[feat] Add FLAVA model (#16654)

* [WIP] Add FLAVA model

This PR aims to add [FLAVA](ihttps://arxiv.org/abs/2112.04482) model to the transformers repo.

Following checklist delineates the list of things to be done for this PR
to be complete:

[x] Flava init
[x] Flava base models
[x] Flava layers
[x] Flava Configs
[x] Flava encoders
[x] Flava pretraining models
[ ] Flava classification/retrieval models (To be added in a separate PR)
[x] Documentation updates 
[x] Imports updates 
[x] Argstring updates
[x] Flava pretrained checkpoints 
[x] Flava tests
[x] Flava processors 
[x] Sanity check
[x] Lint

src/transformers/utils/dummy_pt_objects.py

@@ -1787,6 +1787,58 @@ class FlaubertWithLMHeadModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class FlavaForPreTraining(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class FlavaImageCodebook(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class FlavaImageModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class FlavaModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class FlavaMultimodalModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class FlavaPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class FlavaTextModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 FNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

src/transformers/utils/dummy_vision_objects.py

@@ -59,6 +59,20 @@ class DPTFeatureExtractor(metaclass=DummyObject):
         requires_backends(self, ["vision"])
 
 
+class FlavaFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["vision"])
+
+
+class FlavaProcessor(metaclass=DummyObject):
+    _backends = ["vision"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["vision"])
+
+
 class GLPNFeatureExtractor(metaclass=DummyObject):
     _backends = ["vision"]
 

