# Standard libraries
import itertools
import numpy as np

# PyTorch
import torch
import torch.nn as nn

# Local
from . import JPEG_utils as utils


class y_dequantize(nn.Module):
    """Dequantize Y channel
    Inputs:
        image(tensor): batch x height x width
        factor(float): compression factor
    Outputs:
        image(tensor): batch x height x width
    """

    def __init__(self, factor=1):
        super(y_dequantize, self).__init__()
        self.y_table = utils.y_table
        self.factor = factor

    def forward(self, image):
        return image * (self.y_table * self.factor)


class c_dequantize(nn.Module):
    """Dequantize CbCr channel
    Inputs:
        image(tensor): batch x height x width
        factor(float): compression factor
    Outputs:
        image(tensor): batch x height x width
    """

    def __init__(self, factor=1):
        super(c_dequantize, self).__init__()
        self.factor = factor
        self.c_table = utils.c_table

    def forward(self, image):
        return image * (self.c_table * self.factor)


class idct_8x8(nn.Module):
    """Inverse discrete Cosine Transformation
    Input:
        dcp(tensor): batch x height x width
    Output:
        image(tensor): batch x height x width
    """

    def __init__(self):
        super(idct_8x8, self).__init__()
        alpha = np.array([1.0 / np.sqrt(2)] + [1] * 7)
        self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float())
        tensor = np.zeros((8, 8, 8, 8), dtype=np.float32)
        for x, y, u, v in itertools.product(range(8), repeat=4):
            tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos(
                (2 * v + 1) * y * np.pi / 16
            )
        self.tensor = nn.Parameter(torch.from_numpy(tensor).float())

    def forward(self, image):

        image = image * self.alpha
        result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128
        result.view(image.shape)
        return result


class block_merging(nn.Module):
    """Merge pathces into image
    Inputs:
        patches(tensor) batch x height*width/64, height x width
        height(int)
        width(int)
    Output:
        image(tensor): batch x height x width
    """

    def __init__(self):
        super(block_merging, self).__init__()

    def forward(self, patches, height, width):
        k = 8
        batch_size = patches.shape[0]
        # print(patches.shape) # (1,1024,8,8)
        image_reshaped = patches.view(batch_size, height // k, width // k, k, k)
        image_transposed = image_reshaped.permute(0, 1, 3, 2, 4)
        return image_transposed.contiguous().view(batch_size, height, width)


class chroma_upsampling(nn.Module):
    """Upsample chroma layers
    Input:
        y(tensor): y channel image
        cb(tensor): cb channel
        cr(tensor): cr channel
    Ouput:
        image(tensor): batch x height x width x 3
    """

    def __init__(self):
        super(chroma_upsampling, self).__init__()

    def forward(self, y, cb, cr):
        def repeat(x, k=2):
            height, width = x.shape[1:3]
            x = x.unsqueeze(-1)
            x = x.repeat(1, 1, k, k)
            x = x.view(-1, height * k, width * k)
            return x

        cb = repeat(cb)
        cr = repeat(cr)

        return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3)


class ycbcr_to_rgb_jpeg(nn.Module):
    """Converts YCbCr image to RGB JPEG
    Input:
        image(tensor): batch x height x width x 3
    Outpput:
        result(tensor): batch x 3 x height x width
    """

    def __init__(self):
        super(ycbcr_to_rgb_jpeg, self).__init__()

        matrix = np.array(
            [[1.0, 0.0, 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]],
            dtype=np.float32,
        ).T
        self.shift = nn.Parameter(torch.tensor([0, -128.0, -128.0]))
        self.matrix = nn.Parameter(torch.from_numpy(matrix))

    def forward(self, image):
        result = torch.tensordot(image + self.shift, self.matrix, dims=1)
        # result = torch.from_numpy(result)
        result.view(image.shape)
        return result.permute(0, 3, 1, 2)


class decompress_jpeg(nn.Module):
    """Full JPEG decompression algortihm
    Input:
        compressed(dict(tensor)): batch x h*w/64 x 8 x 8
        rounding(function): rounding function to use
        factor(float): Compression factor
    Ouput:
        image(tensor): batch x 3 x height x width
    """

    # def __init__(self, height, width, rounding=torch.round, factor=1):
    def __init__(self, rounding=torch.round, factor=1):
        super(decompress_jpeg, self).__init__()
        self.c_dequantize = c_dequantize(factor=factor)
        self.y_dequantize = y_dequantize(factor=factor)
        self.idct = idct_8x8()
        self.merging = block_merging()
        # comment this line if no subsampling
        self.chroma = chroma_upsampling()
        self.colors = ycbcr_to_rgb_jpeg()

        # self.height, self.width = height, width

    def forward(self, y, cb, cr, height, width):
        components = {"y": y, "cb": cb, "cr": cr}
        # height = y.shape[0]
        # width = y.shape[1]
        self.height = height
        self.width = width
        for k in components.keys():
            if k in ("cb", "cr"):
                comp = self.c_dequantize(components[k])
                # comment this line if no subsampling
                height, width = int(self.height / 2), int(self.width / 2)
                # height, width = int(self.height), int(self.width)

            else:
                comp = self.y_dequantize(components[k])
                # comment this line if no subsampling
                height, width = self.height, self.width
            comp = self.idct(comp)
            components[k] = self.merging(comp, height, width)
            #
        # comment this line if no subsampling
        image = self.chroma(components["y"], components["cb"], components["cr"])
        # image = torch.cat([components['y'].unsqueeze(3), components['cb'].unsqueeze(3), components['cr'].unsqueeze(3)], dim=3)
        image = self.colors(image)

        image = torch.min(
            255 * torch.ones_like(image), torch.max(torch.zeros_like(image), image)
        )
        return image / 255