# What does this PR do?

Fixes #23688 

Contrary to our description in the docstring, PT's `top_p` was not enforcing `min_tokens_to_keep` when it was 1 (the default). TF and FLAX were fine. This PR corrects it, and adds a check on `min_tokens_to_keep` (must be a non-negative integer)
Generate: PT's `top_p` enforces `min_tokens_to_keep` when it is `1`. # What does this PR do?

Fixes #23688 

Contrary to our description in the docstring, PT's `top_p` was not enforcing `min_tokens_to_keep` when it was 1 (the default). TF and FLAX were fine. This PR corrects it, and adds a check on `min_tokens_to_keep` (must be a non-negative integer)
# 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."""


from __future__ import annotations

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 ...modeling_tf_outputs import (
    TFBaseModelOutputWithPastAndCrossAttentions,
    TFBaseModelOutputWithPoolingAndCrossAttentions,
    TFMaskedLMOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
    TFMaskedLanguageModelingLoss,
    TFModelInputType,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_layoutlm import LayoutLMConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "LayoutLMConfig"

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.config = config
        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.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.config.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.config.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 (`tf.Tensor`): output embedding tensor.
        """
        assert not (input_ids is None and inputs_embeds is None)

        if input_ids is not None:
            check_embeddings_within_bounds(input_ids, self.config.vocab_size)
            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 `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)
        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
        final_embeddings = (
            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)

        self.is_decoder = config.is_decoder

    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,
        encoder_hidden_states: tf.Tensor,
        encoder_attention_mask: tf.Tensor,
        past_key_value: Tuple[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)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size)
            value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size)
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
            value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
            key_layer = tf.concat([past_key_value[0], key_layer], axis=2)
            value_layer = tf.concat([past_key_value[1], value_layer], axis=2)
        else:
            key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
            value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)

        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)

        if self.is_decoder:
            # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_layer, value_layer)

        # 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 = stable_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,)

        if self.is_decoder:
            outputs = outputs + (past_key_value,)
        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,
        encoder_hidden_states: tf.Tensor,
        encoder_attention_mask: tf.Tensor,
        past_key_value: Tuple[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,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
        )
        # add attentions (possibly with past_key_value) if we output them
        outputs = (attention_output,) + self_outputs[1:]

        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.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder:
                raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = TFLayoutLMAttention(config, name="crossattention")
        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,
        encoder_hidden_states: tf.Tensor | None,
        encoder_attention_mask: tf.Tensor | None,
        past_key_value: Tuple[tf.Tensor] | None,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(
            input_tensor=hidden_states,
            attention_mask=attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=None,
            encoder_attention_mask=None,
            past_key_value=self_attn_past_key_value,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = self_attention_outputs[0]

        # if decoder, the last output is tuple of self-attn cache
        if self.is_decoder:
            outputs = self_attention_outputs[1:-1]
            present_key_value = self_attention_outputs[-1]
        else:
            outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        cross_attn_present_key_value = None
        if self.is_decoder and encoder_hidden_states is not None:
            if not hasattr(self, "crossattention"):
                raise ValueError(
                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
                    " by setting `config.add_cross_attention=True`"
                )

            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            cross_attention_outputs = self.crossattention(
                input_tensor=attention_output,
                attention_mask=attention_mask,
                head_mask=head_mask,
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                past_key_value=cross_attn_past_key_value,
                output_attentions=output_attentions,
                training=training,
            )
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights

            # add cross-attn cache to positions 3,4 of present_key_value tuple
            cross_attn_present_key_value = cross_attention_outputs[-1]
            present_key_value = present_key_value + cross_attn_present_key_value

        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,) + outputs  # add attentions if we output them

        # if decoder, return the attn key/values as the last output
        if self.is_decoder:
            outputs = outputs + (present_key_value,)

