import tqdm
import argparse
import pandas as pd
import os
import sys

dir_path = os.path.dirname(os.path.realpath(__file__))
parent_dir_path = os.path.abspath(os.path.join(dir_path, os.pardir))
sys.path.insert(0, parent_dir_path)

import os
import tqdm
import argparse
import pandas as pd
import sentencepiece as spm

from pathlib import Path
from tempfile import NamedTemporaryFile
import sys
import os
import re
from examples.speech_to_text.data_utils import (
    load_df_from_tsv,
    save_df_to_tsv,
    gen_vocab,
)


from pathlib import Path
from examples.speech_to_text.data_utils import (
    load_df_from_tsv,
    save_df_to_tsv,
    gen_config_yaml,
)
from fairseq.data.audio.data_cfg import S2SDataConfig

MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"]


def learn_spm_vocab(args, train_text):
    with NamedTemporaryFile(mode="w") as f:
        for t in train_text:
            f.write(t + "\n")
        gen_vocab(
            Path(f.name),
            Path(args.output_dir).absolute() / f"spm_{args.vocab_type}_{args.lang}",
            args.vocab_type,
            args.vocab_size,
        )


def process(args):
    s2st_tsv_dir = Path(args.s2st_tsv_dir)
    s2tt_tsv_dir = Path(args.s2tt_tsv_dir)
    s2tt_tsv_dir.mkdir(exist_ok=True)
    train_text = []
    for split in ["train", "dev", "test"]:
        manifest = {c: [] for c in MANIFEST_COLUMNS}
        df = load_df_from_tsv(s2st_tsv_dir / f"{split}.tsv")
        data = list(df.T.to_dict().values())
        for item in tqdm.tqdm(data):
            manifest["id"].append(item["id"])
            manifest["audio"].append(item["src_audio"])
            manifest["n_frames"].append(item["src_n_frames"])
            manifest["tgt_text"].append(item["tgt_text"])
            manifest["speaker"].append("None")

            if split == "train":
                train_text.append(item["src_text"])
                train_text.append(item["tgt_text"])
        df = pd.DataFrame.from_dict(manifest)
        save_df_to_tsv(df, s2tt_tsv_dir / f"{split}.tsv")

    with NamedTemporaryFile(mode="w") as f:
        for t in train_text:
            f.write(t + "\n")
        gen_vocab(
            Path(f.name),
            Path(s2tt_tsv_dir).absolute() / f"spm_unigram_joint",
            "unigram",
            10000,
        )


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--s2st-tsv-dir")
    parser.add_argument("--s2tt-tsv-dir")
    args = parser.parse_args()
    process(args)


if __name__ == "__main__":
    main()