[LayoutLMv3] Add TensorFlow implementation (#18678)

Co-authored-by: Esben Toke Christensen <esben.christensen@visma.com>
Co-authored-by: Lasse Reedtz <lasse.reedtz@visma.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

docs/source/en/index.mdx

@@ -245,7 +245,7 @@ Flax), PyTorch, and/or TensorFlow.
 |          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          LayoutLM           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |         LayoutLMv2          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|         LayoutLMv3          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|         LayoutLMv3          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            LeViT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |

docs/source/en/model_doc/layoutlmv3.mdx

@@ -37,7 +37,7 @@ alt="drawing" width="600"/>
 
 <small> LayoutLMv3 architecture. Taken from the <a href="https://arxiv.org/abs/2204.08387">original paper</a>. </small>
 
-This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/layoutlmv3).
+This model was contributed by [nielsr](https://huggingface.co/nielsr). The TensorFlow version of this model was added by [chriskoo](https://huggingface.co/chriskoo), [tokec](https://huggingface.co/tokec), and [lre](https://huggingface.co/lre). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/layoutlmv3).
 
 
 ## LayoutLMv3Config
@@ -84,3 +84,23 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 
 [[autodoc]] LayoutLMv3ForQuestionAnswering
     - forward
+
+## TFLayoutLMv3Model
+
+[[autodoc]] TFLayoutLMv3Model
+    - call
+
+## TFLayoutLMv3ForSequenceClassification
+
+[[autodoc]] TFLayoutLMv3ForSequenceClassification
+    - call
+
+## TFLayoutLMv3ForTokenClassification
+
+[[autodoc]] TFLayoutLMv3ForTokenClassification
+    - call
+
+## TFLayoutLMv3ForQuestionAnswering
+
+[[autodoc]] TFLayoutLMv3ForQuestionAnswering
+    - call

docs/source/it/index.mdx

@@ -221,7 +221,7 @@ tokenizer (chiamato "slow"). Un tokenizer "fast" supportato dalla libreria 🤗
 |          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          LayoutLM           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |         LayoutLMv2          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|         LayoutLMv3          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|         LayoutLMv3          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            LUKE             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |

src/transformers/__init__.py

@@ -2343,6 +2343,16 @@ else:
             "TFLayoutLMPreTrainedModel",
         ]
     )
+    _import_structure["models.layoutlmv3"].extend(
+        [
+            "TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFLayoutLMv3ForQuestionAnswering",
+            "TFLayoutLMv3ForSequenceClassification",
+            "TFLayoutLMv3ForTokenClassification",
+            "TFLayoutLMv3Model",
+            "TFLayoutLMv3PreTrainedModel",
+        ]
+    )
     _import_structure["models.led"].extend(["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"])
     _import_structure["models.longformer"].extend(
         [
@@ -4801,6 +4811,14 @@ if TYPE_CHECKING:
             TFHubertModel,
             TFHubertPreTrainedModel,
         )
+        from .models.layoutlmv3 import (
+            TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFLayoutLMv3ForQuestionAnswering,
+            TFLayoutLMv3ForSequenceClassification,
+            TFLayoutLMv3ForTokenClassification,
+            TFLayoutLMv3Model,
+            TFLayoutLMv3PreTrainedModel,
+        )
         from .models.led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel
         from .models.longformer import (
             TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,

src/transformers/models/auto/modeling_tf_auto.py

@@ -52,6 +52,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("gptj", "TFGPTJModel"),
         ("hubert", "TFHubertModel"),
         ("layoutlm", "TFLayoutLMModel"),
+        ("layoutlmv3", "TFLayoutLMv3Model"),
         ("led", "TFLEDModel"),
         ("longformer", "TFLongformerModel"),
         ("lxmert", "TFLxmertModel"),
@@ -268,6 +269,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("gpt2", "TFGPT2ForSequenceClassification"),
         ("gptj", "TFGPTJForSequenceClassification"),
         ("layoutlm", "TFLayoutLMForSequenceClassification"),
+        ("layoutlmv3", "TFLayoutLMv3ForSequenceClassification"),
         ("longformer", "TFLongformerForSequenceClassification"),
         ("mobilebert", "TFMobileBertForSequenceClassification"),
         ("mpnet", "TFMPNetForSequenceClassification"),
@@ -297,6 +299,7 @@ TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
         ("flaubert", "TFFlaubertForQuestionAnsweringSimple"),
         ("funnel", "TFFunnelForQuestionAnswering"),
         ("gptj", "TFGPTJForQuestionAnswering"),
+        ("layoutlmv3", "TFLayoutLMv3ForQuestionAnswering"),
         ("longformer", "TFLongformerForQuestionAnswering"),
         ("mobilebert", "TFMobileBertForQuestionAnswering"),
         ("mpnet", "TFMPNetForQuestionAnswering"),
@@ -316,7 +319,6 @@ TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
     ]
 )
 
