# 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-v2 model."""


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 (
    TFBaseModelOutput,
    TFMaskedLMOutput,
    TFQuestionAnsweringModelOutput,
    TFSequenceClassifierOutput,
    TFTokenClassifierOutput,
)
from ...modeling_tf_utils import (
    TFMaskedLanguageModelingLoss,
    TFModelInputType,
    TFPreTrainedModel,
    TFQuestionAnsweringLoss,
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
    unpack_inputs,
)
from ...tf_utils import shape_list, stable_softmax
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_deberta_v2 import DebertaV2Config


logger = logging.get_logger(__name__)


_CONFIG_FOR_DOC = "DebertaV2Config"
_TOKENIZER_FOR_DOC = "DebertaV2Tokenizer"
_CHECKPOINT_FOR_DOC = "kamalkraj/deberta-v2-xlarge"

TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "kamalkraj/deberta-v2-xlarge",
    # See all DeBERTa models at https://huggingface.co/models?filter=deberta-v2
]


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaContextPooler with Deberta->DebertaV2
class TFDebertaV2ContextPooler(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, **kwargs):
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(config.pooler_hidden_size, name="dense")
        self.dropout = TFDebertaV2StableDropout(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


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaXSoftmax with Deberta->DebertaV2
class TFDebertaV2XSoftmax(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


# Copied from transformers.models.deberta.modeling_tf_deberta.get_mask
def get_mask(input, dropout):
    mask = tf.cast(
        1 - tf.compat.v1.distributions.Bernoulli(probs=1 - dropout).sample(sample_shape=shape_list(input)), tf.bool
    )
    return mask, dropout


@tf.custom_gradient
# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaXDropout
def TFDebertaV2XDropout(input, local_ctx):
    mask, dropout = get_mask(input, local_ctx)
    scale = tf.convert_to_tensor(1.0 / (1 - dropout), dtype=tf.float32)
    input = tf.cond(dropout > 0, lambda: tf.where(mask, 0.0, input) * scale, lambda: input)

    def custom_grad(upstream_grad):
        return tf.cond(
            scale > 1, lambda: (tf.where(mask, 0.0, upstream_grad) * scale, None), lambda: (upstream_grad, None)
        )

    return input, custom_grad


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaStableDropout with Deberta->DebertaV2
class TFDebertaV2StableDropout(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 = tf.convert_to_tensor(drop_prob, dtype=tf.float32)

    def call(self, inputs: tf.Tensor, training: tf.Tensor = False):
        if training and self.drop_prob > 0:
            return TFDebertaV2XDropout(inputs, self.drop_prob)
        return inputs


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaSelfOutput with Deberta->DebertaV2
class TFDebertaV2SelfOutput(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, **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 = TFDebertaV2StableDropout(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


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaAttention with Deberta->DebertaV2
class TFDebertaV2Attention(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, **kwargs):
        super().__init__(**kwargs)
        self.self = TFDebertaV2DisentangledSelfAttention(config, name="self")
        self.dense_output = TFDebertaV2SelfOutput(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


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaIntermediate with Deberta->DebertaV2
class TFDebertaV2Intermediate(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, **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.deberta.modeling_tf_deberta.TFDebertaOutput with Deberta->DebertaV2
class TFDebertaV2Output(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, **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 = TFDebertaV2StableDropout(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


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaLayer with Deberta->DebertaV2
class TFDebertaV2Layer(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, **kwargs):
        super().__init__(**kwargs)

        self.attention = TFDebertaV2Attention(config, name="attention")
        self.intermediate = TFDebertaV2Intermediate(config, name="intermediate")
        self.bert_output = TFDebertaV2Output(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 TFDebertaV2ConvLayer(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, **kwargs):
        super().__init__(**kwargs)

        self.kernel_size = getattr(config, "conv_kernel_size", 3)
        # groups = getattr(config, "conv_groups", 1)
        self.conv_act = get_tf_activation(getattr(config, "conv_act", "tanh"))
        self.padding = (self.kernel_size - 1) // 2
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = TFDebertaV2StableDropout(config.hidden_dropout_prob, name="dropout")
        self.config = config

