import gc
import hashlib
import os
import queue
import threading
import warnings

import librosa
import numpy as np
import onnxruntime as ort
import soundfile as sf
import torch
from tqdm import tqdm

warnings.filterwarnings("ignore")
stem_naming = {'Vocals': 'Instrumental', 'Other': 'Instruments', 'Instrumental': 'Vocals', 'Drums': 'Drumless', 'Bass': 'Bassless'}


class MDXModel:
    def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
        print("[~] Initializing MDXModel...")
        self.dim_f = dim_f
        self.dim_t = dim_t
        self.dim_c = 4
        self.n_fft = n_fft
        self.hop = hop
        self.stem_name = stem_name
        self.compensation = compensation

        self.n_bins = self.n_fft // 2 + 1
        self.chunk_size = hop * (self.dim_t - 1)
        self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)

        out_c = self.dim_c

        self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device)
        print("[+] MDXModel initialized")

    def stft(self, x):
        print("[~] Performing STFT...")
        x = x.reshape([-1, self.chunk_size])
        x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
        x = torch.view_as_real(x)
        x = x.permute([0, 3, 1, 2])
        x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 4, self.n_bins, self.dim_t])
        print("[+] STFT completed")
        return x[:, :, :self.dim_f]

    def istft(self, x, freq_pad=None):
        print("[~] Performing inverse STFT...")
        freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad
        x = torch.cat([x, freq_pad], -2)
        x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t])
        x = x.permute([0, 2, 3, 1])
        x = x.contiguous()
        x = torch.view_as_complex(x)
        x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
        print("[+] Inverse STFT completed")
        return x.reshape([-1, 2, self.chunk_size])


class MDX:
    DEFAULT_SR = 44100
    DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
    DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR

    DEFAULT_PROCESSOR = 0

    def __init__(self, model_path: str, params: MDXModel, processor=DEFAULT_PROCESSOR):
        print("[~] Initializing MDX...")
        self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
        self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']

        self.model = params

        print(f"[~] Loading ONNX model from {model_path}...")
        self.ort = ort.InferenceSession(model_path, providers=self.provider)
        print("[~] Preloading model...")
        self.ort.run(None, {'input': torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
        self.process = lambda spec: self.ort.run(None, {'input': spec.cpu().numpy()})[0]

        self.prog = None
        print("[+] MDX initialized")

    @staticmethod
    def get_hash(model_path):
        print(f"[~] Calculating hash for model: {model_path}")
        try:
            with open(model_path, 'rb') as f:
                f.seek(- 10000 * 1024, 2)
                model_hash = hashlib.md5(f.read()).hexdigest()
        except:
            model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
        print(f"[+] Model hash: {model_hash}")
        return model_hash

    @staticmethod
    def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
        print("[~] Segmenting wave...")
        if combine:
            processed_wave = None
            for segment_count, segment in enumerate(wave):
                start = 0 if segment_count == 0 else margin_size
                end = None if segment_count == len(wave) - 1 else -margin_size
                if margin_size == 0:
                    end = None
                if processed_wave is None:
                    processed_wave = segment[:, start:end]
                else:
                    processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
        else:
            processed_wave = []
            sample_count = wave.shape[-1]

            if chunk_size <= 0 or chunk_size > sample_count:
                chunk_size = sample_count

            if margin_size > chunk_size:
                margin_size = chunk_size

            for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
                margin = 0 if segment_count == 0 else margin_size
                end = min(skip + chunk_size + margin_size, sample_count)
                start = skip - margin

                cut = wave[:, start:end].copy()
                processed_wave.append(cut)

                if end == sample_count:
                    break
        print("[+] Wave segmentation completed")
        return processed_wave

    def pad_wave(self, wave):
        print("[~] Padding wave...")
        n_sample = wave.shape[1]
        trim = self.model.n_fft // 2
        gen_size = self.model.chunk_size - 2 * trim
        pad = gen_size - n_sample % gen_size

        wave_p = np.concatenate((np.zeros((2, trim)), wave, np.zeros((2, pad)), np.zeros((2, trim))), 1)

        mix_waves = []
        for i in range(0, n_sample + pad, gen_size):
            waves = np.array(wave_p[:, i:i + self.model.chunk_size])
            mix_waves.append(waves)

        mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
        print(f"[+] Wave padded. Shape: {mix_waves.shape}")
        return mix_waves, pad, trim

    def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
        print(f"[~] Processing wave segment {_id}...")
        mix_waves = mix_waves.split(1)
        with torch.no_grad():
            pw = []
            for mix_wave in mix_waves:
                self.prog.update()
                spec = self.model.stft(mix_wave)
                processed_spec = torch.tensor(self.process(spec))
                processed_wav = self.model.istft(processed_spec.to(self.device))
                processed_wav = processed_wav[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).cpu().numpy()
                pw.append(processed_wav)
        processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
        q.put({_id: processed_signal})
        print(f"[+] Wave segment {_id} processed")
        return processed_signal

