import glob
import logging
import os

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sentence_transformers import LoggingHandler, SentenceTransformer

# from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
# from sklearn.model_selection import train_test_split

if not os.path.exists("./plots"):
    os.mkdir("./plots")

# Configure logging
logging.basicConfig(
    format="%(asctime)s - %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=logging.INFO,
    handlers=[LoggingHandler()],
)


def evaluate(model, city_from, city_to):
    city_to = model.encode(city_to)
    city_from = model.encode(city_from)
    return np.dot(city_to, city_from) / (
        np.linalg.norm(city_to) * np.linalg.norm(city_from)
    )


def calculate_similarity(data, base_model, trained_model):
    # MAX_DISTANCE = 20_037.5
    # data["distance"] /= MAX_DISTANCE
    data["similarity_before"] = data.apply(
        lambda x: evaluate(base_model, x["city_from"], x["city_to"]), axis=1
    )

    data["similarity_after"] = data.apply(
        lambda x: evaluate(trained_model, x["city_from"], x["city_to"]), axis=1
    )
    return data


def make_plot(data):
    fig, ax = plt.subplots()

    ax.scatter(
        data["distance"],
        data["similarity_before"],
        color="r",
        alpha=0.1,
        label="before",
    )
    ax.scatter(
        data["distance"], data["similarity_after"], color="b", alpha=0.1, label="after"
    )
    ax.set_xlabel("distance between cities (km)")
    ax.set_ylabel("similarity between vectors\n(cosine)")
    fig.legend(loc="upper right")
    return fig


if __name__ == "__main__":
    model_name = "sentence-transformers/all-MiniLM-L6-v2"
    base_model = SentenceTransformer(model_name, device="cuda")

    data = pd.read_csv("city_distances_full.csv")
    # data_sample = data.sample(1_000)
    checkpoint_dir = "checkpoints_absmax_split"  # no slash
    for checkpoint in sorted(glob.glob(f"{checkpoint_dir}/*")):
        data_sample = data.sample(1_000)
        trained_model = SentenceTransformer(checkpoint, device="cuda")

        data_sample = calculate_similarity(data_sample, base_model, trained_model)
        fig = make_plot(data_sample)
        fig.savefig(f"./plots/progress_{checkpoint.split('/')[1]}.png", dpi=600)