transformers-4.33.3/src/transformers/models/deberta/modeling_tf_deberta.py

# coding=utf-8
# Copyright 2021 Microsoft and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 DeBERTa model."""


from __future__ import annotations

import math
from typing import Dict, Optional, Sequence, Tuple, Union

import numpy as np
import tensorflow as tf

from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
    TFBaseModelOutput,
    TFMaskedLMOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
    TFMaskedLanguageModelingLoss,
    TFModelInputType,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
    unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_deberta import DebertaConfig


logger = logging.get_logger(__name__)


_CONFIG_FOR_DOC = "DebertaConfig"
_CHECKPOINT_FOR_DOC = "kamalkraj/deberta-base"

TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "kamalkraj/deberta-base",
    # See all DeBERTa models at https://huggingface.co/models?filter=DeBERTa
]


class TFDebertaContextPooler(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(config.pooler_hidden_size, name="dense")
        self.dropout = TFDebertaStableDropout(config.pooler_dropout, name="dropout")
        self.config = config

    def call(self, hidden_states, training: bool = False):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        context_token = hidden_states[:, 0]
        context_token = self.dropout(context_token, training=training)
        pooled_output = self.dense(context_token)
        pooled_output = get_tf_activation(self.config.pooler_hidden_act)(pooled_output)
        return pooled_output

    @property
    def output_dim(self) -> int:
        return self.config.hidden_size


class TFDebertaXSoftmax(tf.keras.layers.Layer):
    """
    Masked Softmax which is optimized for saving memory

    Args:
        input (`tf.Tensor`): The input tensor that will apply softmax.
        mask (`tf.Tensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
        dim (int): The dimension that will apply softmax
    """

    def __init__(self, axis=-1, **kwargs):
        super().__init__(**kwargs)
        self.axis = axis

    def call(self, inputs: tf.Tensor, mask: tf.Tensor):
        rmask = tf.logical_not(tf.cast(mask, tf.bool))
        output = tf.where(rmask, float("-inf"), inputs)
        output = stable_softmax(output, self.axis)
        output = tf.where(rmask, 0.0, output)
        return output


class TFDebertaStableDropout(tf.keras.layers.Layer):
    """
    Optimized dropout module for stabilizing the training

    Args:
        drop_prob (float): the dropout probabilities
    """

    def __init__(self, drop_prob, **kwargs):
        super().__init__(**kwargs)
        self.drop_prob = drop_prob

    @tf.custom_gradient
    def xdropout(self, inputs):
        """
        Applies dropout to the inputs, as vanilla dropout, but also scales the remaining elements up by 1/drop_prob.
        """
        mask = tf.cast(
            1
            - tf.compat.v1.distributions.Bernoulli(probs=1.0 - self.drop_prob).sample(sample_shape=shape_list(inputs)),
            tf.bool,
        )
        scale = tf.convert_to_tensor(1.0 / (1 - self.drop_prob), dtype=tf.float32)
        if self.drop_prob > 0:
            inputs = tf.where(mask, 0.0, inputs) * scale

        def grad(upstream):
            if self.drop_prob > 0:
                return tf.where(mask, 0.0, upstream) * scale
            else:
                return upstream

        return inputs, grad

    def call(self, inputs: tf.Tensor, training: tf.Tensor = False):
        if training:
            return self.xdropout(inputs)
        return inputs


class TFDebertaLayerNorm(tf.keras.layers.Layer):
    """LayerNorm module in the TF style (epsilon inside the square root)."""

    def __init__(self, size, eps=1e-12, **kwargs):
        super().__init__(**kwargs)
        self.size = size
        self.eps = eps

    def build(self, input_shape):
        self.gamma = self.add_weight(shape=[self.size], initializer=tf.ones_initializer(), name="weight")
        self.beta = self.add_weight(shape=[self.size], initializer=tf.zeros_initializer(), name="bias")
        return super().build(input_shape)

    def call(self, x: tf.Tensor) -> tf.Tensor:
        mean = tf.reduce_mean(x, axis=[-1], keepdims=True)
        variance = tf.reduce_mean(tf.square(x - mean), axis=[-1], keepdims=True)
        std = tf.math.sqrt(variance + self.eps)
        return self.gamma * (x - mean) / std + self.beta