-
 TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Token Classification mapping
@@ -331,6 +333,7 @@ TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("flaubert", "TFFlaubertForTokenClassification"),
         ("funnel", "TFFunnelForTokenClassification"),
         ("layoutlm", "TFLayoutLMForTokenClassification"),
+        ("layoutlmv3", "TFLayoutLMv3ForTokenClassification"),
         ("longformer", "TFLongformerForTokenClassification"),
         ("mobilebert", "TFMobileBertForTokenClassification"),
         ("mpnet", "TFMPNetForTokenClassification"),
@@ -373,7 +376,6 @@ TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict(
     ]
 )
 
-
 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)

src/transformers/models/layoutlmv3/__init__.py

@@ -21,6 +21,7 @@ from typing import TYPE_CHECKING
 from ...utils import (
     OptionalDependencyNotAvailable,
     _LazyModule,
+    is_tf_available,
     is_tokenizers_available,
     is_torch_available,
     is_vision_available,
@@ -60,6 +61,21 @@ else:
         "LayoutLMv3PreTrainedModel",
     ]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_layoutlmv3"] = [
+        "TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFLayoutLMv3ForQuestionAnswering",
+        "TFLayoutLMv3ForSequenceClassification",
+        "TFLayoutLMv3ForTokenClassification",
+        "TFLayoutLMv3Model",
+        "TFLayoutLMv3PreTrainedModel",
+    ]
+
 try:
     if not is_vision_available():
         raise OptionalDependencyNotAvailable()
@@ -101,6 +117,21 @@ if TYPE_CHECKING:
             LayoutLMv3PreTrainedModel,
         )
 
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_layoutlmv3 import (
+            TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFLayoutLMv3ForQuestionAnswering,
+            TFLayoutLMv3ForSequenceClassification,
+            TFLayoutLMv3ForTokenClassification,
+            TFLayoutLMv3Model,
+            TFLayoutLMv3PreTrainedModel,
+        )
+
     try:
         if not is_vision_available():
             raise OptionalDependencyNotAvailable()

src/transformers/utils/dummy_tf_objects.py

@@ -1316,6 +1316,44 @@ class TFHubertPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFLayoutLMv3ForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMv3ForSequenceClassification(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMv3ForTokenClassification(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMv3Model(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMv3PreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 class TFLEDForConditionalGeneration(metaclass=DummyObject):
     _backends = ["tf"]
 

