# Copyright (c) 2024 ByteDance. All Rights Reserved.
# Part of the code is from https://github.com/xingyizhou/UniDet/blob/master/projects/UniDet/unidet/data/multi_dataset_dataloader.py (Apache-2.0 License)
import copy
import logging
import numpy as np
import operator
import torch
import torch.utils.data
import json
from detectron2.utils.comm import get_world_size
from detectron2.utils.logger import _log_api_usage, log_first_n

from detectron2.config import configurable
from detectron2.data import samplers
from torch.utils.data.sampler import BatchSampler, Sampler
from detectron2.data.common import DatasetFromList, MapDataset
from detectron2.data.dataset_mapper import DatasetMapper
from detectron2.data.build import get_detection_dataset_dicts, build_batch_data_loader
from detectron2.data.samplers import TrainingSampler, RepeatFactorTrainingSampler
from detectron2.data.build import worker_init_reset_seed, print_instances_class_histogram
from detectron2.data.build import filter_images_with_only_crowd_annotations
from detectron2.data.build import filter_images_with_few_keypoints
from detectron2.data.build import check_metadata_consistency
from detectron2.data.catalog import MetadataCatalog, DatasetCatalog
from detectron2.utils import comm
import itertools
import math
from collections import defaultdict
from typing import Optional


def _custom_train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None):
    sampler_name = cfg.DATALOADER.SAMPLER_TRAIN # "MultiDatasetSampler"
    if 'MultiDataset' in sampler_name: # True
        dataset_dicts = get_detection_dataset_dicts_with_source(
            cfg.DATASETS.TRAIN,
            filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
            if cfg.MODEL.KEYPOINT_ON else 0,
            proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
        )
    else: # False
        dataset_dicts = get_detection_dataset_dicts(
            cfg.DATASETS.TRAIN,
            filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
            if cfg.MODEL.KEYPOINT_ON else 0,
            proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
        )

    if mapper is None: # False
        mapper = DatasetMapper(cfg, True)

    if sampler is not None:
        pass
    elif sampler_name == "TrainingSampler": # False
        sampler = TrainingSampler(len(dataset))
    elif sampler_name == "MultiDatasetSampler": # True
        sampler = MultiDatasetSampler(
            dataset_dicts,
            dataset_ratio = cfg.DATALOADER.DATASET_RATIO,
            use_rfs = cfg.DATALOADER.USE_RFS,
            dataset_ann = cfg.DATALOADER.DATASET_ANN,
            repeat_threshold = cfg.DATALOADER.REPEAT_THRESHOLD,
            #seed = 2025,
        )
    elif sampler_name == "RepeatFactorTrainingSampler": # False
        repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
            dataset_dicts, cfg.DATALOADER.REPEAT_THRESHOLD
        )
        sampler = RepeatFactorTrainingSampler(repeat_factors)
    else:
        raise ValueError("Unknown training sampler: {}".format(sampler_name))

    return {
        "dataset": dataset_dicts,
        "sampler": sampler,
        "mapper": mapper,
        "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, # 64
        "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING,
        "num_workers": cfg.DATALOADER.NUM_WORKERS, # 8
        'multi_dataset_grouping': cfg.DATALOADER.MULTI_DATASET_GROUPING, # True
        'use_diff_bs_size': cfg.DATALOADER.USE_DIFF_BS_SIZE, # True
        'dataset_bs': cfg.DATALOADER.DATASET_BS, # [8, 32]
        'num_datasets': len(cfg.DATASETS.TRAIN) # 2
    }