tests/models/flava/test_feature_extraction_flava.py

+# coding=utf-8
+# Copyright 2022 Meta Platforms authors and HuggingFace Inc.
+#
+# 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 random
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import FlavaFeatureExtractor
+    from transformers.models.flava.feature_extraction_flava import (
+        FLAVA_CODEBOOK_MEAN,
+        FLAVA_CODEBOOK_STD,
+        FLAVA_IMAGE_MEAN,
+        FLAVA_IMAGE_STD,
+    )
+else:
+    FLAVA_IMAGE_MEAN = FLAVA_IMAGE_STD = FLAVA_CODEBOOK_MEAN = FLAVA_CODEBOOK_STD = None
+
+
+class FlavaFeatureExtractionTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=224,
+        do_center_crop=True,
+        crop_size=224,
+        resample=None,
+        do_normalize=True,
+        image_mean=FLAVA_IMAGE_MEAN,
+        image_std=FLAVA_IMAGE_STD,
+        input_size_patches=14,
+        total_mask_patches=75,
+        mask_group_max_patches=None,
+        mask_group_min_patches=16,
+        mask_group_min_aspect_ratio=0.3,
+        mask_group_max_aspect_ratio=None,
+        codebook_do_resize=True,
+        codebook_size=112,
+        codebook_resample=None,
+        codebook_do_center_crop=True,
+        codebook_crop_size=112,
+        codebook_do_map_pixels=True,
+        codebook_do_normalize=True,
+        codebook_image_mean=FLAVA_CODEBOOK_MEAN,
+        codebook_image_std=FLAVA_CODEBOOK_STD,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.do_resize = do_resize
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.size = size
+        self.resample = resample if resample is not None else Image.BICUBIC
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size
+
+        self.input_size_patches = input_size_patches
+        self.total_mask_patches = total_mask_patches
+        self.mask_group_max_patches = mask_group_max_patches
+        self.mask_group_min_patches = mask_group_min_patches
+        self.mask_group_min_aspect_ratio = mask_group_min_aspect_ratio
+        self.mask_group_max_aspect_ratio = mask_group_max_aspect_ratio
+
+        self.codebook_do_resize = codebook_do_resize
+        self.codebook_size = codebook_size
+        self.codebook_resample = codebook_resample if codebook_resample is not None else Image.LANCZOS
+        self.codebook_do_center_crop = codebook_do_center_crop
+        self.codebook_crop_size = codebook_crop_size
+        self.codebook_do_map_pixels = codebook_do_map_pixels
+        self.codebook_do_normalize = codebook_do_normalize
+        self.codebook_image_mean = codebook_image_mean
+        self.codebook_image_std = codebook_image_std
+
+    def prepare_feat_extract_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_normalize": self.do_normalize,
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "resample": self.resample,
+            "do_center_crop": self.do_center_crop,
+            "crop_size": self.crop_size,
+            "input_size_patches": self.input_size_patches,
+            "total_mask_patches": self.total_mask_patches,
+            "mask_group_max_patches": self.mask_group_max_patches,
+            "mask_group_min_patches": self.mask_group_min_patches,
+            "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
+            "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
+            "codebook_do_resize": self.codebook_do_resize,
+            "codebook_size": self.codebook_size,
+            "codebook_resample": self.codebook_resample,
+            "codebook_do_center_crop": self.codebook_do_center_crop,
+            "codebook_crop_size": self.codebook_crop_size,
+            "codebook_do_map_pixels": self.codebook_do_map_pixels,
+            "codebook_do_normalize": self.codebook_do_normalize,
+            "codebook_image_mean": self.codebook_image_mean,
+            "codebook_image_std": self.codebook_image_std,
+        }
+
+    def get_expected_image_size(self):
+        return (self.size, self.size) if not isinstance(self.size, tuple) else self.size
+
+    def get_expected_mask_size(self):
+        return (
+            (self.input_size_patches, self.input_size_patches)
+            if not isinstance(self.input_size_patches, tuple)
+            else self.input_size_patches
+        )
+
+    def get_expected_codebook_image_size(self):
+        if not isinstance(self.codebook_size, tuple):
+            return (self.codebook_size, self.codebook_size)
+        else:
+            return self.codebook_size
+
+
+@require_torch
+@require_vision
+class FlavaFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
+
+    feature_extraction_class = FlavaFeatureExtractor if is_vision_available() else None
+    maxDiff = None
+
+    def setUp(self):
+        self.feature_extract_tester = FlavaFeatureExtractionTester(self)
+
+    @property
+    def feat_extract_dict(self):
+        return self.feature_extract_tester.prepare_feat_extract_dict()
+
+    def test_feat_extract_properties(self):
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        self.assertTrue(hasattr(feature_extractor, "image_mean"))
+        self.assertTrue(hasattr(feature_extractor, "image_std"))
+        self.assertTrue(hasattr(feature_extractor, "do_normalize"))
+        self.assertTrue(hasattr(feature_extractor, "do_resize"))
+        self.assertTrue(hasattr(feature_extractor, "resample"))
+        self.assertTrue(hasattr(feature_extractor, "crop_size"))
+        self.assertTrue(hasattr(feature_extractor, "do_center_crop"))
+        self.assertTrue(hasattr(feature_extractor, "masking_generator"))
+        self.assertTrue(hasattr(feature_extractor, "codebook_do_resize"))
+        self.assertTrue(hasattr(feature_extractor, "codebook_size"))
+        self.assertTrue(hasattr(feature_extractor, "codebook_resample"))
+        self.assertTrue(hasattr(feature_extractor, "codebook_do_center_crop"))
+        self.assertTrue(hasattr(feature_extractor, "codebook_crop_size"))
+        self.assertTrue(hasattr(feature_extractor, "codebook_do_map_pixels"))
+        self.assertTrue(hasattr(feature_extractor, "codebook_do_normalize"))
+        self.assertTrue(hasattr(feature_extractor, "codebook_image_mean"))
+        self.assertTrue(hasattr(feature_extractor, "codebook_image_std"))
+
+    def test_batch_feature(self):
+        pass
+
+    def test_call_pil(self):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        # create random PIL images
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
+        for image in image_inputs:
+            self.assertIsInstance(image, Image.Image)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt")
+
+        # Test no bool masked pos
+        self.assertFalse("bool_masked_pos" in encoded_images)
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
+
+        self.assertEqual(
+            encoded_images.pixel_values.shape,
+            (1, self.feature_extract_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = feature_extractor(image_inputs, return_tensors="pt")
+        expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
+
+        # Test no bool masked pos
+        self.assertFalse("bool_masked_pos" in encoded_images)
+
+        self.assertEqual(
+            encoded_images.pixel_values.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def _test_call_framework(self, instance_class, prepare_kwargs):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        # create random tensors
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, **prepare_kwargs)
+        for image in image_inputs:
+            self.assertIsInstance(image, instance_class)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt")
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
+        self.assertEqual(
+            encoded_images.pixel_values.shape,
+            (1, self.feature_extract_tester.num_channels, expected_height, expected_width),
+        )
+
+        encoded_images = feature_extractor(image_inputs, return_image_mask=True, return_tensors="pt")
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
+        self.assertEqual(
+            encoded_images.pixel_values.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_mask_size()
+        self.assertEqual(
+            encoded_images.bool_masked_pos.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+        # Test batched
+        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+        # Test masking
+        encoded_images = feature_extractor(image_inputs, return_image_mask=True, return_tensors="pt")
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
+        self.assertEqual(
+            encoded_images.pixel_values.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_mask_size()
+        self.assertEqual(
+            encoded_images.bool_masked_pos.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_call_numpy(self):
+        self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})
+
+    def test_call_pytorch(self):
+        self._test_call_framework(torch.Tensor, prepare_kwargs={"torchify": True})
+
+    def test_masking(self):
+        # Initialize feature_extractor
+        random.seed(1234)
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_image_mask=True, return_tensors="pt")
+        self.assertEqual(encoded_images.bool_masked_pos.sum().item(), 75)
+
+    def test_codebook_pixels(self):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        # create random PIL images
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
+        for image in image_inputs:
+            self.assertIsInstance(image, Image.Image)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_codebook_pixels=True, return_tensors="pt")
+        expected_height, expected_width = self.feature_extract_tester.get_expected_codebook_image_size()
+        self.assertEqual(
+            encoded_images.codebook_pixel_values.shape,
+            (1, self.feature_extract_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = feature_extractor(image_inputs, return_codebook_pixels=True, return_tensors="pt")
+        expected_height, expected_width = self.feature_extract_tester.get_expected_codebook_image_size()
+        self.assertEqual(
+            encoded_images.codebook_pixel_values.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )

tests/models/flava/test_modeling_flava.py

+# coding=utf-8
+# Copyright 2022 Meta Platforms authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Testing suite for the PyTorch FLAVA model. """
+
+
+import inspect
+import os
+import random
+import tempfile
+import unittest
+
+import numpy as np
+
+import requests
+from transformers import (
+    FlavaConfig,
+    FlavaImageCodebookConfig,
+    FlavaImageConfig,
+    FlavaMultimodalConfig,
+    FlavaTextConfig,
+)
+from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    _config_zero_init,
+    floats_tensor,
+    ids_tensor,
+    random_attention_mask,
+)
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import (
+        FlavaForPreTraining,
+        FlavaImageCodebook,
+        FlavaImageModel,
+        FlavaModel,
+        FlavaMultimodalModel,
+        FlavaTextModel,
+    )
+    from transformers.models.flava.modeling_flava import (
+        FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST,
+        FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
+    )
+else:
+    FlavaModel = None
+    FlavaForPreTraining = None
+    torch = {}
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import FlavaProcessor
+
+
+class FlavaImageModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        qkv_bias=True,
+        mask_token=True,
+        vocab_size=8192,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.qkv_bias = qkv_bias
+        self.mask_token = mask_token
+        self.vocab_size = vocab_size
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        num_patches = self.image_size // self.patch_size
+        bool_masked_pos = (
+            torch.rand((self.batch_size, num_patches, num_patches), device=pixel_values.device) < 0.9
+        ).long()
+        config = self.get_config()
+        return config, pixel_values, bool_masked_pos
+
+    def get_config(self):
+        return FlavaImageConfig(
+            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,
+            initializer_range=self.initializer_range,
+            layer_norm_eps=self.layer_norm_eps,
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            qkv_bias=self.qkv_bias,
+            mask_token=self.mask_token,
+            vocab_size=self.vocab_size,
+        )
+
+    def create_and_check_model(self, config, pixel_values, bool_masked_pos):
+        model = FlavaImageModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values, bool_masked_pos)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        image_size = (self.image_size, self.image_size)
+        patch_size = (self.patch_size, self.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, bool_masked_pos = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values, "bool_masked_pos": bool_masked_pos}
+        return config, inputs_dict
+
+
+@require_torch
+class FlavaImageModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as FLAVA does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (FlavaImageModel,) if is_torch_available() else ()
+
+    test_pruning = False
+    test_torchscript = False
+    test_resize_embeddings = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = FlavaImageModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=FlavaImageConfig, has_text_modality=False, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_inputs_embeds(self):
+        # FLAVA 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(), (nn.Module))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    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.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        # in FLAVA, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
+        image_size = (self.model_tester.image_size, self.model_tester.image_size)
+        patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        seq_len = num_patches + 1
+
+        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)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = 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)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            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)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            added_hidden_states = 1
+            self.assertEqual(out_len + added_hidden_states, len(outputs))
+
+            self_attentions = 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, seq_len, seq_len],
+            )
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                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)
+
+            # FLAVA has a different seq_length
+            image_size = (self.model_tester.image_size, self.model_tester.image_size)
+            patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
+            num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+            seq_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_training(self):
+        pass
+
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    # skip this test as FlavaImageModel has no base class and is
+    # not available in MODEL_MAPPING
+    def test_save_load_fast_init_from_base(self):
+        pass
+
+    # skip this test as FlavaImageModel has no base class and is
+    # not available in MODEL_MAPPING
+    def test_save_load_fast_init_to_base(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = FlavaImageModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+class FlavaTextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        vocab_size=30522,
+        type_vocab_size=2,
+        max_position_embeddings=512,
+        position_embedding_type="absolute",
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=0,
+        qkv_bias=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.seq_length = seq_length
+        self.vocab_size = vocab_size
+        self.type_vocab_size = type_vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.position_embedding_type = position_embedding_type
+        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.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.qkv_bias = qkv_bias
+        self.pad_token_id = pad_token_id
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        if input_mask is not None:
+            batch_size, seq_length = input_mask.shape
+            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
+            for batch_idx, start_index in enumerate(rnd_start_indices):
+                input_mask[batch_idx, :start_index] = 1
+                input_mask[batch_idx, start_index:] = 0
+
+        token_type_ids = None
