dataset_code/sft_sftnews/offload/utils_framepack.py

import math
import random
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from einops import rearrange, repeat

from diffusers.training_utils import compute_density_for_timestep_sampling


DEFAULT_PROMPT_TEMPLATE = {
    "template": (
        "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
        "1. The main content and theme of the video."
        "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
        "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
        "4. background environment, light, style and atmosphere."
        "5. camera angles, movements, and transitions used in the video:<|eot_id|>"
        "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
    ),
    "crop_start": 95,
}

def get_config_value(args, name):
    if hasattr(args, name):
        return getattr(args, name)
    elif hasattr(args, 'training_config') and hasattr(args.training_config, name):
        return getattr(args.training_config, name)
    else:
        raise AttributeError(f"Neither args nor args.training_config has attribute '{name}'")

# Copied from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video.HunyuanVideoPipeline._get_llama_prompt_embeds
def _get_llama_prompt_embeds(
    tokenizer,
    text_encoder,
    prompt: Union[str, List[str]],
    prompt_template: Dict[str, Any],
    num_videos_per_prompt: int = 1,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
    max_sequence_length: int = 256,
    num_hidden_layers_to_skip: int = 2,
) -> Tuple[torch.Tensor, torch.Tensor]:
    device = device
    dtype = dtype

    prompt = [prompt] if isinstance(prompt, str) else prompt
    batch_size = len(prompt)

    prompt = [prompt_template["template"].format(p) for p in prompt]

    crop_start = prompt_template.get("crop_start", None)
    if crop_start is None:
        prompt_template_input = tokenizer(
            prompt_template["template"],
            padding="max_length",
            return_tensors="pt",
            return_length=False,
            return_overflowing_tokens=False,
            return_attention_mask=False,
        )
        crop_start = prompt_template_input["input_ids"].shape[-1]
        # Remove <|eot_id|> token and placeholder {}
        crop_start -= 2

    max_sequence_length += crop_start
    text_inputs = tokenizer(
        prompt,
        max_length=max_sequence_length,
        padding="max_length",
        truncation=True,
        return_tensors="pt",
        return_length=False,
        return_overflowing_tokens=False,
        return_attention_mask=True,
    )
    text_input_ids = text_inputs.input_ids.to(device=device)
    prompt_attention_mask = text_inputs.attention_mask.to(device=device)

    prompt_embeds = text_encoder(
        input_ids=text_input_ids,
        attention_mask=prompt_attention_mask,
        output_hidden_states=True,
    ).hidden_states[-(num_hidden_layers_to_skip + 1)]
    prompt_embeds = prompt_embeds.to(dtype=dtype)

    if crop_start is not None and crop_start > 0:
        prompt_embeds = prompt_embeds[:, crop_start:]
        prompt_attention_mask = prompt_attention_mask[:, crop_start:]

    # duplicate text embeddings for each generation per prompt, using mps friendly method
    _, seq_len, _ = prompt_embeds.shape
    prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
    prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
    prompt_attention_mask = prompt_attention_mask.repeat(1, num_videos_per_prompt)
    prompt_attention_mask = prompt_attention_mask.view(batch_size * num_videos_per_prompt, seq_len)

    return prompt_embeds, prompt_attention_mask


# Copied from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video.HunyuanVideoPipeline._get_clip_prompt_embeds
def _get_clip_prompt_embeds(
    tokenizer_2,
    text_encoder_2,
    prompt: Union[str, List[str]],
    num_videos_per_prompt: int = 1,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
    max_sequence_length: int = 77,
) -> torch.Tensor:
    device = device
    dtype = dtype

    prompt = [prompt] if isinstance(prompt, str) else prompt
    batch_size = len(prompt)

    text_inputs = tokenizer_2(
        prompt,
        padding="max_length",
        max_length=max_sequence_length,
        truncation=True,
        return_tensors="pt",
    )

    text_input_ids = text_inputs.input_ids
    untruncated_ids = tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids
    if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
        _ = tokenizer_2.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])

    prompt_embeds = text_encoder_2(text_input_ids.to(device), output_hidden_states=False).pooler_output

