''' train_fns.py
Functions for the main loop of training different conditional image models
'''
import torch
import torch.nn as nn
import torchvision
import os

import utils
import losses
import numpy as np
import sys
from PIL import Image
import torchvision.transforms as transforms

# Dummy training function for debugging


def dummy_training_function():
    def train(x, y):
        return {}
    return train


def select_loss(config):
    if config['loss_type'] == 'hinge':
        return losses.loss_hinge_dis, losses.loss_hinge_gen
    elif 'kl' in config['loss_type']:
        temp = float(config['loss_type'].split('_')[-1])
        print('temp = ', temp)

        def dis_f(x, y):
            return losses.loss_kl_dis(x, y, temp)

        def gen_f(x):
            return losses.loss_kl_gen(x, temp)

        return dis_f, gen_f
    else:
        raise ValueError('loss not defined')


def GAN_training_function(G, D, GD, z_, y_, ema, state_dict, config):
    discriminator_loss, generator_loss = select_loss(config)

    def train(x, y):
        G.optim.zero_grad()
        D.optim.zero_grad()
        # How many chunks to split x and y into?
        x = torch.split(x, config['batch_size'])
        y = torch.split(y, config['batch_size'])
        counter = 0

        # Optionally toggle D and G's "require_grad"
        if config['toggle_grads']:
            utils.toggle_grad(D, True)
            utils.toggle_grad(G, False)

        for step_index in range(config['num_D_steps']):
            # If accumulating gradients, loop multiple times before an optimizer step
            D.optim.zero_grad()
            for accumulation_index in range(config['num_D_accumulations']):
                z_.sample_()
                if not config['conditional']:
                    # only feed in 0's for y if "unconditional"
                    y_.zero_()
                    y_counter = torch.zeros_like(
                        y[counter]).to(y_.device).long()
                else:
                    y_.sample_()
                    y_counter = y[counter]

                real_samples = x[counter]

                D_fake, D_real = GD(z_[:config['batch_size']], y_[:config['batch_size']], real_samples,
                                    y_counter, train_G=False, split_D=config['split_D'])

                D_loss_real, D_loss_fake = discriminator_loss(
                    D_fake, D_real)
                D_loss = D_loss_real + D_loss_fake

                D_loss.backward()
                counter += 1

            # Optionally apply ortho reg in D
            if config['D_ortho'] > 0.0:
                # Debug print to indicate we're using ortho reg in D.
                print('using modified ortho reg in D')
                utils.ortho(D, config['D_ortho'])

            D.optim.step()

        # Optionally toggle "requires_grad"
        if config['toggle_grads']:
            utils.toggle_grad(D, False)
            utils.toggle_grad(G, True)

        # Zero G's gradients by default before training G, for safety
        G.optim.zero_grad()

        # If accumulating gradients, loop multiple times
        for accumulation_index in range(config['num_G_accumulations']):
            z_.sample_()
            y_.sample_()
            if not config['conditional']:
                y_.zero_()
            D_fake = GD(z_, y_, train_G=True, split_D=config['split_D'])
            G_loss = generator_loss(
                D_fake) / float(config['num_G_accumulations'])
            G_loss.backward()

        # Optionally apply modified ortho reg in G
        if config['G_ortho'] > 0.0:
            # Debug print to indicate we're using ortho reg in G
            print('using modified ortho reg in G')
            # Don't ortho reg shared, it makes no sense. Really we should blacklist any embeddings for this
            utils.ortho(G, config['G_ortho'],
                        blacklist=[param for param in G.shared.parameters()])
        G.optim.step()

