import torch
from torch import nn
from torchvision import models


# 无融合模型
class MOAFNoFusion(nn.Module):
    def __init__(self):
        super().__init__()
        shuff = models.shufflenet_v2_x0_5(weights="DEFAULT")
        self.features = nn.Sequential(
            shuff.conv1, shuff.maxpool,
            shuff.stage2, shuff.stage3,
            shuff.stage4, shuff.conv5
        )
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.regressor = nn.Sequential(
            nn.Flatten(),
            nn.Linear(1024, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 1)
        )

    def forward(self, image, params):
        x = self.features(image)
        x = self.avgpool(x)
        x = self.regressor(x)
        return x


# 参数嵌入模型
class ParamEmbedding(nn.Module):
    def __init__(self, in_dim=3, hidden_dim=64, out_dim=128):
        super().__init__()
        self.embedding = nn.Sequential(
            nn.Linear(in_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, out_dim),
            nn.ReLU(),
            nn.LayerNorm(out_dim)
        )

    def forward(self, params):
        # min-max 归一化参数
        normalized_params = torch.stack([
            (params[:, 0] - 10.0) / 90.0,
            params[:, 1] / 1.25,
            (params[:, 2] - 1.0) / 0.5
        ], dim=1)
        return self.embedding(normalized_params)


# FiLM 融合块
class FiLMBlock(nn.Module):
    def __init__(self, param_emb_dim=128, feat_channels=128):
        super().__init__()
        self.gamma_gen = nn.Linear(param_emb_dim, feat_channels)
        self.beta_gen = nn.Linear(param_emb_dim, feat_channels)

    def forward(self, feature_map, param_emb):
        gamma = self.gamma_gen(param_emb).unsqueeze(-1).unsqueeze(-1)
        beta = self.beta_gen(param_emb).unsqueeze(-1).unsqueeze(-1)
        return feature_map * (1.0 + gamma) + beta


# 通道交叉注意力融合块
class ChannelCrossAttention(nn.Module):
    def __init__(self, param_emb_dim=128, feat_channels=128):
        super().__init__()
        self.param_emb_dim = param_emb_dim
        self.feat_channels = feat_channels
        self.hidden_dim = max(16, self.feat_channels // 4)

        self.global_pool = nn.AdaptiveAvgPool2d(1)

        self.bottle_neck = nn.Sequential(
            nn.Linear(self.param_emb_dim + self.feat_channels, self.hidden_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.hidden_dim, self.feat_channels),
            nn.Sigmoid()
        )

    def forward(self, feature_map, param_emb):
        b, c, h, w = feature_map.shape
        pooled = self.global_pool(feature_map).view(b, c)
        cat = torch.cat([pooled, param_emb], dim=1)
        weights = self.bottle_neck(cat)
        weights4d = weights.view(b, c, 1, 1)
        return feature_map * (1.0 + weights4d)


# SE块
class SEBlock(nn.Module):
    def __init__(self, feat_channels=128):
        super().__init__()
        self.feat_channels = feat_channels
        self.hidden_dim = max(16, self.feat_channels // 4)

        self.global_pool = nn.AdaptiveAvgPool2d(1)

        self.bottle_neck = nn.Sequential(
            nn.Linear(self.feat_channels, self.hidden_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.hidden_dim, self.feat_channels),
            nn.Sigmoid()
        )

    def forward(self, feature_map):
        b, c, h, w = feature_map.shape
        pooled = self.global_pool(feature_map).view(b, c)
        weights = self.bottle_neck(pooled)
        weights4d = weights.view(b, c, 1, 1)
        return feature_map * (1.0 + weights4d)


# 仅返回特征图的恒等变换
class FusionIdentity(nn.Module):
    def forward(self, feature_map, param_emb):
        return feature_map


# 使用 FiLM 融合的模型
class MOAFWithFiLM(nn.Module):
    def __init__(self, fusion_level=None):
        super().__init__()
        shuff = models.shufflenet_v2_x0_5(weights="DEFAULT")
        self.cbrm = nn.Sequential(shuff.conv1, shuff.maxpool)
        self.stage2 = shuff.stage2
        self.stage3 = shuff.stage3
        self.stage4 = shuff.stage4
        self.cbr2 = shuff.conv5

        self.param_embedding = ParamEmbedding()

        if fusion_level is None:
            fusion_level = [2]

        self.film_block0 = FiLMBlock(feat_channels=48) if 0 in fusion_level else FusionIdentity()
        self.film_block1 = FiLMBlock(feat_channels=96) if 1 in fusion_level else FusionIdentity()
        self.film_block2 = FiLMBlock(feat_channels=192) if 2 in fusion_level else FusionIdentity()

        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.regressor = nn.Sequential(
            nn.Flatten(),
            nn.Linear(1024, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 1)
        )

    def forward(self, image, params):
        x = self.cbrm(image)
        x = self.stage2(x)
        param_emb = self.param_embedding(params)
        x = self.film_block0(x, param_emb)
        x = self.stage3(x)
        x = self.film_block1(x, param_emb)
        x = self.stage4(x)
        x = self.film_block2(x, param_emb)
        x = self.cbr2(x)
        x = self.avgpool(x)
        x = self.regressor(x)
        return x


