"""
 Copyright (c) 2021 Intel Corporation
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
      http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
"""

import io
import os.path as osp
from shutil import copyfile

import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau

try:
    from torch.utils.tensorboard import SummaryWriter

    TENSORBOARD_AVAILABLE = True
except ImportError:
    TENSORBOARD_AVAILABLE = False

try:
    import matplotlib.pyplot as plt
    import PIL.Image
    from torchvision.transforms import ToTensor

    IMG_PACKAGES_AVAILABLE = True
except ImportError:
    IMG_PACKAGES_AVAILABLE = False

from nncf.torch.checkpoint_loading import load_state
from nncf.torch.accuracy_aware_training.utils import is_main_process
from nncf.common.utils.helpers import configure_accuracy_aware_paths
from nncf.common.utils.logger import logger as nncf_logger
from nncf.common.utils.tensorboard import prepare_for_tensorboard
from nncf.common.accuracy_aware_training.runner import BaseAccuracyAwareTrainingRunner
from nncf.common.accuracy_aware_training.runner import BaseAdaptiveCompressionLevelTrainingRunner


class PTAccuracyAwareTrainingRunner(BaseAccuracyAwareTrainingRunner):
    """
    BaseAccuracyAwareTrainingRunner
    The Training Runner implementation for PyTorch training code.
    """

    def __init__(self, accuracy_aware_training_params,
                 lr_updates_needed=True, verbose=True,
                 validate_every_n_epochs=None,
                 dump_checkpoints=True):
        super().__init__(accuracy_aware_training_params, verbose,
                         validate_every_n_epochs,
                         dump_checkpoints)

        self._base_lr_reduction_factor_during_search = 0.5
        self.lr_updates_needed = lr_updates_needed

    def initialize_training_loop_fns(self, train_epoch_fn, validate_fn, configure_optimizers_fn,
                                     dump_checkpoint_fn, tensorboard_writer=None, log_dir=None):
        super().initialize_training_loop_fns(train_epoch_fn, validate_fn, configure_optimizers_fn, dump_checkpoint_fn,
                                             tensorboard_writer=tensorboard_writer, log_dir=log_dir)
        self._log_dir = self._log_dir if self._log_dir is not None \
            else 'runs'
        self._log_dir = configure_accuracy_aware_paths(self._log_dir)
        self._checkpoint_save_dir = self._log_dir
        if self._tensorboard_writer is None and TENSORBOARD_AVAILABLE:
            self._tensorboard_writer = SummaryWriter(self._log_dir)

    def retrieve_uncompressed_model_accuracy(self, model):
        if hasattr(model, 'original_model_accuracy') or hasattr(model.module, 'original_model_accuracy'):
            if isinstance(model, (torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel)):
                self.uncompressed_model_accuracy = model.module.original_model_accuracy
            else:
                self.uncompressed_model_accuracy = model.original_model_accuracy
        else:
            raise RuntimeError('Original model does not contain the pre-calculated reference metric value')

    def train_epoch(self, model, compression_controller):
        compression_controller.scheduler.epoch_step()
        # assuming that epoch number is only used for logging in train_fn:
        self._train_epoch_fn(compression_controller,
                             model,
                             epoch=self.cumulative_epoch_count,
                             optimizer=self.optimizer,
                             lr_scheduler=self.lr_scheduler)
        if self.lr_scheduler is not None and self.lr_updates_needed:
            self.lr_scheduler.step(self.training_epoch_count if not isinstance(self.lr_scheduler, ReduceLROnPlateau)
                                   else self.best_val_metric_value)
        self.training_epoch_count += 1
        self.cumulative_epoch_count += 1

    def validate(self, model):
        with torch.no_grad():
            self.current_val_metric_value = self._validate_fn(model, epoch=self.cumulative_epoch_count)
        is_better_by_accuracy = (not self.is_higher_metric_better) != (
                self.current_val_metric_value > self.best_val_metric_value)
        if is_better_by_accuracy:
            self.best_val_metric_value = self.current_val_metric_value

        if is_main_process():
            self.add_tensorboard_scalar('val/accuracy_aware/metric_value',
                                        self.current_val_metric_value, self.cumulative_epoch_count)

        return self.current_val_metric_value

    def configure_optimizers(self):
        self.optimizer, self.lr_scheduler = self._configure_optimizers_fn()

    def reset_training(self):
        self.configure_optimizers()
        for param_group in self.optimizer.param_groups:
            param_group['lr'] *= self._base_lr_reduction_factor_during_search
        if self.lr_scheduler is not None:
            self.lr_scheduler.base_lrs = [base_lr * self._base_lr_reduction_factor_during_search
                                          for base_lr in self.lr_scheduler.base_lrs]
        self.training_epoch_count = 0
        self.best_val_metric_value = 0

    def dump_statistics(self, model, compression_controller):
        statistics = compression_controller.statistics()

        if is_main_process():
            if self.verbose:
                nncf_logger.info(statistics.to_str())
                # dump best checkpoint for current target compression rate
            if self.dump_checkpoints:
                self.dump_checkpoint(model, compression_controller)
            for key, value in prepare_for_tensorboard(statistics).items():
                if isinstance(value, (int, float)):
                    self.add_tensorboard_scalar('compression/statistics/{0}'.format(key),
                                                value, self.cumulative_epoch_count)

    def _save_best_checkpoint(self, checkpoint_path):
        if self.best_val_metric_value == self.current_val_metric_value:
            best_checkpoint_filename = 'acc_aware_checkpoint_best.pth'
            best_path = osp.join(self._checkpoint_save_dir, best_checkpoint_filename)
            self._best_checkpoint = best_path
            copyfile(checkpoint_path, best_path)

