import os
import glob
import pickle
import numpy as np
import miditoolkit
import collections

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# ================================================== #  
#  Configuration                                     #
# ================================================== #  
BEAT_RESOL = 480
BAR_RESOL = BEAT_RESOL * 4
TICK_RESOL = BEAT_RESOL // 4
INSTR_NAME_MAP = {'piano': 0}
MIN_BPM = 40
MIN_VELOCITY = 40
NOTE_SORTING = 1 #  0: ascending / 1: descending

DEFAULT_VELOCITY_BINS = np.linspace(0,  128, 64+1, dtype=np.int)
DEFAULT_BPM_BINS      = np.linspace(32, 224, 64+1, dtype=np.int)
DEFAULT_SHIFT_BINS    = np.linspace(-60, 60, 60+1, dtype=np.int)
DEFAULT_DURATION_BINS = np.arange(
        BEAT_RESOL/8, BEAT_RESOL*8+1, BEAT_RESOL/8)

# ================================================== #  


def traverse_dir(
        root_dir,
        extension=('mid', 'MID', 'midi'),
        amount=None,
        str_=None,
        is_pure=False,
        verbose=False,
        is_sort=False,
        is_ext=True):
    if verbose:
        print('[*] Scanning...')
    file_list = []
    cnt = 0
    for root, _, files in os.walk(root_dir):
        for file in files:
            if file.endswith(extension):
                if (amount is not None) and (cnt == amount):
                    break
                if str_ is not None:
                    if str_ not in file:
                        continue
                mix_path = os.path.join(root, file)
                pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
                if not is_ext:
                    ext = pure_path.split('.')[-1]
                    pure_path = pure_path[:-(len(ext)+1)]
                if verbose:
                    print(pure_path)
                file_list.append(pure_path)
                cnt += 1
    if verbose:
        print('Total: %d files' % len(file_list))
        print('Done!!!')
    if is_sort:
        file_list.sort()
    return file_list


def proc_one(path_midi, path_outfile):
    # --- load --- #
    midi_obj = miditoolkit.midi.parser.MidiFile(path_midi)

    # collect emotion tag
    emo_tag = path_midi.split('/')[-1][:2]
    
    # load notes
    instr_notes = collections.defaultdict(list)
    for instr in midi_obj.instruments:
        # skip 
        if instr.name not in INSTR_NAME_MAP.keys():
            continue

        # process
        instr_idx = INSTR_NAME_MAP[instr.name]
        for note in instr.notes:
            note.instr_idx=instr_idx
            instr_notes[instr_idx].append(note)
        if NOTE_SORTING == 0:
            instr_notes[instr_idx].sort(
                key=lambda x: (x.start, x.pitch))
        elif NOTE_SORTING == 1:
            instr_notes[instr_idx].sort(
                key=lambda x: (x.start, -x.pitch))
        else:
            raise ValueError(' [x] Unknown type of sorting.')

    # load chords
    chords = []
    for marker in midi_obj.markers:
        if marker.text.split('_')[0] != 'global' and \
        'Boundary' not in marker.text.split('_')[0]:
            chords.append(marker)
    chords.sort(key=lambda x: x.time)

    # load tempos
    tempos = midi_obj.tempo_changes
    tempos.sort(key=lambda x: x.time)

    # load labels
    labels = []
    for marker in midi_obj.markers:
        if 'Boundary' in marker.text.split('_')[0]:
            labels.append(marker)
    labels.sort(key=lambda x: x.time)

    # load global bpm
    gobal_bpm = 120
    for marker in midi_obj.markers:
        if marker.text.split('_')[0] == 'global' and \
            marker.text.split('_')[1] == 'bpm':
            gobal_bpm = int(marker.text.split('_')[2])
        
    # --- process items to grid --- #
    # compute empty bar offset at head
    first_note_time = min([instr_notes[k][0].start for k in instr_notes.keys()])
    last_note_time = max([instr_notes[k][-1].start for k in instr_notes.keys()])

    quant_time_first = int(np.round(first_note_time  / TICK_RESOL) * TICK_RESOL)
    offset = quant_time_first // BAR_RESOL # empty bar
    last_bar = int(np.ceil(last_note_time / BAR_RESOL)) - offset
    print(' > offset:', offset)
    print(' > last_bar:', last_bar)

