# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

from __future__ import print_function

import os

import PIL
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from torchvision.utils import make_grid


def array2image(ndarray):
    return PIL.Image.fromarray(np.uint8(ndarray)).convert('RGB')


def tensor_to_ndarray(tensor, nrow=1, padding=0, normalize=True):
    grid = make_grid(tensor, nrow, padding, normalize)
    return grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()


def irregular_hole_synthesize(image, mask):
    img_np = np.array(image).astype("uint8")
    mask_np = np.array(mask).astype("uint8")
    mask_np = mask_np / 255
    img_new = img_np * (1 - mask_np) + mask_np * 255
    return PIL.Image.fromarray(img_new.astype("uint8")).convert("RGB")


# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
    if isinstance(image_tensor, list):
        image_numpy = []
        for i in range(len(image_tensor)):
            image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
        return image_numpy
    image_numpy = image_tensor.cpu().float().numpy()
    if normalize:
        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
    else:
        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
    image_numpy = np.clip(image_numpy, 0, 255)
    if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:
        image_numpy = image_numpy[:, :, 0]
    return image_numpy.astype(imtype)


# Converts a one-hot tensor into a colorful label map
def tensor2label(label_tensor, n_label, imtype=np.uint8):
    if n_label == 0:
        return tensor2im(label_tensor, imtype)
    label_tensor = label_tensor.cpu().float()
    if label_tensor.size()[0] > 1:
        label_tensor = label_tensor.max(0, keepdim=True)[1]
    label_tensor = Colorize(n_label)(label_tensor)
    label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
    return label_numpy.astype(imtype)


def scale_tensor(img_tensor, default_scale=256):
    _, _, w, h = img_tensor.shape
    if w < h:
        ow = default_scale
        oh = h / w * default_scale
    else:
        oh = default_scale
        ow = w / h * default_scale

    oh = int(round(oh / 16) * 16)
    ow = int(round(ow / 16) * 16)

    return F.interpolate(img_tensor, [ow, oh], mode="bilinear")


def data_transforms(img, size="full_size", method=Image.BICUBIC):
    if size == "full_size":
        ow, oh = img.size
        h = int(round(oh / 16) * 16)
        w = int(round(ow / 16) * 16)
        if (h == oh) and (w == ow):
            return img
        return img.resize((w, h), method)

    elif size == "scale_256":
        ow, oh = img.size
        pw, ph = ow, oh
        if ow < oh:
            ow = 256
            oh = ph / pw * 256
        else:
            oh = 256
            ow = pw / ph * 256

        h = int(round(oh / 16) * 16)
        w = int(round(ow / 16) * 16)
        if (h == ph) and (w == pw):
            return img
        return img.resize((w, h), method)


def save_image(image_numpy, image_path):
    image_pil = Image.fromarray(image_numpy)
    image_pil.save(image_path)


def mkdirs(paths):
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)


def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)