class TFDebertaSelfOutput(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, **kwargs):
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(config.hidden_size, name="dense")
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="dropout")

    def call(self, hidden_states, input_tensor, training: bool = False):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class TFDebertaAttention(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, **kwargs):
        super().__init__(**kwargs)
        self.self = TFDebertaDisentangledSelfAttention(config, name="self")
        self.dense_output = TFDebertaSelfOutput(config, name="output")
        self.config = config

    def call(
        self,
        input_tensor: tf.Tensor,
        attention_mask: tf.Tensor,
        query_states: tf.Tensor = None,
        relative_pos: tf.Tensor = None,
        rel_embeddings: tf.Tensor = None,
        output_attentions: bool = False,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        self_outputs = self.self(
            hidden_states=input_tensor,
            attention_mask=attention_mask,
            query_states=query_states,
            relative_pos=relative_pos,
            rel_embeddings=rel_embeddings,
            output_attentions=output_attentions,
            training=training,
        )
        if query_states is None:
            query_states = input_tensor
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=query_states, training=training
        )

        output = (attention_output,) + self_outputs[1:]

        return output


class TFDebertaIntermediate(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, **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


class TFDebertaOutput(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, **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 = TFDebertaStableDropout(config.hidden_dropout_prob, name="dropout")

    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(hidden_states, training=training)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)

        return hidden_states


class TFDebertaLayer(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, **kwargs):
        super().__init__(**kwargs)

        self.attention = TFDebertaAttention(config, name="attention")
        self.intermediate = TFDebertaIntermediate(config, name="intermediate")
        self.bert_output = TFDebertaOutput(config, name="output")

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        query_states: tf.Tensor = None,
        relative_pos: tf.Tensor = None,
        rel_embeddings: tf.Tensor = None,
        output_attentions: bool = False,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        attention_outputs = self.attention(
            input_tensor=hidden_states,
            attention_mask=attention_mask,
            query_states=query_states,
            relative_pos=relative_pos,
            rel_embeddings=rel_embeddings,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = attention_outputs[0]
        intermediate_output = self.intermediate(hidden_states=attention_output)
        layer_output = self.bert_output(
            hidden_states=intermediate_output, input_tensor=attention_output, training=training
        )
        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them

        return outputs


class TFDebertaEncoder(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, **kwargs):
        super().__init__(**kwargs)

        self.layer = [TFDebertaLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
        self.relative_attention = getattr(config, "relative_attention", False)
        self.config = config
        if self.relative_attention:
            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
            if self.max_relative_positions < 1:
                self.max_relative_positions = config.max_position_embeddings

    def build(self, input_shape):
        if self.relative_attention:
            self.rel_embeddings = self.add_weight(
                name="rel_embeddings.weight",
                shape=[self.max_relative_positions * 2, self.config.hidden_size],
                initializer=get_initializer(self.config.initializer_range),
            )
        return super().build(input_shape)

    def get_rel_embedding(self):
        rel_embeddings = self.rel_embeddings if self.relative_attention else None
        return rel_embeddings

    def get_attention_mask(self, attention_mask):
        if len(shape_list(attention_mask)) <= 2:
            extended_attention_mask = tf.expand_dims(tf.expand_dims(attention_mask, 1), 2)
            attention_mask = extended_attention_mask * tf.expand_dims(tf.squeeze(extended_attention_mask, -2), -1)
            attention_mask = tf.cast(attention_mask, tf.uint8)
        elif len(shape_list(attention_mask)) == 3:
            attention_mask = tf.expand_dims(attention_mask, 1)

        return attention_mask

    def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
        if self.relative_attention and relative_pos is None:
            q = shape_list(query_states)[-2] if query_states is not None else shape_list(hidden_states)[-2]
            relative_pos = build_relative_position(q, shape_list(hidden_states)[-2])
        return relative_pos

    def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        query_states: tf.Tensor = None,
        relative_pos: tf.Tensor = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        training: bool = False,
    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        attention_mask = self.get_attention_mask(attention_mask)
        relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)

        if isinstance(hidden_states, Sequence):
            next_kv = hidden_states[0]
        else:
            next_kv = hidden_states

        rel_embeddings = self.get_rel_embedding()

