import pickle

import Levenshtein
import numpy as np
import pandas as pd
import plotly.figure_factory as ff

import config


def get_statistics_for_sample(start_msg, end_msg, row=None):
    edit_ops = Levenshtein.editops(start_msg, end_msg)
    n_deletes = sum([1 if op == "delete" else 0 for op, _, _ in edit_ops])
    n_inserts = sum([1 if op == "insert" else 0 for op, _, _ in edit_ops])
    n_replaces = sum([1 if op == "replace" else 0 for op, _, _ in edit_ops])

    n_changes = n_deletes + n_inserts + n_replaces
    n_deletes += n_replaces
    n_inserts += n_replaces

    return {
        "deletions": n_deletes,
        "insertions": n_inserts,
        "changes": n_changes,
        "deletions_norm": n_deletes / len(start_msg),
        "insertions_norm": n_inserts / len(end_msg),
        "changes_norm": n_changes / len(end_msg),
        "lendiff": abs(len(start_msg) - len(end_msg)),
        "editdist": row["editdist"] if row is not None else Levenshtein.distance(start_msg, end_msg),
    }


def get_statistics_for_row(row):
    if "commit_msg_start" in row:
        start = row["commit_msg_start"]
    else:
        start = row["G_text"]
    if "commit_msg_end" in row:
        end = row["commit_msg_end"]
    else:
        end = row["E_text"]
    return get_statistics_for_sample(start, end, row=row)


def get_statistics_for_df(df: pd.DataFrame):
    stats = [get_statistics_for_row(row) for _, row in df.iterrows()]

    assert len(stats) > 0

    return {stat_name: np.asarray([e[stat_name] for e in stats]) for stat_name in stats[0]}


def build_plotly_chart(stat_golden, stat_e2s, stat_s2e, stat_e2s_s2e, stat_name):
    hist_data = [
        stat_golden,
        stat_e2s,
        stat_s2e,
        stat_e2s_s2e,
        np.concatenate((stat_e2s, stat_s2e, stat_e2s_s2e), axis=0),
    ]

    group_labels = ["Golden", "e2s", "s2e", "e2s+s2e", "Synthetic"]

    fig = ff.create_distplot(hist_data, group_labels, bin_size=0.05, show_rug=False, show_hist=False)

    fig.update_layout(title_text=stat_name)

    with open(config.OUTPUT_CHARTS_DIR / f"{stat_name}_data.pkl", "wb") as f:
        pickle.dump(hist_data, f)

    return fig