# function/attack/attack_api_det.py
"""
Detection attack helpers - detection-specific attack interface for YOLOv7-like models.

(修改点：_save_adv_tensor 支持 ref_path，用于保持原图片的格式与尺寸；generate() 在保存时传入 ref_path)
"""
import os
from pathlib import Path
from typing import List, Optional, Tuple
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F

# Optional imports from yolov7 utils (letterbox / nms). If missing, use simple fallback.
try:
    from utils.datasets import letterbox
    from utils.general import non_max_suppression
    _HAS_YOLO_UTILS = True
except Exception:
    _HAS_YOLO_UTILS = False

# -------------------------
# Helpers: image <-> tensor
# -------------------------
def _letterbox_to_tensor(path: str, img_size: int = 640, device='cpu') -> Tuple[torch.Tensor, Tuple[int,int], Tuple[int,int,int]]:
    img0 = Image.open(path).convert('RGB')
    w0, h0 = img0.size

    if _HAS_YOLO_UTILS:
        np_img = np.array(img0)
        img_lb, ratio, (dw, dh) = letterbox(np_img, new_shape=img_size)
        img_arr = img_lb
        try:
            r = float(ratio) if ratio is not None else min(img_size / h0, img_size / w0)
        except:
            r = min(img_size / h0, img_size / w0)
    else:
        r = min(img_size / h0, img_size / w0)
        new_w, new_h = int(w0 * r), int(h0 * r)
        img_resized = img0.resize((new_w, new_h), Image.BILINEAR)
        canvas = Image.new('RGB', (img_size, img_size), (114, 114, 114))
        dw = (img_size - new_w) // 2
        dh = (img_size - new_h) // 2
        canvas.paste(img_resized, (dw, dh))
        img_arr = np.array(canvas)

    img_bgr = img_arr[:, :, ::-1].transpose(2, 0, 1).copy()
    tensor = torch.from_numpy(img_bgr).float().unsqueeze(0) / 255.0  # 1,C,H,W
    return tensor.to(device), (w0, h0), (int(dw) if 'dw' in locals() else 0, int(dh) if 'dh' in locals() else 0, float(r))

def _tensor_to_pil_rgb(tensor_bchw: torch.Tensor) -> Image.Image:
    arr = (tensor_bchw.detach().cpu().squeeze(0).permute(1, 2, 0).numpy() * 255.0).round().astype('uint8')  # HWC BGR
    arr = arr[:, :, ::-1]  # to RGB
    return Image.fromarray(arr)

# -------------------------
# DetectionEvasionAttacker
# -------------------------
class DetectionEvasionAttacker:
    def __init__(self, model: torch.nn.Module, device: str = 'cuda:0', nc: int = 5, img_size: int = 640, verbose: bool = False):
        self.device = torch.device(device if isinstance(device, str) else device)
        self.model = model.to(self.device)
        self.nc = int(nc)
        self.img_size = int(img_size)
        self.verbose = verbose

    def _model_dtype(self):
        try:
            return next(self.model.parameters()).dtype
        except StopIteration:
            return torch.float32

    def _align_tensor_dtype(self, x: torch.Tensor) -> torch.Tensor:
        x = x.to(self.device)
        target_dtype = self._model_dtype()
        if x.dtype != target_dtype:
            x = x.to(dtype=target_dtype)
        return x

    def _preds_forward(self, imgs: torch.Tensor) -> torch.Tensor:
        imgs = self._align_tensor_dtype(imgs)
        out = self.model(imgs)
        if isinstance(out, (tuple, list)):
            preds = out[0]
        else:
            preds = out
        if torch.is_tensor(preds):
            return preds
        try:
            return torch.stack(preds, dim=0).to(self.device)
        except Exception as e:
            raise RuntimeError(f"Cannot parse model(...) output as preds tensor: {e}")

    def _try_loss_forward(self, imgs: torch.Tensor, targets) -> torch.Tensor:
        imgs = self._align_tensor_dtype(imgs)
        out = self.model(imgs, targets)
        if isinstance(out, (tuple, list)) and len(out) >= 1 and torch.is_tensor(out[0]):
            return out[0]
        if isinstance(out, dict) and 'loss' in out:
            return out['loss']
        raise RuntimeError("Model does not return training loss via model(imgs, targets).")

