# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

import sys
import argparse
import os
from util import util
import torch
import models
import data
import pickle


class BaseOptions:
    def __init__(self):
        self.initialized = False

    def initialize(self, parser):
        # experiment specifics
        parser.add_argument(
            "--name",
            type=str,
            default="label2coco",
            help="name of the experiment. It decides where to store samples and models",
        )

        parser.add_argument(
            "--gpu_ids", type=str, default="0", help="gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU"
        )
        parser.add_argument(
            "--checkpoints_dir", type=str, default="./checkpoints", help="models are saved here"
        )
        parser.add_argument("--model", type=str, default="pix2pix", help="which model to use")
        parser.add_argument(
            "--norm_G",
            type=str,
            default="spectralinstance",
            help="instance normalization or batch normalization",
        )
        parser.add_argument(
            "--norm_D",
            type=str,
            default="spectralinstance",
            help="instance normalization or batch normalization",
        )
        parser.add_argument(
            "--norm_E",
            type=str,
            default="spectralinstance",
            help="instance normalization or batch normalization",
        )
        parser.add_argument("--phase", type=str, default="train", help="train, val, test, etc")

        # input/output sizes
        parser.add_argument("--batchSize", type=int, default=1, help="input batch size")
        parser.add_argument(
            "--preprocess_mode",
            type=str,
            default="scale_width_and_crop",
            help="scaling and cropping of images at load time.",
            choices=(
                "resize_and_crop",
                "crop",
                "scale_width",
                "scale_width_and_crop",
                "scale_shortside",
                "scale_shortside_and_crop",
                "fixed",
                "none",
                "resize",
            ),
        )
        parser.add_argument(
            "--load_size",
            type=int,
            default=1024,
            help="Scale images to this size. The final image will be cropped to --crop_size.",
        )
        parser.add_argument(
            "--crop_size",
            type=int,
            default=512,
            help="Crop to the width of crop_size (after initially scaling the images to load_size.)",
        )
        parser.add_argument(
            "--aspect_ratio",
            type=float,
            default=1.0,
            help="The ratio width/height. The final height of the load image will be crop_size/aspect_ratio",
        )
        parser.add_argument(
            "--label_nc",
            type=int,
            default=182,
            help="# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.",
        )
        parser.add_argument(
            "--contain_dontcare_label",
            action="store_true",
            help="if the label map contains dontcare label (dontcare=255)",
        )
        parser.add_argument("--output_nc", type=int, default=3, help="# of output image channels")

        # for setting inputs
        parser.add_argument("--dataroot", type=str, default="./datasets/cityscapes/")
        parser.add_argument("--dataset_mode", type=str, default="coco")
        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(
            "--no_flip",
            action="store_true",
            help="if specified, do not flip the images for data argumentation",
        )
        parser.add_argument("--nThreads", default=0, type=int, help="# threads for loading data")
        parser.add_argument(
            "--max_dataset_size",
            type=int,
            default=sys.maxsize,
            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(
            "--load_from_opt_file",
            action="store_true",
            help="load the options from checkpoints and use that as default",
        )
        parser.add_argument(
            "--cache_filelist_write",
            action="store_true",
            help="saves the current filelist into a text file, so that it loads faster",
        )
        parser.add_argument(
            "--cache_filelist_read", action="store_true", help="reads from the file list cache"
        )

        # for displays
        parser.add_argument("--display_winsize", type=int, default=400, help="display window size")

        # for generator
        parser.add_argument(
            "--netG", type=str, default="spade", help="selects model to use for netG (pix2pixhd | spade)"
        )
        parser.add_argument("--ngf", type=int, default=64, help="# of gen filters in first conv layer")
        parser.add_argument(
            "--init_type",
            type=str,
            default="xavier",
            help="network initialization [normal|xavier|kaiming|orthogonal]",
        )
        parser.add_argument(
            "--init_variance", type=float, default=0.02, help="variance of the initialization distribution"
        )
        parser.add_argument("--z_dim", type=int, default=256, help="dimension of the latent z vector")
        parser.add_argument(
            "--no_parsing_map", action="store_true", help="During training, we do not use the parsing map"
        )

