"""Image Prediction task"""
import copy
import logging
import os
import pickle
import warnings

import numpy as np
import pandas as pd
from .._gluoncv import ImageClassification
try:
    import timm
except ImportError:
    timm = None

from autogluon.common.utils.log_utils import set_logger_verbosity, verbosity2loglevel
from autogluon.core.constants import MULTICLASS, BINARY, REGRESSION
from autogluon.core.data.label_cleaner import LabelCleaner
from autogluon.core.utils import get_gpu_count_all
from autogluon.core.utils.try_import import try_import_d8
from autogluon.core.utils.utils import generate_train_test_split

from ..configs.presets_configs import unpack, _check_gpu_memory_presets
from ..utils import sanitize_batch_size

__all__ = ['ImagePredictor']


logger = logging.getLogger(__name__)  # return autogluon root logger


class ImagePredictor(object):
    """AutoGluon Predictor for predicting image category based on their whole contents

    Parameters
    ----------
    label : str, default = 'label'
        Name of the column that contains the target variable to predict.
    problem_type : str, default = None
        Type of prediction problem. Options: ('multiclass'). If problem_type = None, the prediction problem type is inferred
         based on the provided dataset. Currently only multiclass(or single class vs. background) classification is supported.
    eval_metric : str, default = None
        Metric by which to evaluate the data with. Options: ('accuracy').
        Currently only supports accuracy for multiclass classification.
    path : str, default = None
        The directory for saving logs or intermediate data. If unspecified, will create a sub-directory under
        current working directory.
    verbosity : int, default = 2
        Verbosity levels range from 0 to 4 and control how much information is printed.
        Higher levels correspond to more detailed print statements (you can set verbosity = 0 to suppress warnings).
        If using logging, you can alternatively control amount of information printed via logger.setLevel(L),
        where L ranges from 0 to 50 (Note: higher values of L correspond to fewer print statements, opposite of verbosity levels)
    """
    # Dataset is a subclass of `pd.DataFrame`, with `image` and `label` columns.
    Dataset = ImageClassification.Dataset

    def __init__(self, label='label', problem_type=None, eval_metric=None, path=None, verbosity=2):
        self._problem_type = problem_type
        self._eval_metric = eval_metric
        if path is None:
            path = os.getcwd()
        self._log_dir = path
        self._verbosity = verbosity
        self._classifier = None
        self._label_cleaner = None
        self._fit_summary = {}
        self._label = label
        self._label_inner = 'label'
        self._train_classes = None
        assert isinstance(self._label, str)
        os.makedirs(self._log_dir, exist_ok=True)

    @property
    def path(self):
        return self._log_dir

    @unpack('image_predictor')
    def fit(self,
            train_data,
            tuning_data=None,
            time_limit='auto',
            presets=None,
            hyperparameters=None,
            **kwargs):
        """Automatic fit process for image prediction.

        Parameters
        ----------
        train_data : pd.DataFrame
            Training data, can be a dataframe like image dataset.
            For dataframe like datasets, `image` and `label` columns are required.
            `image`: raw image paths. `label`: categorical integer id, starting from 0.
        tuning_data : pd.DataFrame, default = None
            Another dataset containing validation data reserved for model selection and hyperparameter-tuning,
            can be a dataframe like image dataset.
            If `None`, the validation dataset will be randomly split from `train_data` according to `holdout_frac`.
        time_limit : int, default = 'auto' (defaults to 2 hours if no presets detected)
            Time limit in seconds, if `None`, will run until all tuning and training finished.
            If `time_limit` is hit during `fit`, the HPO process will interrupt and return the current best configuration.
        presets : list or str or dict, default = ['medium_quality_faster_train']
            List of preset configurations for various arguments in `fit()`. Can significantly impact predictive accuracy, memory-footprint, and inference latency of trained models,
            and various other properties of the returned `predictor`.
            It is recommended to specify presets and avoid specifying most other `fit()` arguments or model hyperparameters prior to becoming familiar with AutoGluon.
            As an example, to get the most accurate overall predictor (regardless of its efficiency), set `presets='best_quality'`.
            To get good quality with faster inference speed, set `presets='good_quality_faster_inference'`
            Any user-specified arguments in `fit()` will override the values used by presets.
            If specifying a list of presets, later presets will override earlier presets if they alter the same argument.
            For precise definitions of the provided presets, see file: `autogluon/vision/configs/presets_configs.py`.
            Users can specify custom presets by passing in a dictionary of argument values as an element to the list.
            Available Presets: ['best_quality', 'high_quality_fast_inference', 'good_quality_faster_inference', 'medium_quality_faster_train']
            It is recommended to only use one `quality` based preset in a given call to `fit()` as they alter many of the same arguments and are not compatible with each-other.

