"""
Generate a large batch of image samples from a model and save them as a large
numpy array. This can be used to produce samples for FID evaluation.
"""

import sys
sys.path.append('../')
import argparse
import os

import numpy as np
import torch as th
import torch.distributed as dist
import matplotlib.pyplot as plt
from improved_diffusion import dist_util, logger
from improved_diffusion.script_util import (
    NUM_CLASSES,
    model_and_diffusion_defaults,
    create_model_and_diffusion,
    add_dict_to_argparser,
    args_to_dict,
)
from PIL import Image
from torchvision.utils import save_image

def main():
    args = create_argparser().parse_args()

    dist_util.setup_dist()
    logger.configure()

    logger.log("creating model and diffusion...")
    model, diffusion = create_model_and_diffusion(
        **args_to_dict(args, model_and_diffusion_defaults().keys())
    )
    model.load_state_dict(
        dist_util.load_state_dict(args.model_path, map_location="cpu")
    )
    model.to(dist_util.dev())
    model.eval() # EVALUATION MODE

    logger.log("sampling...")
    all_images = []
    all_labels = []
    while len(all_images) * args.batch_size < args.num_samples:
        model_kwargs = {}
        if args.class_cond:
            # classes = th.randint(
            #     low=0, high=NUM_CLASSES, size=(args.batch_size,), device=dist_util.dev()
            # )
            classes = th.ones((args.batch_size,), device=dist_util.dev(), dtype=th.int32) * 4
            model_kwargs["y"] = classes


        sample_fn = (
            diffusion.p_sample_loop if not args.use_ddim else diffusion.ddim_sample_loop
        )

        z = th.randn((args.batch_size, 512), device=dist_util.dev())
        # z = -th.ones((args.batch_size, 512), device=dist_util.dev())
        model_kwargs["z"] = z
        sample = sample_fn(
            model,
            (args.batch_size, 1, args.image_size, args.image_size),
            clip_denoised=args.clip_denoised,
            model_kwargs=model_kwargs,
        )

        # DO THIS FOR RGB IMAGES ONLY
        # sample = ((sample + 1) * 127.5).clamp(0, 255).to(th.uint8)
        # sample = sample.permute(0, 2, 3, 1)
        # sample = sample.to(th.uint8).contiguous()

        gathered_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())]
        dist.all_gather(gathered_samples, sample)  # gather not supported with NCCL
        all_images.extend([sample.cpu().numpy() for sample in gathered_samples])

        # IF CLASS CONDITIONAL GENERATION
        if args.class_cond:
            gathered_labels = [
                th.zeros_like(classes) for _ in range(dist.get_world_size())
            ]
            dist.all_gather(gathered_labels, classes)
            all_labels.extend([labels.cpu().numpy() for labels in gathered_labels])
        logger.log(f"created {len(all_images) * args.batch_size} samples") # PRINTS HOW MANY SAMPLES HAVE BEEN GENERATED
        break

    ls = []
    arr = np.concatenate(all_images, axis=0)
    arr = arr[: args.num_samples]
    temp = arr[:16]

    temp = th.tensor(temp, dtype=th.float32)
    save_image(temp, './mnist_representation.png')

    # img = Image.fromarray(temp, 'RGB')
    # img.save('test_img.jpeg')

    if args.class_cond:
        label_arr = np.concatenate(all_labels, axis=0)
        label_arr = label_arr[: args.num_samples]
    if dist.get_rank() == 0:
        shape_str = "x".join([str(x) for x in arr.shape])
        out_path = os.path.join(logger.get_dir(), f"samples_{shape_str}.npz")
        logger.log(f"saving to {out_path}")
        if args.class_cond:
            np.savez(out_path, arr, label_arr)
        else:
            np.savez(out_path, arr)

    dist.barrier()
    logger.log("sampling complete")


def create_argparser():
    defaults = dict(
        clip_denoised=True,
        num_samples=10000,
        batch_size=16,
        use_ddim=False,
        model_path="",
    )
    defaults.update(model_and_diffusion_defaults())
    parser = argparse.ArgumentParser()
    add_dict_to_argparser(parser, defaults)
    return parser


if __name__ == "__main__":
    main()