        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.config = config
        self.layer = [TFLayoutLMLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        encoder_hidden_states: tf.Tensor | None,
        encoder_attention_mask: tf.Tensor | None,
        past_key_values: Tuple[Tuple[tf.Tensor]] | None,
        use_cache: Optional[bool],
        output_attentions: bool,
        output_hidden_states: bool,
        return_dict: bool,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

        next_decoder_cache = () if use_cache else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            past_key_value = past_key_values[i] if past_key_values is not None else None

            layer_outputs = layer_module(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                head_mask=head_mask[i],
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                training=training,
            )
            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)
                if self.config.add_cross_attention and encoder_hidden_states is not None:
                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

        # 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, all_cross_attentions] if v is not None
            )

        return TFBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_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.config = config
        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.config.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.config.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.config.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

    @unpack_inputs
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
        encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = shape_list(input_ids)
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if attention_mask is None:
            attention_mask = tf.fill(dims=input_shape, value=1)

        if token_type_ids is None:
            token_type_ids = tf.fill(dims=input_shape, value=0)
        if bbox is None:
            bbox = tf.fill(dims=input_shape + [4], value=0)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            bbox=bbox,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            training=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(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 head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.config.num_hidden_layers

        encoder_outputs = self.encoder(
            hidden_states=embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            # Need to pass these required positional arguments to `Encoder`
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=None,
            past_key_values=None,
            use_cache=False,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None

        if not return_dict:
            return (
                sequence_output,
                pooled_output,
            ) + encoder_outputs[1:]

        return TFBaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_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 [`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.

    <Tip>

    TensorFlow models and layers in `transformers` accept two formats as input:

    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional argument.

    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
    positional argument:

    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `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:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!

    </Tip>

    Args:
        config ([`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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

LAYOUTLM_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
            [`PreTrainedTokenizer.encode`] for details.

            [What are input IDs?](../glossary#input-ids)
        bbox (`Numpy array` or `tf.Tensor` of shape `({0}, 4)`, *optional*):
            Bounding Boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-
            1]`.
        attention_mask (`Numpy array` or `tf.Tensor` of shape `({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#attention-mask)
        token_type_ids (`Numpy array` or `tf.Tensor` of shape `({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#token-type-ids)
        position_ids (`Numpy array` or `tf.Tensor` of shape `({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#position-ids)
        head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(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 (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`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 (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        training (`bool`, *optional*, defaults to `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")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @replace_return_docstrings(
        output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
        encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
    ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFLayoutLMModel
        >>> import tensorflow as tf

        >>> tokenizer = AutoTokenizer.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
        ```"""
        outputs = self.layoutlm(
            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,
        )

        return outputs


@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

    @unpack_inputs
    @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: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(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:

        ```python
        >>> from transformers import AutoTokenizer, TFLayoutLMForMaskedLM
        >>> import tensorflow as tf

        >>> tokenizer = AutoTokenizer.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
        ```"""
        outputs = self.layoutlm(
            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,
        )
        sequence_output = outputs[0]
        prediction_scores = self.mlm(sequence_output=sequence_output, training=training)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores)

        if not 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,
        )


@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",
        )

    @unpack_inputs
    @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: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification
        >>> import tensorflow as tf

        >>> tokenizer = AutoTokenizer.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
        ```"""
        outputs = self.layoutlm(
            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,
        )
        pooled_output = outputs[1]
        pooled_output = self.dropout(inputs=pooled_output, training=training)
        logits = self.classifier(inputs=pooled_output)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)

        if not 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,
        )


@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",
        )

    @unpack_inputs
    @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: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

        Returns:

        Examples:

        ```python
        >>> import tensorflow as tf
        >>> from transformers import AutoTokenizer, TFLayoutLMForTokenClassification

        >>> tokenizer = AutoTokenizer.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
        ```"""
        outputs = self.layoutlm(
            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,
        )
        sequence_output = outputs[0]
        sequence_output = self.dropout(inputs=sequence_output, training=training)
        logits = self.classifier(inputs=sequence_output)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)

        if not 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,
        )


@add_start_docstrings(
    """
    LayoutLM Model with a span classification head on top for extractive question-answering tasks such as
    [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span
    start logits` and `span end logits`).
    """,
    LAYOUTLM_START_DOCSTRING,
)
class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnsweringLoss):
    # 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",
    ]

    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.qa_outputs = tf.keras.layers.Dense(
            units=config.num_labels,
            kernel_initializer=get_initializer(config.initializer_range),
            name="qa_outputs",
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        start_positions: np.ndarray | tf.Tensor | None = None,
        end_positions: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
        r"""
        start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.