@configurable(from_config=_custom_train_loader_from_config)
def build_custom_train_loader(
        dataset, *, mapper, sampler, 
        total_batch_size=16, # 64
        aspect_ratio_grouping=True, 
        num_workers=0, # 8
        num_datasets=1, # 2
        multi_dataset_grouping=False, # True
        use_diff_bs_size=False, # True
        dataset_bs=[] # [8, 32]
    ):
    """
    Modified from detectron2.data.build.build_custom_train_loader, but supports
    different samplers
    """
    if isinstance(dataset, list):
        dataset = DatasetFromList(dataset, copy=False)
    if mapper is not None: # True
        dataset = MapDataset(dataset, mapper)
    if sampler is None: # False
        sampler = TrainingSampler(len(dataset))
    assert isinstance(sampler, torch.utils.data.sampler.Sampler)
    if multi_dataset_grouping: # True
        return build_multi_dataset_batch_data_loader(
            use_diff_bs_size,
            dataset_bs,
            dataset,
            sampler,
            total_batch_size,
            num_datasets=num_datasets,
            num_workers=num_workers,
        )
    else: # False
        return build_batch_data_loader(
            dataset,
            sampler,
            total_batch_size,
            aspect_ratio_grouping=aspect_ratio_grouping,
            num_workers=num_workers,
        )


def build_multi_dataset_batch_data_loader(
    use_diff_bs_size, dataset_bs,
    dataset, sampler, total_batch_size, num_datasets, num_workers=0
):
    """
    """
    world_size = get_world_size()
    assert (
        total_batch_size > 0 and total_batch_size % world_size == 0
    ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format(
        total_batch_size, world_size
    )

    batch_size = total_batch_size // world_size
    data_loader = torch.utils.data.DataLoader(
        dataset,
        sampler=sampler,
        num_workers=num_workers,
        batch_sampler=None,
        collate_fn=operator.itemgetter(0),  # don't batch, but yield individual elements
        worker_init_fn=worker_init_reset_seed,
    )  # yield individual mapped dict
    if use_diff_bs_size:
        return DIFFMDAspectRatioGroupedDataset(
            data_loader, dataset_bs, num_datasets)
    else:
        return MDAspectRatioGroupedDataset(
            data_loader, batch_size, num_datasets)


def get_detection_dataset_dicts_with_source(
    dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None
):
    assert len(dataset_names)
    dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names]
    for dataset_name, dicts in zip(dataset_names, dataset_dicts):
        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
    
    for source_id, (dataset_name, dicts) in \
        enumerate(zip(dataset_names, dataset_dicts)):
        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
        for d in dicts:
            d['dataset_source'] = source_id # add "dataset_source" to original dict

        if "annotations" in dicts[0]:
            try:
                class_names = MetadataCatalog.get(dataset_name).thing_classes
                check_metadata_consistency("thing_classes", dataset_name)
                print_instances_class_histogram(dicts, class_names)
            except AttributeError:  # class names are not available for this dataset
                pass

    assert proposal_files is None

    dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) # connect multiple iterable objects to one

    has_instances = "annotations" in dataset_dicts[0]
    if filter_empty and has_instances:
        dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts)
    if min_keypoints > 0 and has_instances:
        dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)

    return dataset_dicts


class MultiDatasetSampler(Sampler):
    def __init__(
        self, 
        dataset_dicts, 
        dataset_ratio,
        use_rfs, # [True, False]
        dataset_ann,
        repeat_threshold=0.001,
        seed: Optional[int] = None,
        ):
        """
        """
        sizes = [0 for _ in range(len(dataset_ratio))]
        for d in dataset_dicts:
            sizes[d['dataset_source']] += 1 # size of each dataset
        print('dataset sizes', sizes)
        self.sizes = sizes
        assert len(dataset_ratio) == len(sizes), \
            'length of dataset ratio {} should be equal to number if dataset {}'.format(
                len(dataset_ratio), len(sizes)
            )
        if seed is None:
            seed = comm.shared_random_seed() # seed shared across all GPUs
        self._seed = int(seed)
        self._rank = comm.get_rank()
        self._world_size = comm.get_world_size()
        
        self.dataset_ids =  torch.tensor(
            [d['dataset_source'] for d in dataset_dicts], dtype=torch.long)