tests/models/layoutlmv3/test_modeling_tf_layoutlmv3.py

@@ -0,0 +1,497 @@
+# coding=utf-8
+# Copyright 2022 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 LayoutLMv3 model. """
+
+import copy
+import inspect
+import unittest
+
+import numpy as np
+
+from transformers import is_tf_available, is_vision_available
+from transformers.models.auto import get_values
+from transformers.testing_utils import require_tf, slow
+from transformers.utils import cached_property
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import (
+        TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
+        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
+        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
+        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
+        LayoutLMv3Config,
+        TFLayoutLMv3ForQuestionAnswering,
+        TFLayoutLMv3ForSequenceClassification,
+        TFLayoutLMv3ForTokenClassification,
+        TFLayoutLMv3Model,
+    )
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import LayoutLMv3FeatureExtractor
+
+
+class TFLayoutLMv3ModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        num_channels=3,
+        image_size=4,
+        patch_size=2,
+        text_seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=36,
+        num_hidden_layers=3,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        coordinate_size=6,
+        shape_size=6,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+        range_bbox=1000,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_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.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.coordinate_size = coordinate_size
+        self.shape_size = shape_size
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.scope = scope
+        self.range_bbox = range_bbox
+
+        # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
+        self.text_seq_length = text_seq_length
+        self.image_seq_length = (image_size // patch_size) ** 2 + 1
+        self.seq_length = self.text_seq_length + self.image_seq_length
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.text_seq_length], self.vocab_size)
+
+        bbox = ids_tensor([self.batch_size, self.text_seq_length, 4], self.range_bbox)
+        bbox = bbox.numpy()
+        # Ensure that bbox is legal
+        for i in range(bbox.shape[0]):
+            for j in range(bbox.shape[1]):
+                if bbox[i, j, 3] < bbox[i, j, 1]:
+                    tmp_coordinate = bbox[i, j, 3]
+                    bbox[i, j, 3] = bbox[i, j, 1]
+                    bbox[i, j, 1] = tmp_coordinate
+                if bbox[i, j, 2] < bbox[i, j, 0]:
+                    tmp_coordinate = bbox[i, j, 2]
+                    bbox[i, j, 2] = bbox[i, j, 0]
+                    bbox[i, j, 0] = tmp_coordinate
+        bbox = tf.constant(bbox)
+
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.text_seq_length])
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.text_seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.text_seq_length], self.num_labels)
+
+        config = LayoutLMv3Config(
+            vocab_size=self.vocab_size,
+            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,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            initializer_range=self.initializer_range,
+            coordinate_size=self.coordinate_size,
+            shape_size=self.shape_size,
+            input_size=self.image_size,
+            patch_size=self.patch_size,
+        )
+
+        return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
+
+    def create_and_check_model(self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask):
+        model = TFLayoutLMv3Model(config=config)
+
+        # text + image
+        result = model(input_ids, pixel_values=pixel_values, training=False)
+        result = model(
+            input_ids,
+            bbox=bbox,
+            pixel_values=pixel_values,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            training=False,
+        )
+        result = model(input_ids, bbox=bbox, pixel_values=pixel_values, training=False)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+        # text only
+        result = model(input_ids, training=False)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size)
+        )
+
+        # image only
+        result = model({"pixel_values": pixel_values}, training=False)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.image_seq_length, self.hidden_size)
+        )
+
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFLayoutLMv3ForSequenceClassification(config=config)
+        result = model(
+            input_ids,
+            bbox=bbox,
+            pixel_values=pixel_values,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            labels=sequence_labels,
+            training=False,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_for_token_classification(
+        self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, token_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFLayoutLMv3ForTokenClassification(config=config)
+        result = model(
+            input_ids,
+            bbox=bbox,
+            pixel_values=pixel_values,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            labels=token_labels,
+            training=False,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.num_labels))
+
+    def create_and_check_for_question_answering(
+        self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
+    ):
+        config.num_labels = 2
+        model = TFLayoutLMv3ForQuestionAnswering(config=config)
+        result = model(
+            input_ids,
+            bbox=bbox,
+            pixel_values=pixel_values,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            start_positions=sequence_labels,
+            end_positions=sequence_labels,
+            training=False,
+        )
+        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
+        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (config, input_ids, bbox, pixel_values, token_type_ids, input_mask, _, _) = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "bbox": bbox,
+            "pixel_values": pixel_values,
+            "token_type_ids": token_type_ids,
+            "attention_mask": input_mask,
+        }
+        return config, inputs_dict
+
+
+@require_tf
+class TFLayoutLMv3ModelTest(TFModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            TFLayoutLMv3Model,
+            TFLayoutLMv3ForQuestionAnswering,
+            TFLayoutLMv3ForSequenceClassification,
+            TFLayoutLMv3ForTokenClassification,
+        )
+        if is_tf_available()
+        else ()
+    )
+
+    test_pruning = False
+    test_resize_embeddings = False
+    test_onnx = False
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
+        inputs_dict = copy.deepcopy(inputs_dict)
+
+        if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
+            inputs_dict = {
+                k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
+                if isinstance(v, tf.Tensor) and v.ndim > 0
+                else v
+                for k, v in inputs_dict.items()
+            }
+
+        if return_labels:
+            if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
+                inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
+            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):
+                inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