            Note that depending on your specific hardware limitation(# gpu, size of gpu memory...) your mileage may vary a lot, you may choose lower quality presets if necessary, and
            try to reduce `batch_size` if OOM("RuntimeError: CUDA error: out of memory") happens frequently during the `fit`.

            In-depth Preset Info:
                # Best predictive accuracy with little consideration to inference time or model size. Achieve even better results by specifying a large time_limit value.
                # Recommended for applications that benefit from the best possible model accuracy.
                best_quality={
                    'hyperparameters': {
                        'model': Categorical('coat_lite_small', 'twins_pcpvt_base', 'swin_base_patch4_window7_224'),
                        'lr': Real(1e-5, 1e-2, log=True),
                        'batch_size': Categorical(8, 16, 32, 64, 128),
                        'epochs': 200,
                        'early_stop_patience': 50
                        },
                    'hyperparameter_tune_kwargs': {
                        'num_trials': 1024,
                        'searcher': 'random',
                    },
                    'time_limit': 12*3600,
                },

                # Good predictive accuracy with fast inference.
                # Recommended for applications that require reasonable inference speed and/or model size.
                good_quality_fast_inference={
                    'hyperparameters': {
                        'model': Categorical('resnet50d', 'efficientnet_b1', 'mobilenetv3_large_100'),
                        'lr': Real(1e-4, 1e-2, log=True),
                        'batch_size': Categorical(8, 16, 32, 64, 128),
                        'epochs': 150,
                        'early_stop_patience': 20
                        },
                    'hyperparameter_tune_kwargs': {
                        'num_trials': 512,
                        'searcher': 'random',
                    },
                    'time_limit': 8*3600,
                },

                # Medium predictive accuracy with very fast inference and very fast training time.
                medium_quality_faster_train={
                    'hyperparameters': {
                        'model': 'resnet50d',
                        'lr': 0.01,
                        'batch_size': 64,
                        'epochs': 50,
                        'early_stop_patience': 5
                        },
                    'time_limit': 1*3600,
                },

