"""
checkpoint.py
-------------
Utilities for saving and loading model checkpoints.
Saves projection layer + LoRA adapter weights separately
(not the full frozen LLM), keeping checkpoint size small.
"""

import torch
from pathlib import Path
from typing import Optional


def save_checkpoint(model, output_dir: str, name: str = "checkpoint"):
    """
    Save trainable model weights:
      - projection layer weights
      - LoRA adapter weights (via PEFT save)

    Does NOT save frozen encoder or full LLM weights.

    Args:
        model:      CXRVisionLanguageModel
        output_dir: directory to save into
        name:       checkpoint name
    """
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    # Save projection layer
    proj_path = output_dir / f"{name}_projection.pt"
    torch.save(model.projection.state_dict(), proj_path)
    print(f"[Checkpoint] Projection saved → {proj_path}")

    # Save ITC head if present (Stage-1 ITC mode). Tiny; only exists when
    # the model was built with build_itc_head=True.
    if getattr(model, "itc_head", None) is not None:
        itc_path = output_dir / f"{name}_itc_head.pt"
        torch.save(model.itc_head.state_dict(), itc_path)
        print(f"[Checkpoint] ITC head saved → {itc_path}")

    # Save LoRA adapters via PEFT — skipped in ITC Stage-1 (llm not loaded).
    if getattr(model, "llm", None) is not None:
        lora_dir = output_dir / f"{name}_lora"
        model.llm.save_pretrained(str(lora_dir))
        print(f"[Checkpoint] LoRA adapters saved → {lora_dir}")
    else:
        print("[Checkpoint] llm is None (ITC Stage-1) — LoRA save skipped.")

    # Save CheXpert classifier if it exists and was trained
    if model.chexpert_classifier is not None:
        clf_path = output_dir / f"{name}_chexpert_classifier.pt"
        torch.save(model.chexpert_classifier.state_dict(), clf_path)
        print(f"[Checkpoint] CheXpert classifier saved → {clf_path}")


def load_checkpoint(
    model,
    checkpoint_path: str,
    load_lora: bool = True,
    strict: bool = False,
):
    """
    Load trainable weights from checkpoint.

    Args:
        model:            CXRVisionLanguageModel
        checkpoint_path:  path to checkpoint dir OR .pt file
        load_lora:        whether to load LoRA adapters
        strict:           strict state dict loading
    """
    checkpoint_path = Path(checkpoint_path)

    # If given a .pt file, treat its parent as the checkpoint dir
    if checkpoint_path.suffix == ".pt":
        ckpt_dir  = checkpoint_path.parent
        ckpt_name = checkpoint_path.stem
    else:
        ckpt_dir  = checkpoint_path
        ckpt_name = "checkpoint"

    # Load projection
    proj_path = ckpt_dir / f"{ckpt_name}_projection.pt"
    if proj_path.exists():
        state = torch.load(proj_path, map_location="cpu")
        model.projection.load_state_dict(state, strict=strict)
        print(f"[Checkpoint] Projection loaded ← {proj_path}")
    else:
        print(f"[Checkpoint] No projection file found at {proj_path}, skipping.")

    # Load ITC head (Stage-1 ITC mode only; present iff model.itc_head built)
    if getattr(model, "itc_head", None) is not None:
        itc_path = ckpt_dir / f"{ckpt_name}_itc_head.pt"
        if itc_path.exists():
            state = torch.load(itc_path, map_location="cpu")
            model.itc_head.load_state_dict(state, strict=strict)
            print(f"[Checkpoint] ITC head loaded ← {itc_path}")

    # Load LoRA — skipped when llm not loaded (ITC Stage-1) or no dir present.
    if load_lora and getattr(model, "llm", None) is not None:
        lora_dir = ckpt_dir / f"{ckpt_name}_lora"
        # Defensive: PEFT raises an opaque HFValidationError when the dir
        # exists but `adapter_config.json` is missing (a partially-written
        # or partially-downloaded checkpoint). Surface a clearer message so
        # the user knows the fix: delete the dir and resume from HF Hub.
        if lora_dir.is_dir() and not (lora_dir / "adapter_config.json").is_file():
            raise FileNotFoundError(
                f"[load_checkpoint] {lora_dir} exists but adapter_config.json "
                f"is missing — checkpoint is partially-written/downloaded. "
                f"Fix: delete the parent checkpoint folder "
                f"({lora_dir.parent}) and rerun with --mode resume so it "
                f"gets re-pulled from HF Hub, OR rm -rf the stage2_instruct "
                f"folder to train Stage 2 fresh from stage1_final."
            )
        if lora_dir.exists():
            from peft import PeftModel
            # is_trainable=True is REQUIRED on resume: PEFT defaults to
            # inference mode (requires_grad=False on all LoRA params), which
            # would shrink the trainable set to projection-only (~5 tensors)
            # and break optimizer state loading with a param-group size
            # mismatch against the saved 261-tensor stage-2 optimizer.
            model.llm = PeftModel.from_pretrained(
                model.llm.base_model.model,
                str(lora_dir),
                is_trainable=True,
            )
            print(f"[Checkpoint] LoRA adapters loaded ← {lora_dir}")
        else:
            print(f"[Checkpoint] No LoRA dir found at {lora_dir}, skipping.")

    # Load CheXpert classifier
    if model.chexpert_classifier is not None:
        clf_path = ckpt_dir / f"{ckpt_name}_chexpert_classifier.pt"
        if clf_path.exists():
            state = torch.load(clf_path, map_location="cpu")
            model.chexpert_classifier.load_state_dict(state, strict=strict)
            print(f"[Checkpoint] CheXpert classifier loaded ← {clf_path}")