import os
import os.path as osp
import logging
import random
import argparse

import torchvision

import utils
import utils.options as option
import utils.util as util
from models.tacotron2.text import sequence_to_text
from trainer.ExtensibleTrainer import ExtensibleTrainer
from data import create_dataset, create_dataloader
from tqdm import tqdm
import torch
import numpy as np
from scipy.io import wavfile


def forward_pass(model, data, output_dir, opt, b):
    with torch.no_grad():
        model.feed_data(data, 0)
        model.test()

    if 'real_text' in opt['eval'].keys():
        real = data[opt['eval']['real_text']][0]
        print(f'{b} Real text: "{real}"')

    pred_seq = model.eval_state[opt['eval']['gen_text']][0][0]  # Grab first sequence, which should represent the most likely sequence.
    return sequence_to_text(pred_seq)


if __name__ == "__main__":
    # Set seeds
    torch.manual_seed(5555)
    random.seed(5555)
    np.random.seed(5555)

    #### options
    torch.backends.cudnn.benchmark = True
    want_metrics = False
    parser = argparse.ArgumentParser()
    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_gpt_asr_hf.yml')
    opt = option.parse(parser.parse_args().opt, is_train=False)
    opt = option.dict_to_nonedict(opt)
    utils.util.loaded_options = opt

    util.mkdirs(
        (path for key, path in opt['path'].items()
         if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
    util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
                      screen=True, tofile=True)
    logger = logging.getLogger('base')
    logger.info(option.dict2str(opt))

    test_loaders = []
    for phase, dataset_opt in sorted(opt['datasets'].items()):
        test_set, collate_fn = create_dataset(dataset_opt, return_collate=True)
        test_loader = create_dataloader(test_set, dataset_opt, collate_fn=collate_fn)
        logger.info('Number of test texts in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set)))
        test_loaders.append(test_loader)

    model = ExtensibleTrainer(opt)

    batch = 0
    output = open('results.tsv', 'w')
    for test_loader in test_loaders:
        dataset_dir = opt['path']['results_root']
        util.mkdir(dataset_dir)

        tq = tqdm(test_loader)
        for data in tq:
            #if data['clip'].shape[-1] > opt['networks']['asr_gen']['kwargs']['max_mel_frames']*255:
            #    continue
            pred = forward_pass(model, data, dataset_dir, opt, batch)
            pred = pred.replace('_', '')
            output.write(f'{pred}\t{os.path.basename(data["filenames"][0])}\n')
            print(pred)
            output.flush()
            batch += 1