"""Implements evaluation of trained models"""

import time
import warnings
from pathlib import Path
import pickle
import numpy as np
import torch
from torchvision import transforms
from PIL import ImageFile

from cirtorch.datasets.genericdataset import ImagesFromList

from asmk import asmk_method, kernel as kern_pkg
from ..networks import how_net
from ..utils import score_helpers, data_helpers, logging

ImageFile.LOAD_TRUNCATED_IMAGES = True
warnings.filterwarnings("ignore", r"^Possibly corrupt EXIF data", category=UserWarning)


def evaluate_demo(demo_eval, evaluation, globals):
    """Demo evaluating a trained network

    :param dict demo_eval: Demo-related options
    :param dict evaluation: Evaluation-related options
    :param dict globals: Global options
    """
    globals["device"] = torch.device("cpu")
    if demo_eval['gpu_id'] is not None:
        globals["device"] = torch.device(("cuda:%s" % demo_eval['gpu_id']))

    # Handle net_path when directory
    net_path = Path(demo_eval['exp_folder']) / demo_eval['net_path']
    if net_path.is_dir() and (net_path / "epochs/model_best.pth").exists():
        net_path = net_path / "epochs/model_best.pth"

    # Load net
    state = _convert_checkpoint(torch.load(net_path, map_location='cpu'))
    net = how_net.init_network(**state['net_params']).to(globals['device'])
    net.load_state_dict(state['state_dict'])
    globals["transform"] = transforms.Compose([transforms.ToTensor(), \
                transforms.Normalize(**dict(zip(["mean", "std"], net.runtime['mean_std'])))])

    # Eval
    if evaluation['global_descriptor']['datasets']:
        eval_global(net, evaluation['inference'], globals, **evaluation['global_descriptor'])

    if evaluation['multistep']:
        eval_asmk_multistep(net, evaluation['inference'], evaluation['multistep'], globals, **evaluation['local_descriptor'])
    elif evaluation['local_descriptor']['datasets']:
        eval_asmk(net, evaluation['inference'], globals, **evaluation['local_descriptor'])


def eval_global(net, inference, globals, *, datasets):
    """Evaluate global descriptors"""
    net.eval()
    time0 = time.time()
    logger = globals["logger"]
    logger.info("Starting global evaluation")

    results = {}
    for dataset in datasets:
        images, qimages, bbxs, gnd = data_helpers.load_dataset(dataset, data_root=globals['root_path'])
        logger.info(f"Evaluating {dataset}")

        with logging.LoggingStopwatch("extracting database images", logger.info, logger.debug):
            dset = ImagesFromList(root='', images=images, imsize=inference['image_size'], bbxs=None,
                                  transform=globals['transform'])
            vecs = how_net.extract_vectors(net, dset, globals["device"], scales=inference['scales'])
        with logging.LoggingStopwatch("extracting query images", logger.info, logger.debug):
            qdset = ImagesFromList(root='', images=qimages, imsize=inference['image_size'], bbxs=bbxs,
                                   transform=globals['transform'])
            qvecs = how_net.extract_vectors(net, qdset, globals["device"], scales=inference['scales'])

        vecs, qvecs = vecs.numpy(), qvecs.numpy()
        ranks = np.argsort(-np.dot(vecs, qvecs.T), axis=0)
        results[dataset] = score_helpers.compute_map_and_log(dataset, ranks, gnd, logger=logger)

    logger.info(f"Finished global evaluation in {int(time.time()-time0) // 60} min")
    return results


def eval_asmk(net, inference, globals, *, datasets, codebook_training, asmk):
    """Evaluate local descriptors with ASMK"""
    net.eval()
    time0 = time.time()
    logger = globals["logger"]
    logger.info("Starting asmk evaluation")

    asmk = asmk_method.ASMKMethod.initialize_untrained(asmk)
    asmk = asmk_train_codebook(net, inference, globals, logger, codebook_training=codebook_training,
                               asmk=asmk, cache_path=None)

