from __future__ import annotations

import os
from abc import ABC, abstractmethod

import numpy as np


class EyeClassifier(ABC):
    @property
    @abstractmethod
    def name(self) -> str:
        pass

    @abstractmethod
    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
        pass


class GeometricOnlyClassifier(EyeClassifier):
    @property
    def name(self) -> str:
        return "geometric"

    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
        return 1.0


class YOLOv11Classifier(EyeClassifier):
    def __init__(self, checkpoint_path: str, device: str = "cpu"):
        from ultralytics import YOLO

        self._model = YOLO(checkpoint_path)
        self._device = device

        names = self._model.names
        self._attentive_idx = None
        for idx, cls_name in names.items():
            if cls_name in ("open", "attentive"):
                self._attentive_idx = idx
                break
        if self._attentive_idx is None:
            self._attentive_idx = max(names.keys())
        print(f"[YOLO] Classes: {names}, attentive_idx={self._attentive_idx}")

    @property
    def name(self) -> str:
        return "yolo"

    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
        if not crops_bgr:
            return 1.0
        results = self._model.predict(crops_bgr, device=self._device, verbose=False)
        scores = [float(r.probs.data[self._attentive_idx]) for r in results]
        return sum(scores) / len(scores) if scores else 1.0


class EyeCNNClassifier(EyeClassifier):
    """Loader for the custom PyTorch EyeCNN (trained on Kaggle eye crops)."""

    def __init__(self, checkpoint_path: str, device: str = "cpu"):
        import torch
        import torch.nn as nn

        class EyeCNN(nn.Module):
            def __init__(self, num_classes=2, dropout_rate=0.3):
                super().__init__()
                self.conv_layers = nn.Sequential(
                    nn.Conv2d(3, 32, 3, 1, 1), nn.BatchNorm2d(32), nn.ReLU(), nn.MaxPool2d(2, 2),
                    nn.Conv2d(32, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(2, 2),
                    nn.Conv2d(64, 128, 3, 1, 1), nn.BatchNorm2d(128), nn.ReLU(), nn.MaxPool2d(2, 2),
                    nn.Conv2d(128, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.ReLU(), nn.MaxPool2d(2, 2),
                )
                self.fc_layers = nn.Sequential(
                    nn.AdaptiveAvgPool2d((1, 1)), nn.Flatten(),
                    nn.Linear(256, 512), nn.ReLU(), nn.Dropout(dropout_rate),
                    nn.Linear(512, num_classes),
                )

            def forward(self, x):
                return self.fc_layers(self.conv_layers(x))

        self._device = torch.device(device)
        checkpoint = torch.load(checkpoint_path, map_location=self._device, weights_only=False)
        dropout_rate = checkpoint.get("config", {}).get("dropout_rate", 0.35)
        self._model = EyeCNN(num_classes=2, dropout_rate=dropout_rate)
        self._model.load_state_dict(checkpoint["model_state_dict"])
        self._model.to(self._device)
        self._model.eval()

        self._transform = None  # built lazily

    def _get_transform(self):
        if self._transform is None:
            from torchvision import transforms
            self._transform = transforms.Compose([
                transforms.ToPILImage(),
                transforms.Resize((96, 96)),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=[0.485, 0.456, 0.406],
                    std=[0.229, 0.224, 0.225],
                ),
            ])
        return self._transform

    @property
    def name(self) -> str:
        return "eye_cnn"

    def predict_score(self, crops_bgr: list[np.ndarray]) -> float:
        if not crops_bgr:
            return 1.0

        import torch
        import cv2

        transform = self._get_transform()
        scores = []
        for crop in crops_bgr:
            if crop is None or crop.size == 0:
                scores.append(1.0)
                continue
            rgb = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)
            tensor = transform(rgb).unsqueeze(0).to(self._device)
            with torch.no_grad():
                output = self._model(tensor)
                prob = torch.softmax(output, dim=1)[0, 1].item()  # prob of "open"
            scores.append(prob)
        return sum(scores) / len(scores)


_EXT_TO_BACKEND = {".pth": "cnn", ".pt": "yolo"}


def load_eye_classifier(
    path: str | None = None,
    backend: str = "yolo",
    device: str = "cpu",
) -> EyeClassifier:
    if backend == "geometric":
        return GeometricOnlyClassifier()

    if path is None:
        print(f"[CLASSIFIER] No model path for backend {backend!r}, falling back to geometric")
        return GeometricOnlyClassifier()

    ext = os.path.splitext(path)[1].lower()
    inferred = _EXT_TO_BACKEND.get(ext)
    if inferred and inferred != backend:
        print(f"[CLASSIFIER] File extension {ext!r} implies backend {inferred!r}, "
              f"overriding requested {backend!r}")
        backend = inferred

    print(f"[CLASSIFIER] backend={backend!r}, path={path!r}")

    if backend == "cnn":
        return EyeCNNClassifier(path, device=device)

    if backend == "yolo":
        try:
            return YOLOv11Classifier(path, device=device)
        except ImportError:
            print("[CLASSIFIER] ultralytics required for YOLO. pip install ultralytics")
            raise

    raise ValueError(
        f"Unknown eye backend {backend!r}. Choose from: yolo, cnn, geometric"
    )