# Copyright 2021-2022 The Alibaba DAMO NLP Team Authors.
# Copyright 2020 Microsoft and the Hugging Face Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch DeBERTa-v2 model."""

from collections.abc import Sequence
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import LayerNorm
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import softmax_backward_data

from modelscope.metainfo import Models
from modelscope.models import Model, TorchModel
from modelscope.models.builder import MODELS
from modelscope.outputs import AttentionBackboneModelOutput
from modelscope.utils import logger as logging
from modelscope.utils.constant import Tasks
from .configuration import DebertaV2Config

logger = logging.get_logger(__name__)


# Copied from transformers.models.deberta.modeling_deberta.ContextPooler
class ContextPooler(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.pooler_hidden_size,
                               config.pooler_hidden_size)
        self.dropout = StableDropout(config.pooler_dropout)
        self.config = config

    def forward(self, hidden_states):
        # 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)
        pooled_output = self.dense(context_token)
        pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)
        return pooled_output

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


# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2
class XSoftmax(torch.autograd.Function):
    """
    Masked Softmax which is optimized for saving memory

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

    Example:

    ```python
    >>> import torch
    >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax

    >>> # Make a tensor
    >>> x = torch.randn([4, 20, 100])

    >>> # Create a mask
    >>> mask = (x > 0).int()

    >>> # Specify the dimension to apply softmax
    >>> dim = -1

    >>> y = XSoftmax.apply(x, mask, dim)
    ```"""

    @staticmethod
    def forward(self, input, mask, dim):
        self.dim = dim
        rmask = ~(mask.to(torch.bool))

        output = input.masked_fill(rmask,
                                   torch.tensor(torch.finfo(input.dtype).min))
        output = torch.softmax(output, self.dim)
        output.masked_fill_(rmask, 0)
        self.save_for_backward(output)
        return output

    @staticmethod
    def backward(self, grad_output):
        (output, ) = self.saved_tensors
        inputGrad = softmax_backward_data(self, grad_output, output, self.dim,
                                          output)
        return inputGrad, None, None

    @staticmethod
    def symbolic(g, self, mask, dim):
        import torch.onnx.symbolic_helper as sym_help
        from torch.onnx.symbolic_opset9 import masked_fill, softmax

        mask_cast_value = g.op(
            'Cast', mask, to_i=sym_help.cast_pytorch_to_onnx['Long'])
        r_mask = g.op(
            'Cast',
            g.op('Sub',
                 g.op('Constant', value_t=torch.tensor(1, dtype=torch.int64)),
                 mask_cast_value),
            to_i=sym_help.cast_pytorch_to_onnx['Byte'],
        )
        output = masked_fill(
            g, self, r_mask,
            g.op(
                'Constant',
                value_t=torch.tensor(torch.finfo(self.type().dtype()).min)))
        output = softmax(g, output, dim)
        return masked_fill(
            g, output, r_mask,
            g.op('Constant', value_t=torch.tensor(0, dtype=torch.uint8)))


# Copied from transformers.models.deberta.modeling_deberta.DropoutContext
class DropoutContext(object):

    def __init__(self):
        self.dropout = 0
        self.mask = None
        self.scale = 1
        self.reuse_mask = True


# Copied from transformers.models.deberta.modeling_deberta.get_mask
def get_mask(input, local_context):
    if not isinstance(local_context, DropoutContext):
        dropout = local_context
        mask = None
    else:
        dropout = local_context.dropout
        dropout *= local_context.scale
        mask = local_context.mask if local_context.reuse_mask else None

    if dropout > 0 and mask is None:
        mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(
            torch.bool)

    if isinstance(local_context, DropoutContext):
        if local_context.mask is None:
            local_context.mask = mask

    return mask, dropout


# Copied from transformers.models.deberta.modeling_deberta.XDropout
class XDropout(torch.autograd.Function):
    """Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""

    @staticmethod
    def forward(ctx, input, local_ctx):
        mask, dropout = get_mask(input, local_ctx)
        ctx.scale = 1.0 / (1 - dropout)
        if dropout > 0:
            ctx.save_for_backward(mask)
            return input.masked_fill(mask, 0) * ctx.scale
        else:
            return input

