#!/usr/bin/env python3
"""SOTAKA Song Convert engine — Seed-VC (unified Space)."""

from __future__ import annotations

import os
import sys
import warnings
from pathlib import Path
from typing import Optional, Tuple

os.environ.setdefault("TORCHDYNAMO_DISABLE", "1")
os.environ.setdefault("TORCH_COMPILE_DISABLE", "1")

ROOT = Path(__file__).resolve().parent
SEED_VC = ROOT / "seed-vc"
sys.path.insert(0, str(SEED_VC))
os.environ.setdefault("HF_HUB_CACHE", str(SEED_VC / "checkpoints" / "hf_cache"))


def _seed_cwd():
    """Seed-VC uses relative checkpoint paths — run under its folder."""
    os.chdir(SEED_VC)

import librosa
import numpy as np
import soundfile as sf
import torch
import torchaudio
import yaml

try:
    import spaces
except ImportError:

    class spaces:  # type: ignore
        @staticmethod
        def GPU(*_a, **_k):
            def deco(fn):
                return fn

            return deco


warnings.simplefilter("ignore")

OUT_DIR = ROOT / "outputs"
OUT_DIR.mkdir(exist_ok=True)
MAX_SOURCE_SEC = int(os.environ.get("SEEDVC_MAX_SOURCE_SEC", "60"))

_bundle = None


def _device() -> torch.device:
    if torch.cuda.is_available():
        return torch.device("cuda")
    return torch.device("cpu")


def _crossfade(chunk1, chunk2, overlap):
    fade_out = np.cos(np.linspace(0, np.pi / 2, overlap)) ** 2
    fade_in = np.cos(np.linspace(np.pi / 2, 0, overlap)) ** 2
    chunk2[:overlap] = chunk2[:overlap] * fade_in + chunk1[-overlap:] * fade_out
    return chunk2


def _adjust_f0_semitones(f0_sequence, n_semitones):
    return f0_sequence * (2 ** (n_semitones / 12))


def load_bundle():
    """Load Seed-VC singing (F0) model + helpers once."""
    global _bundle
    if _bundle is not None:
        return _bundle

    _seed_cwd()
    from modules.commons import build_model, load_checkpoint, recursive_munch
    from modules.audio import mel_spectrogram
    from modules.campplus.DTDNN import CAMPPlus
    from modules.rmvpe import RMVPE
    from hf_utils import load_custom_model_from_hf
    from transformers import AutoFeatureExtractor, WhisperModel

    device = _device()
    dit_checkpoint_path, dit_config_path = load_custom_model_from_hf(
        "Plachta/Seed-VC",
        "DiT_seed_v2_uvit_whisper_base_f0_44k_bigvgan_pruned_ft_ema_v2.pth",
        "config_dit_mel_seed_uvit_whisper_base_f0_44k.yml",
    )
    config = yaml.safe_load(open(dit_config_path, "r"))
    model_params = recursive_munch(config["model_params"])
    model_params.dit_type = "DiT"
    model = build_model(model_params, stage="DiT")
    hop_length = config["preprocess_params"]["spect_params"]["hop_length"]
    sr = config["preprocess_params"]["sr"]

    model, _, _, _ = load_checkpoint(
        model,
        None,
        dit_checkpoint_path,
        load_only_params=True,
        ignore_modules=[],
        is_distributed=False,
    )
    for key in model:
        model[key].eval()
        model[key].to(device)
    model.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)

    campplus_ckpt_path = load_custom_model_from_hf(
        "funasr/campplus", "campplus_cn_common.bin", config_filename=None
    )
    campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
    campplus_model.load_state_dict(torch.load(campplus_ckpt_path, map_location="cpu"))
    campplus_model.eval()
    campplus_model.to(device)

    from modules.bigvgan import bigvgan

    bigvgan_model = bigvgan.BigVGAN.from_pretrained(
        model_params.vocoder.name, use_cuda_kernel=False
    )
    bigvgan_model.remove_weight_norm()
    vocoder_fn = bigvgan_model.eval().to(device)

    whisper_name = model_params.speech_tokenizer.name
    whisper_model = WhisperModel.from_pretrained(
        whisper_name, torch_dtype=torch.float16
    ).to(device)
    del whisper_model.decoder
    whisper_feature_extractor = AutoFeatureExtractor.from_pretrained(whisper_name)

    def semantic_fn(waves_16k):
        ori_inputs = whisper_feature_extractor(
            [waves_16k.squeeze(0).cpu().numpy()],
            sampling_rate=16000,
            return_tensors="pt",
            return_attention_mask=True,
        )
        ori_input_features = whisper_model._mask_input_features(
            ori_inputs.input_features, attention_mask=ori_inputs.attention_mask
        ).to(device)
        with torch.no_grad():
            ori_outputs = whisper_model.encoder(
                ori_input_features.to(whisper_model.encoder.dtype),
                head_mask=None,
                output_attentions=False,
                output_hidden_states=False,
                return_dict=True,
            )
        S_ori = ori_outputs.last_hidden_state.to(torch.float32)
        S_ori = S_ori[:, : waves_16k.size(-1) // 320 + 1]
        return S_ori

