// Copyright 2020, Microsoft and the HuggingFace Inc. team.
// Copyright 2022 Guillaume Becquin
// 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.
//
// Derived from rust-bert (https://github.com/guillaume-be/rust-bert)

use super::common::{XDropout, x_softmax};
use super::config::{DebertaConfig, PositionAttentionType, PositionAttentionTypes};
use anyhow::{Result, bail};
use std::borrow::Borrow;
use tch::nn::{Module, Path};
use tch::{Device, Kind, Tensor, nn};

// ---------------------------------------------------------------------------
// BaseDebertaLayerNorm trait
// ---------------------------------------------------------------------------

pub trait BaseDebertaLayerNorm {
    fn new<'p, P>(p: P, size: i64, variance_epsilon: f64) -> Self
    where
        P: Borrow<nn::Path<'p>>;
}

impl BaseDebertaLayerNorm for nn::LayerNorm {
    fn new<'p, P>(p: P, size: i64, variance_epsilon: f64) -> Self
    where
        P: Borrow<Path<'p>>,
    {
        let layer_norm_config = nn::LayerNormConfig {
            eps: variance_epsilon,
            ..Default::default()
        };
        nn::layer_norm(p, vec![size], layer_norm_config)
    }
}

// ---------------------------------------------------------------------------
// DisentangledSelfAttention trait
// ---------------------------------------------------------------------------

pub trait DisentangledSelfAttention {
    fn new<'p, P>(p: P, config: &DebertaConfig) -> Self
    where
        P: Borrow<nn::Path<'p>>;

    fn forward_t(
        &self,
        hidden_states: &Tensor,
        attention_mask: &Tensor,
        query_states: Option<&Tensor>,
        relative_pos: Option<&Tensor>,
        relative_embeddings: Option<&Tensor>,
        train: bool,
    ) -> Result<(Tensor, Option<Tensor>)>;
}

// ---------------------------------------------------------------------------
// DebertaSelfOutput
// ---------------------------------------------------------------------------

pub struct DebertaSelfOutput<LN: BaseDebertaLayerNorm + Module> {
    dense: nn::Linear,
    layer_norm: LN,
    dropout: XDropout,
}

impl<LN: BaseDebertaLayerNorm + Module> DebertaSelfOutput<LN> {
    pub fn new<'p, P>(p: P, config: &DebertaConfig) -> DebertaSelfOutput<LN>
    where
        P: Borrow<nn::Path<'p>>,
    {
        let p = p.borrow();
        let dense = nn::linear(
            p / "dense",
            config.hidden_size,
            config.hidden_size,
            Default::default(),
        );
        let layer_norm = LN::new(
            p / "LayerNorm",
            config.hidden_size,
            config.layer_norm_eps.unwrap_or(1e-7),
        );
        let dropout = XDropout::new(config.hidden_dropout_prob);
        DebertaSelfOutput {
            dense,
            layer_norm,
            dropout,
        }
    }

    pub fn forward_t(&self, hidden_states: &Tensor, input_tensor: &Tensor, train: bool) -> Tensor {
        self.layer_norm.forward(
            &(hidden_states
                .apply(&self.dense)
                .apply_t(&self.dropout, train)
                + input_tensor),
        )
    }
}

// ---------------------------------------------------------------------------
// DebertaAttention
// ---------------------------------------------------------------------------

pub struct DebertaAttention<SA, LN>
where
    SA: DisentangledSelfAttention,
    LN: BaseDebertaLayerNorm + Module,
{
    self_attention: SA,
    self_output: DebertaSelfOutput<LN>,
}

impl<SA, LN> DebertaAttention<SA, LN>
where
    SA: DisentangledSelfAttention,
    LN: BaseDebertaLayerNorm + Module,
{
    pub fn new<'p, P>(p: P, config: &DebertaConfig) -> Self
    where
        P: Borrow<nn::Path<'p>>,
    {
        let p = p.borrow();
        let self_attention = SA::new(p / "self", config);
        let self_output = DebertaSelfOutput::new(p / "output", config);

        DebertaAttention {
            self_attention,
            self_output,
        }
    }

    pub fn forward_t(
        &self,
        hidden_states: &Tensor,
        attention_mask: &Tensor,
        query_states: Option<&Tensor>,
        relative_pos: Option<&Tensor>,
        relative_embeddings: Option<&Tensor>,
        train: bool,
    ) -> Result<(Tensor, Option<Tensor>)> {
        let (self_output, attention_matrix) = self.self_attention.forward_t(
            hidden_states,
            attention_mask,
            query_states,
            relative_pos,
            relative_embeddings,
            train,
        )?;

        let query_states = query_states.unwrap_or(hidden_states);

