"""
This is a slightly altered version of sem_seg_evaluation.py from Detectron2.

All distributed training references have been removed as Determined will handle
those process.

"""

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import itertools
import json
import logging
import os
from collections import OrderedDict

import numpy as np
import PIL.Image as Image
import pycocotools.mask as mask_util
import torch
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.evaluation import DatasetEvaluator
from detectron2.utils.comm import all_gather, is_main_process, synchronize
from fvcore.common.file_io import PathManager


class SemSegEvaluator(DatasetEvaluator):
    """
    Evaluate semantic segmentation
    """

    def __init__(self, dataset_name, distributed, num_classes, ignore_label=255, output_dir=None):
        """
        Args:
            dataset_name (str): name of the dataset to be evaluated.
            distributed (True): if True, will collect results from all ranks for evaluation.
                Otherwise, will evaluate the results in the current process.
            num_classes (int): number of classes
            ignore_label (int): value in semantic segmentation ground truth. Predictions for the
            corresponding pixels should be ignored.
            output_dir (str): an output directory to dump results.
        """
        self._dataset_name = dataset_name
        self._distributed = distributed
        self._output_dir = output_dir
        self._num_classes = num_classes
        self._ignore_label = ignore_label
        self._N = num_classes + 1

        self._cpu_device = torch.device("cpu")
        self._logger = logging.getLogger(__name__)

        self.input_file_to_gt_file = {
            dataset_record["file_name"]: dataset_record["sem_seg_file_name"]
            for dataset_record in DatasetCatalog.get(dataset_name)
        }

        meta = MetadataCatalog.get(dataset_name)
        # Dict that maps contiguous training ids to COCO category ids
        try:
            c2d = meta.stuff_dataset_id_to_contiguous_id
            self._contiguous_id_to_dataset_id = {v: k for k, v in c2d.items()}
        except AttributeError:
            self._contiguous_id_to_dataset_id = None
        self._class_names = meta.stuff_classes

        self.evaluator_name = "SemSegEvaluator"

    def reset(self):
        self._conf_matrix = np.zeros((self._N, self._N), dtype=np.int64)
        self._predictions = []

    def process(self, inputs, outputs):
        """
        Args:
            inputs: the inputs to a model.
                It is a list of dicts. Each dict corresponds to an image and
                contains keys like "height", "width", "file_name".
            outputs: the outputs of a model. It is either list of semantic segmentation predictions
                (Tensor [H, W]) or list of dicts with key "sem_seg" that contains semantic
                segmentation prediction in the same format.
        """
        values = []
        for input, output in zip(inputs, outputs):
            output = output["sem_seg"].argmax(dim=0).to(self._cpu_device)
            pred = np.array(output, dtype=np.int)
            with PathManager.open(self.input_file_to_gt_file[input["file_name"]], "rb") as f:
                gt = np.array(Image.open(f), dtype=np.int)

            gt[gt == self._ignore_label] = self._num_classes

            self._conf_matrix += np.bincount(
                self._N * pred.reshape(-1) + gt.reshape(-1), minlength=self._N**2
            ).reshape(self._N, self._N)

            values.extend(self.encode_json_sem_seg(pred, input["file_name"]))

        return {self.evaluator_name: {"predictions": values, "conf_matrix": self._conf_matrix}}

    def setup_data(self, values):
        self._predictions = values[self.evaluator_name]["predictions"]
        conf_matrix_list = values[self.evaluator_name]["conf_matrix"]

        self._conf_matrix = np.zeros_like(self._conf_matrix)
        for conf_matrix in conf_matrix_list:
            self._conf_matrix += conf_matrix

    def evaluate(self):
        """
        Evaluates standard semantic segmentation metrics (http://cocodataset.org/#stuff-eval):
        * Mean intersection-over-union averaged across classes (mIoU)
        * Frequency Weighted IoU (fwIoU)
        * Mean pixel accuracy averaged across classes (mACC)
        * Pixel Accuracy (pACC)
        """
        if self._output_dir:
            PathManager.mkdirs(self._output_dir)
            file_path = os.path.join(self._output_dir, "sem_seg_predictions.json")
            with PathManager.open(file_path, "w") as f:
                f.write(json.dumps(self._predictions))

        acc = np.full(self._num_classes, np.nan, dtype=np.float)
        iou = np.full(self._num_classes, np.nan, dtype=np.float)
        tp = self._conf_matrix.diagonal()[:-1].astype(np.float)
        pos_gt = np.sum(self._conf_matrix[:-1, :-1], axis=0).astype(np.float)
        class_weights = pos_gt / np.sum(pos_gt)
        pos_pred = np.sum(self._conf_matrix[:-1, :-1], axis=1).astype(np.float)
        acc_valid = pos_gt > 0
        acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid]
        iou_valid = (pos_gt + pos_pred) > 0
        union = pos_gt + pos_pred - tp
        iou[acc_valid] = tp[acc_valid] / union[acc_valid]
        macc = np.sum(acc[acc_valid]) / np.sum(acc_valid)
        miou = np.sum(iou[acc_valid]) / np.sum(iou_valid)
        fiou = np.sum(iou[acc_valid] * class_weights[acc_valid])
        pacc = np.sum(tp) / np.sum(pos_gt)

        res = {}
        res["mIoU"] = 100 * miou
        res["fwIoU"] = 100 * fiou
        for i, name in enumerate(self._class_names):
            res["IoU-{}".format(name)] = 100 * iou[i]
        res["mACC"] = 100 * macc
        res["pACC"] = 100 * pacc
        for i, name in enumerate(self._class_names):
            res["ACC-{}".format(name)] = 100 * acc[i]

        if self._output_dir:
            file_path = os.path.join(self._output_dir, "sem_seg_evaluation.pth")
            with PathManager.open(file_path, "wb") as f:
                torch.save(res, f)
        results = OrderedDict({"sem_seg": res})
        self._logger.info(results)
        return results

    def encode_json_sem_seg(self, sem_seg, input_file_name):
        """
        Convert semantic segmentation to COCO stuff format with segments encoded as RLEs.
        See http://cocodataset.org/#format-results
        """
        json_list = []
        for label in np.unique(sem_seg):
            if self._contiguous_id_to_dataset_id is not None:
                assert (
                    label in self._contiguous_id_to_dataset_id
                ), "Label {} is not in the metadata info for {}".format(label, self._dataset_name)
                dataset_id = self._contiguous_id_to_dataset_id[label]
            else:
                dataset_id = int(label)
            mask = (sem_seg == label).astype(np.uint8)
            mask_rle = mask_util.encode(np.array(mask[:, :, None], order="F"))[0]
            mask_rle["counts"] = mask_rle["counts"].decode("utf-8")
            json_list.append(
                {"file_name": input_file_name, "category_id": dataset_id, "segmentation": mask_rle}
            )
        return json_list