                # Medium predictive accuracy with very fast inference.
                # Comparing with `medium_quality_faster_train` it uses faster model but explores more hyperparameters.
                medium_quality_faster_inference={
                    'hyperparameters': {
                        'model': Categorical('resnet18', 'mobilenetv3_small_100', 'resnet18_v1b'),
                        'lr': Categorical(0.01, 0.005, 0.001),
                        'batch_size': Categorical(64, 128),
                        'epochs': Categorical(50, 100),
                        'early_stop_patience': 10
                        },
                    'hyperparameter_tune_kwargs': {
                        'num_trials': 32,
                        'searcher': 'random',
                    },
                    'time_limit': 2*3600,
                },
        hyperparameters : dict, default = None
            Extra hyperparameters for specific models.
            Accepted args includes(not limited to):
            epochs : int, default value based on network
                The `epochs` for model training.
            net : mx.gluon.Block
                The custom network. If defined, the model name in config will be ignored so your
                custom network will be used for training rather than pulling it from model zoo.
            optimizer : mx.Optimizer
                The custom optimizer object. If defined, the optimizer will be ignored in config but this
                object will be used in training instead.
            batch_size : int
                Mini batch size
            lr : float
                Trainer learning rate for optimization process.
            early_stop_patience : int, default=10
                Number of epochs with no improvement after which train is early stopped. Use `None` to disable.
            early_stop_min_delta : float, default=1e-4
                The small delta value to ignore when evaluating the metric. A large delta helps stablize the early
                stopping strategy against tiny fluctuation, e.g. 0.5->0.49->0.48->0.499->0.500001 is still considered as
                a good timing for early stopping.
            early_stop_baseline : float, default=None
                The minimum(baseline) value to trigger early stopping. For example, with `early_stop_baseline=0.5`,
                early stopping won't be triggered if the metric is less than 0.5 even if plateau is detected.
                Use `None` to disable.
            early_stop_max_value : float, default=None
                The max value for metric, early stop training instantly once the max value is achieved. Use `None` to disable.
            You can get the list of accepted hyperparameters in `config.yaml` saved by this predictor.
        **kwargs :
            holdout_frac : float, default = 0.1
                The random split ratio for `tuning_data` if `tuning_data==None`.
            random_state : int, default = None
                The random_state(seed) for shuffling data, only used if `tuning_data==None`.
                Note that the `random_state` only affect the splitting process, not model training.
                If not specified(None), will leave the original random sampling intact.
            nthreads_per_trial : int, default = (# cpu cores)
                Number of CPU threads for each trial, if `None`, will detect the # cores on current instance.
            ngpus_per_trial : int, default = (# gpus)
                Number of GPUs to use for each trial, if `None`, will detect the # gpus on current instance.
            hyperparameter_tune_kwargs: dict, default = None
                num_trials : int, default = 1
                    The limit of HPO trials that can be performed within `time_limit`. The HPO process will be terminated
                    when `num_trials` trials have finished or wall clock `time_limit` is reached, whichever comes first.
                searcher : str, default = 'random'
                    Searcher strategy for HPO, 'random' by default.
                    Options include: ‘random’ (random search), ‘bayesopt’ (Gaussian process Bayesian optimization),
                    ‘grid’ (grid search).
                max_reward : float, default = None
                    The reward threashold for stopping criteria. If `max_reward` is reached during HPO, the scheduler
                    will terminate earlier to reduce time cost.
                scheduler_options : dict, default = None
                    Extra options for HPO scheduler, please refer to :class:`autogluon.core.Searcher` for details.
        """
        if self._problem_type is None:
            # options: multiclass, binary, regression
            self._problem_type = MULTICLASS
        assert self._problem_type in (MULTICLASS, BINARY, REGRESSION), f"Invalid problem_type: {self._problem_type}"
        if self._eval_metric is None:
            if self._problem_type == REGRESSION:
                # options: rmse
                self._eval_metric = 'rmse'
                logger.log(20, 'ImagePredictor sets rmse as default eval_metric for regression problems.')
            else:
                # options: accuracy
                self._eval_metric = 'accuracy'
                logger.log(20, 'ImagePredictor sets accuracy as default eval_metric for classification problems.')
        # init/validate kwargs
        kwargs = self._validate_kwargs(kwargs)
        # unpack
        num_trials = kwargs['hyperparameter_tune_kwargs']['num_trials']
        nthreads_per_trial = kwargs['nthreads_per_trial']
        ngpus_per_trial = kwargs['ngpus_per_trial']
        holdout_frac = kwargs['holdout_frac']
        random_state = kwargs['random_state']
        scheduler = kwargs['hyperparameter_tune_kwargs']['scheduler']
        searcher = kwargs['hyperparameter_tune_kwargs']['searcher']
        max_reward = kwargs['hyperparameter_tune_kwargs']['max_reward']
        scheduler_options = kwargs['hyperparameter_tune_kwargs']['scheduler_options']
        # deep copy to avoid inplace overwrite
        train_data = copy.deepcopy(train_data)
        tuning_data = copy.deepcopy(tuning_data)

        log_level = verbosity2loglevel(self._verbosity)
        set_logger_verbosity(self._verbosity)
        if presets:
            if not isinstance(presets, list):
                presets = [presets]
            logger.log(20, f'Presets specified: {presets}')

        if time_limit == 'auto':
            # no presets, no user specified time_limit
            time_limit = 7200
            logger.log(20, f'`time_limit=auto` set to `time_limit={time_limit}`.')

        use_rec = False
        if isinstance(train_data, str) and train_data == 'imagenet':
            # FIXME: imagenet does not work, crashes in validating data due to empty DataFrames.
            logger.warning('ImageNet is a huge dataset which cannot be downloaded directly, ' +
                           'please follow the data preparation tutorial in GluonCV.' +
                           'The following record files(symlinks) will be used: \n' +
                           'rec_train : ~/.mxnet/datasets/imagenet/rec/train.rec\n' +
                           'rec_train_idx : ~/.mxnet/datasets/imagenet/rec/train.idx\n' +
                           'rec_val : ~/.mxnet/datasets/imagenet/rec/val.rec\n' +
                           'rec_val_idx : ~/.mxnet/datasets/imagenet/rec/val.idx\n')
            train_data = pd.DataFrame({'image': [], self._label_inner: []})
            tuning_data = pd.DataFrame({'image': [], self._label_inner: []})
            use_rec = True
        if isinstance(train_data, str):
            try_import_d8()
            from d8.image_classification import Dataset as D8D
            names = D8D.list()
            if train_data.lower() in names:
                train_data = D8D.get(train_data)
            else:
                valid_names = '\n'.join(names)
                raise ValueError(f'`train_data` {train_data} is not among valid list {valid_names}')
            if tuning_data is None:
                train_data, tuning_data = train_data.split(1 - holdout_frac)
        if isinstance(tuning_data, str):
            try_import_d8()
            from d8.image_classification import Dataset as D8D
            names = D8D.list()
            if tuning_data.lower() in names:
                tuning_data = D8D.get(tuning_data)
            else:
                valid_names = '\n'.join(names)
                raise ValueError(f'`tuning_data` {tuning_data} is not among valid list {valid_names}')

