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/models/layoutlm/modeling_tf_layoutlm.py

+# coding=utf-8
+# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. 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.
+""" TF 2.0 LayoutLM model. """
+
+import math
+import warnings
+from typing import Dict, Optional, Tuple, Union
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
+from ...modeling_tf_outputs import (
+    TFBaseModelOutput,
+    TFBaseModelOutputWithPooling,
+    TFMaskedLMOutput,
+    TFSequenceClassifierOutput,
+    TFTokenClassifierOutput,
+)
+from ...modeling_tf_utils import (
+    TFMaskedLanguageModelingLoss,
+    TFModelInputType,
+    TFPreTrainedModel,
+    TFSequenceClassificationLoss,
+    TFTokenClassificationLoss,
+    get_initializer,
+    input_processing,
+    keras_serializable,
+    shape_list,
+)
+from ...utils import logging
+from .configuration_layoutlm import LayoutLMConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LayoutLMConfig"
+_TOKENIZER_FOR_DOC = "LayoutLMTokenizer"
+
+TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "microsoft/layoutlm-base-uncased",
+    "microsoft/layoutlm-large-uncased",
+]
+
+
+class TFLayoutLMEmbeddings(tf.keras.layers.Layer):
+    """Construct the embeddings from word, position and token_type embeddings."""
+
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.vocab_size = config.vocab_size
+        self.type_vocab_size = config.type_vocab_size
+        self.hidden_size = config.hidden_size
+        self.max_position_embeddings = config.max_position_embeddings
+        self.max_2d_position_embeddings = config.max_2d_position_embeddings
+        self.initializer_range = config.initializer_range
+        self.embeddings_sum = tf.keras.layers.Add()
+        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+
+    def build(self, input_shape: tf.TensorShape):
+        with tf.name_scope("word_embeddings"):
+            self.weight = self.add_weight(
+                name="weight",
+                shape=[self.vocab_size, self.hidden_size],
+                initializer=get_initializer(self.initializer_range),
+            )
+
+        with tf.name_scope("token_type_embeddings"):
+            self.token_type_embeddings = self.add_weight(
+                name="embeddings",
+                shape=[self.type_vocab_size, self.hidden_size],
+                initializer=get_initializer(self.initializer_range),
+            )
+
+        with tf.name_scope("position_embeddings"):
+            self.position_embeddings = self.add_weight(
+                name="embeddings",
+                shape=[self.max_position_embeddings, self.hidden_size],
+                initializer=get_initializer(self.initializer_range),
+            )
+
+        with tf.name_scope("x_position_embeddings"):
+            self.x_position_embeddings = self.add_weight(
+                name="embeddings",
+                shape=[self.max_2d_position_embeddings, self.hidden_size],
+                initializer=get_initializer(self.initializer_range),
+            )
+
+        with tf.name_scope("y_position_embeddings"):
+            self.y_position_embeddings = self.add_weight(
+                name="embeddings",
+                shape=[self.max_2d_position_embeddings, self.hidden_size],
+                initializer=get_initializer(self.initializer_range),
+            )
+
+        with tf.name_scope("h_position_embeddings"):
+            self.h_position_embeddings = self.add_weight(
+                name="embeddings",
+                shape=[self.max_2d_position_embeddings, self.hidden_size],
+                initializer=get_initializer(self.initializer_range),
+            )
+
+        with tf.name_scope("w_position_embeddings"):
+            self.w_position_embeddings = self.add_weight(
+                name="embeddings",
+                shape=[self.max_2d_position_embeddings, self.hidden_size],
+                initializer=get_initializer(self.initializer_range),
+            )
+
+        super().build(input_shape)
+
+    def call(
+        self,
+        input_ids: tf.Tensor = None,
+        bbox: tf.Tensor = None,
+        position_ids: tf.Tensor = None,
+        token_type_ids: tf.Tensor = None,
+        inputs_embeds: tf.Tensor = None,
+        training: bool = False,
+    ) -> tf.Tensor:
+        """
+        Applies embedding based on inputs tensor.