        Ok((
            self.self_output
                .forward_t(&self_output, query_states, train),
            attention_matrix,
        ))
    }
}

// ---------------------------------------------------------------------------
// V2 relative position helpers
// ---------------------------------------------------------------------------

pub fn make_log_bucket_position(
    relative_pos: &Tensor,
    bucket_size: i64,
    max_position: i64,
) -> Tensor {
    let sign = relative_pos.sign();
    let mid = bucket_size / 2;
    let abs_pos = relative_pos.abs().where_scalarother(
        &relative_pos
            .lt(mid)
            .logical_and(&relative_pos.gt(-mid))
            .logical_not(),
        mid - 1,
    );
    let log_pos = (((&abs_pos / mid).log() / (((max_position - 1) / mid) as f64).ln()) * (mid - 1))
        .ceil()
        + mid;
    relative_pos.where_self(
        &abs_pos.less_equal(mid),
        &(log_pos * sign).to_kind(Kind::Int64),
    )
}

pub fn build_relative_position(
    query_size: i64,
    key_size: i64,
    bucket_size: i64,
    max_position: i64,
    device: Device,
) -> Tensor {
    let q_ids = Tensor::arange(query_size, (Kind::Int64, device));
    let k_ids = Tensor::arange(key_size, (Kind::Int64, device));
    let mut rel_pos_ids = q_ids.unsqueeze(-1) - k_ids.tile([q_ids.size()[0], 1]);
    if (bucket_size > 0) & (max_position > 0) {
        rel_pos_ids = make_log_bucket_position(&rel_pos_ids, bucket_size, max_position);
    }
    rel_pos_ids.slice(0, 0, query_size, 1).unsqueeze(0)
}

// ---------------------------------------------------------------------------
// DebertaV2DisentangledSelfAttention
// ---------------------------------------------------------------------------

pub struct DebertaV2DisentangledSelfAttention {
    query_proj: nn::Linear,
    key_proj: nn::Linear,
    value_proj: nn::Linear,
    pos_key_proj: Option<nn::Linear>,
    pos_query_proj: Option<nn::Linear>,
    position_buckets: Option<i64>,
    pos_embed_size: Option<i64>,
    dropout: XDropout,
    num_attention_heads: i64,
    pos_att_type: PositionAttentionTypes,
    max_relative_positions: Option<i64>,
    pos_dropout: Option<XDropout>,
    output_attentions: bool,
}

impl DebertaV2DisentangledSelfAttention {
    fn transpose_for_scores(&self, x: &Tensor) -> Tensor {
        let mut new_shape = x.size();
        let _ = new_shape.pop();
        new_shape.extend_from_slice(&[self.num_attention_heads, -1]);
        let x = x.view(new_shape.as_slice());
        x.permute([0, 2, 1, 3])
            .contiguous()
            .view([-1, x.size()[1], *x.size().last().unwrap()])
    }

    fn disentangled_att_bias(
        &self,
        query_layer: &Tensor,
        key_layer: &Tensor,
        relative_pos: Option<&Tensor>,
        relative_embeddings: &Tensor,
        scale_factor: f64,
    ) -> Result<Tensor> {
        let mut key_layer_size = key_layer.size();
        key_layer_size.reverse();
        let mut query_layer_size = query_layer.size();
        query_layer_size.reverse();

        let calc_relative_pos = if relative_pos.is_none() {
            Some(build_relative_position(
                query_layer_size[1],
                key_layer_size[1],
                self.position_buckets.unwrap_or(-1),
                self.max_relative_positions.unwrap_or(-1),
                query_layer.device(),
            ))
        } else {
            None
        };
        let relative_pos = relative_pos.unwrap_or_else(|| calc_relative_pos.as_ref().unwrap());
        let relative_pos = match &relative_pos.dim() {
            2 => relative_pos.unsqueeze(0).unsqueeze(0),
            3 => relative_pos.unsqueeze(1),
            4 => relative_pos.shallow_clone(),
            _ => {
                bail!(
                    "Expected relative position of dimensions 2, 3 or 4, got {}",
                    relative_pos.dim()
                )
            }
        };

