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	# -- coding: utf-8 --
	"""Master_MIDI_Dataset_GPU_Search_and_Filter.ipynb

	Automatically generated by Colaboratory.

	Original file is located at
	https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Extras/Master_MIDI_Dataset_GPU_Search_and_Filter.ipynb

	# Master MIDI Dataset GPU Search and Filter (ver. 7.0)

	***

	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/Los-Angeles-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/Los-Angeles-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/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 Los Angeles MIDI Dataset
	print('=' * 70)
	print('Downloading Los Angeles MIDI Dataset...Please wait...')
	print('=' * 70)

	hf_hub_download(repo_id='projectlosangeles/Los-Angeles-MIDI-Dataset',
	filename='Los-Angeles-MIDI-Dataset-Ver-4-0-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 Los Angeles MIDI Dataset
	# %cd /content/Main-MIDI-Dataset/

	print('=' * 70)
	print('Unzipping Los Angeles MIDI Dataset...Please wait...')
	!unzip 'Los-Angeles-MIDI-Dataset-Ver-4-0-CC-BY-NC-SA.zip'
	print('=' * 70)

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

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

	#@title Create Los Angeles 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 Los Angeles MIDI Dataset Signatures Data

	print('=' * 70)
	print('Loading LAMDa Signatures Data...')
	sigs_data = pickle.load(open('/content/Main-MIDI-Dataset/SIGNATURES_DATA/LAMDa_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 Master 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('Master 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) + 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]))
	checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord)

	sig_token = TMIDIX.ALL_CHORDS.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 Los Angeles MIDI Dataset Kilo-Chords Data
	search_matching_type = "Full-Kilo-Chords" # @param ["Full-Kilo-Chords", "Unique-Kilo-Chords"]

	print('=' * 70)
	print('Loading LAMDa Kilo-Chords Data...')
	kilo_chords = pickle.load(open('/content/Main-MIDI-Dataset/KILO_CHORDS_DATA/LAMDa_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 Master 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('Master 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]))
	checked_tones_chord = TMIDIX.check_and_fix_tones_chord(tones_chord)

	chord_token = TMIDIX.ALL_CHORDS.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 RESULTS)"""

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

	print('=' * 70)

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

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

	# %cd /content/Output-MIDI-Dataset/
	!zip -r Master_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/Master_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)

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