import torch
import time
import numpy as np
from  config import Config as cfg

class TrainerBase:
    """
    Base class for iterative trainer with hooks.

    The only assumption we made here is: the training runs in a loop.
    A subclass can implement what the loop is.
    We made no assumptions about the existence of dataloader, optimizer, model, etc.

    Attributes:
        iter(int): the current iteration.

        start_iter(int): The iteration to start with.
            By convention the minimum possible value is 0.

        max_iter(int): The iteration to end training.

        storage(EventStorage): An EventStorage that's opened during the course of training.
    """

    def __init__(self):
        pass

    def train(self, start_iter: int, max_iter: int):
        """
        Args:
            start_iter, max_iter (int): See docs above
        """
        # logger = logging.getLogger(__name__)
        # logger.info("Starting training from iteration {}".format(start_iter))

        self.iter = self.start_iter = start_iter
        self.max_iter = max_iter
        for self.iter in range(start_iter, max_iter):
            self.run_step()
        # with EventStorage(start_iter) as self.storage:
        #     try:
        #
        #         for self.iter in range(start_iter, max_iter):
        #             self.before_step()
        #             self.run_step()
        #             self.after_step()
        #     finally:
        #         self.after_train()

    def run_step(self):
        raise NotImplementedError


class SimpleTrainer(TrainerBase):
    """
    A simple trainer for the most common type of task:
    single-cost single-optimizer single-data-source iterative optimization.
    It assumes that every step, you:

    1. Compute the loss with a data from the data_loader.
    2. Compute the gradients with the above loss.
    3. Update the model with the optimizer.

    If you want to do anything fancier than this,
    either subclass TrainerBase and implement your own `run_step`,
    or write your own training loop.
    """

    def __init__(self, model, data_loader, optimizer):
        """
        Args:
            model: a torch Module. Takes a data from data_loader and returns a
                dict of losses.
            data_loader: an iterable. Contains data to be used to call model.
            optimizer: a torch optimizer.
        """
        super().__init__()

        """
        We set the model to training mode in the trainer.
        However it's valid to train a model that's in eval mode.
        If you want your model (or a submodule of it) to behave
        like evaluation during training, you can overwrite its train() method.
        """
        model.train()

        self.model = model
        self.data_loader = data_loader
        self._data_loader_iter = iter(data_loader)
        self.optimizer = optimizer

    def run_step(self):
        """
        Implement the standard training logic described above.
        """
        assert self.model.training, "[SimpleTrainer] model was changed to eval mode!"
        start = time.perf_counter()
        """
        If your want to do something with the data, you can wrap the dataloader.
        """
        data = next(self._data_loader_iter)
        data_time = time.perf_counter() - start

        """
        If your want to do something with the losses, you can wrap the model.
        """
        loss_dict = self.model(data)
        losses = sum(loss for loss in loss_dict.values())
        self._detect_anomaly(losses, loss_dict)

        metrics_dict = loss_dict
        metrics_dict["data_time"] = data_time
        self._write_metrics(metrics_dict)

        """
        If you need accumulate gradients or something similar, you can
        wrap the optimizer with your custom `zero_grad()` method.
        """
        self.optimizer.zero_grad()
        losses.backward()

        """
        If you need gradient clipping/scaling or other processing, you can
        wrap the optimizer with your custom `step()` method.
        """
        self.optimizer.step()

    def _detect_anomaly(self, losses, loss_dict):
        if not torch.isfinite(losses).all():
            raise FloatingPointError(
                "Loss became infinite or NaN at iteration={}!\nloss_dict = {}".format(
                    self.iter, loss_dict
                )
            )

    def _write_metrics(self, metrics_dict: dict):
        """
        Args:
            metrics_dict (dict): dict of scalar metrics
        """
        metrics_dict = {
            k: v.detach().cpu().item() if isinstance(v, torch.Tensor) else float(v)
            for k, v in metrics_dict.items()
        }
        # gather metrics among all workers for logging
        # This assumes we do DDP-style training, which is currently the only
        # supported method in detectron2.
        all_metrics_dict = comm.gather(metrics_dict)

        if comm.is_main_process():
            if "data_time" in all_metrics_dict[0]:
                # data_time among workers can have high variance. The actual latency
                # caused by data_time is the maximum among workers.
                data_time = np.max([x.pop("data_time") for x in all_metrics_dict])
                self.storage.put_scalar("data_time", data_time)

            # average the rest metrics
            metrics_dict = {
                k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()
            }
            total_losses_reduced = sum(loss for loss in metrics_dict.values())

            self.storage.put_scalar("total_loss", total_losses_reduced)
            if len(metrics_dict) > 1:
                self.storage.put_scalars(**metrics_dict)

class DefaultTrainer(SimpleTrainer):
    """
    A trainer with default training logic. Compared to `SimpleTrainer`, it
    contains the following logic in addition:

    1. Create model, optimizer, scheduler, dataloader from the given config.
    2. Load a checkpoint or `cfg.MODEL.WEIGHTS`, if exists.
    3. Register a few common hooks.

    It is created to simplify the **standard model training workflow** and reduce code boilerplate
    for users who only need the standard training workflow, with standard features.
    It means this class makes *many assumptions* about your training logic that
    may easily become invalid in a new research. In fact, any assumptions beyond those made in the
    :class:`SimpleTrainer` are too much for research.

    The code of this class has been annotated about restrictive assumptions it mades.
    When they do not work for you, you're encouraged to write your own training logic.