    results = {}
    for dataset in datasets:
        dataset_name = dataset if isinstance(dataset, str) else dataset['name']
        images, qimages, bbxs, gnd = data_helpers.load_dataset(dataset, data_root=globals['root_path'])
        logger.info(f"Evaluating '{dataset_name}'")

        asmk_dataset = asmk_index_database(net, inference, globals, logger, asmk=asmk, images=images)
        asmk_query_ivf(net, inference, globals, logger, dataset=dataset, asmk_dataset=asmk_dataset,
                       qimages=qimages, bbxs=bbxs, gnd=gnd, results=results,
                       cache_path=globals["exp_path"] / "query_results.pkl")

    logger.info(f"Finished asmk evaluation in {int(time.time()-time0) // 60} min")
    return results


def eval_asmk_multistep(net, inference, multistep, globals, *, datasets, codebook_training, asmk):
    """Evaluate local descriptors with ASMK"""
    valid_steps = ["train_codebook", "aggregate_database", "build_ivf", "query_ivf", "aggregate_build_query"]
    assert multistep['step'] in valid_steps, multistep['step']

    net.eval()
    time0 = time.time()
    logger = globals["logger"]
    (globals["exp_path"] / "eval").mkdir(exist_ok=True)
    logger.info(f"Starting asmk evaluation step '{multistep['step']}'")

    # Handle partitioning
    partition = {"suffix": "", "norm_start": 0, "norm_end": 1}
    if multistep.get("partition"):
        total, index = multistep['partition']
        partition = {"suffix": f":{total}_{str(index).zfill(len(str(total-1)))}",
                     "norm_start": index / total,
                     "norm_end": (index+1) / total}
        if multistep['step'] == "aggregate_database" or multistep['step'] == "query_ivf":
            logger.info(f"Processing partition '{total}_{index}'")

    # Handle distractors
    distractors_path = None
    distractors = multistep.get("distractors")
    if distractors:
        distractors_path = globals["exp_path"] / f"eval/{distractors}.ivf.pkl"

    # Train codebook
    asmk = asmk_method.ASMKMethod.initialize_untrained(asmk)
    cdb_path = globals["exp_path"] / "eval/codebook.pkl"
    if multistep['step'] == "train_codebook":
        asmk_train_codebook(net, inference, globals, logger, codebook_training=codebook_training,
                            asmk=asmk, cache_path=cdb_path)
        return None

    asmk = asmk.train_codebook(None, cache_path=cdb_path)

    results = {}
    for dataset in datasets:
        dataset_name = database_name = dataset if isinstance(dataset, str) else dataset['name']
        if distractors and multistep['step'] != "aggregate_database":
            dataset_name = f"{distractors}_{database_name}"
        images, qimages, bbxs, gnd = data_helpers.load_dataset(dataset, data_root=globals['root_path'])
        logger.info(f"Processing dataset '{dataset_name}'")

        # Infer database
        if multistep['step'] == "aggregate_database":
            agg_path = globals["exp_path"] / f"eval/{database_name}.agg{partition['suffix']}.pkl"
            asmk_aggregate_database(net, inference, globals, logger, asmk=asmk, images=images,
                                    partition=partition, cache_path=agg_path)

        # Build ivf
        elif multistep['step'] == "build_ivf":
            ivf_path = globals["exp_path"] / f"eval/{dataset_name}.ivf.pkl"
            asmk_build_ivf(globals, logger, asmk=asmk, cache_path=ivf_path, database_name=database_name,
                           distractors=distractors, distractors_path=distractors_path)

