import copy
import pytest
import numpy as np
from umap import UMAP
from sklearn.decomposition import PCA

from bertopic import BERTopic


@pytest.mark.parametrize("dim_model", [UMAP, PCA])
@pytest.mark.parametrize("embeddings,shape,n_components", [(np.random.rand(100, 128), 100, 5),
                                                           (np.random.rand(10, 256), 10, 5),
                                                           (np.random.rand(50, 15), 50, 10)])
def test_reduce_dimensionality(dim_model, embeddings, shape, n_components):
    model = BERTopic(umap_model=dim_model(n_components=n_components))
    umap_embeddings = model._reduce_dimensionality(embeddings)
    assert umap_embeddings.shape == (shape, n_components)


@pytest.mark.parametrize('model', [('kmeans_pca_topic_model'),
                                   ('base_topic_model'),
                                   ('custom_topic_model'),
                                   ('merged_topic_model'),
                                   ('reduced_topic_model'),
                                   ('online_topic_model')])
def test_custom_reduce_dimensionality(model, request):
    embeddings = np.random.rand(500, 128)
    topic_model = copy.deepcopy(request.getfixturevalue(model))
    umap_embeddings = topic_model._reduce_dimensionality(embeddings)
    assert umap_embeddings.shape[1] < embeddings.shape[1]