import torch
import torch.nn as nn
from einops import rearrange


class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn

    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs)


class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout=0.0):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout),
        )

    def forward(self, x):
        return self.net(x)


class Attention(nn.Module):
    def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, selfatt=True):
        super().__init__()
        inner_dim = dim_head * heads
        project_out = not (heads == 1 and dim_head == dim)

        self.heads = heads
        self.scale = dim_head**-0.5

        self.attend = nn.Softmax(dim=-1)
        if selfatt:
            self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
        else:
            self.to_q = nn.Linear(dim, inner_dim, bias=False)
            self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)

        self.to_out = (
            nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout))
            if project_out
            else nn.Identity()
        )

    def forward(self, x, z=None):
        if z is None:
            qkv = self.to_qkv(x).chunk(3, dim=-1)
        else:
            q = self.to_q(x)
            k, v = self.to_kv(z).chunk(2, dim=-1)
            qkv = (q, k, v)

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), qkv)

        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale

        attn = self.attend(dots)

        out = torch.matmul(attn, v)
        out = rearrange(out, "b h n d -> b n (h d)")

        vis = True
        weights = attn if vis else None
        return self.to_out(out), weights


class Transformer(nn.Module):
    def __init__(
        self,
        dim=2048,
        depth=2,
        heads=12,
        dim_head=64,
        mlp_dim=1536,
        dropout=0.0,
        selfatt=True,
    ):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                nn.ModuleList(
                    [
                        PreNorm(
                            dim,
                            Attention(
                                dim,
                                heads=heads,
                                dim_head=dim_head,
                                dropout=dropout,
                                selfatt=selfatt,
                            ),
                        ),
                        PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout)),
                    ]
                )
            )

    def forward(self, x, z=None):
        attn_weights = []
        for attn, ff in self.layers:
            out, weights = attn(x, z=z)
            attn_weights.append(weights)
            x = out + x
            x = ff(x) + x

        return x