+
+        Returns:
+            final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
+        """
+        assert not (input_ids is None and inputs_embeds is None)
+
+        if input_ids is not None:
+            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
+
+        input_shape = shape_list(inputs_embeds)[:-1]
+
+        if token_type_ids is None:
+            token_type_ids = tf.fill(dims=input_shape, value=0)
+
+        if position_ids is None:
+            position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
+
+        if position_ids is None:
+            position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
+
+        if bbox is None:
+            bbox = bbox = tf.fill(input_shape + [4], value=0)
+        try:
+            left_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 0])
+            upper_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 1])
+            right_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 2])
+            lower_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 3])
+        except IndexError as e:
+            raise IndexError("The :obj:`bbox`coordinate values should be within 0-1000 range.") from e
+        h_position_embeddings = tf.gather(self.h_position_embeddings, bbox[:, :, 3] - bbox[:, :, 1])
+        w_position_embeddings = tf.gather(self.w_position_embeddings, bbox[:, :, 2] - bbox[:, :, 0])
+
+        position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
+        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
+        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
+        final_embeddings = self.embeddings_sum(
+            inputs=[
+                inputs_embeds,
+                position_embeds,
+                token_type_embeds,
+                left_position_embeddings,
+                upper_position_embeddings,
+                right_position_embeddings,
+                lower_position_embeddings,
+                h_position_embeddings,
+                w_position_embeddings,
+            ]
+        )
+        final_embeddings = self.LayerNorm(inputs=final_embeddings)
+        final_embeddings = self.dropout(inputs=final_embeddings, training=training)
+
+        return final_embeddings
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->LayoutLM
+class TFLayoutLMSelfAttention(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number "
+                f"of attention heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
+
+        self.query = tf.keras.layers.Dense(
+            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
+        )
+        self.key = tf.keras.layers.Dense(
+            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
+        )
+        self.value = tf.keras.layers.Dense(
+            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
+        )
+        self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
+        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
+        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
+
+        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
+        return tf.transpose(tensor, perm=[0, 2, 1, 3])
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        attention_mask: tf.Tensor,
+        head_mask: tf.Tensor,
+        output_attentions: bool,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        batch_size = shape_list(hidden_states)[0]
+        mixed_query_layer = self.query(inputs=hidden_states)
+        mixed_key_layer = self.key(inputs=hidden_states)
+        mixed_value_layer = self.value(inputs=hidden_states)
+        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
+        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
+        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        # (batch size, num_heads, seq_len_q, seq_len_k)
+        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
+        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
+        attention_scores = tf.divide(attention_scores, dk)
+
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in TFLayoutLMModel call() function)
+            attention_scores = tf.add(attention_scores, attention_mask)
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = tf.nn.softmax(logits=attention_scores, axis=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(inputs=attention_probs, training=training)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = tf.multiply(attention_probs, head_mask)
+
+        attention_output = tf.matmul(attention_probs, value_layer)
+        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
+
+        # (batch_size, seq_len_q, all_head_size)
+        attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
+        outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
+
+        return outputs
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->LayoutLM
+class TFLayoutLMSelfOutput(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense = tf.keras.layers.Dense(
+            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+        )
+        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+
+    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+        hidden_states = self.dense(inputs=hidden_states)
+        hidden_states = self.dropout(inputs=hidden_states, training=training)
+        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
+
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->LayoutLM
+class TFLayoutLMAttention(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.self_attention = TFLayoutLMSelfAttention(config, name="self")
+        self.dense_output = TFLayoutLMSelfOutput(config, name="output")
+
+    def prune_heads(self, heads):
+        raise NotImplementedError
+
+    def call(
+        self,
+        input_tensor: tf.Tensor,
+        attention_mask: tf.Tensor,
+        head_mask: tf.Tensor,
+        output_attentions: bool,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        self_outputs = self.self_attention(
+            hidden_states=input_tensor,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            training=training,
+        )
+        attention_output = self.dense_output(
+            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
+        )
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+
+        return outputs
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->LayoutLM
+class TFLayoutLMIntermediate(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense = tf.keras.layers.Dense(
+            units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+        )
+
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        hidden_states = self.dense(inputs=hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->LayoutLM
+class TFLayoutLMOutput(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense = tf.keras.layers.Dense(
+            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+        )
+        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+
+    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+        hidden_states = self.dense(inputs=hidden_states)
+        hidden_states = self.dropout(inputs=hidden_states, training=training)
+        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