        # If we have an ema, update it, regardless of if we test with it or not
        if config['ema']:
            ema.update(state_dict['itr'])

        out = {'G_loss': float(G_loss.item()),
               'D_loss_real': float(D_loss_real.item()),
               'D_loss_fake': float(D_loss_fake.item())}
        # Return G's loss and the components of D's loss.
        return out
    return train


def save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y,
                    state_dict, config, experiment_name):
    utils.save_weights(G, D, state_dict, config['weights_root'],
                       experiment_name, None, G_ema if config['ema'] else None)
    # Save an additional copy to mitigate accidental corruption if process
    # is killed during a save (it's happened to me before -.-)
    if config['num_save_copies'] > 0:
        utils.save_weights(G, D, state_dict, config['weights_root'],
                           experiment_name,
                           'copy%d' % state_dict['save_num'],
                           G_ema if config['ema'] else None)
        state_dict['save_num'] = (
            state_dict['save_num'] + 1) % config['num_save_copies']

    # Use EMA G for samples or non-EMA?
    which_G = G_ema if config['ema'] and config['use_ema'] else G

    # Accumulate standing statistics?
    if config['accumulate_stats']:
        if not config['conditional']:
            y_.zero_()
        utils.accumulate_standing_stats(G_ema if config['ema'] and config['use_ema'] else G,
                                        z_, y_, config['n_classes'],
                                        config['num_standing_accumulations'])

    # Save a random sample sheet with fixed z and y
    with torch.no_grad():
        if config['parallel']:
            fixed_Gz = nn.parallel.data_parallel(
                which_G, (fixed_z, which_G.shared(fixed_y)))
        else:
            fixed_Gz = which_G(fixed_z, which_G.shared(fixed_y))
    if not os.path.isdir('%s/%s' % (config['samples_root'], experiment_name)):
        os.mkdir('%s/%s' % (config['samples_root'], experiment_name))
    image_filename = '%s/%s/fixed_samples%d.jpg' % (config['samples_root'],
                                                    experiment_name,
                                                    state_dict['itr'])
    torchvision.utils.save_image(fixed_Gz.float().cpu(), image_filename,
                                 nrow=int(fixed_Gz.shape[0] ** 0.5), normalize=True)
    # For now, every time we save, also save sample sheets
    utils.sample_sheet(which_G,
                       classes_per_sheet=utils.classes_per_sheet_dict[config['dataset']],
                       num_classes=config['n_classes'],
                       samples_per_class=10, parallel=config['parallel'],
                       samples_root=config['samples_root'],
                       experiment_name=experiment_name,
                       folder_number=state_dict['itr'],
                       z_=z_)
    # Also save interp sheets
    for fix_z, fix_y in zip([False, False, True], [False, True, False]):
        utils.interp_sheet(which_G,
                           num_per_sheet=16,
                           num_midpoints=8,
                           num_classes=config['n_classes'],
                           parallel=config['parallel'],
                           samples_root=config['samples_root'],
                           experiment_name=experiment_name,
                           folder_number=state_dict['itr'],
                           sheet_number=0,
                           fix_z=fix_z, fix_y=fix_y, device='cuda')


def update_FID(G, D, G_ema, state_dict, config, FID, experiment_name, test_log):
    print('Itr %d: PYTORCH UNOFFICIAL FID is %5.4f' %
          (state_dict['itr'], FID))
    # If improved over previous best metric, save approrpiate copy
    if ((config['which_best'] == 'IS' and IS_mean > state_dict['best_IS'])
            or (config['which_best'] == 'FID' and FID < state_dict['best_FID'])):
        print('%s improved over previous best, saving checkpoint...' %
              config['which_best'])
        utils.save_weights(G, D, state_dict, config['weights_root'],
                           experiment_name, 'best%d' % state_dict['save_best_num'],
                           G_ema if config['ema'] else None)
        state_dict['save_best_num'] = (
            state_dict['save_best_num'] + 1) % config['num_best_copies']
    state_dict['best_FID'] = min(state_dict['best_FID'], FID)
    # Log results to file
    test_log.log(itr=int(state_dict['itr']), IS_mean=float(0),
                 IS_std=float(0), FID=float(FID))
