_mask=inputs["head_mask"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            interpolate_pos_encoding=inputs["interpolate_pos_encoding"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
+    def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=output.last_hidden_state,
+            pooler_output=output.pooler_output,
+            hidden_states=hs,
+            attentions=attns,
+        )
+
+
+class TFViTPooler(tf.keras.layers.Layer):
+    def __init__(self, config: ViTConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense = tf.keras.layers.Dense(
+            units=config.hidden_size,
+            kernel_initializer=get_initializer(config.initializer_range),
+            activation="tanh",
+            name="dense",
+        )
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(inputs=first_token_tensor)
+
+        return pooled_output
+
+
+@add_start_docstrings(
+    """
+    ViT 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.
+    """,
+    VIT_START_DOCSTRING,
+)
+class TFViTForImageClassification(TFViTPreTrainedModel, TFSequenceClassificationLoss):
+    def __init__(self, config: ViTConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.num_labels = config.num_labels
+        self.vit = TFViTMainLayer(config, add_pooling_layer=False, name="vit")
+
+        # Classifier head
+        self.classifier = tf.keras.layers.Dense(
+            units=config.num_labels,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="classifier",
+        )
+
+    @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        training: Optional[bool] = False,
+        **kwargs,
+    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
+        r"""
+        labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the image classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Returns:
+
+        Examples::
+
+            >>> from transformers import ViTFeatureExtractor, TFViTForImageClassification
+            >>> from PIL import Image
+            >>> import requests
+
+            >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+            >>> image = Image.open(requests.get(url, stream=True).raw)
+
+            >>> feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
+            >>> model = TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+
+            >>> inputs = feature_extractor(images=image, return_tensors="tf")
+            >>> outputs = model(**inputs)
+            >>> logits = outputs.logits
+            >>> # model predicts one of the 1000 ImageNet classes
+            >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
+            >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
+        """
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=pixel_values,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            return_dict=return_dict,
+            labels=labels,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        if "input_ids" in inputs:
+            inputs["pixel_values"] = inputs.pop("input_ids")
+
+        outputs = self.vit(
+            pixel_values=inputs["pixel_values"],
+            head_mask=inputs["head_mask"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            interpolate_pos_encoding=inputs["interpolate_pos_encoding"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+        sequence_output = outputs[0]
+        logits = self.classifier(inputs=sequence_output[:, 0, :])
+        loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=logits)
+
+        if not inputs["return_dict"]:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFSequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+        return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)

src/transformers/utils/dummy_tf_objects.py

@@ -176,6 +176,9 @@ class TFAlbertPreTrainedModel:
 TF_MODEL_FOR_CAUSAL_LM_MAPPING = None
 
 
+TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = None
+
+
 TF_MODEL_FOR_MASKED_LM_MAPPING = None
 
 
@@ -224,6 +227,15 @@ class TFAutoModelForCausalLM:
         requires_backends(cls, ["tf"])
 
 
+class TFAutoModelForImageClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
 class TFAutoModelForMaskedLM:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tf"])
@@ -1971,6 +1983,29 @@ class TFTransfoXLPreTrainedModel:
         requires_backends(cls, ["tf"])
 
 
+class TFViTForImageClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFViTModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
+class TFViTPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
 TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/test_modeling_common.py

@@ -1476,6 +1476,8 @@ class ModelTesterMixin:
                     tf_inputs_dict[key] = tensor
                 elif key == "input_values":
                     tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                elif key == "pixel_values":
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
                 else:
                     tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
 
@@ -1525,6 +1527,8 @@ class ModelTesterMixin:
                     tf_inputs_dict[key] = tf.convert_to_tensor(tensor, dtype=tf.int32)
                 elif key == "input_values":
                     tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                elif key == "pixel_values":
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
                 else:
                     tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
 

tests/test_modeling_tf_common.py

@@ -49,6 +49,7 @@ if is_tf_available():
 
     from transformers import (
         TF_MODEL_FOR_CAUSAL_LM_MAPPING,
+        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_MASKED_LM_MAPPING,
         TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
@@ -126,7 +127,10 @@ class TFModelTesterMixin:
             elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                 inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
                 inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
-            elif model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
+            elif model_class in [
+                *get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
+                *get_values(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
+            ]:
                 inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
             elif model_class in get_values(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING):
                 inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
@@ -460,6 +464,8 @@ class TFModelTesterMixin:
                     pt_inputs_dict[name] = key
                 elif name == "input_values":
                     pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
+                elif name == "pixel_values":
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                 else:
                     pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
 
