from __future__ import annotations

from typing import Any

import cv2
import numpy as np

from sahi.logger import logger
from sahi.models.base import DetectionModel
from sahi.prediction import ObjectPrediction
from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
from sahi.utils.cv import get_coco_segmentation_from_bool_mask
from sahi.utils.import_utils import check_requirements


class UltralyticsDetectionModel(DetectionModel):
    """Detection model for Ultralytics YOLO models.

    Supports both PyTorch (.pt) and ONNX (.onnx) models.
    """

    def __init__(self, *args, **kwargs):
        self.fuse: bool = kwargs.pop("fuse", False)
        existing_packages = getattr(self, "required_packages", None) or []
        self.required_packages = [*list(existing_packages), "ultralytics"]
        super().__init__(*args, **kwargs)

    def load_model(self):
        """Detection model is initialized and set to self.model.

        Supports both PyTorch (.pt) and ONNX (.onnx) models.
        """

        from ultralytics import YOLO

        if self.model_path and ".onnx" in self.model_path:
            check_requirements(["onnx", "onnxruntime"])

        try:
            model = YOLO(self.model_path)
            # Only call .to(device) for PyTorch models, not ONNX
            if self.model_path and not self.model_path.endswith(".onnx"):
                model.to(self.device)
            self.set_model(model)
            if self.fuse and hasattr(model, "fuse"):
                model.fuse()

        except Exception as e:
            raise TypeError("model_path is not a valid Ultralytics model path: ", e)

    def set_model(self, model: Any, **kwargs):
        """Sets the underlying Ultralytics model.

        Args:
            model: Any
                A Ultralytics model
        """

        self.model = model
        # set category_mapping
        if not self.category_mapping:
            category_mapping = {str(ind): category_name for ind, category_name in enumerate(self.category_names)}
            self.category_mapping = category_mapping

    def perform_inference(self, image: np.ndarray):
        """Prediction is performed using self.model and the prediction result is set to self._original_predictions.

        Args:
            image: np.ndarray
                A numpy array that contains the image to be predicted. 3 channel image should be in RGB order.
        """

        # Confirm model is loaded

        import torch

        if self.model is None:
            raise ValueError("Model is not loaded, load it by calling .load_model()")

        kwargs = {"cfg": self.config_path, "verbose": False, "conf": self.confidence_threshold, "device": self.device}

        if self.image_size is not None:
            kwargs = {"imgsz": self.image_size, **kwargs}

        prediction_result = self.model(image[:, :, ::-1], **kwargs)  # YOLO expects numpy arrays to have BGR

        # Handle different result types for PyTorch vs ONNX models
        # ONNX models might return results in a different format
        if self.has_mask:
            from ultralytics.engine.results import Masks

            if not prediction_result[0].masks:
                # Create empty masks if none exist
                if hasattr(self.model, "device"):
                    device = self.model.device
                else:
                    device = "cpu"  # Default for ONNX models
                prediction_result[0].masks = Masks(
                    torch.tensor([], device=device), prediction_result[0].boxes.orig_shape
                )

            # We do not filter results again as confidence threshold is already applied above
            prediction_result = [
                (
                    result.boxes.data,
                    result.masks.data,
                )
                for result in prediction_result
            ]
        elif self.is_obb:
            # For OBB task, get OBB points in xyxyxyxy format
            device = getattr(self.model, "device", "cpu")
            prediction_result = [
                (
                    # Get OBB data: xyxy, conf, cls
                    torch.cat(
                        [
                            result.obb.xyxy,  # box coordinates
                            result.obb.conf.unsqueeze(-1),  # confidence scores
                            result.obb.cls.unsqueeze(-1),  # class ids
                        ],
                        dim=1,
                    )
                    if result.obb is not None
                    else torch.empty((0, 6), device=device),
                    # Get OBB points in (N, 4, 2) format
                    result.obb.xyxyxyxy if result.obb is not None else torch.empty((0, 4, 2), device=device),
                )
                for result in prediction_result
            ]
        else:  # If model doesn't do segmentation or OBB then no need to check masks
            # We do not filter results again as confidence threshold is already applied above
            prediction_result = [result.boxes.data for result in prediction_result]

        self._original_predictions = prediction_result
        self._original_shape = image.shape

    @property
    def category_names(self):
        # For ONNX models, names might not be available, use category_mapping
        if hasattr(self.model, "names") and self.model.names:
            return self.model.names.values()
        elif self.category_mapping:
            return list(self.category_mapping.values())
        else:
            raise ValueError("Category names not available. Please provide category_mapping for ONNX models.")