        // This method only gets called if relative attention is True
        let att_span = self.pos_embed_size.unwrap();
        let relative_embeddings = relative_embeddings
            .slice(0, 0, 2 * att_span, 1)
            .unsqueeze(0);

        let key_proj = self.pos_key_proj.as_ref().unwrap_or(&self.key_proj);
        let query_proj = self.pos_query_proj.as_ref().unwrap_or(&self.query_proj);

        let pos_query_layer = self
            .transpose_for_scores(&relative_embeddings.apply(query_proj))
            .repeat([query_layer.size()[0] / self.num_attention_heads, 1, 1]);
        let pos_key_layer = self
            .transpose_for_scores(&relative_embeddings.apply(key_proj))
            .repeat([query_layer.size()[0] / self.num_attention_heads, 1, 1]);

        let mut score = Tensor::zeros([1], (query_layer.kind(), query_layer.device()));

        let c2p_pos = if self.pos_att_type.has_type(PositionAttentionType::c2p)
            | self.pos_att_type.has_type(PositionAttentionType::p2p)
        {
            let scale = *pos_key_layer.size().last().unwrap() as f64 * scale_factor;
            let c2p_att = query_layer.bmm(&pos_key_layer.transpose(-1, -2));
            let c2p_pos = relative_pos.clamp(0, att_span * 2 - 1);
            let c2p_att = c2p_att.gather(
                -1,
                &c2p_pos.squeeze_dim(0).expand(
                    [
                        query_layer.size()[0],
                        query_layer.size()[1],
                        *relative_pos.size().last().unwrap(),
                    ],
                    true,
                ),
                false,
            );
            score = score + c2p_att / scale;
            Some(c2p_pos)
        } else {
            None
        };

        if self.pos_att_type.has_type(PositionAttentionType::p2c) {
            let scale = *pos_query_layer.size().last().unwrap() as f64 * scale_factor;
            let r_pos = if key_layer_size[1] != query_layer_size[1] {
                build_relative_position(
                    key_layer_size[1],
                    key_layer_size[1],
                    self.position_buckets.unwrap_or(-1),
                    self.max_relative_positions.unwrap_or(-1),
                    query_layer.device(),
                )
                .unsqueeze(0)
            } else {
                relative_pos.shallow_clone()
            };

            let p2c_pos = (-r_pos + att_span).clamp(0, 2 * att_span - 1);

            let p2c_att = key_layer
                .bmm(&pos_query_layer.transpose(-1, -2))
                .gather(
                    -1,
                    &p2c_pos.squeeze_dim(0).expand(
                        [query_layer.size()[0], key_layer_size[1], key_layer_size[1]],
                        true,
                    ),
                    false,
                )
                .transpose(-1, -2);
            score = score + p2c_att / scale;
        }

        if self.pos_att_type.has_type(PositionAttentionType::p2p) {
            let pos_query = pos_query_layer.slice(2, att_span, None, 1);
            let p2p_att = pos_query.matmul(&pos_key_layer.transpose(-1, -2));
            let mut expand_size = query_layer.size()[..2].to_vec();
            expand_size.append(&mut p2p_att.size().into_iter().skip(2).collect());
            let p2p_att = p2p_att.gather(
                -1,
                &c2p_pos.unwrap().expand(
                    [
                        query_layer.size()[0],
                        query_layer.size()[1],
                        query_layer.size()[2],
                        *relative_pos.size().last().unwrap(),
                    ],
                    true,
                ),
                false,
            );
            score = score + p2p_att;
        }

        Ok(score)
    }
}

impl DisentangledSelfAttention for DebertaV2DisentangledSelfAttention {
    fn new<'p, P>(p: P, config: &DebertaConfig) -> DebertaV2DisentangledSelfAttention
    where
        P: Borrow<nn::Path<'p>>,
    {
        let p = p.borrow();

        let num_attention_heads = config.num_attention_heads;
        let query_proj = nn::linear(
            p / "query_proj",
            config.hidden_size,
            config.hidden_size,
            Default::default(),
        );
        let key_proj = nn::linear(
            p / "key_proj",
            config.hidden_size,
            config.hidden_size,
            Default::default(),
        );
        let value_proj = nn::linear(
            p / "value_proj",
            config.hidden_size,
            config.hidden_size,
            Default::default(),
        );
        let share_attention_key = config.share_att_key.unwrap_or(false);
        let pos_att_type = config.pos_att_type.clone().unwrap_or_default();
        let relative_attention = config.relative_attention.unwrap_or(false);

        let (
            max_relative_positions,
            pos_dropout,
            pos_key_proj,
            pos_query_proj,
            position_buckets,
            pos_embed_size,
        ) = if relative_attention {
            let position_buckets = config.position_buckets.unwrap_or(-1);
            let mut max_relative_positions = config.max_relative_positions.unwrap_or(-1);
            if max_relative_positions < 1 {
                max_relative_positions = config.max_position_embeddings;
            }
            let pos_ebd_size = if position_buckets > 0 {
                position_buckets
            } else {
                max_relative_positions
            };
            let pos_dropout = Some(XDropout::new(config.hidden_dropout_prob));

