# -*- coding: utf-8 -*-
"""Monster_MIDI_Dataset_GPU_Search_and_Filter.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/github/asigalov61/Monster-MIDI-Dataset/blob/main/Monster_MIDI_Dataset_GPU_Search_and_Filter.ipynb

# Monster MIDI Dataset GPU Search and Filter (ver. 3.0)

***

Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools

***

#### Project Los Angeles

#### Tegridy Code 2024

***

# (GPU CHECK)
"""

# @title NVIDIA GPU Check
!nvidia-smi

"""# (SETUP ENVIRONMENT)"""

#@title Install all dependencies (run only once per session)

!git clone --depth 1 https://github.com/asigalov61/Monster-MIDI-Dataset
!pip install huggingface_hub

#@title Import all needed modules

print('Loading core modules... Please wait...')

import os
import copy
from collections import Counter
import random
import pickle
from tqdm import tqdm
import pprint
import statistics
import shutil

import locale
locale.getpreferredencoding = lambda: "UTF-8"

import cupy as cp

from huggingface_hub import hf_hub_download

from google.colab import files

print('Loading TMIDIX module...')
os.chdir('/content/Monster-MIDI-Dataset')

import TMIDIX

os.chdir('/content/')

print('Creating IO dirs... Please wait...')

if not os.path.exists('/content/Master-MIDI-Dataset'):
    os.makedirs('/content/Master-MIDI-Dataset')

if not os.path.exists('/content/Output-MIDI-Dataset'):
    os.makedirs('/content/Output-MIDI-Dataset')

print('Done!')
print('Enjoy! :)')

"""# (DOWNLOAD AND UNZIP MAIN MIDI DATASET)"""

#@title Download Monster MIDI Dataset
print('=' * 70)
print('Downloading Monster MIDI Dataset...Please wait...')
print('=' * 70)

hf_hub_download(repo_id='projectlosangeles/Monster-MIDI-Dataset',
                filename='Monster-MIDI-Dataset-Ver-1-0-CC-BY-NC-SA.zip',
                repo_type="dataset",
                local_dir='/content/Main-MIDI-Dataset',
                local_dir_use_symlinks=False)
print('=' * 70)
print('Done! Enjoy! :)')
print('=' * 70)

# Commented out IPython magic to ensure Python compatibility.
#@title Unzip Monster MIDI Dataset
# %cd /content/Main-MIDI-Dataset/

print('=' * 70)
print('Unzipping Monster MIDI Dataset...Please wait...')
!unzip 'Monster-MIDI-Dataset-Ver-1-0-CC-BY-NC-SA.zip'
print('=' * 70)

print('Done! Enjoy! :)')
print('=' * 70)
# %cd /content/

"""# (CREATE MAIN MIDI DATASET FILES LIST)"""

#@title Create Monster MIDI Dataset files list
print('=' * 70)
print('Creating dataset files list...')
dataset_addr = "/content/Main-MIDI-Dataset/MIDIs"

# os.chdir(dataset_addr)
filez = list()
for (dirpath, dirnames, filenames) in os.walk(dataset_addr):
    filez += [os.path.join(dirpath, file) for file in filenames]

if filez == []:
    print('Could not find any MIDI files. Please check Dataset dir...')
    print('=' * 70)

print('=' * 70)
print('Randomizing file list...')
random.shuffle(filez)
print('=' * 70)

LAMD_files_list = []

for f in tqdm(filez):
  LAMD_files_list.append([f.split('/')[-1].split('.mid')[0], f])
print('Done!')
print('=' * 70)

"""# (SIGNATURES SEARCH)"""

# @title Load Monster MIDI Dataset Signatures Data

print('=' * 70)
print('Loading Monster MIDI Dataset Signatures Data...')
sigs_data = pickle.load(open('/content/Main-MIDI-Dataset/SIGNATURES_DATA/MONSTER_SIGNATURES_DATA.pickle', 'rb'))
print('=' * 70)

print('Prepping signatures...')
print('=' * 70)

random.shuffle(sigs_data)

signatures_file_names = []
sigs_matrixes = [ [0]*(len(TMIDIX.ALL_CHORDS)+256) for i in range(len(sigs_data))]

idx = 0
for s in tqdm(sigs_data):

  signatures_file_names.append(s[0])

  for ss in s[1]:
    sigs_matrixes[idx][ss[0]] = ss[1]

  idx += 1

print('=' * 70)
print('Loading signatures...')
print('=' * 70)

signatures_data_full = cp.array(sigs_matrixes)

print('Done!')
print('=' * 70)

