from pathlib import Path

import torch
from tqdm import tqdm

import variables as V
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomApply

from ml_lib.audio_toolset.audio_augmentation import Speed
from ml_lib.audio_toolset.audio_io import AudioToMel, NormalizeLocal, MelToImage

# Dataset and Dataloaders
# =============================================================================

# Transforms
from ml_lib.audio_toolset.mel_augmentation import NoiseInjection, LoudnessManipulator, ShiftTime, MaskAug
from ml_lib.utils.logging import Logger
from ml_lib.utils.model_io import SavedLightningModels
from ml_lib.utils.transforms import ToTensor
from ml_lib.visualization.tools import Plotter
from util.config import MConfig

# Datasets
from datasets.binar_masks import BinaryMasksDataset


def prepare_dataloader(config_obj):
    mel_transforms = Compose([
        AudioToMel(sr=config_obj.data.sr, n_mels=config_obj.data.n_mels, n_fft=config_obj.data.n_fft,
                   hop_length=config_obj.data.hop_length),
        MelToImage()])
    transforms = Compose([NormalizeLocal(), ToTensor()])
    aug_transforms = Compose([
            NoiseInjection(0.4),
            LoudnessManipulator(0.4),
            ShiftTime(0.3),
            MaskAug(0.2),
        NormalizeLocal(), ToTensor()
    ])

    dataset: Dataset = BinaryMasksDataset(config_obj.data.root, setting='test',
                                          mel_transforms=mel_transforms, transforms=transforms
                                          )
    # noinspection PyTypeChecker
    return DataLoader(dataset, batch_size=config_obj.train.batch_size,
                      num_workers=config_obj.data.worker, shuffle=False)


def restore_logger_and_model(log_dir):
    model = SavedLightningModels.load_checkpoint(models_root_path=log_dir, n=-2)
    model = model.restore()
    if torch.cuda.is_available():
        model.cuda()
    else:
        model.cpu()
    return model


if __name__ == '__main__':
    outpath = Path('output')
    model_type = 'CC'
    parameters = 'CC_213adb16e46592c5a405abfbd693835e/'
    version = 'version_41'
    model_path = Path('/home/steffen/projects/inter_challenge_2020/output/CC/CC_fd2020a7ead9d5c80609a7364741f24b/version_40')
    config_filename = 'config.ini'
    inference_out = 'manual_test_out.csv'

    config = MConfig()
    config.read_file((Path(model_path) / config_filename).open('r'))
    test_dataloader = prepare_dataloader(config)

    loaded_model = restore_logger_and_model(model_path)
    loaded_model.eval()

    with (model_path / inference_out).open(mode='w') as outfile:
        outfile.write(f'file_name,prediction\n')

        for batch in tqdm(test_dataloader, total=len(test_dataloader)):
            batch_x, file_name = batch
            y = loaded_model(batch_x.unsqueeze(0).to(device='cuda' if torch.cuda.is_available() else 'cpu')).main_out
            prediction = (y.squeeze() >= 0.5).int().item()
            prediction = 'clear' if prediction == V.CLEAR else 'mask'
            outfile.write(f'{file_name},{prediction}\n')
    print('Done')