import os

import librosa
import numpy as np
import soundfile as sf
import math
import json
import hashlib
from tqdm import tqdm


def crop_center(h1, h2):
    h1_shape = h1.size()
    h2_shape = h2.size()

    if h1_shape[3] == h2_shape[3]:
        return h1
    elif h1_shape[3] < h2_shape[3]:
        raise ValueError('h1_shape[3] must be greater than h2_shape[3]')

    # s_freq = (h2_shape[2] - h1_shape[2]) // 2
    # e_freq = s_freq + h1_shape[2]
    s_time = (h1_shape[3] - h2_shape[3]) // 2
    e_time = s_time + h2_shape[3]
    h1 = h1[:, :, :, s_time:e_time]

    return h1


def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, reverse=False):
    if reverse:
        wave_left = np.flip(np.asfortranarray(wave[0]))
        wave_right = np.flip(np.asfortranarray(wave[1]))
    elif mid_side:
        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
    else:
        wave_left = np.asfortranarray(wave[0])
        wave_right = np.asfortranarray(wave[1])

    spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
    
    spec = np.asfortranarray([spec_left, spec_right])

    return spec
   
   
def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, reverse=False):
    import threading

    if reverse:
        wave_left = np.flip(np.asfortranarray(wave[0]))
        wave_right = np.flip(np.asfortranarray(wave[1]))
    elif mid_side:
        wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
        wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
    else:
        wave_left = np.asfortranarray(wave[0])
        wave_right = np.asfortranarray(wave[1])
   
    def run_thread(**kwargs):
        global spec_left
        spec_left = librosa.stft(**kwargs)

    thread = threading.Thread(target=run_thread, kwargs={'y': wave_left, 'n_fft': n_fft, 'hop_length': hop_length})
    thread.start()
    spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
    thread.join()   
    
    spec = np.asfortranarray([spec_left, spec_right])

    return spec
    
    
def combine_spectrograms(specs, mp):
    l = min([specs[i].shape[2] for i in specs])    
    spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64)
    offset = 0
    bands_n = len(mp.param['band'])
    
    for d in range(1, bands_n + 1):
        h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start']
        spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l]
        offset += h
        
    if offset > mp.param['bins']:
        raise ValueError('Too much bins')
        
    # lowpass fiter
    if mp.param['pre_filter_start'] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:   
        if bands_n == 1:
            spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
        else:
            gp = 1        
            for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']):
                g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0)
                gp = g
                spec_c[:, b, :] *= g
                
    return np.asfortranarray(spec_c)
    

def spectrogram_to_image(spec, mode='magnitude'):
    if mode == 'magnitude':
        if np.iscomplexobj(spec):
            y = np.abs(spec)
        else:
            y = spec
        y = np.log10(y ** 2 + 1e-8)
    elif mode == 'phase':
        if np.iscomplexobj(spec):
            y = np.angle(spec)
        else:
            y = spec

    y -= y.min()
    y *= 255 / y.max()
    img = np.uint8(y)

    if y.ndim == 3:
        img = img.transpose(1, 2, 0)
        img = np.concatenate([
            np.max(img, axis=2, keepdims=True), img
        ], axis=2)

    return img


def reduce_vocal_aggressively(X, y, softmask):
    v = X - y
    y_mag_tmp = np.abs(y)
    v_mag_tmp = np.abs(v)

    v_mask = v_mag_tmp > y_mag_tmp
    y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)

    return y_mag * np.exp(1.j * np.angle(y))


def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
    if min_range < fade_size * 2:
        raise ValueError('min_range must be >= fade_area * 2')

    mag = mag.copy()

    idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
    starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
    ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
    uninformative = np.where(ends - starts > min_range)[0]
    if len(uninformative) > 0:
        starts = starts[uninformative]
        ends = ends[uninformative]
        old_e = None
        for s, e in zip(starts, ends):
            if old_e is not None and s - old_e < fade_size:
                s = old_e - fade_size * 2

            if s != 0:
                weight = np.linspace(0, 1, fade_size)
                mag[:, :, s:s + fade_size] += weight * ref[:, :, s:s + fade_size]
            else:
                s -= fade_size

            if e != mag.shape[2]:
                weight = np.linspace(1, 0, fade_size)
                mag[:, :, e - fade_size:e] += weight * ref[:, :, e - fade_size:e]
            else:
                e += fade_size

