#
# Copyright (c) 2021-2022, 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.
#

import pytest
from ..utils.utils import _benchmark_algo, fixture_generation_helper
from ..utils.utils import bench_step  # noqa: F401
from ... import datagen

#
# Core tests
#


@pytest.fixture(**fixture_generation_helper({
                    'n_samples': [1000, 10000],
                    'n_features': [5, 500]
                }))
def blobs1(request):
    data = datagen.gen_data(
        'classification',
        'cupy',
        n_samples=request.param['n_samples'],
        n_features=request.param['n_features']
    )
    return data, None


@pytest.fixture(scope='session')
def blobs2(request):
    dataset_kwargs = {
        'dataset_type': 'blobs',
        'n_samples': 10000,
        'n_features': 100
    }
    dataset = datagen.gen_data(
        dataset_kwargs['dataset_type'],
        'cupy',
        n_samples=dataset_kwargs['n_samples'],
        n_features=dataset_kwargs['n_features']
    )
    return dataset, dataset_kwargs


@pytest.fixture(scope='session')
def blobs3(request):
    dataset_kwargs = {
        'dataset_type': 'blobs',
        'n_samples': 50000,
        'n_features': 100
    }
    dataset = datagen.gen_data(
        dataset_kwargs['dataset_type'],
        'cupy',
        n_samples=dataset_kwargs['n_samples'],
        n_features=dataset_kwargs['n_features']
    )
    return dataset, dataset_kwargs


def bench_mnmg_kmeans(gpubenchmark, bench_step, blobs1, client):  # noqa: F811
    _benchmark_algo(gpubenchmark, 'MNMG.KMeans',
                    bench_step, blobs1, client=client)


def bench_mnmg_dbscan(gpubenchmark, bench_step, blobs2, client):  # noqa: F811
    _benchmark_algo(gpubenchmark, 'MNMG.DBSCAN',
                    bench_step, blobs2, client=client)


def bench_mnmg_nearest_neighbors(gpubenchmark, bench_step,  # noqa: F811
                                 blobs2, client):
    _benchmark_algo(gpubenchmark, 'MNMG.NearestNeighbors',
                    bench_step, blobs2, client=client)


@pytest.mark.parametrize('algo_name', ['MNMG.UMAP-Unsupervised',
                                       'MNMG.UMAP-Supervised'])
def bench_mnmg_umap(gpubenchmark, algo_name, bench_step,  # noqa: F811
                    blobs2, client):
    _benchmark_algo(gpubenchmark, algo_name,
                    bench_step, blobs2, client=client)


@pytest.mark.parametrize('algo_name', ['MNMG.tSVD',
                                       'MNMG.PCA'])
@pytest.mark.parametrize('n_components', [2, 10, 50])
def bench_mnmg_dimensionality_reduction(gpubenchmark, algo_name,
                                        bench_step, blobs3,  # noqa: F811
                                        client, n_components):
    _benchmark_algo(gpubenchmark, algo_name,
                    bench_step, blobs3,
                    setup_kwargs={'n_components': n_components},
                    client=client)