        # data sanity check
        train_data = self._validate_data(train_data)
        train_labels = _get_valid_labels(train_data)
        self._label_cleaner = LabelCleaner.construct(problem_type=self._problem_type, y=train_labels, y_uncleaned=train_labels)
        train_labels_cleaned = self._label_cleaner.transform(train_labels)
        if train_labels_cleaned.dtype.kind in ('i', 'u'):
            train_labels_cleaned = train_labels_cleaned.astype('int64')
        # converting to internal label set
        _set_valid_labels(train_data, train_labels_cleaned)
        tuning_data_validated = False
        if tuning_data is None:
            train_data, tuning_data, _, _ = generate_train_test_split(X=train_data, y=train_data[self._label_inner], problem_type=self._problem_type, test_size=holdout_frac)
            logger.info('Randomly split train_data into train[%d]/validation[%d] splits.',
                              len(train_data), len(tuning_data))
            train_data = train_data.reset_index(drop=True)
            tuning_data = tuning_data.reset_index(drop=True)
            tuning_data_validated = True

        train_data = self._validate_data(train_data)
        if isinstance(train_data, self.Dataset):
            train_data = self.Dataset(train_data, classes=train_data.classes)
        if tuning_data is not None and not tuning_data_validated:
            tuning_data = self._validate_data(tuning_data)
            # converting to internal label set
            tuning_labels_cleaned = self._label_cleaner.transform(_get_valid_labels(tuning_data))
            if tuning_labels_cleaned.dtype.kind in ('i', 'u'):
                tuning_labels_cleaned = tuning_labels_cleaned.astype('int64')
            _set_valid_labels(tuning_data, tuning_labels_cleaned)
            if isinstance(tuning_data, self.Dataset):
                tuning_data = self.Dataset(tuning_data, classes=tuning_data.classes)

        if self._classifier is not None:
            logging.getLogger("ImageClassificationEstimator").propagate = True
            self._classifier._logger.setLevel(log_level)
            self._fit_summary = self._classifier.fit(train_data, tuning_data, 1 - holdout_frac, random_state, resume=False)
            if hasattr(self._classifier, 'fit_history'):
                self._fit_summary['fit_history'] = self._classifier.fit_history()
            return self

