from random import choice
from string import ascii_uppercase
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
import os

import sys
from configs import global_config, paths_config
import wandb

from training.coaches.multi_id_coach import MultiIDCoach
from training.coaches.single_id_coach import SingleIDCoach
from utils.ImagesDataset import ImagesDataset


def run_PTI(run_name="", use_wandb=False, use_multi_id_training=False):
    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
    os.environ["CUDA_VISIBLE_DEVICES"] = global_config.cuda_visible_devices

    if run_name == "":
        global_config.run_name = "".join(choice(ascii_uppercase) for i in range(12))
    else:
        global_config.run_name = run_name

    if use_wandb:
        run = wandb.init(
            project=paths_config.pti_results_keyword,
            reinit=True,
            name=global_config.run_name,
        )
    global_config.pivotal_training_steps = 1
    global_config.training_step = 1

    embedding_dir_path = f"{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}"
    os.makedirs(embedding_dir_path, exist_ok=True)

    dataset = ImagesDataset(
        paths_config.input_data_path,
        transforms.Compose(
            [
                transforms.Resize((256, 256)),
                transforms.ToTensor(),
                transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
            ]
        ),
    )

    dataloader = DataLoader(dataset, batch_size=1, shuffle=False)

    if use_multi_id_training:
        coach = MultiIDCoach(dataloader, use_wandb)
    else:
        coach = SingleIDCoach(dataloader, use_wandb)

    coach.train()

    return global_config.run_name


if __name__ == "__main__":
    run_name = f"pti_{paths_config.year}"        
    print(run_name)
    run_PTI(run_name=run_name, use_wandb=False, use_multi_id_training=False)