#!/bin/bash
set -euo pipefail

PROGPATH=$(realpath "$0")
PROGDIR=$(dirname "${PROGPATH}")

source "${PROGDIR}/shell.functions"

if [ "${HELP}" == "true" ] ; then
    cat <<EOF >&2
Usage: $0 [ --cleanup-archives ] [ --cleanup-input-files ] [ --data-dir=<data_dir> ]

    --cleanup-archives    : Automatically clean up any downloaded archvies after
                            extraction.
    --cleanup-intermediate-files
                          : Automatically clean up the intermediate files after they've
                          : converted to 16k.
    <data_dir>            : Path to the data directory.
                          :    Default: ${DATA_DIR}

EOF
    exit 1
fi

mkdir -p "${DATA_DIR}/training_datasets/downloads" || :
cd "${DATA_DIR}/training_datasets"

HF_RIR_REPO_ID="TaterTotterson/MIT_environmental_impulse_responses"
HF_RIR_API_URL="https://huggingface.co/api/datasets/${HF_RIR_REPO_ID}"
HF_RIR_SOURCE_KEY="hf_mit_environmental_impulse_responses"
AUDIO_DIR="./mit_rirs"
mkdir -p "${AUDIO_DIR}" || :
AUDIO16K_DIR="./mit_rirs_16k"
mkdir -p "${AUDIO16K_DIR}" || :
AUDIO_FILECOUNT="./downloads/mit_rir_filecount"
AUDIO_IN_GLOB="*.wav"

declare -A filecounts=( [${HF_RIR_SOURCE_KEY}]=0 )
get_filecounts filecounts "${AUDIO_FILECOUNT}"

echo "===== Checking MIT environmental RIRs ====="

download_hf_mit_rirs() {
    source ${DATA_DIR}/.venv/bin/activate
    python - "${HF_RIR_REPO_ID}" "${HF_RIR_API_URL}" "${AUDIO_DIR}" <<-'EOF'
import json
import sys
import urllib.parse
import urllib.request
from pathlib import Path

repo_id = sys.argv[1]
api_url = sys.argv[2]
audio_dir = Path(sys.argv[3])
audio_dir.mkdir(parents=True, exist_ok=True)

request = urllib.request.Request(api_url, headers={"User-Agent": "WakeWordTrainer/1.0"})
with urllib.request.urlopen(request, timeout=30) as response:
    metadata = json.loads(response.read().decode("utf-8"))

files = sorted(
    sibling.get("rfilename", "")
    for sibling in metadata.get("siblings", [])
    if str(sibling.get("rfilename", "")).startswith("16khz/")
    and str(sibling.get("rfilename", "")).lower().endswith(".wav")
)
if not files:
    raise SystemExit("Hugging Face MIT RIR dataset did not list any 16khz WAV files")

downloaded = 0
skipped = 0
for rel in files:
    target = audio_dir / rel
    if target.exists() and target.stat().st_size > 0:
        skipped += 1
        continue
    target.parent.mkdir(parents=True, exist_ok=True)
    encoded = urllib.parse.quote(rel, safe="/")
    url = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{encoded}"
    with urllib.request.urlopen(url, timeout=60) as response:
        target.write_bytes(response.read())
    if not target.exists() or target.stat().st_size == 0:
        raise SystemExit(f"download failed for {rel}")
    downloaded += 1

print(f"   Hugging Face MIT environmental RIR download complete ({downloaded} downloaded, {skipped} reused)")
print(f"   MIT environmental RIR files available: {len(files)}")
EOF
}

converter() {
    source ${DATA_DIR}/.venv/bin/activate
    python - "${AUDIO_DIR}" "${AUDIO16K_DIR}" <<-EOF
import os, sys, subprocess, scipy.io.wavfile, numpy as np
from pathlib import Path
import soundfile as sf
import librosa
from tqdm import tqdm

def write_wav(dst: Path, data: np.ndarray, sr: int):
    x = np.clip(data, -1.0, 1.0)
    scipy.io.wavfile.write(dst, sr, (x * 32767).astype(np.int16))

rir_in = Path(sys.argv[1])
rir_out = Path(sys.argv[2])

waves = list(rir_in.rglob("*.wav"))
try:
    print("   MIT environmental RIR normalizing to 16k…")
    # Normalize to 16k mono
    for p in tqdm(waves, desc="   MIT environmental RIR (resample 16k mono)"):
        outfile = Path(rir_out / p.name)
        if outfile.exists():
            continue
        a, sr = sf.read(p, always_2d=False)
        if a.ndim > 1:
            a = a[:, 0]
        if sr != 16000:
            a, _ = librosa.load(p, sr=16000, mono=True)
        write_wav(outfile, a, 16000)
    print("   MIT environmental RIR normalization complete")
except Exception as e2:
    print(f"   MIT environmental RIR preparation failed: {e2}")
    raise
EOF
}

expected_filecount=${filecounts[${HF_RIR_SOURCE_KEY}]}
actual_filecount=$(find "${AUDIO16K_DIR}" -name '*.wav' 2>/dev/null | wc -l) || :
write_filecount=false

if [ "${actual_filecount}" -ne 0 ] && [ "${actual_filecount}" -eq "${expected_filecount}" ] ; then
    echo "   Existing ${AUDIO16K_DIR} valid"
else
    actual_filecount=$(find "${AUDIO_DIR}" -name "${AUDIO_IN_GLOB}" 2>/dev/null | wc -l) || :
    if [ "${actual_filecount}" -eq 0 ] || [ "${expected_filecount}" -eq 0 ] || [ "${actual_filecount}" -ne "${expected_filecount}" ] ; then
        rm -rf "${AUDIO_DIR}" || :
        mkdir -p "${AUDIO_DIR}" || :
        echo "   Downloading MIT environmental impulse responses from Hugging Face mirror"
        download_hf_mit_rirs
    fi

    converter
    actual_filecount=$(find "${AUDIO16K_DIR}" -name "*.wav" 2>/dev/null | wc -l) || :
    filecounts[${HF_RIR_SOURCE_KEY}]="${actual_filecount}"
    write_filecount=true
fi

if ${write_filecount} ; then
    write_filecounts filecounts "${AUDIO_FILECOUNT}"
fi

if "${CLEANUP_INTERMEDIATE_FILES}" && [ -d "${AUDIO_DIR}" ]; then
    echo "   Cleaning up ${AUDIO_DIR}"
    rm -rf "${AUDIO_DIR}"
fi

echo "   MIT environmental RIRs complete"
exit 0