#@title Monster MIDI Dataset Search and Filter

#@markdown DO NOT FORGET TO UPLOAD YOUR MASTER DATASET TO "Master-MIDI-Dataset" FOLDER

#@markdown NOTE: You can stop the search at any time to render partial results

number_of_top_matches_MIDIs_to_collect = 30 #@param {type:"slider", min:5, max:50, step:1}
search_matching_type = "Ratios" # @param ["Ratios", "Distances", "Correlations"]
maximum_match_ratio_to_search_for = 1 #@param {type:"slider", min:0, max:1, step:0.001}
match_results_weight = 2 # @param {type:"slider", min:0.1, max:3, step:0.1}
match_lengths_weight = 1 # @param {type:"slider", min:0.1, max:3, step:0.1}
match_counts_weight = 1 # @param {type:"slider", min:0.1, max:3, step:0.1}
distances_norm_order = 3 # @param {type:"slider", min:1, max:10, step:1}
epsilon = 0.5 # @param {type:"slider", min:0.001, max:1, step:0.001}
match_drums = False # @param {type:"boolean"}

print('=' * 70)
print('Monster MIDI Dataset GPU Search and Filter')
print('=' * 70)

###########

search_settings_string = ''

if match_drums:
  search_settings_string += 'Chords_Drums'
else:
  search_settings_string += 'Chords'

if search_matching_type == 'Distances':
  search_settings_string += '_O_' + str(distances_norm_order)

search_settings_string += '_W_'
search_settings_string += str(match_results_weight) + '_'
search_settings_string += str(match_lengths_weight) + '_'
search_settings_string += str(match_counts_weight)

search_settings_string += '_E_' + str(epsilon)

###########

print('Loading MIDI files...')
print('This may take a while on a large dataset in particular.')

dataset_addr = "/content/Master-MIDI-Dataset"

filez = list()

for (dirpath, dirnames, filenames) in os.walk(dataset_addr):
    for file in filenames:
        if file.endswith(('.mid', '.midi', '.kar')):
            filez.append(os.path.join(dirpath, file))

print('=' * 70)

if filez:

  print('Randomizing file list...')
  random.shuffle(filez)
  print('=' * 70)

  ###################

  if not os.path.exists('/content/Output-MIDI-Dataset/'+search_matching_type+'_'+search_settings_string):
      os.makedirs('/content/Output-MIDI-Dataset/'+search_matching_type+'_'+search_settings_string)

  ###################

  input_files_count = 0
  files_count = 0

  for f in filez:
    try:

          input_files_count += 1

          fn = os.path.basename(f)
          fn1 = os.path.splitext(fn)[0]
          ext = os.path.splitext(f)[1]

          print('Processing MIDI File #', files_count+1, 'out of', len(filez))
          print('MIDI file name', fn)
          print('-' * 70)

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

          raw_score = TMIDIX.midi2single_track_ms_score(open(f, 'rb').read())
          escore = TMIDIX.advanced_score_processor(raw_score, return_score_analysis=False, return_enhanced_score_notes=True)[0]

          for e in escore:
            e[1] = int(e[1] / 16)
            e[2] = int(e[2] / 16)

          drums_offset = len(TMIDIX.ALL_CHORDS_SORTED) + 128

          src_sigs = []

          for i in range(-6, 6):

            escore_copy = copy.deepcopy(escore)

            for e in escore_copy:
              e[4] += i

            cscore = TMIDIX.chordify_score([1000, escore_copy])

            sig = []
            dsig = []

            for c in cscore:

              pitches = sorted(set([p[4] for p in c if p[3] != 9]))
              drums = sorted(set([p[4]+drums_offset for p in c if p[3] == 9]))

              if pitches:
                if len(pitches) > 1:
                  tones_chord = sorted(set([p % 12 for p in pitches]))

                  try:
                      sig_token = TMIDIX.ALL_CHORDS_SORTED.index(tones_chord) + 128
                  except:
                      checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord)
                      sig_token = TMIDIX.ALL_CHORDS_SORTED.index(checked_tones_chord) + 128