            mag[:, :, s + fade_size:e - fade_size] += ref[:, :, s + fade_size:e - fade_size]
            old_e = e

    return mag
    

def align_wave_head_and_tail(a, b):
    l = min([a[0].size, b[0].size])  
    
    return a[:l,:l], b[:l,:l]
    

def cache_or_load(mix_path, inst_path, mp):
    mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
    inst_basename = os.path.splitext(os.path.basename(inst_path))[0]

    cache_dir = 'mph{}'.format(hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode('utf-8')).hexdigest())
    mix_cache_dir = os.path.join('cache', cache_dir)
    inst_cache_dir = os.path.join('cache', cache_dir)

    os.makedirs(mix_cache_dir, exist_ok=True)
    os.makedirs(inst_cache_dir, exist_ok=True)

    mix_cache_path = os.path.join(mix_cache_dir, mix_basename + '.npy')
    inst_cache_path = os.path.join(inst_cache_dir, inst_basename + '.npy')

    if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path):
        X_spec_m = np.load(mix_cache_path)
        y_spec_m = np.load(inst_cache_path)
    else:
        X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
         
        for d in range(len(mp.param['band']), 0, -1):            
            bp = mp.param['band'][d]
                    
            if d == len(mp.param['band']): # high-end band
                X_wave[d], _ = librosa.load(
                    mix_path, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
                y_wave[d], _ = librosa.load(
                    inst_path, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
            else: # lower bands
                X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
                y_wave[d] = librosa.resample(y_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
            
            X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
            
            X_spec_s[d] = wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
            y_spec_s[d] = wave_to_spectrogram(y_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
            
        del X_wave, y_wave
                 
        X_spec_m = combine_spectrograms(X_spec_s, mp)
        y_spec_m = combine_spectrograms(y_spec_s, mp)
        
        if X_spec_m.shape != y_spec_m.shape:
            raise ValueError('The combined spectrograms are different: ' + mix_path)

        _, ext = os.path.splitext(mix_path)

        np.save(mix_cache_path, X_spec_m)
        np.save(inst_cache_path, y_spec_m)

    return X_spec_m, y_spec_m


def spectrogram_to_wave(spec, hop_length, mid_side, reverse):
    spec_left = np.asfortranarray(spec[0])
    spec_right = np.asfortranarray(spec[1])

    wave_left = librosa.istft(spec_left, hop_length=hop_length)
    wave_right = librosa.istft(spec_right, hop_length=hop_length)

    if reverse:
        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
    elif mid_side:
        return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
    else:
        return np.asfortranarray([wave_left, wave_right])
    
    
def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse):
    import threading

    spec_left = np.asfortranarray(spec[0])
    spec_right = np.asfortranarray(spec[1])
    
    def run_thread(**kwargs):
        global wave_left
        wave_left = librosa.istft(**kwargs)
        
    thread = threading.Thread(target=run_thread, kwargs={'stft_matrix': spec_left, 'hop_length': hop_length})
    thread.start()
    wave_right = librosa.istft(spec_right, hop_length=hop_length)
    thread.join()   
    
    if reverse:
        return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
    elif mid_side:
        return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
    else:
        return np.asfortranarray([wave_left, wave_right])
    
    
def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
    wave_band = {}
    bands_n = len(mp.param['band'])    
    offset = 0

    for d in range(1, bands_n + 1):
        bp = mp.param['band'][d]
        spec_s = np.ndarray(shape=(2, bp['n_fft'] // 2 + 1, spec_m.shape[2]), dtype=complex)
        h = bp['crop_stop'] - bp['crop_start']
        spec_s[:, bp['crop_start']:bp['crop_stop'], :] = spec_m[:, offset:offset+h, :]
        
        offset += h
        if d == bands_n: # higher
            if extra_bins_h: # if --high_end_process bypass
                max_bin = bp['n_fft'] // 2
                spec_s[:, max_bin-extra_bins_h:max_bin, :] = extra_bins[:, :extra_bins_h, :]
            if bp['hpf_start'] > 0:
                spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
            if bands_n == 1:
                wave = spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse'])
            else:
                wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']))
        else:
            sr = mp.param['band'][d+1]['sr']
            if d == 1: # lower
                spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
                wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']), bp['sr'], sr, res_type="sinc_fastest")
            else: # mid
                spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
                spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
                wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']))
                wave = librosa.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
        
