def visualize_documents(
    topic_model,
    docs: List[str],
    topics: List[int] = None,
    embeddings: np.ndarray = None,
    reduced_embeddings: np.ndarray = None,
    sample: float = None,
    hide_annotations: bool = False,
    hide_document_hover: bool = False,
    custom_labels: Union[bool, str] = False,
    title: str = "<b>Documents and Topics</b>",
    width: int = 1200,
    height: int = 750,
):
    """Visualize documents and their topics in 2D.

    Arguments:
        topic_model: A fitted BERTopic instance.
        docs: The documents you used when calling either `fit` or `fit_transform`
        topics: A selection of topics to visualize.
                Not to be confused with the topics that you get from `.fit_transform`.
                For example, if you want to visualize only topics 1 through 5:
                `topics = [1, 2, 3, 4, 5]`.
        embeddings: The embeddings of all documents in `docs`.
        reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
        sample: The percentage of documents in each topic that you would like to keep.
                Value can be between 0 and 1. Setting this value to, for example,
                0.1 (10% of documents in each topic) makes it easier to visualize
                millions of documents as a subset is chosen.
        hide_annotations: Hide the names of the traces on top of each cluster.
        hide_document_hover: Hide the content of the documents when hovering over
                             specific points. Helps to speed up generation of visualization.
        custom_labels: If bool, whether to use custom topic labels that were defined using
                       `topic_model.set_topic_labels`.
                       If `str`, it uses labels from other aspects, e.g., "Aspect1".
        title: Title of the plot.
        width: The width of the figure.
        height: The height of the figure.

    Examples:
    To visualize the topics simply run:

    ```python
    topic_model.visualize_documents(docs)
    ```

    Do note that this re-calculates the embeddings and reduces them to 2D.
    The advised and preferred pipeline for using this function is as follows:

    ```python
    from sklearn.datasets import fetch_20newsgroups
    from sentence_transformers import SentenceTransformer
    from bertopic import BERTopic
    from umap import UMAP

    # Prepare embeddings
    docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
    sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
    embeddings = sentence_model.encode(docs, show_progress_bar=False)

    # Train BERTopic
    topic_model = BERTopic().fit(docs, embeddings)

    # Reduce dimensionality of embeddings, this step is optional
    # reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)

    # Run the visualization with the original embeddings
    topic_model.visualize_documents(docs, embeddings=embeddings)

    # Or, if you have reduced the original embeddings already:
    topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
    ```

    Or if you want to save the resulting figure:

    ```python
    fig = topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
    fig.write_html("path/to/file.html")
    ```

    <iframe src="../../getting_started/visualization/documents.html"
    style="width:1000px; height: 800px; border: 0px;""></iframe>
    """
    topic_per_doc = topic_model.topics_

    # Sample the data to optimize for visualization and dimensionality reduction
    if sample is None or sample > 1:
        sample = 1

    indices = []
    for topic in set(topic_per_doc):
        s = np.where(np.array(topic_per_doc) == topic)[0]
        size = len(s) if len(s) < 100 else int(len(s) * sample)
        indices.extend(np.random.choice(s, size=size, replace=False))
    indices = np.array(indices)

    df = pd.DataFrame({"topic": np.array(topic_per_doc)[indices]})
    df["doc"] = [docs[index] for index in indices]
    df["topic"] = [topic_per_doc[index] for index in indices]

    # Extract embeddings if not already done
    if sample is None:
        if embeddings is None and reduced_embeddings is None:
            embeddings_to_reduce = topic_model._extract_embeddings(df.doc.to_list(), method="document")
        else:
            embeddings_to_reduce = embeddings
    else:
        if embeddings is not None:
            embeddings_to_reduce = embeddings[indices]
        elif embeddings is None and reduced_embeddings is None:
            embeddings_to_reduce = topic_model._extract_embeddings(df.doc.to_list(), method="document")

    # Reduce input embeddings
    if reduced_embeddings is None:
        try:
            from umap import UMAP

            umap_model = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric="cosine").fit(embeddings_to_reduce)
            embeddings_2d = umap_model.embedding_
        except (ImportError, ModuleNotFoundError):
            raise ModuleNotFoundError(
                "UMAP is required if the embeddings are not yet reduced in dimensionality. Please install it using `pip install umap-learn`."
            )
    elif sample is not None and reduced_embeddings is not None:
        embeddings_2d = reduced_embeddings[indices]
    elif sample is None and reduced_embeddings is not None:
        embeddings_2d = reduced_embeddings

    unique_topics = set(topic_per_doc)
    if topics is None:
        topics = unique_topics

    # Combine data
    df["x"] = embeddings_2d[:, 0]
    df["y"] = embeddings_2d[:, 1]

    # Prepare text and names
    if isinstance(custom_labels, str):
        names = [[[str(topic), None]] + topic_model.topic_aspects_[custom_labels][topic] for topic in unique_topics]
        names = ["_".join([label[0] for label in labels[:4]]) for labels in names]
        names = [label if len(label) < 30 else label[:27] + "..." for label in names]
    elif topic_model.custom_labels_ is not None and custom_labels:
        names = [topic_model.custom_labels_[topic + topic_model._outliers] for topic in unique_topics]
    else:
        names = [
            f"{topic}_" + "_".join([word for word, value in topic_model.get_topic(topic)][:3])
            for topic in unique_topics
        ]

