# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved

import itertools
import logging
import numpy as np
from collections import Counter
import torch.utils.data
from tabulate import tabulate
from termcolor import colored

from detectron2.utils.logger import _log_api_usage, log_first_n
from detectron2.data.catalog import DatasetCatalog, MetadataCatalog
import torch.utils.data
from detectron2.config import configurable
from detectron2.data.build import (
    build_batch_data_loader,
    trivial_batch_collator,
    load_proposals_into_dataset,
    filter_images_with_only_crowd_annotations,
    filter_images_with_few_keypoints,
    print_instances_class_histogram,
)

from detectron2.data.common import DatasetFromList, MapDataset
from detectron2.data.dataset_mapper import DatasetMapper
from detectron2.data.detection_utils import check_metadata_consistency
from detectron2.data.samplers import (
    InferenceSampler,
    RandomSubsetTrainingSampler,
    RepeatFactorTrainingSampler,
    TrainingSampler,
)

"""
This file contains the default logic to build a dataloader for training or testing.
"""

__all__ = [
    "build_detection_train_loader",
    "build_detection_test_loader",
]


def print_classification_instances_class_histogram(dataset_dicts, class_names):
    """
    Args:
        dataset_dicts (list[dict]): list of dataset dicts.
        class_names (list[str]): list of class names (zero-indexed).
    """
    num_classes = len(class_names)
    hist_bins = np.arange(num_classes + 1)
    histogram = np.zeros((num_classes,), dtype=np.int)
    for entry in dataset_dicts:
        classes = np.asarray([entry["category_id"]], dtype=np.int)
        if len(classes):
            assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}"
            assert (
                classes.max() < num_classes
            ), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes"
        histogram += np.histogram(classes, bins=hist_bins)[0]

    N_COLS = min(6, len(class_names) * 2)

    def short_name(x):
        # make long class names shorter. useful for lvis
        if len(x) > 13:
            return x[:11] + ".."
        return x

    data = list(
        itertools.chain(
            *[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)]
        )
    )
    total_num_instances = sum(data[1::2])
    data.extend([None] * (N_COLS - (len(data) % N_COLS)))
    if num_classes > 1:
        data.extend(["total", total_num_instances])
    data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)])
    table = tabulate(
        data,
        headers=["category", "#instances"] * (N_COLS // 2),
        tablefmt="pipe",
        numalign="left",
        stralign="center",
    )
    log_first_n(
        logging.INFO,
        "Distribution of instances among all {} categories:\n".format(num_classes)
        + colored(table, "cyan"),
        key="message",
    )


def wrap_metas(dataset_dict, **kwargs):
    def _assign_attr(data_dict: dict, **kwargs):
        assert not any(
            [key in data_dict for key in kwargs]
        ), "Assigned attributes should not exist in the original sample."
        data_dict.update(kwargs)
        return data_dict

    return [_assign_attr(sample, meta=kwargs) for sample in dataset_dict]


def get_detection_dataset_dicts(
    names, filter_empty=True, min_keypoints=0, proposal_files=None
):
    """
    Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.

    Args:
        names (str or list[str]): a dataset name or a list of dataset names
        filter_empty (bool): whether to filter out images without instance annotations
        min_keypoints (int): filter out images with fewer keypoints than
            `min_keypoints`. Set to 0 to do nothing.
        proposal_files (list[str]): if given, a list of object proposal files
            that match each dataset in `names`.

    Returns:
        list[dict]: a list of dicts following the standard dataset dict format.
    """
    if isinstance(names, str):
        names = [names]
    assert len(names), names
    dataset_dicts = [
        wrap_metas(DatasetCatalog.get(dataset_name), dataset_name=dataset_name)
        for dataset_name in names
    ]
    for dataset_name, dicts in zip(names, dataset_dicts):
        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)

    if proposal_files is not None:
        assert len(names) == len(proposal_files)
        # load precomputed proposals from proposal files
        dataset_dicts = [
            load_proposals_into_dataset(dataset_i_dicts, proposal_file)
            for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files)
        ]

    dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))

    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)

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

    assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names))
    return dataset_dicts


def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None):
    if dataset is None:
        dataset = 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,
        )
        _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0])

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

    if sampler is None:
        sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
        logger = logging.getLogger(__name__)
        logger.info("Using training sampler {}".format(sampler_name))
        if sampler_name == "TrainingSampler":
            sampler = TrainingSampler(len(dataset))
        elif sampler_name == "RepeatFactorTrainingSampler":
            repeat_factors = (
                RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
                    dataset, cfg.DATALOADER.REPEAT_THRESHOLD
                )
            )
            sampler = RepeatFactorTrainingSampler(repeat_factors)
        elif sampler_name == "RandomSubsetTrainingSampler":
            sampler = RandomSubsetTrainingSampler(
                len(dataset), cfg.DATALOADER.RANDOM_SUBSET_RATIO
            )
        else:
            raise ValueError("Unknown training sampler: {}".format(sampler_name))

    return {
        "dataset": dataset,
        "sampler": sampler,
        "mapper": mapper,
        "total_batch_size": cfg.SOLVER.IMS_PER_BATCH,
        "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING,
        "num_workers": cfg.DATALOADER.NUM_WORKERS,
    }


