import ast
from pathlib import Path
import logging
import warnings as _warnings
import torch


def _find_vision():
    for p in [
        Path(__file__).resolve().parent / "unsloth" / "models" / "vision.py",
        Path(__file__).resolve().parents[1] / "unsloth" / "models" / "vision.py",
        Path(
            "/mnt/disks/unslothai/ubuntu/workspace_25/github_review/unsloth-pr-5053-staging-3/unsloth/models/vision.py"
        ),
    ]:
        if p.exists():
            return p
    raise FileNotFoundError("vision.py not found")


def _load_fns():
    tree = ast.parse(_find_vision().read_text())
    ns = {"torch": torch, "warnings": _warnings, "logger": logging.getLogger("test")}
    for node in tree.body:
        if isinstance(node, ast.FunctionDef) and node.name in {
            "_infer_device_map_from_loaded_model",
            "_attach_bnb_multidevice_hooks",
        }:
            exec(
                compile(
                    ast.Module(body = [node], type_ignores = []),
                    str(_find_vision()),
                    "exec",
                ),
                ns,
            )
    return ns["_infer_device_map_from_loaded_model"], ns[
        "_attach_bnb_multidevice_hooks"
    ]


class _P:
    def __init__(self, dev):
        self.device = torch.device(dev) if isinstance(dev, str) else dev


class _FakeMod:
    def __init__(self, params = None, buffers = None, children = None, hf_device_map = None):
        self._p = list(params or [])
        self._b = list(buffers or [])
        self._c = list(children or [])
        self.hf_device_map = hf_device_map

    def named_parameters(self, recurse = True, remove_duplicate = False):
        for n, d in self._p:
            yield n, _P(d)
        if recurse:
            for cn, cm in self._c:
                for pn, pp in cm.named_parameters(
                    recurse = True, remove_duplicate = remove_duplicate
                ):
                    yield f"{cn}.{pn}", pp

    def parameters(self, recurse = True):
        for _, p in self.named_parameters(recurse = recurse):
            yield p

    def named_buffers(self, recurse = True):
        for n, d in self._b:
            yield n, _P(d)
        if recurse:
            for cn, cm in self._c:
                for bn, bb in cm.named_buffers(recurse = True):
                    yield f"{cn}.{bn}", bb

    def named_children(self):
        yield from self._c


def test_infer_three_level_deep_mixed():
    """Split at the third level of nesting: the algorithm must recurse deep
    enough to distinguish grandchildren on different devices."""
    infer, _ = _load_fns()
    g1 = _FakeMod(params = [("w", "cuda:0")])
    g2 = _FakeMod(params = [("w", "cuda:1")])
    level2 = _FakeMod(children = [("g1", g1), ("g2", g2)])
    level1 = _FakeMod(children = [("l2", level2)])
    root = _FakeMod(children = [("l1", level1)])
    dm = infer(root)
    assert dm.get("l1.l2.g1") == torch.device("cuda", 0)
    assert dm.get("l1.l2.g2") == torch.device("cuda", 1)
    # Intermediate levels that are mixed must NOT collapse prematurely
    assert "l1" not in dm or len({dm.get("l1"), dm.get("l1.l2.g1")}) > 1