# All imports
# Math
import math
import random
import cv2
import numpy as np
from scipy.stats import pearsonr

# from scipy.sparse.linalg import svds
# from sklearn.decomposition import NMF
from sklearn.decomposition import LatentDirichletAllocation

# from sklearn.cluster import KMeans

# Torch
import torch
import torchvision.transforms as transforms
from torchvision.datasets import Caltech101
from torchvision.models import resnet50, ResNet50_Weights

import tensorly as tl

# OS and env
import json
import os
from os import getenv
from dotenv import load_dotenv
import warnings
from joblib import dump, load

load_dotenv()

# MongoDB
from pymongo import MongoClient

# Visualizing
import matplotlib.pyplot as plt



valid_classification_methods = {
    "m-nn": 1,
    "decision-tree": 2,
    "ppr": 3,
}

def getCollection(db, collection):
    """Load feature descriptor collection from MongoDB"""
    client = MongoClient("mongodb://localhost:27017")
    return client[db][collection]


def euclidean_distance_measure(img_1_fd, img_2_fd):
    img_1_fd_reshaped = img_1_fd.flatten()
    img_2_fd_reshaped = img_2_fd.flatten()

    # Calculate Euclidean distance
    return math.dist(img_1_fd_reshaped, img_2_fd_reshaped)


valid_feature_models = {
    "cm": "cm_fd",
    "hog": "hog_fd",
    "avgpool": "avgpool_fd",
    "layer3": "layer3_fd",
    "fc": "fc_fd",
    "resnet": "resnet_fd",
}