    def _proxy_loss_from_preds(self, preds: torch.Tensor) -> torch.Tensor:
        if preds is None:
            return torch.tensor(0.0, device=self.device)
        if preds.ndim == 3 and preds.shape[-1] >= 6:
            obj = preds[..., 4].unsqueeze(-1)
            cls_scores = preds[..., 5:5 + self.nc]
            cls_prob = torch.softmax(cls_scores, dim=-1)
            per_box = obj * cls_prob
            per_image = per_box.sum(dim=1)
            loss = per_image.sum()
            return loss
        return preds.sum()

    def generate_fgsm_from_loss(self, imgs: torch.Tensor, targets, eps: float = 8/255.0) -> torch.Tensor:
        self.model.train()
        imgs = imgs.to(self.device)
        imgs = self._align_tensor_dtype(imgs)
        orig = imgs.detach().clone()
        adv = orig.clone().detach().requires_grad_(True)
        loss = self._try_loss_forward(adv, targets)
        loss.backward()
        grad = adv.grad.detach()
        adv = adv + eps * grad.sign()
        adv = torch.max(torch.min(adv, orig + eps), orig - eps)
        adv = torch.clamp(adv, 0.0, 1.0)
        return adv.detach()

    def generate_pgd_from_loss(self, imgs: torch.Tensor, targets, eps: float = 8/255.0, alpha: float = 2/255.0, iters: int = 10, random_start: bool = True) -> torch.Tensor:
        self.model.train()
        imgs = imgs.to(self.device)
        imgs = self._align_tensor_dtype(imgs)
        orig = imgs.detach().clone()
        if random_start:
            adv = orig + (torch.rand_like(orig) * 2 - 1) * eps
        else:
            adv = orig.clone()
        adv = torch.clamp(adv, 0.0, 1.0).detach().requires_grad_(True)
        for i in range(iters):
            if adv.grad is not None:
                adv.grad.zero_()
            loss = self._try_loss_forward(adv, targets)
            loss.backward()
            grad = adv.grad.detach()
            adv = adv + alpha * grad.sign()
            adv = torch.max(torch.min(adv, orig + eps), orig - eps)
            adv = torch.clamp(adv, 0.0, 1.0).detach().requires_grad_(True)
            if self.verbose and i % 5 == 0:
                try:
                    print(f"[PGD loss] iter {i} loss {loss.item():.4f}")
                except:
                    pass
        return adv.detach()

    def generate_fgsm_proxy(self, imgs: torch.Tensor, eps: float = 8/255.0) -> torch.Tensor:
        self.model.eval()
        imgs = imgs.to(self.device)
        imgs = self._align_tensor_dtype(imgs)
        orig = imgs.detach().clone()
        adv = orig.clone().detach().requires_grad_(True)
        preds = self._preds_forward(adv)
        loss = self._proxy_loss_from_preds(preds)
        loss.backward()
        grad = adv.grad.detach()
        adv = adv - eps * grad.sign()
        adv = torch.max(torch.min(adv, orig + eps), orig - eps)
        adv = torch.clamp(adv, 0.0, 1.0)
        return adv.detach()

    def generate_pgd_proxy(self, imgs: torch.Tensor, eps: float = 8/255.0, alpha: float = 2/255.0, iters: int = 10, random_start: bool = True) -> torch.Tensor:
        self.model.eval()
        imgs = imgs.to(self.device)
        imgs = self._align_tensor_dtype(imgs)
        orig = imgs.detach().clone()
        if random_start:
            adv = orig + (torch.rand_like(orig) * 2 - 1) * eps
        else:
            adv = orig.clone()
        adv = torch.clamp(adv, 0.0, 1.0).detach().requires_grad_(True)
        for i in range(iters):
            if adv.grad is not None:
                adv.grad.zero_()
            preds = self._preds_forward(adv)
            loss = self._proxy_loss_from_preds(preds)
            loss.backward()
            grad = adv.grad.detach()
            adv = adv - alpha * grad.sign()
            adv = torch.max(torch.min(adv, orig + eps), orig - eps)
            adv = torch.clamp(adv, 0.0, 1.0).detach().requires_grad_(True)
            if self.verbose and i % 5 == 0:
                try:
                    print(f"[PGD proxy] iter {i} loss {loss.item():.4f}")
                except:
                    pass
        return adv.detach()