+
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, input_mask
+
+    def get_config(self):
+        return FlavaTextConfig(
+            vocab_size=self.vocab_size,
+            type_vocab_size=self.type_vocab_size,
+            max_position_embeddings=self.max_position_embeddings,
+            position_embedding_type=self.position_embedding_type,
+            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,
+            initializer_range=self.initializer_range,
+            layer_norm_eps=self.layer_norm_eps,
+            pad_token_id=self.pad_token_id,
+            qkv_bias=self.qkv_bias,
+        )
+
+    def create_and_check_model(self, config, input_ids, token_type_ids, input_mask):
+        model = FlavaTextModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask)
+            result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, token_type_ids, input_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class FlavaTextModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (FlavaTextModel,) if is_torch_available() else ()
+    test_pruning = False
+    test_head_masking = False
+    test_torchscript = False
+
+    def setUp(self):
+        self.model_tester = FlavaTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=FlavaTextConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_training(self):
+        pass
+
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    def test_inputs_embeds(self):
+        # FLAVA does not use inputs_embeds
+        pass
+
+    # skip this test as FlavaTextModel has no base class and is
+    # not available in MODEL_MAPPING
+    def test_save_load_fast_init_from_base(self):
+        pass
+
+    # skip this test as FlavaTextModel has no base class and is
+    # not available in MODEL_MAPPING
+    def test_save_load_fast_init_to_base(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = FlavaTextModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+class FlavaMultimodalModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        seq_length=44,
+        use_input_mask=True,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        qkv_bias=True,
+        ce_ignore_index=-100,
+        use_cls_token=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.use_input_mask = use_input_mask
+        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.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.qkv_bias = qkv_bias
+        self.ce_ignore_index = ce_ignore_index
+        self.use_cls_token = use_cls_token
+
+    def prepare_config_and_inputs(self):
+        hidden_states = floats_tensor([self.batch_size, self.seq_length - 1, self.hidden_size])
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        if input_mask is not None:
+            batch_size, seq_length = input_mask.shape
+            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
+            for batch_idx, start_index in enumerate(rnd_start_indices):
+                input_mask[batch_idx, :start_index] = 1
+                input_mask[batch_idx, start_index:] = 0
+
+        config = self.get_config()
+
+        return config, hidden_states, input_mask
+
+    def get_config(self):
+        return FlavaMultimodalConfig(
+            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,
+            initializer_range=self.initializer_range,
+            layer_norm_eps=self.layer_norm_eps,
+            qkv_bias=self.qkv_bias,
+            use_cls_token=self.use_cls_token,
+            ce_ignore_index=self.ce_ignore_index,
+        )
+
+    def create_and_check_model(self, config, hidden_states, input_mask):
+        model = FlavaMultimodalModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(hidden_states, attention_mask=input_mask)
+            result = model(hidden_states)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, hidden_states, input_mask = config_and_inputs
+        inputs_dict = {"hidden_states": hidden_states, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class FlavaMultimodalModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (FlavaMultimodalModel,) if is_torch_available() else ()
+    test_pruning = False
+    test_head_masking = False
+    test_resize_embeddings = False
+    test_torchscript = False
+
+    def setUp(self):
+        self.model_tester = FlavaMultimodalModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=FlavaMultimodalConfig, has_text_modality=False, hidden_size=37
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_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.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["hidden_states"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_model_common_attributes(self):
+        # No embedding in multimodal model
+        pass
+
+    def test_training(self):
+        pass
+
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    def test_inputs_embeds(self):
+        # FLAVA does not use inputs_embeds
+        pass
+
+    # skip this test as FlavaMultimodalModel has no base class and is
+    # not available in MODEL_MAPPING
+    def test_save_load_fast_init_from_base(self):
+        pass
+
+    # skip this test as FlavaMultimodalModel has no base class and is
+    # not available in MODEL_MAPPING
+    def test_save_load_fast_init_to_base(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = FlavaMultimodalModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+class FlavaImageCodebookTester:
+    def __init__(self, parent, batch_size=12, image_size=112, num_channels=3):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.num_channels = num_channels
+
+    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 FlavaImageCodebookConfig()
+
+    def create_and_check_model(self, config, pixel_values):
+        model = FlavaImageCodebook(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values)
+        self.parent.assertEqual(
+            result.shape, (self.batch_size, config.vocab_size, self.image_size // 8, self.image_size // 8)
+        )
+
+    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_torch
+class FlavaImageCodebookTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (FlavaImageCodebook,) if is_torch_available() else ()
+    test_pruning = False
+    test_head_masking = False
+    test_resize_embeddings = False
+    test_torchscript = False
+    has_attentions = False
+
+    def setUp(self):
+        self.model_tester = FlavaImageCodebookTester(self)
+        self.config_tester = ConfigTester(self, config_class=FlavaImageCodebookConfig, has_text_modality=False)
+