    def build(self, input_shape):
        with tf.name_scope("conv"):
            self.conv_kernel = self.add_weight(
                name="kernel",
                shape=[self.kernel_size, self.config.hidden_size, self.config.hidden_size],
                initializer=get_initializer(self.config.initializer_range),
            )
            self.conv_bias = self.add_weight(
                name="bias", shape=[self.config.hidden_size], initializer=tf.zeros_initializer()
            )
        return super().build(input_shape)

    def call(
        self, hidden_states: tf.Tensor, residual_states: tf.Tensor, input_mask: tf.Tensor, training: bool = False
    ) -> tf.Tensor:
        out = tf.nn.conv2d(
            tf.expand_dims(hidden_states, 1),
            tf.expand_dims(self.conv_kernel, 0),
            strides=1,
            padding=[[0, 0], [0, 0], [self.padding, self.padding], [0, 0]],
        )
        out = tf.squeeze(tf.nn.bias_add(out, self.conv_bias), 1)
        rmask = tf.cast(1 - input_mask, tf.bool)
        out = tf.where(tf.broadcast_to(tf.expand_dims(rmask, -1), shape_list(out)), 0.0, out)
        out = self.dropout(out, training=training)
        out = self.conv_act(out)

        layer_norm_input = residual_states + out
        output = self.LayerNorm(layer_norm_input)

        if input_mask is None:
            output_states = output
        else:
            if len(shape_list(input_mask)) != len(shape_list(layer_norm_input)):
                if len(shape_list(input_mask)) == 4:
                    input_mask = tf.squeeze(tf.squeeze(input_mask, axis=1), axis=1)
                input_mask = tf.cast(tf.expand_dims(input_mask, axis=2), tf.float32)

            output_states = output * input_mask

        return output_states


class TFDebertaV2Encoder(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, **kwargs):
        super().__init__(**kwargs)

        self.layer = [TFDebertaV2Layer(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

            self.position_buckets = getattr(config, "position_buckets", -1)
            self.pos_ebd_size = self.max_relative_positions * 2

            if self.position_buckets > 0:
                self.pos_ebd_size = self.position_buckets * 2

        self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("|")]

        if "layer_norm" in self.norm_rel_ebd:
            self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")

        self.conv = TFDebertaV2ConvLayer(config, name="conv") if getattr(config, "conv_kernel_size", 0) > 0 else None

    def build(self, input_shape):
        if self.relative_attention:
            self.rel_embeddings = self.add_weight(
                name="rel_embeddings.weight",
                shape=[self.pos_ebd_size, 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
        if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd):
            rel_embeddings = self.LayerNorm(rel_embeddings)
        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],
                bucket_size=self.position_buckets,
                max_position=self.max_relative_positions,
            )
        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]]:
        if len(shape_list(attention_mask)) <= 2:
            input_mask = attention_mask
        else:
            input_mask = tf.cast(tf.math.reduce_sum(attention_mask, axis=-2) > 0, dtype=tf.uint8)

        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)

        next_kv = hidden_states

        rel_embeddings = self.get_rel_embedding()
        output_states = next_kv
        for i, layer_module in enumerate(self.layer):

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (output_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,
            )
            output_states = layer_outputs[0]

            if i == 0 and self.conv is not None:
                output_states = self.conv(hidden_states, output_states, input_mask)

            next_kv = output_states

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

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

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

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


def make_log_bucket_position(relative_pos, bucket_size, max_position):
    sign = tf.math.sign(relative_pos)
    mid = bucket_size // 2
    abs_pos = tf.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, tf.math.abs(relative_pos))
    log_pos = (
        tf.math.ceil(
            tf.cast(tf.math.log(abs_pos / mid), tf.float32) / tf.math.log((max_position - 1) / mid) * (mid - 1)
        )
        + mid
    )
    bucket_pos = tf.cast(
        tf.where(abs_pos <= mid, tf.cast(relative_pos, tf.float32), log_pos * tf.cast(sign, tf.float32)), tf.int32
    )
    return bucket_pos


def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):
    """
    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
        bucket_size (int): the size of position bucket
        max_position (int): the maximum allowed absolute position

    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.expand_dims(k_ids, axis=0), [shape_list(q_ids)[0], 1])
    if bucket_size > 0 and max_position > 0:
        rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)
    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 take_along_axis(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 TFDebertaV2DisentangledSelfAttention(tf.keras.layers.Layer):
    """
    Disentangled self-attention module

