import logging
import os

from funasr.datasets.small_datasets.dataset import ESPnetDataset
from funasr.datasets.small_datasets.preprocessor import build_preprocess
from funasr.samplers.length_batch_sampler import LengthBatchSampler


def build_dataloader(args, mode="train"):
    preprocess_fn = build_preprocess(args, train=mode == "train")
    dest_sample_rate = args.frontend_conf["fs"] if (
                args.frontend_conf is not None and "fs" in args.frontend_conf) else 16000
    if mode == "train":
        data_path_and_name_and_type = args.train_data_path_and_name_and_type
        shape_files = args.train_shape_file
    elif mode == "valid":
        data_path_and_name_and_type = args.valid_data_path_and_name_and_type
        shape_files = args.valid_shape_file
    else:
        raise NotImplementedError(f"mode={mode}")
    dataset = ESPnetDataset(
        data_path_and_name_and_type,
        preprocess=preprocess_fn,
        dest_sample_rate=dest_sample_rate,
    )
    if os.path.exists(os.path.join(data_path_and_name_and_type[0][0].parent, "utt2category")):
        utt2category_file = os.path.join(data_path_and_name_and_type[0][0].parent, "utt2category")
    else:
        utt2category_file = None

    dataset_conf = args.dataset_conf
    batch_sampler = LengthBatchSampler(
        batch_bins=dataset_conf["batch_size"],
        shape_files=shape_files,
        sort_in_batch=dataset_conf["sort_in_batch"] if hasattr(dataset_conf, "sort_in_batch") else "descending",
        sort_batch=dataset_conf["sort_batch"] if hasattr(dataset_conf, "sort_batch") else "ascending",
        drop_last=False,
        padding=True,
    )

    batches = list(batch_sampler)
    bs_list = [len(batch) for batch in batches]
    logging.info(f"[{mode}] dataset:\n{dataset}")
    logging.info(f"[{mode}] Batch sampler: {batch_sampler}")
    logging.info(
        f"[{mode}] mini-batch sizes summary: N-batch={len(bs_list)}, "
        f"mean={np.mean(bs_list):.1f}, min={np.min(bs_list)}, max={np.max(bs_list)}"
    )