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import pytest
import os

from os import path

from tvm.driver import tvmc
from tvm.driver.tvmc.model import TVMCModel, TVMCPackage, TVMCResult
from tvm.runtime.module import BenchmarkResult


def test_tvmc_workflow(keras_simple):
    pytest.importorskip("tensorflow")

    tvmc_model = tvmc.load(keras_simple)
    tuning_records = tvmc.tune(tvmc_model, target="llvm", enable_autoscheduler=True, trials=2)
    tvmc_package = tvmc.compile(tvmc_model, tuning_records=tuning_records, target="llvm")
    result = tvmc.run(tvmc_package, device="cpu")
    assert type(tvmc_model) is TVMCModel
    assert type(tvmc_package) is TVMCPackage
    assert type(result) is TVMCResult
    assert path.exists(tuning_records)
    assert type(result.outputs) is dict
    assert type(result.times) is BenchmarkResult
    assert "output_0" in result.outputs.keys()


def test_save_load_model(keras_simple, tmpdir_factory):
    pytest.importorskip("onnx")

    tmpdir = tmpdir_factory.mktemp("data")
    tvmc_model = tvmc.load(keras_simple)

    # Create tuning artifacts
    tvmc.tune(tvmc_model, target="llvm", trials=2)

    # Create package artifacts
    tvmc.compile(tvmc_model, target="llvm")

    # Save the model to disk
    model_path = os.path.join(tmpdir, "saved_model.tar")
    tvmc_model.save(model_path)

    # Load the model into a new TVMCModel
    new_tvmc_model = TVMCModel(model_path=model_path)

    # Check that the two models match.
    assert str(new_tvmc_model.mod) == str(tvmc_model.mod)
    # Check that tuning records and the compiled package are recoverable.
    assert path.exists(new_tvmc_model.default_package_path())
    assert path.exists(new_tvmc_model.default_tuning_records_path())