        # for instance-wise features
        parser.add_argument(
            "--no_instance", action="store_true", help="if specified, do *not* add instance map as input"
        )
        parser.add_argument(
            "--nef", type=int, default=16, help="# of encoder filters in the first conv layer"
        )
        parser.add_argument("--use_vae", action="store_true", help="enable training with an image encoder.")
        parser.add_argument(
            "--tensorboard_log", action="store_true", help="use tensorboard to record the resutls"
        )

        # parser.add_argument('--img_dir',)
        parser.add_argument(
            "--old_face_folder", type=str, default="", help="The folder name of input old face"
        )
        parser.add_argument(
            "--old_face_label_folder", type=str, default="", help="The folder name of input old face label"
        )

        parser.add_argument("--injection_layer", type=str, default="all", help="")

        self.initialized = True
        return parser

    def gather_options(self):
        # initialize parser with basic options
        if not self.initialized:
            parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
            parser = self.initialize(parser)

        # get the basic options
        opt, unknown = parser.parse_known_args()

        # modify model-related parser options
        model_name = opt.model
        model_option_setter = models.get_option_setter(model_name)
        parser = model_option_setter(parser, self.isTrain)

        # modify dataset-related parser options
        # dataset_mode = opt.dataset_mode
        # dataset_option_setter = data.get_option_setter(dataset_mode)
        # parser = dataset_option_setter(parser, self.isTrain)

        opt, unknown = parser.parse_known_args()

        # if there is opt_file, load it.
        # The previous default options will be overwritten
        if opt.load_from_opt_file:
            parser = self.update_options_from_file(parser, opt)

        opt = parser.parse_args()
        self.parser = parser
        return opt

    def print_options(self, opt):
        message = ""
        message += "----------------- Options ---------------\n"
        for k, v in sorted(vars(opt).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 option_file_path(self, opt, makedir=False):
        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
        if makedir:
            util.mkdirs(expr_dir)
        file_name = os.path.join(expr_dir, "opt")
        return file_name

    def save_options(self, opt):
        file_name = self.option_file_path(opt, makedir=True)
        with open(file_name + ".txt", "wt") as opt_file:
            for k, v in sorted(vars(opt).items()):
                comment = ""
                default = self.parser.get_default(k)
                if v != default:
                    comment = "\t[default: %s]" % str(default)
                opt_file.write("{:>25}: {:<30}{}\n".format(str(k), str(v), comment))

        with open(file_name + ".pkl", "wb") as opt_file:
            pickle.dump(opt, opt_file)

    def update_options_from_file(self, parser, opt):
        new_opt = self.load_options(opt)
        for k, v in sorted(vars(opt).items()):
            if hasattr(new_opt, k) and v != getattr(new_opt, k):
                new_val = getattr(new_opt, k)
                parser.set_defaults(**{k: new_val})
        return parser

    def load_options(self, opt):
        file_name = self.option_file_path(opt, makedir=False)
        new_opt = pickle.load(open(file_name + ".pkl", "rb"))
        return new_opt

    def parse(self, save=False):

        opt = self.gather_options()
        opt.isTrain = self.isTrain  # train or test
        opt.contain_dontcare_label = False

        self.print_options(opt)
        if opt.isTrain:
            self.save_options(opt)

        # Set semantic_nc based on the option.
        # This will be convenient in many places
        opt.semantic_nc = (
            opt.label_nc + (1 if opt.contain_dontcare_label else 0) + (0 if opt.no_instance else 1)
        )

        # set gpu ids
        str_ids = opt.gpu_ids.split(",")
        opt.gpu_ids = []
        for str_id in str_ids:
            int_id = int(str_id)
            if int_id >= 0:
                opt.gpu_ids.append(int_id)

        if len(opt.gpu_ids) > 0:
            print("The main GPU is ")
            print(opt.gpu_ids[0])
            torch.cuda.set_device(opt.gpu_ids[0])

        assert (
            len(opt.gpu_ids) == 0 or opt.batchSize % len(opt.gpu_ids) == 0
        ), "Batch size %d is wrong. It must be a multiple of # GPUs %d." % (opt.batchSize, len(opt.gpu_ids))

        self.opt = opt
        return self.opt