        # Query ivf
        elif multistep['step'] == "query_ivf":
            asmk_dataset = asmk.build_ivf(None, None, cache_path=globals["exp_path"] / f"eval/{dataset_name}.ivf.pkl")
            start, end = int(len(qimages)*partition['norm_start']), int(len(qimages)*partition['norm_end'])
            bbxs = bbxs[start:end] if bbxs is not None else None
            results_path = globals["exp_path"] / f"eval/{dataset_name}.results{partition['suffix']}.pkl"
            asmk_query_ivf(net, inference, globals, logger, dataset=dataset, asmk_dataset=asmk_dataset,
                           qimages=qimages[start:end], bbxs=bbxs, gnd=gnd, results=results,
                           cache_path=results_path, imid_offset=start)

        # All 3 dataset steps
        elif multistep['step'] == "aggregate_build_query":
            if multistep.get("partition"):
                raise NotImplementedError("Partitions within step 'aggregate_build_query' are not" \
                                          " supported, use separate steps")
            results_path = globals["exp_path"] / "query_results.pkl"
            if gnd is None and results_path.exists():
                logger.debug("Step results already exist")
                continue
            asmk_dataset = asmk_index_database(net, inference, globals, logger, asmk=asmk, images=images,
                                               distractors_path=distractors_path)
            asmk_query_ivf(net, inference, globals, logger, dataset=dataset, asmk_dataset=asmk_dataset,
                           qimages=qimages, bbxs=bbxs, gnd=gnd, results=results, cache_path=results_path)

    logger.info(f"Finished asmk evaluation step '{multistep['step']}' in {int(time.time()-time0) // 60} min")
    return results

#
# Separate steps
#

def asmk_train_codebook(net, inference, globals, logger, *, codebook_training, asmk, cache_path):
    """Asmk evaluation step 'train_codebook'"""
    if cache_path and cache_path.exists():
        return asmk.train_codebook(None, cache_path=cache_path)

    images = data_helpers.load_dataset('train', data_root=globals['root_path'])[0]
    images = images[:codebook_training['images']]
    dset = ImagesFromList(root='', images=images, imsize=inference['image_size'], bbxs=None,
                          transform=globals['transform'])
    infer_opts = {"scales": codebook_training['scales'], "features_num": inference['features_num']}
    des_train = how_net.extract_vectors_local(net, dset, globals["device"], **infer_opts)[0]
    asmk = asmk.train_codebook(des_train, cache_path=cache_path)
    logger.info(f"Codebook trained in {asmk.metadata['train_codebook']['train_time']:.1f}s")
    return asmk

def asmk_aggregate_database(net, inference, globals, logger, *, asmk, images, partition, cache_path):
    """Asmk evaluation step 'aggregate_database'"""
    if cache_path.exists():
        logger.debug("Step results already exist")
        return
    codebook = asmk.codebook
    kernel = kern_pkg.ASMKKernel(codebook, **asmk.params['build_ivf']['kernel'])
    start, end = int(len(images)*partition['norm_start']), int(len(images)*partition['norm_end'])
    data_opts = {"imsize": inference['image_size'], "transform": globals['transform']}
    infer_opts = {"scales": inference['scales'], "features_num": inference['features_num']}
    # Aggregate database
    dset = ImagesFromList(root='', images=images[start:end], bbxs=None, **data_opts)
    vecs, imids, *_ = how_net.extract_vectors_local(net, dset, globals["device"], **infer_opts)
    imids += start
    quantized = codebook.quantize(vecs, imids, **asmk.params["build_ivf"]["quantize"])
    aggregated = kernel.aggregate(*quantized, **asmk.params["build_ivf"]["aggregate"])
    with cache_path.open("wb") as handle:
        pickle.dump(dict(zip(["des", "word_ids", "image_ids"], aggregated)), handle)