+
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->LayoutLM
+class TFLayoutLMLayer(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.attention = TFLayoutLMAttention(config, name="attention")
+        self.intermediate = TFLayoutLMIntermediate(config, name="intermediate")
+        self.bert_output = TFLayoutLMOutput(config, name="output")
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        attention_mask: tf.Tensor,
+        head_mask: tf.Tensor,
+        output_attentions: bool,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        attention_outputs = self.attention(
+            input_tensor=hidden_states,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            training=training,
+        )
+        attention_output = attention_outputs[0]
+        intermediate_output = self.intermediate(hidden_states=attention_output)
+        layer_output = self.bert_output(
+            hidden_states=intermediate_output, input_tensor=attention_output, training=training
+        )
+        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
+
+        return outputs
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->LayoutLM
+class TFLayoutLMEncoder(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.layer = [TFLayoutLMLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        attention_mask: tf.Tensor,
+        head_mask: tf.Tensor,
+        output_attentions: bool,
+        output_hidden_states: bool,
+        return_dict: bool,
+        training: bool = False,
+    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_outputs = layer_module(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                head_mask=head_mask[i],
+                output_attentions=output_attentions,
+                training=training,
+            )
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        # Add last layer
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+
+        return TFBaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+        )
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->LayoutLM
+class TFLayoutLMPooler(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense = tf.keras.layers.Dense(
+            units=config.hidden_size,
+            kernel_initializer=get_initializer(config.initializer_range),
+            activation="tanh",
+            name="dense",
+        )
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(inputs=first_token_tensor)
+
+        return pooled_output
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->LayoutLM
+class TFLayoutLMPredictionHeadTransform(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense = tf.keras.layers.Dense(
+            units=config.hidden_size,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="dense",
+        )
+
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = get_tf_activation(config.hidden_act)
+        else:
+            self.transform_act_fn = config.hidden_act
+
+        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        hidden_states = self.dense(inputs=hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(inputs=hidden_states)
+
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->LayoutLM
+class TFLayoutLMLMPredictionHead(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
+        super().__init__(**kwargs)
+
+        self.vocab_size = config.vocab_size
+        self.hidden_size = config.hidden_size
+
+        self.transform = TFLayoutLMPredictionHeadTransform(config, name="transform")
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.input_embeddings = input_embeddings
+
+    def build(self, input_shape: tf.TensorShape):
+        self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
+
+        super().build(input_shape)
+
+    def get_output_embeddings(self) -> tf.keras.layers.Layer:
+        return self.input_embeddings
+
+    def set_output_embeddings(self, value: tf.Variable):
+        self.input_embeddings.weight = value
+        self.input_embeddings.vocab_size = shape_list(value)[0]
+
+    def get_bias(self) -> Dict[str, tf.Variable]:
+        return {"bias": self.bias}
+
+    def set_bias(self, value: tf.Variable):
+        self.bias = value["bias"]
+        self.vocab_size = shape_list(value["bias"])[0]
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        hidden_states = self.transform(hidden_states=hidden_states)
+        seq_length = shape_list(hidden_states)[1]
+        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
+        hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
+        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
+        hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
+
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->LayoutLM
+class TFLayoutLMMLMHead(tf.keras.layers.Layer):
+    def __init__(self, config: LayoutLMConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
+        super().__init__(**kwargs)
+
+        self.predictions = TFLayoutLMLMPredictionHead(config, input_embeddings, name="predictions")
+
+    def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
+        prediction_scores = self.predictions(hidden_states=sequence_output)
+
+        return prediction_scores
+
+
+@keras_serializable
+class TFLayoutLMMainLayer(tf.keras.layers.Layer):
+    config_class = LayoutLMConfig
+
+    def __init__(self, config: LayoutLMConfig, add_pooling_layer: bool = True, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
+
+        self.embeddings = TFLayoutLMEmbeddings(config, name="embeddings")
+        self.encoder = TFLayoutLMEncoder(config, name="encoder")
+        self.pooler = TFLayoutLMPooler(config, name="pooler") if add_pooling_layer else None
+
+    def get_input_embeddings(self) -> tf.keras.layers.Layer:
+        return self.embeddings
+
+    def set_input_embeddings(self, value: tf.Variable):
+        self.embeddings.weight = value
+        self.embeddings.vocab_size = shape_list(value)[0]
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        raise NotImplementedError
+
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        bbox: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+        **kwargs,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            bbox=bbox,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif inputs["input_ids"] is not None:
+            input_shape = shape_list(inputs["input_ids"])
+        elif inputs["inputs_embeds"] is not None:
+            input_shape = shape_list(inputs["inputs_embeds"])[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if inputs["attention_mask"] is None:
+            inputs["attention_mask"] = tf.fill(dims=input_shape, value=1)
+
+        if inputs["token_type_ids"] is None:
+            inputs["token_type_ids"] = tf.fill(dims=input_shape, value=0)
+        if inputs["bbox"] is None:
+            inputs["bbox"] = tf.fill(dims=input_shape + [4], value=0)
+
+        embedding_output = self.embeddings(
+            input_ids=inputs["input_ids"],
+            bbox=inputs["bbox"],
+            position_ids=inputs["position_ids"],
+            token_type_ids=inputs["token_type_ids"],
+            inputs_embeds=inputs["inputs_embeds"],
+            training=inputs["training"],
+        )
+
+        # We create a 3D attention mask from a 2D tensor mask.