@@ -504,6 +510,8 @@ class TFModelTesterMixin:
                     pt_inputs_dict[name] = torch.from_numpy(key).to(torch.long)
                 elif name == "input_values":
                     pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
+                elif name == "pixel_values":
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                 else:
                     pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
             # need to rename encoder-decoder "inputs" for PyTorch
@@ -605,7 +613,7 @@ class TFModelTesterMixin:
 
         for model_class in self.all_model_classes:
             if self.is_encoder_decoder:
-                input_ids = {
+                inputs = {
                     "decoder_input_ids": tf.keras.Input(
                         batch_shape=(2, max_input),
                         name="decoder_input_ids",
@@ -613,10 +621,22 @@ class TFModelTesterMixin:
                     ),
                     "input_ids": tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32"),
                 }
+            # TODO: A better way to handle vision models
+            elif model_class.__name__ in ["TFViTModel", "TFViTForImageClassification"]:
+                inputs = tf.keras.Input(
+                    batch_shape=(
+                        3,
+                        self.model_tester.num_channels,
+                        self.model_tester.image_size,
+                        self.model_tester.image_size,
+                    ),
+                    name="pixel_values",
+                    dtype="float32",
+                )
             elif model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
-                input_ids = tf.keras.Input(batch_shape=(4, 2, max_input), name="input_ids", dtype="int32")
+                inputs = tf.keras.Input(batch_shape=(4, 2, max_input), name="input_ids", dtype="int32")
             else:
-                input_ids = tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32")
+                inputs = tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32")
 
             # Prepare our model
             model = model_class(config)
@@ -626,14 +646,14 @@ class TFModelTesterMixin:
                 model.save_pretrained(tmpdirname, saved_model=False)
                 model = model_class.from_pretrained(tmpdirname)
 
-            outputs_dict = model(input_ids)
+            outputs_dict = model(inputs)
             hidden_states = outputs_dict[0]
 
             # Add a dense layer on top to test integration with other keras modules
             outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
 
             # Compile extended model
-            extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
+            extended_model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
             extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
 
     def test_keyword_and_dict_args(self):
@@ -647,6 +667,8 @@ class TFModelTesterMixin:
 
             inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
             input_ids = inputs_keywords.pop("input_ids", None)
+            if input_ids is None:
+                input_ids = inputs_keywords.pop("pixel_values", None)
             outputs_keywords = model(input_ids, **inputs_keywords)
             output_dict = outputs_dict[0].numpy()
             output_keywords = outputs_keywords[0].numpy()
@@ -1236,7 +1258,8 @@ class TFModelTesterMixin:
 
                 # Test that model correctly compute the loss with kwargs
                 prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
-                input_ids = prepared_for_class.pop("input_ids")
+                input_name = "input_ids" if "input_ids" in prepared_for_class else "pixel_values"
+                input_ids = prepared_for_class.pop(input_name)
 
                 loss = model(input_ids, **prepared_for_class)[0]
                 self.assertEqual(loss.shape, [loss_size])
@@ -1255,7 +1278,7 @@ class TFModelTesterMixin:
                 signature_names = list(signature.keys())
 
                 # Create a dictionary holding the location of the tensors in the tuple
-                tuple_index_mapping = {0: "input_ids"}
+                tuple_index_mapping = {0: input_name}
                 for label_key in label_keys:
                     label_key_index = signature_names.index(label_key)
                     tuple_index_mapping[label_key_index] = label_key