        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_outputs = layer_module(
                hidden_states=next_kv,
                attention_mask=attention_mask,
                query_states=query_states,
                relative_pos=relative_pos,
                rel_embeddings=rel_embeddings,
                output_attentions=output_attentions,
                training=training,
            )
            hidden_states = layer_outputs[0]

            if query_states is not None:
                query_states = hidden_states
                if isinstance(hidden_states, Sequence):
                    next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
            else:
                next_kv = hidden_states

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)

        return TFBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )


def build_relative_position(query_size, key_size):
    """
    Build relative position according to the query and key

    We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
    \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q -
    P_k\\)

    Args:
        query_size (int): the length of query
        key_size (int): the length of key

    Return:
        `tf.Tensor`: A tensor with shape [1, query_size, key_size]

    """
    q_ids = tf.range(query_size, dtype=tf.int32)
    k_ids = tf.range(key_size, dtype=tf.int32)
    rel_pos_ids = q_ids[:, None] - tf.tile(tf.reshape(k_ids, [1, -1]), [query_size, 1])
    rel_pos_ids = rel_pos_ids[:query_size, :]
    rel_pos_ids = tf.expand_dims(rel_pos_ids, axis=0)
    return tf.cast(rel_pos_ids, tf.int64)


def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
    shapes = [
        shape_list(query_layer)[0],
        shape_list(query_layer)[1],
        shape_list(query_layer)[2],
        shape_list(relative_pos)[-1],
    ]
    return tf.broadcast_to(c2p_pos, shapes)


def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
    shapes = [
        shape_list(query_layer)[0],
        shape_list(query_layer)[1],
        shape_list(key_layer)[-2],
        shape_list(key_layer)[-2],
    ]
    return tf.broadcast_to(c2p_pos, shapes)


def pos_dynamic_expand(pos_index, p2c_att, key_layer):
    shapes = shape_list(p2c_att)[:2] + [shape_list(pos_index)[-2], shape_list(key_layer)[-2]]
    return tf.broadcast_to(pos_index, shapes)


def torch_gather(x, indices, gather_axis):
    if gather_axis < 0:
        gather_axis = tf.rank(x) + gather_axis

    if gather_axis != tf.rank(x) - 1:
        pre_roll = tf.rank(x) - 1 - gather_axis
        permutation = tf.roll(tf.range(tf.rank(x)), pre_roll, axis=0)
        x = tf.transpose(x, perm=permutation)
        indices = tf.transpose(indices, perm=permutation)
    else:
        pre_roll = 0

    flat_x = tf.reshape(x, (-1, tf.shape(x)[-1]))
    flat_indices = tf.reshape(indices, (-1, tf.shape(indices)[-1]))
    gathered = tf.gather(flat_x, flat_indices, batch_dims=1)
    gathered = tf.reshape(gathered, tf.shape(indices))

    if pre_roll != 0:
        permutation = tf.roll(tf.range(tf.rank(x)), -pre_roll, axis=0)
        gathered = tf.transpose(gathered, perm=permutation)

    return gathered


class TFDebertaDisentangledSelfAttention(tf.keras.layers.Layer):
    """
    Disentangled self-attention module

    Parameters:
        config (`str`):
            A model config class instance with the configuration to build a new model. The schema is similar to
            *BertConfig*, for more details, please refer [`DebertaConfig`]