    # process notes
    intsr_gird = dict()
    for key in instr_notes.keys():
        notes = instr_notes[key]
        note_grid = collections.defaultdict(list)
        for note in notes:
            note.start = note.start - offset * BAR_RESOL
            note.end = note.end - offset * BAR_RESOL

            # quantize start
            quant_time = int(np.round(note.start / TICK_RESOL) * TICK_RESOL)

            # velocity
            note.velocity = DEFAULT_VELOCITY_BINS[
                np.argmin(abs(DEFAULT_VELOCITY_BINS-note.velocity))]
            note.velocity = max(MIN_VELOCITY, note.velocity)

            # shift of start
            note.shift = note.start - quant_time 
            note.shift = DEFAULT_SHIFT_BINS[np.argmin(abs(DEFAULT_SHIFT_BINS-note.shift))]

            # duration
            note_duration = note.end - note.start
            if note_duration > BAR_RESOL:
                note_duration = BAR_RESOL
            ntick_duration = int(np.round(note_duration / TICK_RESOL) * TICK_RESOL)
            note.duration = ntick_duration

            # append
            note_grid[quant_time].append(note)
        
        # set to track
        intsr_gird[key] = note_grid.copy()

    # process chords
    chord_grid = collections.defaultdict(list)
    for chord in chords:
        # quantize
        chord.time = chord.time - offset * BAR_RESOL
        chord.time  = 0 if chord.time < 0 else chord.time 
        quant_time = int(np.round(chord.time / TICK_RESOL) * TICK_RESOL)

        # append
        chord_grid[quant_time].append(chord)

    # process tempo
    tempo_grid = collections.defaultdict(list)
    for tempo in tempos:
        # quantize
        tempo.time = tempo.time - offset * BAR_RESOL
        tempo.time = 0 if tempo.time < 0 else tempo.time
        quant_time = int(np.round(tempo.time / TICK_RESOL) * TICK_RESOL)
        tempo.tempo = DEFAULT_BPM_BINS[np.argmin(abs(DEFAULT_BPM_BINS-tempo.tempo))]

        # append
        tempo_grid[quant_time].append(tempo)

    # process boundary
    label_grid = collections.defaultdict(list)
    for label in labels:
        # quantize
        label.time = label.time - offset * BAR_RESOL
        label.time = 0 if label.time < 0 else label.time
        quant_time = int(np.round(label.time / TICK_RESOL) * TICK_RESOL)

        # append
        label_grid[quant_time] = [label]
        
    # process global bpm
    gobal_bpm = DEFAULT_BPM_BINS[np.argmin(abs(DEFAULT_BPM_BINS-gobal_bpm))]

    # collect
    song_data = {
        'notes': intsr_gird,
        'chords': chord_grid,
        'tempos': tempo_grid,
        'labels': label_grid,
        'metadata': {
            'global_bpm': gobal_bpm,
            'last_bar': last_bar,
            'emotion': emo_tag
        }
    }

    # save
    fn = os.path.basename(path_outfile)
    os.makedirs(path_outfile[:-len(fn)], exist_ok=True)
    pickle.dump(song_data, open(path_outfile, 'wb'))
    
    return song_data

if __name__ == '__main__':
     # paths
    path_indir = './midi_analyzed/fixed'
    path_outdir = './corpus/fixed'
    os.makedirs(path_outdir, exist_ok=True)

    # list files
    midifiles = traverse_dir(
        path_indir,
        is_pure=True,
        is_sort=True)
    n_files = len(midifiles)
    print('num fiels:', n_files)

    # run all
    for fidx in range(n_files):
        path_midi = midifiles[fidx]
        print('{}/{}'.format(fidx, n_files))

        # paths
        path_infile = os.path.join(path_indir, path_midi)
        path_outfile = os.path.join(path_outdir, path_midi+'.pkl')

        # proc
        _ = proc_one(path_infile, path_outfile)