                elif len(pitches) == 1:
                  sig_token = pitches[0]

                sig.append(sig_token)

              if drums:
                dsig.extend(drums)

            sig_p = dict.fromkeys(sig+dsig, 0)
            for item in sig+dsig:
              sig_p[item] += 1

            fsig = [list(v) for v in sig_p.items()]

            src_sig_mat = [0] * (len(TMIDIX.ALL_CHORDS)+256)

            for s in fsig:

              src_sig_mat[s[0]] = s[1]

            src_sigs.append(src_sig_mat)

          src_signatures = cp.stack(cp.array(src_sigs))

          if not match_drums:
            src_signatures = src_signatures[:,:drums_offset]
            signatures_data = signatures_data_full[:,:drums_offset]
          else:
            signatures_data = signatures_data_full

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

          print('Searching for matches...Please wait...')
          print('-' * 70)

          lower_threshold = 0.0
          upper_threshold = maximum_match_ratio_to_search_for
          filter_size = number_of_top_matches_MIDIs_to_collect

          final_ratios = []

          avg_idxs = []

          all_filtered_means = []
          all_filtered_idxs = []
          all_filtered_tvs = []

          tv_idx = -6

          for target_sig in tqdm(src_signatures):

            comps_lengths = cp.vstack((cp.repeat(cp.sum(target_sig != 0), signatures_data.shape[0]), cp.sum(signatures_data != 0, axis=1)))
            comps_lengths_ratios = cp.divide(cp.min(comps_lengths, axis=0), cp.max(comps_lengths, axis=0))

            comps_counts_sums = cp.vstack((cp.repeat(cp.sum(target_sig), signatures_data.shape[0]), cp.sum(signatures_data, axis=1)))
            comps_counts_sums_ratios = cp.divide(cp.min(comps_counts_sums, axis=0), cp.max(comps_counts_sums, axis=0))

            if search_matching_type == 'Ratios':

              ratios = cp.where(target_sig != 0, cp.divide(cp.minimum(signatures_data, target_sig), cp.maximum(signatures_data, target_sig)), epsilon)
              results = cp.mean(ratios, axis=1)

            elif search_matching_type == 'Distances':

              distances = cp.power(cp.sum(cp.power(cp.abs(signatures_data - target_sig), distances_norm_order), axis=1), 1 / distances_norm_order)

              distances_mean = cp.mean(distances)
              distances_std = cp.std(distances)

              results = 1 - cp.divide((distances - distances_mean), distances_std)

            elif search_matching_type == 'Correlations':

                main_array_mean = cp.mean(signatures_data, axis=1, keepdims=True)
                main_array_std = cp.std(signatures_data, axis=1, keepdims=True)
                target_array_mean = cp.mean(target_sig)
                target_array_std = cp.std(target_sig)

                signatures_data_normalized = cp.where(main_array_std != 0, (signatures_data - main_array_mean) / main_array_std, epsilon)
                target_sig_normalized = cp.where(target_array_std != 0, (target_sig - target_array_mean) / target_array_std, epsilon)

                correlations = cp.divide(cp.einsum('ij,j->i', signatures_data_normalized, target_sig_normalized), (signatures_data.shape[1] - 1))
                scaled_correlations = cp.divide(correlations, cp.sqrt(cp.sum(correlations**2)))
                exp = cp.exp(scaled_correlations - cp.max(scaled_correlations))
                results = cp.multiply(cp.divide(exp, cp.sum(exp)), 1e5)

            results_weight = match_results_weight
            comp_lengths_weight = match_lengths_weight
            comp_counts_sums_weight = match_counts_weight

            results = cp.divide(cp.add(cp.add(results_weight, comp_lengths_weight), comp_counts_sums_weight), cp.add(cp.add(cp.divide(results_weight, cp.where(results !=0, results, epsilon)), cp.divide(comp_lengths_weight, cp.where(comps_lengths_ratios !=0, comps_lengths_ratios, epsilon))), cp.divide(comp_counts_sums_weight, cp.where(comps_counts_sums_ratios !=0, comps_counts_sums_ratios, epsilon))))

            unique_means = cp.unique(results)
            sorted_means = cp.sort(unique_means)[::-1]

            filtered_means = sorted_means[(sorted_means >= lower_threshold) & (sorted_means <= upper_threshold)][:filter_size]