    @staticmethod
    def backward(ctx, grad_output):
        if ctx.scale > 1:
            (mask, ) = ctx.saved_tensors
            return grad_output.masked_fill(mask, 0) * ctx.scale, None
        else:
            return grad_output, None

    @staticmethod
    def symbolic(g: torch._C.Graph, input: torch._C.Value,
                 local_ctx: Union[float, DropoutContext]) -> torch._C.Value:
        from torch.onnx import symbolic_opset12

        dropout_p = local_ctx
        if isinstance(local_ctx, DropoutContext):
            dropout_p = local_ctx.dropout
        # StableDropout only calls this function when training.
        train = True
        # TODO: We should check if the opset_version being used to export
        # is > 12 here, but there's no good way to do that. As-is, if the
        # opset_version < 12, export will fail with a CheckerError.
        # Once https://github.com/pytorch/pytorch/issues/78391 is fixed, do something like:
        # if opset_version < 12:
        #   return torch.onnx.symbolic_opset9.dropout(g, input, dropout_p, train)
        return symbolic_opset12.dropout(g, input, dropout_p, train)


# Copied from transformers.models.deberta.modeling_deberta.StableDropout
class StableDropout(nn.Module):
    """
    Optimized dropout module for stabilizing the training

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

    def __init__(self, drop_prob):
        super().__init__()
        self.drop_prob = drop_prob
        self.count = 0
        self.context_stack = None

    def forward(self, x):
        """
        Call the module

        Args:
            x (`torch.tensor`): The input tensor to apply dropout
        """
        if self.training and self.drop_prob > 0:
            return XDropout.apply(x, self.get_context())
        return x

    def clear_context(self):
        self.count = 0
        self.context_stack = None

    def init_context(self, reuse_mask=True, scale=1):
        if self.context_stack is None:
            self.context_stack = []
        self.count = 0
        for c in self.context_stack:
            c.reuse_mask = reuse_mask
            c.scale = scale

    def get_context(self):
        if self.context_stack is not None:
            if self.count >= len(self.context_stack):
                self.context_stack.append(DropoutContext())
            ctx = self.context_stack[self.count]
            ctx.dropout = self.drop_prob
            self.count += 1
            return ctx
        else:
            return self.drop_prob


# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm
class DebertaV2SelfOutput(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
        self.dropout = StableDropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2
class DebertaV2Attention(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.self = DisentangledSelfAttention(config)
        self.output = DebertaV2SelfOutput(config)
        self.config = config

    def forward(
        self,
        hidden_states,
        attention_mask,
        output_attentions=False,
        query_states=None,
        relative_pos=None,
        rel_embeddings=None,
    ):
        self_output = self.self(
            hidden_states,
            attention_mask,
            output_attentions,
            query_states=query_states,
            relative_pos=relative_pos,
            rel_embeddings=rel_embeddings,
        )
        if output_attentions:
            self_output, att_matrix = self_output
        if query_states is None:
            query_states = hidden_states
        attention_output = self.output(self_output, query_states)

        if output_attentions:
            return (attention_output, att_matrix)
        else:
            return attention_output


# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2
class DebertaV2Intermediate(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm
class DebertaV2Output(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
        self.dropout = StableDropout(config.hidden_dropout_prob)
        self.config = config

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2
class DebertaV2Layer(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.attention = DebertaV2Attention(config)
        self.intermediate = DebertaV2Intermediate(config)
        self.output = DebertaV2Output(config)

    def forward(
        self,
        hidden_states,
        attention_mask,
        query_states=None,
        relative_pos=None,
        rel_embeddings=None,
        output_attentions=False,
    ):
        attention_output = self.attention(
            hidden_states,
            attention_mask,
            output_attentions=output_attentions,
            query_states=query_states,
            relative_pos=relative_pos,
            rel_embeddings=rel_embeddings,
        )
        if output_attentions:
            attention_output, att_matrix = attention_output
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        if output_attentions:
            return (layer_output, att_matrix)
        else:
            return layer_output


class ConvLayer(nn.Module):

    def __init__(self, config):
        super().__init__()
        kernel_size = getattr(config, 'conv_kernel_size', 3)
        groups = getattr(config, 'conv_groups', 1)
        self.conv_act = getattr(config, 'conv_act', 'tanh')
        self.conv = nn.Conv1d(
            config.hidden_size,
            config.hidden_size,
            kernel_size,
            padding=(kernel_size - 1) // 2,
            groups=groups)
        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
        self.dropout = StableDropout(config.hidden_dropout_prob)
        self.config = config

    def forward(self, hidden_states, residual_states, input_mask):
        out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(
            0, 2, 1).contiguous()
        rmask = (1 - input_mask).bool()
        out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)
        out = ACT2FN[self.conv_act](self.dropout(out))