+            elif model_class in get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING):
+                inputs_dict["labels"] = tf.zeros(
+                    (self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=tf.int32
+                )
+
+        return inputs_dict
+
+    def setUp(self):
+        self.model_tester = TFLayoutLMv3ModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=LayoutLMv3Config, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_loss_computation(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            if getattr(model, "hf_compute_loss", None):
+                # The number of elements in the loss should be the same as the number of elements in the label
+                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
+                added_label = prepared_for_class[
+                    sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True)[0]
+                ]
+                expected_loss_size = added_label.shape.as_list()[:1]
+
+                # 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")
+
+                loss = model(input_ids, **prepared_for_class)[0]
+                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
+
+                # Test that model correctly compute the loss when we mask some positions
+                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
+                input_ids = prepared_for_class.pop("input_ids")
+                if "labels" in prepared_for_class:
+                    labels = prepared_for_class["labels"].numpy()
+                    if len(labels.shape) > 1 and labels.shape[1] != 1:
+                        labels[0] = -100
+                        prepared_for_class["labels"] = tf.convert_to_tensor(labels)
+                        loss = model(input_ids, **prepared_for_class)[0]
+                        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
+                        self.assertTrue(not np.any(np.isnan(loss.numpy())))
+
+                # Test that model correctly compute the loss with a dict
+                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
+                loss = model(prepared_for_class)[0]
+                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
+
+                # Test that model correctly compute the loss with a tuple
+                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
+
+                # Get keys that were added with the _prepare_for_class function
+                label_keys = prepared_for_class.keys() - inputs_dict.keys()
+                signature = inspect.signature(model.call).parameters
+                signature_names = list(signature.keys())
+
+                # Create a dictionary holding the location of the tensors in the tuple
+                tuple_index_mapping = {0: "input_ids"}
+                for label_key in label_keys:
+                    label_key_index = signature_names.index(label_key)
+                    tuple_index_mapping[label_key_index] = label_key
+                sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
+                # Initialize a list with their default values, update the values and convert to a tuple
+                list_input = []
+
+                for name in signature_names:
+                    if name != "kwargs":
+                        list_input.append(signature[name].default)
+
+                for index, value in sorted_tuple_index_mapping:
+                    list_input[index] = prepared_for_class[value]
+
+                tuple_input = tuple(list_input)
+
+                # Send to model
+                loss = model(tuple_input[:-1])[0]
+
+                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
+
+    def test_model(self):
+        (
+            config,
+            input_ids,
+            bbox,
+            pixel_values,
+            token_type_ids,
+            input_mask,
+            _,
+            _,
+        ) = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(config, input_ids, bbox, pixel_values, token_type_ids, input_mask)
+
+    def test_model_various_embeddings(self):
+        (
+            config,
+            input_ids,
+            bbox,
+            pixel_values,
+            token_type_ids,
+            input_mask,
+            _,
+            _,
+        ) = self.model_tester.prepare_config_and_inputs()
+        for type in ["absolute", "relative_key", "relative_key_query"]:
+            config.position_embedding_type = type
+            self.model_tester.create_and_check_model(config, input_ids, bbox, pixel_values, token_type_ids, input_mask)
+
+    def test_for_sequence_classification(self):
+        (
+            config,
+            input_ids,
+            bbox,
+            pixel_values,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            _,
+        ) = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(
+            config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
+        )
+
+    def test_for_token_classification(self):
+        (
+            config,
+            input_ids,
+            bbox,
+            pixel_values,
+            token_type_ids,
+            input_mask,
+            _,
+            token_labels,
+        ) = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(
+            config, input_ids, bbox, pixel_values, token_type_ids, input_mask, token_labels
+        )
+
+    def test_for_question_answering(self):
+        (
+            config,
+            input_ids,
+            bbox,
+            pixel_values,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            _,
+        ) = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_question_answering(
+            config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels
+        )
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFLayoutLMv3Model.from_pretrained(model_name)
+            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_tf
+class TFLayoutLMv3ModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_feature_extractor(self):
+        return LayoutLMv3FeatureExtractor(apply_ocr=False) if is_vision_available() else None
+
+    @slow
+    def test_inference_no_head(self):
+        model = TFLayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base")
+
+        feature_extractor = self.default_feature_extractor
+        image = prepare_img()
+        pixel_values = feature_extractor(images=image, return_tensors="tf").pixel_values
+
+        input_ids = tf.constant([[1, 2]])
+        bbox = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]), axis=0)
+
+        # forward pass
+        outputs = model(input_ids=input_ids, bbox=bbox, pixel_values=pixel_values, training=False)
+
+        # verify the logits
+        expected_shape = (1, 199, 768)
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
+
+        expected_slice = tf.constant(
+            [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]
+        )
+
+        self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))

utils/documentation_tests.txt

@@ -38,6 +38,7 @@ src/transformers/models/gptj/modeling_gptj.py
 src/transformers/models/hubert/modeling_hubert.py
 src/transformers/models/layoutlmv2/modeling_layoutlmv2.py
 src/transformers/models/layoutlmv3/modeling_layoutlmv3.py
+src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py
 src/transformers/models/longformer/modeling_longformer.py
 src/transformers/models/longformer/modeling_tf_longformer.py
 src/transformers/models/longt5/modeling_longt5.py