    # -------------------------
    # Batch helpers & generate API
    # -------------------------
    def _save_adv_tensor(self, tensor: torch.Tensor, out_path: str, ref_path: Optional[str] = None):
        """
        Save adv tensor (1,C,H,W) as an image.
        If ref_path is provided, mimic ref image's size & format (e.g., JPEG 448x416).
        """
        pil = _tensor_to_pil_rgb(tensor)  # PIL RGB
        # If a reference image provided, try to read its format/size/mode
        target_size = None
        target_format = None
        try:
            if ref_path is not None and os.path.exists(ref_path):
                ref_im = Image.open(ref_path)
                target_format = ref_im.format  # e.g., 'JPEG' or 'PNG'
                target_size = ref_im.size  # (W,H)
        except Exception:
            target_format = None
            target_size = None

        if target_size is not None:
            # resize to reference size (width, height)
            pil = pil.resize(target_size, Image.BILINEAR)

        # Ensure directory exists
        os.makedirs(os.path.dirname(out_path), exist_ok=True)
        # Decide save format by reference or by extension
        ext = Path(out_path).suffix.lower()
        fmt = None
        if target_format:
            fmt = target_format.upper()
        else:
            if ext in ('.jpg', '.jpeg'):
                fmt = 'JPEG'
            elif ext in ('.png',):
                fmt = 'PNG'
            else:
                fmt = 'PNG'

        # Save accordingly
        if fmt == 'JPEG':
            pil.save(out_path, format='JPEG', quality=95)
        else:
            pil.save(out_path, format=fmt)

    def generate(self, method: str = "FGSM",
             img_paths: Optional[List[str]] = None,
             source_folder: Optional[str] = None,
             save_path: Optional[str] = None,
             batch: int = 4,
             img_size: int = None,
             use_loss: bool = False,
             targets_list: Optional[List] = None,
             **kwargs):
        """
        General generate API supporting multiple methods and per-method kwargs.

        Parameters of interest in kwargs:
        - eps (float)
        - alpha (float)
        - iters/int (int)
        - random_start (bool)
        - norm ('inf','2','1')
        - other method-specific params forwarded.

        img_paths: if provided, use this list (preferred).
        source_folder: fallback to read all images from folder.
        """
        if img_paths is None:
            if source_folder:
                p = Path(source_folder)
                img_paths = sorted([str(x) for x in p.glob('*') if x.suffix.lower() in ('.jpg','.jpeg','.png')])
            else:
                raise ValueError("Either img_paths or source_folder must be provided")
        # clip / cast
        if img_size is None:
            img_size = self.img_size
        else:
            img_size = int(img_size)

        method_key = method.upper()
        # default args
        eps = float(kwargs.get('eps', kwargs.get('epsilon', 8/255.0)))
        alpha = float(kwargs.get('alpha', 2/255.0))
        iters = int(kwargs.get('iters', kwargs.get('steps', 10)))
        random_start = bool(kwargs.get('random_start', True))
        norm = str(kwargs.get('norm', 'inf')).lower()

        # Prepare image list
        if len(img_paths) == 0:
            raise ValueError("No images to attack")
        piclist = []
        advs = []

        # helper to chunk
        for i in range(0, len(img_paths), batch):
            batch_paths = img_paths[i:i + batch]
            batch_tensors = []
            for pth in batch_paths:
                t, _, _ = _letterbox_to_tensor(pth, img_size=img_size, device=self.device)
                t = t.float()
                batch_tensors.append(t.squeeze(0))
            x = torch.stack(batch_tensors, dim=0)  # B,C,H,W