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

        if input_mask is None:
            output_states = output
        else:
            if input_mask.dim() != layer_norm_input.dim():
                if input_mask.dim() == 4:
                    input_mask = input_mask.squeeze(1).squeeze(1)
                input_mask = input_mask.unsqueeze(2)

            input_mask = input_mask.to(output.dtype)
            output_states = output * input_mask

        return output_states


class DebertaV2Encoder(nn.Module):
    """Modified BertEncoder with relative position bias support"""

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

        self.layer = nn.ModuleList(
            [DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])
        self.relative_attention = getattr(config, 'relative_attention', False)

        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)
            pos_ebd_size = self.max_relative_positions * 2

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

            self.rel_embeddings = nn.Embedding(pos_ebd_size,
                                               config.hidden_size)

        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 = LayerNorm(
                config.hidden_size,
                config.layer_norm_eps,
                elementwise_affine=True)

        self.conv = ConvLayer(config) if getattr(config, 'conv_kernel_size',
                                                 0) > 0 else None
        self.gradient_checkpointing = False

    def get_rel_embedding(self):
        rel_embeddings = self.rel_embeddings.weight 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 attention_mask.dim() <= 2:
            extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
            attention_mask = extended_attention_mask * extended_attention_mask.squeeze(
                -2).unsqueeze(-1)
            attention_mask = attention_mask.byte()
        elif attention_mask.dim() == 3:
            attention_mask = attention_mask.unsqueeze(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 = query_states.size(
                -2) if query_states is not None else hidden_states.size(-2)
            relative_pos = build_relative_position(
                q,
                hidden_states.size(-2),
                bucket_size=self.position_buckets,
                max_position=self.max_relative_positions)
        return relative_pos

    def forward(
        self,
        hidden_states,
        attention_mask,
        output_hidden_states=True,
        output_attentions=False,
        query_states=None,
        relative_pos=None,
        return_dict=True,
    ):
        if attention_mask.dim() <= 2:
            input_mask = attention_mask
        else:
            input_mask = (attention_mask.sum(-2) > 0).byte()
        attention_mask = self.get_attention_mask(attention_mask)
        relative_pos = self.get_rel_pos(hidden_states, query_states,
                                        relative_pos)

        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

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

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):

                    def custom_forward(*inputs):
                        return module(*inputs, output_attentions)

                    return custom_forward

                output_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module),
                    next_kv,
                    attention_mask,
                    query_states,
                    relative_pos,
                    rel_embeddings,
                )
            else:
                output_states = layer_module(
                    next_kv,
                    attention_mask,
                    query_states=query_states,
                    relative_pos=relative_pos,
                    rel_embeddings=rel_embeddings,
                    output_attentions=output_attentions,
                )

            if output_attentions:
                output_states, att_m = output_states

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

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

            if output_attentions:
                all_attentions = all_attentions + (att_m, )

        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 BaseModelOutput(
            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 = torch.sign(relative_pos)
    mid = bucket_size // 2
    abs_pos = torch.where(
        (relative_pos < mid) & (relative_pos > -mid),
        torch.tensor(mid - 1).type_as(relative_pos),
        torch.abs(relative_pos),
    )
    log_pos = (
        torch.ceil(
            torch.log(abs_pos / mid)
            / torch.log(torch.tensor(
                (max_position - 1) / mid)) * (mid - 1)) + mid)
    bucket_pos = torch.where(abs_pos <= mid, relative_pos.type_as(log_pos),
                             log_pos * sign)
    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:
        `torch.LongTensor`: A tensor with shape [1, query_size, key_size]

    """
    q_ids = torch.arange(0, query_size)
    k_ids = torch.arange(0, key_size)
    rel_pos_ids = q_ids[:, None] - k_ids[None, :]
    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.to(torch.long)
    rel_pos_ids = rel_pos_ids[:query_size, :]
    rel_pos_ids = rel_pos_ids.unsqueeze(0)
    return rel_pos_ids


