from copy import deepcopy
import torch
import numpy as np
from scripts.utils_train import update_ema
import pandas as pd

class Trainer:
    def __init__(self, diffusion, train_iter, lr, weight_decay, steps, device=torch.device('cuda:1')):
        self.diffusion = diffusion
        self.ema_model = deepcopy(self.diffusion._denoise_fn)
        for param in self.ema_model.parameters():
            param.detach_()

        self.train_iter = train_iter
        self.steps = steps
        self.init_lr = lr
        self.optimizer = torch.optim.AdamW(self.diffusion.parameters(), lr=lr, weight_decay=weight_decay)
        self.device = device
        self.loss_history = pd.DataFrame(columns=['step', 'mloss', 'gloss', 'loss'])
        self.log_every = 100
        self.print_every = 500
        self.ema_every = 1000

    def _anneal_lr(self, step):
        frac_done = step / self.steps
        lr = self.init_lr * (1 - frac_done)
        for param_group in self.optimizer.param_groups:
            param_group["lr"] = lr

    def _run_step(self, x, out_dict):
        x = x.to(self.device)
        for k in out_dict:
            out_dict[k] = out_dict[k].long().to(self.device)
        self.optimizer.zero_grad()
        loss_multi, loss_gauss = self.diffusion.mixed_loss(x, out_dict)
        loss = loss_multi + loss_gauss
        loss.backward()
        self.optimizer.step()

        return loss_multi, loss_gauss

    def run_loop(self):
        step = 0
        curr_loss_multi = 0.0
        curr_loss_gauss = 0.0

        curr_count = 0
        while step < self.steps:
            x, out_dict = next(self.train_iter)
            out_dict = {'y': out_dict}
            batch_loss_multi, batch_loss_gauss = self._run_step(x, out_dict)

            self._anneal_lr(step)

            curr_count += len(x)
            curr_loss_multi += batch_loss_multi.item() * len(x)
            curr_loss_gauss += batch_loss_gauss.item() * len(x)

            if (step + 1) % self.log_every == 0:
                mloss = np.around(curr_loss_multi / curr_count, 4)
                gloss = np.around(curr_loss_gauss / curr_count, 4)
                if (step + 1) % self.print_every == 0:
                    print(f'Step {(step + 1)}/{self.steps} MLoss: {mloss} GLoss: {gloss} Sum: {mloss + gloss}')
                self.loss_history.loc[len(self.loss_history)] =[step + 1, mloss, gloss, mloss + gloss]
                curr_count = 0
                curr_loss_gauss = 0.0
                curr_loss_multi = 0.0

            update_ema(self.ema_model.parameters(), self.diffusion._denoise_fn.parameters())

            step += 1