from PIL import Image
import numpy as np
import torch
import torch.nn as nn
from torchvision import transforms
import transformer as transformer
import StyTR as StyTR
from collections import OrderedDict
import tensorflow_hub as tfhub
import tensorflow as tf
import os
import cv2 
import paddlehub as phub


############################################# TRANSFORMER ############################################

def style_transform(h,w):
    k = (h,w)
    transform_list = []    
    transform_list.append(transforms.CenterCrop((h,w)))
    transform_list.append(transforms.ToTensor())
    transform = transforms.Compose(transform_list)
    return transform

def content_transform():
    transform_list = []   
    transform_list.append(transforms.ToTensor())
    transform = transforms.Compose(transform_list)
    return transform

def StyleTransformer(content_img: Image, style_img: Image):
    vgg_path        = 'vgg_normalised.pth'
    decoder_path    = 'decoder_iter_160000.pth'
    Trans_path      = 'transformer_iter_160000.pth'
    embedding_path  = 'embedding_iter_160000.pth'
    # Advanced options
    content_size=640
    style_size=640

    vgg = StyTR.vgg
    vgg.load_state_dict(torch.load(vgg_path))
    vgg = nn.Sequential(*list(vgg.children())[:44])

    decoder = StyTR.decoder
    Trans = transformer.Transformer()
    embedding = StyTR.PatchEmbed()

    decoder.eval()
    Trans.eval()
    vgg.eval()

    new_state_dict = OrderedDict()
    state_dict = torch.load(decoder_path)
    decoder.load_state_dict(state_dict)

    new_state_dict = OrderedDict()
    state_dict = torch.load(Trans_path)
    Trans.load_state_dict(state_dict)

    new_state_dict = OrderedDict()
    state_dict = torch.load(embedding_path)
    embedding.load_state_dict(state_dict)

    network = StyTR.StyTrans(vgg,decoder,embedding,Trans)
    network.eval()

    content_tf = content_transform() 
    style_tf   = style_transform(style_size,style_size)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    network.to(device)
    content = content_tf(content_img.convert("RGB"))   
    style = style_tf(style_img.convert("RGB"))
    style = style.to(device).unsqueeze(0)
    content = content.to(device).unsqueeze(0)
    with torch.no_grad():
        output= network(content,style)  
    output = output[0].cpu().squeeze()
    output = output.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
    return Image.fromarray(output)
   
############################################## STYLE-GAN #############################################


def StyleGAN(content_image, style_image):
    style_transfer_model = tfhub.load("https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2")

    content_image = tf.convert_to_tensor(content_image, np.float32)[tf.newaxis, ...] / 255.
    style_image = tf.convert_to_tensor(style_image, np.float32)[tf.newaxis, ...] / 255.
    output = style_transfer_model(content_image, style_image)
    stylized_image = output[0]
    return Image.fromarray(np.uint8(stylized_image[0] * 255))

########################################### STYLE PROJECTION ##########################################



def styleProjection(content_image,style_image):
    stylepro_artistic = phub.Module(name="stylepro_artistic")
    result = stylepro_artistic.style_transfer(
    images=[{
        'content': np.array(content_image.convert('RGB') )[:, :, ::-1],
        'styles': [np.array(style_image.convert('RGB') )[:, :, ::-1]]
    }])

    return Image.fromarray(np.uint8(result[0]['data'])[:,:,::-1]).convert('RGB')


def create_styledSofa(content_image,style_image):
    output = StyleTransformer(content_image,style_image)
    return output