def asmk_build_ivf(globals, logger, *, asmk, cache_path, database_name, distractors, distractors_path):
    """Asmk evaluation step 'build_ivf'"""
    if cache_path.exists():
        logger.debug("Step results already exist")
        return asmk.build_ivf(None, None, cache_path=cache_path)
    builder = asmk.create_ivf_builder(cache_path=cache_path)
    # Build ivf
    if not builder.loaded_from_cache:
        if distractors:
            builder.initialize_with_distractors(distractors_path)
            logger.debug(f"Loaded ivf with distractors '{distractors}'")
        for path in sorted(globals["exp_path"].glob(f"eval/{database_name}.agg*.pkl")):
            with path.open("rb") as handle:
                des = pickle.load(handle)
            builder.ivf.add(des['des'], des['word_ids'], des['image_ids'])
            logger.info(f"Indexed '{path.name}'")
    asmk_dataset = asmk.add_ivf_builder(builder)
    logger.debug(f"IVF stats: {asmk_dataset.metadata['build_ivf']['ivf_stats']}")
    return asmk_dataset

def asmk_index_database(net, inference, globals, logger, *, asmk, images, distractors_path=None):
    """Asmk evaluation step 'aggregate_database' and 'build_ivf'"""
    data_opts = {"imsize": inference['image_size'], "transform": globals['transform']}
    infer_opts = {"scales": inference['scales'], "features_num": inference['features_num']}
    # Index database vectors
    dset = ImagesFromList(root='', images=images, bbxs=None, **data_opts)
    vecs, imids, *_ = how_net.extract_vectors_local(net, dset, globals["device"], **infer_opts)
    asmk_dataset = asmk.build_ivf(vecs, imids, distractors_path=distractors_path)
    logger.info(f"Indexed images in {asmk_dataset.metadata['build_ivf']['index_time']:.2f}s")
    logger.debug(f"IVF stats: {asmk_dataset.metadata['build_ivf']['ivf_stats']}")
    return asmk_dataset

def asmk_query_ivf(net, inference, globals, logger, *, dataset, asmk_dataset, qimages, bbxs, gnd,
                   results, cache_path, imid_offset=0):
    """Asmk evaluation step 'query_ivf'"""
    if gnd is None and cache_path and cache_path.exists():
        logger.debug("Step results already exist")
        return
    data_opts = {"imsize": inference['image_size'], "transform": globals['transform']}
    infer_opts = {"scales": inference['scales'], "features_num": inference['features_num']}
    # Query vectors
    qdset = ImagesFromList(root='', images=qimages, bbxs=bbxs, **data_opts)
    qvecs, qimids, *_ = how_net.extract_vectors_local(net, qdset, globals["device"], **infer_opts)
    qimids += imid_offset
    metadata, query_ids, ranks, scores = asmk_dataset.query_ivf(qvecs, qimids)
    logger.debug(f"Average query time (quant+aggr+search) is {metadata['query_avg_time']:.3f}s")
    # Evaluate
    if gnd is not None:
        results[dataset] = score_helpers.compute_map_and_log(dataset, ranks.T, gnd, logger=logger)
    with cache_path.open("wb") as handle:
        pickle.dump({"metadata": metadata, "query_ids": query_ids, "ranks": ranks, "scores": scores}, handle)

#
# Helpers
#

def _convert_checkpoint(state):
    """Enable loading checkpoints in the old format"""
    if "_version" not in state:
        # Old checkpoint format
        meta = state['meta']
        state['net_params'] = {
            "architecture": meta['architecture'],
            "pretrained": True,
            "skip_layer": meta['skip_layer'],
            "dim_reduction": {"dim": meta["dim"]},
            "smoothing": {"kernel_size": meta["feat_pool_k"]},
            "runtime": {
                "mean_std": [meta['mean'], meta['std']],
                "image_size": 1024,
                "features_num": 1000,
                "scales": [2.0, 1.414, 1.0, 0.707, 0.5, 0.353, 0.25],
                "training_scales": [1],
            },
        }

        state_dict = state['state_dict']
        state_dict['dim_reduction.weight'] = state_dict.pop("whiten.weight")
        state_dict['dim_reduction.bias'] = state_dict.pop("whiten.bias")

        state['_version'] = "how/2020"

    return state