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

CACHE_T = 2


class RMS_norm(nn.Module):
    def __init__(self, dim, channel_first=True, images=True, bias=False):
        super().__init__()
        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
        shape = (dim, *broadcastable_dims) if channel_first else (dim,)

        self.channel_first = channel_first
        self.scale = dim**0.5
        self.gamma = nn.Parameter(torch.ones(shape))
        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0

    def forward(self, x):
        return F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias


class CausalConv3d(nn.Conv3d):
    """
    Causal 3d convolusion.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._padding = (self.padding[2], self.padding[2], self.padding[1], self.padding[1], 2 * self.padding[0], 0)
        self.padding = (0, 0, 0)

    def forward(self, x, cache_x=None):
        padding = list(self._padding)
        if cache_x is not None and self._padding[4] > 0:
            cache_x = cache_x.to(x.device)
            # print(cache_x.shape, x.shape)
            x = torch.cat([cache_x, x], dim=2)
            padding[4] -= cache_x.shape[2]
            # print('cache!')
        x = F.pad(x, padding, mode="replicate")  # mode='replicate'
        # print(x[0,0,:,0,0])

        return super().forward(x)


class PixelShuffle3d(nn.Module):
    def __init__(self, ff, hh, ww):
        super().__init__()
        self.ff = ff
        self.hh = hh
        self.ww = ww

    def forward(self, x):
        # x: (B, C, F, H, W)
        return rearrange(x, "b c (f ff) (h hh) (w ww) -> b (c ff hh ww) f h w", ff=self.ff, hh=self.hh, ww=self.ww)


class Buffer_LQ4x_Proj(nn.Module):
    def __init__(self, in_dim, out_dim, layer_num=30):
        super().__init__()
        self.ff = 1
        self.hh = 16
        self.ww = 16
        self.hidden_dim1 = 2048
        self.hidden_dim2 = 3072
        self.layer_num = layer_num

        self.pixel_shuffle = PixelShuffle3d(self.ff, self.hh, self.ww)

        self.conv1 = CausalConv3d(in_dim * self.ff * self.hh * self.ww, self.hidden_dim1, (4, 3, 3), stride=(2, 1, 1), padding=(1, 1, 1))  # f -> f/2 h -> h w -> w
        self.norm1 = RMS_norm(self.hidden_dim1, images=False)
        self.act1 = nn.SiLU()

        self.conv2 = CausalConv3d(self.hidden_dim1, self.hidden_dim2, (4, 3, 3), stride=(2, 1, 1), padding=(1, 1, 1))  # f -> f/2 h -> h w -> w
        self.norm2 = RMS_norm(self.hidden_dim2, images=False)
        self.act2 = nn.SiLU()

        self.linear_layers = nn.ModuleList([nn.Linear(self.hidden_dim2, out_dim) for _ in range(layer_num)])

        self.clip_idx = 0

    def forward(self, video):
        self.clear_cache()
        # x: (B, C, F, H, W)

        t = video.shape[2]
        iter_ = 1 + (t - 1) // 4
        first_frame = video[:, :, :1, :, :].repeat(1, 1, 3, 1, 1)
        video = torch.cat([first_frame, video], dim=2)
        # print(video.shape)

        out_x = []
        for i in range(iter_):
            x = self.pixel_shuffle(video[:, :, i * 4 : (i + 1) * 4, :, :])
            cache1_x = x[:, :, -CACHE_T:, :, :].clone()
            self.cache["conv1"] = cache1_x
            x = self.conv1(x, self.cache["conv1"])
            x = self.norm1(x)
            x = self.act1(x)
            cache2_x = x[:, :, -CACHE_T:, :, :].clone()
            self.cache["conv2"] = cache2_x
            if i == 0:
                continue
            x = self.conv2(x, self.cache["conv2"])
            x = self.norm2(x)
            x = self.act2(x)
            out_x.append(x)
        out_x = torch.cat(out_x, dim=2)
        # print(out_x.shape)
        out_x = rearrange(out_x, "b c f h w -> b (f h w) c")
        outputs = []
        for i in range(self.layer_num):
            outputs.append(self.linear_layers[i](out_x))
        return outputs

    def clear_cache(self):
        self.cache = {}
        self.cache["conv1"] = None
        self.cache["conv2"] = None
        self.clip_idx = 0

    def stream_forward(self, video_clip):
        if self.clip_idx == 0:
            # self.clear_cache()
            first_frame = video_clip[:, :, :1, :, :].repeat(1, 1, 3, 1, 1)
            video_clip = torch.cat([first_frame, video_clip], dim=2)
            x = self.pixel_shuffle(video_clip)
            cache1_x = x[:, :, -CACHE_T:, :, :].clone()
            self.cache["conv1"] = cache1_x
            x = self.conv1(x, self.cache["conv1"])
            x = self.norm1(x)
            x = self.act1(x)
            cache2_x = x[:, :, -CACHE_T:, :, :].clone()
            self.cache["conv2"] = cache2_x
            self.clip_idx += 1
            return None
        else:
            x = self.pixel_shuffle(video_clip)
            cache1_x = x[:, :, -CACHE_T:, :, :].clone()
            self.cache["conv1"] = cache1_x
            x = self.conv1(x, self.cache["conv1"])
            x = self.norm1(x)
            x = self.act1(x)
            cache2_x = x[:, :, -CACHE_T:, :, :].clone()
            self.cache["conv2"] = cache2_x
            x = self.conv2(x, self.cache["conv2"])
            x = self.norm2(x)
            x = self.act2(x)
            out_x = rearrange(x, "b c f h w -> b (f h w) c")
            outputs = []
            for i in range(self.layer_num):
                outputs.append(self.linear_layers[i](out_x))
            self.clip_idx += 1
            return outputs