Nathan Fradet Natooz

## benchmark_midi_parsing.py
#!/usr/bin/python3 python

"""Benchmark for Python MIDI parsing libraries.
"""

import random
from pathlib import Path
from time import time
from typing import Sequence

## slurm_loop_sbatch.sh
#!/bin/bash

# Here the job is relaunched if it was not 100% completed, that is until a specific file exists
# The --wait argument allows to hang the execution until the job execution ends, before resubmitting it if needed

# Set vars
NAME=$1
JOB_FILE="train_$NAME.sh"
TRAIN_FILE="runs/gen_MMD/TSD_$NAME/train_results.json"

## estimate_training_time.py
"""
Small script estimating the training time of models, depending on number of nodes / GPUs / batch size...
"""


from pprint import pformat
from math import ceil


def sec_to_hours(nb_sec: float, no_seconds: bool = False) -> str:

## test_chords_detection.py
#!/usr/bin/python3 python

"""Test chord detection methods

"""

from dataclasses import dataclass
from typing import List, Tuple, Dict, cast
from copy import copy
	#!/usr/bin/python3 python

	"""Benchmark for Python MIDI parsing libraries.
	"""

	import random
	from pathlib import Path
	from time import time
	from typing import Sequence
	#!/bin/bash

	# Here the job is relaunched if it was not 100% completed, that is until a specific file exists
	# The --wait argument allows to hang the execution until the job execution ends, before resubmitting it if needed

	# Set vars
	NAME=$1
	JOB_FILE="train_$NAME.sh"
	TRAIN_FILE="runs/gen_MMD/TSD_$NAME/train_results.json"
	"""
	Small script estimating the training time of models, depending on number of nodes / GPUs / batch size...
	"""


	from pprint import pformat
	from math import ceil


	def sec_to_hours(nb_sec: float, no_seconds: bool = False) -> str:
	#!/usr/bin/python3 python

	"""Test chord detection methods

	"""

	from dataclasses import dataclass
	from typing import List, Tuple, Dict, cast
	from copy import copy