+    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_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.forward)
+            # 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_common_attributes(self):
+        # No embedding in multimodal model
+        pass
+
+    def test_training(self):
+        pass
+
+    def test_hidden_states_output(self):
+        pass
+
+    def test_retain_grad_hidden_states_attentions(self):
+        # no attentions
+        pass
+
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    def test_inputs_embeds(self):
+        # FLAVA does not use inputs_embeds
+        pass
+
+    def test_model_outputs_equivalence(self):
+        pass
+
+    # skip this test as FlavaImageCodebook has no base class and is
+    # not available in MODEL_MAPPING
+    def test_save_load_fast_init_from_base(self):
+        pass
+
+    # skip this test as FlavaImageCodebook has no base class and is
+    # not available in MODEL_MAPPING
+    def test_save_load_fast_init_to_base(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = FlavaImageCodebook.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+class FlavaModelTester:
+    model_class = FlavaModel
+
+    def __init__(
+        self,
+        parent,
+        is_training=True,
+        hidden_size=32,
+        projection_dim=32,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+    ):
+        self.parent = parent
+        self.image_model_tester = FlavaImageModelTester(parent)
+        self.text_model_tester = FlavaTextModelTester(parent)
+        self.multimodal_model_tester = FlavaMultimodalModelTester(parent)
+        self.image_codebook_tester = FlavaImageCodebookTester(parent)
+        self.is_training = is_training
+        self.config_tester = ConfigTester(self, config_class=FlavaConfig, hidden_size=37)
+        self.hidden_size = hidden_size
+        self.projection_dim = projection_dim
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def prepare_config_and_inputs_for_common(self):
+        _, pixel_values, bool_masked_pos = self.image_model_tester.prepare_config_and_inputs()
+        _, input_ids, token_type_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+
+        config = self.get_config()
+
+        return config, {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "attention_mask": attention_mask,
+            "pixel_values": pixel_values,
+            "bool_masked_pos": bool_masked_pos,
+        }
+
+    def get_config(self):
+        return FlavaConfig.from_configs(
+            self.image_model_tester.get_config(),
+            self.text_model_tester.get_config(),
+            self.multimodal_model_tester.get_config(),
+            self.image_codebook_tester.get_config(),
+            hidden_size=self.hidden_size,
+            projection_dim=self.projection_dim,
+            initializer_range=self.initializer_range,
+            layer_norm_eps=self.layer_norm_eps,
+        )
+
+    def create_and_check_model(self, config, inputs):
+        self._test_model(config, inputs, test_image=True)
+        self._test_model(config, inputs, test_text=True)
+        self._test_model(config, inputs, test_image=True, test_text=True)
+
+    def _test_model(self, config, inputs, test_image=False, test_text=False):
+        model = self.model_class(config).to(torch_device).eval()
+        with torch.no_grad():
+            result = model(
+                input_ids=inputs["input_ids"] if test_text else None,
+                attention_mask=inputs["attention_mask"] if test_text else None,
+                token_type_ids=inputs["token_type_ids"] if test_text else None,
+                pixel_values=inputs["pixel_values"] if test_image else None,
+                bool_masked_pos=inputs["bool_masked_pos"] if test_image else None,
+            )
+        image_size = (self.image_model_tester.image_size, self.image_model_tester.image_size)
+        patch_size = (self.image_model_tester.patch_size, self.image_model_tester.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+
+        if test_image:
+            self.parent.assertEqual(
+                result.image_embeddings.shape,
+                (self.image_model_tester.batch_size, num_patches + 1, self.image_model_tester.hidden_size),
+            )
+        else:
+            self.parent.assertIsNone(result.image_embeddings)
+
+        if test_text:
+            self.parent.assertEqual(
+                result.text_embeddings.shape,
+                (
+                    self.text_model_tester.batch_size,
+                    self.text_model_tester.seq_length,
+                    self.text_model_tester.hidden_size,
+                ),
+            )
+        else:
+            self.parent.assertIsNone(result.text_embeddings)
+
+        if test_image and test_text:
+            self.parent.assertEqual(
+                result.multimodal_embeddings.shape,
+                (
+                    self.multimodal_model_tester.batch_size,
+                    self.text_model_tester.seq_length + num_patches + 2,
+                    self.multimodal_model_tester.hidden_size,
+                ),
+            )
+        else:
+            self.parent.assertIsNone(result.multimodal_embeddings)
+
+
+@require_torch
+class FlavaModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (FlavaModel,) if is_torch_available() else ()
+    class_for_tester = FlavaModelTester
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+
+    def setUp(self):
+        self.model_tester = self.class_for_tester(self)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    # hidden_states are tested in individual model tests
+    def test_hidden_states_output(self):
+        pass
+
+    # input_embeds are tested in individual model tests
+    def test_inputs_embeds(self):
+        pass
+
+    # tested in individual model tests
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    # FlavaModel does not have input/output embeddings
+    def test_model_common_attributes(self):
+        pass
+
+    # override as the `logit_scale` parameter initilization is different for FLAVA
+    def test_initialization(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            for name, param in model.named_parameters():
+                if param.requires_grad:
+                    # check if `logit_scale` is initilized as per the original implementation
+                    if name == "logit_scale" or name == "flava.logit_scale":
+                        self.assertAlmostEqual(
+                            param.data.item(),
+                            np.log(1 / 0.07),