    def dump_checkpoint(self, model, compression_controller):
        if self._dump_checkpoint_fn is not None:
            self._dump_checkpoint_fn(model, compression_controller, self, self._log_dir)
        else:
            checkpoint = {
                'epoch': self.cumulative_epoch_count + 1,
                'state_dict': model.state_dict(),
                'compression_state': compression_controller.get_compression_state(),
                'best_metric_val': self.best_val_metric_value,
                'current_val_metric_value': self.current_val_metric_value,
                'optimizer': self.optimizer.state_dict(),
                'scheduler': compression_controller.scheduler.get_state()
            }
            checkpoint_path = osp.join(self._checkpoint_save_dir, 'acc_aware_checkpoint_last.pth')
            torch.save(checkpoint, checkpoint_path)
            self._save_best_checkpoint(checkpoint_path)

    def add_tensorboard_scalar(self, key, data, step):
        if self.verbose and self._tensorboard_writer is not None:
            self._tensorboard_writer.add_scalar(key, data, step)

    def update_training_history(self, compression_rate, best_metric_value):
        best_accuracy_budget = best_metric_value - self.minimal_tolerable_accuracy
        self._compressed_training_history.append((compression_rate, best_accuracy_budget))

        if IMG_PACKAGES_AVAILABLE:
            plt.figure()
            plt.plot(self.compressed_training_history.keys(),
                     self.compressed_training_history.values())
            buf = io.BytesIO()
            plt.savefig(buf, format='jpeg')
            buf.seek(0)
            image = PIL.Image.open(buf)
            image = ToTensor()(image)
            if self._tensorboard_writer is not None:
                self._tensorboard_writer.add_image('compression/accuracy_aware/acc_budget_vs_comp_rate',
                                                   image,
                                                   global_step=len(self.compressed_training_history))

    @property
    def compressed_training_history(self):
        return dict(self._compressed_training_history)

    def load_best_checkpoint(self, model):
        resuming_checkpoint_path = self._best_checkpoint
        nncf_logger.info('Loading the best checkpoint found during training '
                         '{}...'.format(resuming_checkpoint_path))
        resuming_checkpoint = torch.load(resuming_checkpoint_path, map_location='cpu')
        resuming_model_state_dict = resuming_checkpoint.get('state_dict', resuming_checkpoint)
        load_state(model, resuming_model_state_dict, is_resume=True)


class PTAdaptiveCompressionLevelTrainingRunner(PTAccuracyAwareTrainingRunner,
                                               BaseAdaptiveCompressionLevelTrainingRunner):
    def __init__(self, accuracy_aware_training_params,
                 lr_updates_needed=True, verbose=True,
                 minimal_compression_rate=0.05,
                 maximal_compression_rate=0.95,
                 validate_every_n_epochs=None,
                 dump_checkpoints=True):

        PTAccuracyAwareTrainingRunner.__init__(self, accuracy_aware_training_params,
                                               lr_updates_needed, verbose,
                                               validate_every_n_epochs,
                                               dump_checkpoints)

        BaseAdaptiveCompressionLevelTrainingRunner.__init__(self, accuracy_aware_training_params,
                                                            verbose,
                                                            minimal_compression_rate=minimal_compression_rate,
                                                            maximal_compression_rate=maximal_compression_rate,
                                                            validate_every_n_epochs=validate_every_n_epochs,
                                                            dump_checkpoints=dump_checkpoints)

    def update_training_history(self, compression_rate, best_metric_value):
        best_accuracy_budget = best_metric_value - self.minimal_tolerable_accuracy
        self._compressed_training_history.append((compression_rate, best_accuracy_budget))

        if IMG_PACKAGES_AVAILABLE:
            plt.figure()
            plt.plot(self.compressed_training_history.keys(),
                     self.compressed_training_history.values())
            buf = io.BytesIO()
            plt.savefig(buf, format='jpeg')
            buf.seek(0)
            image = PIL.Image.open(buf)
            image = ToTensor()(image)
            self._tensorboard_writer.add_image('compression/accuracy_aware/acc_budget_vs_comp_rate',
                                               image,
                                               global_step=len(self.compressed_training_history))

    def _save_best_checkpoint(self, checkpoint_path):
        if self.best_val_metric_value == self.current_val_metric_value:
            best_checkpoint_filename = 'acc_aware_checkpoint_best_compression_rate_' \
                                       '{comp_rate:.3f}.pth'.format(comp_rate=self.compression_rate_target)
            best_path = osp.join(self._checkpoint_save_dir, best_checkpoint_filename)
            self._best_checkpoints[self.compression_rate_target] = best_path
            copyfile(checkpoint_path, best_path)

    def load_best_checkpoint(self, model):
        # load checkpoint with highest compression rate and positive acc budget
        possible_checkpoint_rates = [comp_rate for (comp_rate, acc_budget) in self._compressed_training_history
                                     if acc_budget >= 0]
        if not possible_checkpoint_rates:
            nncf_logger.warning('Could not produce a compressed model satisfying the set accuracy '
                                'degradation criterion during training. Increasing the number of training '
                                'epochs')
        best_checkpoint_compression_rate = max(possible_checkpoint_rates)
        resuming_checkpoint_path = self._best_checkpoints[best_checkpoint_compression_rate]
        nncf_logger.info('Loading the best checkpoint found during training '
                         '{}...'.format(resuming_checkpoint_path))
        resuming_checkpoint = torch.load(resuming_checkpoint_path, map_location='cpu')
        resuming_model_state_dict = resuming_checkpoint.get('state_dict', resuming_checkpoint)
        load_state(model, resuming_model_state_dict, is_resume=True)

    @property
    def compressed_training_history(self):
        return dict(self._compressed_training_history)
