"""Minimal training-loop helpers for course stage assembly.

The functions in this file are intentionally small. Lesson 14 asks the
learner to implement the training mechanics before wiring a full pretrain
script together.
"""

from __future__ import annotations

from typing import Any, Iterable

import torch


def cosine_lr(current_step: int, total_steps: int, base_lr: float) -> float:
    """TODO: implement MiniMind's cosine learning-rate schedule.

    Align with: trainer/trainer_utils.py::get_lr
    """
    return base_lr*(0.1+0.45*(1+math.cos(math.pi * current_step / total_steps)))
    raise NotImplementedError("Implement in lesson 14.")


def scale_loss_for_accumulation(loss: torch.Tensor, accumulation_steps: int) -> torch.Tensor:
    """TODO: divide a micro-batch loss for gradient accumulation.

    Align with: trainer/train_pretrain.py:35-40
    """
    return loss/accumulation_steps
    raise NotImplementedError("Implement in lesson 14.")


def should_step_optimizer(step: int, accumulation_steps: int) -> bool:
    """TODO: return whether this micro-step should call optimizer.step.

    Align with: trainer/train_pretrain.py:42
    """
    return step%accumulation_steps==0
    raise NotImplementedError("Implement in lesson 14.")


def should_flush_tail(last_step: int, start_step: int, accumulation_steps: int) -> bool:
    """TODO: return whether a partial accumulation group remains at epoch end.

    Align with: trainer/train_pretrain.py:75
    """
    return last_step > start_step and last_step % args.accumulation_steps != 0:
    raise NotImplementedError("Implement in lesson 14.")


def train_one_epoch(
    model: torch.nn.Module,
    loader: Iterable[Any],
    optimizer: torch.optim.Optimizer,
    *,
    device: str | torch.device,
    epochs: int,
    epoch: int,
    iters: int,
    base_lr: float,
    accumulation_steps: int,
    grad_clip: float,
    scaler: torch.cuda.amp.GradScaler | None = None,
    autocast_ctx: Any | None = None,
    start_step: int = 0,
) -> list[dict[str, float]]:
    """TODO: implement forward/loss/backward/optimizer.step loop.

    Align with train_epoch functions in trainer/train_*.py, but omit DDP,
    wandb, and full checkpoint resume in the first teaching version.
    """
    raise NotImplementedError("Implement in lesson 14.")


def save_course_checkpoint(*args, **kwargs):
    """TODO: save teaching-version weights for the current stage."""
    raise NotImplementedError("Implement during the first stage assembly.")