    return wave.T


def fft_lp_filter(spec, bin_start, bin_stop):
    g = 1.0
    for b in range(bin_start, bin_stop):
        g -= 1 / (bin_stop - bin_start)
        spec[:, b, :] = g * spec[:, b, :]
        
    spec[:, bin_stop:, :] *= 0

    return spec


def fft_hp_filter(spec, bin_start, bin_stop):
    g = 1.0
    for b in range(bin_start, bin_stop, -1):
        g -= 1 / (bin_start - bin_stop)
        spec[:, b, :] = g * spec[:, b, :]
    
    spec[:, 0:bin_stop+1, :] *= 0

    return spec
    
    
def mirroring(a, spec_m, input_high_end, mp):
    if 'mirroring' == a:
        mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
        mirror = mirror * np.exp(1.j * np.angle(input_high_end))
        
        return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)
        
    if 'mirroring2' == a:
        mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
        mi = np.multiply(mirror, input_high_end)
        
        return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)


def ensembling(a, specs):
    for i in range(1, len(specs)):
        if i == 1:
            spec = specs[i-1]
    
        ln = min([spec.shape[2], specs[i].shape[2]])
        spec = spec[:,:,:ln]
        specs[i] = specs[i][:,:,:ln]
        
        if 'min_mag' == a:
            spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
        if 'max_mag' == a:
            spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)

    return spec
    

if __name__ == "__main__":
    import cv2
    import sys
    import time
    import argparse
    from model_param_init import ModelParameters
    
    p = argparse.ArgumentParser()
    p.add_argument('--algorithm', '-a', type=str, choices=['invert', 'min_mag', 'max_mag', 'deep'], default='min_mag')
    p.add_argument('--model_params', '-m', type=str, default=os.path.join('modelparams', '1band_sr44100_hl512.json'))
    p.add_argument('--output_name', '-o', type=str, default='output')
    p.add_argument('--vocals_only', '-v', action='store_true')
    p.add_argument('input', nargs='+')
    args = p.parse_args()
  
    start_time = time.time()
    
    if args.algorithm == 'invert' and len(args.input) != 2:
        raise ValueError('There should be two input files.')    
    
    if args.algorithm != 'invert' and len(args.input) < 2:
        raise ValueError('There must be at least two input files.')
    
    wave, specs = {}, {}
    mp = ModelParameters(args.model_params)
     
    for i in range(len(args.input)):    
        for d in range(len(mp.param['band']), 0, -1):          
            bp = mp.param['band'][d]
            spec = {}
            
            if d == len(mp.param['band']): # high-end band                
                wave[d], _ = librosa.load(
                    args.input[i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
                
                if len(wave[d].shape) == 1: # mono to stereo
                    wave[d] = np.array([wave[d], wave[d]])
            else: # lower bands
                wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
                       
            spec[d] = wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
            
        specs[i] = combine_spectrograms(spec, mp)
        
    del wave

    if args.algorithm == 'deep':
        d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1])
        v_spec = d_spec - specs[1]
        sf.write(os.path.join('{}.wav'.format(args.output_name)), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr'])   
        
    if args.algorithm == 'invert':
        specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
        v_spec = specs[0] - specs[1]

        if not args.vocals_only:
            X_mag = np.abs(specs[0])
            y_mag = np.abs(specs[1])
            v_mag = np.abs(v_spec)

            X_image = spectrogram_to_image(X_mag)
            y_image = spectrogram_to_image(y_mag)
            v_image = spectrogram_to_image(v_mag)

            cv2.imwrite('{}_X.png'.format(args.output_name), X_image)
            cv2.imwrite('{}_y.png'.format(args.output_name), y_image)
            cv2.imwrite('{}_v.png'.format(args.output_name), v_image)    
                
            sf.write('{}_X.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[0], mp), mp.param['sr'])
            sf.write('{}_y.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[1], mp), mp.param['sr'])
            
        sf.write('{}_v.wav'.format(args.output_name), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr'])    
    else:    
        if not args.algorithm == 'deep':
            sf.write(os.path.join('ensembled','{}.wav'.format(args.output_name)), cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp), mp.param['sr'])

    #print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))