+        # Sizes are [batch_size, 1, 1, to_seq_length]
+        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+        # this attention mask is more simple than the triangular masking of causal attention
+        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+        extended_attention_mask = tf.reshape(inputs["attention_mask"], (input_shape[0], 1, 1, input_shape[1]))
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
+        one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
+        ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
+        extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        if inputs["head_mask"] is not None:
+            raise NotImplementedError
+        else:
+            inputs["head_mask"] = [None] * self.config.num_hidden_layers
+
+        encoder_outputs = self.encoder(
+            hidden_states=embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=inputs["head_mask"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None
+
+        if not inputs["return_dict"]:
+            return (
+                sequence_output,
+                pooled_output,
+            ) + encoder_outputs[1:]
+
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class TFLayoutLMPreTrainedModel(TFPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = LayoutLMConfig
+    base_model_prefix = "layoutlm"
+
+
+LAYOUTLM_START_DOCSTRING = r"""
+
+    This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
+    generic methods the library implements for all its model (such as downloading or saving, resizing the input
+    embeddings, pruning heads etc.)
+
+    This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
+    it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
+    and behavior.
+
+    .. note::
+
+        TF 2.0 models accepts two formats as inputs:
+
+        - having all inputs as keyword arguments (like PyTorch models), or
+        - having all inputs as a list, tuple or dict in the first positional arguments.
+
+        This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
+        the tensors in the first argument of the model call function: :obj:`model(inputs)`.
+
+        If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
+        the first positional argument :
+
+        - a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
+        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+          :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
+        - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+          :obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+    Args:
+        config (:class:`~transformers.LayoutLMConfig`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the :meth:`~transformers.TFPreTrainedModel.from_pretrained` method to load the
+            model weights.
+"""
+
+LAYOUTLM_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using :class:`~transformers.LayoutLMTokenizer`. See
+            :func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
+            details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+        bbox (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0}, 4)`, `optional`):
+            Bounding Boxes of each input sequence tokens. Selected in the range ``[0,
+            config.max_2d_position_embeddings- 1]``.
+        attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
+            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            `What are attention masks? <../glossary.html#attention-mask>`__
+        token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
+            1]``:
+
+            - 0 corresponds to a `sentence A` token,
+            - 1 corresponds to a `sentence B` token.
+
+            `What are token type IDs? <../glossary.html#token-type-ids>`__
+        position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
+            config.max_position_embeddings - 1]``.
+
+            `What are position IDs? <../glossary.html#position-ids>`__
+        head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        inputs_embeds (:obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `optional`):
+            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
+            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
+            vectors than the model's internal embedding lookup matrix.
+        output_attentions (:obj:`bool`, `optional`):
+            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
+            tensors for more detail.
