# Copyright (c) 2019-2023, NVIDIA CORPORATION.
#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

from cuml.dask.common.dask_arr_utils import to_dask_cudf
from cuml.internals.safe_imports import gpu_only_import
import pytest
from cuml.internals.safe_imports import cpu_only_import

np = cpu_only_import("numpy")
cp = gpu_only_import("cupy")


@pytest.mark.mg
@pytest.mark.parametrize("nrows", [1000])
@pytest.mark.parametrize("ncols", [20])
@pytest.mark.parametrize("n_parts", [67])
@pytest.mark.parametrize("input_type", ["dataframe", "array"])
def test_pca_fit(nrows, ncols, n_parts, input_type, client):

    from cuml.dask.decomposition import PCA as daskPCA
    from sklearn.decomposition import PCA

    from cuml.dask.datasets import make_blobs

    X, _ = make_blobs(
        n_samples=nrows,
        n_features=ncols,
        centers=1,
        n_parts=n_parts,
        cluster_std=0.5,
        random_state=10,
        dtype=np.float32,
    )

    if input_type == "dataframe":
        X_train = to_dask_cudf(X)
        X_cpu = X_train.compute().to_pandas().values
    elif input_type == "array":
        X_train = X
        X_cpu = cp.asnumpy(X_train.compute())

    try:

        cupca = daskPCA(n_components=5, whiten=True)
        cupca.fit(X_train)
    except Exception as e:
        print(str(e))

    skpca = PCA(n_components=5, whiten=True, svd_solver="full")
    skpca.fit(X_cpu)

    from cuml.testing.utils import array_equal

    all_attr = [
        "singular_values_",
        "components_",
        "explained_variance_",
        "explained_variance_ratio_",
    ]

    for attr in all_attr:
        with_sign = False if attr in ["components_"] else True
        cuml_res = getattr(cupca, attr)
        if type(cuml_res) == np.ndarray:
            cuml_res = cuml_res.to_numpy()
        skl_res = getattr(skpca, attr)
        assert array_equal(cuml_res, skl_res, 1e-1, with_sign=with_sign)


@pytest.mark.mg
@pytest.mark.parametrize("nrows", [1000])
@pytest.mark.parametrize("ncols", [20])
@pytest.mark.parametrize("n_parts", [46])
def test_pca_fit_transform_fp32(nrows, ncols, n_parts, client):

    from cuml.dask.decomposition import PCA as daskPCA
    from cuml.dask.datasets import make_blobs

    X_cudf, _ = make_blobs(
        n_samples=nrows,
        n_features=ncols,
        centers=1,
        n_parts=n_parts,
        cluster_std=1.5,
        random_state=10,
        dtype=np.float32,
    )

    cupca = daskPCA(n_components=20, whiten=True)
    res = cupca.fit_transform(X_cudf)
    res = res.compute()
    assert res.shape[0] == nrows and res.shape[1] == 20


@pytest.mark.mg
@pytest.mark.parametrize("nrows", [1000])
@pytest.mark.parametrize("ncols", [20])
@pytest.mark.parametrize("n_parts", [33])
def test_pca_fit_transform_fp64(nrows, ncols, n_parts, client):

    from cuml.dask.decomposition import PCA as daskPCA
    from cuml.dask.datasets import make_blobs

    X_cudf, _ = make_blobs(
        n_samples=nrows,
        n_features=ncols,
        centers=1,
        n_parts=n_parts,
        cluster_std=1.5,
        random_state=10,
        dtype=np.float64,
    )

    cupca = daskPCA(n_components=30, whiten=False)
    res = cupca.fit_transform(X_cudf)
    res = res.compute()
    assert res.shape[0] == nrows and res.shape[1] == 30


@pytest.mark.mg
@pytest.mark.parametrize("nrows", [1000])
@pytest.mark.parametrize("ncols", [20])
@pytest.mark.parametrize("n_parts", [28])
def test_pca_fit_transform_fp32_noncomponents(nrows, ncols, n_parts, client):
    # Tests the case when n_components is not passed for MG scenarios
    from cuml.dask.decomposition import PCA as daskPCA
    from cuml.dask.datasets import make_blobs

    X_cudf, _ = make_blobs(
        n_samples=nrows,
        n_features=ncols,
        centers=1,
        n_parts=n_parts,
        cluster_std=1.5,
        random_state=10,
        dtype=np.float32,
    )

    cupca = daskPCA(whiten=False)
    res = cupca.fit_transform(X_cudf)
    res = res.compute()
    assert res.shape[0] == nrows and res.shape[1] == 20