            filtered_idxs = cp.nonzero(cp.in1d(results, filtered_means))[0]

            all_filtered_means.extend(results[filtered_idxs].tolist())

            all_filtered_idxs.extend(filtered_idxs.tolist())

            filtered_tvs = [tv_idx] * filtered_idxs.shape[0]

            all_filtered_tvs.extend(filtered_tvs)

            tv_idx += 1

          f_results = sorted(zip(all_filtered_means, all_filtered_idxs, all_filtered_tvs), key=lambda x: x[0], reverse=True)

          triplet_dict = {}

          for triplet in f_results:

              if triplet[0] not in triplet_dict:
                triplet_dict[triplet[0]] = triplet
              else:
                if triplet[2] == 0:
                  triplet_dict[triplet[0]] = triplet

          filtered_results = list(triplet_dict.values())[:filter_size]

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

          print('Done!')
          print('-' * 70)
          print('Max match ratio:', filtered_results[0][0])
          print('Max match transpose value:', filtered_results[0][2])
          print('Max match signature index:', filtered_results[0][1])
          print('Max match file name:', signatures_file_names[filtered_results[0][1]])
          print('-' * 70)
          print('Copying max ratios MIDIs...')

          for fr in filtered_results:

            max_ratio_index = fr[1]

            ffn = signatures_file_names[fr[1]]
            ffn_idx = [y[0] for y in LAMD_files_list].index(ffn)

            ff = LAMD_files_list[ffn_idx][1]

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

            dir_str = str(fn1)
            copy_path = '/content/Output-MIDI-Dataset/'+search_matching_type+'_'+search_settings_string+'/'+dir_str
            if not os.path.exists(copy_path):
                os.mkdir(copy_path)

            fff = str(fr[0] * 100) + '_' + str(fr[2]) + '_' + ffn + '.mid'

            shutil.copy2(ff, copy_path+'/'+fff)

          shutil.copy2(f, copy_path+'/'+fn)

          #======================================================='''
          print('Done!')
          print('=' * 70)

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

          # Processed files counter
          files_count += 1

    except KeyboardInterrupt:
        print('Quitting...')
        print('Total number of processed MIDI files', files_count)
        print('=' * 70)
        break

    except Exception as ex:
        print('WARNING !!!')
        print('=' * 70)
        print('Bad file:', f)
        print('Error detected:', ex)
        print('=' * 70)
        continue

    print('Total number of processed MIDI files', files_count)
    print('=' * 70)

else:
  print('Could not find any MIDI files. Please check Dataset dir...')
  print('=' * 70)

"""# (KILO-CHORDS SEARCH)"""

#@title Load Monster MIDI Dataset Kilo-Chords Data
search_matching_type = "Full-Kilo-Chords" # @param ["Full-Kilo-Chords", "Unique-Kilo-Chords"]

print('=' * 70)
print('Loading Monster MIDI Dataset Kilo-Chords Data...')
kilo_chords = pickle.load(open('/content/Main-MIDI-Dataset/KILO_CHORDS_DATA/MONSTER_KILO_CHORDS_DATA.pickle', 'rb'))
print('=' * 70)

print('Prepping Kilo-Chords...')
print('=' * 70)

random.shuffle(kilo_chords)

if search_matching_type == 'Full-Kilo-Chords':

  kilo_chords_file_names = []

  for kc in tqdm(kilo_chords):

    kilo_chords_file_names.append(kc[0])

    kcho = kc[1]

    kcho += [0] * (1000 - len(kcho))

  print('=' * 70)
  print('Loading Kilo-Chords...')
  print('=' * 70)

  kilo_chords_data = cp.array([kc[1] for kc in kilo_chords])

else:

  kilo_chords_file_names = []

  kilo_chords_matrixes = [ [0]*(len(TMIDIX.ALL_CHORDS)+128) for i in range(len(kilo_chords))]

  idx = 0
  for kc in tqdm(kilo_chords):

    kilo_chords_file_names.append(kc[0])

    for c in kc[1]:
      kilo_chords_matrixes[idx][c] += 1

    idx += 1

  print('=' * 70)
  print('Loading Kilo-Chords...')
  print('=' * 70)

  kilo_chords_data = cp.array(kilo_chords_matrixes)

print('Done!')
print('=' * 70)