+                            delta=1e-3,
+                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                        )
+                    else:
+                        self.assertIn(
+                            ((param.data.mean() * 1e9).round() / 1e9).item(),
+                            [0.0, 1.0],
+                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                        )
+
+    def _create_and_check_torchscript(self, config, inputs_dict):
+        if not self.test_torchscript:
+            return
+
+        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
+        configs_no_init.torchscript = True
+        configs_no_init.return_dict = False
+        configs_no_init.return_loss = False
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            model.to(torch_device)
+            model.eval()
+
+            try:
+                input_ids = inputs_dict["input_ids"]
+                pixel_values = inputs_dict["pixel_values"]  # FLAVA needs pixel_values
+
+                if "input_ids_masked" in inputs_dict:
+                    # For pretraining
+                    inputs = (input_ids, inputs_dict["input_ids_masked"], pixel_values)
+                else:
+                    inputs = (input_ids, pixel_values)
+
+                traced_model = torch.jit.trace(model, inputs)
+            except RuntimeError:
+                self.fail("Couldn't trace module.")
+
+            with tempfile.TemporaryDirectory() as tmp_dir_name:
+                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
+
+                try:
+                    torch.jit.save(traced_model, pt_file_name)
+                except Exception:
+                    self.fail("Couldn't save module.")
+
+                try:
+                    loaded_model = torch.jit.load(pt_file_name)
+                except Exception:
+                    self.fail("Couldn't load module.")
+
+            model.to(torch_device)
+            model.eval()
+
+            loaded_model.to(torch_device)
+            loaded_model.eval()
+
+            model_state_dict = model.state_dict()
+            loaded_model_state_dict = loaded_model.state_dict()
+            # Non persistent buffers won't be in original state dict
+            loaded_model_state_dict.pop("text_model.embeddings.token_type_ids", None)
+
+            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
+
+            models_equal = True
+            for layer_name, p1 in model_state_dict.items():
+                p2 = loaded_model_state_dict[layer_name]
+                if p1.data.ne(p2.data).sum() > 0:
+                    models_equal = False
+
+            self.assertTrue(models_equal)
+
+    def test_load_image_text_config(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # Save FlavaConfig and check if we can load FlavaImageConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            image_config = FlavaImageConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.image_config.to_dict(), image_config.to_dict())
+
+        # Save FlavaConfig and check if we can load FlavaTextConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            text_config = FlavaTextConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
+
+        # Save FlavaConfig and check if we can load FlavaMultimodalConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            multimodal_config = FlavaMultimodalConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.multimodal_config.to_dict(), multimodal_config.to_dict())
+
+    # overwrite from common since FlavaModel/TFFlavaModel return FLAVAOutput/TFFLAVAOutput
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = FlavaModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+class FlavaForPreTrainingTester(FlavaModelTester):
+    model_class = FlavaForPreTraining
+
+    def prepare_config_and_inputs_for_common(self):
+        _, pixel_values, bool_masked_pos = self.image_model_tester.prepare_config_and_inputs()
+        _, input_ids, token_type_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        config = self.get_config()
+
+        input_ids_masked = input_ids.detach().clone()
+        input_ids_masked[:, 1:3] = 100
+        mlm_labels = input_ids.detach().clone()
+        mlm_labels[:, :] = config.ce_ignore_index
+        mlm_labels[:, 1:3] = input_ids[:, 1:3]
+        mim_labels = torch.randint(
+            0, self.image_model_tester.vocab_size, bool_masked_pos.size(), device=bool_masked_pos.device
+        ).long()
+        mim_labels[bool_masked_pos.ne(True)] = config.ce_ignore_index
+        itm_labels = torch.ones(mlm_labels.size(0), device=bool_masked_pos.device).long()
+
+        return config, {
+            "input_ids": input_ids,
+            "input_ids_masked": input_ids_masked,
+            "token_type_ids": token_type_ids,
+            "attention_mask": attention_mask,
+            "pixel_values": pixel_values,
+            "bool_masked_pos": bool_masked_pos,
+            "mlm_labels": mlm_labels,
+            "mim_labels": mim_labels,
+            "itm_labels": itm_labels,
+            "return_loss": True,
+        }
+
+    def _test_model(self, config, inputs, test_image=False, test_text=False):
+        model = self.model_class(config).to(torch_device).eval()
+        with torch.no_grad():
+            result = model(
+                input_ids=inputs["input_ids"] if test_text else None,
+                input_ids_masked=inputs["input_ids_masked"] if test_text else None,
+                attention_mask=inputs["attention_mask"] if test_text else None,
+                token_type_ids=inputs["token_type_ids"] if test_text else None,
+                pixel_values=inputs["pixel_values"] if test_image else None,
+                bool_masked_pos=inputs["bool_masked_pos"] if test_image else None,
+                mlm_labels=inputs["mlm_labels"],
+                mim_labels=inputs["mim_labels"],
+                itm_labels=inputs["itm_labels"],
+                return_loss=inputs["return_loss"],
+            )
+        image_size = (self.image_model_tester.image_size, self.image_model_tester.image_size)
+        patch_size = (self.image_model_tester.patch_size, self.image_model_tester.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+
+        if test_image:
+            self.parent.assertEqual(
+                result.image_embeddings.shape,
+                (self.image_model_tester.batch_size, num_patches + 1, self.image_model_tester.hidden_size),
+            )
+            if not test_text:
+                self.parent.assertEqual(
+                    result.loss_info.mim.dim(),
+                    0,
+                )
+                self.parent.assertEqual(
+                    result.mim_logits.shape,
+                    (inputs["bool_masked_pos"].sum().item(), self.image_model_tester.vocab_size),
+                )
+
+        else:
+            self.parent.assertIsNone(result.image_embeddings)
+
+        if test_text:
+            self.parent.assertEqual(
+                result.text_embeddings.shape,
+                (
+                    self.text_model_tester.batch_size,
+                    self.text_model_tester.seq_length,
+                    self.text_model_tester.hidden_size,
+                ),
+            )
+            if not test_image:
+                self.parent.assertEqual(result.loss_info.mlm.dim(), 0)
+                self.parent.assertEqual(
+                    result.mlm_logits.shape,
+                    (
+                        (inputs["mlm_labels"] != self.multimodal_model_tester.ce_ignore_index).sum().item(),
+                        self.text_model_tester.vocab_size,
+                    ),
+                )
+        else:
+            self.parent.assertIsNone(result.text_embeddings)
+
+        if test_image and test_text:
+            self.parent.assertEqual(
+                result.multimodal_masked_embeddings.shape,
+                (
+                    self.multimodal_model_tester.batch_size,
+                    self.text_model_tester.seq_length + num_patches + 2,
+                    self.multimodal_model_tester.hidden_size,
+                ),
+            )
+            self.parent.assertEqual(
+                result.itm_logits.shape,
+                (self.text_model_tester.batch_size, 2),
+            )
+            self.parent.assertEqual(
+                result.mmm_text_logits.shape,
+                (
+                    (inputs["mlm_labels"] != self.multimodal_model_tester.ce_ignore_index).sum().item(),
+                    self.text_model_tester.vocab_size,
+                ),
+            )
+            self.parent.assertEqual(
+                result.mmm_image_logits.shape,
+                (inputs["bool_masked_pos"].sum().item(), self.image_model_tester.vocab_size),
+            )
+            self.parent.assertEqual(
+                result.contrastive_logits_per_image.shape,
+                (self.image_model_tester.batch_size, self.text_model_tester.batch_size),
+            )
+            self.parent.assertEqual(
+                result.contrastive_logits_per_text.shape,
+                (self.text_model_tester.batch_size, self.image_model_tester.batch_size),
+            )
+
+            for item in [
+                result.loss_info.global_contrastive,
+                result.loss_info.itm,
+                result.loss_info.mmm_text,
+                result.loss_info.mmm_image,
+            ]:
+                self.parent.assertEqual(item.dim(), 0)
+
+            for item in [result.loss_info.mim, result.loss_info.mlm]:
+                self.parent.assertIsNone(item)
+
+        else:
+            self.parent.assertIsNone(result.multimodal_masked_embeddings)
+            for item in [
+                result.loss_info.global_contrastive,
+                result.loss_info.itm,
+                result.loss_info.mmm_text,
+                result.loss_info.mmm_image,
+            ]:
+                self.parent.assertIsNone(item)
+
+        self.parent.assertIsNone(result.multimodal_embeddings)
+
+
+@require_torch
+class FlavaForPreTrainingTest(FlavaModelTest):
+    all_model_classes = (FlavaForPreTraining,) if is_torch_available() else ()
+    class_for_tester = FlavaForPreTrainingTester
+    test_torchscript = False
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@require_vision
+@require_torch
+class FlavaModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model_name = "facebook/flava-full"
+        model = FlavaModel.from_pretrained(model_name).to(torch_device)
+        processor = FlavaProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        inputs = processor(
+            text=["a photo of a cat", "a photo of a dog"],
+            images=[image, image],
+            padding="max_length",
+            max_length=77,
+            return_tensors="pt",
+        ).to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, return_dict=True)
+
+        # verify the embeddings
+        self.assertAlmostEqual(outputs.image_embeddings.sum().item(), -1352.53540, places=4)
+        self.assertAlmostEqual(outputs.text_embeddings.sum().item(), -198.98225, places=4)
+        self.assertAlmostEqual(outputs.multimodal_embeddings.sum().item(), -3988.51367, places=4)
+
+
+@require_vision
+@require_torch
+class FlavaForPreTrainingIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model_name = "facebook/flava-full"
+        model = FlavaForPreTraining.from_pretrained(model_name).to(torch_device)
+        processor = FlavaProcessor.from_pretrained(model_name)
+        torch.manual_seed(1)
+        random.seed(1)
+
+        image = prepare_img()
+        inputs = processor(
+            text=["a photo of a cat", "a photo of a dog"],
+            images=[image, image],
+            padding="max_length",
+            max_length=77,
+            return_tensors="pt",
+            return_codebook_pixels=True,
+            return_image_mask=True,
+        )
+        inputs["input_ids_masked"] = inputs["input_ids"].clone()
+        inputs["input_ids_masked"][0, 4:6] = 103
+        inputs["mlm_labels"] = inputs["input_ids"].clone()
+        inputs["mlm_labels"][:, :] = -100
+        inputs["mlm_labels"][0, 4:6] = inputs["input_ids"][0, 4:6]
+        inputs = inputs.to(torch_device)
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        self.assertEqual(
+            outputs.contrastive_logits_per_image.shape,
+            torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
+        )
+        self.assertEqual(
+            outputs.contrastive_logits_per_text.shape,
+            torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
+        )
+
+        expected_logits = torch.tensor([[16.1291, 8.4033], [16.1291, 8.4033]], device=torch_device)
+        self.assertTrue(torch.allclose(outputs.contrastive_logits_per_image, expected_logits, atol=1e-3))
+        self.assertAlmostEqual(outputs.loss_info.mmm_text.item(), 1.75533199)
+        self.assertAlmostEqual(outputs.loss_info.mmm_image.item(), 7.0290069, places=4)
+        self.assertAlmostEqual(outputs.loss.item(), 11.0626, places=4)

