"""Object Detection task"""
import copy
import pickle
import platform
import logging
import warnings
import os

import pandas as pd
import numpy as np
from autogluon.common.utils.log_utils import set_logger_verbosity, verbosity2loglevel
from autogluon.core.utils import ResourceManager
from .._gluoncv import ObjectDetection
from ..configs.presets_configs import unpack, _check_gpu_memory_presets

__all__ = ['ObjectDetector']

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


class ObjectDetector(object):
    """AutoGluon Predictor for detecting objects in images

    Parameters
    ----------
    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 `bbox` columns.
    Dataset = ObjectDetection.Dataset

    def __init__(self, path=None, verbosity=2):
        warnings.warn(
            f"AutoGluon ObjectDetector will be deprecated in v0.7. "
            f"Please use AutoGluon MultiModalPredictor instead for more functionalities and better support. "
            f"Visit https://auto.gluon.ai/stable/tutorials/multimodal/index.html for more details!",
            DeprecationWarning,
        )
        if path is None:
            path = os.getcwd()
        self._log_dir = path
        self._verbosity = verbosity
        self._detector = None
        self._fit_summary = {}
        os.makedirs(self._log_dir, exist_ok=True)

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

    @unpack('object_detector')
    def fit(self,
            train_data,
            tuning_data=None,
            time_limit='auto',
            presets=None,
            hyperparameters=None,
            **kwargs):
        """Automatic fit process for object detection.
        Tip: if you observe very slow training speed only happening at the first epoch and your overall time budget
        is not large, you may disable `CUDNN_AUTOTUNE` by setting the environment variable
        `export MXNET_CUDNN_AUTOTUNE_DEFAULT=0` before running your python script or
        insert `import os; os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0'` before any code block.
        The tuning is beneficial in terms of training speed in the long run, but may cost your noticeable overhead at
        the beginning of each trial.

        Parameters
        ----------
        train_data : pd.DataFrame or str
            Training data, can be a dataframe like image dataset.
            For more details of how to construct a object detection dataset, please checkout:
            `http://preview.d2l.ai/d8/main/object_detection/getting_started.html`.
            If a string is provided, will search for d8 datasets.
        tuning_data : pd.DataFrame or str, default = None
            Holdout tuning data for validation, reserved for model selection and hyperparameter-tuning,
            can be a dataframe like image dataset.
            If a string is provided, will search for k8 datasets.
            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': {
                        'transfer': 'faster_rcnn_fpn_resnet101_v1d_coco',
                        'lr': Real(1e-5, 1e-3, log=True),
                        'batch_size': Categorical(4, 8),
                        'epochs': 30,
                        'early_stop_patience': 50
                        },
                    'hyperparameter_tune_kwargs': {
                        'num_trials': 128,
                        'searcher': 'random',
                    },
                    'time_limit': 24*3600,
                },

                # Good predictive accuracy with fast inference.
                # Recommended for applications that require reasonable inference speed and/or model size.
                good_quality_fast_inference={
                    'hyperparameters': {
                        'transfer': Categorical('ssd_512_resnet50_v1_coco',
                                                'yolo3_darknet53_coco',
                                                'center_net_resnet50_v1b_coco'),
                        'lr': Real(1e-4, 1e-2, log=True),
                        'batch_size': Categorical(8, 16, 32, 64),
                        'epochs': 50,
                        'early_stop_patience': 20
                        },
                    'hyperparameter_tune_kwargs': {
                        'num_trials': 512,
                        'searcher': 'random',
                    },
                    'time_limit': 12*3600,
                },

                # Medium predictive accuracy with very fast inference and very fast training time.
                # This is the default preset in AutoGluon, but should generally only be used for quick prototyping.
                medium_quality_faster_train={
                    'hyperparameters': {
                        'transfer': 'ssd_512_resnet50_v1_coco',
                        'lr': 0.01,
                        'batch_size': Categorical(8, 16),
                        'epochs': 30,
                        'early_stop_patience': 5
                        },
                    'hyperparameter_tune_kwargs': {
                        'num_trials': 16,
                        'searcher': 'random',
                    },
                    'time_limit': 2*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': {
                        'transfer': Categorical('center_net_resnet18_v1b_coco', 'yolo3_mobilenet1.0_coco'),
                        'lr': Categorical(0.01, 0.005, 0.001),
                        'batch_size': Categorical(32, 64, 128),
                        'epochs': Categorical(30, 50),
                        'early_stop_patience': 10
                        },
                    'hyperparameter_tune_kwargs': {
                        'num_trials': 32,
                        'searcher': 'random',
                    },
                    'time_limit': 4*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.
            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 stabilize 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), ‘grid’ (grid search).
                max_reward : float, default = None
                    The reward threshold 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.
        """
        # 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']

        log_level = verbosity2loglevel(self._verbosity)
        set_logger_verbosity(self._verbosity)
        if platform.system() == 'Windows':
            logger.log(40, '=============================================================================\n'
                           'WARNING: Windows OS detected, but ObjectDetector is not supported on Windows!\n'
                           'You may run into many errors. Consider running on Linux instead.\n'
                           '=============================================================================\n')
        logger.log(30, '=============================================================================\n'
                       'WARNING: ObjectDetector is deprecated as of v0.4.0 and may contain various bugs and issues!\n'
                       'In a future release ObjectDetector may be entirely reworked to use Torch as a backend.\n'
                       'This future change will likely be API breaking.'
                       'Users should ensure they update their code that depends on ObjectDetector when upgrading to future AutoGluon releases.\n'
                       'For more information, refer to ObjectDetector refactor GitHub issue: https://github.com/autogluon/autogluon/issues/1559\n'
                       '=============================================================================\n')
        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}`.')

