import torch
import torch.nn as nn
import torchvision.models as models
from modelscope.msdatasets import MsDataset
from utils import MODEL_DIR


def get_backbone(ver, backbone_list):
    for bb in backbone_list:
        if ver == bb["ver"]:
            return bb

    print("Backbone name not found, using default option - alexnet.")
    return backbone_list[0]


def model_info(m_ver):
    backbone_list = MsDataset.load(
        "monetjoe/cv_backbones", split="train"
    )
    backbone = get_backbone(m_ver, backbone_list)
    m_type = str(backbone["type"])
    input_size = int(backbone["input_size"])
    return m_type, input_size


def Classifier(cls_num: int, output_size: int, linear_output: bool):
    q = (1.0 * output_size / cls_num) ** 0.25
    l1 = int(q * cls_num)
    l2 = int(q * l1)
    l3 = int(q * l2)

    if linear_output:
        return torch.nn.Sequential(
            nn.Dropout(),
            nn.Linear(output_size, l3),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(l3, l2),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(l2, l1),
            nn.ReLU(inplace=True),
            nn.Linear(l1, cls_num),
        )

    else:
        return torch.nn.Sequential(
            nn.Dropout(),
            nn.Conv2d(output_size, l3, kernel_size=(1, 1), stride=(1, 1)),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d(output_size=(1, 1)),
            nn.Flatten(),
            nn.Linear(l3, l2),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(l2, l1),
            nn.ReLU(inplace=True),
            nn.Linear(l1, cls_num),
        )


class EvalNet:
    model = None
    m_type = "squeezenet"
    input_size = 224
    output_size = 512

    def __init__(self, log_name: str, cls_num=4):
        saved_model_path = f"{MODEL_DIR}/{log_name}/save.pt"
        m_ver = "_".join(log_name.split("_")[:-1])
        self.m_type, self.input_size = model_info(m_ver)

        if not hasattr(models, m_ver):
            print("Unsupported model.")
            exit()

        self.model = eval("models.%s()" % m_ver)
        linear_output = self._set_outsize()
        self._set_classifier(cls_num, linear_output)
        checkpoint = torch.load(saved_model_path, map_location="cpu")
        if torch.cuda.is_available():
            checkpoint = torch.load(saved_model_path)

        self.model.load_state_dict(checkpoint, False)
        self.model.eval()

    def _set_outsize(self, debug_mode=False):
        for name, module in self.model.named_modules():
            if (
                str(name).__contains__("classifier")
                or str(name).__eq__("fc")
                or str(name).__contains__("head")
            ):
                if isinstance(module, torch.nn.Linear):
                    self.output_size = module.in_features
                    if debug_mode:
                        print(
                            f"{name}(Linear): {self.output_size} -> {module.out_features}"
                        )
                    return True

                if isinstance(module, torch.nn.Conv2d):
                    self.output_size = module.in_channels
                    if debug_mode:
                        print(
                            f"{name}(Conv2d): {self.output_size} -> {module.out_channels}"
                        )
                    return False

        return False

    def _set_classifier(self, cls_num, linear_output):
        if self.m_type == "convnext":
            del self.model.classifier[2]
            self.model.classifier = nn.Sequential(
                *list(self.model.classifier)
                + list(Classifier(cls_num, self.output_size, linear_output))
            )
            return

        if hasattr(self.model, "classifier"):
            self.model.classifier = Classifier(cls_num, self.output_size, linear_output)
            return

        elif hasattr(self.model, "fc"):
            self.model.fc = Classifier(cls_num, self.output_size, linear_output)
            return

        elif hasattr(self.model, "head"):
            self.model.head = Classifier(cls_num, self.output_size, linear_output)
            return

        self.model.heads.head = Classifier(cls_num, self.output_size, linear_output)

    def forward(self, x):
        if torch.cuda.is_available():
            x = x.cuda()
            self.model = self.model.cuda()

        if self.m_type == "googlenet" and self.training:
            return self.model(x)[0]
        else:
            return self.model(x)