@torch.jit.script
# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand
def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
    return c2p_pos.expand([
        query_layer.size(0),
        query_layer.size(1),
        query_layer.size(2),
        relative_pos.size(-1)
    ])


@torch.jit.script
# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand
def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
    return c2p_pos.expand([
        query_layer.size(0),
        query_layer.size(1),
        key_layer.size(-2),
        key_layer.size(-2)
    ])


@torch.jit.script
# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand
def pos_dynamic_expand(pos_index, p2c_att, key_layer):
    return pos_index.expand(p2c_att.size()[:2]
                            + (pos_index.size(-2), key_layer.size(-2)))


class DisentangledSelfAttention(nn.Module):
    """
    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):
        super().__init__()
        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 = nn.Linear(
            config.hidden_size, self.all_head_size, bias=True)
        self.key_proj = nn.Linear(
            config.hidden_size, self.all_head_size, bias=True)
        self.value_proj = nn.Linear(
            config.hidden_size, self.all_head_size, 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 = StableDropout(config.hidden_dropout_prob)

            if not self.share_att_key:
                if 'c2p' in self.pos_att_type:
                    self.pos_key_proj = nn.Linear(
                        config.hidden_size, self.all_head_size, bias=True)
                if 'p2c' in self.pos_att_type:
                    self.pos_query_proj = nn.Linear(config.hidden_size,
                                                    self.all_head_size)

        self.dropout = StableDropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x, attention_heads):
        new_x_shape = x.size()[:-1] + (attention_heads, -1)
        x = x.view(new_x_shape)
        return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1),
                                                       x.size(-1))

    def forward(
        self,
        hidden_states,
        attention_mask,
        output_attentions=False,
        query_states=None,
        relative_pos=None,
        rel_embeddings=None,
    ):
        """
        Call the module

        Args:
            hidden_states (`torch.FloatTensor`):
                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 (`torch.ByteTensor`):
                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.

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

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

            relative_pos (`torch.LongTensor`):
                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 (`torch.FloatTensor`):
                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 = torch.sqrt(
            torch.tensor(query_layer.size(-1), dtype=torch.float)
            * scale_factor)
        attention_scores = torch.bmm(query_layer, key_layer.transpose(
            -1, -2)) / torch.tensor(
                scale, dtype=query_layer.dtype)
        if self.relative_attention:
            rel_embeddings = self.pos_dropout(rel_embeddings)
            rel_att = self.disentangled_attention_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 = attention_scores
        attention_scores = attention_scores.view(-1, self.num_attention_heads,
                                                 attention_scores.size(-2),
                                                 attention_scores.size(-1))

        # bsz x height x length x dimension
        attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)
        attention_probs = self.dropout(attention_probs)
        context_layer = torch.bmm(
            attention_probs.view(-1, attention_probs.size(-2),
                                 attention_probs.size(-1)), value_layer)
        context_layer = (
            context_layer.view(-1, self.num_attention_heads,
                               context_layer.size(-2),
                               context_layer.size(-1)).permute(0, 2, 1,
                                                               3).contiguous())
        new_context_layer_shape = context_layer.size()[:-2] + (-1, )
        context_layer = context_layer.view(new_context_layer_shape)
        if output_attentions:
            return (context_layer, attention_probs)
        else:
            return context_layer

    def disentangled_attention_bias(self, query_layer, key_layer, relative_pos,
                                    rel_embeddings, scale_factor):
        if relative_pos is None:
            q = query_layer.size(-2)
            relative_pos = build_relative_position(
                q,
                key_layer.size(-2),
                bucket_size=self.position_buckets,
                max_position=self.max_relative_positions)
        if relative_pos.dim() == 2:
            relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
        elif relative_pos.dim() == 3:
            relative_pos = relative_pos.unsqueeze(1)
        # bsz x height x query x key
        elif relative_pos.dim() != 4:
            raise ValueError(
                f'Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}'
            )

        att_span = self.pos_ebd_size
        relative_pos = relative_pos.long().to(query_layer.device)