    @property
    def num_categories(self):
        """Returns number of categories."""
        if hasattr(self.model, "names") and self.model.names:
            return len(self.model.names)
        elif self.category_mapping:
            return len(self.category_mapping)
        else:
            raise ValueError("Cannot determine number of categories. Please provide category_mapping for ONNX models.")

    @property
    def has_mask(self):
        """Returns if model output contains segmentation mask."""
        # Check if model has 'task' attribute (for both .pt and .onnx models)
        if hasattr(self.model, "overrides") and "task" in self.model.overrides:
            return self.model.overrides["task"] == "segment"
        # For ONNX models, task might be stored differently
        elif hasattr(self.model, "task"):
            return self.model.task == "segment"
        # For ONNX models without task info, check model path
        elif self.model_path and isinstance(self.model_path, str):
            return "seg" in self.model_path.lower()
        return False

    @property
    def is_obb(self):
        """Returns if model output contains oriented bounding boxes."""
        # Check if model has 'task' attribute (for both .pt and .onnx models)
        if hasattr(self.model, "overrides") and "task" in self.model.overrides:
            return self.model.overrides["task"] == "obb"
        # For ONNX models, task might be stored differently
        elif hasattr(self.model, "task"):
            return self.model.task == "obb"
        # For ONNX models without task info, check model path
        elif self.model_path and isinstance(self.model_path, str):
            return "obb" in self.model_path.lower()
        return False

    def _create_object_prediction_list_from_original_predictions(
        self,
        shift_amount_list: list[list[int]] | None = [[0, 0]],
        full_shape_list: list[list[int]] | None = None,
    ):
        """self._original_predictions is converted to a list of prediction.ObjectPrediction and set to
        self._object_prediction_list_per_image.

        Args:
            shift_amount_list: list of list
                To shift the box and mask predictions from sliced image to full sized image, should
                be in the form of List[[shift_x, shift_y],[shift_x, shift_y],...]
            full_shape_list: list of list
                Size of the full image after shifting, should be in the form of
                List[[height, width],[height, width],...]
        """
        original_predictions = self._original_predictions

        # compatibility for sahi v0.8.15
        shift_amount_list = fix_shift_amount_list(shift_amount_list)
        full_shape_list = fix_full_shape_list(full_shape_list)

        # handle all predictions
        object_prediction_list_per_image = []

        for image_ind, image_predictions in enumerate(original_predictions):
            shift_amount = shift_amount_list[image_ind]
            full_shape = None if full_shape_list is None else full_shape_list[image_ind]
            object_prediction_list = []

            # Extract boxes and optional masks/obb
            if self.has_mask or self.is_obb:
                boxes = image_predictions[0].cpu().detach().numpy()
                masks_or_points = image_predictions[1].cpu().detach().numpy()
            else:
                boxes = image_predictions.data.cpu().detach().numpy()
                masks_or_points = None

            # Process each prediction
            for pred_ind, prediction in enumerate(boxes):
                # Get bbox coordinates
                bbox = prediction[:4].tolist()
                score = prediction[4]
                category_id = int(prediction[5])
                category_name = self.category_mapping[str(category_id)]

                # Fix box coordinates
                bbox = [max(0, coord) for coord in bbox]
                if full_shape is not None:
                    bbox[0] = min(full_shape[1], bbox[0])
                    bbox[1] = min(full_shape[0], bbox[1])
                    bbox[2] = min(full_shape[1], bbox[2])
                    bbox[3] = min(full_shape[0], bbox[3])

                # Ignore invalid predictions
                if not (bbox[0] < bbox[2]) or not (bbox[1] < bbox[3]):
                    logger.warning(f"ignoring invalid prediction with bbox: {bbox}")
                    continue

                # Get segmentation or OBB points
                segmentation = None
                if masks_or_points is not None:
                    if self.has_mask:
                        bool_mask = masks_or_points[pred_ind]
                        # Resize mask to original image size
                        bool_mask = cv2.resize(
                            bool_mask.astype(np.uint8), (self._original_shape[1], self._original_shape[0])
                        )
                        segmentation = get_coco_segmentation_from_bool_mask(bool_mask)
                    else:  # is_obb
                        obb_points = masks_or_points[pred_ind]  # Get OBB points for this prediction
                        segmentation = [obb_points.reshape(-1).tolist()]

                    if len(segmentation) == 0:
                        continue

                # Create and append object prediction
                object_prediction = ObjectPrediction(
                    bbox=bbox,
                    category_id=category_id,
                    score=score,
                    segmentation=segmentation,
                    category_name=category_name,
                    shift_amount=shift_amount,
                    full_shape=self._original_shape[:2] if full_shape is None else full_shape,  # (height, width)
                )
                object_prediction_list.append(object_prediction)

            object_prediction_list_per_image.append(object_prediction_list)

        self._object_prediction_list_per_image = object_prediction_list_per_image