import textwrap as tr
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, stop_after_attempt, wait_random_exponential

import r_openai as openai


@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
def get_embedding(text: str, engine="text-similarity-davinci-001") -> List[float]:

    # replace newlines, which can negatively affect performance.
    text = text.replace("\n", " ")

    return openai.Embedding.create(input=[text], engine=engine)["data"][0]["embedding"]


@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
def get_embeddings(
    list_of_text: List[str], engine="text-similarity-babbage-001"
) -> List[List[float]]:
    assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."

    # replace newlines, which can negatively affect performance.
    list_of_text = [text.replace("\n", " ") for text in list_of_text]

    data = openai.Embedding.create(input=list_of_text, engine=engine).data
    data = sorted(data, key=lambda x: x["index"])  # maintain the same order as input.
    return [d["embedding"] for d in data]


def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))


def plot_multiclass_precision_recall(
    y_score, y_true_untransformed, class_list, classifier_name
):
    """
    Precision-Recall plotting for a multiclass problem. It plots average precision-recall, per class precision recall and reference f1 contours.

    Code slightly modified, but heavily based on https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html
    """
    n_classes = len(class_list)
    y_true = pd.concat(
        [(y_true_untransformed == class_list[i]) for i in range(n_classes)], axis=1
    ).values

    # For each class
    precision = dict()
    recall = dict()
    average_precision = dict()
    for i in range(n_classes):
        precision[i], recall[i], _ = precision_recall_curve(y_true[:, i], y_score[:, i])
        average_precision[i] = average_precision_score(y_true[:, i], y_score[:, i])

    # A "micro-average": quantifying score on all classes jointly
    precision_micro, recall_micro, _ = precision_recall_curve(
        y_true.ravel(), y_score.ravel()
    )
    average_precision_micro = average_precision_score(y_true, y_score, average="micro")
    print(
        str(classifier_name)
        + " - Average precision score over all classes: {0:0.2f}".format(
            average_precision_micro
        )
    )

    # setup plot details
    plt.figure(figsize=(9, 10))
    f_scores = np.linspace(0.2, 0.8, num=4)
    lines = []
    labels = []
    for f_score in f_scores:
        x = np.linspace(0.01, 1)
        y = f_score * x / (2 * x - f_score)
        (l,) = plt.plot(x[y >= 0], y[y >= 0], color="gray", alpha=0.2)
        plt.annotate("f1={0:0.1f}".format(f_score), xy=(0.9, y[45] + 0.02))

    lines.append(l)
    labels.append("iso-f1 curves")
    (l,) = plt.plot(recall_micro, precision_micro, color="gold", lw=2)
    lines.append(l)
    labels.append(
        "average Precision-recall (auprc = {0:0.2f})" "".format(average_precision_micro)
    )

    for i in range(n_classes):
        (l,) = plt.plot(recall[i], precision[i], lw=2)
        lines.append(l)
        labels.append(
            "Precision-recall for class `{0}` (auprc = {1:0.2f})"
            "".format(class_list[i], average_precision[i])
        )

    fig = plt.gcf()
    fig.subplots_adjust(bottom=0.25)
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel("Recall")
    plt.ylabel("Precision")
    plt.title(f"{classifier_name}: Precision-Recall curve for each class")
    plt.legend(lines, labels)


def distances_from_embeddings(
    query_embedding: List[float],
    embeddings: List[List[float]],
    distance_metric="cosine",
) -> List[List]:
    """Return the distances between a query embedding and a list of embeddings."""
    distance_metrics = {
        "cosine": spatial.distance.cosine,
        "L1": spatial.distance.cityblock,
        "L2": spatial.distance.euclidean,
        "Linf": spatial.distance.chebyshev,
    }
    distances = [
        distance_metrics[distance_metric](query_embedding, embedding)
        for embedding in embeddings
    ]
    return distances


def indices_of_nearest_neighbors_from_distances(distances) -> np.ndarray:
    """Return a list of indices of nearest neighbors from a list of distances."""
    return np.argsort(distances)


def pca_components_from_embeddings(
    embeddings: List[List[float]], n_components=2
) -> np.ndarray:
    """Return the PCA components of a list of embeddings."""
    pca = PCA(n_components=n_components)
    array_of_embeddings = np.array(embeddings)
    return pca.fit_transform(array_of_embeddings)


def tsne_components_from_embeddings(
    embeddings: List[List[float]], n_components=2, **kwargs
) -> np.ndarray:
    """Returns t-SNE components of a list of embeddings."""
    # use better defaults if not specified
    if "init" not in kwargs.keys():
        kwargs["init"] = "pca"
    if "learning_rate" not in kwargs.keys():
        kwargs["learning_rate"] = "auto"
    tsne = TSNE(n_components=n_components, **kwargs)
    array_of_embeddings = np.array(embeddings)
    return tsne.fit_transform(array_of_embeddings)


def chart_from_components(
    components: np.ndarray,
    labels: Optional[List[str]] = None,
    strings: Optional[List[str]] = None,
    x_title="Component 0",
    y_title="Component 1",
    mark_size=5,
    **kwargs,
):
    """Return an interactive 2D chart of embedding components."""
    empty_list = ["" for _ in components]
    data = pd.DataFrame(
        {
            x_title: components[:, 0],
            y_title: components[:, 1],
            "label": labels if labels else empty_list,
            "string": ["<br>".join(tr.wrap(string, width=30)) for string in strings]
            if strings
            else empty_list,
        }
    )
    chart = px.scatter(
        data,
        x=x_title,
        y=y_title,
        color="label" if labels else None,
        symbol="label" if labels else None,
        hover_data=["string"] if strings else None,
        **kwargs,
    ).update_traces(marker=dict(size=mark_size))
    return chart


def chart_from_components_3D(
    components: np.ndarray,
    labels: Optional[List[str]] = None,
    strings: Optional[List[str]] = None,
    x_title: str = "Component 0",
    y_title: str = "Component 1",
    z_title: str = "Compontent 2",
    mark_size: int = 5,
    **kwargs,
):
    """Return an interactive 3D chart of embedding components."""
    empty_list = ["" for _ in components]
    data = pd.DataFrame(
        {
            x_title: components[:, 0],
            y_title: components[:, 1],
            z_title: components[:, 2],
            "label": labels if labels else empty_list,
            "string": ["<br>".join(tr.wrap(string, width=30)) for string in strings]
            if strings
            else empty_list,
        }
    )
    chart = px.scatter_3d(
        data,
        x=x_title,
        y=y_title,
        z=z_title,
        color="label" if labels else None,
        symbol="label" if labels else None,
        hover_data=["string"] if strings else None,
        **kwargs,
    ).update_traces(marker=dict(size=mark_size))
    return chart