    # Visualize
    fig = go.Figure()

    # Outliers and non-selected topics
    non_selected_topics = set(unique_topics).difference(topics)
    if len(non_selected_topics) == 0:
        non_selected_topics = [-1]

    selection = df.loc[df.topic.isin(non_selected_topics), :]
    selection["text"] = ""
    selection.loc[len(selection), :] = [
        None,
        None,
        selection.x.mean(),
        selection.y.mean(),
        "Other documents",
    ]

    fig.add_trace(
        go.Scattergl(
            x=selection.x,
            y=selection.y,
            hovertext=selection.doc if not hide_document_hover else None,
            hoverinfo="text",
            mode="markers+text",
            name="other",
            showlegend=False,
            marker=dict(color="#CFD8DC", size=5, opacity=0.5),
        )
    )

    # Selected topics
    for name, topic in zip(names, unique_topics):
        if topic in topics and topic != -1:
            selection = df.loc[df.topic == topic, :]
            selection["text"] = ""

            if not hide_annotations:
                selection.loc[len(selection), :] = [
                    None,
                    None,
                    selection.x.mean(),
                    selection.y.mean(),
                    name,
                ]

            fig.add_trace(
                go.Scattergl(
                    x=selection.x,
                    y=selection.y,
                    hovertext=selection.doc if not hide_document_hover else None,
                    hoverinfo="text",
                    text=selection.text,
                    mode="markers+text",
                    name=name,
                    textfont=dict(
                        size=12,
                    ),
                    marker=dict(size=5, opacity=0.5),
                )
            )

    # Add grid in a 'plus' shape
    x_range = (
        df.x.min() - abs((df.x.min()) * 0.15),
        df.x.max() + abs((df.x.max()) * 0.15),
    )
    y_range = (
        df.y.min() - abs((df.y.min()) * 0.15),
        df.y.max() + abs((df.y.max()) * 0.15),
    )
    fig.add_shape(
        type="line",
        x0=sum(x_range) / 2,
        y0=y_range[0],
        x1=sum(x_range) / 2,
        y1=y_range[1],
        line=dict(color="#CFD8DC", width=2),
    )
    fig.add_shape(
        type="line",
        x0=x_range[0],
        y0=sum(y_range) / 2,
        x1=x_range[1],
        y1=sum(y_range) / 2,
        line=dict(color="#9E9E9E", width=2),
    )
    fig.add_annotation(x=x_range[0], y=sum(y_range) / 2, text="D1", showarrow=False, yshift=10)
    fig.add_annotation(y=y_range[1], x=sum(x_range) / 2, text="D2", showarrow=False, xshift=10)

    # Stylize layout
    fig.update_layout(
        template="simple_white",
        title={
            "text": f"{title}",
            "x": 0.5,
            "xanchor": "center",
            "yanchor": "top",
            "font": dict(size=22, color="Black"),
        },
        width=width,
        height=height,
    )

    fig.update_xaxes(visible=False)
    fig.update_yaxes(visible=False)
    return fig
Visualize documents and their topics in 2D.

    Arguments:
        topic_model: A fitted BERTopic instance.
        docs: The documents you used when calling either `fit` or `fit_transform`
        topics: A selection of topics to visualize.
                Not to be confused with the topics that you get from `.fit_transform`.
                For example, if you want to visualize only topics 1 through 5:
                `topics = [1, 2, 3, 4, 5]`.
        embeddings: The embeddings of all documents in `docs`.
        reduced_embeddings: The 2D reduced embeddings of all documents in `docs`.
        sample: The percentage of documents in each topic that you would like to keep.
                Value can be between 0 and 1. Setting this value to, for example,
                0.1 (10% of documents in each topic) makes it easier to visualize
                millions of documents as a subset is chosen.
        hide_annotations: Hide the names of the traces on top of each cluster.
        hide_document_hover: Hide the content of the documents when hovering over
                             specific points. Helps to speed up generation of visualization.
        custom_labels: If bool, whether to use custom topic labels that were defined using
                       `topic_model.set_topic_labels`.
                       If `str`, it uses labels from other aspects, e.g., "Aspect1".
        title: Title of the plot.
        width: The width of the figure.
        height: The height of the figure.

    Examples:
    To visualize the topics simply run:

    ```python
    topic_model.visualize_documents(docs)
    ```

    Do note that this re-calculates the embeddings and reduces them to 2D.
    The advised and preferred pipeline for using this function is as follows:

    ```python
    from sklearn.datasets import fetch_20newsgroups
    from sentence_transformers import SentenceTransformer
    from bertopic import BERTopic
    from umap import UMAP

    # Prepare embeddings
    docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
    sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
    embeddings = sentence_model.encode(docs, show_progress_bar=False)

    # Train BERTopic
    topic_model = BERTopic().fit(docs, embeddings)

    # Reduce dimensionality of embeddings, this step is optional
    # reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)

    # Run the visualization with the original embeddings
    topic_model.visualize_documents(docs, embeddings=embeddings)

    # Or, if you have reduced the original embeddings already:
    topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
    ```

    Or if you want to save the resulting figure:

    ```python
    fig = topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)
    fig.write_html("path/to/file.html")
    ```

    <iframe src="../../getting_started/visualization/documents.html"
    style="width:1000px; height: 800px; border: 0px;""></iframe>
    
bertopic/plotting/_documents.py