        rel_embeddings = rel_embeddings[0:att_span * 2, :].unsqueeze(0)
        if self.share_att_key:
            pos_query_layer = self.transpose_for_scores(
                self.query_proj(rel_embeddings),
                self.num_attention_heads).repeat(
                    query_layer.size(0) // self.num_attention_heads, 1, 1)
            pos_key_layer = self.transpose_for_scores(
                self.key_proj(rel_embeddings),
                self.num_attention_heads).repeat(
                    query_layer.size(0) // self.num_attention_heads, 1, 1)
        else:
            if 'c2p' in self.pos_att_type:
                pos_key_layer = self.transpose_for_scores(
                    self.pos_key_proj(rel_embeddings),
                    self.num_attention_heads).repeat(
                        query_layer.size(0) // self.num_attention_heads, 1,
                        1)  # .split(self.all_head_size, dim=-1)
            if 'p2c' in self.pos_att_type:
                pos_query_layer = self.transpose_for_scores(
                    self.pos_query_proj(rel_embeddings),
                    self.num_attention_heads).repeat(
                        query_layer.size(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 = torch.sqrt(
                torch.tensor(pos_key_layer.size(-1), dtype=torch.float)
                * scale_factor)
            c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2))
            c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)
            c2p_att = torch.gather(
                c2p_att,
                dim=-1,
                index=c2p_pos.squeeze(0).expand([
                    query_layer.size(0),
                    query_layer.size(1),
                    relative_pos.size(-1)
                ]),
            )
            score += c2p_att / torch.tensor(scale, dtype=c2p_att.dtype)

        # position->content
        if 'p2c' in self.pos_att_type:
            scale = torch.sqrt(
                torch.tensor(pos_query_layer.size(-1), dtype=torch.float)
                * scale_factor)
            if key_layer.size(-2) != query_layer.size(-2):
                r_pos = build_relative_position(
                    key_layer.size(-2),
                    key_layer.size(-2),
                    bucket_size=self.position_buckets,
                    max_position=self.max_relative_positions,
                ).to(query_layer.device)
                r_pos = r_pos.unsqueeze(0)
            else:
                r_pos = relative_pos

            p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
            p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2))
            p2c_att = torch.gather(
                p2c_att,
                dim=-1,
                index=p2c_pos.squeeze(0).expand([
                    query_layer.size(0),
                    key_layer.size(-2),
                    key_layer.size(-2)
                ]),
            ).transpose(-1, -2)
            score += p2c_att / torch.tensor(scale, dtype=p2c_att.dtype)

        return score


# Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm
class DebertaV2Embeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, config):
        super().__init__()
        pad_token_id = getattr(config, 'pad_token_id', 0)
        self.embedding_size = getattr(config, 'embedding_size',
                                      config.hidden_size)
        self.word_embeddings = nn.Embedding(
            config.vocab_size, self.embedding_size, padding_idx=pad_token_id)

        self.position_biased_input = getattr(config, 'position_biased_input',
                                             True)
        if not self.position_biased_input:
            self.position_embeddings = None
        else:
            self.position_embeddings = nn.Embedding(
                config.max_position_embeddings, self.embedding_size)

        if config.type_vocab_size > 0:
            self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
                                                      self.embedding_size)

        if self.embedding_size != config.hidden_size:
            self.embed_proj = nn.Linear(
                self.embedding_size, config.hidden_size, bias=False)
        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
        self.dropout = StableDropout(config.hidden_dropout_prob)
        self.config = config

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer(
            'position_ids',
            torch.arange(config.max_position_embeddings).expand((1, -1)))

    def forward(self,
                input_ids=None,
                token_type_ids=None,
                position_ids=None,
                mask=None,
                inputs_embeds=None):
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]

        if token_type_ids is None:
            token_type_ids = torch.zeros(
                input_shape, dtype=torch.long, device=self.position_ids.device)

