import click
import os
import sys
import pickle
import numpy as np
from PIL import Image
import torch
from configs import paths_config, hyperparameters, global_config
from IPython.display import display
import matplotlib.pyplot as plt
from scripts.latent_editor_wrapper import LatentEditorWrapper

image_dir_name = 'images'
use_multi_id_training = False
global_config.device = 'cuda'
paths_config.e4e = 'e4e_ffhq_encode.pt'
paths_config.input_data_id = image_dir_name
paths_config.input_data_path = f'{image_dir_name}'
paths_config.stylegan2_ada_ffhq = 'ffhq.pkl'
paths_config.checkpoints_dir = 'checkpoints'
paths_config.style_clip_pretrained_mappers = ''
hyperparameters.use_locality_regularization = False
hyperparameters.lpips_type = 'squeeze'

from scripts.run_pti import run_PTI

def load_generator(model_id):
  with open(f'{paths_config.checkpoints_dir}/model_{model_id}_file.pt', 'rb') as f_new: 
    new_G = torch.load(f_new).cuda()
  return new_G
  
def tensor_to_pil(img):
  img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()[0] 
  plt.axis('off') 
  resized_image = Image.fromarray(img,mode='RGB').resize((256,256)) 
  return resized_image

def tune():
    model_id = run_PTI(run_name='',use_wandb=False, use_multi_id_training=False)
    w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}'
    embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/file'
    w_pivot = torch.load(f'{embedding_dir}/0.pt')
    new_G = load_generator(model_id)
    new_image = new_G.synthesis(w_pivot, noise_mode='const', force_fp32 = True)
    tensor_to_pil(new_image).save("output/out.png")
   
#----------------------------------------------------------------------------
if __name__ == '__main__':
    tune()

#----------------------------------------------------------------------------