from __future__ import annotations

import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from skimage import transform
from skimage.measure import find_contours

from src.config.endpoints import MEDSAM_CHECKPOINT, MEDSAM_DEVICE, MEDSAM_IMAGE_SIZE
from src.interfaces.segmenter import Segmenter
from src.schemas.segmentation import MaskResult


def _preprocess_image(image: Image.Image, target_size: int) -> tuple[torch.Tensor, int, int]:
    """Resize to target_size × target_size, normalize to [0,1], return tensor + original H, W."""
    img_np = np.asarray(image.convert("RGB"), dtype=np.uint8)
    H, W = img_np.shape[:2]

    img_resized = transform.resize(
        img_np,
        (target_size, target_size),
        order=3,
        preserve_range=True,
        anti_aliasing=True,
    ).astype(np.float32)

    img_normalized = (img_resized - img_resized.min()) / np.clip(
        img_resized.max() - img_resized.min(), a_min=1e-8, a_max=None
    )

    # (H, W, 3) → (1, 3, H, W)
    tensor = torch.from_numpy(img_normalized).permute(2, 0, 1).unsqueeze(0).float()
    return tensor, H, W


def _bbox_normalized_to_medsam(
    bbox_normalized: list[int],
    H: int,
    W: int,
    target_size: int,
) -> np.ndarray:
    """Convert MedGemma [y0, x0, y1, x1] in [0,1000] to MedSAM [x0,y0,x1,y1] in target_size scale."""
    y0_n, x0_n, y1_n, x1_n = bbox_normalized
    # to pixel coords in original image
    x0_px = x0_n / 1000.0 * W
    y0_px = y0_n / 1000.0 * H
    x1_px = x1_n / 1000.0 * W
    y1_px = y1_n / 1000.0 * H
    # scale to target_size
    box_scaled = np.array([[x0_px, y0_px, x1_px, y1_px]]) / np.array([W, H, W, H]) * target_size
    return box_scaled


def _extract_polygon(mask: np.ndarray) -> list[list[int]] | None:
    """Return the largest contour of a binary mask as [[x, y], ...] pixel coords.

    find_contours returns (row, col) = (y, x), so we swap to [x, y].
    """
    contours = find_contours(mask.astype(float), level=0.5)
    if not contours:
        return None
    largest = max(contours, key=len)
    return [[int(round(pt[1])), int(round(pt[0]))] for pt in largest]


@torch.no_grad()
def _medsam_inference(
    model: object,
    img_embed: torch.Tensor,
    box_scaled: np.ndarray,
    H: int,
    W: int,
) -> np.ndarray:
    """Run MedSAM mask decoder and return a binary H×W uint8 mask."""
    box_torch = torch.as_tensor(box_scaled, dtype=torch.float, device=img_embed.device)
    if box_torch.dim() == 2:
        box_torch = box_torch[:, None, :]  # (B, 1, 4)

    sparse_emb, dense_emb = model.prompt_encoder(points=None, boxes=box_torch, masks=None)
    low_res_logits, _ = model.mask_decoder(
        image_embeddings=img_embed,
        image_pe=model.prompt_encoder.get_dense_pe(),
        sparse_prompt_embeddings=sparse_emb,
        dense_prompt_embeddings=dense_emb,
        multimask_output=False,
    )

    low_res_pred = F.interpolate(
        torch.sigmoid(low_res_logits),
        size=(H, W),
        mode="bilinear",
        align_corners=False,
    )
    mask = (low_res_pred.squeeze().cpu().numpy() > 0.5).astype(np.uint8)
    return mask


class MedSAMClient(Segmenter):
    """Wraps MedSAM (ViT-B) for prompt-guided segmentation (Stage 2)."""

    def __init__(
        self,
        checkpoint_path: str = MEDSAM_CHECKPOINT,
        device: str = MEDSAM_DEVICE,
    ) -> None:
        from segment_anything import sam_model_registry  # import guarded for GPU-only envs

        self.device = device
        self._model = sam_model_registry["vit_b"](checkpoint=checkpoint_path)
        self._model = self._model.to(device)
        self._model.eval()

    def segment(
        self,
        image: Image.Image,
        bbox_normalized: list[int],
        finding_label: str,
        image_path: str,
        image_index: int,
    ) -> MaskResult:
        try:
            img_tensor, H, W = _preprocess_image(image, MEDSAM_IMAGE_SIZE)
            img_tensor = img_tensor.to(self.device)

            with torch.no_grad():
                img_embed = self._model.image_encoder(img_tensor)

            box_scaled = _bbox_normalized_to_medsam(bbox_normalized, H, W, MEDSAM_IMAGE_SIZE)
            mask = _medsam_inference(self._model, img_embed, box_scaled, H, W)
            polygon = _extract_polygon(mask)

            return MaskResult(
                finding_label=finding_label,
                image_path=image_path,
                image_index=image_index,
                bbox_normalized=bbox_normalized,
                mask=mask,
                polygon=polygon,
                status="success",
            )

        except Exception as exc:
            return MaskResult(
                finding_label=finding_label,
                image_path=image_path,
                image_index=image_index,
                bbox_normalized=bbox_normalized,
                mask=None,
                polygon=None,
                status="failed",
                error=str(exc),
            )