"""Strict topology/source audit for KD and VESPO student checkpoints."""

from __future__ import annotations

import json
from pathlib import Path
from typing import Iterable

import torch


SUPPORTED_CHECKPOINT_FORMATS = {
    "moss-sherry-kd-qat-v1",
    "moss-sherry-vespo-v1",
    # This bounded pilot keeps optimizer/head state in the same document, but
    # its ``student`` view and topology config are standalone and deployable.
    "moss-speaker-state-pilot-checkpoint-v1",
}


def audit_quantized_student_state(
    state: dict[str, torch.Tensor],
    *,
    e2e: bool,
    required_quantized_keys: Iterable[str] = (),
) -> None:
    """Validate required FP32 masters and the tied embedding checkpoint view."""

    if not all(isinstance(key, str) and torch.is_tensor(value) for key, value in state.items()):
        raise ValueError("checkpoint student state must map string keys to tensors")
    required = set(required_quantized_keys)
    missing = sorted(required - set(state))
    if missing:
        raise ValueError(f"checkpoint is missing quantized tensors: {missing[:20]}")
    rounded = sorted(key for key in required if state[key].dtype != torch.float32)
    if rounded:
        details = [(key, str(state[key].dtype)) for key in rounded[:20]]
        raise ValueError(f"quantized master tensors must be FP32: {details}")
    embed_key = "model.language_model.embed_tokens.weight"
    head_key = "lm_head.weight"
    if e2e:
        if embed_key not in state or head_key not in state:
            raise ValueError("checkpoint is missing tied embedding/lm_head weights")
        if not torch.equal(state[embed_key], state[head_key]):
            raise ValueError("checkpoint embedding and lm_head weights disagree")


def audited_student_checkpoint(
    path: Path,
    *,
    e2e: bool,
    embed_bits: int,
    embed_group_size: int,
    required_quantized_keys: Iterable[str] = (),
) -> tuple[dict[str, torch.Tensor], str, int, str | None]:
    document = torch.load(path, map_location="cpu", weights_only=False)
    bare_snapshot = False
    if isinstance(document, dict) and isinstance(document.get("student"), dict):
        state = document["student"]
        topology = document.get("config")
        version = str(document.get("version") or f"kd-{int(document.get('step', 0))}")
        step = int(document.get("step", 0))
        checkpoint_format = document.get("format")
        if checkpoint_format not in SUPPORTED_CHECKPOINT_FORMATS:
            raise ValueError(f"unsupported student checkpoint format: {checkpoint_format!r}")
    elif isinstance(document, dict) and all(torch.is_tensor(value) for value in document.values()):
        bare_snapshot = True
        state = document
        metadata_path = path.with_name("metadata.json")
        if not metadata_path.is_file():
            raise ValueError("bare VESPO snapshot requires sibling metadata.json")
        topology = json.loads(metadata_path.read_text(encoding="utf-8"))
        if topology.get("schema") != "vespo-snapshot-v1":
            raise ValueError("bare VESPO snapshot metadata schema is invalid")
        if topology.get("dtype") != "mixed_fp32_quant_bf16_residual":
            raise ValueError(
                f"legacy/rounded VESPO snapshot dtype is not allowed: {topology.get('dtype')!r}"
            )
        version = str(topology.get("version") or path.parent.name)
        step = int(topology.get("step", 0))
        checkpoint_format = "vespo-snapshot-v1"
    else:
        raise ValueError(f"unsupported student checkpoint format: {path}")
    if not isinstance(topology, dict):
        raise ValueError("checkpoint is missing topology config")
    recorded_e2e = topology.get("e2e")
    if type(recorded_e2e) is not bool or recorded_e2e != e2e:
        raise ValueError(f"checkpoint e2e topology mismatch: {topology.get('e2e')!r} != {e2e}")
    recorded_bits = topology.get("embed_bits")
    recorded_group = topology.get("embed_group_size")
    if e2e:
        # The original W4 e2e trainer predates explicit embed metadata.  Missing
        # values are unambiguous only for that legacy W4 full checkpoint.
        if not (embed_bits == 4 and not bare_snapshot and recorded_bits is None):
            if recorded_bits != embed_bits:
                raise ValueError(
                    f"checkpoint embedding precision mismatch: {recorded_bits!r} != {embed_bits}"
                )
        if embed_bits == 3 and recorded_group != embed_group_size:
            raise ValueError(
                f"checkpoint embedding group mismatch: {recorded_group!r} != {embed_group_size}"
            )
    audit_quantized_student_state(
        state,
        e2e=e2e,
        required_quantized_keys=required_quantized_keys,
    )
    return state, version, step, checkpoint_format