tests/test_modeling_tf_vit.py

+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Testing suite for the TensorFlow ViT model. """
+
+
+import inspect
+import os
+import tempfile
+import unittest
+
+from transformers import ViTConfig
+from transformers.file_utils import cached_property, is_tf_available, is_vision_available
+from transformers.testing_utils import require_tf, require_vision, slow, tooslow
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import TFViTForImageClassification, TFViTModel
+    from transformers.models.vit.modeling_tf_vit import to_2tuple
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ViTFeatureExtractor
+
+
+class TFViTModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        use_labels=True,
+        hidden_size=32,
+        num_hidden_layers=5,
+        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,
+        num_labels=3,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.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
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return ViTConfig(
+            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,
+        )
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = TFViTModel(config=config)
+        result = model(pixel_values, training=False)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        image_size = to_2tuple(self.image_size)
+        patch_size = to_2tuple(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))
+
+        # Test with an image with different size than the one specified in config.
+        image_size = self.image_size // 2
+        pixel_values = pixel_values[:, :, :image_size, :image_size]
+        result = model(pixel_values, interpolate_pos_encoding=True, training=False)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        image_size = to_2tuple(image_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))
+
+    def create_and_check_for_image_classification(self, config, pixel_values, labels):
+        config.num_labels = self.type_sequence_label_size
+        model = TFViTForImageClassification(config)
+        result = model(pixel_values, labels=labels, training=False)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+
+        # Test with an image with different size than the one specified in config.
+        image_size = self.image_size // 2
+        pixel_values = pixel_values[:, :, :image_size, :image_size]
+        result = model(pixel_values, interpolate_pos_encoding=True, training=False)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_tf
+class TFViTModelTest(TFModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_tf_common.py, as ViT does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
+
+    test_resize_embeddings = False
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFViTModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ViTConfig, has_text_modality=False, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_inputs_embeds(self):
+        # ViT does not use inputs_embeds
+        pass
+
+    def test_graph_mode_with_inputs_embeds(self):
+        # ViT does not use inputs_embeds
+        pass
+
+    def test_model_common_attributes(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer))
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.call)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    # overwrite from common since `encoder_seq_length` and `encoder_key_length` are calculated
+    # in a different way than in text models.
+    @tooslow
+    def test_saved_model_creation_extended(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = True
+
+        if hasattr(config, "use_cache"):
+            config.use_cache = True
+
+        # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
+        image_size = to_2tuple(self.model_tester.image_size)
+        patch_size = to_2tuple(self.model_tester.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        seq_len = num_patches + 1
+        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
+        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
+
+        for model_class in self.all_model_classes:
+            class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+            model = model_class(config)
+            num_out = len(model(class_inputs_dict))
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname, saved_model=True)
+                saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
+                model = tf.keras.models.load_model(saved_model_dir)
+                outputs = model(class_inputs_dict)
+
+                if self.is_encoder_decoder:
+                    output_hidden_states = outputs["encoder_hidden_states"]
+                    output_attentions = outputs["encoder_attentions"]
+                else:
+                    output_hidden_states = outputs["hidden_states"]
+                    output_attentions = outputs["attentions"]
+
+                self.assertEqual(len(outputs), num_out)
+
+                expected_num_layers = getattr(
+                    self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+                )
+
+                self.assertEqual(len(output_hidden_states), expected_num_layers)
+                self.assertListEqual(
+                    list(output_hidden_states[0].shape[-2:]),
+                    [seq_len, self.model_tester.hidden_size],
+                )
+
+                self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
+                self.assertListEqual(
+                    list(output_attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+                )
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
+        image_size = to_2tuple(self.model_tester.image_size)
+        patch_size = to_2tuple(self.model_tester.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        seq_len = num_patches + 1
+        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
+        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class(config)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            self.assertListEqual(
+                list(attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+            )
+            out_len = len(outputs)
+
+            # Check attention is always last and order is fine
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
+
+            if hasattr(self.model_tester, "num_hidden_states_types"):
+                added_hidden_states = self.model_tester.num_hidden_states_types
+            elif self.is_encoder_decoder:
+                added_hidden_states = 2
+            else:
+                added_hidden_states = 1
+            self.assertEqual(out_len + added_hidden_states, len(outputs))
+
+            self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
+
+            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
+            self.assertListEqual(
+                list(self_attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+            )
+
+    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_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            # ViT has a different seq_length
+            image_size = to_2tuple(self.model_tester.image_size)
+            patch_size = to_2tuple(self.model_tester.patch_size)
+            num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+            seq_length = num_patches + 1
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [seq_length, self.model_tester.hidden_size],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    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)
+
+    @slow
+    def test_model_from_pretrained(self):
+
+        model = TFViTModel.from_pretrained("google/vit-base-patch16-224", from_pt=True)
+        self.assertIsNotNone(model)
+
+
+# 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
+class TFViTModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_feature_extractor(self):
+        return ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224") if is_vision_available() else None
+
+    @slow
+    def test_inference_image_classification_head(self):
+        model = TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224", from_pt=True)
+
+        feature_extractor = self.default_feature_extractor
+        image = prepare_img()
+        inputs = feature_extractor(images=image, return_tensors="tf")
+
+        # forward pass
+        outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = tf.TensorShape((1, 1000))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = tf.constant([-0.2744, 0.8215, -0.0836])
+
+        tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4)