import torch
from torch import nn, einsum

from einops import rearrange, repeat
from einops.layers.torch 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.):
        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.):
        super().__init__()
        inner_dim = dim_head *  heads
        self.heads = heads
        self.scale = dim_head ** -0.5

        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        b, n, _, h = *x.shape, self.heads
        qkv = self.to_qkv(x).chunk(3, dim = -1)
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)

        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale

        attn = dots.softmax(dim=-1)

        out = einsum('b h i j, b h j d -> b h i d', attn, v)
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        self.norm = nn.LayerNorm(dim)
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
            ]))

    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x
        return self.norm(x)

class STAM(nn.Module):
    def __init__(
        self,
        *,
        dim,
        image_size,
        patch_size,
        num_frames,
        num_classes,
        space_depth,
        space_heads,
        space_mlp_dim,
        time_depth,
        time_heads,
        time_mlp_dim,
        space_dim_head = 64,
        time_dim_head = 64,
        dropout = 0.,
        emb_dropout = 0.
    ):
        super().__init__()
        assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
        num_patches = (image_size // patch_size) ** 2
        patch_dim = 3 * patch_size ** 2

        self.to_patch_embedding = nn.Sequential(
            Rearrange('b f c (h p1) (w p2) -> b f (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
            nn.Linear(patch_dim, dim),
        )

        self.pos_embedding = nn.Parameter(torch.randn(num_frames, num_patches + 1, dim))
        self.space_cls_token = nn.Parameter(torch.randn(1, dim))
        self.time_cls_token = nn.Parameter(torch.randn(1, dim))
        self.dropout = nn.Dropout(emb_dropout)

        self.space_transformer = Transformer(dim, space_depth, space_heads, space_dim_head, space_mlp_dim, dropout)
        self.time_transformer = Transformer(dim, time_depth, time_heads, time_dim_head, time_mlp_dim, dropout)
        self.mlp_head = nn.Linear(dim, num_classes)

    def forward(self, video):
        x = self.to_patch_embedding(video)
        b, f, *_ = x.shape

        # concat space CLS tokens

        space_cls_tokens = repeat(self.space_cls_token, 'n d -> b f n d', b = b, f = f)
        x = torch.cat((space_cls_tokens, x), dim = -2)

        # positional embedding

        x += self.pos_embedding
        x = self.dropout(x)

        # space attention

        x = rearrange(x, 'b f ... -> (b f) ...')
        x = self.space_transformer(x)
        x = rearrange(x[:, 0], '(b f) ... -> b f ...', b = b)  # select CLS token out of each frame

        # concat time CLS tokens

        time_cls_tokens = repeat(self.time_cls_token, 'n d -> b n d', b = b)
        x = torch.cat((time_cls_tokens, x), dim = -2)

        # time attention

        x = self.time_transformer(x)

        # final mlp

        return self.mlp_head(x[:, 0])
