"""
 Copyright 2019 Johns Hopkins University  (Author: Jesus Villalba)
 Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
"""
import os
from collections import OrderedDict as ODict

import logging

import torch
import torch.nn as nn

from ..utils import MetricAcc, TorchDDP
from .xvector_trainer import XVectorTrainer


class XVectorTrainerFromWav(XVectorTrainer):
    """Trainer to train x-vector style models.

       Attributes:
         model: x-Vector model object.
         feat_extractor: feature extractor nn.Module
         optim: pytorch optimizer object or options dict
         epochs: max. number of epochs
         exp_path: experiment output path
         cur_epoch: current epoch
         grad_acc_steps: gradient accumulation steps to simulate larger batch size.
         device: cpu/gpu device
         metrics: extra metrics to compute besides cxe.
         lrsched: learning rate scheduler object or options dict.
         loggers: LoggerList object, loggers write training progress to std. output and file.
         ddp: if True use distributed data parallel training
         ddp_type: type of distributed data parallel in  (ddp, oss_ddp, oss_shared_ddp)
         loss: if None, it uses cross-entropy
         train_mode: training mode in ['train', 'ft-full', 'ft-last-layer']
         use_amp: uses mixed precision training.
         log_interval: number of optim. steps between log outputs
         use_tensorboard: use tensorboard logger
         use_wandb: use wandb logger
         wandb: wandb dictionary of options
         grad_clip: norm to clip gradients, if 0 there is no clipping
         grad_clip_norm: norm type to clip gradients
         swa_start: epoch to start doing swa
         swa_lr: SWA learning rate
         swa_anneal_epochs: SWA learning rate anneal epochs
         cpu_offload: CPU offload of gradients when using fully sharded ddp
    """
    def __init__(self,
                 model,
                 feat_extractor,
                 optim={},
                 epochs=100,
                 exp_path='./train',
                 cur_epoch=0,
                 grad_acc_steps=1,
                 device=None,
                 metrics=None,
                 lrsched=None,
                 loggers=None,
                 ddp=False,
                 ddp_type='ddp',
                 loss=None,
                 train_mode='train',
                 use_amp=False,
                 log_interval=10,
                 use_tensorboard=False,
                 use_wandb=False,
                 wandb={},
                 grad_clip=0,
                 grad_clip_norm=2,
                 swa_start=0,
                 swa_lr=1e-3,
                 swa_anneal_epochs=10,
                 cpu_offload=False):

        super().__init__(model,
                         optim,
                         epochs,
                         exp_path,
                         cur_epoch=cur_epoch,
                         grad_acc_steps=grad_acc_steps,
                         device=device,
                         metrics=metrics,
                         lrsched=lrsched,
                         loggers=loggers,
                         ddp=ddp,
                         ddp_type=ddp_type,
                         loss=loss,
                         train_mode=train_mode,
                         use_amp=use_amp,
                         log_interval=log_interval,
                         use_tensorboard=use_tensorboard,
                         use_wandb=use_wandb,
                         wandb=wandb,
                         grad_clip=grad_clip,
                         grad_clip_norm=grad_clip_norm,
                         swa_start=swa_start,
                         swa_lr=swa_lr,
                         swa_anneal_epochs=swa_anneal_epochs,
                         cpu_offload=cpu_offload)

        self.feat_extractor = feat_extractor
        if device is not None:
            self.feat_extractor.to(device)

        # if ddp:
        #     self.feat_extractor = TorchDDP(self.feat_extractor)

    def train_epoch(self, data_loader):
        """Training epoch loop

           Args:
             data_loader: pytorch data loader returning features and class labels.
        """

        self.model.update_loss_margin(self.cur_epoch)

        metric_acc = MetricAcc(device=self.device)
        batch_metrics = ODict()
        self.set_train_mode()

        for batch, (data, target) in enumerate(data_loader):
            self.loggers.on_batch_begin(batch)
            if batch % self.grad_acc_steps == 0:
                self.optimizer.zero_grad()

            data, target = data.to(self.device), target.to(self.device)
            batch_size = data.shape[0]
            with torch.no_grad():
                feats = self.feat_extractor(data)

            with self.amp_autocast():
                output = self.model(feats, target)
                loss = self.loss(output, target).mean() / self.grad_acc_steps

            if self.use_amp:
                self.grad_scaler.scale(loss).backward()
            else:
                loss.backward()

            if (batch + 1) % self.grad_acc_steps == 0:
                if self.lr_scheduler is not None and not self.in_swa:
                    self.lr_scheduler.on_opt_step()
                self.update_model()

            batch_metrics['loss'] = loss.item() * self.grad_acc_steps
            for k, metric in self.metrics.items():
                batch_metrics[k] = metric(output, target)

            metric_acc.update(batch_metrics, batch_size)
            logs = metric_acc.metrics
            logs['lr'] = self._get_lr()
            self.loggers.on_batch_end(logs=logs, batch_size=batch_size)

        logs = metric_acc.metrics
        logs = ODict(('train_' + k, v) for k, v in logs.items())
        logs['lr'] = self._get_lr()
        return logs

    def validation_epoch(self, data_loader, swa_update_bn=False):
        """Validation epoch loop

        Args:
          data_loader: PyTorch data loader return input/output pairs
        """
        metric_acc = MetricAcc(device=self.device)
        batch_metrics = ODict()
        with torch.no_grad():
            if swa_update_bn:
                log_tag = 'train_'
                self.set_train_mode()
            else:
                log_tag = 'val_'
                self.model.eval()

            for batch, (data, target) in enumerate(data_loader):
                data, target = data.to(self.device), target.to(self.device)
                batch_size = data.shape[0]

                feats = self.feat_extractor(data)
                with self.amp_autocast():
                    output = self.model(feats, **self.amp_args)
                    loss = self.loss(output, target)

                batch_metrics['loss'] = loss.mean().item()
                for k, metric in self.metrics.items():
                    batch_metrics[k] = metric(output, target)

                metric_acc.update(batch_metrics, batch_size)

        logs = metric_acc.metrics
        logs = ODict((log_tag + k, v) for k, v in logs.items())
        return logs
