import pytest
import torch

from pina import LabelTensor, Condition
from pina.model import FeedForward
from pina.trainer import Trainer
from pina.solver import DeepEnsemblePINN
from pina.condition import (
    InputTargetCondition,
    InputEquationCondition,
    DomainEquationCondition,
)
from pina.problem.zoo import Poisson2DSquareProblem as Poisson
from torch._dynamo.eval_frame import OptimizedModule


# define problems
problem = Poisson()
problem.discretise_domain(10)

# add input-output condition to test supervised learning
input_pts = torch.rand(10, len(problem.input_variables))
input_pts = LabelTensor(input_pts, problem.input_variables)
output_pts = torch.rand(10, len(problem.output_variables))
output_pts = LabelTensor(output_pts, problem.output_variables)
problem.conditions["data"] = Condition(input=input_pts, target=output_pts)

# define models
models = [
    FeedForward(
        len(problem.input_variables), len(problem.output_variables), n_layers=1
    )
    for _ in range(5)
]


def test_constructor():
    solver = DeepEnsemblePINN(problem=problem, models=models)

    assert solver.accepted_conditions_types == (
        InputTargetCondition,
        InputEquationCondition,
        DomainEquationCondition,
    )
    assert solver.num_ensemble == 5


@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
@pytest.mark.parametrize("compile", [True, False])
def test_solver_train(batch_size, compile):
    solver = DeepEnsemblePINN(models=models, problem=problem)
    trainer = Trainer(
        solver=solver,
        max_epochs=2,
        accelerator="cpu",
        batch_size=batch_size,
        train_size=1.0,
        val_size=0.0,
        test_size=0.0,
        compile=compile,
    )
    trainer.train()
    if trainer.compile:
        assert all(
            [isinstance(model, OptimizedModule) for model in solver.models]
        )


@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
@pytest.mark.parametrize("compile", [True, False])
def test_solver_validation(batch_size, compile):
    solver = DeepEnsemblePINN(models=models, problem=problem)
    trainer = Trainer(
        solver=solver,
        max_epochs=2,
        accelerator="cpu",
        batch_size=batch_size,
        train_size=0.9,
        val_size=0.1,
        test_size=0.0,
        compile=compile,
    )
    trainer.train()
    if trainer.compile:
        assert all(
            [isinstance(model, OptimizedModule) for model in solver.models]
        )


@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
@pytest.mark.parametrize("compile", [True, False])
def test_solver_test(batch_size, compile):
    solver = DeepEnsemblePINN(models=models, problem=problem)
    trainer = Trainer(
        solver=solver,
        max_epochs=2,
        accelerator="cpu",
        batch_size=batch_size,
        train_size=0.7,
        val_size=0.2,
        test_size=0.1,
        compile=compile,
    )
    trainer.test()
    if trainer.compile:
        assert all(
            [isinstance(model, OptimizedModule) for model in solver.models]
        )


def test_train_load_restore():
    dir = "tests/test_solver/tmp"
    solver = DeepEnsemblePINN(models=models, problem=problem)
    trainer = Trainer(
        solver=solver,
        max_epochs=5,
        accelerator="cpu",
        batch_size=None,
        train_size=0.7,
        val_size=0.2,
        test_size=0.1,
        default_root_dir=dir,
    )
    trainer.train()

    # restore
    new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu")
    new_trainer.train(
        ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/"
        + "epoch=4-step=5.ckpt"
    )

    # loading
    new_solver = DeepEnsemblePINN.load_from_checkpoint(
        f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt",
        problem=problem,
        models=models,
    )

    test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables)
    assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape
    torch.testing.assert_close(
        new_solver.forward(test_pts), solver.forward(test_pts)
    )

    # rm directories
    import shutil

    shutil.rmtree("tests/test_solver/tmp")