+        output_hidden_states (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
+        training (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not to use the model in training mode (some modules like dropout modules have different
+            behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+    "The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.",
+    LAYOUTLM_START_DOCSTRING,
+)
+class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
+    def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm")
+
+    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        bbox: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: Optional[bool] = False,
+        **kwargs,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        r"""
+        Returns:
+
+        Examples::
+
+            >>> from transformers import LayoutLMTokenizer, TFLayoutLMModel
+            >>> import tensorflow as tf
+
+            >>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
+            >>> model = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased')
+
+            >>> words = ["Hello", "world"]
+            >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+            >>> token_boxes = []
+            >>> for word, box in zip(words, normalized_word_boxes):
+            ...     word_tokens = tokenizer.tokenize(word)
+            ...     token_boxes.extend([box] * len(word_tokens))
+            >>> # add bounding boxes of cls + sep tokens
+            >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+            >>> encoding = tokenizer(' '.join(words), return_tensors="tf")
+            >>> input_ids = encoding["input_ids"]
+            >>> attention_mask = encoding["attention_mask"]
+            >>> token_type_ids = encoding["token_type_ids"]
+            >>> bbox = tf.convert_to_tensor([token_boxes])
+
+            >>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
+
+            >>> last_hidden_states = outputs.last_hidden_state
+        """
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            bbox=bbox,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+        outputs = self.layoutlm(
+            input_ids=inputs["input_ids"],
+            bbox=inputs["bbox"],
+            attention_mask=inputs["attention_mask"],
+            token_type_ids=inputs["token_type_ids"],
+            position_ids=inputs["position_ids"],
+            head_mask=inputs["head_mask"],
+            inputs_embeds=inputs["inputs_embeds"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
+    def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=output.last_hidden_state,
+            pooler_output=output.pooler_output,
+            hidden_states=hs,
+            attentions=attns,
+        )
+
+
+@add_start_docstrings("""LayoutLM Model with a `language modeling` head on top. """, LAYOUTLM_START_DOCSTRING)
+class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingLoss):
+    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+    _keys_to_ignore_on_load_unexpected = [
+        r"pooler",
+        r"cls.seq_relationship",
+        r"cls.predictions.decoder.weight",
+        r"nsp___cls",
+    ]
+
+    def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `TFLayoutLMForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm")
+        self.mlm = TFLayoutLMMLMHead(config, input_embeddings=self.layoutlm.embeddings, name="mlm___cls")
+
+    def get_lm_head(self) -> tf.keras.layers.Layer:
+        return self.mlm.predictions
+
+    def get_prefix_bias_name(self) -> str:
+        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
+        return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
+
+    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        bbox: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        training: Optional[bool] = False,
+        **kwargs,
+    ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
+        r"""
+        labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
+            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
+            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
+
+        Returns:
+
+        Examples::
+
+            >>> from transformers import LayoutLMTokenizer, TFLayoutLMForMaskedLM
+            >>> import tensorflow as tf
+
+            >>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
+            >>> model = TFLayoutLMForMaskedLM.from_pretrained('microsoft/layoutlm-base-uncased')
+
+            >>> words = ["Hello", "[MASK]"]
+            >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+            >>> token_boxes = []
+            >>> for word, box in zip(words, normalized_word_boxes):
+            ...     word_tokens = tokenizer.tokenize(word)
+            ...     token_boxes.extend([box] * len(word_tokens))
+            >>> # add bounding boxes of cls + sep tokens
+            >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+            >>> encoding = tokenizer(' '.join(words), return_tensors="tf")
+            >>> input_ids = encoding["input_ids"]
+            >>> attention_mask = encoding["attention_mask"]
+            >>> token_type_ids = encoding["token_type_ids"]
+            >>> bbox = tf.convert_to_tensor([token_boxes])
+
+            >>> labels = tokenizer("Hello world", return_tensors="tf")["input_ids"]
+
+            >>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
+            ...                 labels=labels)
+
+            >>> loss = outputs.loss
+        """
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            bbox=bbox,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            labels=labels,
+            training=training,
+            kwargs_call=kwargs,
+        )
+        outputs = self.layoutlm(
+            input_ids=inputs["input_ids"],
+            bbox=inputs["bbox"],
+            attention_mask=inputs["attention_mask"],
+            token_type_ids=inputs["token_type_ids"],
+            position_ids=inputs["position_ids"],
+            head_mask=inputs["head_mask"],
+            inputs_embeds=inputs["inputs_embeds"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+        sequence_output = outputs[0]
+        prediction_scores = self.mlm(sequence_output=sequence_output, training=inputs["training"])
+        loss = (
+            None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=prediction_scores)
+        )
+
+        if not inputs["return_dict"]:
+            output = (prediction_scores,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFMaskedLMOutput(
+            loss=loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+        return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
+
+
+@add_start_docstrings(
+    """
+    LayoutLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+    pooled output) e.g. for GLUE tasks.
