# Standard libraries
import numpy as np

# PyTorch
import torch
import torch.nn as nn

y_table = np.array(
    [
        [16, 11, 10, 16, 24, 40, 51, 61],
        [12, 12, 14, 19, 26, 58, 60, 55],
        [14, 13, 16, 24, 40, 57, 69, 56],
        [14, 17, 22, 29, 51, 87, 80, 62],
        [18, 22, 37, 56, 68, 109, 103, 77],
        [24, 35, 55, 64, 81, 104, 113, 92],
        [49, 64, 78, 87, 103, 121, 120, 101],
        [72, 92, 95, 98, 112, 100, 103, 99],
    ],
    dtype=np.float32,
).T

y_table = nn.Parameter(torch.from_numpy(y_table).cuda())
#
c_table = np.empty((8, 8), dtype=np.float32)
c_table.fill(99)
c_table[:4, :4] = np.array(
    [[17, 18, 24, 47], [18, 21, 26, 66], [24, 26, 56, 99], [47, 66, 99, 99]]
).T
c_table = nn.Parameter(torch.from_numpy(c_table).cuda())


def diff_round(x):
    """Differentiable rounding function
    Input:
        x(tensor)
    Output:
        x(tensor)
    """
    return torch.round(x) + (x - torch.round(x)) ** 3


def quality_to_factor(quality):
    """Calculate factor corresponding to quality
    Input:
        quality(float): Quality for jpeg compression
    Output:
        factor(float): Compression factor
    """
    if quality < 50:
        quality = 5000.0 / quality
    else:
        quality = 200.0 - quality * 2
    return quality / 100.0
