"""
 Copyright 2020 Tianshu AI Platform. All Rights Reserved.

 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 torch
import torch.nn as nn
from kamal.core.engine.engine import Engine, Event, DefaultEvents, State
from kamal.core import tasks
from kamal.utils import set_mode, move_to_device, get_logger, split_batch
from typing import Callable, Mapping, Any, Sequence
import time
import weakref

class BasicTrainer(Engine):
    def __init__( self, 
                  logger=None,
                  tb_writer=None):
        super(BasicTrainer, self).__init__(logger=logger, tb_writer=tb_writer)

    def setup(self, 
              model: torch.nn.Module, 
              task: tasks.Task,
              dataloader: torch.utils.data.DataLoader,
              optimizer: torch.optim.Optimizer, 
              device: torch.device=None):
        
        if device is None:
            device = torch.device( 'cuda' if torch.cuda.is_available() else 'cpu' )
        self.device = device
        if isinstance(task, Sequence):
            task = tasks.TaskCompose(task)
        self.task = task
        self.model = model
        self.dataloader = dataloader
        self.optimizer = optimizer
        return self

    def run( self, max_iter, start_iter=0, epoch_length=None):
        self.model.to(self.device)
        with set_mode(self.model, training=True):
            super( BasicTrainer, self ).run( self.step_fn, self.dataloader, start_iter=start_iter, max_iter=max_iter, epoch_length=epoch_length)

    def step_fn(self, engine, batch):
        model = self.model
        start_time = time.perf_counter()
        batch = move_to_device(batch, self.device)
        inputs, targets = split_batch(batch)
        outputs = model(inputs)
        loss_dict = self.task.get_loss(outputs, targets) # get loss
        loss = sum( loss_dict.values() )
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        step_time = time.perf_counter() - start_time
        metrics = { loss_name: loss_value.item() for (loss_name, loss_value) in loss_dict.items() }
        metrics.update({
            'total_loss': loss.item(),
            'step_time': step_time,
            'lr': float( self.optimizer.param_groups[0]['lr'] )
        })
        return metrics


class KDTrainer(BasicTrainer):

    def setup(self, 
              student: torch.nn.Module, 
              teacher: torch.nn.Module, 
              task: tasks.Task,
              dataloader: torch.utils.data.DataLoader,
              optimizer: torch.optim.Optimizer, 
              device: torch.device=None):

        super(KDTrainer, self).setup(
            model=student, task=task, dataloader=dataloader, optimizer=optimizer, device=device)
        if isinstance(teacher, (list, tuple)):
            if len(teacher)==1:
                teacher=teacher[0]
            else:
                teacher = nn.ModuleList(teacher)
        self.student = self.model
        self.teacher = teacher
        return self

    def run( self, max_iter, start_iter=0, epoch_length=None):
        self.student.to(self.device)
        self.teacher.to(self.device)

        with set_mode(self.student, training=True), \
             set_mode(self.teacher, training=False):
            super( BasicTrainer, self ).run(
                self.step_fn, self.dataloader, start_iter=start_iter, max_iter=max_iter, epoch_length=epoch_length)

    def step_fn(self, engine, batch):
        model = self.model
        start_time = time.perf_counter()
        batch = move_to_device(batch, self.device)
        inputs, targets = split_batch(batch)
        outputs = model(inputs)
        if isinstance(self.teacher, nn.ModuleList):
            soft_targets = [ t(inputs) for t in self.teacher ]
        else:
            soft_targets = self.teacher(inputs)
        loss_dict = self.task.get_loss(outputs, soft_targets) # get loss
        loss = sum( loss_dict.values() )
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        step_time = time.perf_counter() - start_time
        metrics = { loss_name: loss_value.item() for (loss_name, loss_value) in loss_dict.items() }
        metrics.update({
            'total_loss': loss.item(),
            'step_time': step_time,
            'lr': float( self.optimizer.param_groups[0]['lr'] )
        })
        return metrics