#@title Monster MIDI Dataset Search and Filter

#@markdown DO NOT FORGET TO UPLOAD YOUR MASTER DATASET TO "Master-MIDI-Dataset" FOLDER

#@markdown NOTE: You can stop the search at any time to render partial results

number_of_top_matches_MIDIs_to_collect = 30 #@param {type:"slider", min:5, max:50, step:1}
maximum_match_ratio_to_search_for = 1 #@param {type:"slider", min:0, max:1, step:0.001}
match_results_weight = 2 # @param {type:"slider", min:0.1, max:3, step:0.1}
match_lengths_weight = 1 # @param {type:"slider", min:0.1, max:3, step:0.1}
match_counts_weight = 1 # @param {type:"slider", min:0.1, max:3, step:0.1}
epsilon = 0.5 # @param {type:"slider", min:0.001, max:1, step:0.001}

print('=' * 70)
print('Monster MIDI Dataset GPU Search and Filter')
print('=' * 70)

###########

search_settings_string = ''

search_settings_string += str(search_matching_type).replace('-', '_')

search_settings_string += '_W_'
search_settings_string += str(match_results_weight) + '_'
search_settings_string += str(match_lengths_weight) + '_'
search_settings_string += str(match_counts_weight)

search_settings_string += '_E_' + str(epsilon)

###########

print('Loading MIDI files...')
print('This may take a while on a large dataset in particular.')

dataset_addr = "/content/Master-MIDI-Dataset"

filez = list()

for (dirpath, dirnames, filenames) in os.walk(dataset_addr):
    for file in filenames:
        if file.endswith(('.mid', '.midi', '.kar')):
            filez.append(os.path.join(dirpath, file))

print('=' * 70)

if filez:

  print('Randomizing file list...')
  random.shuffle(filez)
  print('=' * 70)

  ###################

  if not os.path.exists('/content/Output-MIDI-Dataset/'+search_settings_string):
      os.makedirs('/content/Output-MIDI-Dataset/'+search_settings_string)

  ###################

  input_files_count = 0
  files_count = 0

  for f in filez:

    try:

          input_files_count += 1

          fn = os.path.basename(f)
          fn1 = os.path.splitext(fn)[0]
          ext = os.path.splitext(f)[1]

          print('Processing MIDI File #', files_count+1, 'out of', len(filez))
          print('MIDI file name', fn)
          print('-' * 70)

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

          raw_score = TMIDIX.midi2single_track_ms_score(open(f, 'rb').read())
          escore = TMIDIX.advanced_score_processor(raw_score, return_score_analysis=False, return_enhanced_score_notes=True)[0]

          for e in escore:
            e[1] = int(e[1] / 16)
            e[2] = int(e[2] / 16)

          src_kilo_chords = []

          for i in range(-6, 6):

            escore_copy = copy.deepcopy(escore)

            for e in escore_copy:
              e[4] += i

            cscore = TMIDIX.chordify_score([1000, escore_copy])

            kilo_chord = []

            for c in cscore:

              pitches = sorted(set([p[4] for p in c if p[3] != 9]))

              if pitches:
                if len(pitches) > 1:
                  tones_chord = sorted(set([p % 12 for p in pitches]))

                  try:
                      chord_token = TMIDIX.ALL_CHORDS_SORTED.index(tones_chord) + 128
                  except:
                      checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord)
                      chord_token = TMIDIX.ALL_CHORDS_SORTED.index(checked_tones_chord) + 128

                elif len(pitches) == 1:
                  chord_token = pitches[0]

                kilo_chord.append(chord_token)

            if search_matching_type == 'Full-Kilo-Chords':

              kilo_chord = kilo_chord[:1000]
              kilo_chord_matrix = kilo_chord + [0] * (1000 - len(kilo_chord))

            else:

              kilo_chord_matrix = [0] * (len(TMIDIX.ALL_CHORDS)+128)

              for c in kilo_chord:
                kilo_chord_matrix[c] += 1

            src_kilo_chords.append(kilo_chord_matrix)

          src_kilo_chords = cp.stack(cp.array(src_kilo_chords))