    mel_fn_args = {
        "n_fft": config["preprocess_params"]["spect_params"]["n_fft"],
        "win_size": config["preprocess_params"]["spect_params"]["win_length"],
        "hop_size": config["preprocess_params"]["spect_params"]["hop_length"],
        "num_mels": config["preprocess_params"]["spect_params"]["n_mels"],
        "sampling_rate": sr,
        "fmin": config["preprocess_params"]["spect_params"].get("fmin", 0),
        "fmax": None
        if config["preprocess_params"]["spect_params"].get("fmax", "None") == "None"
        else 8000,
        "center": False,
    }
    to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)

    rmvpe_path = load_custom_model_from_hf(
        "lj1995/VoiceConversionWebUI", "rmvpe.pt", None
    )
    rmvpe = RMVPE(rmvpe_path, is_half=False, device=device)

    _bundle = {
        "device": device,
        "model": model,
        "semantic_fn": semantic_fn,
        "vocoder_fn": vocoder_fn,
        "campplus_model": campplus_model,
        "to_mel": to_mel,
        "f0_fn": rmvpe.infer_from_audio,
        "sr": sr,
        "hop_length": hop_length,
        "overlap_frame_len": 16,
    }
    return _bundle


@spaces.GPU(duration=150)
def convert_song(
    source_audio: Optional[str],
    voice_ref: Optional[str],
    diffusion_steps: int = 30,
    pitch_shift: int = 0,
    length_adjust: float = 1.0,
    cfg_rate: float = 0.7,
) -> Tuple[Optional[str], str]:
    if not source_audio:
        return None, "Upload source vocal / song."
    if not voice_ref:
        return None, "Upload your voice reference (8–20s clear audio)."

    _seed_cwd()
    try:
        b = load_bundle()
    except Exception as e:
        return None, f"Failed to load Seed-VC: {e}"

    device = b["device"]
    model = b["model"]
    semantic_fn = b["semantic_fn"]
    vocoder_fn = b["vocoder_fn"]
    campplus_model = b["campplus_model"]
    to_mel = b["to_mel"]
    f0_fn = b["f0_fn"]
    sr = b["sr"]
    hop_length = b["hop_length"]
    overlap_frame_len = b["overlap_frame_len"]
    overlap_wave_len = overlap_frame_len * hop_length
    max_context_window = sr // hop_length * 30

    steps = int(max(10, min(80, diffusion_steps)))
    pitch = int(max(-12, min(12, pitch_shift)))
    length_adjust = float(max(0.5, min(2.0, length_adjust)))
    cfg_rate = float(max(0.0, min(1.0, cfg_rate)))

    try:
        source_np = librosa.load(source_audio, sr=sr)[0]
        ref_np = librosa.load(voice_ref, sr=sr)[0]
        if len(source_np) > sr * MAX_SOURCE_SEC:
            source_np = source_np[: sr * MAX_SOURCE_SEC]
            trim_note = f"Source auto-trimmed to {MAX_SOURCE_SEC}s.\n"
        else:
            trim_note = ""
        ref_np = ref_np[: sr * 25]

        source_audio_t = torch.tensor(source_np).unsqueeze(0).float().to(device)
        ref_audio_t = torch.tensor(ref_np).unsqueeze(0).float().to(device)

        ref_waves_16k = torchaudio.functional.resample(ref_audio_t, sr, 16000)
        converted_waves_16k = torchaudio.functional.resample(source_audio_t, sr, 16000)

        if converted_waves_16k.size(-1) <= 16000 * 30:
            S_alt = semantic_fn(converted_waves_16k)
        else:
            overlapping_time = 5
            S_alt_list = []
            buffer = None
            traversed_time = 0
            while traversed_time < converted_waves_16k.size(-1):
                if buffer is None:
                    chunk = converted_waves_16k[
                        :, traversed_time : traversed_time + 16000 * 30
                    ]
                else:
                    chunk = torch.cat(
                        [
                            buffer,
                            converted_waves_16k[
                                :,
                                traversed_time : traversed_time
                                + 16000 * (30 - overlapping_time),
                            ],
                        ],
                        dim=-1,
                    )
                S_alt = semantic_fn(chunk)
                if traversed_time == 0:
                    S_alt_list.append(S_alt)
                else:
                    S_alt_list.append(S_alt[:, 50 * overlapping_time :])
                buffer = chunk[:, -16000 * overlapping_time :]
                traversed_time += (
                    30 * 16000
                    if traversed_time == 0
                    else chunk.size(-1) - 16000 * overlapping_time
                )
            S_alt = torch.cat(S_alt_list, dim=1)