    # duplicate text embeddings for each generation per prompt, using mps friendly method
    prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt)
    prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, -1)

    return prompt_embeds


# Copied from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video.HunyuanVideoPipeline.encode_prompt
def encode_prompt(
    tokenizer,
    text_encoder,
    tokenizer_2,
    text_encoder_2,
    prompt: Union[str, List[str]],
    prompt_2: Union[str, List[str]] = None,
    prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
    num_videos_per_prompt: int = 1,
    prompt_embeds: Optional[torch.Tensor] = None,
    pooled_prompt_embeds: Optional[torch.Tensor] = None,
    prompt_attention_mask: Optional[torch.Tensor] = None,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
    max_sequence_length: int = 256,
):
    if prompt_embeds is None:
        prompt_embeds, prompt_attention_mask = _get_llama_prompt_embeds(
            tokenizer,
            text_encoder,
            prompt,
            prompt_template,
            num_videos_per_prompt,
            device=device,
            dtype=dtype,
            max_sequence_length=max_sequence_length,
        )

    if pooled_prompt_embeds is None:
        if prompt_2 is None:
            prompt_2 = prompt
        pooled_prompt_embeds = _get_clip_prompt_embeds(
            tokenizer_2,
            text_encoder_2,
            prompt,
            num_videos_per_prompt,
            device=device,
            dtype=dtype,
            max_sequence_length=77,
        )

    return prompt_embeds, pooled_prompt_embeds, prompt_attention_mask


def encode_image(
    feature_extractor,
    image_encoder,
    image: torch.Tensor,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
):
    device = device
    image = (image + 1) / 2.0  # [-1, 1] -> [0, 1]
    image = feature_extractor(images=image, return_tensors="pt", do_rescale=False).to(
        device=device, dtype=image_encoder.dtype
    )
    image_embeds = image_encoder(**image).last_hidden_state
    return image_embeds.to(dtype=dtype)


def get_framepack_input_t2v(
    vae,
    pixel_values,  # [-1, 1], (B, C, F, H, W)
    latent_window_size: int = 9,
    vanilla_sampling: bool = False,
    dtype: Optional[torch.dtype] = None,
    is_keep_x0=False,
):
    # calculate latent frame count from original frame count (4n+1)
    latent_f = (pixel_values.shape[2] - 1) // 4 + 1
    # assert latent_f % latent_window_size == 0

    # calculate the total number of sections (excluding the first frame, divided by window size)
    total_latent_sections = math.floor(latent_f / latent_window_size)  # 2.0
    if total_latent_sections < 1:
        min_frames_needed = latent_window_size * 4 + 1
        raise ValueError(
            f"Not enough frames for FramePack: {pixel_values.shape[2]} frames ({latent_f} latent frames), minimum required: {min_frames_needed} frames ({latent_window_size + 1} latent frames)"
        )

    # actual latent frame count (aligned to section boundaries)
    latent_f_aligned = total_latent_sections * latent_window_size

    # actual video frame count
    frame_count_aligned = (latent_f_aligned - 1) * 4 + 1  # 73
    if frame_count_aligned != pixel_values.shape[2]:  # 73 != 89
        print(
            f"Frame count mismatch: required={frame_count_aligned} != actual={pixel_values.shape[2]}, trimming to {frame_count_aligned}"
        )
        pixel_values = pixel_values[
            :, :, :frame_count_aligned, :, :
        ]  # torch.Size([1, 3, 89, 480, 832]) -> torch.Size([1, 3, 73, 480, 832])

    latent_f = latent_f_aligned  # Update to the aligned value

    # VAE encode
    pixel_values = pixel_values.to(device=vae.device, dtype=vae.dtype)
    latents = vae.encode(pixel_values).latent_dist.sample()
    latents = latents * vae.config.scaling_factor
    latents = latents.to(dtype=dtype)

    all_target_latents = []
    all_target_latent_indices = []
    all_clean_latents = []
    all_clean_latent_indices = []
    all_clean_latents_2x = []
    all_clean_latent_2x_indices = []
    all_clean_latents_4x = []
    all_clean_latent_4x_indices = []
    section_to_video_idx = []

    if vanilla_sampling:
        # Vanilla Sampling Logic
        if is_keep_x0:
            for b in range(latents.shape[0]):
                video_lat = latents[b : b + 1]  # Keep batch dim: 1, C, F_aligned, H, W

                for section_index in range(total_latent_sections):
                    target_start_f = section_index * latent_window_size
                    target_end_f = target_start_f + latent_window_size
                    start_latent = video_lat[:, :, 0:1, :, :]
                    target_latents = video_lat[:, :, target_start_f:target_end_f, :, :]