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

          print('Searching for matches...Please wait...')
          print('-' * 70)

          lower_threshold = 0.0
          upper_threshold = maximum_match_ratio_to_search_for
          filter_size = number_of_top_matches_MIDIs_to_collect

          final_ratios = []

          avg_idxs = []

          all_filtered_means = []
          all_filtered_idxs = []
          all_filtered_tvs = []

          tv_idx = -6

          for target_kc in tqdm(src_kilo_chords):

            comps_lengths = cp.vstack((cp.repeat(cp.sum(target_kc != 0), kilo_chords_data.shape[0]), cp.sum(kilo_chords_data != 0, axis=1)))
            comps_lengths_ratios = cp.divide(cp.min(comps_lengths, axis=0), cp.max(comps_lengths, axis=0))

            comps_counts_sums = cp.vstack((cp.repeat(cp.sum(target_kc), kilo_chords_data.shape[0]), cp.sum(kilo_chords_data, axis=1)))
            comps_counts_sums_ratios = cp.divide(cp.min(comps_counts_sums, axis=0), cp.max(comps_counts_sums, axis=0))

            intersections = cp.where((kilo_chords_data == target_kc), kilo_chords_data, 0)
            results = cp.mean(intersections != 0, axis=1)

            results_weight = match_results_weight
            comp_lengths_weight = match_lengths_weight
            comp_counts_sums_weight = match_counts_weight

            results = cp.divide(cp.add(cp.add(results_weight, comp_lengths_weight), comp_counts_sums_weight), cp.add(cp.add(cp.divide(results_weight, cp.where(results !=0, results, epsilon)), cp.divide(comp_lengths_weight, cp.where(comps_lengths_ratios !=0, comps_lengths_ratios, epsilon))), cp.divide(comp_counts_sums_weight, cp.where(comps_counts_sums_ratios !=0, comps_counts_sums_ratios, epsilon))))

            unique_means = cp.unique(results)
            sorted_means = cp.sort(unique_means)[::-1]

            filtered_means = sorted_means[(sorted_means >= lower_threshold) & (sorted_means <= upper_threshold)][:filter_size]

            filtered_idxs = cp.nonzero(cp.in1d(results, filtered_means))[0]

            all_filtered_means.extend(results[filtered_idxs].tolist())

            all_filtered_idxs.extend(filtered_idxs.tolist())

            filtered_tvs = [tv_idx] * filtered_idxs.shape[0]

            all_filtered_tvs.extend(filtered_tvs)

            tv_idx += 1

          f_results = sorted(zip(all_filtered_means, all_filtered_idxs, all_filtered_tvs), key=lambda x: x[0], reverse=True)

          triplet_dict = {}

          for triplet in f_results:

              if triplet[0] not in triplet_dict:
                triplet_dict[triplet[0]] = triplet
              else:
                if triplet[2] == 0:
                  triplet_dict[triplet[0]] = triplet

          filtered_results = list(triplet_dict.values())[:filter_size]

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

          print('Done!')
          print('-' * 70)
          print('Max match ratio:', filtered_results[0][0])
          print('Max match transpose value:', filtered_results[0][2])
          print('Max match signature index:', filtered_results[0][1])
          print('Max match file name:', kilo_chords_file_names[filtered_results[0][1]])
          print('-' * 70)
          print('Copying max ratios MIDIs...')

          for fr in filtered_results:

            max_ratio_index = fr[1]

            ffn = kilo_chords_file_names[fr[1]]
            ffn_idx = [y[0] for y in LAMD_files_list].index(ffn)

            ff = LAMD_files_list[ffn_idx][1]

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

            dir_str = str(fn1)
            copy_path = '/content/Output-MIDI-Dataset/'+search_settings_string+'/'+dir_str
            if not os.path.exists(copy_path):
                os.mkdir(copy_path)

            fff = str(fr[0] * 100) + '_' + str(fr[2]) + '_' + ffn + '.mid'

            shutil.copy2(ff, copy_path+'/'+fff)

          shutil.copy2(f, copy_path+'/'+fn)

          #======================================================='''
          print('Done!')
          print('=' * 70)

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

          # Processed files counter
          files_count += 1

    except KeyboardInterrupt:
        print('Quitting...')
        print('Total number of processed MIDI files', files_count)
        print('=' * 70)
        break

    except Exception as ex:
        print('WARNING !!!')
        print('=' * 70)
        print('Bad file:', f)
        print('Error detected:', ex)
        print('=' * 70)
        continue

    print('Total number of processed MIDI files', files_count)
    print('=' * 70)

else:
  print('Could not find any MIDI files. Please check Dataset dir...')
  print('=' * 70)

