from src.utils.config_loader import constants
from huggingface_hub import snapshot_download
from zipfile import ZipFile
import numpy as np
import os, shutil
import matplotlib.pyplot as plt
import cv2
import math


def download_hf_dataset(repo_id, allow_patterns=None):
    """Used to download dataset from any public hugging face dataset"""
    snapshot_download(
        repo_id=repo_id,
        repo_type="dataset",
        local_dir=constants.RAW_DATASET_DIR,
        allow_patterns=allow_patterns,
    )


def download_personal_hf_dataset(name):
    """Used to download dataset from a specific hugging face dataset"""
    download_hf_dataset(
        repo_id="Anuj-Panthri/Image-Colorization-Datasets", allow_patterns=f"{name}/*"
    )


def unzip_file(file_path, destination_dir):
    """unzips file to destination_dir"""
    if os.path.exists(destination_dir):
        shutil.rmtree(destination_dir)
    os.makedirs(destination_dir)
    with ZipFile(file_path, "r") as zip:
        zip.extractall(destination_dir)


def is_bw(img: np.ndarray):
    """checks if RGB image is black and white"""
    rg, gb, rb = (
        img[:, :, 0] - img[:, :, 1],
        img[:, :, 1] - img[:, :, 2],
        img[:, :, 0] - img[:, :, 2],
    )
    rg, gb, rb = np.abs(rg).sum(), np.abs(gb).sum(), np.abs(rb).sum()
    avg = np.mean([rg, gb, rb])

    return avg < 10


def print_title(msg: str, max_chars=105):
    n = (max_chars - len(msg)) // 2
    print("=" * n, msg.upper(), "=" * n, sep="")


def scale_L(L):
    return L / 100


def rescale_L(L):
    return L * 100


def scale_AB(AB):
    return AB / 128


def rescale_AB(AB):
    return AB * 128


def show_images_from_paths(
    image_paths: list[str],
    image_size=64,
    cols=4,
    row_size=5,
    col_size=5,
    show_BW=False,
    title=None,
    save=False,
    label="",
):

    n = len(image_paths)
    rows = math.ceil(n / cols)
    fig = plt.figure(figsize=(col_size * cols, row_size * rows))
    if title:
        plt.title(title)
    plt.axis("off")

    for i in range(n):
        fig.add_subplot(rows, cols, i + 1)

        img = cv2.imread(image_paths[i])[:, :, ::-1]
        img = cv2.resize(img, [image_size, image_size])

        if show_BW:
            BW = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
            BW = np.tile(BW, (1, 1, 3))
            img = np.concatenate([BW, img], axis=1)
        plt.imshow(img.astype("uint8"))

    if save:
        os.makedirs(constants.ARTIFACT_DATASET_VISUALIZATION_DIR, exist_ok=True)
        plt.savefig(
            os.path.join(
                constants.ARTIFACT_DATASET_VISUALIZATION_DIR, f"{label}_image.png"
            )
        )
    plt.show()


def see_batch(
    L_batch,
    AB_batch,
    show_L=False,
    cols=4,
    row_size=5,
    col_size=5,
    title=None,
    save=False,
    label="",
):
    n = L_batch.shape[0]
    rows = math.ceil(n / cols)
    fig = plt.figure(figsize=(col_size * cols, row_size * rows))
    if title:
        plt.title(title)
    plt.axis("off")

    for i in range(n):
        fig.add_subplot(rows, cols, i + 1)
        L, AB = L_batch[i], AB_batch[i]
        L, AB = rescale_L(L), rescale_AB(AB)
        #         print(L.shape,AB.shape)
        img = np.concatenate([L, AB], axis=-1)
        img = cv2.cvtColor(img, cv2.COLOR_LAB2RGB) * 255
        #         print(img.min(),img.max())
        if show_L:
            L = np.tile(L, (1, 1, 3)) / 100 * 255
            img = np.concatenate([L, img], axis=1)
        plt.imshow(img.astype("uint8"))
    if save:
        os.makedirs(constants.ARTIFACT_RESULT_VISUALIZATION_DIR, exist_ok=True)
        plt.savefig(
            os.path.join(
                constants.ARTIFACT_RESULT_VISUALIZATION_DIR, f"{label}_image.png"
            )
        )
    plt.show()