    def process_wave(self, wave: np.array, mt_threads=1):
        print(f"[~] Processing wave with {mt_threads} threads...")
        self.prog = tqdm(total=0)
        chunk = wave.shape[-1] // mt_threads
        waves = self.segment(wave, False, chunk)

        q = queue.Queue()
        threads = []
        for c, batch in enumerate(waves):
            mix_waves, pad, trim = self.pad_wave(batch)
            self.prog.total = len(mix_waves) * mt_threads
            thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
            thread.start()
            threads.append(thread)
        for thread in threads:
            thread.join()
        self.prog.close()

        processed_batches = []
        while not q.empty():
            processed_batches.append(q.get())
        processed_batches = [list(wave.values())[0] for wave in
                             sorted(processed_batches, key=lambda d: list(d.keys())[0])]
        assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
        print("[+] Wave processing completed")
        return self.segment(processed_batches, True, chunk)


def run_mdx(model_params, output_dir, model_path, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2):
    print(f"[~] Running MDX on file: {filename}")
    device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')

    device_properties = torch.cuda.get_device_properties(device)
    vram_gb = device_properties.total_memory / 1024**3
    m_threads = 1 if vram_gb < 8 else 2
    print(f"[~] Using {m_threads} threads for processing")

    model_hash = MDX.get_hash(model_path)
    mp = model_params.get(model_hash)
    model = MDXModel(
        device,
        dim_f=mp["mdx_dim_f_set"],
        dim_t=2 ** mp["mdx_dim_t_set"],
        n_fft=mp["mdx_n_fft_scale_set"],
        stem_name=mp["primary_stem"],
        compensation=mp["compensate"]
    )

    mdx_sess = MDX(model_path, model)
    print("[~] Loading audio file...")
    wave, sr = librosa.load(filename, mono=False, sr=44100)
    print("[~] Normalizing input wave...")
    peak = max(np.max(wave), abs(np.min(wave)))
    wave /= peak
    if denoise:
        print("[~] Denoising wave...")
        wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))
        wave_processed *= 0.5
    else:
        print("[~] Processing wave...")
        wave_processed = mdx_sess.process_wave(wave, m_threads)
    wave_processed *= peak
    stem_name = model.stem_name if suffix is None else suffix

    main_filepath = None
    if not exclude_main:
        main_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
        print(f"[~] Writing main output to: {main_filepath}")
        sf.write(main_filepath, wave_processed.T, sr)

    invert_filepath = None
    if not exclude_inversion:
        diff_stem_name = stem_naming.get(stem_name) if invert_suffix is None else invert_suffix
        stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
        invert_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
        print(f"[~] Writing inverted output to: {invert_filepath}")
        sf.write(invert_filepath, (-wave_processed.T * model.compensation) + wave.T, sr)

    if not keep_orig:
        print(f"[~] Removing original file: {filename}")
        os.remove(filename)

    print("[~] Cleaning up...")
    del mdx_sess, wave_processed, wave
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    gc.collect()
    print("[+] MDX processing completed")
    return main_filepath, invert_filepath

def run_roformer(model_params, output_dir, model_name, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2):
    print(f"[~] Running RoFormer on file: {filename}")
    os.makedirs(output_dir, exist_ok=True)
    
    print("[~] Loading audio file...")
    wave, sr = librosa.load(filename, mono=False, sr=44100)
    base_name = os.path.splitext(os.path.basename(filename))[0]
    
    roformer_output_format = 'wav'
    roformer_overlap = 4
    roformer_segment_size = 256
    print(f"[~] Output directory: {output_dir}")
    prompt = f'audio-separator "{filename}" --model_filename {model_name} --output_dir="{output_dir}" --output_format={roformer_output_format} --normalization=0.9 --mdxc_overlap={roformer_overlap} --mdxc_segment_size={roformer_segment_size}'
    print(f"[~] Running command: {prompt}")
    os.system(prompt)

    vocals_file = f"{base_name}_Vocals.wav"
    instrumental_file = f"{base_name}_Instrumental.wav"

    main_filepath = None
    invert_filepath = None

    if not exclude_main:
        main_filepath = os.path.join(output_dir, vocals_file)
        if os.path.exists(os.path.join(output_dir, f"{base_name}_(Vocals)_{model_name.replace('.9755.ckpt', '')}.wav")):
            print(f"[~] Renaming vocals file to: {main_filepath}")
            os.rename(os.path.join(output_dir, f"{base_name}_(Vocals)_{model_name.replace('.9755.ckpt', '')}.wav"), main_filepath)

    if not exclude_inversion:
        invert_filepath = os.path.join(output_dir, instrumental_file)
        if os.path.exists(os.path.join(output_dir, f"{base_name}_(Instrumental)_{model_name.replace('.9755.ckpt', '')}.wav")):
            print(f"[~] Renaming instrumental file to: {invert_filepath}")
            os.rename(os.path.join(output_dir, f"{base_name}_(Instrumental)_{model_name.replace('.9755.ckpt', '')}.wav"), invert_filepath)

    if not keep_orig:
        print(f"[~] Removing original file: {filename}")
        os.remove(filename)

    print("[+] RoFormer processing completed")
    return main_filepath, invert_filepath