        # new HPO task
        if time_limit is not None and num_trials is None:
            num_trials = 99999
        if time_limit is None and num_trials is None:
            raise ValueError('`time_limit` and `num_trials` can not be `None` at the same time, '
                             'otherwise the training will not be terminated gracefully.')
        config = {'log_dir': self._log_dir,
                  'num_trials': 99999 if num_trials is None else max(1, num_trials),
                  'time_limits': 2147483647 if time_limit is None else max(1, time_limit),
                  'searcher': searcher,
                  # needed for gluon-cv TODO: remove after gluon-cv is updated https://github.com/dmlc/gluon-cv/issues/1633
                  'search_strategy': searcher,
                  'scheduler': scheduler,
                  }
        if max_reward is not None:
            config['max_reward'] = max_reward
        if nthreads_per_trial is not None:
            config['nthreads_per_trial'] = nthreads_per_trial
        if ngpus_per_trial is not None:
            config['ngpus_per_trial'] = ngpus_per_trial
        if isinstance(hyperparameters, dict):
            if 'batch_size' in hyperparameters:
                bs = hyperparameters['batch_size']
                _check_gpu_memory_presets(bs, ngpus_per_trial, 4, 256)  # 256MB per sample
            net = hyperparameters.pop('net', None)
            if net is not None:
                config['custom_net'] = net
            optimizer = hyperparameters.pop('optimizer', None)
            if optimizer is not None:
                config['custom_optimizer'] = optimizer
            # check if hyperparameters overwriting existing config
            for k, v in hyperparameters.items():
                if k in config:
                    raise ValueError(f'Overwriting {k} = {config[k]} to {v} by hyperparameters is ambiguous.')
            config.update(hyperparameters)
        if scheduler_options is not None:
            config.update(scheduler_options)
        if use_rec == True:
            config['use_rec'] = True
        if 'early_stop_patience' not in config:
            config['early_stop_patience'] = 10
        if config['early_stop_patience'] == None:
            config['early_stop_patience'] = -1
        # TODO(zhreshold): expose the transform function(or sign function) for converting custom metrics
        if 'early_stop_baseline' not in config or config['early_stop_baseline'] == None:
            config['early_stop_baseline'] = -np.Inf
        if 'early_stop_max_value' not in config or config['early_stop_max_value'] == None:
            config['early_stop_max_value'] = np.Inf
        # batch size cannot be larger than dataset size
        if ngpus_per_trial is not None and ngpus_per_trial > 1:
            min_value = ngpus_per_trial
        else:
            min_value = 1
        bs = sanitize_batch_size(config.get('batch_size', 16), min_value=min_value, max_value=len(train_data))
        config['batch_size'] = bs
        # TODO: remove this once mxnet is deprecated
        if timm is None and config.get('model', None) is None:
            config['model'] = 'resnet50_v1b'
        # verbosity
        if log_level > logging.INFO:
            logging.getLogger("ImageClassificationEstimator").propagate = False
            logging.getLogger("ImageClassificationEstimator").setLevel(log_level)

        task = ImageClassification(config=config, problem_type=self._problem_type)
        # GluonCV can't handle these separately - patching created config
        task.search_strategy = scheduler
        task.scheduler_options['searcher'] = searcher
        task._logger.setLevel(log_level)
        task._logger.propagate = True
        self._train_classes = train_data.classes
        with warnings.catch_warnings(record=True) as w:
            # TODO: MXNetErrorCatcher was removed because it didn't return traceback
            #  Re-add once it returns full traceback regardless of which exception was caught
            self._classifier = task.fit(train_data, tuning_data, 1 - holdout_frac, random_state)
        self._classifier._logger.setLevel(log_level)
        self._classifier._logger.propagate = True
        self._fit_summary = task.fit_summary()
        if hasattr(task, 'fit_history'):
            self._fit_summary['fit_history'] = task.fit_history()
        return self

    def _validate_data(self, data):
        """Check whether data is valid, try to convert with best effort if not"""
        if isinstance(data, pd.DataFrame):
            # TODO(zhreshold): allow custom label column without this renaming trick
            if self._label != self._label_inner and self._label in data.columns:
                # data is deepcopied so it's okay to overwrite directly
                data = data.rename(columns={self._label_inner: '_unused_label', self._label: self._label_inner}, errors='ignore')
        if not (hasattr(data, 'classes') and hasattr(data, 'to_mxnet')):
            if isinstance(data, pd.DataFrame):
                # raw dataframe, try to add metadata automatically
                if self._label_inner in data.columns and 'image' in data.columns:
                    # check image relative/abs path is valid
                    sample = data.iloc[0]['image']
                    if not os.path.isfile(sample):
                        raise OSError(f'Detected invalid image path `{sample}`, please ensure all image paths are absolute or you are using the right working directory.')
                    logger.log(20, 'Converting raw DataFrame to ImageDataset...')
                    if self._problem_type in [MULTICLASS, BINARY]:
                        infer_classes = sorted(data.label.unique().tolist())
                        logger.log(20, f'Detected {len(infer_classes)} unique classes: {infer_classes}')
                    elif self._problem_type == REGRESSION:
                        infer_classes = []
                        logger.log(20, 'Set classes = [] for regression problems')
                    instruction = 'train_data = ImageDataset(train_data, classes=["foo", "bar"])'
                    logger.log(20, f'If you feel the `classes` is inaccurate, please construct the dataset explicitly, e.g. {instruction}')
                    data = ImageClassification.Dataset(data, classes=infer_classes)
                else:
                    err_msg = 'Unable to convert raw DataFrame to ImagePredictor Dataset, ' + \
                              '`image` and `label` columns are required.' + \
                              'You may visit `https://auto.gluon.ai/stable/tutorials/image_prediction/dataset.html` ' + \
                              'for details.'
                    raise AttributeError(err_msg)
            else:
                raise TypeError(f"Unable to process dataset of type: {type(data)}")
        elif isinstance(data, ImageClassification.Dataset):
            assert self._label_inner in data.columns
            assert hasattr(data, 'classes')
            if self._problem_type in [MULTICLASS, BINARY]:
                orig_classes = data.classes
                if not isinstance(data.classes, (tuple, list)):
                    # consider it as an invalid dataset without proper label, try to reconstruct as a normal DataFrame
                    orig_classes = []
                # check whether classes are outdated, no action required if all unique labels is subset of `classes`
                unique_labels = sorted(data[self._label_inner].unique().tolist())
                if not (all(ulabel in orig_classes for ulabel in unique_labels)):
                    data = ImageClassification.Dataset(data, classes=unique_labels)
                    logger.log(20, f'Reset labels to {unique_labels}')
            elif self._problem_type == REGRESSION:
                data = ImageClassification.Dataset(data, classes=[])
                logger.log(20, 'Set classes = [] for regression problems')
        if len(data) < 1:
            raise ValueError('Empty dataset.')
        return data

