from collections import OrderedDict
from typing import Tuple

import numpy as np
import torch
from omegaconf import DictConfig
from PIL import Image


def parse_configs(config: DictConfig) -> Tuple[DictConfig, DictConfig, DictConfig, DictConfig, DictConfig, DictConfig, DictConfig, DictConfig, DictConfig]:
    """Load a config file and return component sections as DictConfigs."""
    rae_config = config.get("stage_1", None)
    stage2_config = config.get("stage_2", None)
    transport_config = config.get("transport", None)
    sampler_config = config.get("sampler", None)
    guidance_config = config.get("guidance", None)
    misc = config.get("misc", None)
    training_config = config.get("training", None)
    eval_config = config.get("eval", None)
    dataset_config = config.get("dataset", None)
    return rae_config, stage2_config, transport_config, sampler_config, guidance_config, misc, training_config, eval_config, dataset_config


def center_crop_arr(pil_image, image_size):
    """
    Center cropping implementation from ADM.
    https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
    """
    while min(*pil_image.size) >= 2 * image_size:
        pil_image = pil_image.resize(
            tuple(x // 2 for x in pil_image.size), resample=Image.BOX
        )

    scale = image_size / min(*pil_image.size)
    pil_image = pil_image.resize(
        tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
    )

    arr = np.array(pil_image)
    crop_y = (arr.shape[0] - image_size) // 2
    crop_x = (arr.shape[1] - image_size) // 2
    return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size])


#################################################################################
#                             Training Helper Functions                         #
#################################################################################
def requires_grad(model, flag=True):
    """
    Set requires_grad flag for all parameters in a model.
    """
    for p in model.parameters():
        p.requires_grad = flag


@torch.no_grad()
def update_ema(ema_model, model, decay=0.9999):
    """Step the EMA model towards the current model."""
    ema_params = OrderedDict(ema_model.named_parameters())
    model_params = OrderedDict(model.named_parameters())
    for name, param in model_params.items():
        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)


def get_autocast_kwargs(args) -> dict:
    """Get autocast kwargs for bf16 or fp32 precision."""
    if args.precision == "bf16":
        return dict(enabled=True, dtype=torch.bfloat16)
    return dict(enabled=False)