from typing import Union
import numpy as np


def findCosineDistance(
    source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> np.float64:
    if isinstance(source_representation, list):
        source_representation = np.array(source_representation)

    if isinstance(test_representation, list):
        test_representation = np.array(test_representation)

    a = np.matmul(np.transpose(source_representation), test_representation)
    b = np.sum(np.multiply(source_representation, source_representation))
    c = np.sum(np.multiply(test_representation, test_representation))
    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))


def findEuclideanDistance(
    source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> np.float64:
    if isinstance(source_representation, list):
        source_representation = np.array(source_representation)

    if isinstance(test_representation, list):
        test_representation = np.array(test_representation)

    euclidean_distance = source_representation - test_representation
    euclidean_distance = np.sum(np.multiply(euclidean_distance, euclidean_distance))
    euclidean_distance = np.sqrt(euclidean_distance)
    return euclidean_distance


def l2_normalize(x: np.ndarray) -> np.ndarray:
    return x / np.sqrt(np.sum(np.multiply(x, x)))


def findThreshold(model_name: str, distance_metric: str) -> float:

    base_threshold = {"cosine": 0.40, "euclidean": 0.55, "euclidean_l2": 0.75}

    thresholds = {
        # "VGG-Face": {"cosine": 0.40, "euclidean": 0.60, "euclidean_l2": 0.86}, # 2622d
        "VGG-Face": {
            "cosine": 0.68,
            "euclidean": 1.17,
            "euclidean_l2": 1.17,
        },  # 4096d - tuned with LFW
        "Facenet": {"cosine": 0.40, "euclidean": 10, "euclidean_l2": 0.80},
        "Facenet512": {"cosine": 0.30, "euclidean": 23.56, "euclidean_l2": 1.04},
        "ArcFace": {"cosine": 0.68, "euclidean": 4.15, "euclidean_l2": 1.13},
        "Dlib": {"cosine": 0.07, "euclidean": 0.6, "euclidean_l2": 0.4},
        "SFace": {"cosine": 0.593, "euclidean": 10.734, "euclidean_l2": 1.055},
        "OpenFace": {"cosine": 0.10, "euclidean": 0.55, "euclidean_l2": 0.55},
        "DeepFace": {"cosine": 0.23, "euclidean": 64, "euclidean_l2": 0.64},
        "DeepID": {"cosine": 0.015, "euclidean": 45, "euclidean_l2": 0.17},
    }

    threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)

    return threshold