    """

    def __init__(self, config: DebertaConfig, **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 of attention "
                f"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.in_proj = tf.keras.layers.Dense(
            self.all_head_size * 3,
            kernel_initializer=get_initializer(config.initializer_range),
            name="in_proj",
            use_bias=False,
        )
        self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []

        self.relative_attention = getattr(config, "relative_attention", False)
        self.talking_head = getattr(config, "talking_head", False)

        if self.talking_head:
            self.head_logits_proj = tf.keras.layers.Dense(
                self.num_attention_heads,
                kernel_initializer=get_initializer(config.initializer_range),
                name="head_logits_proj",
                use_bias=False,
            )
            self.head_weights_proj = tf.keras.layers.Dense(
                self.num_attention_heads,
                kernel_initializer=get_initializer(config.initializer_range),
                name="head_weights_proj",
                use_bias=False,
            )

        self.softmax = TFDebertaXSoftmax(axis=-1)

        if self.relative_attention:
            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
            if self.max_relative_positions < 1:
                self.max_relative_positions = config.max_position_embeddings
            self.pos_dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="pos_dropout")
            if "c2p" in self.pos_att_type:
                self.pos_proj = tf.keras.layers.Dense(
                    self.all_head_size,
                    kernel_initializer=get_initializer(config.initializer_range),
                    name="pos_proj",
                    use_bias=False,
                )
            if "p2c" in self.pos_att_type:
                self.pos_q_proj = tf.keras.layers.Dense(
                    self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="pos_q_proj"
                )

        self.dropout = TFDebertaStableDropout(config.attention_probs_dropout_prob, name="dropout")

    def build(self, input_shape):
        self.q_bias = self.add_weight(
            name="q_bias", shape=(self.all_head_size), initializer=tf.keras.initializers.Zeros()
        )
        self.v_bias = self.add_weight(
            name="v_bias", shape=(self.all_head_size), initializer=tf.keras.initializers.Zeros()
        )
        return super().build(input_shape)

    def transpose_for_scores(self, tensor: tf.Tensor) -> tf.Tensor:
        shape = shape_list(tensor)[:-1] + [self.num_attention_heads, -1]
        # 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=shape)

        # 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,
        query_states: tf.Tensor = None,
        relative_pos: tf.Tensor = None,
        rel_embeddings: tf.Tensor = None,
        output_attentions: bool = False,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        """
        Call the module

        Args:
            hidden_states (`tf.Tensor`):
                Input states to the module usually the output from previous layer, it will be the Q,K and V in
                *Attention(Q,K,V)*

            attention_mask (`tf.Tensor`):
                An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the batch size, *N* is the maximum
                sequence length in which element [i,j] = *1* means the *i* th token in the input can attend to the *j*
                th token.

            return_att (`bool`, optional):
                Whether return the attention matrix.

            query_states (`tf.Tensor`, optional):
                The *Q* state in *Attention(Q,K,V)*.

            relative_pos (`tf.Tensor`):
                The relative position encoding between the tokens in the sequence. It's of shape [*B*, *N*, *N*] with
                values ranging in [*-max_relative_positions*, *max_relative_positions*].

            rel_embeddings (`tf.Tensor`):
                The embedding of relative distances. It's a tensor of shape [\\(2 \\times
                \\text{max_relative_positions}\\), *hidden_size*].


        """
        if query_states is None:
            qp = self.in_proj(hidden_states)  # .split(self.all_head_size, dim=-1)
            query_layer, key_layer, value_layer = tf.split(
                self.transpose_for_scores(qp), num_or_size_splits=3, axis=-1
            )
        else:

            def linear(w, b, x):
                out = tf.matmul(x, w, transpose_b=True)
                if b is not None:
                    out += tf.transpose(b)
                return out

            ws = tf.split(
                tf.transpose(self.in_proj.weight[0]), num_or_size_splits=self.num_attention_heads * 3, axis=0
            )
            qkvw = tf.TensorArray(dtype=tf.float32, size=3)
            for k in tf.range(3):
                qkvw_inside = tf.TensorArray(dtype=tf.float32, size=self.num_attention_heads)
                for i in tf.range(self.num_attention_heads):
                    qkvw_inside = qkvw_inside.write(i, ws[i * 3 + k])
                qkvw = qkvw.write(k, qkvw_inside.concat())
            qkvb = [None] * 3

            q = linear(qkvw[0], qkvb[0], query_states)
            k = linear(qkvw[1], qkvb[1], hidden_states)
            v = linear(qkvw[2], qkvb[2], hidden_states)
            query_layer = self.transpose_for_scores(q)
            key_layer = self.transpose_for_scores(k)
            value_layer = self.transpose_for_scores(v)