        dataset_weight = [torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) \
            for i, (r, s) in enumerate(zip(dataset_ratio, sizes))]
        dataset_weight = torch.cat(dataset_weight)
        
        rfs_factors = []
        st = 0
        for i, s in enumerate(sizes):
            if use_rfs[i]:
                if dataset_ann[i] == 'box':
                    rfs_func = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency
                else:
                    rfs_func = repeat_factors_from_tag_frequency
                rfs_factor = rfs_func(
                    dataset_dicts[st: st + s],
                    repeat_thresh=repeat_threshold)
                rfs_factor = rfs_factor * (s / rfs_factor.sum())
            else:
                rfs_factor = torch.ones(s)
            rfs_factors.append(rfs_factor)
            st = st + s
        rfs_factors = torch.cat(rfs_factors)

        self.weights = dataset_weight * rfs_factors # weights for each element in the dataset_dict
        self.sample_epoch_size = len(self.weights)

    def __iter__(self):
        start = self._rank
        yield from itertools.islice(
            self._infinite_indices(), start, None, self._world_size) # itertools.islice(iterable, start, stop[, step])


    def _infinite_indices(self):
        g = torch.Generator()
        g.manual_seed(self._seed)
        while True:
            ids = torch.multinomial(
                self.weights, self.sample_epoch_size, generator=g, 
                replacement=True) # randomly sample according to the given weights
            nums = [(self.dataset_ids[ids] == i).sum().int().item() \
                for i in range(len(self.sizes))]
            yield from ids


class MDAspectRatioGroupedDataset(torch.utils.data.IterableDataset):
    def __init__(self, dataset, batch_size, num_datasets):
        """
        """
        self.dataset = dataset
        self.batch_size = batch_size
        self._buckets = [[] for _ in range(2 * num_datasets)] # there are (2 x num_datasets) types of data. For each dataset, there are two types: w>h or w<=h

    def __iter__(self):
        for d in self.dataset:
            w, h = d["width"], d["height"]
            aspect_ratio_bucket_id = 0 if w > h else 1
            bucket_id = d['dataset_source'] * 2 + aspect_ratio_bucket_id
            bucket = self._buckets[bucket_id]
            bucket.append(d)
            if len(bucket) == self.batch_size:
                yield bucket[:]
                del bucket[:]


class DIFFMDAspectRatioGroupedDataset(torch.utils.data.IterableDataset):
    def __init__(self, dataset, batch_sizes, num_datasets):
        """
        """
        self.dataset = dataset
        self.batch_sizes = batch_sizes
        self._buckets = [[] for _ in range(2 * num_datasets)]

    def __iter__(self):
        for d in self.dataset:
            w, h = d["width"], d["height"]
            aspect_ratio_bucket_id = 0 if w > h else 1
            bucket_id = d['dataset_source'] * 2 + aspect_ratio_bucket_id
            bucket = self._buckets[bucket_id]
            bucket.append(d)
            if len(bucket) == self.batch_sizes[d['dataset_source']]: # allow different batchsizes
                yield bucket[:]
                del bucket[:]


def repeat_factors_from_tag_frequency(dataset_dicts, repeat_thresh):
    """
    """
    category_freq = defaultdict(int)
    for dataset_dict in dataset_dicts:
        cat_ids = dataset_dict['pos_category_ids']
        for cat_id in cat_ids:
            category_freq[cat_id] += 1
    num_images = len(dataset_dicts)
    for k, v in category_freq.items():
        category_freq[k] = v / num_images

    category_rep = {
        cat_id: max(1.0, math.sqrt(repeat_thresh / cat_freq))
        for cat_id, cat_freq in category_freq.items()
    }

    rep_factors = []
    for dataset_dict in dataset_dicts:
        cat_ids = dataset_dict['pos_category_ids']
        rep_factor = max({category_rep[cat_id] for cat_id in cat_ids}, default=1.0)
        rep_factors.append(rep_factor)

    return torch.tensor(rep_factors, dtype=torch.float32)