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# ============================================================================

import argparse

import torch





class parse_args():

    def __init__(self, isTrain=True, isTest=False):

        self.isTrain = isTrain

        self.isTest = isTest

        self.parser = argparse.ArgumentParser(description='Pytorch CycleGAN training')



    def initialize(self):

        parser = self.parser

        parser.add_argument('--model_ga_path', type=str,

                            default='./checkpoints_8pbs1/maps_cycle_gan/175_pu0_net_G_A.pth',

                            help='path for modelga')

        parser.add_argument('--model_gb_path', type=str,

                            default='./checkpoints_8pbs1/maps_cycle_gan/175_pu0_net_G_B.pth',

                            help='path for modelga')

        parser.add_argument('--prof', type=int, default=1, help='whether to get prof file')

        parser.add_argument('--num_epoch', type=int, default=240, help='whether to get prof file1')

        parser.add_argument('--line_scale', type=float, default=2, help='whether to get prof file1')

        parser.add_argument('--num_epoch_start', type=int, default=0, help='whether to get prof file1')

        parser.add_argument('--loadweight', default='latest', help='whether to get prof file1')

        parser.add_argument('--prof_file', type=str, default='./output.prof', help='whether to get prof file')

        parser.add_argument('--log_path', type=str, default='gpu1p.txt', help='whether to get prof file')

        parser.add_argument('--multiprocessing_distributed', type=int, default=1,

                            help='Use multi-processing distributed training to launch,if it is eaqul to 1 or  more than ,start to  npu/gpu Multi-card training ')

        parser.add_argument('--world_size', type=int, default=1, help='word__size')

        parser.add_argument('--distributed', type=int, default=1,

                            help='whether to use distributed to fastern training,if it is eaqul to 1 or  more than ,start to  npu/gpu Multi-card training')

        parser.add_argument('--rank', default=0, type=int, help='rank')

        parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')

        parser.add_argument('--npu', type=int, default=0, help='whether to use npu to fastern training')

        parser.add_argument('--pu_ids', type=str, default='0,1',

                            help='gpu ids(npu ids): e.g. 0  0,1,2, 0,2. use -1 for CPU')



        parser.add_argument('--isapex', default=True, help='whether to use apex to fastern training')

        parser.add_argument('--apex_type', type=str, default="O1", help='O0,O1,O2,O3')

        parser.add_argument('--loss_scale', default=None, help='loss_scale:1,128,dynamic')

        parser.add_argument('--dataroot', type=str, default='./datasets/maps',

                            help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')

        parser.add_argument('--name', type=str, default='maps_cycle_gan',

                            help='name of the experiment. It decides where to store samples and models')



        parser.add_argument('--checkpoints_dir', type=str, default='./re_checkpoints2p_bs1',

                            help='models are saved here')

        # model parameters

        parser.add_argument('--model', type=str, default='cycle_gan',

                            help='chooses which model to use. [cycle_gan| pix2pix | test | colorization]')

        parser.add_argument('--input_nc', type=int, default=3,

                            help='# of input image channels: 3 for RGB and 1 for grayscale')

        parser.add_argument('--output_nc', type=int, default=3,

                            help='# of output image channels: 3 for RGB and 1 for grayscale')

        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')

        parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')

        parser.add_argument('--netD', type=str, default='basic',

                            help='specify discriminator architecture [basic | n_layers | pixel].'

                                 ' The basic model is a 70x70 PatchGAN. n_layers allows you to '

                                 'specify the layers in the discriminator')

        parser.add_argument('--netG', type=str, default='resnet_9blocks',

                            help='specify generator architecture [resnet_9blocks | resnet_6blocks | '

                                 'unet_256 | unet_128]')

        parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')

        parser.add_argument('--norm', type=str, default='instance',

                            help='instance normalization or batch normalization [instance | batch | none]')

        parser.add_argument('--init_type', type=str, default='normal',

                            help='network initialization [normal | xavier | kaiming | orthogonal]')

        parser.add_argument('--init_gain', type=float, default=0.02,

                            help='scaling factor for normal, xavier and orthogonal.')

        parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')

        # dataset parameters

        parser.add_argument('--dataset_mode', type=str, default='unaligned',

                            help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')

        parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')

        parser.add_argument('--serial_batches', action='store_true',

                            help='if true, takes images in order to make batches, otherwise takes them randomly')

        parser.add_argument('--num_threads', default=8, type=int, help='# threads for loading data')

        parser.add_argument('--batch_size', type=int, default=4, help='input batch size')

        parser.add_argument('--load_size', type=int, default=286, help='scale images to this size')

        parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size')

        parser.add_argument('--max_dataset_size', type=int, default=float("inf"),

                            help='Maximum number of samples allowed per dataset. If the dataset directory '

                                 'contains more than max_dataset_size, only a subset is loaded.')

        parser.add_argument('--preprocess', type=str, default='resize_and_crop',

                            help='scaling and cropping of images at load time [resize_and_crop | crop | '

                                 'scale_width | scale_width_and_crop | none]')

        parser.add_argument('--no_flip', action='store_true',

                            help='if specified, do not flip the images for data augmentation')

        parser.add_argument('--display_winsize', type=int, default=256,

                            help='display window size for both visdom and HTML')

