import torch
import torch.nn as nn
# from spikingjelly.clock_driven.neuron import MultiStepLIFNode
# from spikingjelly.clock_driven import layer, surrogate
from timm.models.layers import to_2tuple, trunc_normal_, DropPath
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
# from einops.layers.torch import Rearrange
import torch.nn.functional as F
from functools import partial
from spiking_layer import LIF, MixedLIF

__all__ = ['spikformer']

class MLP(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_func_lif=LIF, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1_conv = nn.Conv1d(in_features, hidden_features, kernel_size=1, stride=1)
        self.fc1_bn = nn.BatchNorm1d(hidden_features)
        self.fc1_lif = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')

        self.fc2_conv = nn.Conv1d(hidden_features, out_features, kernel_size=1, stride=1)
        self.fc2_bn = nn.BatchNorm1d(out_features)
        self.fc2_lif = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')
        self.c_hidden = hidden_features
        self.c_output = out_features
    def forward(self, x):
        T,B,C,N = x.shape
        x = self.fc1_conv(x.flatten(0,1))
        x = self.fc1_bn(x).reshape(T,B,self.c_hidden,N).contiguous()
        x = self.fc1_lif(x)

        x = self.fc2_conv(x.flatten(0,1))
        x = self.fc2_bn(x).reshape(T,B,C,N).contiguous()
        x = self.fc2_lif(x)
        return x

class SSA(nn.Module):
    def __init__(self, dim, num_heads=8, act_func_lif=LIF, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
        self.dim = dim
        self.num_heads = num_heads
        self.scale = 0.25

        self.q_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1,bias=False)

        self.q_bn = nn.BatchNorm1d(dim)
        self.q_lif = act_func_lif(tau=4.0, detach_reset=True, backend='cupy')

        self.k_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1,bias=False)
        self.k_bn = nn.BatchNorm1d(dim)
        self.k_lif = act_func_lif(tau=4.0, detach_reset=True, backend='cupy')

        self.v_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1,bias=False)
        self.v_bn = nn.BatchNorm1d(dim)
        self.v_lif = act_func_lif(tau=4.0, detach_reset=True, backend='cupy')

        self.attn_drop = nn.Dropout(0.2)
        self.res_lif = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')
        self.attn_lif = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')

        self.proj_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1)
        self.proj_bn = nn.BatchNorm1d(dim)
        self.proj_lif = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')

    def forward(self, x):
        T, B, C, N = x.shape
        x_for_qkv = x.flatten(0, 1)
        q_conv_out = self.q_conv(x_for_qkv)
        q_conv_out = self.q_bn(q_conv_out).reshape(T,B,C,N).contiguous()
        q_conv_out = self.q_lif(q_conv_out)
        q = q_conv_out.transpose(-1, -2).reshape(T, B, N, self.num_heads, C//self.num_heads).permute(0, 1, 3, 2, 4).contiguous()

        k_conv_out = self.k_conv(x_for_qkv)
        k_conv_out = self.k_bn(k_conv_out).reshape(T,B,C,N).contiguous()
        k_conv_out = self.k_lif(k_conv_out)
        k = k_conv_out.transpose(-1, -2).reshape(T, B, N, self.num_heads, C//self.num_heads).permute(0, 1, 3, 2, 4).contiguous()

        v_conv_out = self.v_conv(x_for_qkv)
        v_conv_out = self.v_bn(v_conv_out).reshape(T,B,C,N).contiguous()
        v_conv_out = self.v_lif(v_conv_out)
        v = v_conv_out.transpose(-1, -2).reshape(T, B, N, self.num_heads, C//self.num_heads).permute(0, 1, 3, 2, 4).contiguous()

        attn = (q @ k.transpose(-2, -1))
        x = (attn @ v) * self.scale

        x = x.transpose(3, 4).reshape(T, B, C, N).contiguous()
        x = self.attn_lif(x)
        x = x.flatten(0,1)
        x = self.proj_lif(self.proj_bn(self.proj_conv(x)).reshape(T,B,C,N))

        return x


class Block(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, act_func_lif=LIF, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = SSA(dim, num_heads=num_heads, act_func_lif=act_func_lif, qkv_bias=qkv_bias, qk_scale=qk_scale,
                              attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_func_lif=act_func_lif, drop=drop)

    def forward(self, x):
        x = x + self.attn(x)
        x = x + (self.mlp(x))
        return x


class SPS(nn.Module):
    def __init__(self, img_size_h=128, img_size_w=128, patch_size=4, in_channels=2, embed_dims=256, act_func_lif=LIF):
        super().__init__()
        self.image_size = [img_size_h, img_size_w]
        patch_size = to_2tuple(patch_size)
        self.patch_size = patch_size
        self.C = in_channels
        self.H, self.W = self.image_size[0] // patch_size[0], self.image_size[1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj_conv = nn.Conv2d(in_channels, embed_dims//8, kernel_size=3, stride=1, padding=1, bias=False)
        self.proj_bn = nn.BatchNorm2d(embed_dims//8)
        self.proj_lif = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')
        # self.maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

        self.proj_conv1 = nn.Conv2d(embed_dims//8, embed_dims//4, kernel_size=3, stride=1, padding=1, bias=False)
        self.proj_bn1 = nn.BatchNorm2d(embed_dims//4)
        self.proj_lif1 = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')
        # self.maxpool1 = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