"""# (DOWNLOAD SEARCH RESULTS)"""

# Commented out IPython magic to ensure Python compatibility.
#@title Zip and download all search results

print('=' * 70)

try:
    os.remove('Monster_MIDI_Dataset_Search_Results.zip')
except OSError:
    pass

print('Zipping... Please wait...')
print('=' * 70)

# %cd /content/Output-MIDI-Dataset/
!zip -r Monster_MIDI_Dataset_Search_Results.zip *
# %cd /content/

print('=' * 70)
print('Done!')
print('=' * 70)

print('Downloading final zip file...')
print('=' * 70)

files.download('/content/Output-MIDI-Dataset/Monster_MIDI_Dataset_Search_Results.zip')

print('Done!')
print('=' * 70)

# @title Delete search results directory and files

#@markdown WARNING: This can't be undone so make sure you downloaded the search results first

print('=' * 70)
print('Deleting... Please wait...')
print('=' * 70)

!rm -rf /content/Output-MIDI-Dataset
print('Done!')
print('=' * 70)

"""# (META DATA SEARCH)"""

#@title Load Monster MIDI Dataset Metadata
print('=' * 70)
print('Loading Monster MIDI Dataset Metadata...')
meta_data = pickle.load(open('/content/Main-MIDI-Dataset/META_DATA/MONSTER_META_DATA.pickle', 'rb'))
print('Done!')
print('=' * 70)
print('Enjoy!')
print('=' * 70)

#@title Monster MIDI Dataset Metadata Search

#@markdown You can search the metadata by search query or by MIDI md5 hash file name

search_query = "Come To My Window" #@param {type:"string"}
md5_hash_MIDI_file_name = "68dfb00f24f5ebd9bb52823fa9a04c3e" #@param {type:"string"}
case_sensitive_search = False #@param {type:"boolean"}

fields_to_search = ['track_name',
                    'text_event',
                    'lyric',
                    'copyright_text_event',
                    'marker',
                    'text_event_08',
                    'text_event_09',
                    'text_event_0a',
                    'text_event_0b',
                    'text_event_0c',
                    'text_event_0d',
                    'text_event_0e',
                    'text_event_0f',
                    ]

print('=' * 70)
print('Los Angeles MIDI Dataset Metadata Search')
print('=' * 70)
print('Searching...')
print('=' * 70)

if md5_hash_MIDI_file_name != '':
  for d in tqdm(meta_data):
    try:
      if d[0] == md5_hash_MIDI_file_name:
        print('Found!')
        print('=' * 70)
        print('Metadata index:', meta_data.index(d))
        print('MIDI file name:', meta_data[meta_data.index(d)][0])
        print('-' * 70)
        pprint.pprint(['Result:', d[1][:16]], compact = True)
        print('=' * 70)
        break

    except KeyboardInterrupt:
      print('Ending search...')
      print('=' * 70)
      break

    except Exception as e:
      print('WARNING !!!')
      print('=' * 70)
      print('Error detected:', e)
      print('=' * 70)
      continue

  if d[0] != md5_hash_MIDI_file_name:
    print('Not found!')
    print('=' * 70)
    print('md5 hash was not found!')
    print('Ending search...')
    print('=' * 70)

else:
  for d in tqdm(meta_data):
    try:
      for dd in d[1]:
        if dd[0] in fields_to_search:
          if case_sensitive_search:
            if str(search_query) in str(dd[2]):
              print('Found!')
              print('=' * 70)
              print('Metadata index:', meta_data.index(d))
              print('MIDI file name:', meta_data[meta_data.index(d)][0])
              print('-' * 70)
              pprint.pprint(['Result:', dd[2][:16]], compact = True)
              print('=' * 70)

          else:
            if str(search_query).lower() in str(dd[2]).lower():
              print('Found!')
              print('=' * 70)
              print('Metadata index:', meta_data.index(d))
              print('MIDI file name:', meta_data[meta_data.index(d)][0])
              print('-' * 70)
              pprint.pprint(['Result:', dd[2][:16]], compact = True)
              print('=' * 70)

    except KeyboardInterrupt:
      print('Ending search...')
      print('=' * 70)
      break

    except:
      print('Ending search...')
      print('=' * 70)
      break

"""# Congrats! You did it! :)"""