import os
import re
import io
import logging
import argparse

import numpy as np
import pandas as pd
from tqdm.auto import tqdm
from datasets import Dataset, DatasetDict, Features, Image, Value

from audiodiffusion import Mel

logging.basicConfig(level=logging.WARN)
logger = logging.getLogger('audio_to_images')


def main(args):
    mel = Mel(x_res=args.resolution[0],
              y_res=args.resolution[1],
              hop_length=args.hop_length,
              sample_rate=args.sample_rate,
              n_fft=args.n_fft)
    os.makedirs(args.output_dir, exist_ok=True)
    audio_files = [
        os.path.join(root, file) for root, _, files in os.walk(args.input_dir)
        for file in files if re.search("\.(mp3|wav|m4a)$", file, re.IGNORECASE)
    ]
    examples = []
    try:
        for audio_file in tqdm(audio_files):
            try:
                mel.load_audio(audio_file)
            except KeyboardInterrupt:
                raise
            except:
                continue
            for slice in range(mel.get_number_of_slices()):
                image = mel.audio_slice_to_image(slice)
                assert (image.width == args.resolution[0] and image.height
                        == args.resolution[1]), "Wrong resolution"
                # skip completely silent slices
                if all(np.frombuffer(image.tobytes(), dtype=np.uint8) == 255):
                    logger.warn('File %s slice %d is completely silent',
                                audio_file, slice)
                    continue
                with io.BytesIO() as output:
                    image.save(output, format="PNG")
                    bytes = output.getvalue()
                examples.extend([{
                    "image": {
                        "bytes": bytes
                    },
                    "audio_file": audio_file,
                    "slice": slice,
                }])
    except Exception as e:
        print(e)
    finally:
        if len(examples) == 0:
            logger.warn('No valid audio files were found.')
            return
        ds = Dataset.from_pandas(
            pd.DataFrame(examples),
            features=Features({
                "image": Image(),
                "audio_file": Value(dtype="string"),
                "slice": Value(dtype="int16"),
            }),
        )
        dsd = DatasetDict({"train": ds})
        dsd.save_to_disk(os.path.join(args.output_dir))
        if args.push_to_hub:
            dsd.push_to_hub(args.push_to_hub)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=
        "Create dataset of Mel spectrograms from directory of audio files.")
    parser.add_argument("--input_dir", type=str)
    parser.add_argument("--output_dir", type=str, default="data")
    parser.add_argument("--resolution",
                        type=str,
                        default="256",
                        help="Either square resolution or width,height.")
    parser.add_argument("--hop_length", type=int, default=512)
    parser.add_argument("--push_to_hub", type=str, default=None)
    parser.add_argument("--sample_rate", type=int, default=22050)
    parser.add_argument("--n_fft", type=int, default=2048)
    args = parser.parse_args()

    if args.input_dir is None:
        raise ValueError(
            "You must specify an input directory for the audio files.")

    # Handle the resolutions.
    try:
        args.resolution = (int(args.resolution), int(args.resolution))
    except ValueError:
        try:
            args.resolution = tuple(int(x) for x in args.resolution.split(","))
            if len(args.resolution) != 2:
                raise ValueError
        except ValueError:
            raise ValueError(
                "Resolution must be a tuple of two integers or a single integer."
            )
    assert isinstance(args.resolution, tuple)

    main(args)