    Parameters:
        config (`DebertaV2Config`):
            A model config class instance with the configuration to build a new model. The schema is similar to
            *BertConfig*, for more details, please refer [`DebertaV2Config`]

    """

    def __init__(self, config: DebertaV2Config, **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
        _attention_head_size = config.hidden_size // config.num_attention_heads
        self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query_proj = tf.keras.layers.Dense(
            self.all_head_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="query_proj",
            use_bias=True,
        )
        self.key_proj = tf.keras.layers.Dense(
            self.all_head_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="key_proj",
            use_bias=True,
        )
        self.value_proj = tf.keras.layers.Dense(
            self.all_head_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="value_proj",
            use_bias=True,
        )

        self.share_att_key = getattr(config, "share_att_key", 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)

        if self.relative_attention:
            self.position_buckets = getattr(config, "position_buckets", -1)
            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_ebd_size = self.max_relative_positions
            if self.position_buckets > 0:
                self.pos_ebd_size = self.position_buckets

            self.pos_dropout = TFDebertaV2StableDropout(config.hidden_dropout_prob, name="pos_dropout")

            if not self.share_att_key:
                if "c2p" in self.pos_att_type:
                    self.pos_key_proj = tf.keras.layers.Dense(
                        self.all_head_size,
                        kernel_initializer=get_initializer(config.initializer_range),
                        name="pos_proj",
                        use_bias=True,
                    )
                if "p2c" in self.pos_att_type:
                    self.pos_query_proj = tf.keras.layers.Dense(
                        self.all_head_size,
                        kernel_initializer=get_initializer(config.initializer_range),
                        name="pos_q_proj",
                    )
        self.softmax = TFDebertaV2XSoftmax(axis=-1)
        self.dropout = TFDebertaV2StableDropout(config.attention_probs_dropout_prob, name="dropout")

    def transpose_for_scores(self, tensor: tf.Tensor, attention_heads: int) -> tf.Tensor:
        tensor_shape = shape_list(tensor)
        # In graph mode mode, we can't reshape with -1 as the final dimension if the first dimension (batch size) is None
        shape = tensor_shape[:-1] + [attention_heads, tensor_shape[-1] // attention_heads]
        # 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)
        tensor = tf.transpose(tensor, perm=[0, 2, 1, 3])
        x_shape = shape_list(tensor)
        tensor = tf.reshape(tensor, shape=[-1, x_shape[-2], x_shape[-1]])
        return tensor

    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:
            query_states = hidden_states
        query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads)
        key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads)
        value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads)

        rel_att = None
        # Take the dot product between "query" and "key" to get the raw attention scores.
        scale_factor = 1
        if "c2p" in self.pos_att_type:
            scale_factor += 1
        if "p2c" in self.pos_att_type:
            scale_factor += 1
        scale = tf.math.sqrt(tf.cast(shape_list(query_layer)[-1] * scale_factor, tf.float32))
        attention_scores = tf.matmul(query_layer, tf.transpose(key_layer, [0, 2, 1])) / scale
        if self.relative_attention:
            rel_embeddings = self.pos_dropout(rel_embeddings)
            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
        attention_scores = tf.reshape(
            attention_scores,
            (-1, self.num_attention_heads, shape_list(attention_scores)[-2], shape_list(attention_scores)[-1]),
        )