            # Choose implementation
            try:
                if method_key in ("FGSM", "FFGSM"):
                    if use_loss and targets_list:
                        adv = self.generate_fgsm_from_loss(x, targets_list[i:i+batch], eps=eps)
                    else:
                        adv = self.generate_fgsm_proxy(x, eps=eps)
                elif method_key in ("PGD", "PGDL", "BIM", "IFGSM", "I-FGSM", "BASIC_ITERATIVE"):
                    # PGD-like attacks: use proxy or loss versions
                    if use_loss and targets_list:
                        adv = self.generate_pgd_from_loss(x, targets_list[i:i+batch], eps=eps, alpha=alpha, iters=iters, random_start=random_start)
                    else:
                        adv = self.generate_pgd_proxy(x, eps=eps, alpha=alpha, iters=iters, random_start=random_start)
                elif method_key in ("CW", "C&W", "CARLINI", "CARLINI-WAGNER"):
                    try:
                        import torchattacks
                        atk = torchattacks.CW(self.model, c=kwargs.get('c', 1e-3), kappa=kwargs.get('kappa', 0), steps=kwargs.get('cw_steps', 1000))
                        adv = atk(x, torch.zeros(x.size(0), dtype=torch.long, device=self.device))
                    except Exception as e:
                        raise RuntimeError("C&W attack requires 'torchattacks' package or custom implementation. "
                                        "Fallback: use PGD-from-loss for detection models. Error: " + str(e))
                elif method_key in ("DEEPLF", "DEEPOF", "DEEPLFOOL", "DEEPFOOL"):
                    try:
                        import torchattacks
                        atk = torchattacks.DeepFool(self.model)
                        adv = atk(x, torch.zeros(x.size(0), dtype=torch.long, device=self.device))
                    except Exception as e:
                        raise RuntimeError("DeepFool requires 'torchattacks' or custom impl. Fallback use PGD-from-loss.")
                elif method_key in ("AUTOATTACK", "AUTO"):
                    try:
                        import torchattacks
                        atk = torchattacks.AutoAttack(self.model)
                        adv = atk(x, torch.zeros(x.size(0), dtype=torch.long, device=self.device))
                    except Exception as e:
                        raise RuntimeError("AutoAttack requires 'torchattacks' package. Fallback: use PGD.")
                else:
                    # default fallback: PGD proxy
                    if use_loss and targets_list:
                        adv = self.generate_pgd_from_loss(x, targets_list[i:i+batch], eps=eps, alpha=alpha, iters=iters, random_start=random_start)
                    else:
                        adv = self.generate_pgd_proxy(x, eps=eps, alpha=alpha, iters=iters, random_start=random_start)
            except Exception as e:
                raise RuntimeError(f"Attack generation failed for method {method_key} on batch starting at {i}: {e}")

            # save adv images if requested (preserve ref)
            for bi, pth in enumerate(batch_paths):
                tensor_single = adv[bi:bi + 1]  # 1,C,H,W
                if save_path:
                    orig_ext = Path(pth).suffix.lower()
                    fname = Path(pth).stem + f"_adv_{method_key}" + orig_ext
                    outp = os.path.join(save_path, fname)
                    # Save adversarial image
                    self._save_adv_tensor(tensor_single, outp, ref_path=pth)
                    piclist.append(outp)

                    # Calculate and save the perturbation to /jjs_tmp/perturbation/
                    orig_tensor, _, _ = _letterbox_to_tensor(pth, img_size=img_size, device=self.device)
                    perturbation = tensor_single - orig_tensor  # Calculate perturbation
                    perturbation_dir = "jjs_tmp/perturbation/"  # Specify the directory
                    os.makedirs(perturbation_dir, exist_ok=True)  # Ensure directory exists
                    perturbation_path = os.path.join(perturbation_dir, f"{Path(pth).stem}_perturbation.bin")
                    perturbation.detach().cpu().numpy().tofile(perturbation_path)
                    #np.save(perturbation_path, perturbation.detach().cpu().numpy())  # Save perturbation as .npy
                advs.append(tensor_single.detach().cpu().numpy())

        adv_np = np.concatenate(advs, axis=0) if advs else np.zeros((0,3,img_size,img_size), dtype=np.float32)
        return adv_np, None, piclist