        # additional parameters

        parser.add_argument('--epoch', type=str, default='latest',

                            help='which epoch to load? set to latest to use latest cached model')

        parser.add_argument('--load_iter', type=int, default='0',

                            help='which iteration to load? if load_iter > 0, the code will load models by iter_'

                                 '[load_iter]; otherwise, the code will load models by [epoch]')

        parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')

        parser.add_argument('--suffix', default='', type=str,

                            help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')

        parser.add_argument(

            "--cache-dataset",

            dest="cache_dataset",

            help="Cache the datasets for quicker initialization. It also serializes the transforms",

            action="store_true",

        )

        parser.set_defaults(no_dropout=True)  # default CycleGAN did not use dropout

        if (self.isTrain):

            # network saving and loading parameters

            parser.add_argument('--display_freq', type=int, default=400,

                                help='frequency of showing training results on screen')

            parser.add_argument('--display_ncols', type=int, default=4,

                                help='if positive, display all images in a single visdom web panel with '

                                     'certain number of images per row.')

            parser.add_argument('--display_id', type=int, default=-1, help='window id of the web display')

            parser.add_argument('--display_server', type=str, default="http://localhost",

                                help='visdom server of the web display')

            parser.add_argument('--display_env', type=str, default='main',

                                help='visdom display environment name (default is "main")')

            parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display')

            parser.add_argument('--update_html_freq', type=int, default=1000,

                                help='frequency of saving training results to html')

            parser.add_argument('--print_freq', type=int, default=100,

                                help='frequency of showing training results on console')

            parser.add_argument('--no_html', action='store_true',

                                help='do not save intermediate training results to ['

                                     'opt.checkpoints_dir]/[opt.name]/web/')

            # network saving and loading parameters

            parser.add_argument('--save_latest_freq', type=int, default=5000,

                                help='frequency of saving the latest results')

            parser.add_argument('--save_epoch_freq', type=int, default=5,

                                help='frequency of saving checkpoints at the end of epochs')

            parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration')

            parser.add_argument('--continue_train', action='store_true',

                                help='continue training: load the latest model')

            parser.add_argument('--epoch_count', type=int, default=1,

                                help='the starting epoch count, we save the model '

                                     'by <epoch_count>, <epoch_count>+<save_latest_freq>, ...')

            parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')

            # training parameters

            parser.add_argument('--n_epochs', type=int, default=100,

                                help='number of epochs with the initial learning rate')

            parser.add_argument('--n_epochs_decay', type=int, default=100,

                                help='number of epochs to linearly decay learning rate to zero')

            parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')

            parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')

            parser.add_argument('--gan_mode', type=str, default='lsgan',

                                help='the type of GAN objective. [vanilla| lsgan | wgangp]. vanilla GAN loss is'

                                     ' the cross-entropy objective used in the original GAN paper.')

            parser.add_argument('--pool_size', type=int, default=50,

                                help='the size of image buffer that stores previously generated images')

            parser.add_argument('--lr_policy', type=str, default='linear',

                                help='learning rate policy. [linear | step | plateau | cosine]')

            parser.add_argument('--lr_decay_iters', type=int, default=50,

                                help='multiply by a gamma every lr_decay_iters iterations')

            parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)')

            parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)')

            parser.add_argument('--lambda_identity', type=float, default=0.5,

                                help='use identity mapping. Setting lambda_identity other than 0 has an effect of'

                                     ' scaling the weight of the identity mapping loss. For example, if the weight of'

                                     ' the identity loss should be 10 times smaller than the weight of the '

                                     'reconstruction loss, please set lambda_identity = 0.1')

        parser = parser.parse_args()

        parser.process_device_map = self.device_id_to_process_device_map(parser.pu_ids)

        return parser



    def device_id_to_process_device_map(self, device_list):

        devices = device_list.split(",")

        devices = [int(x) for x in devices]

        devices.sort()



        process_device_map = dict()

        for process_id, device_id in enumerate(devices):

            process_device_map[process_id] = device_id

        return process_device_map



    def change_parser(self, isTrain=True, isTest=False):

        self.isTest = isTest

        self.isTrain = isTrain

        self.parser = None

        return self.initialize()



    def printParser(self):

        pasers = self.parser.parse_args()

        message = ''

        message += '----------------- Options ---------------\n'

        for k, v in sorted(vars(pasers).items()):

            comment = ''

            default = self.parser.get_default(k)

            # if v != default:

            #     comment = '\t[default: %s]' % str(default)

            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)

        message += '----------------- End -------------------'

        print(message)



    def init_distributed_mode(self, ngpus_per_node, gpu):

        opt = self.parser.parse_args()

        if opt.multiprocessing_distributed >= 1:

            # For multiprocessing distributed training, rank needs to be the

            # global rank among all the processes

            opt.rank = opt.rank * ngpus_per_node + gpu

        if (opt.npu < 1):

            torch.distributed.init_process_group(backend='nccl', init_method='env://', world_size=opt.world_size,

                                                 rank=opt.rank)

        elif (opt.npu >= 1):

            torch.distributed.init_process_group(backend='hccl', world_size=opt.world_size, rank=opt.rank)