Layout lm tf 2 (#10636)

* Added embeddings layer

* Added layoutlm layers, main model, maskedlm and token classification classes

* Added model classes to tf auto models

* Added model to PT to TF conversion script

* Added model to doc README

* Added tests

* Removed unused imports

* Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py

* Made tests pass!

* Fixed typos in imports and docs

* Fixed a typo in embeddings layer

* Removed imports

* Fixed formatting issues, imports, tests

* Added layoutlm layers, main model, maskedlm and token classification classes

* Added model classes to tf auto models

* Added model to PT to TF conversion script

* Removed unused imports

* Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py

* Made tests pass!

* Fixed typos in imports and docs

* Removed imports

* Fixed small formatting issues

* Removed duplicates import from main __init__.py

* Chnaged deafult arg to true for adding  pooling layer to tf layoutlm

* Fixed formatting issues

* Style

* Added copied from to classes copied from bert

* Fixed doc strings examples to work with layoutlm inputs

* Removed PyTorch reference in doc strings example

* Added integration tests

* Cleaned up initialization file

* Updated model checkpoint identifiers

* Fixed imports
Co-authored-by: Amir Tahmasbi <amir@ehsai.ca>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

docs/source/index.rst

@@ -281,7 +281,7 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

docs/source/model_doc/layoutlm.rst

@@ -130,3 +130,31 @@ LayoutLMForTokenClassification
 
 .. autoclass:: transformers.LayoutLMForTokenClassification
     :members:
+
+
+TFLayoutLMModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLayoutLMModel
+    :members:
+
+
+TFLayoutLMForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLayoutLMForMaskedLM
+    :members:
+
+
+TFLayoutLMForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLayoutLMForSequenceClassification
+    :members:
+
+
+TFLayoutLMForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFLayoutLMForTokenClassification
+    :members:

src/transformers/__init__.py

@@ -1214,6 +1214,17 @@ if is_tf_available():
             "TFXLMRobertaModel",
         ]
     )
+    _import_structure["models.layoutlm"].extend(
+        [
+            "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFLayoutLMForMaskedLM",
+            "TFLayoutLMForSequenceClassification",
+            "TFLayoutLMForTokenClassification",
+            "TFLaoutLMMainLayer",
+            "TFLayoutLMModel",
+            "TFLayoutLMPreTrainedModel",
+        ]
+    )
     _import_structure["models.xlnet"].extend(
         [
             "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -2010,6 +2021,15 @@ if TYPE_CHECKING:
         # Benchmarks
         from .benchmark.benchmark_tf import TensorFlowBenchmark
         from .generation_tf_utils import tf_top_k_top_p_filtering
+        from .modeling_tf_layoutlm import (
+            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFLayoutLMForMaskedLM,
+            TFLayoutLMForSequenceClassification,
+            TFLayoutLMForTokenClassification,
+            TFLayoutLMMainLayer,
+            TFLayoutLMModel,
+            TFLayoutLMPreTrainedModel,
+        )
         from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, shape_list
         from .models.albert import (
             TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,

src/transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -31,6 +31,7 @@ from . import (
     ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
     FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -50,6 +51,7 @@ from . import (
     ElectraConfig,
     FlaubertConfig,
     GPT2Config,
+    LayoutLMConfig,
     LxmertConfig,
     OpenAIGPTConfig,
     RobertaConfig,
@@ -69,6 +71,7 @@ from . import (
     TFElectraForPreTraining,
     TFFlaubertWithLMHeadModel,
     TFGPT2LMHeadModel,
+    TFLayoutLMForMaskedLM,
     TFLxmertForPreTraining,
     TFLxmertVisualFeatureEncoder,
     TFOpenAIGPTLMHeadModel,
@@ -111,6 +114,7 @@ if is_torch_available():
         ElectraForPreTraining,
         FlaubertWithLMHeadModel,
         GPT2LMHeadModel,
+        LayoutLMForMaskedLM,
         LxmertForPreTraining,
         LxmertVisualFeatureEncoder,
         OpenAIGPTLMHeadModel,
@@ -211,6 +215,12 @@ MODEL_CLASSES = {
         RobertaForMaskedLM,
         ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ),
+    "layoutlm": (
+        LayoutLMConfig,
+        TFLayoutLMForMaskedLM,
+        LayoutLMForMaskedLM,
+        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
+    ),
     "roberta-large-mnli": (
         RobertaConfig,
         TFRobertaForSequenceClassification,