tests/models/flava/test_processor_flava.py

+# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+import random
+import shutil
+import tempfile
+import unittest
+
+import numpy as np
+import pytest
+
+from transformers import BertTokenizer, BertTokenizerFast
+from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
+from transformers.testing_utils import require_vision
+from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import FlavaFeatureExtractor, FlavaProcessor
+    from transformers.models.flava.feature_extraction_flava import (
+        FLAVA_CODEBOOK_MEAN,
+        FLAVA_CODEBOOK_STD,
+        FLAVA_IMAGE_MEAN,
+        FLAVA_IMAGE_STD,
+    )
+
+
+@require_vision
+class FlavaProcessorTest(unittest.TestCase):
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        # fmt: off
+        vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
+        # fmt: on
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+
+        with open(self.vocab_file, "w", encoding="utf-8") as fp:
+            fp.write("".join([x + "\n" for x in vocab_tokens]))
+
+        feature_extractor_map = {
+            "image_mean": FLAVA_IMAGE_MEAN,
+            "image_std": FLAVA_IMAGE_STD,
+            "do_normalize": True,
+            "do_resize": True,
+            "size": 224,
+            "do_center_crop": True,
+            "crop_size": 224,
+            "input_size_patches": 14,
+            "total_mask_patches": 75,
+            "mask_group_max_patches": None,
+            "mask_group_min_patches": 16,
+            "mask_group_min_aspect_ratio": 0.3,
+            "mask_group_max_aspect_ratio": None,
+            "codebook_do_resize": True,
+            "codebook_size": 112,
+            "codebook_resample": None,
+            "codebook_do_center_crop": True,
+            "codebook_crop_size": 112,
+            "codebook_do_map_pixels": True,
+            "codebook_do_normalize": True,
+            "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
+            "codebook_image_std": FLAVA_CODEBOOK_STD,
+        }
+
+        self.feature_extractor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
+        with open(self.feature_extractor_file, "w", encoding="utf-8") as fp:
+            json.dump(feature_extractor_map, fp)
+
+    def get_tokenizer(self, **kwargs):
+        return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_rust_tokenizer(self, **kwargs):
+        return BertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_feature_extractor(self, **kwargs):
+        return FlavaFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def prepare_image_inputs(self):
+        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
+        or a list of PyTorch tensors if one specifies torchify=True.
+        """
+
+        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
+
+        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
+
+        return image_inputs
+
+    def test_save_load_pretrained_default(self):
+        tokenizer_slow = self.get_tokenizer()
+        tokenizer_fast = self.get_rust_tokenizer()
+        feature_extractor = self.get_feature_extractor()
+
+        processor_slow = FlavaProcessor(tokenizer=tokenizer_slow, feature_extractor=feature_extractor)
+        processor_slow.save_pretrained(self.tmpdirname)
+        processor_slow = FlavaProcessor.from_pretrained(self.tmpdirname, use_fast=False)
+
+        processor_fast = FlavaProcessor(tokenizer=tokenizer_fast, feature_extractor=feature_extractor)
+        processor_fast.save_pretrained(self.tmpdirname)
+        processor_fast = FlavaProcessor.from_pretrained(self.tmpdirname)
+
+        self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
+        self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
+        self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
+        self.assertIsInstance(processor_slow.tokenizer, BertTokenizer)
+        self.assertIsInstance(processor_fast.tokenizer, BertTokenizerFast)
+
+        self.assertEqual(processor_slow.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+        self.assertEqual(processor_fast.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+        self.assertIsInstance(processor_slow.feature_extractor, FlavaFeatureExtractor)
+        self.assertIsInstance(processor_fast.feature_extractor, FlavaFeatureExtractor)
+
+    def test_save_load_pretrained_additional_features(self):
+        processor = FlavaProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
+        processor.save_pretrained(self.tmpdirname)
+
+        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
+        feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
+
+        processor = FlavaProcessor.from_pretrained(
+            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
+        )
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
+        self.assertIsInstance(processor.tokenizer, BertTokenizerFast)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, FlavaFeatureExtractor)
+
+    def test_feature_extractor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        image_input = self.prepare_image_inputs()
+
+        input_feat_extract = feature_extractor(image_input, return_tensors="np")
+        input_processor = processor(images=image_input, return_tensors="np")
+
+        for key in input_feat_extract.keys():
+            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+        # With rest of the args
+        random.seed(1234)
+        input_feat_extract = feature_extractor(
+            image_input, return_image_mask=True, return_codebook_pixels=True, return_tensors="np"
+        )
+        random.seed(1234)
+        input_processor = processor(
+            images=image_input, return_image_mask=True, return_codebook_pixels=True, return_tensors="np"
+        )
+
+        for key in input_feat_extract.keys():
+            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+    def test_tokenizer(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        input_str = "lower newer"
+
+        encoded_processor = processor(text=input_str)
+
+        encoded_tok = tokenizer(input_str)
+
+        for key in encoded_tok.keys():
+            self.assertListEqual(encoded_tok[key], encoded_processor[key])
+
+    def test_processor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        input_str = "lower newer"
+        image_input = self.prepare_image_inputs()
+
+        inputs = processor(text=input_str, images=image_input)
+
+        self.assertListEqual(list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"])
+
+        # add extra args
+        inputs = processor(text=input_str, images=image_input, return_codebook_pixels=True, return_image_mask=True)
+
+        self.assertListEqual(
+            list(inputs.keys()),
+            [
+                "input_ids",
+                "token_type_ids",
+                "attention_mask",
+                "pixel_values",
+                "codebook_pixel_values",
+                "bool_masked_pos",
+            ],
+        )
+
+        # test if it raises when no input is passed
+        with pytest.raises(ValueError):
+            processor()
+
+    def test_tokenizer_decode(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+
+        processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
+
+        decoded_processor = processor.batch_decode(predicted_ids)
+        decoded_tok = tokenizer.batch_decode(predicted_ids)
+
+        self.assertListEqual(decoded_tok, decoded_processor)

utils/check_repo.py

@@ -146,6 +146,10 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "DetrForSegmentation",
     "DPRReader",
     "FlaubertForQuestionAnswering",
+    "FlavaImageCodebook",
+    "FlavaTextModel",
+    "FlavaImageModel",
+    "FlavaMultimodalModel",
     "GPT2DoubleHeadsModel",
     "LukeForMaskedLM",
     "LukeForEntityClassification",