import os
import sys
from configs.config_abl_skin_var_3 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, UV2MeshLabel
from dataset import DatasetFactory, StaticDataset
from custom_utils.smpl import SMPL
from models.cvae_model import CVAE
from custom_utils.supersmpl_io import writeOBJ


uv2mesh = UV2MeshLabel(res=256)


@tf.function
def step(batch):
    pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])

    mesh_pred = tf.math.argmax(uv2mesh(pred), axis=-1)

    return mesh_pred


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']
    smpl = SMPL(use_one_hot=False)
    model = CVAE(config)

    # 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)

    ids = os.listdir(os.path.join(folder, 'pose_shape'))
    ids = [id.split('_')[0] for id in ids]
    ids = set(ids)

    iteration = '70000'

    load_model(model, 'checkpoints/' + config['name'], iteration=iteration)


    factory = DatasetFactory()
    dataset_test = factory.get_dataset(folder=config['test_dir'], 
                                       batch_size=1, 
                                       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'],
                                       frames_file='/home/workspace/data/dataset/test_ready/frames_pose_cluster_20_sample_10.txt')
    static_dataset_test = StaticDataset(folder=config['test_dir'], 
                                        signature=config['signature_static'], 
                                        resolutions=[config['resolution']], 
                                        in_memory=True)

    # faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
    # ids = os.listdir(faces_folder)
    # ids = [id.split('.')[0] for id in ids]
    # faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }

    for batch in tqdm.tqdm(dataset_test):
        batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))