            let (pos_key_proj, pos_query_proj) = if !share_attention_key {
                let pos_key_proj = if pos_att_type.has_type(PositionAttentionType::c2p)
                    | pos_att_type.has_type(PositionAttentionType::p2p)
                {
                    Some(nn::linear(
                        p / "pos_key_proj",
                        config.hidden_size,
                        config.hidden_size,
                        Default::default(),
                    ))
                } else {
                    None
                };
                let pos_query_proj = if pos_att_type.has_type(PositionAttentionType::p2c)
                    | pos_att_type.has_type(PositionAttentionType::p2p)
                {
                    Some(nn::linear(
                        p / "pos_query_proj",
                        config.hidden_size,
                        config.hidden_size,
                        Default::default(),
                    ))
                } else {
                    None
                };
                (pos_key_proj, pos_query_proj)
            } else {
                (None, None)
            };

            (
                Some(max_relative_positions),
                pos_dropout,
                pos_key_proj,
                pos_query_proj,
                Some(position_buckets),
                Some(pos_ebd_size),
            )
        } else {
            (None, None, None, None, None, None)
        };
        let dropout = XDropout::new(config.attention_probs_dropout_prob);

        let output_attentions = config.output_attentions.unwrap_or(false);

        DebertaV2DisentangledSelfAttention {
            query_proj,
            key_proj,
            num_attention_heads,
            pos_att_type,
            max_relative_positions,
            pos_dropout,
            dropout,
            output_attentions,
            value_proj,
            pos_key_proj,
            pos_query_proj,
            position_buckets,
            pos_embed_size,
        }
    }

    fn forward_t(
        &self,
        hidden_states: &Tensor,
        attention_mask: &Tensor,
        query_states: Option<&Tensor>,
        relative_pos: Option<&Tensor>,
        relative_embeddings: Option<&Tensor>,
        train: bool,
    ) -> Result<(Tensor, Option<Tensor>)> {
        let query_states = query_states.unwrap_or(hidden_states);

        let query_layer = self.transpose_for_scores(&query_states.apply(&self.query_proj));
        let key_layer = self.transpose_for_scores(&query_states.apply(&self.key_proj));
        let value_layer = self.transpose_for_scores(&query_states.apply(&self.value_proj));

        let mut scale_factor = 1;
        if self.pos_att_type.has_type(PositionAttentionType::c2p) {
            scale_factor += 1;
        }
        if self.pos_att_type.has_type(PositionAttentionType::p2c) {
            scale_factor += 1;
        }
        if self.pos_att_type.has_type(PositionAttentionType::p2p) {
            scale_factor += 1;
        }
        let scale = ((query_layer.size().last().unwrap() * scale_factor) as f64).sqrt();
        let mut attention_scores = query_layer.bmm(&key_layer.transpose(-1, -2)) / scale;

        if let (Some(pos_dropout), Some(rel_embeddings)) = (&self.pos_dropout, relative_embeddings)
        {
            let rel_embeddings = rel_embeddings.apply_t(pos_dropout, train);
            let rel_att = self.disentangled_att_bias(
                &query_layer,
                &key_layer,
                relative_pos,
                &rel_embeddings,
                scale_factor as f64,
            )?;
            attention_scores = attention_scores + rel_att;
        }
        let mut reverse_attention_scores_size = attention_scores.size();
        reverse_attention_scores_size.reverse();
        attention_scores = attention_scores.view([
            -1,
            self.num_attention_heads,
            reverse_attention_scores_size[1],
            reverse_attention_scores_size[0],
        ]);

        let attention_probs =
            x_softmax(&attention_scores, attention_mask, -1).apply_t(&self.dropout, train);

        let mut reverse_attention_probs_size = attention_probs.size();
        reverse_attention_probs_size.reverse();
        let context_layer = attention_probs
            .view([
                -1,
                reverse_attention_probs_size[1],
                reverse_attention_probs_size[0],
            ])
            .bmm(&value_layer);

        let mut reverse_context_layer_size = context_layer.size();
        reverse_context_layer_size.reverse();
        let context_layer = context_layer
            .view([
                -1,
                self.num_attention_heads,
                reverse_context_layer_size[1],
                reverse_context_layer_size[0],
            ])
            .permute([0, 2, 1, 3])
            .contiguous();

        let mut new_context_layer_shape = context_layer.size();
        let _ = new_context_layer_shape.pop();
        let _ = new_context_layer_shape.pop();
        new_context_layer_shape.push(-1);

        let context_layer = context_layer.view(new_context_layer_shape.as_slice());

        let attention_probs = if self.output_attentions {
            Some(attention_probs)
        } else {
            None
        };

        Ok((context_layer, attention_probs))
    }
}
