from typing import Optional, Sequence, Union

import click

from lhotse.bin.modes import prepare
from lhotse.recipes.iwslt22_ta import prepare_iwslt22_ta
from lhotse.utils import Pathlike


@prepare.command(context_settings=dict(show_default=True))
@click.argument("corpus_dir", type=click.Path(exists=True, dir_okay=True))
@click.argument("splits", type=click.Path(exists=True, dir_okay=True))
@click.argument("output_dir", type=click.Path())
@click.option(
    "-j",
    "--num-jobs",
    type=int,
    default=1,
    help="How many threads to use (can give good speed-ups with slow disks).",
)
@click.option(
    "--normalize-text",
    default=False,
    help="Whether to perform additional text cleaning and normalization from https://aclanthology.org/2022.iwslt-1.29.pdf.",
)
@click.option(
    "--langs",
    default="",
    help="Comma-separated list of language abbreviations for source and target languages",
)
def iwslt22_ta(
    corpus_dir: Pathlike,
    splits: Pathlike,
    output_dir: Pathlike,
    normalize_text: bool,
    langs: str,
    num_jobs: int,
):
    """
    IWSLT_2022 data preparation.
    \b
    This is conversational telephone speech collected as 8kHz-sampled data.
    The catalog number LDC2022E01 corresponds to the train, dev, and test1
    splits of the iwslt2022 shared task.
    To obtaining this data your institution needs to have an LDC subscription.
    You also should download the predined splits with
    git clone https://github.com/kevinduh/iwslt22-dialect.git
    """
    langs_list = langs.split(",")
    prepare_iwslt22_ta(
        corpus_dir,
        splits,
        output_dir=output_dir,
        num_jobs=num_jobs,
        clean=normalize_text,
        langs=langs_list,
    )