        # bsz x height x length x dimension
        attention_probs = self.softmax(attention_scores, attention_mask)
        attention_probs = self.dropout(attention_probs, training=training)
        context_layer = tf.matmul(
            tf.reshape(attention_probs, [-1, shape_list(attention_probs)[-2], shape_list(attention_probs)[-1]]),
            value_layer,
        )
        context_layer = tf.transpose(
            tf.reshape(
                context_layer,
                [-1, self.num_attention_heads, shape_list(context_layer)[-2], shape_list(context_layer)[-1]],
            ),
            [0, 2, 1, 3],
        )
        # 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
        context_layer_shape = shape_list(context_layer)
        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],
                bucket_size=self.position_buckets,
                max_position=self.max_relative_positions,
            )
        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)
        # bsz x height x query x key
        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 = self.pos_ebd_size
        rel_embeddings = tf.expand_dims(
            rel_embeddings[self.pos_ebd_size - att_span : self.pos_ebd_size + att_span, :], 0
        )
        if self.share_att_key:
            pos_query_layer = tf.tile(
                self.transpose_for_scores(self.query_proj(rel_embeddings), self.num_attention_heads),
                [shape_list(query_layer)[0] // self.num_attention_heads, 1, 1],
            )
            pos_key_layer = tf.tile(
                self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads),
                [shape_list(query_layer)[0] // self.num_attention_heads, 1, 1],
            )
        else:
            if "c2p" in self.pos_att_type:
                pos_key_layer = tf.tile(
                    self.transpose_for_scores(self.pos_key_proj(rel_embeddings), self.num_attention_heads),
                    [shape_list(query_layer)[0] // self.num_attention_heads, 1, 1],
                )  # .split(self.all_head_size, dim=-1)
            if "p2c" in self.pos_att_type:
                pos_query_layer = tf.tile(
                    self.transpose_for_scores(self.pos_query_proj(rel_embeddings), self.num_attention_heads),
                    [shape_list(query_layer)[0] // self.num_attention_heads, 1, 1],
                )  # .split(self.all_head_size, dim=-1)

        score = 0
        # content->position
        if "c2p" in self.pos_att_type:
            scale = tf.math.sqrt(tf.cast(shape_list(pos_key_layer)[-1] * scale_factor, tf.float32))
            c2p_att = tf.matmul(query_layer, tf.transpose(pos_key_layer, [0, 2, 1]))
            c2p_pos = tf.clip_by_value(relative_pos + att_span, 0, att_span * 2 - 1)
            c2p_att = take_along_axis(
                c2p_att,
                tf.broadcast_to(
                    tf.squeeze(c2p_pos, 0),
                    [shape_list(query_layer)[0], shape_list(query_layer)[1], shape_list(relative_pos)[-1]],
                ),
                -1,
            )
            score += c2p_att / scale

        # position->content
        if "p2c" in self.pos_att_type:
            scale = tf.math.sqrt(tf.cast(shape_list(pos_query_layer)[-1] * scale_factor, tf.float32))
            if shape_list(key_layer)[-2] != shape_list(query_layer)[-2]:
                r_pos = build_relative_position(
                    shape_list(key_layer)[-2],
                    shape_list(key_layer)[-2],
                    bucket_size=self.position_buckets,
                    max_position=self.max_relative_positions,
                )
                r_pos = tf.expand_dims(r_pos, 0)
            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, 2, 1]))
            p2c_att = tf.transpose(
                take_along_axis(
                    p2c_att,
                    tf.broadcast_to(
                        tf.squeeze(p2c_pos, 0),
                        [shape_list(query_layer)[0], shape_list(key_layer)[-2], shape_list(key_layer)[-2]],
                    ),
                    -1,
                ),
                [0, 2, 1],
            )
            score += p2c_att / scale

        return score


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaEmbeddings Deberta->DebertaV2
class TFDebertaV2Embeddings(tf.keras.layers.Layer):
    """Construct the embeddings from word, position and token_type embeddings."""

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

        self.vocab_size = config.vocab_size
        self.type_vocab_size = config.type_vocab_size
        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, use_bias=False)
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = TFDebertaV2StableDropout(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.vocab_size, self.embedding_size],
                initializer=get_initializer(self.initializer_range),
            )

        with tf.name_scope("token_type_embeddings"):
            if self.type_vocab_size > 0:
                self.token_type_embeddings = self.add_weight(
                    name="embeddings",
                    shape=[self.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:
            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.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


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaPredictionHeadTransform with Deberta->DebertaV2
class TFDebertaV2PredictionHeadTransform(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, **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(hidden_states)

        return hidden_states


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaLMPredictionHead with Deberta->DebertaV2
class TFDebertaV2LMPredictionHead(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, input_embeddings: tf.keras.layers.Layer, **kwargs):
        super().__init__(**kwargs)

        self.vocab_size = config.vocab_size
        self.hidden_size = config.hidden_size

        self.transform = TFDebertaV2PredictionHeadTransform(config, name="transform")