src/transformers/modeling_tf_pytorch_utils.py

@@ -333,6 +333,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
     all_tf_weights = set(list(tf_weights_map.keys()))
     loaded_pt_weights_data_ptr = {}
     missing_keys_pt = []
+
     for pt_weight_name, pt_weight in current_pt_params_dict.items():
         # Handle PyTorch shared weight ()not duplicated in TF 2.0
         if pt_weight.data_ptr() in loaded_pt_weights_data_ptr:

src/transformers/models/auto/modeling_tf_auto.py

@@ -102,6 +102,12 @@ from ..funnel.modeling_tf_funnel import (
     TFFunnelModel,
 )
 from ..gpt2.modeling_tf_gpt2 import TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2Model
+from ..layoutlm.modeling_tf_layoutlm import (
+    TFLayoutLMForMaskedLM,
+    TFLayoutLMForSequenceClassification,
+    TFLayoutLMForTokenClassification,
+    TFLayoutLMModel,
+)
 from ..led.modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel
 from ..longformer.modeling_tf_longformer import (
     TFLongformerForMaskedLM,
@@ -189,6 +195,7 @@ from .configuration_auto import (
     FlaubertConfig,
     FunnelConfig,
     GPT2Config,
+    LayoutLMConfig,
     LEDConfig,
     LongformerConfig,
     LxmertConfig,
@@ -227,6 +234,7 @@ TF_MODEL_MAPPING = OrderedDict(
         (XLMRobertaConfig, TFXLMRobertaModel),
         (LongformerConfig, TFLongformerModel),
         (RobertaConfig, TFRobertaModel),
+        (LayoutLMConfig, TFLayoutLMModel),
         (BertConfig, TFBertModel),
         (OpenAIGPTConfig, TFOpenAIGPTModel),
         (GPT2Config, TFGPT2Model),
@@ -260,6 +268,7 @@ TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
         (CamembertConfig, TFCamembertForMaskedLM),
         (XLMRobertaConfig, TFXLMRobertaForMaskedLM),
         (RobertaConfig, TFRobertaForMaskedLM),
+        (LayoutLMConfig, TFLayoutLMForMaskedLM),
         (BertConfig, TFBertForPreTraining),
         (OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
         (GPT2Config, TFGPT2LMHeadModel),
@@ -289,6 +298,7 @@ TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
         (XLMRobertaConfig, TFXLMRobertaForMaskedLM),
         (LongformerConfig, TFLongformerForMaskedLM),
         (RobertaConfig, TFRobertaForMaskedLM),
+        (LayoutLMConfig, TFLayoutLMForMaskedLM),
         (BertConfig, TFBertForMaskedLM),
         (OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
         (GPT2Config, TFGPT2LMHeadModel),
@@ -330,6 +340,7 @@ TF_MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
         (XLMRobertaConfig, TFXLMRobertaForMaskedLM),
         (LongformerConfig, TFLongformerForMaskedLM),
         (RobertaConfig, TFRobertaForMaskedLM),
+        (LayoutLMConfig, TFLayoutLMForMaskedLM),
         (BertConfig, TFBertForMaskedLM),
         (MobileBertConfig, TFMobileBertForMaskedLM),
         (FlaubertConfig, TFFlaubertWithLMHeadModel),
@@ -366,6 +377,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         (XLMRobertaConfig, TFXLMRobertaForSequenceClassification),
         (LongformerConfig, TFLongformerForSequenceClassification),
         (RobertaConfig, TFRobertaForSequenceClassification),
+        (LayoutLMConfig, TFLayoutLMForSequenceClassification),
         (BertConfig, TFBertForSequenceClassification),
         (XLNetConfig, TFXLNetForSequenceClassification),
         (MobileBertConfig, TFMobileBertForSequenceClassification),
@@ -414,6 +426,7 @@ TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
         (XLMRobertaConfig, TFXLMRobertaForTokenClassification),
         (LongformerConfig, TFLongformerForTokenClassification),
         (RobertaConfig, TFRobertaForTokenClassification),
+        (LayoutLMConfig, TFLayoutLMForTokenClassification),
         (BertConfig, TFBertForTokenClassification),
         (MobileBertConfig, TFMobileBertForTokenClassification),
         (XLNetConfig, TFXLNetForTokenClassification),