    def _validate_kwargs(self, kwargs):
        """validate and initialize default kwargs"""

        valid_kwargs = {'holdout_frac', 'random_state', 'nthreads_per_trial', 'ngpus_per_trial', 'hyperparameter_tune_kwargs'}
        invalid_kwargs = []
        for key in kwargs:
            if key not in valid_kwargs:
                invalid_kwargs.append(key)
        if invalid_kwargs:
            raise KeyError(f'Invalid kwargs specified: {invalid_kwargs}. Valid kwargs names: {list(valid_kwargs)}')

        kwargs['holdout_frac'] = kwargs.get('holdout_frac', 0.1)
        if not (0 < kwargs['holdout_frac'] < 1.0):
            raise ValueError(f'Range error for `holdout_frac`, expected to be within range (0, 1), given {kwargs["holdout_frac"]}')
        kwargs['random_state'] = kwargs.get('random_state', None)
        kwargs['nthreads_per_trial'] = kwargs.get('nthreads_per_trial', None)
        kwargs['ngpus_per_trial'] = kwargs.get('ngpus_per_trial', None)
        if kwargs['ngpus_per_trial'] is not None and kwargs['ngpus_per_trial'] > 0:
            detected_gpu = self._get_num_gpus_available()
            if detected_gpu < kwargs['ngpus_per_trial']:
                raise ValueError(f"Insufficient detected # gpus {detected_gpu} vs requested {kwargs['ngpus_per_trial']}")
        # tune kwargs
        hpo_tune_args = kwargs.get('hyperparameter_tune_kwargs', {})
        hpo_tune_args['num_trials'] = hpo_tune_args.get('num_trials', 1)
        hpo_tune_args['searcher'] = hpo_tune_args.get('searcher', 'random')
        if not hpo_tune_args['searcher'] in ('random', 'bayesopt', 'grid'):
            raise ValueError(f"Invalid searcher: {hpo_tune_args['searcher']}, supported: ('random', 'bayesopt', 'grid')")
        hpo_tune_args['scheduler'] = hpo_tune_args.get('scheduler', 'local')
        if not hpo_tune_args['scheduler'] in ('fifo', 'local'):
            raise ValueError(f"Invalid searcher: {hpo_tune_args['searcher']}, supported: ('fifo', 'local')")
        hpo_tune_args['max_reward'] = hpo_tune_args.get('max_reward', None)
        if hpo_tune_args['max_reward'] is not None and hpo_tune_args['max_reward'] < 0:
            raise ValueError(f"Expected `max_reward` to be a positive float number between 0 and 1.0, given {hpo_tune_args['max_reward']}")
        hpo_tune_args['scheduler_options'] = hpo_tune_args.get('scheduler_options', None)
        kwargs['hyperparameter_tune_kwargs'] = hpo_tune_args
        return kwargs

    def predict_proba(self, data, as_pandas=True):
        """Predict images as a whole, return the probabilities of each category rather
        than class-labels.

        Parameters
        ----------
        data : str, pd.DataFrame or ndarray
            The input, can be str(filepath), pd.DataFrame with 'image' column, or raw ndarray input.
        as_pandas : bool, default = True
            Whether to return the output as a pandas object (True) or list of numpy array(s) (False).
            Pandas object is a DataFrame.

