import os
import sys
from configs.config_abl_all import config

os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        # Currently, memory growth needs to be the same across GPUs
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        # Memory growth must be set before GPUs have been initialized
        print(e)

import glob
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
from postprocessing import fix_collision

if config['is_skirt']:
    from losses import edge_loss_skirt as edge_loss
else:
    from losses import edge_loss



from losses import collision_ratio, kl_loss #, concept_loss
# from losses import collision_penalty as collision_penalty
from losses import collision_loss as collision_penalty
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
from dataset import DatasetFactory, StaticDataset
from custom_utils.smpl import SMPL
from models.cvae_model import CVAE
from custom_utils.supersmpl_io import writeOBJ




if __name__ == '__main__':
    # init_wandb()
    parser = argparse.ArgumentParser(description='')
    parser.add_argument('--name', type=str)
    args = vars(parser.parse_args())

    smpl = SMPL(use_one_hot=config['use_one_hot'])
    skirt_modifier = '_skirt' if config['is_skirt'] else ''

    folder = config['test_dir']


    # str_split = config['name'].split('_')
    # test_idx = str_split.index('test')
    # checkpoint_name = '_'.join(str_split[:test_idx])
    checkpoint_name = config['name']

    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)


    output_folder = '/home/workspace/data/results/support'


    os.makedirs(output_folder + '/bodies', exist_ok=True)
    os.makedirs(output_folder + '/faces', exist_ok=True)

    config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
                            'mesh_body_posed': (14475, 3,),
                            }
    config['signature_static'] = { 
                                'mesh_mask': (14475,),
                                }

    factory = DatasetFactory()
    dataset_test = factory.get_dataset(folder=config['test_dir'], 
                                       batch_size=config['batch_size'], 
                                       signature=config['signature_dynamic'], 
                                       resolutions=[config['resolution']], 
                                       in_memory=False, 
                                       do_repeat=False,
                                       use_one_hot=config['use_one_hot'],
                                       use_no_orient=config['remove_global_orientation'])
    static_dataset_test = StaticDataset(folder=config['test_dir'], 
                                        signature=config['signature_static'], 
                                        resolutions=[config['resolution']], 
                                        in_memory=False)
   
    for batch in tqdm.tqdm(dataset_test):
        batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))

        # mask = batch['mesh_mask'].numpy().astype(bool)
        body = batch['mesh_body_posed'].numpy()
        # _, faces = maskFacesBatch(np.zeros((body.shape[0],14475,3)), F, mask)

        for i in range(body.shape[0]):
            id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
            writeOBJ(os.path.join(output_folder, 'bodies', id + '_' + frame_nr + '_body_no_orient.obj'), body[i], F)
            # np.save(os.path.join(output_folder, 'faces', id + '_' + frame_nr + '.npy'), faces[i].astype(np.int32))