# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import contextlib
import io
import itertools
import json
import logging
import os
import tempfile
from collections import OrderedDict
from PIL import Image
from tabulate import tabulate
import numpy as np

from cvpods.utils import PathManager, comm

from .evaluator import DatasetEvaluator

logger = logging.getLogger(__name__)


class COCOPanopticEvaluator(DatasetEvaluator):
    """
    Evaluate Panoptic Quality metrics on COCO using PanopticAPI.
    It saves panoptic segmentation prediction in `output_dir`

    It contains a synchronize call and has to be called from all workers.
    """

    def __init__(self, dataset_name, meta, output_dir, dump=False):
        """
        Args:
            dataset_name (str): name of the datasets.
            meta (SimpleNamespace): datasets metadata.
            output_dir (str): output directory to save results for evaluation.
            dump (bool): If True, after the evaluation is completed, a Markdown file
                that records the model evaluation metrics and corresponding scores
                will be generated in the working directory.
        """
        self._dump = dump
        self._metadata = meta
        self._thing_contiguous_id_to_dataset_id = {
            v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
        }
        self._stuff_contiguous_id_to_dataset_id = {
            v: k for k, v in self._metadata.stuff_dataset_id_to_contiguous_id.items()
        }
        PathManager.mkdirs(output_dir)
        self._predictions_json = os.path.join(output_dir, "predictions.json")

    def reset(self):
        self._predictions = []

    def _convert_category_id(self, segment_info):
        isthing = segment_info.pop("isthing", None)
        if isthing is None:
            # the model produces panoptic category id directly. No more conversion needed
            return segment_info
        if isthing is True:
            segment_info["category_id"] = self._thing_contiguous_id_to_dataset_id[
                segment_info["category_id"]
            ]
        else:
            segment_info["category_id"] = self._stuff_contiguous_id_to_dataset_id[
                segment_info["category_id"]
            ]
        return segment_info

    def process(self, inputs, outputs):
        from panopticapi.utils import id2rgb

        for input, output in zip(inputs, outputs):
            panoptic_img, segments_info = output["panoptic_seg"]
            panoptic_img = panoptic_img.cpu().numpy()
            if segments_info is None:
                # If "segments_info" is None, we assume "panoptic_img" is a
                # H*W int32 image storing the panoptic_id in the format of
                # category_id * label_divisor + instance_id. We reserve -1 for
                # VOID label, and add 1 to panoptic_img since the official
                # evaluation script uses 0 for VOID label.
                label_divisor = self._metadata.label_divisor
                segments_info = []
                for panoptic_label in np.unique(panoptic_img):
                    if panoptic_label == -1:
                        # VOID region.
                        continue
                    pred_class = panoptic_label // label_divisor
                    isthing = (
                            pred_class in self._metadata.thing_dataset_id_to_contiguous_id.values()
                    )
                    segments_info.append(
                        {
                            "id": int(panoptic_label) + 1,
                            "category_id": int(pred_class),
                            "isthing": bool(isthing),
                        }
                    )
                # Official evaluation script uses 0 for VOID label.
                panoptic_img += 1

            file_name = os.path.basename(input["file_name"])
            file_name_png = os.path.splitext(file_name)[0] + ".png"
            with io.BytesIO() as out:
                Image.fromarray(id2rgb(panoptic_img)).save(out, format="PNG")
                segments_info = [self._convert_category_id(x) for x in segments_info]
                self._predictions.append(
                    {
                        "image_id": input["image_id"],
                        "file_name": file_name_png,
                        "png_string": out.getvalue(),
                        "segments_info": segments_info,
                    }
                )

    def evaluate(self):
        comm.synchronize()

        self._predictions = comm.gather(self._predictions)
        self._predictions = list(itertools.chain(*self._predictions))
        if not comm.is_main_process():
            return

        gt_json = PathManager.get_local_path(self._metadata.panoptic_json)
        gt_folder = self._metadata.panoptic_root