        query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :])
        value_layer = value_layer + self.transpose_for_scores(self.v_bias[None, None, :])

        rel_att = None
        # Take the dot product between "query" and "key" to get the raw attention scores.
        scale_factor = 1 + len(self.pos_att_type)
        scale = math.sqrt(shape_list(query_layer)[-1] * scale_factor)
        query_layer = query_layer / scale

        attention_scores = tf.matmul(query_layer, tf.transpose(key_layer, [0, 1, 3, 2]))
        if self.relative_attention:
            rel_embeddings = self.pos_dropout(rel_embeddings, training=training)
            rel_att = self.disentangled_att_bias(query_layer, key_layer, relative_pos, rel_embeddings, scale_factor)

        if rel_att is not None:
            attention_scores = attention_scores + rel_att

        if self.talking_head:
            attention_scores = tf.transpose(
                self.head_logits_proj(tf.transpose(attention_scores, [0, 2, 3, 1])), [0, 3, 1, 2]
            )

        attention_probs = self.softmax(attention_scores, attention_mask)
        attention_probs = self.dropout(attention_probs, training=training)
        if self.talking_head:
            attention_probs = tf.transpose(
                self.head_weights_proj(tf.transpose(attention_probs, [0, 2, 3, 1])), [0, 3, 1, 2]
            )

        context_layer = tf.matmul(attention_probs, value_layer)
        context_layer = tf.transpose(context_layer, [0, 2, 1, 3])
        context_layer_shape = shape_list(context_layer)
        # Set the final dimension here explicitly.
        # Calling tf.reshape(context_layer, (*context_layer_shape[:-2], -1)) raises an error when executing
        # the model in graph mode as context_layer is reshaped to (None, 7, None) and Dense layer in TFDebertaV2SelfOutput
        # requires final input dimension to be defined
        new_context_layer_shape = context_layer_shape[:-2] + [context_layer_shape[-2] * context_layer_shape[-1]]
        context_layer = tf.reshape(context_layer, new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs

    def disentangled_att_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
        if relative_pos is None:
            q = shape_list(query_layer)[-2]
            relative_pos = build_relative_position(q, shape_list(key_layer)[-2])
        shape_list_pos = shape_list(relative_pos)
        if len(shape_list_pos) == 2:
            relative_pos = tf.expand_dims(tf.expand_dims(relative_pos, 0), 0)
        elif len(shape_list_pos) == 3:
            relative_pos = tf.expand_dims(relative_pos, 1)
        # bxhxqxk
        elif len(shape_list_pos) != 4:
            raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {len(shape_list_pos)}")

        att_span = tf.cast(
            tf.minimum(
                tf.maximum(shape_list(query_layer)[-2], shape_list(key_layer)[-2]), self.max_relative_positions
            ),
            tf.int64,
        )
        rel_embeddings = tf.expand_dims(
            rel_embeddings[self.max_relative_positions - att_span : self.max_relative_positions + att_span, :], 0
        )

        score = 0

        # content->position
        if "c2p" in self.pos_att_type:
            pos_key_layer = self.pos_proj(rel_embeddings)
            pos_key_layer = self.transpose_for_scores(pos_key_layer)
            c2p_att = tf.matmul(query_layer, tf.transpose(pos_key_layer, [0, 1, 3, 2]))
            c2p_pos = tf.clip_by_value(relative_pos + att_span, 0, att_span * 2 - 1)
            c2p_att = torch_gather(c2p_att, c2p_dynamic_expand(c2p_pos, query_layer, relative_pos), -1)
            score += c2p_att