# TODO can allow dataset as an iterable or IterableDataset to make this function more general
@configurable(from_config=_train_loader_from_config)
def build_detection_train_loader(
    dataset,
    *,
    mapper,
    sampler=None,
    total_batch_size,
    aspect_ratio_grouping=True,
    num_workers=0,
):
    """
    Build a dataloader for object detection with some default features.
    This interface is experimental.

    Args:
        dataset (list or torch.utils.data.Dataset): a list of dataset dicts,
            or a map-style pytorch dataset. They can be obtained by using
            :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
        mapper (callable): a callable which takes a sample (dict) from dataset and
            returns the format to be consumed by the model.
            When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``.
        sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces
            indices to be applied on ``dataset``. Default to :class:`TrainingSampler`,
            which coordinates an infinite random shuffle sequence across all workers.
        total_batch_size (int): total batch size across all workers. Batching
            simply puts data into a list.
        aspect_ratio_grouping (bool): whether to group images with similar
            aspect ratio for efficiency. When enabled, it requires each
            element in dataset be a dict with keys "width" and "height".
        num_workers (int): number of parallel data loading workers

    Returns:
        torch.utils.data.DataLoader:
            a dataloader. Each output from it is a ``list[mapped_element]`` of length
            ``total_batch_size / num_workers``, where ``mapped_element`` is produced
            by the ``mapper``.
    """
    if isinstance(dataset, list):
        dataset = DatasetFromList(dataset, copy=False)
    if mapper is not None:
        dataset = MapDataset(dataset, mapper)
    if sampler is None:
        sampler = TrainingSampler(len(dataset))
    assert isinstance(sampler, torch.utils.data.sampler.Sampler)
    return build_batch_data_loader(
        dataset,
        sampler,
        total_batch_size,
        aspect_ratio_grouping=aspect_ratio_grouping,
        num_workers=num_workers,
    )


def _test_loader_from_config(cfg, dataset_name, mapper=None):
    """
    Uses the given `dataset_name` argument (instead of the names in cfg), because the
    standard practice is to evaluate each test set individually (not combining them).
    """
    if isinstance(dataset_name, str):
        dataset_name = [dataset_name]

    dataset = get_detection_dataset_dicts(
        dataset_name,
        filter_empty=False,
        proposal_files=[
            cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)]
            for x in dataset_name
        ]
        if cfg.MODEL.LOAD_PROPOSALS
        else None,
    )
    if mapper is None:
        mapper = DatasetMapper(cfg, False)
    return {
        "dataset": dataset,
        "mapper": mapper,
        "num_workers": 0,
        "samples_per_gpu": cfg.SOLVER.TEST_IMS_PER_BATCH,
    }


@configurable(from_config=_test_loader_from_config)
def build_detection_test_loader(
    dataset, *, mapper, sampler=None, num_workers=0, samples_per_gpu=1
):
    """
    Similar to `build_detection_train_loader`, but uses a batch size of 1,
    and :class:`InferenceSampler`. This sampler coordinates all workers to
    produce the exact set of all samples.
    This interface is experimental.

    Args:
        dataset (list or torch.utils.data.Dataset): a list of dataset dicts,
            or a map-style pytorch dataset. They can be obtained by using
            :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
        mapper (callable): a callable which takes a sample (dict) from dataset
           and returns the format to be consumed by the model.
           When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.
        sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces
            indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,
            which splits the dataset across all workers.
        num_workers (int): number of parallel data loading workers

    Returns:
        DataLoader: a torch DataLoader, that loads the given detection
        dataset, with test-time transformation and batching.

    Examples:
    ::
        data_loader = build_detection_test_loader(
            DatasetRegistry.get("my_test"),
            mapper=DatasetMapper(...))

        # or, instantiate with a CfgNode:
        data_loader = build_detection_test_loader(cfg, "my_test")
    """
    if isinstance(dataset, list):
        dataset = DatasetFromList(dataset, copy=False)
    if mapper is not None:
        dataset = MapDataset(dataset, mapper)
    if sampler is None:
        sampler = InferenceSampler(len(dataset))
    # Always use 1 image per worker during inference since this is the
    # standard when reporting inference time in papers.
    batch_sampler = torch.utils.data.sampler.BatchSampler(
        sampler, samples_per_gpu, drop_last=False
    )
    data_loader = torch.utils.data.DataLoader(
        dataset,
        num_workers=num_workers,
        batch_sampler=batch_sampler,
        collate_fn=trivial_batch_collator,
    )
    return data_loader