import copy
import pytest
from umap import UMAP
from hdbscan import HDBSCAN
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans, MiniBatchKMeans
from sklearn.decomposition import PCA, IncrementalPCA
from bertopic.vectorizers import OnlineCountVectorizer
from bertopic.representation import KeyBERTInspired, MaximalMarginalRelevance
from bertopic.cluster import BaseCluster
from bertopic.dimensionality import BaseDimensionalityReduction
from sklearn.linear_model import LogisticRegression


@pytest.fixture(scope="session")
def embedding_model():
    model = SentenceTransformer("all-MiniLM-L6-v2")
    return model


@pytest.fixture(scope="session")
def document_embeddings(documents, embedding_model):
    embeddings = embedding_model.encode(documents)
    return embeddings


@pytest.fixture(scope="session")
def reduced_embeddings(document_embeddings):
    reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(document_embeddings)
    return reduced_embeddings


@pytest.fixture(scope="session")
def documents():
    newsgroup_docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data'][:1000]
    return newsgroup_docs


@pytest.fixture(scope="session")
def targets():
    data = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))
    y = data['target'][:1000]
    return y


@pytest.fixture(scope="session")
def base_topic_model(documents, document_embeddings, embedding_model):
    model = BERTopic(embedding_model=embedding_model, calculate_probabilities=True)
    model.umap_model.random_state = 42
    model.hdbscan_model.min_cluster_size = 3
    model.fit(documents, document_embeddings)
    return model


@pytest.fixture(scope="session")
def zeroshot_topic_model(documents, document_embeddings, embedding_model):
    zeroshot_topic_list = ["religion", "cars", "electronics"]
    model = BERTopic(embedding_model=embedding_model, calculate_probabilities=True, zeroshot_topic_list=zeroshot_topic_list, zeroshot_min_similarity=0.5)
    model.umap_model.random_state = 42
    model.hdbscan_model.min_cluster_size = 2
    model.fit(documents, document_embeddings)
    return model


@pytest.fixture(scope="session")
def custom_topic_model(documents, document_embeddings, embedding_model):
    umap_model = UMAP(n_neighbors=15, n_components=6, min_dist=0.0, metric='cosine', random_state=42)
    hdbscan_model = HDBSCAN(min_cluster_size=3, metric='euclidean', cluster_selection_method='eom', prediction_data=True)
    model = BERTopic(umap_model=umap_model, hdbscan_model=hdbscan_model, embedding_model=embedding_model, calculate_probabilities=True).fit(documents, document_embeddings)
    return model

@pytest.fixture(scope="session")
def representation_topic_model(documents, document_embeddings, embedding_model):
    umap_model = UMAP(n_neighbors=15, n_components=6, min_dist=0.0, metric='cosine', random_state=42)
    hdbscan_model = HDBSCAN(min_cluster_size=3, metric='euclidean', cluster_selection_method='eom', prediction_data=True)
    representation_model = {"Main": KeyBERTInspired(), "MMR": [KeyBERTInspired(top_n_words=30), MaximalMarginalRelevance()]}
    model = BERTopic(umap_model=umap_model, hdbscan_model=hdbscan_model, embedding_model=embedding_model, representation_model=representation_model,
                     calculate_probabilities=True).fit(documents, document_embeddings)
    return model

@pytest.fixture(scope="session")
def reduced_topic_model(custom_topic_model, documents):
    model = copy.deepcopy(custom_topic_model)
    model.reduce_topics(documents, nr_topics=12)
    return model


@pytest.fixture(scope="session")
def merged_topic_model(custom_topic_model, documents):
    model = copy.deepcopy(custom_topic_model)

    # Merge once
    topics_to_merge = [[1, 2],
                       [3, 4]]
    model.merge_topics(documents, topics_to_merge)

    # Merge second time
    topics_to_merge = [[5, 6, 7]]
    model.merge_topics(documents, topics_to_merge)
    return model


@pytest.fixture(scope="session")
def kmeans_pca_topic_model(documents, document_embeddings):
    hdbscan_model = KMeans(n_clusters=15, random_state=42)
    dim_model = PCA(n_components=5)
    model = BERTopic(hdbscan_model=hdbscan_model, umap_model=dim_model, embedding_model=embedding_model).fit(documents, document_embeddings)
    return model


@pytest.fixture(scope="session")
def supervised_topic_model(documents, document_embeddings, embedding_model, targets):
    empty_dimensionality_model = BaseDimensionalityReduction()
    clf = LogisticRegression()

    model = BERTopic(
            embedding_model=embedding_model,
            umap_model=empty_dimensionality_model,
            hdbscan_model=clf,
    ).fit(documents, embeddings=document_embeddings, y=targets)
    return model


@pytest.fixture(scope="session")
def online_topic_model(documents, document_embeddings, embedding_model):
    umap_model = IncrementalPCA(n_components=5)
    cluster_model = MiniBatchKMeans(n_clusters=50, random_state=0)
    vectorizer_model = OnlineCountVectorizer(stop_words="english", decay=.01)
    model = BERTopic(umap_model=umap_model, hdbscan_model=cluster_model, vectorizer_model=vectorizer_model, embedding_model=embedding_model)

    topics = []
    for index in range(0, len(documents), 50):
        model.partial_fit(documents[index: index+50], document_embeddings[index: index+50])
        topics.extend(model.topics_)
    model.topics_ = topics
    return model