import os
import sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')

os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '3'

import time
import argparse
import wandb
import tqdm
import random
import numpy as np
import plotly.graph_objects as go
import plotly.express as ex
from PIL import Image
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision.transforms import Resize
from torchvision.transforms.functional import InterpolationMode
from pathlib import Path
import importlib

from data.utils import Frame
from data.dataset import read_frames_file
from utils.utils import save_stl, create_temp_frames
from utils.checkpoints import CheckpointSaver, load_model
from utils.postprocess import fix_collision
from losses.losses import collision_loss, normal_loss, kl_loss
from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary_html, maskFacesBatch, get_summary_str, plot_3d_collision, group_inputs_for_levels
from smpl.smpl_torch import SMPL
from plotly.subplots import make_subplots
from models.pyramid_model import Pyramid
from data.dataset import Dataset
from utils.checkpoints import CheckpointSaver, load_model
from torch.utils.data import DataLoader
import trimesh as tm
from zipfile import ZipFile

from configs.iccvw.pyr_64_128_256_512_abl import config as config_1


uv2mesh = UV2Mesh(res=256, is_skirt=False, is_smpl_orig=config_1['is_cape'])
uv2mesh_skirt = UV2Mesh(res=256, is_skirt=True, is_smpl_orig=config_1['is_cape'])


def step_pyramid(model, batch, config):
    with torch.inference_mode():
        levels_input, conds = group_inputs_for_levels(batch, config)
        out_levels = model([levels_input, conds])
        pred, _, _ = out_levels[-1]

        mesh_pred = uv2mesh(pred) if not config['is_skirt'] else uv2mesh_skirt(pred)

        if config['use_skinning']:
            shape = batch['pose_shape'][:, 72:]
            gender = torch.greater_equal(shape[:, 10] , 0.5).to(dtype=torch.int64)
            shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])

            mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
            if not config['is_cape']:
                mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
            else:
                mesh_pred_posed = mesh_pred_shaped
        else:
            mesh_pred_posed = mesh_pred

    return mesh_pred_posed


if __name__ == '__main__':
    results_folder = Path('/data-local/127_2/results')
    out_folder = Path('temp/supplementary')
    os.makedirs(out_folder, exist_ok=True)

    smpl = SMPL(orig_smpl=config_1['is_cape'], path='smpl', device=config_1['device'])
    dataset_resolutions = []
    dataset_resolutions = [64, 128, 256, 512]

    experiments = {
                #    'inc': { 'nonskirt': ('pyr_64_128_256_512', 100000), 'skirt': ('pyr_64_128_256_512_skirt', 200000) },
                   'e2e': { 'nonskirt': ('pyr_64_128_256_512_e2e', 250000), 'skirt': ('pyr_64_128_256_512_skirt_e2e', 200000) },
                #    'skin': { 'nonskirt': ('pyr_64_128_256_512_skin', 225000), 'skirt': ('pyr_64_128_256_512_skin_skirt', 200000) },
                #    'norm': { 'nonskirt': ('pyr_64_128_256_512_norm', 200000), 'skirt': ('pyr_64_128_256_512_norm_skirt', 200000) },
                #    'reg': { 'nonskirt': ('pyr_64_128_256_512_reg', 200000), 'skirt': ('pyr_64_128_256_512_reg_skirt', 200000) },
    }

    configs = dict()
    models = dict()

    for experiment in experiments.keys():
        configs[experiment] = dict()
        configs[experiment]['nonskirt'] = importlib.import_module('configs.iccvw.' + experiments[experiment]['nonskirt'][0]).config
        configs[experiment]['skirt'] = importlib.import_module('configs.iccvw.' + experiments[experiment]['skirt'][0]).config

        models[experiment] = dict()
        models[experiment]['nonskirt'] = Pyramid(configs[experiment]['nonskirt'])
        models[experiment]['skirt'] = Pyramid(configs[experiment]['skirt'])

        load_model(models[experiment]['nonskirt'], 'checkpoints/iccvw/' + configs[experiment]['nonskirt']['name'], iteration=experiments[experiment]['nonskirt'][1])
        load_model(models[experiment]['skirt'], 'checkpoints/iccvw/' + configs[experiment]['skirt']['name'], iteration=experiments[experiment]['skirt'][1])

        models[experiment]['nonskirt'].eval()
        models[experiment]['skirt'].eval()


