import contextlib
import contextvars
import numpy as np
import pickle
import sys
import types
import weakref
import pytest
import torch

import compilation


def test_package_transfer_drops_live_cuda_runner():
    packaged = compilation.PackagedFP16("/runtime/model.pt2")
    # A native runner cannot be pickled and must never escape its GPU worker.
    packaged.loaded = lambda value: value
    restored = pickle.loads(pickle.dumps(packaged))
    assert restored.package_path == "/runtime/model.pt2"
    assert restored.loaded is None


def test_package_loads_once_across_zerogpu_requests(monkeypatch):
    loads = []

    def load(path):
        loads.append(path)
        return lambda value: value + 1

    monkeypatch.setattr(torch._inductor, "aoti_load_package", load)
    # The real helper reads Linux process maps; it cannot import on Windows.
    cleanup = types.SimpleNamespace(_register_aoti_cleanup=contextlib.nullcontext)
    monkeypatch.setitem(sys.modules, "spaces.zero.torch.aoti", cleanup)
    packaged = compilation.PackagedFP16("/runtime/model.pt2")
    # ZeroGPU workers run every request in a fresh copy of the worker context.
    results = [contextvars.copy_context().run(packaged, 1) for _ in range(3)]
    assert results == [2, 2, 2]
    assert loads == ["/runtime/model.pt2"]


def test_disabled_compilation_preserves_working_model(monkeypatch):
    monkeypatch.setenv("VRMBG_COMPILE", "0")
    model = torch.nn.Linear(2, 1)
    expected = model(torch.ones(1, 2)).detach().clone()
    assert compilation.enable_compiled_model(model) is False
    torch.testing.assert_close(model(torch.ones(1, 2)), expected)


def test_compile_failure_preserves_eager_model(monkeypatch, caplog):
    monkeypatch.setenv("VRMBG_COMPILE", "1")
    monkeypatch.setenv("SPACES_ZERO_GPU", "true")
    monkeypatch.setattr(compilation.spaces, "GPU", lambda **kw: lambda fn: fn)

    def fail(model):
        raise RuntimeError("Compiler unavailable")

    monkeypatch.setattr(compilation, "compile_fp16", fail)
    model = torch.nn.Linear(2, 1)
    model.register_buffer("integer_indices", torch.arange(4))
    expected = model(torch.ones(1, 2)).detach().clone()
    assert compilation.enable_compiled_model(model) is False
    torch.testing.assert_close(model(torch.ones(1, 2)), expected)
    assert "using eager FP16" in caplog.text


def test_zerogpu_startup_enables_compiled_forward(monkeypatch):
    monkeypatch.delenv("VRMBG_COMPILE", raising=False)
    monkeypatch.setenv("SPACES_ZERO_GPU", "true")
    monkeypatch.setattr(compilation.spaces, "GPU", lambda **kw: lambda fn: fn)
    model = torch.nn.Linear(2, 1)
    model.register_buffer("integer_indices", torch.arange(4))

    def compiled(value):
        return value.sum(dim=1, keepdim=True)

    monkeypatch.setattr(compilation, "compile_fp16", lambda model: compiled)
    old_parameter = weakref.ref(model.weight)
    assert compilation.enable_compiled_model(model) is True
    torch.testing.assert_close(model(torch.ones(1, 2)), torch.tensor([[2.0]]))
    assert all(tensor.numel() == 0 for tensor in model.state_dict().values())
    assert old_parameter() is None


def test_apply_failure_is_not_reported_as_working_eager_fallback(monkeypatch):
    monkeypatch.setenv("VRMBG_COMPILE", "1")
    monkeypatch.setattr(compilation.spaces, "GPU", lambda **kw: lambda fn: fn)
    monkeypatch.setattr(compilation, "compile_fp16", lambda model: object())

    def fail_apply(candidate, model):
        model.weight = None
        raise RuntimeError("Apply failed after mutation")

    monkeypatch.setattr(compilation, "apply_compiled_model", fail_apply)
    with pytest.raises(RuntimeError, match="Apply failed"):
        compilation.enable_compiled_model(torch.nn.Linear(2, 1))


def test_local_compilation_errors_require_a_fresh_process(monkeypatch):
    monkeypatch.setenv("VRMBG_COMPILE", "1")
    monkeypatch.delenv("SPACES_ZERO_GPU", raising=False)
    monkeypatch.setattr(compilation.spaces, "GPU", lambda **kw: lambda fn: fn)

    def fail(model):
        raise RuntimeError("CUDA failure")

    monkeypatch.setattr(compilation, "compile_fp16", fail)
    with pytest.raises(RuntimeError, match="CUDA failure"):
        compilation.enable_compiled_model(torch.nn.Linear(2, 1))


@pytest.mark.parametrize("bad", [np.nan, np.inf, 0.7])
def test_quality_check_rejects_invalid_or_changed_mattes(bad):
    reference = np.full((3, 8, 8), 0.5, dtype=np.float32)
    with pytest.raises(RuntimeError):
        compilation.check_mattes(reference, np.full_like(reference, bad))


def test_quality_check_rejects_shape_changes():
    with pytest.raises(RuntimeError):
        compilation.check_mattes(np.zeros((3, 8, 8)), np.zeros((1, 8, 8)))


def test_quality_check_accepts_small_fp16_rounding():
    reference = np.full((3, 8, 8), 0.5, dtype=np.float32)
    compilation.check_mattes(reference, reference + 0.0001)