        Returns
        -------

        pd.DataFrame
            The returned dataframe will contain probs of each category. If more than one image in input,
            the returned dataframe will contain `images` column, and all results are concatenated.
        """
        if self._classifier is None:
            raise RuntimeError('Classifier is not initialized, try `fit` first.')
        assert self._label_cleaner is not None
        try:
            y_pred_proba = self._classifier.predict(data, with_proba=True)
        except AssertionError:
            y_pred_proba = self._classifier.predict(data)
        if isinstance(data, pd.DataFrame):
            y_pred_proba.index = data.index
        if self._problem_type in [MULTICLASS, BINARY]:
            y_pred_proba[list(self._label_cleaner.cat_mappings_dependent_var.values())] = y_pred_proba['image_proba'].to_list()
            ret = y_pred_proba.drop(['image', 'image_proba'], axis=1, errors='ignore')
        elif self._problem_type == REGRESSION:
            ret = y_pred_proba['prediction']
        if as_pandas:
            return ret
        else:
            return ret.to_numpy()

    def predict(self, data, as_pandas=True):
        """Predict images as a whole, return labels(class category).

        Parameters
        ----------
        data : str, pd.DataFrame or ndarray
            The input, can be str(filepath), pd.DataFrame with 'image' column, or raw ndarray input.
        as_pandas : bool, default = True
            Whether to return the output as a pandas object (True) or list of numpy array(s) (False).
            Pandas object is a DataFrame.

        Returns
        -------

        pd.DataFrame
            The returned dataframe will contain labels. If more than one image in input,
            the returned dataframe will contain `images` column, and all results are concatenated.
        """
        if self._problem_type in [REGRESSION]:
            return self.predict_proba(data, as_pandas)

        if self._classifier is None:
            raise RuntimeError('Classifier is not initialized, try `fit` first.')
        assert self._label_cleaner is not None
        proba = self._classifier.predict(data)
        if 'image' in proba.columns:
            # multiple images
            assert isinstance(data, pd.DataFrame) and 'image' in data.columns
            index_name = data.index.name
            if index_name is None:
                # TODO: This crashes if a feature is already named 'index'.
                index_name = 'index'
            y_pred = proba.loc[proba.groupby(["image"])["score"].idxmax()].reset_index(drop=True)
            idx_to_image_map = data[['image']]
            idx_to_image_map = idx_to_image_map.reset_index(drop=False)
            y_pred = idx_to_image_map.merge(y_pred, on='image')
            y_pred = y_pred.set_index(index_name).rename_axis(None)
            ret = self._label_cleaner.inverse_transform(y_pred['id'].rename(self._label))
        else:
            # single image
            ret = proba.loc[[proba["score"].idxmax()]]
            ret = self._label_cleaner.inverse_transform(ret['id'].rename(self._label))
        if as_pandas:
            return ret
        else:
            return ret.to_numpy()

    def predict_feature(self, data, as_pandas=True):
        """Predict images visual feature representations, return the features as numpy (1xD) vector.

        Parameters
        ----------
        data : str, pd.DataFrame or ndarray
            The input, can be str(filepath), pd.DataFrame with 'image' column, or raw ndarray input.
        as_pandas : bool, default = True
            Whether to return the output as a pandas object (True) or list of numpy array(s) (False).
            Pandas object is a DataFrame.

        Returns
        -------

        pd.DataFrame
            The returned dataframe will contain image features. If more than one image in input,
            the returned dataframe will contain `images` column, and all results are concatenated.
        """
        if self._classifier is None:
            raise RuntimeError('Classifier is not initialized, try `fit` first.')
        ret = self._classifier.predict_feature(data)
        if as_pandas:
            return ret
        else:
            return ret.to_numpy()

    def evaluate(self, data):
        """Evaluate model performance on validation data.

