import argparse
from faster_whisper import available_models
from utils.constants import LANGUAGE_CODES
from main import process
from utils.convert import str2bool, str2timeinterval


def main():
    """
    Main entry point for the script.

    Parses command line arguments, processes the inputs using the specified options,
    and performs transcription or translation based on the specified task.
    """
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument("--audio_channel", default="0",
                        type=int, help="audio channel index to use")
    parser.add_argument("--sample_interval", type=str2timeinterval, default=None,
                        help="generate subtitles for a specific \
                              fragment of the video (e.g. 01:02:05-01:03:45)")
    parser.add_argument("--model", default="small",
                        choices=available_models(), help="name of the Whisper model to use")
    parser.add_argument("--device", type=str, default="auto",
                        choices=["cpu", "cuda", "auto"],
                        help="Device to use for computation (\"cpu\", \"cuda\", \"auto\")")
    parser.add_argument("--compute_type", type=str, default="default", choices=[
                        "int8", "int8_float32", "int8_float16", "int8_bfloat16",
                        "int16", "float16", "bfloat16", "float32"],
                        help="Type to use for computation. \
                              See https://opennmt.net/CTranslate2/quantization.html.")
    parser.add_argument("--beam_size", type=int, default=5,
                        help="model parameter, tweak to increase accuracy")
    parser.add_argument("--no_speech_threshold", type=float, default=0.6,
                        help="model parameter, tweak to increase accuracy")
    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True,
                        help="model parameter, tweak to increase accuracy")
    parser.add_argument("--task", type=str, default="transcribe",
                        choices=["transcribe", "translate"],
                        help="whether to perform X->X speech recognition ('transcribe') \
                              or X->English translation ('translate')")
    parser.add_argument("--language", type=str, default="auto",
                        choices=LANGUAGE_CODES,
                        help="What is the origin language of the video? \
                              If unset, it is detected automatically.")

    args = parser.parse_args().__dict__

    process(args)


if __name__ == '__main__':
    main()