        with tempfile.TemporaryDirectory(prefix="panoptic_eval") as pred_dir:
            logger.info("Writing all panoptic predictions to {} ...".format(pred_dir))
            for p in self._predictions:
                with open(os.path.join(pred_dir, p["file_name"]), "wb") as f:
                    f.write(p.pop("png_string"))

            with open(gt_json, "r") as f:
                json_data = json.load(f)
            json_data["annotations"] = self._predictions
            with PathManager.open(self._predictions_json, "w") as f:
                f.write(json.dumps(json_data))

            from panopticapi.evaluation import pq_compute

            with contextlib.redirect_stdout(io.StringIO()):
                pq_res = pq_compute(
                    gt_json,
                    PathManager.get_local_path(self._predictions_json),
                    gt_folder=gt_folder,
                    pred_folder=pred_dir,
                )

        res = {}
        res["PQ"] = 100 * pq_res["All"]["pq"]
        res["SQ"] = 100 * pq_res["All"]["sq"]
        res["RQ"] = 100 * pq_res["All"]["rq"]
        res["PQ_th"] = 100 * pq_res["Things"]["pq"]
        res["SQ_th"] = 100 * pq_res["Things"]["sq"]
        res["RQ_th"] = 100 * pq_res["Things"]["rq"]
        res["PQ_st"] = 100 * pq_res["Stuff"]["pq"]
        res["SQ_st"] = 100 * pq_res["Stuff"]["sq"]
        res["RQ_st"] = 100 * pq_res["Stuff"]["rq"]

        results = OrderedDict({"panoptic_seg": res})
        table = _print_panoptic_results(pq_res)

        if self._dump:
            dump_info_one_task = {
                "task": "panoptic_seg",
                "tables": [table],
            }
            _dump_to_markdown([dump_info_one_task])

        return results


def _print_panoptic_results(pq_res):
    """
    Print evaluation results in the form of a string table.

    Args:
        pq_res (dict): a dictionary containing the evaluation results.

    Returns:
        table (str): a string table containing the model evaluation metrics
            and corresponding scores.
    """
    headers = ["", "PQ", "SQ", "RQ", "#categories"]
    data = []
    for name in ["All", "Things", "Stuff"]:
        row = [name] + [pq_res[name][k] * 100 for k in ["pq", "sq", "rq"]] + [pq_res[name]["n"]]
        data.append(row)
    table = tabulate(
        data, headers=headers, tablefmt="pipe", floatfmt=".3f", stralign="center", numalign="center"
    )
    logger.info("Panoptic Evaluation Results:\n" + table)
    return table


def _dump_to_markdown(dump_infos, md_file="README.md"):
    """
    Dump a Markdown file that records the model evaluation metrics and corresponding scores
    to the current working directory.

    Args:
        dump_infos (list[dict]): dump information for each task.
        md_file (str): markdown file path.
    """
    title = os.getcwd().split("/")[-1]
    with open(md_file, "w") as f:
        f.write("# {}  ".format(title))
        for dump_info_per_task in dump_infos:
            task_name = dump_info_per_task["task"]
            tables = dump_info_per_task["tables"]
            tables = [table.replace("\n", "  \n") for table in tables]
            f.write("\n\n## Evaluation results for {}:  \n\n".format(task_name))
            f.write(tables[0])
            f.write("\n")


if __name__ == "__main__":
    from cvpods.utils import setup_logger

    logger = setup_logger()
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("--gt-json")
    parser.add_argument("--gt-dir")
    parser.add_argument("--pred-json")
    parser.add_argument("--pred-dir")
    args = parser.parse_args()

    from panopticapi.evaluation import pq_compute

    with contextlib.redirect_stdout(io.StringIO()):
        pq_res = pq_compute(
            args.gt_json, args.pred_json, gt_folder=args.gt_dir, pred_folder=args.pred_dir
        )
        _print_panoptic_results(pq_res)