        Parameters
        ----------
        data : pd.DataFrame or iterator
            The validation data.
        """
        if self._classifier is None:
            raise RuntimeError('Classifier not initialized, try `fit` first.')
        assert self._train_classes is not None
        if isinstance(data, pd.DataFrame) and not isinstance(data, ImageClassification.Dataset):
            assert self._label in data.columns, f'{self._label} is not present in evaluation data'
            # note that evaluation data must use the same classes as training data, otherwise incorrect result
            if self._problem_type in [MULTICLASS, BINARY]:
                data = ImageClassification.Dataset(data, classes=self._train_classes)
            else:
                data = ImageClassification.Dataset(data, classes=[])
        ret = self._classifier.evaluate(data, metric_name=self._eval_metric)
        # TODO: remove the switch if mxnet is deprecated
        if isinstance(ret, dict):
            return ret
        elif isinstance(ret, tuple):
            assert len(ret) == 2
            return {'top1': ret[0], 'top5': ret[1]}
        else:
            return {self._eval_metric: ret}

    def fit_summary(self):
        """Return summary of last `fit` process.

        Returns
        -------
        dict
            The summary of last `fit` process. Major keys are ('train_acc', 'val_acc', 'total_time',...)

        """
        return copy.copy(self._fit_summary)

    def save(self, path=None):
        """Dump predictor to disk.

        Parameters
        ----------
        path : str, default = None
            The path of saved copy. If not specified(None), will automatically save to `self.path` directory
            with filename `image_predictor.ag`

        """
        if path is None:
            path = os.path.join(self.path, 'image_predictor.ag')
        with open(path, 'wb') as fid:
            pickle.dump(self, fid)

    @classmethod
    def load(cls, path, verbosity=2):
        """Load previously saved predictor.

        Parameters
        ----------
        path : str
            The file name for saved pickle file. If `path` is a directory, will try to load the file `image_predictor.ag` in
            this directory.
        verbosity : int, default = 2
            Verbosity levels range from 0 to 4 and control how much information is printed.
            Higher levels correspond to more detailed print statements (you can set verbosity = 0 to suppress warnings).
            If using logging, you can alternatively control amount of information printed via logger.setLevel(L),
            where L ranges from 0 to 50 (Note: higher values of L correspond to fewer print statements, opposite of verbosity levels)

        """
        if os.path.isdir(path):
            path = os.path.join(path, 'image_predictor.ag')
        with open(path, 'rb') as fid:
            obj = pickle.load(fid)
        obj._verbosity = verbosity
        return obj

    @classmethod
    def list_models(cls):
        """Get the list of supported model names in model zoo that
        can be used for image classification.

        Returns
        -------
        tuple of str
            A tuple of supported model names in str.

        """
        return tuple(_SUPPORTED_MODELS)

    @staticmethod
    def _get_num_gpus_available():
        return get_gpu_count_all()


def _get_valid_labels(data):
    ret = None
    if isinstance(data, pd.DataFrame):
        ret = data['label']
    else:
        try_import_d8()
        from d8.image_classification import Dataset as D8D
        if isinstance(data, D8D):
            ret = data.df['class_name']
    if ret is None:
        raise ValueError('Dataset must be pandas.DataFrame or d8.image_classification.Dataset')
    return ret


def _set_valid_labels(data, label):
    if isinstance(data, pd.DataFrame):
        data['label'] = label
    else:
        try_import_d8()
        from d8.image_classification import Dataset as D8D
        if isinstance(data, D8D):
            data.df['class_name'] = label
        else:
            raise ValueError('Dataset must be pandas.DataFrame or d8.image_classification.Dataset')


def _get_supported_models():
    try:
        import mxnet as _mxnet
    except ImportError:
        _mxnet = None
    if _mxnet is not None:
        from gluoncv.model_zoo import get_model_list
        all_models = get_model_list()
        blacklist = ['ssd', 'faster_rcnn', 'mask_rcnn', 'fcn', 'deeplab',
                    'psp', 'icnet', 'fastscnn', 'danet', 'yolo', 'pose',
                    'center_net', 'siamrpn', 'monodepth',
                    'ucf101', 'kinetics', 'voc', 'coco', 'citys', 'mhpv1',
                    'ade', 'hmdb51', 'sthsth', 'otb']
        cls_models = [m for m in all_models if not any(x in m for x in blacklist)]
    else:
        cls_models = []
    # add timm backend supported models
    try:
        import torch as _torch
    except ImportError:
        _torch = None
    try:
        import timm as _timm
    except ImportError:
        _timm = None
    if _timm is not None:
        cls_models += list(_timm.list_models())
    elif _torch is None:
        logger.warning('timm installed but torch is required to enable it.')
    else:
        logger.warning('cannot import timm, possibly due to missing torchvision')
    return cls_models

_SUPPORTED_MODELS = _get_supported_models()
