"""Multi-Head Attention layer definition."""

import math
import torch
import torch.nn as nn
from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
from typing import Tuple


class RelPositionMultiHeadedAttention(MultiHeadedAttention):
    """Multi-Head Attention layer with relative position encoding.
    Paper: https://arxiv.org/abs/1901.02860
    Args:
        n_head (int): The number of heads.
        n_feat (int): The number of features.
        dropout_rate (float): Dropout rate.
    """

    def __init__(
        self,
        n_head,
        n_feat,
        dropout_rate,
        do_rel_shift=False,
        adaptive_scale=False,
        init_weights=False,
    ):
        """Construct an RelPositionMultiHeadedAttention object."""
        super().__init__(n_head, n_feat, dropout_rate)
        # linear transformation for positional encoding
        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
        # these two learnable bias are used in matrix c and matrix d
        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
        self.do_rel_shift = do_rel_shift
        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
        torch.nn.init.xavier_uniform_(self.pos_bias_u)
        torch.nn.init.xavier_uniform_(self.pos_bias_v)
        self.adaptive_scale = adaptive_scale
        self.ada_scale = nn.Parameter(
            torch.ones([1, 1, n_feat]), requires_grad=adaptive_scale
        )
        self.ada_bias = nn.Parameter(
            torch.zeros([1, 1, n_feat]), requires_grad=adaptive_scale
        )
        if init_weights:
            self.init_weights()

    def init_weights(self):
        input_max = (self.h * self.d_k) ** -0.5
        torch.nn.init.uniform_(self.linear_q.weight, -input_max, input_max)
        torch.nn.init.uniform_(self.linear_q.bias, -input_max, input_max)
        torch.nn.init.uniform_(self.linear_k.weight, -input_max, input_max)
        torch.nn.init.uniform_(self.linear_k.bias, -input_max, input_max)
        torch.nn.init.uniform_(self.linear_v.weight, -input_max, input_max)
        torch.nn.init.uniform_(self.linear_v.bias, -input_max, input_max)
        torch.nn.init.uniform_(self.linear_pos.weight, -input_max, input_max)
        torch.nn.init.uniform_(self.linear_out.weight, -input_max, input_max)
        torch.nn.init.uniform_(self.linear_out.bias, -input_max, input_max)

    def rel_shift(self, x, zero_triu: bool = False):
        """Compute relative positinal encoding.
        Args:
            x (torch.Tensor): Input tensor (batch, time, size).
            zero_triu (bool): If true, return the lower triangular part of
                the matrix.
        Returns:
            torch.Tensor: Output tensor.
        """

        zero_pad = torch.zeros(
            (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype
        )
        x_padded = torch.cat([zero_pad, x], dim=-1)

        x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2))
        x = x_padded[:, :, 1:].view_as(x)

        if zero_triu:
            ones = torch.ones((x.size(2), x.size(3)))
            x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]

        return x

    def forward_attention(
        self,
        value: torch.Tensor,
        scores: torch.Tensor,
        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
    ) -> torch.Tensor:
        """Compute attention context vector.

        Args:
            value (torch.Tensor): Transformed value, size
                (#batch, n_head, time2, d_k).
            scores (torch.Tensor): Attention score, size
                (#batch, n_head, time1, time2).
            mask (torch.Tensor): Mask, size (#batch, 1, time2) or
                (#batch, time1, time2), (0, 0, 0) means fake mask.

        Returns:
            torch.Tensor: Transformed value (#batch, time1, d_model)
                weighted by the attention score (#batch, time1, time2).

        """
        n_batch = value.size(0)
        # NOTE(xcsong): When will `if mask.size(2) > 0` be True?
        #   1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
        #           1st chunk to ease the onnx export.]
        #   2. pytorch training
        if mask.size(2) > 0:  # time2 > 0
            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
            # For last chunk, time2 might be larger than scores.size(-1)
            mask = mask[:, :, :, : scores.size(-1)]  # (batch, 1, *, time2)
            scores = scores.masked_fill(mask, -float("inf"))
            # (batch, head, time1, time2)
            attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
        # NOTE(xcsong): When will `if mask.size(2) > 0` be False?
        #   1. onnx(16/-1, -1/-1, 16/0)
        #   2. jit (16/-1, -1/-1, 16/0, 16/4)
        else:
            attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)

        p_attn = self.dropout(attn)
        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
        x = (
            x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
        )  # (batch, time1, d_model)

        return self.linear_out(x)  # (batch, time1, d_model)

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
        pos_emb: torch.Tensor = torch.empty(0),
        cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
        Args:
            query (torch.Tensor): Query tensor (#batch, time1, size).
            key (torch.Tensor): Key tensor (#batch, time2, size).
            value (torch.Tensor): Value tensor (#batch, time2, size).
            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
                (#batch, time1, time2), (0, 0, 0) means fake mask.
            pos_emb (torch.Tensor): Positional embedding tensor
                (#batch, time2, size).
            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
                where `cache_t == chunk_size * num_decoding_left_chunks`
                and `head * d_k == size`
        Returns:
            torch.Tensor: Output tensor (#batch, time1, d_model).
            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
                where `cache_t == chunk_size * num_decoding_left_chunks`
                and `head * d_k == size`
        """
        if self.adaptive_scale:
            query = self.ada_scale * query + self.ada_bias
            key = self.ada_scale * key + self.ada_bias
            value = self.ada_scale * value + self.ada_bias
        q, k, v = self.forward_qkv(query, key, value)
        q = q.transpose(1, 2)  # (batch, time1, head, d_k)

        # NOTE(xcsong):
        #   when export onnx model, for 1st chunk, we feed
        #       cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
        #       or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
        #       In all modes, `if cache.size(0) > 0` will alwayse be `True`
        #       and we will always do splitting and
        #       concatnation(this will simplify onnx export). Note that
        #       it's OK to concat & split zero-shaped tensors(see code below).
        #   when export jit  model, for 1st chunk, we always feed
        #       cache(0, 0, 0, 0) since jit supports dynamic if-branch.
        # >>> a = torch.ones((1, 2, 0, 4))
        # >>> b = torch.ones((1, 2, 3, 4))
        # >>> c = torch.cat((a, b), dim=2)
        # >>> torch.equal(b, c)        # True
        # >>> d = torch.split(a, 2, dim=-1)
        # >>> torch.equal(d[0], d[1])  # True
        if cache.size(0) > 0:
            key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1)
            k = torch.cat([key_cache, k], dim=2)
            v = torch.cat([value_cache, v], dim=2)
        # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
        #   non-trivial to calculate `next_cache_start` here.
        new_cache = torch.cat((k, v), dim=-1)

        n_batch_pos = pos_emb.size(0)
        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
        p = p.transpose(1, 2)  # (batch, head, time1, d_k)

        # (batch, head, time1, d_k)
        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
        # (batch, head, time1, d_k)
        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)

        # compute attention score
        # first compute matrix a and matrix c
        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
        # (batch, head, time1, time2)
        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))

        # compute matrix b and matrix d
        # (batch, head, time1, time2)
        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
        # Remove rel_shift since it is useless in speech recognition,
        # and it requires special attention for streaming.
        if self.do_rel_shift:
            matrix_bd = self.rel_shift(matrix_bd)

        scores = (matrix_ac + matrix_bd) / math.sqrt(
            self.d_k
        )  # (batch, head, time1, time2)

        return self.forward_attention(v, scores, mask), new_cache