        self.proj_conv2 = nn.Conv2d(embed_dims//4, embed_dims//2, kernel_size=3, stride=1, padding=1, bias=False)
        self.proj_bn2 = nn.BatchNorm2d(embed_dims//2)
        self.proj_lif2 = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')
        self.maxpool2 = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

        self.proj_conv3 = nn.Conv2d(embed_dims//2, embed_dims, kernel_size=3, stride=1, padding=1, bias=False)
        self.proj_bn3 = nn.BatchNorm2d(embed_dims)
        self.proj_lif3 = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')
        self.maxpool3 = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

        self.rpe_conv = nn.Conv2d(embed_dims, embed_dims, kernel_size=3, stride=1, padding=1, bias=False)
        self.rpe_bn = nn.BatchNorm2d(embed_dims)
        self.rpe_lif = act_func_lif(tau=4.0, v_threshold=0.5, detach_reset=True, backend='cupy')
    def forward(self, x):
        T, B, C, H, W = x.shape
        x = self.proj_conv(x.flatten(0, 1)) # have some fire value
        x = self.proj_bn(x).reshape(T,B,-1,H,W).contiguous()
        x = self.proj_lif(x).flatten(0,1).contiguous()
        # x = self.maxpool(x)

        x = self.proj_conv1(x)
        x = self.proj_bn1(x).reshape(T, B, -1, H, W).contiguous()
        x = self.proj_lif1(x).flatten(0, 1).contiguous()
        # x = self.maxpool1(x)

        x = self.proj_conv2(x)
        x = self.proj_bn2(x).reshape(T, B, -1, H, W).contiguous()
        x = self.proj_lif2(x).flatten(0, 1).contiguous()
        x = self.maxpool2(x)

        x = self.proj_conv3(x)
        x = self.proj_bn3(x).reshape(T, B, -1, H//2, W//2).contiguous()
        x = self.proj_lif3(x).flatten(0, 1).contiguous()
        x = self.maxpool3(x)

        x_rpe = self.rpe_bn(self.rpe_conv(x)).reshape(T,B,-1,H//4,W//4).contiguous()
        x_rpe = self.rpe_lif(x_rpe).flatten(0,1)
        x = x + x_rpe
        x = x.reshape(T, B, -1, (H//4)*(H//4)).contiguous()

        return x

class Spikformer(nn.Module):
    def __init__(self,
                 pretrained=False, pretrained_cfg=None,
                 img_size_h=128, img_size_w=128, patch_size=16, in_channels=2, num_classes=11,
                 embed_dims=[64, 128, 256], num_heads=[1, 2, 4], mlp_ratios=[4, 4, 4], qkv_bias=False, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, act_func_lif=LIF,
                 depths=[6, 8, 6], sr_ratios=[8, 4, 2], **kwargs
                 ):
        super().__init__()
        self.num_classes = num_classes
        self.depths = depths

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depths)]  # stochastic depth decay rule

        patch_embed = SPS(img_size_h=img_size_h,
                                 img_size_w=img_size_w,
                                 patch_size=patch_size,
                                 in_channels=in_channels,
                                 embed_dims=embed_dims,
                                 act_func_lif=act_func_lif)
        num_patches = patch_embed.num_patches
        pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dims))
        block = nn.ModuleList([Block(
            dim=embed_dims, num_heads=num_heads, mlp_ratio=mlp_ratios, qkv_bias=qkv_bias,
            qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[j],
            norm_layer=norm_layer, act_func_lif=act_func_lif, sr_ratio=sr_ratios)
            for j in range(depths)])

        setattr(self, f"patch_embed", patch_embed)
        setattr(self, f"pos_embed", pos_embed)
        setattr(self, f"block", block)

        self.norm = norm_layer(embed_dims)
        # classification head 这里不需要脉冲，因为输入的是在T时长平均发射值
        self.head = nn.Linear(embed_dims, num_classes) if num_classes > 0 else nn.Identity()

        pos_embed = getattr(self, f"pos_embed")
        trunc_normal_(pos_embed, std=.02)
        self.apply(self._init_weights)

    @torch.jit.ignore
    def _get_pos_embed(self, pos_embed, patch_embed, H, W):
        if H * W == self.patch_embed1.num_patches:
            return pos_embed
        else:
            return F.interpolate(
                pos_embed.reshape(1, patch_embed.H, patch_embed.W, -1).permute(0, 3, 1, 2),
                size=(H, W), mode="bilinear").reshape(1, -1, H * W).permute(0, 2, 1)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def forward_features(self, x):

        block = getattr(self, f"block")
        patch_embed = getattr(self, f"patch_embed")
        x = patch_embed(x)
        for blk in block:
            x = blk(x)
        return x.mean(3)

    def forward(self, x):
        x = x.permute(1, 0, 2, 3, 4)  # [T, N, 2, *, *]  T, N, C, H, W
        x_f = self.forward_features(x)
        x = self.head(x_f.mean(0))
        return x, x_f


@register_model
def spikformer(pretrained=False, **kwargs):
    model = Spikformer(
        img_size_h=48, img_size_w=48,
        patch_size=4, embed_dims=256, num_heads=16, mlp_ratios=4,
        in_channels=2, num_classes=10, qkv_bias=False,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=2, sr_ratios=1,
        **kwargs
    )
    model.default_cfg = _cfg()
    return model