src/transformers/models/layoutlm/__init__.py

@@ -18,7 +18,9 @@
 
 from typing import TYPE_CHECKING
 
-from ...file_utils import _BaseLazyModule, is_tokenizers_available, is_torch_available
+from ...file_utils import _BaseLazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig
+from .tokenization_layoutlm import LayoutLMTokenizer
 
 
 _import_structure = {
@@ -38,6 +40,17 @@ if is_torch_available():
         "LayoutLMModel",
     ]
 
+if is_tf_available():
+    _import_structure["modeling_tf_layoutlm"] = [
+        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFLayoutLMForMaskedLM",
+        "TFLayoutLMForTokenClassification",
+        "TFLayoutLMForSequenceClassification",
+        "TFLayoutLMMainLayer",
+        "TFLayoutLMModel",
+        "TFLayoutLMPreTrainedModel",
+    ]
+
 
 if TYPE_CHECKING:
     from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig
@@ -54,6 +67,16 @@ if TYPE_CHECKING:
             LayoutLMForTokenClassification,
             LayoutLMModel,
         )
+    if is_tf_available():
+        from .modeling_tf_layoutlm import (
+            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFLayoutLMForMaskedLM,
+            TFLayoutLMForSequenceClassification,
+            TFLayoutLMForTokenClassification,
+            TFLayoutLMMainLayer,
+            TFLayoutLMModel,
+            TFLayoutLMPreTrainedModel,
+        )
 
 else:
     import importlib

src/transformers/utils/dummy_tf_objects.py

@@ -16,6 +16,59 @@ def tf_top_k_top_p_filtering(*args, **kwargs):
     requires_tf(tf_top_k_top_p_filtering)
 
 
+TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFLayoutLMForMaskedLM:
+    def __init__(self, *args, **kwargs):
+        requires_tf(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_tf(self)
+
+
+class TFLayoutLMForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_tf(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_tf(self)
+
+
+class TFLayoutLMForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_tf(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_tf(self)
+
+
+class TFLayoutLMMainLayer:
+    def __init__(self, *args, **kwargs):
+        requires_tf(self)
+
+
+class TFLayoutLMModel:
+    def __init__(self, *args, **kwargs):
+        requires_tf(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_tf(self)
+
+
+class TFLayoutLMPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_tf(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_tf(self)
+
+
 class TFPreTrainedModel:
     def __init__(self, *args, **kwargs):
         requires_tf(self)

