# ------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------

"""
Plotting utilities to visualize training logs.
"""
from pathlib import Path, PurePath

import matplotlib.pyplot as plt
import pandas as pd
import torch

import seaborn as sns


def plot_logs(
    logs, fields=("class_error", "loss_bbox_unscaled", "mAP"), ewm_col=0, log_name="log.txt"
):
    """
    Function to plot specific fields from training log(s). Plots both training and test results.

    :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file
              - fields = which results to plot from each log file - plots both training and test for each field.
              - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots
              - log_name = optional, name of log file if different than default 'log.txt'.

    :: Outputs - matplotlib plots of results in fields, color coded for each log file.
               - solid lines are training results, dashed lines are test results.

    """
    func_name = "plot_utils.py::plot_logs"

    # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path,
    # convert single Path to list to avoid 'not iterable' error

    if not isinstance(logs, list):
        if isinstance(logs, PurePath):
            logs = [logs]
            print(f"{func_name} info: logs param expects a list argument, converted to list[Path].")
        else:
            raise ValueError(
                f"{func_name} - invalid argument for logs parameter.\n \
            Expect list[Path] or single Path obj, received {type(logs)}"
            )

    # verify valid dir(s) and that every item in list is Path object
    for i, dir in enumerate(logs):
        if not isinstance(dir, PurePath):
            raise ValueError(
                f"{func_name} - non-Path object in logs argument of {type(dir)}: \n{dir}"
            )
        if dir.exists():
            continue
        raise ValueError(f"{func_name} - invalid directory in logs argument:\n{dir}")

    # load log file(s) and plot
    dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs]

    fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5))

    for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))):
        for j, field in enumerate(fields):
            if field == "mAP":
                coco_eval = (
                    pd.DataFrame(pd.np.stack(df.test_coco_eval.dropna().values)[:, 1])
                    .ewm(com=ewm_col)
                    .mean()
                )
                axs[j].plot(coco_eval, c=color)
            else:
                df.interpolate().ewm(com=ewm_col).mean().plot(
                    y=[f"train_{field}", f"test_{field}"],
                    ax=axs[j],
                    color=[color] * 2,
                    style=["-", "--"],
                )
    for ax, field in zip(axs, fields):
        ax.legend([Path(p).name for p in logs])
        ax.set_title(field)


def plot_precision_recall(files, naming_scheme="iter"):
    if naming_scheme == "exp_id":
        # name becomes exp_id
        names = [f.parts[-3] for f in files]
    elif naming_scheme == "iter":
        names = [f.stem for f in files]
    else:
        raise ValueError(f"not supported {naming_scheme}")
    fig, axs = plt.subplots(ncols=2, figsize=(16, 5))
    for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names):
        data = torch.load(f)
        # precision is n_iou, n_points, n_cat, n_area, max_det
        precision = data["precision"]
        recall = data["params"].recThrs
        scores = data["scores"]
        # take precision for all classes, all areas and 100 detections
        precision = precision[0, :, :, 0, -1].mean(1)
        scores = scores[0, :, :, 0, -1].mean(1)
        prec = precision.mean()
        rec = data["recall"][0, :, 0, -1].mean()
        print(
            f"{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, "
            + f"score={scores.mean():0.3f}, "
            + f"f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}"
        )
        axs[0].plot(recall, precision, c=color)
        axs[1].plot(recall, scores, c=color)

    axs[0].set_title("Precision / Recall")
    axs[0].legend(names)
    axs[1].set_title("Scores / Recall")
    axs[1].legend(names)
    return fig, axs