"""Base Inferencer for Torch and OpenVINO."""

# Copyright (C) 2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

from __future__ import annotations

from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, cast

import cv2
import numpy as np
from omegaconf import DictConfig, OmegaConf
from skimage.morphology import dilation
from skimage.segmentation import find_boundaries
from torch import Tensor

from anomal_util.data.utils import read_image
from anomal_util.post_processing import ImageResult, compute_mask
from anomal_util.post_processing.normalization.cdf import normalize as normalize_cdf
from anomal_util.post_processing.normalization.cdf import standardize
from anomal_util.post_processing.normalization.min_max import normalize as normalize_min_max


class Inferencer(ABC):
    """Abstract class for the inference.

    This is used by both Torch and OpenVINO inference.
    """

    @abstractmethod
    def load_model(self, path: str | Path) -> Any:
        """Load Model."""
        raise NotImplementedError

    @abstractmethod
    def pre_process(self, image: np.ndarray) -> np.ndarray | Tensor:
        """Pre-process."""
        raise NotImplementedError

    @abstractmethod
    def forward(self, image: np.ndarray | Tensor) -> np.ndarray | Tensor:
        """Forward-Pass input to model."""
        raise NotImplementedError

    @abstractmethod
    def post_process(self, predictions: np.ndarray | Tensor, metadata: dict[str, Any] | None) -> dict[str, Any]:
        """Post-Process."""
        raise NotImplementedError

    def predict(
        self,
        image: str | Path | np.ndarray,
        metadata: dict[str, Any] | None = None,
    ) -> ImageResult:
        """Perform a prediction for a given input image.

        The main workflow is (i) pre-processing, (ii) forward-pass, (iii) post-process.

        Args:
            image (Union[str, np.ndarray]): Input image whose output is to be predicted.
                It could be either a path to image or numpy array itself.

            metadata: Metadata information such as shape, threshold.

        Returns:
            ImageResult: Prediction results to be visualized.
        """
        if metadata is None:
            if hasattr(self, "metadata"):
                metadata = getattr(self, "metadata")
            else:
                metadata = {}
        if isinstance(image, (str, Path)):
            image_arr: np.ndarray = read_image(image)
        else:  # image is already a numpy array. Kept for mypy compatibility.
            image_arr = image
        metadata["image_shape"] = image_arr.shape[:2]

        processed_image = self.pre_process(image_arr)
        predictions = self.forward(processed_image)
        output = self.post_process(predictions, metadata=metadata)

        return ImageResult(
            image=image_arr,
            pred_score=output["pred_score"],
            pred_label=output["pred_label"],
            anomaly_map=output["anomaly_map"],
            pred_mask=output["pred_mask"],
            pred_boxes=output["pred_boxes"],
            box_labels=output["box_labels"],
        )

    @staticmethod
    def _superimpose_segmentation_mask(metadata: dict, anomaly_map: np.ndarray, image: np.ndarray) -> np.ndarray:
        """Superimpose segmentation mask on top of image.

        Args:
            metadata (dict): Metadata of the image which contains the image size.
            anomaly_map (np.ndarray): Anomaly map which is used to extract segmentation mask.
            image (np.ndarray): Image on which segmentation mask is to be superimposed.

        Returns:
            np.ndarray: Image with segmentation mask superimposed.
        """
        pred_mask = compute_mask(anomaly_map, 0.5)  # assumes predictions are normalized.
        image_height = metadata["image_shape"][0]
        image_width = metadata["image_shape"][1]
        pred_mask = cv2.resize(pred_mask, (image_width, image_height))
        boundaries = find_boundaries(pred_mask)
        outlines = dilation(boundaries, np.ones((7, 7)))
        image[outlines] = [255, 0, 0]
        return image

    def __call__(self, image: np.ndarray) -> ImageResult:
        """Call predict on the Image.

        Args:
            image (np.ndarray): Input Image

        Returns:
            ImageResult: Prediction results to be visualized.
        """
        return self.predict(image)

    @staticmethod
    def _normalize(
        pred_scores: Tensor | np.float32,
        metadata: dict | DictConfig,
        anomaly_maps: Tensor | np.ndarray | None = None,
    ) -> tuple[np.ndarray | Tensor | None, float]:
        """Applies normalization and resizes the image.

        Args:
            pred_scores (Tensor | np.float32): Predicted anomaly score
            metadata (dict | DictConfig): Meta data. Post-processing step sometimes requires
                additional meta data such as image shape. This variable comprises such info.
            anomaly_maps (Tensor | np.ndarray | None): Predicted raw anomaly map.

        Returns:
            tuple[np.ndarray | Tensor | None, float]: Post processed predictions that are ready to be
                visualized and predicted scores.
        """

        # min max normalization
        if "min" in metadata and "max" in metadata:
            if anomaly_maps is not None:
                anomaly_maps = normalize_min_max(
                    anomaly_maps,
                    metadata["pixel_threshold"],
                    metadata["min"],
                    metadata["max"],
                )
            pred_scores = normalize_min_max(
                pred_scores,
                metadata["image_threshold"],
                metadata["min"],
                metadata["max"],
            )

        # standardize pixel scores
        if "pixel_mean" in metadata.keys() and "pixel_std" in metadata.keys():
            if anomaly_maps is not None:
                anomaly_maps = standardize(
                    anomaly_maps, metadata["pixel_mean"], metadata["pixel_std"], center_at=metadata["image_mean"]
                )
                anomaly_maps = normalize_cdf(anomaly_maps, metadata["pixel_threshold"])

        # standardize image scores
        if "image_mean" in metadata.keys() and "image_std" in metadata.keys():
            pred_scores = standardize(pred_scores, metadata["image_mean"], metadata["image_std"])
            pred_scores = normalize_cdf(pred_scores, metadata["image_threshold"])

        return anomaly_maps, float(pred_scores)

    def _load_metadata(self, path: str | Path | dict | None = None) -> dict | DictConfig:
        """Loads the meta data from the given path.

        Args:
            path (str | Path | dict | None, optional): Path to JSON file containing the metadata.
                If no path is provided, it returns an empty dict. Defaults to None.

        Returns:
            dict | DictConfig: Dictionary containing the metadata.
        """
        metadata: dict[str, float | np.ndarray | Tensor] | DictConfig = {}
        if path is not None:
            config = OmegaConf.load(path)
            metadata = cast(DictConfig, config)
        return metadata
