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

import numpy as np
import pycocotools.mask as mask_util
from pycocotools.coco import COCO

import torch

from ..data.datasets.coco import convert_to_coco_json
from cvpods.evaluation.fast_eval_api import COCOeval_opt as COCOeval
from cvpods.structures import Boxes, BoxMode, pairwise_iou
from cvpods.utils import PathManager, comm, create_small_table, create_table_with_header

from .evaluator import DatasetEvaluator
from .registry import EVALUATOR


@EVALUATOR.register()
class COCOEvaluator(DatasetEvaluator):
    """
    Evaluate object proposal, instance detection/segmentation, keypoint detection
    outputs using COCO's metrics and APIs.
    """

    def __init__(self, dataset_name, meta, cfg, distributed, output_dir=None, dump=False):
        """
        Args:
            dataset_name (str): name of the dataset to be evaluated.
                It must have either the following corresponding metadata:

                    "json_file": the path to the COCO format annotation

                Or it must be in cvpods's standard dataset format
                so it can be converted to COCO format automatically.
            meta (SimpleNamespace): dataset metadata.
            cfg (CfgNode): cvpods Config instance.
            distributed (True): if True, will collect results from all ranks for evaluation.
                Otherwise, will evaluate the results in the current process.
            output_dir (str): optional, an output directory to dump all
                results predicted on the dataset. The dump contains two files:

                1. "instance_predictions.pth" a file in torch serialization
                   format that contains all the raw original predictions.
                2. "coco_instances_results.json" a json file in COCO's result
                   format.

            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.cfg = cfg
        self._tasks = self._tasks_from_config(cfg)
        self._distributed = distributed
        self._output_dir = output_dir

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

        self._metadata = meta
        if not hasattr(self._metadata, "json_file"):
            self._logger.warning(
                f"json_file was not found in MetaDataCatalog for '{dataset_name}'."
                " Trying to convert it to COCO format ..."
            )

            cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json")
            self._metadata.json_file = cache_path
            convert_to_coco_json(dataset_name, cache_path)

        json_file = PathManager.get_local_path(self._metadata.json_file)
        with contextlib.redirect_stdout(io.StringIO()):
            self._coco_api = COCO(json_file)

        self._kpt_oks_sigmas = cfg.TEST.KEYPOINT_OKS_SIGMAS
        # Test set json files do not contain annotations (evaluation must be
        # performed using the COCO evaluation server).
        self._do_evaluation = "annotations" in self._coco_api.dataset

    def reset(self):
        self._predictions = []
        self._coco_results = []
        self._dump_infos = []  # per task

    def _tasks_from_config(self, cfg):
        """
        Returns:
            tuple[str]: tasks that can be evaluated under the given configuration.
        """
        tasks = ("bbox",)
        if cfg.MODEL.MASK_ON:
            tasks = tasks + ("segm",)
        if cfg.MODEL.KEYPOINT_ON:
            tasks = tasks + ("keypoints",)
        return tasks

    def process(self, inputs, outputs):
        """
        Args:
            inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
                It is a list of dict. Each dict corresponds to an image and
                contains keys like "height", "width", "file_name", "image_id".
            outputs: the outputs of a COCO model. It is a list of dicts with key
                "instances" that contains :class:`Instances`.
        """
        for input, output in zip(inputs, outputs):
            prediction = {"image_id": input["image_id"]}

            # TODO this is ugly
            if "instances" in output:
                instances = output["instances"].to(self._cpu_device)
                prediction["instances"] = instances_to_coco_json(instances, input["image_id"])
            if "proposals" in output:
                prediction["proposals"] = output["proposals"].to(self._cpu_device)
            self._predictions.append(prediction)

    def evaluate(self):
        if self._distributed:
            comm.synchronize()
            self._predictions = comm.gather(self._predictions, dst=0)
            self._predictions = list(itertools.chain(*self._predictions))

            if not comm.is_main_process():
                return {}

        if len(self._predictions) == 0:
            self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
            return {}

        if self._output_dir:
            PathManager.mkdirs(self._output_dir)
            file_path = os.path.join(self._output_dir, "instances_predictions.pth")
            with PathManager.open(file_path, "wb") as f:
                torch.save(self._predictions, f)

        self._results = OrderedDict()
        if "proposals" in self._predictions[0]:
            self._eval_box_proposals()
        if "instances" in self._predictions[0]:
            self._eval_predictions(set(self._tasks))

        if self._dump:
            extra_infos = {
                "title": os.path.basename(os.getcwd()),
                "seed": self.cfg.SEED,
            }
            _dump_to_markdown(extra_infos, self._dump_infos)