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if self.position_embeddings is not None:
            position_embeddings = self.position_embeddings(position_ids.long())
        else:
            position_embeddings = torch.zeros_like(inputs_embeds)

        embeddings = inputs_embeds
        if self.position_biased_input:
            embeddings += position_embeddings
        if self.config.type_vocab_size > 0:
            token_type_embeddings = self.token_type_embeddings(token_type_ids)
            embeddings += token_type_embeddings

        if self.embedding_size != self.config.hidden_size:
            embeddings = self.embed_proj(embeddings)

        embeddings = self.LayerNorm(embeddings)

        if mask is not None:
            if mask.dim() != embeddings.dim():
                if mask.dim() == 4:
                    mask = mask.squeeze(1).squeeze(1)
                mask = mask.unsqueeze(2)
            mask = mask.to(embeddings.dtype)

            embeddings = embeddings * mask

        embeddings = self.dropout(embeddings)
        return embeddings


# Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2
class DebertaV2PreTrainedModel(TorchModel, PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = DebertaV2Config
    base_model_prefix = 'deberta'
    _keys_to_ignore_on_load_missing = ['position_ids']
    _keys_to_ignore_on_load_unexpected = ['position_embeddings']
    supports_gradient_checkpointing = True

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

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(
                mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(
                mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, DebertaV2Encoder):
            module.gradient_checkpointing = value

    @classmethod
    def _instantiate(cls, **kwargs):
        model_dir = kwargs.pop('model_dir', None)
        if model_dir is None:
            ponet_config = DebertaV2Config(**kwargs)
            model = cls(ponet_config)
        else:
            model = super(
                Model,
                cls).from_pretrained(pretrained_model_name_or_path=model_dir)
        return model


@MODELS.register_module(Tasks.backbone, module_name=Models.deberta_v2)
# Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2
class DebertaV2Model(DebertaV2PreTrainedModel):
    """The bare DeBERTa_v2 Model transformer outputting raw hidden-states without any specific head on top.

    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 PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    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.
    """

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

        self.embeddings = DebertaV2Embeddings(config)
        self.encoder = DebertaV2Encoder(config)
        self.z_steps = 0
        self.config = config
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, new_embeddings):
        self.embeddings.word_embeddings = new_embeddings

    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(
            'The prune function is not implemented in DeBERTa model.')

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, AttentionBackboneModelOutput]:
        r"""
        Args:
        input_ids (`torch.LongTensor` of shape `('batch_size, sequence_length')`):
            Indices of input sequence tokens in the vocabulary.

        attention_mask (`torch.FloatTensor` of shape `('batch_size, sequence_length')`, *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**.

        token_type_ids (`torch.LongTensor` of shape `('batch_size, sequence_length')`, *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.

        position_ids (`torch.LongTensor` of shape `('batch_size, sequence_length')`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

        inputs_embeds (`torch.FloatTensor` of shape `('batch_size, sequence_length', 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 dataclass instead of a plain tuple.

        Returns:
            Returns `modelscope.outputs.AttentionBackboneModelOutput`

        Examples:
            >>> from modelscope.models import Model
            >>> from modelscope.preprocessors import Preprocessor
            >>> model = Model.from_pretrained('damo/nlp_debertav2_fill-mask_chinese-lite', task='backbone')
            >>> preprocessor = Preprocessor.from_pretrained('damo/nlp_debertav2_fill-mask_chinese-lite')
            >>> print(model(**preprocessor('这是个测试')))
        """

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else
            self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        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 = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError(
                'You have to specify either input_ids or inputs_embeds')

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(
                input_shape, dtype=torch.long, device=device)

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

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask,
            output_hidden_states=True,
            output_attentions=output_attentions,
            return_dict=return_dict,
        )
        encoded_layers = encoder_outputs[1]

        if self.z_steps > 1:
            hidden_states = encoded_layers[-2]
            layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
            query_states = encoded_layers[-1]
            rel_embeddings = self.encoder.get_rel_embedding()
            attention_mask = self.encoder.get_attention_mask(attention_mask)
            rel_pos = self.encoder.get_rel_pos(embedding_output)
            for layer in layers[1:]:
                query_states = layer(
                    hidden_states,
                    attention_mask,
                    output_attentions=False,
                    query_states=query_states,
                    relative_pos=rel_pos,
                    rel_embeddings=rel_embeddings,
                )
                encoded_layers.append(query_states)

        sequence_output = encoded_layers[-1]

        if not return_dict:
            return (sequence_output, ) + encoder_outputs[
                (1 if output_hidden_states else 2):]

        return AttentionBackboneModelOutput(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states
            if output_hidden_states else None,
            attentions=encoder_outputs.attentions,
        )