+    """,
+    LAYOUTLM_START_DOCSTRING,
+)
+class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceClassificationLoss):
+    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+    _keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"]
+    _keys_to_ignore_on_load_missing = [r"dropout"]
+
+    def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.num_labels = config.num_labels
+
+        self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm")
+        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.classifier = tf.keras.layers.Dense(
+            units=config.num_labels,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="classifier",
+        )
+
+    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        bbox: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        training: Optional[bool] = False,
+        **kwargs,
+    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
+        r"""
+        labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Returns:
+
+        Examples::
+
+            >>> from transformers import LayoutLMTokenizer, TFLayoutLMForSequenceClassification
+            >>> import tensorflow as tf
+
+            >>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
+            >>> model = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased')
+
+            >>> words = ["Hello", "world"]
+            >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+            >>> token_boxes = []
+            >>> for word, box in zip(words, normalized_word_boxes):
+            ...     word_tokens = tokenizer.tokenize(word)
+            ...     token_boxes.extend([box] * len(word_tokens))
+            >>> # add bounding boxes of cls + sep tokens
+            >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+            >>> encoding = tokenizer(' '.join(words), return_tensors="tf")
+            >>> input_ids = encoding["input_ids"]
+            >>> attention_mask = encoding["attention_mask"]
+            >>> token_type_ids = encoding["token_type_ids"]
+            >>> bbox = tf.convert_to_tensor([token_boxes])
+            >>> sequence_label = tf.convert_to_tensor([1])
+
+            >>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
+            ...                 labels=sequence_label)
+
+            >>> loss = outputs.loss
+            >>> logits = outputs.logits
+        """
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            bbox=bbox,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            labels=labels,
+            training=training,
+            kwargs_call=kwargs,
+        )
+        outputs = self.layoutlm(
+            input_ids=inputs["input_ids"],
+            bbox=inputs["bbox"],
+            attention_mask=inputs["attention_mask"],
+            token_type_ids=inputs["token_type_ids"],
+            position_ids=inputs["position_ids"],
+            head_mask=inputs["head_mask"],
+            inputs_embeds=inputs["inputs_embeds"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+        pooled_output = outputs[1]
+        pooled_output = self.dropout(inputs=pooled_output, training=inputs["training"])
+        logits = self.classifier(inputs=pooled_output)
+        loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=logits)
+
+        if not inputs["return_dict"]:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFSequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+        return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
+
+
+@add_start_docstrings(
+    """
+    LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+    Named-Entity-Recognition (NER) tasks.
+    """,
+    LAYOUTLM_START_DOCSTRING,
+)
+class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassificationLoss):
+    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+    _keys_to_ignore_on_load_unexpected = [
+        r"pooler",
+        r"mlm___cls",
+        r"nsp___cls",
+        r"cls.predictions",
+        r"cls.seq_relationship",
+    ]
+    _keys_to_ignore_on_load_missing = [r"dropout"]
+
+    def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.num_labels = config.num_labels
+
+        self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm")
+        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.classifier = tf.keras.layers.Dense(
+            units=config.num_labels,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="classifier",
+        )
+
+    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        input_ids: Optional[TFModelInputType] = None,
+        bbox: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        training: Optional[bool] = False,
+        **kwargs,
+    ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
+        r"""
+        labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
+            1]``.
+
+        Returns:
+
+        Examples::
+
+            >>> from transformers import LayoutLMTokenizer, TFLayoutLMForTokenClassification
+            >>> import torch
+
+            >>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
+            >>> model = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased')
+
+            >>> words = ["Hello", "world"]
+            >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+            >>> token_boxes = []
+            >>> for word, box in zip(words, normalized_word_boxes):
+            ...     word_tokens = tokenizer.tokenize(word)
+            ...     token_boxes.extend([box] * len(word_tokens))
+            >>> # add bounding boxes of cls + sep tokens
+            >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+            >>> encoding = tokenizer(' '.join(words), return_tensors="tf")
+            >>> input_ids = encoding["input_ids"]
+            >>> attention_mask = encoding["attention_mask"]
+            >>> token_type_ids = encoding["token_type_ids"]
+            >>> bbox = tf.convert_to_tensor([token_boxes])
+            >>> token_labels = tf.convert_to_tensor([1,1,0,0])
+
+            >>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
+            ...                 labels=token_labels)
+
+            >>> loss = outputs.loss
+            >>> logits = outputs.logits
+        """
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            bbox=bbox,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            labels=labels,
+            training=training,
+            kwargs_call=kwargs,
+        )
+        outputs = self.layoutlm(
+            input_ids=inputs["input_ids"],
+            bbox=inputs["bbox"],
+            attention_mask=inputs["attention_mask"],
+            token_type_ids=inputs["token_type_ids"],
+            position_ids=inputs["position_ids"],
+            head_mask=inputs["head_mask"],
+            inputs_embeds=inputs["inputs_embeds"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+        sequence_output = outputs[0]
+        sequence_output = self.dropout(inputs=sequence_output, training=inputs["training"])
+        logits = self.classifier(inputs=sequence_output)
+        loss = None if inputs["labels"] is None else self.compute_loss(labels=inputs["labels"], logits=logits)
+
+        if not inputs["return_dict"]:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFTokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+        return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)

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)