        # Copy so the caller can do whatever with results
        return copy.deepcopy(self._results)

    def _eval_predictions(self, tasks):
        """
        Evaluate self._predictions on the given tasks.
        Fill self._results with the metrics of the tasks.
        """
        self._logger.info("Preparing results for COCO format ...")
        self._coco_results = list(itertools.chain(*[x["instances"] for x in self._predictions]))

        # unmap the category ids for COCO
        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
            reverse_id_mapping = {
                v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
            }
            for result in self._coco_results:
                category_id = result["category_id"]
                assert (
                    category_id in reverse_id_mapping
                ), "A prediction has category_id={}, which is not available in the dataset.".format(
                    category_id
                )
                result["category_id"] = reverse_id_mapping[category_id]

        if self._output_dir:
            file_path = os.path.join(self._output_dir, "coco_instances_results.json")
            self._logger.info("Saving results to {}".format(file_path))
            with PathManager.open(file_path, "w") as f:
                f.write(json.dumps(self._coco_results))
                f.flush()

        if not self._do_evaluation:
            self._logger.info("Annotations are not available for evaluation.")
            return

        self._logger.info("Evaluating predictions ...")
        for task in sorted(tasks):
            coco_eval, summary = (
                _evaluate_predictions_on_coco(
                    self._coco_api, self._coco_results, task, kpt_oks_sigmas=self._kpt_oks_sigmas
                )
                if len(self._coco_results) > 0
                else None  # cocoapi does not handle empty results very well
            )
            self._logger.info("\n" + summary.getvalue())
            res = self._derive_coco_results(
                coco_eval, task, summary, class_names=self._metadata.thing_classes
            )
            self._results[task] = res

    def _eval_box_proposals(self):
        """
        Evaluate the box proposals in self._predictions.
        Fill self._results with the metrics for "box_proposals" task.
        """
        if self._output_dir:
            # Saving generated box proposals to file.
            # Predicted box_proposals are in XYXY_ABS mode.
            bbox_mode = BoxMode.XYXY_ABS.value
            ids, boxes, objectness_logits = [], [], []
            for prediction in self._predictions:
                ids.append(prediction["image_id"])
                boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy())
                objectness_logits.append(prediction["proposals"].objectness_logits.numpy())

            proposal_data = {
                "boxes": boxes,
                "objectness_logits": objectness_logits,
                "ids": ids,
                "bbox_mode": bbox_mode,
            }
            with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f:
                pickle.dump(proposal_data, f)

        if not self._do_evaluation:
            self._logger.info("Annotations are not available for evaluation.")
            return

        self._logger.info("Evaluating bbox proposals ...")
        res = {}
        areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
        for limit in [100, 1000]:
            for area, suffix in areas.items():
                stats = _evaluate_box_proposals(
                    self._predictions, self._coco_api, area=area, limit=limit
                )
                key = "AR{}@{:d}".format(suffix, limit)
                res[key] = float(stats["ar"].item() * 100)
        self._logger.info("Proposal metrics: \n" + create_small_table(res))
        self._results["box_proposals"] = res

    def _derive_coco_results(self, coco_eval, iou_type, summary, class_names=None):
        """
        Derive the desired score numbers from summarized COCOeval.

        Args:
            coco_eval (None or COCOEval): None represents no predictions from model.
            iou_type (str): specific evaluation task,
                optional values are: "bbox", "segm", "keypoints".
            class_names (None or list[str]): if provided, will use it to predict
                per-category AP.

        Returns:
            a dict of {metric name: score}
        """

        metrics = {
            "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
            "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
            "keypoints": ["AP", "AP50", "AP75", "APm", "APl"],
        }[iou_type]

        if coco_eval is None:
            self._logger.warn("No predictions from the model!")
            return {metric: float("nan") for metric in metrics}

        # the standard metrics
        results = {
            metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
            for idx, metric in enumerate(metrics)
        }
        small_table = create_small_table(results)
        self._logger.info("Evaluation results for {}: \n".format(iou_type) + small_table)
        if not np.isfinite(sum(results.values())):
            self._logger.info("Note that some metrics cannot be computed.")