        # data sanity check
        train_data = self._validate_data(train_data)
        if tuning_data is not None:
            # FIXME: Use ImagePredictor's tuning_data split logic when None, currently this does not perform an ideal split.
            tuning_data = self._validate_data(tuning_data)

        if self._detector is not None:
            self._detector._logger.setLevel(log_level)
            self._detector._logger.propagate = True
            self._fit_summary = self._detector.fit(train_data, tuning_data, 1 - holdout_frac, random_state, resume=False)
            if hasattr(self._detector, 'fit_history'):
                self._fit_summary['fit_history'] = self._detector.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 kwargs['hyperparameter_tune_kwargs']['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),
                '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, 1280)  # 1280MB per sample
            # 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 '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
        # verbosity
        if log_level > logging.INFO:
            logging.getLogger('gluoncv.auto.tasks.object_detection').propagate = False
            for logger_name in ('SSDEstimator', 'CenterNetEstimator', 'YOLOv3Estimator', 'FasterRCNNEstimator'):
                logging.getLogger(logger_name).setLevel(log_level)
                logging.getLogger(logger_name).propagate = False
        task = ObjectDetection(config=config)
        task.search_strategy = scheduler
        task.scheduler_options['searcher'] = searcher
        task._logger.setLevel(log_level)
        task._logger.propagate = True
        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._detector = task.fit(train_data, tuning_data, 1 - holdout_frac, random_state)
        self._detector._logger.setLevel(log_level)
        self._detector._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 len(data) < 1:
            raise ValueError('Empty dataset.')
        if not (hasattr(data, 'classes') and hasattr(data, 'to_mxnet')):
            if isinstance(data, pd.DataFrame):
                # raw dataframe, try to add metadata automatically
                infer_classes = []
                if '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.')
                if 'rois' in data.columns and 'image' in data.columns:
                    sample = data.iloc[0]['rois']
                    for sample_key in ('class', 'xmin', 'ymin', 'xmax', 'ymax'):
                        assert sample_key in sample, f'key `{sample_key}` required in `rois`'
                    class_column = data.rois.apply(lambda x: x.get('class', 'unknown'))
                    infer_classes = class_column.unique().tolist()
                    data['rois'] = data['rois'].apply(lambda x: x.update({'difficult': x.get('difficult', 0)} or x))
                    data = ObjectDetection.Dataset(data.sort_values('image').reset_index(drop=True), classes=infer_classes)
                elif 'image' in data and 'class' in data and 'xmin' in data and 'ymin' in data and 'xmax' in data and 'ymax' in data:
                    infer_classes = data['class'].unique().tolist()
                    if 'difficult' not in data.columns:
                        data['difficult'] = 0
                    data = ObjectDetection.Dataset(data.sort_values('image').reset_index(drop=True), classes=infer_classes)
                    data = data.pack()
                    data.classes = infer_classes
                else:
                    err_msg = 'Unable to convert raw DataFrame to ObjectDetector Dataset, ' + \
                              '`image` and `rois` columns are required.' + \
                              'You may visit `https://auto.gluon.ai/stable/tutorials/object_detection/dataset.html` ' + \
                              'for details.'
                    raise AttributeError(err_msg)
                logger.log(20, 'Converting raw DataFrame to ObjectDetector.Dataset...')
                logger.log(20, f'Detected {len(infer_classes)} unique classes: {infer_classes}')
                instruction = 'train_data = ObjectDetector.Dataset(train_data, classes=["foo", "bar"])'
                logger.log(20, f'If you feel the `classes` is inaccurate, please construct the dataset explicitly, e.g. {instruction}')
        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', 'grid'):
            raise ValueError(f"Invalid searcher: {hpo_tune_args['searcher']}, supported: ('random', 'grid')")
        hpo_tune_args['scheduler'] = hpo_tune_args.get('scheduler', 'local')
        if not hpo_tune_args['scheduler'] in ['local']:
            raise ValueError(f"Invalid searcher: {hpo_tune_args['searcher']}, supported: ['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(self, data, as_pandas=True):
        """Predict objects in image, return the confidences, bounding boxes of each predicted object.

        Parameters
        ----------
        data : str, pd.DataFrame or ndarray
            The input data, 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 (`pred_score`, `pred_bbox`, `pred_id`).
            If more than one image in input, the returned dataframe will contain `images` column,
            and all results are concatenated.
        """
        if self._detector is None:
            raise RuntimeError('Detector is not initialized, try `fit` first.')
        ret = self._detector.predict(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._detector is None:
            raise RuntimeError('Detector not initialized, try `fit` first.')
        return self._detector.evaluate(data)

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

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

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

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

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

        """
        if path is None:
            path = os.path.join(self.path, 'object_detector.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 `object_detector.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, 'object_detector.ag')
        with open(path, 'rb') as fid:
            obj = pickle.load(fid)
        obj._verbosity = verbosity
        return obj

    @staticmethod
    def _get_num_gpus_available():
        return ResourceManager.get_gpu_count_all()