    Also note that the behavior of this class, like other functions/classes in
    this file, is not stable, since it is meant to represent the "common default behavior".
    It is only guaranteed to work well with the standard models and training workflow in detectron2.
    To obtain more stable behavior, write your own training logic with other public APIs.

    Attributes:
        scheduler:
        checkpointer (DetectionCheckpointer):
        cfg (CfgNode):
    """

    def __init__(self, model , data_loader, optimizer):
        """
        Args:
            cfg (CfgNode):
        """
        # Assume these objects must be constructed in this order.
        model =model
        # if cfg.FREEZE_ALL:
        #     # Freeze model parameters except for viewpoint
        #     for name,param in model.named_parameters():
        #         if param.requires_grad and 'viewpoint' not in name:
        #             param.requires_grad = False
        optimizer = optimizer
        data_loader = data_loader
        super().__init__(model, data_loader, optimizer)

        # self.scheduler = self.build_lr_scheduler(cfg, optimizer)
        # Assume no other objects need to be checkpointed.
        # We can later make it checkpoint the stateful hooks
        # self.checkpointer = DetectionCheckpointer(
        #     # Assume you want to save checkpoints together with logs/statistics
        #     model,
        #     cfg.OUTPUT_DIR,
        #     optimizer=optimizer,
        #     scheduler=self.scheduler,
        # )
        self.start_iter = 0
        self.max_iter = cfg.Model.Output.MAX_ITER
        self.cfg = cfg

        # self.register_hooks(self.build_hooks())

    def resume_or_load(self, resume=True):
        """
        If `resume==True`, and last checkpoint exists, resume from it.

        Otherwise, load a model specified by the config.

        Args:
            resume (bool): whether to do resume or not
        """
        # The checkpoint stores the training iteration that just finished, thus we start
        # at the next iteration (or iter zero if there's no checkpoint).
        self.checkpointer._set_resume(resume)
        self.start_iter = (
            self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume).get(
                "iteration", -1
            )
            + 1
        )


    def build_hooks(self):
        """
        Build a list of default hooks.

        Returns:
            list[HookBase]:
        """
        cfg = self.cfg.clone()
        cfg.defrost()
        cfg.DATALOADER.NUM_WORKERS = 0  # save some memory and time for PreciseBN

        ret = [
            hooks.IterationTimer(),
            hooks.LRScheduler(self.optimizer, self.scheduler),
            hooks.PreciseBN(
                # Run at the same freq as (but before) evaluation.
                cfg.TEST.EVAL_PERIOD,
                self.model,
                # Build a new data loader to not affect training
                self.build_train_loader(cfg),
                cfg.TEST.PRECISE_BN.NUM_ITER,
            )
            if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
            else None,
        ]

        # Do PreciseBN before checkpointer, because it updates the model and need to
        # be saved by checkpointer.
        # This is not always the best: if checkpointing has a different frequency,
        # some checkpoints may have more precise statistics than others.
        if comm.is_main_process():
            ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))

        def test_and_save_results():
            self._last_eval_results = self.test(self.cfg, self.model)
            return self._last_eval_results

        # Do evaluation after checkpointer, because then if it fails,
        # we can use the saved checkpoint to debug.
        ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))

        if comm.is_main_process():
            # run writers in the end, so that evaluation metrics are written
            ret.append(hooks.PeriodicWriter(self.build_writers()))
        return ret

    def build_writers(self):
        """
        Build a list of default writers, that write metrics to the screen,
        a json file, and a tensorboard event file respectively.

        Returns:
            list[Writer]: a list of objects that have a ``.write`` method.
        """
        # Assume the default print/log frequency.
        return [
            # It may not always print what you want to see, since it prints "common" metrics only.
            CommonMetricPrinter(self.max_iter),
            JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, "metrics.json")),
            TensorboardXWriter(self.cfg.OUTPUT_DIR),
        ]

    def train(self):
        """
        Run training.

        Returns:
            OrderedDict of results, if evaluation is enabled. Otherwise None.
        """
        super().train(self.start_iter, self.max_iter)
        if hasattr(self, "_last_eval_results") and comm.is_main_process():
            verify_results(self.cfg, self._last_eval_results)
            return self._last_eval_results


    @classmethod
    def build_evaluator(cls, cfg, dataset_name):
        """
        Returns:
            DatasetEvaluator
        """
        raise NotImplementedError

    @classmethod
    def test(cls, cfg, model, evaluators=None):
        """
        Args:
            cfg (CfgNode):
            model (nn.Module):
            evaluators (list[DatasetEvaluator] or None): if None, will call
                :meth:`build_evaluator`. Otherwise, must have the same length as
                `cfg.DATASETS.TEST`.

        Returns:
            dict: a dict of result metrics
        """
        logger = logging.getLogger(__name__)
        if isinstance(evaluators, DatasetEvaluator):
            evaluators = [evaluators]
        if evaluators is not None:
            assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
                len(cfg.DATASETS.TEST), len(evaluators)
            )

        results = OrderedDict()
        for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
            data_loader = cls.build_test_loader(cfg, dataset_name)
            # When evaluators are passed in as arguments,
            # implicitly assume that evaluators can be created before data_loader.
            evaluator = (
                evaluators[idx]
                if evaluators is not None
                else cls.build_evaluator(cfg, dataset_name)
            )
            results_i = inference_on_dataset(model, data_loader, evaluator)
            results[dataset_name] = results_i
            if comm.is_main_process():
                assert isinstance(
                    results_i, dict
                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
                    results_i
                )
                logger.info("Evaluation results for {} in csv format:".format(dataset_name))
                print_csv_format(results_i)

        if len(results) == 1:
            results = list(results.values())[0]
        return results