        # position->content
        if "p2c" in self.pos_att_type:
            pos_query_layer = self.pos_q_proj(rel_embeddings)
            pos_query_layer = self.transpose_for_scores(pos_query_layer)
            pos_query_layer /= tf.math.sqrt(tf.cast(shape_list(pos_query_layer)[-1] * scale_factor, dtype=tf.float32))
            if shape_list(query_layer)[-2] != shape_list(key_layer)[-2]:
                r_pos = build_relative_position(shape_list(key_layer)[-2], shape_list(key_layer)[-2])
            else:
                r_pos = relative_pos
            p2c_pos = tf.clip_by_value(-r_pos + att_span, 0, att_span * 2 - 1)
            p2c_att = tf.matmul(key_layer, tf.transpose(pos_query_layer, [0, 1, 3, 2]))
            p2c_att = tf.transpose(
                torch_gather(p2c_att, p2c_dynamic_expand(p2c_pos, query_layer, key_layer), -1), [0, 1, 3, 2]
            )
            if shape_list(query_layer)[-2] != shape_list(key_layer)[-2]:
                pos_index = tf.expand_dims(relative_pos[:, :, :, 0], -1)
                p2c_att = torch_gather(p2c_att, pos_dynamic_expand(pos_index, p2c_att, key_layer), -2)
            score += p2c_att

        return score


class TFDebertaEmbeddings(tf.keras.layers.Layer):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.config = config
        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
        self.hidden_size = config.hidden_size
        self.max_position_embeddings = config.max_position_embeddings
        self.position_biased_input = getattr(config, "position_biased_input", True)
        self.initializer_range = config.initializer_range
        if self.embedding_size != config.hidden_size:
            self.embed_proj = tf.keras.layers.Dense(
                config.hidden_size,
                kernel_initializer=get_initializer(config.initializer_range),
                name="embed_proj",
                use_bias=False,
            )
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="dropout")

    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.embedding_size],
                initializer=get_initializer(self.initializer_range),
            )

        with tf.name_scope("token_type_embeddings"):
            if self.config.type_vocab_size > 0:
                self.token_type_embeddings = self.add_weight(
                    name="embeddings",
                    shape=[self.config.type_vocab_size, self.embedding_size],
                    initializer=get_initializer(self.initializer_range),
                )
            else:
                self.token_type_embeddings = None

        with tf.name_scope("position_embeddings"):
            if self.position_biased_input:
                self.position_embeddings = self.add_weight(
                    name="embeddings",
                    shape=[self.max_position_embeddings, self.hidden_size],
                    initializer=get_initializer(self.initializer_range),
                )
            else:
                self.position_embeddings = None

        super().build(input_shape)

    def call(
        self,
        input_ids: tf.Tensor = None,
        position_ids: tf.Tensor = None,
        token_type_ids: tf.Tensor = None,
        inputs_embeds: tf.Tensor = None,
        mask: tf.Tensor = None,
        training: bool = False,
    ) -> tf.Tensor:
        """
        Applies embedding based on inputs tensor.

        Returns:
            final_embeddings (`tf.Tensor`): output embedding tensor.
        """
        if input_ids is None and inputs_embeds is None:
            raise ValueError("Need to provide either `input_ids` or `input_embeds`.")

        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)

        final_embeddings = inputs_embeds
        if self.position_biased_input:
            position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
            final_embeddings += position_embeds
        if self.config.type_vocab_size > 0:
            token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
            final_embeddings += token_type_embeds

        if self.embedding_size != self.hidden_size:
            final_embeddings = self.embed_proj(final_embeddings)

        final_embeddings = self.LayerNorm(final_embeddings)

        if mask is not None:
            if len(shape_list(mask)) != len(shape_list(final_embeddings)):
                if len(shape_list(mask)) == 4:
                    mask = tf.squeeze(tf.squeeze(mask, axis=1), axis=1)
                mask = tf.cast(tf.expand_dims(mask, axis=2), tf.float32)

            final_embeddings = final_embeddings * mask

        final_embeddings = self.dropout(final_embeddings, training=training)

        return final_embeddings


class TFDebertaPredictionHeadTransform(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, **kwargs):
        super().__init__(**kwargs)