# 使用交叉注意力融合的模型
class MOAFWithChannelCrossAttention(nn.Module):
    def __init__(self, fusion_level=None):
        super().__init__()
        shuff = models.shufflenet_v2_x0_5(weights="DEFAULT")
        self.cbrm = nn.Sequential(shuff.conv1, shuff.maxpool)
        self.stage2 = shuff.stage2
        self.stage3 = shuff.stage3
        self.stage4 = shuff.stage4
        self.cbr2 = shuff.conv5

        self.param_embedding = ParamEmbedding()

        if fusion_level is None:
            fusion_level = [2]

        self.cca_block0 = ChannelCrossAttention(feat_channels=48) if 0 in fusion_level else FusionIdentity()
        self.cca_block1 = ChannelCrossAttention(feat_channels=96) if 1 in fusion_level else FusionIdentity()
        self.cca_block2 = ChannelCrossAttention(feat_channels=192) if 2 in fusion_level else FusionIdentity()

        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.regressor = nn.Sequential(
            nn.Flatten(),
            nn.Linear(1024, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 1)
        )

    def forward(self, image, params):
        x = self.cbrm(image)
        x = self.stage2(x)
        param_emb = self.param_embedding(params)
        x = self.cca_block0(x, param_emb)
        x = self.stage3(x)
        x = self.cca_block1(x, param_emb)
        x = self.stage4(x)
        x = self.cca_block2(x, param_emb)
        x = self.cbr2(x)
        x = self.avgpool(x)
        x = self.regressor(x)
        return x


# 使用 SE 块但无融合的模型
class MOAFWithSE(nn.Module):
    def __init__(self, fusion_level=None):
        super().__init__()
        shuff = models.shufflenet_v2_x0_5(weights="DEFAULT")
        self.cbrm = nn.Sequential(shuff.conv1, shuff.maxpool)
        self.stage2 = shuff.stage2
        self.stage3 = shuff.stage3
        self.stage4 = shuff.stage4
        self.cbr2 = shuff.conv5

        if fusion_level is None:
            fusion_level = [0]

        self.se_block0 = SEBlock(feat_channels=48) if 0 in fusion_level else nn.Identity()
        self.se_block1 = SEBlock(feat_channels=96) if 1 in fusion_level else nn.Identity()
        self.se_block2 = SEBlock(feat_channels=192) if 2 in fusion_level else nn.Identity()

        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.regressor = nn.Sequential(
            nn.Flatten(),
            nn.Linear(1024, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 1)
        )

    def forward(self, image, params):
        x = self.cbrm(image)
        x = self.stage2(x)
        x = self.se_block0(x)
        x = self.stage3(x)
        x = self.se_block1(x)
        x = self.stage4(x)
        x = self.se_block2(x)
        x = self.cbr2(x)
        x = self.avgpool(x)
        x = self.regressor(x)
        return x

# 多回归头模型
class MOAFWithMMLP(nn.Module):
    def __init__(self, num_lenses=7):
        super().__init__()
        shuff = models.shufflenet_v2_x0_5(weights="DEFAULT")
        self.features = nn.Sequential(
            shuff.conv1, shuff.maxpool,
            shuff.stage2, shuff.stage3,
            shuff.stage4, shuff.conv5
        )
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.num_lenses = num_lenses
        self.regressors = nn.ModuleList([
            self._create_regressor_head() for _ in range(num_lenses)
        ])

    def _create_regressor_head(self):
        return nn.Sequential(
            nn.Flatten(),
            nn.Linear(1024, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 1)
        )

    def _find_lens_id(self, params):
        batch_size = params.shape[0]
        lens_ids = []
        
        for i in range(batch_size):
            mag, na, rix = params[i][0].item(), params[i][1].item(), params[i][2].item()
            
            # 直接的条件匹配（类似于switch-case）
            if mag == 10 and na == 0.25 and rix == 1.0000:
                lens_ids.append(0)  # obj1
            elif mag == 10 and na == 0.30 and rix == 1.0000:
                lens_ids.append(1)  # obj2
            elif mag == 20 and na == 0.70 and rix == 1.0000:
                lens_ids.append(2)  # obj3
            elif mag == 20 and na == 0.80 and rix == 1.0000:
                lens_ids.append(3)  # obj4
            elif mag == 40 and na == 0.65 and rix == 1.0000:
                lens_ids.append(4)  # obj5
            elif mag == 100 and na == 0.80 and rix == 1.0000:
                lens_ids.append(5)  # obj6
            elif mag == 100 and na == 1.25 and rix == 1.4730:
                lens_ids.append(6)  # obj7
            else:
                lens_ids.append(0)
        
        return torch.tensor(lens_ids, dtype=torch.long, device=params.device)

    def forward(self, image, params):
        x = self.features(image)
        x = self.avgpool(x)
        
        lens_ids = self._find_lens_id(params)
        batch_size = params.size(0)
        outputs = []
        for i in range(batch_size):
            current_lens_id = lens_ids[i]
            # 确保lens_id在有效范围内
            if current_lens_id < 0 or current_lens_id >= self.num_lenses:
                current_lens_id = 0
            
            # 选择对应的回归头
            head_output = self.regressors[current_lens_id](x[i].unsqueeze(0))
            outputs.append(head_output)
        
        return torch.cat(outputs, dim=0)