import torch
import torchaudio
import librosa
from modules.commons import build_model, load_checkpoint, recursive_munch
import yaml
from hf_utils import load_custom_model_from_hf
import numpy as np

# Загрузка моделей и конфигураций
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Загрузка конфигурации и модели DiT
dit_checkpoint_path, dit_config_path = load_custom_model_from_hf(
    "Plachta/Seed-VC",
    "DiT_seed_v2_uvit_whisper_small_wavenet_bigvgan_pruned.pth",
    "config_dit_mel_seed_uvit_whisper_small_wavenet.yml"
)

config = yaml.safe_load(open(dit_config_path, 'r'))
model_params = recursive_munch(config['model_params'])
model = build_model(model_params, stage='DiT')
hop_length = config['preprocess_params']['spect_params']['hop_length']
sr = config['preprocess_params']['sr']

# Загрузка контрольных точек модели
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)

# Загрузка дополнительной модели CAMPPlus
from modules.campplus.DTDNN import CAMPPlus

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)

# Загрузка модели BigVGAN
from modules.bigvgan import bigvgan

bigvgan_model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_22khz_80band_256x', use_cuda_kernel=False)
bigvgan_model.remove_weight_norm()
bigvgan_model = bigvgan_model.eval().to(device)

# Загрузка модели FAcodec
ckpt_path, config_path = load_custom_model_from_hf("Plachta/FAcodec", 'pytorch_model.bin', 'config.yml')

codec_config = yaml.safe_load(open(config_path))
codec_model_params = recursive_munch(codec_config['model_params'])
codec_encoder = build_model(codec_model_params, stage="codec")

ckpt_params = torch.load(ckpt_path, map_location="cpu")

for key in codec_encoder:
    codec_encoder[key].load_state_dict(ckpt_params[key], strict=False)
_ = [codec_encoder[key].eval() for key in codec_encoder]
_ = [codec_encoder[key].to(device) for key in codec_encoder]

# Загрузка модели Whisper
from transformers import AutoFeatureExtractor, WhisperModel

whisper_name = model_params.speech_tokenizer.whisper_name if hasattr(model_params.speech_tokenizer, 'whisper_name') else "openai/whisper-small"
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)

# Функция для генерации мел-спектрограммы
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": 0,
    "fmax": None,
    "center": False
}
from modules.audio import mel_spectrogram

to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)

# Модель с F0 условием
dit_checkpoint_path_f0, dit_config_path_f0 = load_custom_model_from_hf(
    "Plachta/Seed-VC",
    "DiT_seed_v2_uvit_whisper_base_f0_44k_bigvgan_pruned_ft_ema.pth",
    "config_dit_mel_seed_uvit_whisper_base_f0_44k.yml"
)

config_f0 = yaml.safe_load(open(dit_config_path_f0, 'r'))
model_params_f0 = recursive_munch(config_f0['model_params'])
model_f0 = build_model(model_params_f0, stage='DiT')
hop_length_f0 = config_f0['preprocess_params']['spect_params']['hop_length']
sr_f0 = config_f0['preprocess_params']['sr']

# Загрузка контрольных точек модели с F0
model_f0, _, _, _ = load_checkpoint(
    model_f0, None, dit_checkpoint_path_f0,
    load_only_params=True, ignore_modules=[], is_distributed=False
)
for key in model_f0:
    model_f0[key].eval()
    model_f0[key].to(device)
model_f0.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)

# Загрузка F0-экстрактора RMVPE
from modules.rmvpe import RMVPE

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

# Параметры мел-спектрограммы для F0
mel_fn_args_f0 = {
    "n_fft": config_f0['preprocess_params']['spect_params']['n_fft'],
    "win_size": config_f0['preprocess_params']['spect_params']['win_length'],
    "hop_size": config_f0['preprocess_params']['spect_params']['hop_length'],
    "num_mels": config_f0['preprocess_params']['spect_params']['n_mels'],
    "sampling_rate": sr_f0,
    "fmin": 0,
    "fmax": None,
    "center": False
}
to_mel_f0 = lambda x: mel_spectrogram(x, **mel_fn_args_f0)

