from __future__ import annotations

import subprocess
import sys
from dataclasses import dataclass
from pathlib import Path


@dataclass(frozen=True)
class TrainingRun:
    command: list[str]
    returncode: int | None = None


def training_script_path() -> Path:
    return Path(__file__).resolve().parent.parent / 'training' / 'train_service_intent_extractor.py'


def build_training_command(
    *,
    dataset_path: str | Path,
    model_name_or_path: str | Path,
    output_dir: str | Path,
    quantized_output_dir: str | Path | None = None,
    epochs: int = 3,
    batch_size: int = 4,
    learning_rate: float = 3e-5,
    max_source_length: int = 192,
    max_target_length: int = 256,
    skip_quantize: bool = False,
) -> list[str]:
    command = [
        sys.executable,
        str(training_script_path()),
        '--dataset',
        str(dataset_path),
        '--model-name-or-path',
        str(model_name_or_path),
        '--output-dir',
        str(output_dir),
        '--epochs',
        str(epochs),
        '--batch-size',
        str(batch_size),
        '--learning-rate',
        str(learning_rate),
        '--max-source-length',
        str(max_source_length),
        '--max-target-length',
        str(max_target_length),
    ]
    if skip_quantize:
        command.append('--skip-quantize')
    elif quantized_output_dir:
        command.extend(['--quantized-output-dir', str(quantized_output_dir)])
    return command


def run_training(command: list[str], *, cwd: str | Path | None = None) -> TrainingRun:
    completed = subprocess.run(command, cwd=str(cwd) if cwd else None, check=False)
    return TrainingRun(command=command, returncode=completed.returncode)