'Plan or execute binary, source-classification and real-ratio training configurations.'
import argparse
import json
from pathlib import Path
import subprocess
import sys

ROOT = Path(__file__).resolve().parents[2]
SEEDS = [7, 13, 21, 42, 84]
N_GRID = [900, 90, 45, 18]


def build_jobs(kind, fixed_split, output, mapping, real_ratio=0.1):
    if kind == 'ratio':
        return [job for ratio in [0.01, 1.0] for job in build_jobs('ratio_binary', fixed_split, output / f'ratio_{ratio}', mapping, ratio)]
    jobs = []
    for seed in SEEDS if kind == 'binary' else [42]:
        splits = output / 'splits' / f'seed{seed}'
        jobs.append([sys.executable, str(ROOT/'scripts/data/build_training_splits.py'),
            '--ai-train', str(fixed_split/'ai_train_pool.csv'), '--ai-val', str(fixed_split/'ai_validation.csv'),
            '--real-train', str(fixed_split/'real_train_pool.csv'), '--real-val', str(fixed_split/'real_validation.csv'),
            '--output-dir', str(splits), '--n-grid', ','.join(map(str,N_GRID)),
            '--real-ratio', str(real_ratio), '--seed', str(seed)])
        tag = 'training_split' if kind != 'source' else 'source_split'
        split = splits/f'{tag}_n900_realratio_{str(real_ratio).replace(".", "p")}_seed{seed}.json'
        for model in ['univfd','cnndetect']:
            script=ROOT/f'train_{model}{"_multiclass" if kind=="source" else ""}.py'
            for n in ([18, 900] if kind == 'ratio_binary' else N_GRID):
                run=output/'runs'/f'{kind}_{model}'
                if seed!=42: run=run/f'seed{seed}'
                run=run/f'n{n}'
                jobs.append([sys.executable,str(script),'--split-file',str(split),'--mapping',str(mapping),
                    '--sample-sizes',str(n),'--total-samples-per-model','9000','--train-ratio','1.0',
                    '--real-ratio',str(real_ratio),'--batch-size','64','--lr','0.0001',
                    '--num-validations','10','--seed',str(seed),'--output-dir',str(run)])
    return jobs


def main():
    p=argparse.ArgumentParser(description=__doc__)
    p.add_argument('--kind',choices=['binary','source','ratio'],default='binary')
    p.add_argument('--split-dir',type=Path,required=True)
    p.add_argument('--mapping',type=Path,required=True)
    p.add_argument('--output-dir',type=Path,default=ROOT/'outputs')
    p.add_argument('--execute',action='store_true')
    a=p.parse_args()
    jobs=build_jobs(a.kind,a.split_dir.resolve(),a.output_dir.resolve(),a.mapping.resolve())
    a.output_dir.mkdir(parents=True,exist_ok=True)
    (a.output_dir/f'{a.kind}_release_jobs.json').write_text(json.dumps({
        'protocol':'release-v1-resampled-final',
        'checkpoint':'final','commands':jobs},indent=2),encoding='utf-8')
    if a.execute:
        for job in jobs: subprocess.run(job,cwd=ROOT,check=True)
    print(f'{len(jobs)} commands {"executed" if a.execute else "planned (not executed)"}')

if __name__=='__main__':main()