# Загрузка модели BigVGAN для 44kHz
bigvgan_44k_model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=False)
bigvgan_44k_model.remove_weight_norm()
bigvgan_44k_model = bigvgan_44k_model.eval().to(device)

def adjust_f0_semitones(f0_sequence, n_semitones):
    factor = 2 ** (n_semitones / 12)
    return f0_sequence * factor

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

# Параметры для обработки потоков и чанков
max_context_window = sr // hop_length * 30
overlap_frame_len = 16
overlap_wave_len = overlap_frame_len * hop_length
bitrate = "320k"

@torch.no_grad()
def voice_conversion(source, target, diffusion_steps, length_adjust, inference_cfg_rate, f0_condition, auto_f0_adjust, pitch_shift):
    """
    Функция для голосового преобразования.

    Параметры:
    - source (str): Путь к исходному аудио файлу.
    - target (str): Путь к целевому аудио файлу (голос, на который нужно преобразовать).
    - diffusion_steps (int): Количество шагов диффузии.
    - length_adjust (float): Коэффициент регулировки длины.
    - inference_cfg_rate (float): Коэффициент CFG для инференса.
    - f0_condition (bool): Использовать ли условие F0.
    - auto_f0_adjust (bool): Автоматически ли корректировать F0.
    - pitch_shift (int): Сдвиг тона в полутонах.

    Возвращает:
    - tuple: (частота дискретизации, numpy массив аудио данных)
    """
    inference_module = model_f0 if f0_condition else model
    mel_fn = to_mel_f0 if f0_condition else to_mel
    bigvgan_fn = bigvgan_44k_model if f0_condition else bigvgan_model
    sr_used = sr_f0 if f0_condition else sr

    # Загрузка аудио
    source_audio, _ = librosa.load(source, sr=sr_used)
    ref_audio, _ = librosa.load(target, sr=sr_used)

    # Ограничение длины целевого аудио
    ref_audio = ref_audio[:sr_used * 25]

    # Преобразование аудио в тензоры
    source_audio = torch.tensor(source_audio).unsqueeze(0).float().to(device)
    ref_audio = torch.tensor(ref_audio).unsqueeze(0).float().to(device)

    # Ресемплирование для Whisper
    ref_waves_16k = torchaudio.functional.resample(ref_audio, sr_used, 16000)
    converted_waves_16k = torchaudio.functional.resample(source_audio, sr_used, 16000)

    # Извлечение признаков с помощью Whisper
    if converted_waves_16k.size(-1) <= 16000 * 30:
        alt_inputs = whisper_feature_extractor([converted_waves_16k.squeeze(0).cpu().numpy()],
                                               return_tensors="pt",
                                               return_attention_mask=True,
                                               sampling_rate=16000)
        alt_input_features = whisper_model._mask_input_features(
            alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
        alt_outputs = whisper_model.encoder(
            alt_input_features.to(whisper_model.encoder.dtype),
            head_mask=None,
            output_attentions=False,
            output_hidden_states=False,
            return_dict=True,
        )
        S_alt = alt_outputs.last_hidden_state.to(torch.float32)
        S_alt = S_alt[:, :converted_waves_16k.size(-1) // 320 + 1]
    else:
        # Обработка длинного аудио в чанках
        overlapping_time = 5  # секунд
        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)
            alt_inputs = whisper_feature_extractor([chunk.squeeze(0).cpu().numpy()],
                                                   return_tensors="pt",
                                                   return_attention_mask=True,
                                                   sampling_rate=16000)
            alt_input_features = whisper_model._mask_input_features(
                alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
            alt_outputs = whisper_model.encoder(
                alt_input_features.to(whisper_model.encoder.dtype),
                head_mask=None,
                output_attentions=False,
                output_hidden_states=False,
                return_dict=True,
            )
            S_alt = alt_outputs.last_hidden_state.to(torch.float32)
            S_alt = S_alt[:, :chunk.size(-1) // 320 + 1]
            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)

    # Извлечение признаков из референсного аудио
    ori_waves_16k = torchaudio.functional.resample(ref_audio, sr_used, 16000)
    ori_inputs = whisper_feature_extractor([ori_waves_16k.squeeze(0).cpu().numpy()],
                                           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[:, :ori_waves_16k.size(-1) // 320 + 1]

    mel = mel_fn(source_audio.to(device).float())
    mel2 = mel_fn(ref_audio.to(device).float())

