from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor

from astrai.inference.core.cache import KvcacheView
from astrai.model.components.linear import Linear
from astrai.model.components.norm import RMSNorm
from astrai.model.components.rope import apply_rotary_emb


def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
    bs, slen, n_heads, head_dim = x.shape
    if n_rep == 1:
        return x
    return (
        x[:, :, :, None, :]
        .expand(bs, slen, n_heads, n_rep, head_dim)
        .reshape(bs, slen, n_heads * n_rep, head_dim)
    )


class GQA(nn.Module):
    def __init__(
        self,
        dim: int,
        n_heads: int,
        n_kv_heads: int,
        use_qk_norm: bool,
        norm_eps: float,
        use_gated_attention: bool,
        layer_id: int,
    ):
        super().__init__()
        assert dim % n_heads == 0
        assert n_heads % n_kv_heads == 0

        self.head_dim = dim // n_heads
        self.layer_id = layer_id
        self.dim = dim
        self.n_heads = n_heads
        self.n_kv_heads = n_kv_heads
        self.n_rep = n_heads // n_kv_heads
        self.use_qk_norm = use_qk_norm
        self.use_gated_attention = use_gated_attention

        self.q_proj = Linear(dim, n_heads * self.head_dim)
        self.k_proj = Linear(dim, n_kv_heads * self.head_dim)
        self.v_proj = Linear(dim, n_kv_heads * self.head_dim)
        self.o_proj = Linear(dim, dim)

        if self.use_qk_norm:
            self.q_norm = RMSNorm(self.head_dim, norm_eps)
            self.k_norm = RMSNorm(self.head_dim, norm_eps)

        if self.use_gated_attention:
            self.gate = Linear(dim, dim)

    def _split_heads(self, x: Tensor, n_heads) -> Tensor:
        batch_size, seq_len, _ = x.shape
        x = x.reshape(batch_size, seq_len, n_heads, self.head_dim)
        return x

    def forward(
        self,
        x: Tensor,
        rotary_emb: Tensor,
        attn_mask: Tensor = None,
        paged_cache: Optional[KvcacheView] = None,
    ) -> Tensor:
        is_causal = attn_mask is None

        q = self._split_heads(self.q_proj(x), self.n_heads)
        k = self._split_heads(self.k_proj(x), self.n_kv_heads)
        v = self._split_heads(self.v_proj(x), self.n_kv_heads)
        q, k = apply_rotary_emb(q, rotary_emb), apply_rotary_emb(k, rotary_emb)

        if self.use_qk_norm:
            q, k = self.q_norm(q), self.k_norm(k)

        if paged_cache is not None:
            paged_cache.write(self.layer_id, k, v)
            k, v = paged_cache.gather(self.layer_id)

        k, v = repeat_kv(k, self.n_rep), repeat_kv(v, self.n_rep)

        q, k, v = q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3)
        sdqa_out = (
            F.scaled_dot_product_attention(q, k, v, attn_mask, is_causal=is_causal)
            .permute(0, 2, 1, 3)
            .contiguous()
            .flatten(2)
        )

        if self.use_gated_attention:
            sdqa_out = sdqa_out * F.sigmoid(self.gate(x))

        out = self.o_proj(sdqa_out)
        return out


class MLA(nn.Module):
    def __init__(
        self,
        dim: int,
        n_heads: int,
        n_kv_heads: int,
        kv_lora_rank: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        norm_eps: float,
        use_gated_attention: bool,
        layer_id: int,
    ):
        super().__init__()
        self.dim = dim
        self.n_heads = n_heads
        self.n_kv_heads = n_kv_heads
        self.kv_lora_rank = kv_lora_rank
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.head_dim = qk_nope_head_dim + qk_rope_head_dim
        self.layer_id = layer_id
        self.n_rep = n_heads // n_kv_heads
        self.use_gated_attention = use_gated_attention

        self.q_proj = Linear(dim, n_heads * self.head_dim, bias=False)
        self.kv_a_proj = Linear(dim, kv_lora_rank, bias=False)
        self.kv_norm = RMSNorm(kv_lora_rank, norm_eps)

        self.kv_b_proj = Linear(
            kv_lora_rank,
            n_kv_heads * (self.head_dim + qk_rope_head_dim + self.head_dim),
        )

        self.o_proj = Linear(dim, dim, bias=False)

        if use_gated_attention:
            self.gate = Linear(dim, dim, bias=False)

    def forward(
        self,
        x: Tensor,
        rotary_emb: Tensor,
        attn_mask: Tensor = None,
        paged_cache: Optional[KvcacheView] = None,
    ) -> Tensor:
        bsz, seq_len, _ = x.size()
        is_causal = attn_mask is None

        q = self.q_proj(x)
        q = q.view(bsz, seq_len, self.n_heads, self.head_dim)

        kv_compressed = self.kv_a_proj(x)
        kv_compressed = self.kv_norm(kv_compressed)

        kv = self.kv_b_proj(kv_compressed)
        kv = kv.view(bsz, seq_len, self.n_kv_heads, -1)

        k_nope, k_rope, v = torch.split(
            kv, [self.qk_nope_head_dim, self.qk_rope_head_dim, self.head_dim], dim=-1
        )

        q_nope, q_rope = (
            q[..., : self.qk_nope_head_dim],
            q[..., self.qk_rope_head_dim :],
        )
        q_rope = apply_rotary_emb(q_rope, rotary_emb)
        k_rope = apply_rotary_emb(k_rope, rotary_emb)

        q = torch.cat([q_nope, q_rope], dim=-1)
        k = torch.cat([k_nope, k_rope], dim=-1)

        if paged_cache is not None:
            paged_cache.write(self.layer_id, k, v)
            k, v = paged_cache.gather(self.layer_id)

        q = q.permute(0, 2, 1, 3)
        k = k.permute(0, 2, 1, 3)
        v = v.permute(0, 2, 1, 3)

        attn_out = F.scaled_dot_product_attention(
            q, k, v, attn_mask, is_causal=is_causal
        )
        attn_out = attn_out.permute(0, 2, 1, 3).contiguous().flatten(2)

        if self.use_gated_attention:
            attn_out = attn_out * F.sigmoid(self.gate(x))

        out = self.o_proj(attn_out)
        return out