        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)

        self.dense = tf.keras.layers.Dense(
            units=self.embedding_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(hidden_states)

        return hidden_states


class TFDebertaLMPredictionHead(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
        super().__init__(**kwargs)

        self.config = config
        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)

        self.transform = TFDebertaPredictionHeadTransform(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.embedding_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


class TFDebertaOnlyMLMHead(tf.keras.layers.Layer):
    def __init__(self, config: DebertaConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
        super().__init__(**kwargs)
        self.predictions = TFDebertaLMPredictionHead(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 TFDebertaMainLayer(tf.keras.layers.Layer):
    config_class = DebertaConfig

    def __init__(self, config: DebertaConfig, **kwargs):
        super().__init__(**kwargs)

        self.config = config

        self.embeddings = TFDebertaEmbeddings(config, name="embeddings")
        self.encoder = TFDebertaEncoder(config, name="encoder")

    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,
        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,
        inputs_embeds: 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[TFBaseModelOutput, 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)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            mask=attention_mask,
            training=training,
        )

        encoder_outputs = self.encoder(
            hidden_states=embedding_output,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = encoder_outputs[0]

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

        return TFBaseModelOutput(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class TFDebertaPreTrainedModel(TFPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = DebertaConfig
    base_model_prefix = "deberta"


DEBERTA_START_DOCSTRING = r"""
    The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled
    Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build
    on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
    improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

    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>

    Parameters:
        config ([`DebertaConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

DEBERTA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

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

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`np.ndarray` 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 (`np.ndarray` 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 (`np.ndarray` 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)
        inputs_embeds (`np.ndarray` or `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.
"""


@add_start_docstrings(
    "The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.",
    DEBERTA_START_DOCSTRING,
)
class TFDebertaModel(TFDebertaPreTrainedModel):
    def __init__(self, config: DebertaConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.deberta = TFDebertaMainLayer(config, name="deberta")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | 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,
        inputs_embeds: 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[TFBaseModelOutput, Tuple[tf.Tensor]]:
        outputs = self.deberta(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            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("""DeBERTa Model with a `language modeling` head on top.""", DEBERTA_START_DOCSTRING)
class TFDebertaForMaskedLM(TFDebertaPreTrainedModel, TFMaskedLanguageModelingLoss):
    def __init__(self, config: DebertaConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        if config.is_decoder:
            logger.warning(
                "If you want to use `TFDebertaForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )

        self.deberta = TFDebertaMainLayer(config, name="deberta")
        self.mlm = TFDebertaOnlyMLMHead(config, input_embeddings=self.deberta.embeddings, name="cls")

    def get_lm_head(self) -> tf.keras.layers.Layer:
        return self.mlm.predictions

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | 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,
        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]`
        """
        outputs = self.deberta(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            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(
    """
    DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    DEBERTA_START_DOCSTRING,
)
class TFDebertaForSequenceClassification(TFDebertaPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: DebertaConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.num_labels = config.num_labels

        self.deberta = TFDebertaMainLayer(config, name="deberta")
        self.pooler = TFDebertaContextPooler(config, name="pooler")

        drop_out = getattr(config, "cls_dropout", None)
        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
        self.dropout = TFDebertaStableDropout(drop_out, name="cls_dropout")
        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(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | 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,
        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).
        """
        outputs = self.deberta(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        sequence_output = outputs[0]
        pooled_output = self.pooler(sequence_output, training=training)
        pooled_output = self.dropout(pooled_output, training=training)
        logits = self.classifier(pooled_output)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)

        if not return_dict:
            output = (logits,) + outputs[1:]

            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(
    """
    DeBERTa 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.
    """,
    DEBERTA_START_DOCSTRING,
)
class TFDebertaForTokenClassification(TFDebertaPreTrainedModel, TFTokenClassificationLoss):
    def __init__(self, config: DebertaConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.num_labels = config.num_labels

        self.deberta = TFDebertaMainLayer(config, name="deberta")
        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(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | 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,
        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]`.
        """
        outputs = self.deberta(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            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(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[1:]
            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(
    """
    DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    DEBERTA_START_DOCSTRING,
)
class TFDebertaForQuestionAnswering(TFDebertaPreTrainedModel, TFQuestionAnsweringLoss):
    def __init__(self, config: DebertaConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.num_labels = config.num_labels

        self.deberta = TFDebertaMainLayer(config, name="deberta")
        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(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: TFModelInputType | 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,
        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.
        """
        outputs = self.deberta(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            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,
        )