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

    # Извлечение стиля с помощью CAMPPlus
    feat2 = torchaudio.compliance.kaldi.fbank(ref_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))

    if f0_condition:
        # Извлечение F0 с помощью RMVPE
        F0_ori = rmvpe.infer_from_audio(ref_waves_16k[0], thred=0.5)
        F0_alt = rmvpe.infer_from_audio(converted_waves_16k[0], thred=0.5)

        F0_ori = torch.from_numpy(F0_ori).to(device)[None]
        F0_alt = torch.from_numpy(F0_alt).to(device)[None]

        voiced_F0_ori = F0_ori[F0_ori > 1]
        voiced_F0_alt = F0_alt[F0_alt > 1]

        log_f0_alt = torch.log(F0_alt + 1e-5)
        voiced_log_f0_ori = torch.log(voiced_F0_ori + 1e-5)
        voiced_log_f0_alt = torch.log(voiced_F0_alt + 1e-5)
        median_log_f0_ori = torch.median(voiced_log_f0_ori)
        median_log_f0_alt = torch.median(voiced_log_f0_alt)

        # Корректировка F0
        shifted_log_f0_alt = log_f0_alt.clone()
        if auto_f0_adjust:
            shifted_log_f0_alt[F0_alt > 1] = log_f0_alt[F0_alt > 1] - median_log_f0_alt + median_log_f0_ori
        shifted_f0_alt = torch.exp(shifted_log_f0_alt)
        if pitch_shift != 0:
            shifted_f0_alt[F0_alt > 1] = adjust_f0_semitones(shifted_f0_alt[F0_alt > 1], pitch_shift)
    else:
        F0_ori = None
        F0_alt = None
        shifted_f0_alt = None

    # Регулировка длины
    cond, _, codes, commitment_loss, codebook_loss = inference_module.length_regulator(
        S_alt, ylens=target_lengths, n_quantizers=3, f0=shifted_f0_alt
    )
    prompt_condition, _, codes, commitment_loss, codebook_loss = inference_module.length_regulator(
        S_ori, ylens=target2_lengths, n_quantizers=3, f0=F0_ori
    )

    max_source_window = max_context_window - mel2.size(2)
    processed_frames = 0
    generated_wave_chunks = []

    # Генерация аудио по частям
    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)
        vc_target = inference_module.cfm.inference(
            cat_condition,
            torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
            mel2, style2, None, diffusion_steps,
            inference_cfg_rate=inference_cfg_rate
        )
        vc_target = vc_target[:, :, mel2.size(-1):]
        vc_wave = bigvgan_fn(vc_target)[0]
        if processed_frames == 0:
            if is_last_chunk:
                output_wave = vc_wave[0].cpu().numpy()
                generated_wave_chunks.append(output_wave)
                break
            output_wave = vc_wave[0, :-overlap_wave_len].cpu().numpy()
            generated_wave_chunks.append(output_wave)
            previous_chunk = vc_wave[0, -overlap_wave_len:]
            processed_frames += vc_target.size(2) - overlap_frame_len
        elif is_last_chunk:
            output_wave = crossfade(previous_chunk.cpu().numpy(), vc_wave[0].cpu().numpy(), overlap_wave_len)
            generated_wave_chunks.append(output_wave)
            processed_frames += vc_target.size(2) - overlap_frame_len
            break
        else:
            output_wave = crossfade(
                previous_chunk.cpu().numpy(),
                vc_wave[0, :-overlap_wave_len].cpu().numpy(),
                overlap_wave_len
            )
            generated_wave_chunks.append(output_wave)
            previous_chunk = vc_wave[0, -overlap_wave_len:]
            processed_frames += vc_target.size(2) - overlap_frame_len

    # Объединение всех чанков в одно аудио
    full_output_wave = np.concatenate(generated_wave_chunks)

    # Нормализация аудио
    max_val = np.max(np.abs(full_output_wave))
    if max_val > 1.0:
        full_output_wave = full_output_wave / max_val

    return sr_used, full_output_wave