# Copyright 2025 Eli Lilly and Company
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Tests for the `.fit()` method."""

import jax.numpy as jnp
import numpyro.distributions as dist
import pytest
from conftest import lm
from jax import random
from jax.typing import ArrayLike
from numpyro import deterministic, sample
from numpyro.infer import SVI, Trace_ELBO
from numpyro.infer.autoguide import AutoNormal
from numpyro.optim import Adam
from sklearn.exceptions import DataConversionWarning

from aimz._exceptions import KernelValidationError
from aimz.model import ImpactModel
from aimz.utils._validation import _is_fitted


class TestKernelSignatureValidation:
    """Test class for validating parameter compatibility with the kernel signature."""

    def test_extra_parameters(self) -> None:
        """Extra parameters not present in the kernel raise an error."""

        def kernel(X: ArrayLike, y: ArrayLike | None = None) -> None:
            pass

        with pytest.warns(
            UserWarning,
            match="Legacy `uint32` PRNGKey detected; converting to a typed key array.",
        ):
            im = ImpactModel(
                kernel,
                rng_key=random.PRNGKey(42),
                inference=SVI(
                    kernel,
                    guide=AutoNormal(kernel),
                    optim=Adam(step_size=1e-3),
                    loss=Trace_ELBO(),
                ),
            )
        with pytest.raises(TypeError):
            im.fit(X=jnp.ones((3, 1)), y=jnp.ones((3,)), batch_size=3, extra=True)

    def test_missing_parameters(self) -> None:
        """Missing required parameters in the kernel raise an error."""

        def kernel(X: ArrayLike, arg: object, y: ArrayLike | None = None) -> None:
            pass

        im = ImpactModel(
            kernel,
            rng_key=random.key(42),
            inference=SVI(
                kernel,
                guide=AutoNormal(kernel),
                optim=Adam(step_size=1e-3),
                loss=Trace_ELBO(),
            ),
        )
        with pytest.raises(TypeError):
            im.fit(X=jnp.ones((10, 1)), y=jnp.ones((10,)), batch_size=3)


class TestKernelBodyValidation:
    """Test class for validating parameter compatibility with the kernel body."""

    def test_missing_output_site(self) -> None:
        """Missing output sample site in the kernel raises an error."""

        def kernel(X: ArrayLike, y: ArrayLike | None = None) -> None:
            sample("z", dist.Normal(0.0, 1.0), obs=y)

        im = ImpactModel(
            kernel,
            rng_key=random.key(42),
            inference=SVI(
                kernel,
                guide=AutoNormal(kernel),
                optim=Adam(step_size=1e-3),
                loss=Trace_ELBO(),
            ),
        )
        with pytest.raises(KernelValidationError):
            im.fit(X=jnp.ones((10, 1)), y=jnp.ones((10,)), batch_size=3)

    def test_sample_output_site(self) -> None:
        """Raises error if output site is not a sample site."""

        def kernel(X: ArrayLike, y: ArrayLike | None = None) -> None:
            deterministic("y", jnp.zeros_like(y))

        im = ImpactModel(
            kernel,
            rng_key=random.key(42),
            inference=SVI(
                kernel,
                guide=AutoNormal(kernel),
                optim=Adam(step_size=1e-3),
                loss=Trace_ELBO(),
            ),
        )
        with pytest.raises(KernelValidationError):
            im.fit(X=jnp.ones((10, 1)), y=jnp.ones((10,)), batch_size=3)

    def test_unobserved_output_site(self) -> None:
        """Raises error if output site is not observed."""

        def kernel(X: ArrayLike, y: ArrayLike | None = None) -> None:
            sample("y", dist.Normal(0.0, 1.0))

        im = ImpactModel(
            kernel,
            rng_key=random.key(42),
            inference=SVI(
                kernel,
                guide=AutoNormal(kernel),
                optim=Adam(step_size=1e-3),
                loss=Trace_ELBO(),
            ),
        )
        with pytest.raises(KernelValidationError):
            im.fit(X=jnp.ones((10, 1)), y=jnp.ones((10,)), batch_size=3)

    def test_parameter_site_conflict(self) -> None:
        """Raises an error if a parameter name conflicts with a site name."""

        def kernel(X: ArrayLike, y: ArrayLike | None = None) -> None:
            sample("X", dist.Normal(0.0, 1.0))
            sample("y", dist.Normal(0.0, 1.0), obs=y)

        im = ImpactModel(
            kernel,
            rng_key=random.key(42),
            inference=SVI(
                kernel,
                guide=AutoNormal(kernel),
                optim=Adam(step_size=1e-3),
                loss=Trace_ELBO(),
            ),
        )
        with pytest.raises(KernelValidationError):
            im.fit(X=jnp.ones((10, 1)), y=jnp.ones((10,)), batch_size=3)


def test_fit_unexpected_y_shape() -> None:
    """Calling `.fit()` with an unexpected shape of `y` raises a warning."""

    def kernel(X: ArrayLike, y: ArrayLike | None = None) -> None:
        sample("y", dist.Normal(0.0, 1.0), obs=y)

    im = ImpactModel(
        kernel,
        rng_key=random.key(42),
        inference=SVI(
            kernel,
            guide=AutoNormal(kernel),
            optim=Adam(step_size=1e-3),
            loss=Trace_ELBO(),
        ),
    )
    with pytest.warns(DataConversionWarning):
        im.fit(X=jnp.zeros((3, 2)), y=jnp.zeros((3, 1)), batch_size=3)


def test_fit_nan_warning(synthetic_data: tuple[ArrayLike, ArrayLike]) -> None:
    """Test that `.fit()` emits a RuntimeWarning when NaN is in the loss."""
    X, y = synthetic_data
    im = ImpactModel(
        lm,
        rng_key=random.key(42),
        inference=SVI(
            lm,
            guide=AutoNormal(lm),
            optim=Adam(step_size=1e3),
            loss=Trace_ELBO(),
        ),
    )
    with pytest.warns(RuntimeWarning):
        im.fit(X, y, batch_size=len(X), epochs=3)

    with pytest.warns(RuntimeWarning):
        im.fit_on_batch(X, y)


@pytest.mark.parametrize("vi", [lm], indirect=True)
def test_fit_lm(synthetic_data: tuple[ArrayLike, ArrayLike], vi: SVI) -> None:
    """Test the `.fit()` method of `ImpactModel`."""
    X, y = synthetic_data
    im = ImpactModel(lm, rng_key=random.key(42), inference=vi)
    im.fit(X=X, y=y, batch_size=3)
    assert _is_fitted(im), "Model fitting check failed"