        Returns:

        Examples:

        ```python
        >>> import tensorflow as tf
        >>> from transformers import AutoTokenizer, TFLayoutLMForQuestionAnswering
        >>> from datasets import load_dataset

        >>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
        >>> model = TFLayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")

        >>> dataset = load_dataset("nielsr/funsd", split="train")
        >>> example = dataset[0]
        >>> question = "what's his name?"
        >>> words = example["words"]
        >>> boxes = example["bboxes"]

        >>> encoding = tokenizer(
        ...     question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="tf"
        ... )
        >>> bbox = []
        >>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
        ...     if s == 1:
        ...         bbox.append(boxes[w])
        ...     elif i == tokenizer.sep_token_id:
        ...         bbox.append([1000] * 4)
        ...     else:
        ...         bbox.append([0] * 4)
        >>> encoding["bbox"] = tf.convert_to_tensor([bbox])

        >>> word_ids = encoding.word_ids(0)
        >>> outputs = model(**encoding)
        >>> loss = outputs.loss
        >>> start_scores = outputs.start_logits
        >>> end_scores = outputs.end_logits
        >>> start, end = word_ids[tf.math.argmax(start_scores, -1)[0]], word_ids[tf.math.argmax(end_scores, -1)[0]]
        >>> print(" ".join(words[start : end + 1]))
        M. Hamann P. Harper, P. Martinez
        ```"""

        outputs = self.layoutlm(
            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,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(inputs=sequence_output)
        start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
        start_logits = tf.squeeze(input=start_logits, axis=-1)
        end_logits = tf.squeeze(input=end_logits, axis=-1)
        loss = None

        if start_positions is not None and end_positions is not None:
            labels = {"start_position": start_positions}
            labels["end_position"] = end_positions
            loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TFQuestionAnsweringModelOutput(
            loss=loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

# 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."""


from __future__ import annotations

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 ...modeling_tf_outputs import (
    TFBaseModelOutputWithPastAndCrossAttentions,
    TFBaseModelOutputWithPoolingAndCrossAttentions,
    TFMaskedLMOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
    TFMaskedLanguageModelingLoss,
    TFModelInputType,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_layoutlm import LayoutLMConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "LayoutLMConfig"

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.config = config
        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.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.config.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.config.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 (`tf.Tensor`): output embedding tensor.
        """
        assert not (input_ids is None and inputs_embeds is None)

        if input_ids is not None:
            check_embeddings_within_bounds(input_ids, self.config.vocab_size)
            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 `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)
        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
        final_embeddings = (
            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)

        self.is_decoder = config.is_decoder

    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,
        encoder_hidden_states: tf.Tensor,
        encoder_attention_mask: tf.Tensor,
        past_key_value: Tuple[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)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size)
            value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size)
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
            value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
            key_layer = tf.concat([past_key_value[0], key_layer], axis=2)
            value_layer = tf.concat([past_key_value[1], value_layer], axis=2)
        else:
            key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
            value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)

        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)

        if self.is_decoder:
            # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_layer, value_layer)

        # 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 = stable_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,)

        if self.is_decoder:
            outputs = outputs + (past_key_value,)
        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,
        encoder_hidden_states: tf.Tensor,
        encoder_attention_mask: tf.Tensor,
        past_key_value: Tuple[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,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
        )
        # add attentions (possibly with past_key_value) if we output them
        outputs = (attention_output,) + self_outputs[1:]