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.input_embeddings = input_embeddings

    def build(self, input_shape: tf.TensorShape):
        self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")

        super().build(input_shape)

    def get_output_embeddings(self) -> tf.keras.layers.Layer:
        return self.input_embeddings

    def set_output_embeddings(self, value: tf.Variable):
        self.input_embeddings.weight = value
        self.input_embeddings.vocab_size = shape_list(value)[0]

    def get_bias(self) -> Dict[str, tf.Variable]:
        return {"bias": self.bias}

    def set_bias(self, value: tf.Variable):
        self.bias = value["bias"]
        self.vocab_size = shape_list(value["bias"])[0]

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.transform(hidden_states=hidden_states)
        seq_length = shape_list(hidden_states)[1]
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
        hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
        hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)

        return hidden_states


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaOnlyMLMHead with Deberta->DebertaV2
class TFDebertaV2OnlyMLMHead(tf.keras.layers.Layer):
    def __init__(self, config: DebertaV2Config, input_embeddings: tf.keras.layers.Layer, **kwargs):
        super().__init__(**kwargs)
        self.predictions = TFDebertaV2LMPredictionHead(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


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaMainLayer with Deberta->DebertaV2
class TFDebertaV2MainLayer(tf.keras.layers.Layer):
    config_class = DebertaV2Config

    def __init__(self, config: DebertaV2Config, **kwargs):
        super().__init__(**kwargs)

        self.config = config

        self.embeddings = TFDebertaV2Embeddings(config, name="embeddings")
        self.encoder = TFDebertaV2Encoder(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: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> 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,
        )


# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaPreTrainedModel with Deberta->DebertaV2
class TFDebertaV2PreTrainedModel(TFPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = DebertaV2Config
    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>

    TF 2.0 models accepts two formats as inputs:

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

    This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
    tensors in the first argument of the model call function: `model(inputs)`.

    If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
    first positional argument :

    - a single Tensor with `input_ids` only and nothing else: `model(inputs_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})`

    </Tip>

    Parameters:
        config ([`DebertaV2Config`]): 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 [`DebertaV2Tokenizer`]. 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,
)
# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaModel with Deberta->DebertaV2
class TFDebertaV2Model(TFDebertaV2PreTrainedModel):
    def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.deberta = TFDebertaV2MainLayer(config, name="deberta")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
    ) -> 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

    def serving_output(self, output: TFBaseModelOutput) -> TFBaseModelOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)


@add_start_docstrings("""DeBERTa Model with a `language modeling` head on top.""", DEBERTA_START_DOCSTRING)
# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaForMaskedLM with Deberta->DebertaV2
class TFDebertaV2ForMaskedLM(TFDebertaV2PreTrainedModel, TFMaskedLanguageModelingLoss):
    def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        if config.is_decoder:
            logger.warning(
                "If you want to use `TFDebertaV2ForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )

        self.deberta = TFDebertaV2MainLayer(config, name="deberta")
        self.mlm = TFDebertaV2OnlyMLMHead(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(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
    ) -> 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,
        )

    def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)


@add_start_docstrings(
    """
    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,
)
# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaForSequenceClassification with Deberta->DebertaV2
class TFDebertaV2ForSequenceClassification(TFDebertaV2PreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.num_labels = config.num_labels

        self.deberta = TFDebertaV2MainLayer(config, name="deberta")
        self.pooler = TFDebertaV2ContextPooler(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 = TFDebertaV2StableDropout(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(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
    ) -> 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,
        )

    def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)


@add_start_docstrings(
    """
    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,
)
# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaForTokenClassification with Deberta->DebertaV2
class TFDebertaV2ForTokenClassification(TFDebertaV2PreTrainedModel, TFTokenClassificationLoss):
    def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.num_labels = config.num_labels

        self.deberta = TFDebertaV2MainLayer(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(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
    ) -> 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,
        )

    def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)


@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,
)
# Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaForQuestionAnswering with Deberta->DebertaV2
class TFDebertaV2ForQuestionAnswering(TFDebertaV2PreTrainedModel, TFQuestionAnsweringLoss):
    def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.num_labels = config.num_labels

        self.deberta = TFDebertaV2MainLayer(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(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
        end_positions: Optional[Union[np.ndarray, tf.Tensor]] = 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,
        )

    def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

        return TFQuestionAnsweringModelOutput(
            start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
        )
