import os
from pprint import pformat

import colossalai
import torch
import torch.distributed as dist
from colossalai.cluster import DistCoordinator
from mmengine.runner import set_random_seed
from tqdm import tqdm

from opensora.acceleration.parallel_states import set_sequence_parallel_group
from opensora.datasets import save_sample
from opensora.models.text_encoder.t5 import text_preprocessing
from opensora.registry import MODELS, SCHEDULERS, build_module
from opensora.utils.config_utils import parse_configs
from opensora.utils.inference_utils import get_save_path_name, load_prompts, prepare_multi_resolution_info
from opensora.utils.misc import all_exists, create_logger, is_distributed, is_main_process, to_torch_dtype


def main():
    torch.set_grad_enabled(False)
    # ======================================================
    # configs & runtime variables
    # ======================================================
    # == parse configs ==
    cfg = parse_configs(training=False)

    # == device and dtype ==
    device = "cuda" if torch.cuda.is_available() else "cpu"
    cfg_dtype = cfg.get("dtype", "fp32")
    assert cfg_dtype in ["fp16", "bf16", "fp32"], f"Unknown mixed precision {cfg_dtype}"
    dtype = to_torch_dtype(cfg.get("dtype", "bf16"))
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True

    # == init distributed env ==
    if is_distributed():
        colossalai.launch_from_torch({})
        coordinator = DistCoordinator()
        enable_sequence_parallelism = coordinator.world_size > 1
        if enable_sequence_parallelism:
            set_sequence_parallel_group(dist.group.WORLD)
    else:
        coordinator = None
        enable_sequence_parallelism = False
    set_random_seed(seed=cfg.seed)

    # == init logger ==
    logger = create_logger()
    logger.info("Training configuration:\n %s", pformat(cfg.to_dict()))
    verbose = cfg.get("verbose", 1)
    progress_wrap = tqdm if verbose == 1 else (lambda x: x)

    # ======================================================
    # build model & load weights
    # ======================================================
    logger.info("Building models...")
    # == build text-encoder and vae ==
    text_encoder = build_module(cfg.text_encoder, MODELS, device=device)
    vae = build_module(cfg.vae, MODELS).to(device, dtype).eval()

    # == build diffusion model ==
    input_size = (cfg.get("num_frames", None), *cfg.get("image_size", (None, None)))
    latent_size = vae.get_latent_size(input_size)
    model = (
        build_module(
            cfg.model,
            MODELS,
            input_size=latent_size,
            in_channels=vae.out_channels,
            caption_channels=text_encoder.output_dim,
            model_max_length=text_encoder.model_max_length,
            enable_sequence_parallelism=enable_sequence_parallelism,
        )
        .to(device, dtype)
        .eval()
    )
    text_encoder.y_embedder = model.y_embedder  # HACK: for classifier-free guidance

    # == build scheduler ==
    scheduler = build_module(cfg.scheduler, SCHEDULERS)

    # ======================================================
    # inference
    # ======================================================
    # == load prompts ==
    prompts = cfg.get("prompt", None)
    start_idx = cfg.get("start_index", 0)
    if prompts is None:
        assert cfg.get("prompt_path", None) is not None, "Prompt or prompt_path must be provided"
        prompts = load_prompts(cfg.prompt_path, start_idx, cfg.get("end_index", None))

    # == prepare arguments ==
    image_size = cfg.image_size
    num_frames = cfg.num_frames
    fps = cfg.fps
    save_fps = cfg.fps // cfg.get("frame_interval", 1)
    multi_resolution = cfg.get("multi_resolution", None)
    batch_size = cfg.get("batch_size", 1)
    num_sample = cfg.get("num_sample", 1)

    save_dir = cfg.save_dir
    os.makedirs(save_dir, exist_ok=True)
    sample_name = cfg.get("sample_name", None)
    prompt_as_path = cfg.get("prompt_as_path", False)

    # == Iter over all samples ==
    for i in progress_wrap(range(0, len(prompts), batch_size)):
        # == prepare batch prompts ==
        batch_prompts_raw = prompts[i : i + batch_size]
        batch_prompts = [text_preprocessing(prompt) for prompt in batch_prompts_raw]

        # == multi-resolution info ==
        model_args = prepare_multi_resolution_info(
            multi_resolution, len(batch_prompts), image_size, num_frames, fps, device, dtype
        )

        # == Iter over number of sampling for one prompt ==
        for k in range(num_sample):
            # == prepare save paths ==
            save_paths = [
                get_save_path_name(
                    save_dir,
                    sample_name=sample_name,
                    sample_idx=start_idx + idx,
                    prompt=batch_prompts_raw[idx],
                    prompt_as_path=prompt_as_path,
                    num_sample=num_sample,
                    k=k,
                )
                for idx in range(len(batch_prompts))
            ]

            # NOTE: Skip if the sample already exists
            # This is useful for resuming sampling VBench
            if prompt_as_path and all_exists(save_paths):
                continue

            # == sampling ==
            z = torch.randn(len(batch_prompts), vae.out_channels, *latent_size, device=device, dtype=dtype)
            samples = scheduler.sample(
                model,
                text_encoder,
                z=z,
                prompts=batch_prompts,
                device=device,
                additional_args=model_args,
                progress=verbose >= 2,
            )
            samples = vae.decode(samples.to(dtype), num_frames=num_frames)

            # == save samples ==
            if is_main_process():
                for idx, sample in enumerate(samples):
                    if verbose >= 2:
                        logger.info("Prompt: %s", batch_prompts_raw[idx])
                    save_path = save_paths[idx]
                    save_sample(
                        sample,
                        fps=save_fps,
                        save_path=save_path,
                        verbose=verbose >= 2,
                    )
        start_idx += len(batch_prompts)
    logger.info("Inference finished.")
    logger.info("Saved samples to %s", save_dir)


if __name__ == "__main__":
    main()