        if class_names is None:  # or len(class_names) <= 1:
            return results
        # Compute per-category AP
        # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
        precisions = coco_eval.eval["precision"]
        # precision has dims (iou, recall, cls, area range, max dets)
        assert len(class_names) == precisions.shape[2]

        results_per_category = {}
        for idx, name in enumerate(class_names):
            # area range index 0: all area ranges
            # max dets index -1: typically 100 per image
            precision = precisions[:, :, idx, 0, -1]
            precision = precision[precision > -1]
            ap = np.mean(precision) if precision.size else float("nan")
            results_per_category[name] = float(ap * 100)
            # results_per_category.append(("{}".format(name), float(ap * 100)))

        # tabulate it
        table = create_table_with_header(results_per_category, headers=["category", "AP"])
        self._logger.info("Per-category {} AP: \n".format(iou_type) + table)

        results.update({"AP-" + name: ap for name, ap in results_per_category.items()})
        if self._dump:
            dump_info_one_task = {
                "task": iou_type,
                "summary": summary.getvalue(),
                "tables": [small_table, table],
            }
            self._dump_infos.append(dump_info_one_task)
        return results


def instances_to_coco_json(instances, img_id):
    """
    Dump an "Instances" object to a COCO-format json that's used for evaluation.
    Args:
        instances (Instances):
        img_id (int): the image id
    Returns:
        list[dict]: list of json annotations in COCO format.
    """
    num_instance = len(instances)
    if num_instance == 0:
        return []

    boxes = instances.pred_boxes.tensor.numpy()
    boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
    boxes = boxes.tolist()
    scores = instances.scores.tolist()
    classes = instances.pred_classes.tolist()

    has_mask = instances.has("pred_masks")
    if has_mask:
        # use RLE to encode the masks, because they are too large and takes memory
        # since this evaluator stores outputs of the entire dataset
        rles = [
            mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
            for mask in instances.pred_masks
        ]
        for rle in rles:
            # "counts" is an array encoded by mask_util as a byte-stream. Python3's
            # json writer which always produces strings cannot serialize a bytestream
            # unless you decode it. Thankfully, utf-8 works out (which is also what
            # the pycocotools/_mask.pyx does).
            rle["counts"] = rle["counts"].decode("utf-8")

    has_keypoints = instances.has("pred_keypoints")
    if has_keypoints:
        keypoints = instances.pred_keypoints

    results = []
    for k in range(num_instance):
        result = {
            "image_id": img_id,
            "category_id": classes[k],
            "bbox": boxes[k],
            "score": scores[k],
        }
        if has_mask:
            result["segmentation"] = rles[k]
        if has_keypoints:
            # In COCO annotations,
            # keypoints coordinates are pixel indices.
            # However our predictions are floating point coordinates.
            # Therefore we subtract 0.5 to be consistent with the annotation format.
            # This is the inverse of data loading logic in `datasets/coco.py`.
            keypoints[k][:, :2] -= 0.5
            result["keypoints"] = keypoints[k].flatten().tolist()
        results.append(result)
    return results


# inspired from Detectron:
# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa
def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None):
    """
    Evaluate detection proposal recall metrics. This function is a much
    faster alternative to the official COCO API recall evaluation code. However,
    it produces slightly different results.
    """
    # Record max overlap value for each gt box
    # Return vector of overlap values
    areas = {
        "all": 0,
        "small": 1,
        "medium": 2,
        "large": 3,
        "96-128": 4,
        "128-256": 5,
        "256-512": 6,
        "512-inf": 7,
    }
    area_ranges = [
        [0 ** 2, 1e5 ** 2],  # all
        [0 ** 2, 32 ** 2],  # small
        [32 ** 2, 96 ** 2],  # medium
        [96 ** 2, 1e5 ** 2],  # large
        [96 ** 2, 128 ** 2],  # 96-128
        [128 ** 2, 256 ** 2],  # 128-256
        [256 ** 2, 512 ** 2],  # 256-512
        [512 ** 2, 1e5 ** 2],
    ]  # 512-inf
    assert area in areas, "Unknown area range: {}".format(area)
    area_range = area_ranges[areas[area]]
    gt_overlaps = []
    num_pos = 0

    for prediction_dict in dataset_predictions:
        predictions = prediction_dict["proposals"]

        # sort predictions in descending order
        # TODO maybe remove this and make it explicit in the documentation
        inds = predictions.objectness_logits.sort(descending=True)[1]
        predictions = predictions[inds]

        ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"])
        anno = coco_api.loadAnns(ann_ids)
        gt_boxes = [
            BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
            for obj in anno
            if obj["iscrowd"] == 0
        ]
        gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4)  # guard against no boxes
        gt_boxes = Boxes(gt_boxes)
        gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0])

        if len(gt_boxes) == 0 or len(predictions) == 0:
            continue

        valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
        gt_boxes = gt_boxes[valid_gt_inds]

        num_pos += len(gt_boxes)

        if len(gt_boxes) == 0:
            continue

        if limit is not None and len(predictions) > limit:
            predictions = predictions[:limit]

        overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes)

        _gt_overlaps = torch.zeros(len(gt_boxes))
        for j in range(min(len(predictions), len(gt_boxes))):
            # find which proposal box maximally covers each gt box
            # and get the iou amount of coverage for each gt box
            max_overlaps, argmax_overlaps = overlaps.max(dim=0)

            # find which gt box is 'best' covered (i.e. 'best' = most iou)
            gt_ovr, gt_ind = max_overlaps.max(dim=0)
            assert gt_ovr >= 0
            # find the proposal box that covers the best covered gt box
            box_ind = argmax_overlaps[gt_ind]
            # record the iou coverage of this gt box
            _gt_overlaps[j] = overlaps[box_ind, gt_ind]
            assert _gt_overlaps[j] == gt_ovr
            # mark the proposal box and the gt box as used
            overlaps[box_ind, :] = -1
            overlaps[:, gt_ind] = -1

        # append recorded iou coverage level
        gt_overlaps.append(_gt_overlaps)
    gt_overlaps = torch.cat(gt_overlaps, dim=0)
    gt_overlaps, _ = torch.sort(gt_overlaps)

    if thresholds is None:
        step = 0.05
        thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
    recalls = torch.zeros_like(thresholds)
    # compute recall for each iou threshold
    for i, t in enumerate(thresholds):
        recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
    # ar = 2 * np.trapz(recalls, thresholds)
    ar = recalls.mean()
    return {
        "ar": ar,
        "recalls": recalls,
        "thresholds": thresholds,
        "gt_overlaps": gt_overlaps,
        "num_pos": num_pos,
    }


def _evaluate_predictions_on_coco(coco_gt, coco_results, iou_type, kpt_oks_sigmas=None):
    """
    Evaluate the coco results using COCOEval API.
    """
    assert len(coco_results) > 0

    if iou_type == "segm":
        coco_results = copy.deepcopy(coco_results)
        # When evaluating mask AP, if the results contain bbox, cocoapi will
        # use the box area as the area of the instance, instead of the mask area.
        # This leads to a different definition of small/medium/large.
        # We remove the bbox field to let mask AP use mask area.
        for c in coco_results:
            c.pop("bbox", None)

    coco_dt = coco_gt.loadRes(coco_results)
    coco_eval = COCOeval(coco_gt, coco_dt, iou_type)
    # Use the COCO default keypoint OKS sigmas unless overrides are specified
    if kpt_oks_sigmas:
        coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)

    if iou_type == "keypoints":
        num_keypoints = len(coco_results[0]["keypoints"]) // 3
        assert len(coco_eval.params.kpt_oks_sigmas) == num_keypoints, (
            "[COCOEvaluator] The length of cfg.TEST.KEYPOINT_OKS_SIGMAS (default: 17) "
            "must be equal to the number of keypoints. However the prediction has {} "
            "keypoints! For more information please refer to "
            "http://cocodataset.org/#keypoints-eval.".format(num_keypoints)
        )

    coco_eval.evaluate()
    coco_eval.accumulate()

    redirect_string = io.StringIO()
    with contextlib.redirect_stdout(redirect_string):
        coco_eval.summarize()
    redirect_string.getvalue()
    return coco_eval, redirect_string


def _dump_to_markdown(extra_infos, 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:
        extra_infos (dict): extra_infos to dump.
        dump_infos (list[dict]): dump information for each task.
        md_file (str): markdown file path.
    """
    with open(md_file, "w") as f:
        title = extra_infos["title"]
        seed = extra_infos["seed"]
        f.write("# {}  \n\nseed: {}".format(title, seed))
        for task_info in dump_infos:
            task_name = task_info["task"]
            f.write("\n\n## Evaluation results for {}:  \n\n".format(task_name))
            f.write("```  \n" + task_info["summary"] + "```  \n")
            overall_table, detail_table = [
                table.replace("\n", "  \n") for table in task_info["tables"]
            ]
            header = "\n\n### Per-category {} AP:  \n\n".format(task_name)
            f.write(overall_table + header + detail_table + "\n")