tests/test_modeling_tf_layoutlm.py

+# coding=utf-8
+# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, The Hugging Face Team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+from transformers import LayoutLMConfig, is_tf_available
+from transformers.testing_utils import require_tf, slow
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers.models.layoutlm.modeling_tf_layoutlm import (
+        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
+        TFLayoutLMForMaskedLM,
+        TFLayoutLMForSequenceClassification,
+        TFLayoutLMForTokenClassification,
+        TFLayoutLMModel,
+    )
+
+
+class TFLayoutLMModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+        range_bbox=1000,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        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.num_labels = num_labels
+        self.num_choices = num_choices
+        self.scope = scope
+        self.range_bbox = range_bbox
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        # convert bbox to numpy since TF does not support item assignment
+        bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_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]:
+                    t = bbox[i, j, 3]
+                    bbox[i, j, 3] = bbox[i, j, 1]
+                    bbox[i, j, 1] = t
+                if bbox[i, j, 2] < bbox[i, j, 0]:
+                    t = bbox[i, j, 2]
+                    bbox[i, j, 2] = bbox[i, j, 0]
+                    bbox[i, j, 0] = t
+        bbox = tf.convert_to_tensor(bbox)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+        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)
+
+        sequence_labels = None
+        token_labels = None
+        choice_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.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = LayoutLMConfig(
+            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,
+        )
+
+        return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def create_and_check_model(
+        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFLayoutLMModel(config=config)
+
+        result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
+        result = model(input_ids, bbox, token_type_ids=token_type_ids)
+        result = model(input_ids, bbox)
+        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 create_and_check_for_masked_lm(
+        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFLayoutLMForMaskedLM(config=config)
+
+        result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFLayoutLMForSequenceClassification(config=config)
+
+        result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_for_token_classification(
+        self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFLayoutLMForTokenClassification(config=config)
+
+        result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            bbox,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "bbox": bbox,
+            "token_type_ids": token_type_ids,
+            "attention_mask": input_mask,
+        }
+        return config, inputs_dict
+
+
+@require_tf
+class LayoutLMModelTest(TFModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (TFLayoutLMModel, TFLayoutLMForMaskedLM, TFLayoutLMForTokenClassification, TFLayoutLMForSequenceClassification)
+        if is_tf_available()
+        else ()
+    )
+    test_head_masking = False
+    test_onnx = True
+    onnx_min_opset = 10
+
+    def setUp(self):
+        self.model_tester = TFLayoutLMModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, 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_model_various_embeddings(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        for type in ["absolute", "relative_key", "relative_key_query"]:
+            config_and_inputs[0].position_embedding_type = type
+            self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = TFLayoutLMModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+def prepare_layoutlm_batch_inputs():
+    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
+    # fmt: off
+    input_ids = tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]])  # noqa: E231
+    attention_mask = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],])  # noqa: E231
+    bbox = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]])  # noqa: E231
+    token_type_ids = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])  # noqa: E231
+    # these are sequence labels (i.e. at the token level)
+    labels = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]])  # noqa: E231
+    # fmt: on
+
+    return input_ids, attention_mask, bbox, token_type_ids, labels
+
+
+@require_tf
+class TFLayoutLMModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_forward_pass_no_head(self):
+        model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
+
+        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
+
+        # forward pass
+        outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
+
+        # test the sequence output on [0, :3, :3]
+        expected_slice = tf.convert_to_tensor(
+            [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],
+        )
+
+        self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3))
+
+        # test the pooled output on [1, :3]
+        expected_slice = tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])
+
+        self.assertTrue(np.allclose(outputs.pooler_output[1, :3], expected_slice, atol=1e-3))
+
+    @slow
+    def test_forward_pass_sequence_classification(self):
+        # initialize model with randomly initialized sequence classification head
+        model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2)
+
+        input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs()
+
+        # forward pass
+        outputs = model(
+            input_ids=input_ids,
+            bbox=bbox,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            labels=tf.convert_to_tensor([1, 1]),
+        )
+
+        # test whether we get a loss as a scalar
+        loss = outputs.loss
+        expected_shape = (2,)
+        self.assertEqual(loss.shape, expected_shape)
+
+        # test the shape of the logits
+        logits = outputs.logits
+        expected_shape = (2, 2)
+        self.assertEqual(logits.shape, expected_shape)
+
+    @slow
+    def test_forward_pass_token_classification(self):
+        # initialize model with randomly initialized token classification head
+        model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13)
+
+        input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
+
+        # forward pass
+        outputs = model(
+            input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
+        )
+
+        # test the shape of the logits
+        logits = outputs.logits
+        expected_shape = tf.convert_to_tensor((2, 25, 13))
+        self.assertEqual(logits.shape, expected_shape)