        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.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder:
                raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
            self.crossattention = TFLayoutLMAttention(config, name="crossattention")
        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,
        encoder_hidden_states: tf.Tensor | None,
        encoder_attention_mask: tf.Tensor | None,
        past_key_value: Tuple[tf.Tensor] | None,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(
            input_tensor=hidden_states,
            attention_mask=attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=None,
            encoder_attention_mask=None,
            past_key_value=self_attn_past_key_value,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = self_attention_outputs[0]

        # if decoder, the last output is tuple of self-attn cache
        if self.is_decoder:
            outputs = self_attention_outputs[1:-1]
            present_key_value = self_attention_outputs[-1]
        else:
            outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        cross_attn_present_key_value = None
        if self.is_decoder and encoder_hidden_states is not None:
            if not hasattr(self, "crossattention"):
                raise ValueError(
                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
                    " by setting `config.add_cross_attention=True`"
                )

            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            cross_attention_outputs = self.crossattention(
                input_tensor=attention_output,
                attention_mask=attention_mask,
                head_mask=head_mask,
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                past_key_value=cross_attn_past_key_value,
                output_attentions=output_attentions,
                training=training,
            )
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights

            # add cross-attn cache to positions 3,4 of present_key_value tuple
            cross_attn_present_key_value = cross_attention_outputs[-1]
            present_key_value = present_key_value + cross_attn_present_key_value

        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,) + outputs  # add attentions if we output them

        # if decoder, return the attn key/values as the last output
        if self.is_decoder:
            outputs = outputs + (present_key_value,)

        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.config = config
        self.layer = [TFLayoutLMLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        head_mask: tf.Tensor,
        encoder_hidden_states: tf.Tensor | None,
        encoder_attention_mask: tf.Tensor | None,
        past_key_values: Tuple[Tuple[tf.Tensor]] | None,
        use_cache: Optional[bool],
        output_attentions: bool,
        output_hidden_states: bool,
        return_dict: bool,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

        next_decoder_cache = () if use_cache else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            past_key_value = past_key_values[i] if past_key_values is not None else None

            layer_outputs = layer_module(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                head_mask=head_mask[i],
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                training=training,
            )
            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)
                if self.config.add_cross_attention and encoder_hidden_states is not None:
                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

        # 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, all_cross_attentions] if v is not None
            )

        return TFBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_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.config = config
        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.config.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.config.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.config.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

    @unpack_inputs
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
        encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = shape_list(input_ids)
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if attention_mask is None:
            attention_mask = tf.fill(dims=input_shape, value=1)

        if token_type_ids is None:
            token_type_ids = tf.fill(dims=input_shape, value=0)
        if bbox is None:
            bbox = tf.fill(dims=input_shape + [4], value=0)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            bbox=bbox,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            training=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(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 head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.config.num_hidden_layers

        encoder_outputs = self.encoder(
            hidden_states=embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            # Need to pass these required positional arguments to `Encoder`
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=None,
            past_key_values=None,
            use_cache=False,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None

        if not return_dict:
            return (
                sequence_output,
                pooled_output,
            ) + encoder_outputs[1:]

        return TFBaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_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 [`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.

    <Tip>

    TensorFlow models and layers in `transformers` accept two formats as input:

    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional argument.

    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
    positional argument:

    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `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:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!

    </Tip>

    Args:
        config ([`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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

LAYOUTLM_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
            [`PreTrainedTokenizer.encode`] for details.

            [What are input IDs?](../glossary#input-ids)
        bbox (`Numpy array` or `tf.Tensor` of shape `({0}, 4)`, *optional*):
            Bounding Boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-
            1]`.
        attention_mask (`Numpy array` or `tf.Tensor` of shape `({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#attention-mask)
        token_type_ids (`Numpy array` or `tf.Tensor` of shape `({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#token-type-ids)
        position_ids (`Numpy array` or `tf.Tensor` of shape `({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#position-ids)
        head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(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 (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`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 (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        training (`bool`, *optional*, defaults to `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")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @replace_return_docstrings(
        output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC
    )
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
        encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
    ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFLayoutLMModel
        >>> import tensorflow as tf

