import re
import importlib
import torch
import numpy as np
from PIL import Image
import os
import argparse
import dill as pickle


def save_obj(obj, name):
    with open(name, 'wb') as f:
        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)


def load_obj(name):
    with open(name, 'rb') as f:
        return pickle.load(f)


# returns a configuration for creating a generator
# |default_opt| should be the opt of the current experiment
# |**kwargs|: if any configuration should be overriden, it can be specified here


def copyconf(default_opt, **kwargs):
    conf = argparse.Namespace(**vars(default_opt))
    for key in kwargs:
        print(key, kwargs[key])
        setattr(conf, key, kwargs[key])
    return conf


def tile_images(imgs, picturesPerRow=4):
    """ Code borrowed from
    https://stackoverflow.com/questions/26521365/cleanly-tile-numpy-array-of-images-stored-in-a-flattened-1d-format/26521997
    """

    # Padding
    if imgs.shape[0] % picturesPerRow == 0:
        rowPadding = 0
    else:
        rowPadding = picturesPerRow - imgs.shape[0] % picturesPerRow
    if rowPadding > 0:
        imgs = np.concatenate([imgs, np.zeros((rowPadding, *imgs.shape[1:]), dtype=imgs.dtype)], axis=0)

    # Tiling Loop (The conditionals are not necessary anymore)
    tiled = []
    for i in range(0, imgs.shape[0], picturesPerRow):
        tiled.append(np.concatenate([imgs[j] for j in range(i, i + picturesPerRow)], axis=1))

    tiled = np.concatenate(tiled, axis=0)
    return tiled


# 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, tile=False):
    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

    if image_tensor.dim() == 4:
        # transform each image in the batch
        images_np = []
        for b in range(image_tensor.size(0)):
            one_image = image_tensor[b]
            one_image_np = tensor2im(one_image)
            images_np.append(one_image_np.reshape(1, *one_image_np.shape))
        images_np = np.concatenate(images_np, axis=0)
        if tile:
            images_tiled = tile_images(images_np)
            return images_tiled
        else:
            return images_np

    if image_tensor.dim() == 2:
        image_tensor = image_tensor.unsqueeze(0)
    image_numpy = image_tensor.detach().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:
        image_numpy = image_numpy[:, :, 0]
    return image_numpy.astype(imtype)


def save_image(image_numpy, image_path, create_dir=False):
    if create_dir:
        os.makedirs(os.path.dirname(image_path), exist_ok=True)
    if len(image_numpy.shape) == 2:
        image_numpy = np.expand_dims(image_numpy, axis=2)
    if image_numpy.shape[2] == 1:
        image_numpy = np.repeat(image_numpy, 3, 2)
    image_pil = Image.fromarray(image_numpy)

    # save to png
    image_pil.save(image_path)
    # image_pil.save(image_path.replace('.jpg', '.png'))


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)


def atoi(text):
    return int(text) if text.isdigit() else text


def natural_keys(text):
    '''
    alist.sort(key=natural_keys) sorts in human order
    http://nedbatchelder.com/blog/200712/human_sorting.html
    (See Toothy's implementation in the comments)
    '''
    return [atoi(c) for c in re.split('(\d+)', text)]


def natural_sort(items):
    items.sort(key=natural_keys)


def str2bool(v):
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')


def find_class_in_module(target_cls_name, module):
    target_cls_name = target_cls_name.replace('_', '').lower()
    clslib = importlib.import_module(module)
    cls = None
    for name, clsobj in clslib.__dict__.items():
        if name.lower() == target_cls_name:
            cls = clsobj

    if cls is None:
        print("In %s, there should be a class whose name matches %s in lowercase without underscore(_)" % (module,
                                                                                                           target_cls_name))
        exit(0)

    return cls


def save_network(net, label, epoch, opt):
    save_filename = '%s_net_%s.pth' % (epoch, label)
    save_path = os.path.join(opt.checkpoints_dir, opt.name, save_filename)
    torch.save(net.cpu().state_dict(), save_path)
    if len(opt.gpu_ids) and torch.cuda.is_available():
        net.cuda()


def load_network(net, label, epoch, opt):
    save_filename = '%s_net_%s.pth' % (epoch, label)
    save_dir = os.path.join(opt.checkpoints_dir, opt.name)
    save_path = os.path.join(save_dir, save_filename)
    weights = torch.load(save_path)
    net.load_state_dict(weights)
    return net


def copy_state_dict(state_dict, model, strip=None):
    tgt_state = model.state_dict()
    copied_names = set()
    for name, param in state_dict.items():
        if strip is not None and name.startswith(strip):
            name = name[len(strip):]
        if name not in tgt_state:
            continue
        if isinstance(param, torch.nn.Parameter):
            param = param.data
        if param.size() != tgt_state[name].size():
            print('mismatch:', name, param.size(), tgt_state[name].size())
            continue
        tgt_state[name].copy_(param)
        copied_names.add(name)

    missing = set(tgt_state.keys()) - copied_names
    if len(missing) > 0:
        print("missing keys in state_dict:", missing)


###############################################################################
# Code from
# https://github.com/ycszen/pytorch-seg/blob/master/transform.py
# Modified so it complies with the Citscape label map colors
###############################################################################
def uint82bin(n, count=8):
    """returns the binary of integer n, count refers to amount of bits"""
    return ''.join([str((n >> y) & 1) for y in range(count - 1, -1, -1)])
