import numpy as np
import torch
from skimage.exposure import match_histograms


def calc_mean_std(feat, eps=1e-5):
    # eps is a small value added to the variance to avoid divide-by-zero.
    size = feat.size()
    assert len(size) == 4
    N, C = size[:2]
    feat_var = feat.view(N, C, -1).var(dim=2) + eps
    feat_std = feat_var.sqrt().view(N, C, 1, 1)
    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
    return feat_mean, feat_std


def adaptive_instance_normalization(content_feat, style_feat):
    assert content_feat.size()[:2] == style_feat.size()[:2]
    size = content_feat.size()
    style_mean, style_std = calc_mean_std(style_feat)
    content_mean, content_std = calc_mean_std(content_feat)

    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(
        size
    )
    return normalized_feat * style_std.expand(size) + style_mean.expand(size)


## AdaMean
def adaptive_mean_normalization(content_feat, style_feat):
    assert content_feat.size()[:2] == style_feat.size()[:2]
    size = content_feat.size()
    style_mean, style_std = calc_mean_std(style_feat)
    content_mean, content_std = calc_mean_std(content_feat)

    normalized_feat = content_feat - content_mean.expand(size)
    return normalized_feat + style_mean.expand(size)


## AdaStd
def adaptive_std_normalization(content_feat, style_feat):
    assert content_feat.size()[:2] == style_feat.size()[:2]
    size = content_feat.size()
    style_mean, style_std = calc_mean_std(style_feat)
    content_mean, content_std = calc_mean_std(content_feat)

    normalized_feat = (content_feat) / content_std.expand(size)
    return normalized_feat * style_std.expand(size)


## EFDM
def exact_feature_distribution_matching(content_feat, style_feat):
    assert content_feat.size() == style_feat.size()
    B, C, W, H = (
        content_feat.size(0),
        content_feat.size(1),
        content_feat.size(2),
        content_feat.size(3),
    )
    value_content, index_content = torch.sort(
        content_feat.view(B, C, -1)
    )  # sort conduct a deep copy here.
    value_style, _ = torch.sort(
        style_feat.view(B, C, -1)
    )  # sort conduct a deep copy here.
    inverse_index = index_content.argsort(-1)
    new_content = content_feat.view(B, C, -1) + (
        value_style.gather(-1, inverse_index) - content_feat.view(B, C, -1).detach()
    )

    return new_content.view(B, C, W, H)


## HM
def histogram_matching(content_feat, style_feat):
    assert content_feat.size() == style_feat.size()
    B, C, W, H = (
        content_feat.size(0),
        content_feat.size(1),
        content_feat.size(2),
        content_feat.size(3),
    )
    x_view = content_feat.view(-1, W, H)
    image1_temp = match_histograms(
        np.array(x_view.detach().clone().cpu().float().transpose(0, 2)),
        np.array(
            style_feat.view(-1, W, H).detach().clone().cpu().float().transpose(0, 2)
        ),
    )
    image1_temp = (
        torch.from_numpy(image1_temp)
        .float()
        .to(content_feat.device)
        .transpose(0, 2)
        .view(B, C, W, H)
    )
    return content_feat + (image1_temp - content_feat).detach()


def _calc_feat_flatten_mean_std(feat):
    # takes 3D feat (C, H, W), return mean and std of array within channels
    assert feat.size()[0] == 3
    assert isinstance(feat, torch.FloatTensor)
    feat_flatten = feat.view(3, -1)
    mean = feat_flatten.mean(dim=-1, keepdim=True)
    std = feat_flatten.std(dim=-1, keepdim=True)
    return feat_flatten, mean, std


def _mat_sqrt(x):
    U, D, V = torch.svd(x)
    return torch.mm(torch.mm(U, D.pow(0.5).diag()), V.t())


def coral(source: torch.Tensor, target: torch.Tensor):
    # assume both source and target are 3D array (C, H, W)
    # Note: flatten -> f
    
    source_f, source_f_mean, source_f_std = _calc_feat_flatten_mean_std(source.to('cpu'))
    source_f_norm = (
        source_f - source_f_mean.expand_as(source_f)
    ) / source_f_std.expand_as(source_f)
    source_f_cov_eye = torch.mm(source_f_norm, source_f_norm.t()) + torch.eye(3)

    target_f, target_f_mean, target_f_std = _calc_feat_flatten_mean_std(target.to('cpu'))
    target_f_norm = (
        target_f - target_f_mean.expand_as(target_f)
    ) / target_f_std.expand_as(target_f)
    target_f_cov_eye = torch.mm(target_f_norm, target_f_norm.t()) + torch.eye(3)

    source_f_norm_transfer = torch.mm(
        _mat_sqrt(target_f_cov_eye),
        torch.mm(torch.inverse(_mat_sqrt(source_f_cov_eye)), source_f_norm),
    )

    source_f_transfer = source_f_norm_transfer * target_f_std.expand_as(
        source_f_norm
    ) + target_f_mean.expand_as(source_f_norm)
    
    return source_f_transfer.view(source.size())