    # frames = ['00054_1_Dress', '00005_1_Jumpsuit']
    # frames = ['03849_27_Dress', '00054_1_Dress', '01852_70_Tshirt']
    # frames = ['01852_70_Tshirt'] # , '03849_27_Dress', '00054_1_Dress'
    # frames = [Frame(frame_str=frame) for frame in frames]
            
    frames_file = configs['e2e']['nonskirt']['train_frames']
    with open(os.path.join(config_1['train_dir'], frames_file), 'r') as f:
        frames = f.readlines()
    frames_1 = [Frame(frames.split()[0], int(frames.split()[1]), frames.split()[2]) for frames in frames]
    
    frames_file = configs['e2e']['skirt']['train_frames']
    with open(os.path.join(config_1['train_dir'], frames_file), 'r') as f:
        frames = f.readlines()
    frames_2 = [Frame(frames.split()[0], int(frames.split()[1]), frames.split()[2]) for frames in frames]
    
    frames = frames_1 + frames_2
    random.shuffle(frames)

    body_faces = smpl.f

    for frame in frames:
        create_temp_frames(config_1['train_dir'], frame)
        dataset = Dataset(config_1['train_dir'], config_1['signature'], device=config_1['device'], frames_file='frames_temp.txt', use_cache=True, resolutions=dataset_resolutions, is_cape=False)
        dataloader = DataLoader(dataset, batch_size=4, drop_last=True, shuffle=True, )

        model_picker = 'skirt' if frame.garment in ['Dress', 'Skirt'] else 'nonskirt'
        
        fix_data = np.load(Path(dataset.folder) / 'mesh_fixed' / f'{frame.id}_{frame.garment}.npz')
        mask = fix_data['mask']
        faces = fix_data['faces']

        batch = next(iter(dataloader))
        body = batch['mesh_body_posed'][0].cpu().numpy()
        gt = batch['mesh_posed'][0].cpu().numpy()

        # save_stl(out_folder / f'{frame}_gt.stl', gt[mask, :], faces)
        # save_stl(out_folder / f'{frame}_gt_body.stl', body, body_faces)

        for experiment in experiments.keys():

            pred = step_pyramid(models[experiment][model_picker], batch, configs[experiment][model_picker])

            mesh_posed_fixed = fix_collision(pred, batch['mesh_body_posed'], batch['mesh_mask']).cpu().numpy()
            mesh_posed_fixed = mesh_posed_fixed[0, mask, :]
            try:
                m = tm.Trimesh(mesh_posed_fixed, faces, process=False, validate=False)
                tm.smoothing.filter_humphrey(m, iterations=2)
                verts = m.vertices
            except:
                verts = mesh_posed_fixed
                print('failed to apply smoothing')

            # save_stl(out_folder / f'{frame}_{experiment}_body.stl', body, body_faces)
            # save_stl(out_folder / f'{frame}_{experiment}_mesh.stl', verts, faces)

            fig = go.Figure(data=[go.Mesh3d(x=verts[:,0], y=verts[:,2], z=verts[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1),
                                  go.Mesh3d(x=body[:,0], y=body[:,2], z=body[:,1], i=body_faces[:,0], j=body_faces[:,1], k=body_faces[:,2], color='gray', opacity=1)
                                  ])
            fig.update_layout(scene=dict(aspectmode='cube'))
            fig.update_layout(scene=dict(xaxis=dict(range=[-1, 1], ), yaxis=dict(range=[-1, 1], ), zaxis=dict(range=[-1, 1], ), ), )
            fig.update_layout(title_text=f'{frame} {experiment}')
            fig.show()
            pass

        pass