        ori_waves_16k = torchaudio.functional.resample(ref_audio_t, sr, 16000)
        S_ori = semantic_fn(ori_waves_16k)

        mel = to_mel(source_audio_t.float())
        mel2 = to_mel(ref_audio_t.float())
        target_lengths = torch.LongTensor([int(mel.size(2) * length_adjust)]).to(
            mel.device
        )
        target2_lengths = torch.LongTensor([mel2.size(2)]).to(mel2.device)

        feat2 = torchaudio.compliance.kaldi.fbank(
            ori_waves_16k, num_mel_bins=80, dither=0, sample_frequency=16000
        )
        feat2 = feat2 - feat2.mean(dim=0, keepdim=True)
        style2 = campplus_model(feat2.unsqueeze(0))

        F0_ori = f0_fn(ori_waves_16k[0], thred=0.03)
        F0_alt = f0_fn(converted_waves_16k[0], thred=0.03)
        F0_ori = torch.from_numpy(F0_ori).float().to(device)[None]
        F0_alt = torch.from_numpy(F0_alt).float().to(device)[None]

        log_f0_alt = torch.log(F0_alt + 1e-5)
        shifted_log_f0_alt = log_f0_alt.clone()
        shifted_f0_alt = torch.exp(shifted_log_f0_alt)
        if pitch != 0:
            shifted_f0_alt[F0_alt > 1] = _adjust_f0_semitones(
                shifted_f0_alt[F0_alt > 1], pitch
            )

        cond, _, _, _, _ = model.length_regulator(
            S_alt, ylens=target_lengths, n_quantizers=3, f0=shifted_f0_alt
        )
        prompt_condition, _, _, _, _ = model.length_regulator(
            S_ori, ylens=target2_lengths, n_quantizers=3, f0=F0_ori
        )
        interpolated_shifted_f0_alt = torch.nn.functional.interpolate(
            shifted_f0_alt.unsqueeze(1), size=cond.size(1), mode="nearest"
        ).squeeze(1)

        max_source_window = max_context_window - mel2.size(2)
        processed_frames = 0
        generated_wave_chunks = []
        previous_chunk = None

        with torch.no_grad():
            while processed_frames < cond.size(1):
                chunk_cond = cond[
                    :, processed_frames : processed_frames + max_source_window
                ]
                is_last_chunk = processed_frames + max_source_window >= cond.size(1)
                cat_condition = torch.cat([prompt_condition, chunk_cond], dim=1)
                with torch.autocast(
                    device_type=device.type, dtype=torch.float16, enabled=device.type == "cuda"
                ):
                    vc_target = model.cfm.inference(
                        cat_condition,
                        torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
                        mel2,
                        style2,
                        None,
                        steps,
                        inference_cfg_rate=cfg_rate,
                    )
                    vc_target = vc_target[:, :, mel2.size(-1) :]
                    vc_wave = vocoder_fn(vc_target.float()).squeeze().cpu()
                if vc_wave.ndim == 1:
                    vc_wave = vc_wave.unsqueeze(0)

                if processed_frames == 0:
                    if is_last_chunk:
                        generated_wave_chunks.append(vc_wave[0].numpy())
                        break
                    generated_wave_chunks.append(vc_wave[0, :-overlap_wave_len].numpy())
                    previous_chunk = vc_wave[0, -overlap_wave_len:]
                    processed_frames += vc_target.size(2) - overlap_frame_len
                elif is_last_chunk:
                    generated_wave_chunks.append(
                        _crossfade(
                            previous_chunk.numpy(), vc_wave[0].numpy(), overlap_wave_len
                        )
                    )
                    break
                else:
                    generated_wave_chunks.append(
                        _crossfade(
                            previous_chunk.numpy(),
                            vc_wave[0, :-overlap_wave_len].numpy(),
                            overlap_wave_len,
                        )
                    )
                    previous_chunk = vc_wave[0, -overlap_wave_len:]
                    processed_frames += vc_target.size(2) - overlap_frame_len

        wav = np.concatenate(generated_wave_chunks).astype(np.float32)
        out = OUT_DIR / "SOTAKA_Song.wav"
        sf.write(out, wav, sr)
        return str(out), (
            trim_note
            + f"Engine: Seed-VC (ZeroGPU)\n"
            f"device={device} | steps={steps} | pitch={pitch}\n"
            f"duration={len(wav)/sr:.1f}s | saved SOTAKA_Song.wav"
        )
    except Exception as e:
        return None, f"Conversion error: {e}"