"""Test bpnet contrib
"""
import pytest
from bpnet.cli.contrib import bpnet_contrib, ContribFile
import keras.backend as K


@pytest.mark.parametrize("method", ['deeplift', 'grad'])
def test_contrib_bias_model(tmp_path, method, trained_model_w_bias):
    """Test whether we can compute differnet contribution scores
    """
    K.clear_session()
    fpath = tmp_path / 'imp-score.h5'
    bpnet_contrib(str(trained_model_w_bias),
                  str(fpath),
                  method=method)

    cf = ContribFile(fpath)
    assert cf.get_contrib()['Task1'].shape[-1] == 4


@pytest.mark.parametrize("method", ['deeplift', 'grad'])
def test_contrib_default_model(tmp_path, method, trained_model):
    """Test whether we can compute differnet contribution scores
    """
    K.clear_session()
    fpath = tmp_path / 'imp-score.h5'
    bpnet_contrib(str(trained_model),
                  str(fpath),
                  method=method)

    cf = ContribFile(fpath)
    assert cf.get_contrib()['Task1'].shape[-1] == 4


@pytest.mark.parametrize("method", ['grad'])
def test_contrib_regions(tmp_path, method, trained_model, regions):
    """Test different scenarios regarding subsetting
    """
    K.clear_session()
    bpnet_contrib(str(trained_model),
                  str(tmp_path / 'imp-score.h5'),
                  method=method,
                  regions=str(regions))


@pytest.mark.parametrize("method", ['grad'])
def test_contrib_dataspec(tmp_path, method, trained_model, dataspec_bias, regions):
    """Test different scenarios regarding subsetting
    """
    K.clear_session()
    bpnet_contrib(str(trained_model),
                  str(tmp_path / 'imp-score.h5'),
                  method=method,
                  dataspec=str(dataspec_bias),
                  regions=str(regions))


@pytest.mark.parametrize("method", ['grad'])
def test_contrib_fasta_file(tmp_path, method, trained_model, fasta_file, regions):
    """Test different scenarios regarding subsetting
    """
    K.clear_session()
    bpnet_contrib(str(trained_model),
                  str(tmp_path / 'imp-score.h5'),
                  method=method,
                  fasta_file=str(fasta_file),
                  regions=str(regions))


@pytest.mark.parametrize("method", ['grad'])
def test_contrib_dataspec_fasta_file(tmp_path, method, trained_model, dataspec_bias, fasta_file):
    """Test different scenarios regarding subsetting
    """
    K.clear_session()
    with pytest.raises(ValueError):
        bpnet_contrib(str(trained_model),
                      str(tmp_path / 'imp-score.h5'),
                      method=method,
                      fasta_file=str(fasta_file),
                      dataspec=str(dataspec_bias))