        >>> tokenizer = AutoTokenizer.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
        ```"""
        outputs = self.layoutlm(
            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,
        )

        return outputs


@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

    @unpack_inputs
    @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: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(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:

        ```python
        >>> from transformers import AutoTokenizer, TFLayoutLMForMaskedLM
        >>> import tensorflow as tf

        >>> tokenizer = AutoTokenizer.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
        ```"""
        outputs = self.layoutlm(
            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,
        )
        sequence_output = outputs[0]
        prediction_scores = self.mlm(sequence_output=sequence_output, training=training)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores)

        if not 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,
        )


@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",
        )

    @unpack_inputs
    @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: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification
        >>> import tensorflow as tf

        >>> tokenizer = AutoTokenizer.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
        ```"""
        outputs = self.layoutlm(
            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,
        )
        pooled_output = outputs[1]
        pooled_output = self.dropout(inputs=pooled_output, training=training)
        logits = self.classifier(inputs=pooled_output)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)

        if not 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,
        )


@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",
        )

    @unpack_inputs
    @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: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

        Returns:

        Examples:

        ```python
        >>> import tensorflow as tf
        >>> from transformers import AutoTokenizer, TFLayoutLMForTokenClassification

        >>> tokenizer = AutoTokenizer.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
        ```"""
        outputs = self.layoutlm(
            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,
        )
        sequence_output = outputs[0]
        sequence_output = self.dropout(inputs=sequence_output, training=training)
        logits = self.classifier(inputs=sequence_output)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)

        if not 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,
        )


@add_start_docstrings(
    """
    LayoutLM Model with a span classification head on top for extractive question-answering tasks such as
    [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span
    start logits` and `span end logits`).
    """,
    LAYOUTLM_START_DOCSTRING,
)
class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnsweringLoss):
    # 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",
    ]

    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.qa_outputs = tf.keras.layers.Dense(
            units=config.num_labels,
            kernel_initializer=get_initializer(config.initializer_range),
            name="qa_outputs",
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
        input_ids: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        start_positions: np.ndarray | tf.Tensor | None = None,
        end_positions: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
        r"""
        start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.

        Returns:

        Examples:

        ```python
        >>> import tensorflow as tf
        >>> from transformers import AutoTokenizer, TFLayoutLMForQuestionAnswering
        >>> from datasets import load_dataset

        >>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
        >>> model = TFLayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")

        >>> dataset = load_dataset("nielsr/funsd", split="train")
        >>> example = dataset[0]
        >>> question = "what's his name?"
        >>> words = example["words"]
        >>> boxes = example["bboxes"]

        >>> encoding = tokenizer(
        ...     question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="tf"
        ... )
        >>> bbox = []
        >>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
        ...     if s == 1:
        ...         bbox.append(boxes[w])
        ...     elif i == tokenizer.sep_token_id:
        ...         bbox.append([1000] * 4)
        ...     else:
        ...         bbox.append([0] * 4)
        >>> encoding["bbox"] = tf.convert_to_tensor([bbox])

        >>> word_ids = encoding.word_ids(0)
        >>> outputs = model(**encoding)
        >>> loss = outputs.loss
        >>> start_scores = outputs.start_logits
        >>> end_scores = outputs.end_logits
        >>> start, end = word_ids[tf.math.argmax(start_scores, -1)[0]], word_ids[tf.math.argmax(end_scores, -1)[0]]
        >>> print(" ".join(words[start : end + 1]))
        M. Hamann P. Harper, P. Martinez
        ```"""

        outputs = self.layoutlm(
            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,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(inputs=sequence_output)
        start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
        start_logits = tf.squeeze(input=start_logits, axis=-1)
        end_logits = tf.squeeze(input=end_logits, axis=-1)
        loss = None

        if start_positions is not None and end_positions is not None:
            labels = {"start_position": start_positions}
            labels["end_position"] = end_positions
            loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TFQuestionAnsweringModelOutput(
            loss=loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