                    # Clean latents preparation (Vanilla)
                    if section_index == 0:
                        clean_latents_total_count = 2 + 2 + 16
                    else:
                        clean_latents_total_count = 1 + 2 + 16
                    history_latents = torch.zeros(
                        size=(
                            1,
                            16,
                            clean_latents_total_count,
                            video_lat.shape[-2],
                            video_lat.shape[-1],
                        ),
                        device=video_lat.device,
                        dtype=video_lat.dtype,
                    )

                    history_start_f = 0
                    video_start_f = target_start_f - clean_latents_total_count
                    copy_count = clean_latents_total_count

                    if video_start_f < 0:
                        history_start_f = -video_start_f
                        copy_count = clean_latents_total_count - history_start_f
                        video_start_f = 0
                    if copy_count > 0:
                        history_latents[:, :, history_start_f:] = video_lat[
                            :, :, video_start_f : video_start_f + copy_count, :, :
                        ]

                    # indices generation (Vanilla): copy from FramePack-F1
                    if section_index == 0:
                        indices = torch.arange(0, sum([16, 2, 2, latent_window_size])).unsqueeze(0)
                        (
                            clean_latent_4x_indices,
                            clean_latent_2x_indices,
                            clean_latent_indices,
                            latent_indices,
                        ) = indices.split([16, 2, 2, latent_window_size], dim=1)
                        clean_latents_4x, clean_latents_2x, clean_latents = history_latents.split([16, 2, 2], dim=2)
                    else:
                        indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0)
                        (
                            clean_latent_indices_start,
                            clean_latent_4x_indices,
                            clean_latent_2x_indices,
                            clean_latent_1x_indices,
                            latent_indices,
                        ) = indices.split([1, 16, 2, 1, latent_window_size], dim=1)
                        clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)

                        clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents.split([16, 2, 1], dim=2)
                        clean_latents = torch.cat([start_latent, clean_latents_1x], dim=2)

                    all_target_latents.append(target_latents)
                    all_target_latent_indices.append(latent_indices)
                    all_clean_latents.append(clean_latents)
                    all_clean_latent_indices.append(clean_latent_indices)
                    all_clean_latents_2x.append(clean_latents_2x)
                    all_clean_latent_2x_indices.append(clean_latent_2x_indices)
                    all_clean_latents_4x.append(clean_latents_4x)
                    all_clean_latent_4x_indices.append(clean_latent_4x_indices)
                    section_to_video_idx.append(b)
        else:
            for b in range(latents.shape[0]):
                video_lat = latents[b : b + 1]  # Keep batch dim: 1, C, F_aligned, H, W

                for section_index in range(total_latent_sections):
                    target_start_f = section_index * latent_window_size
                    target_end_f = target_start_f + latent_window_size
                    target_latents = video_lat[:, :, target_start_f:target_end_f, :, :]

                    # Clean latents preparation (Vanilla)
                    clean_latents_total_count = 2 + 2 + 16
                    history_latents = torch.zeros(
                        size=(
                            1,
                            16,
                            clean_latents_total_count,
                            video_lat.shape[-2],
                            video_lat.shape[-1],
                        ),
                        device=video_lat.device,
                        dtype=video_lat.dtype,
                    )

                    history_start_f = 0
                    video_start_f = target_start_f - clean_latents_total_count
                    copy_count = clean_latents_total_count

                    if video_start_f < 0:
                        history_start_f = -video_start_f
                        copy_count = clean_latents_total_count - history_start_f
                        video_start_f = 0
                    if copy_count > 0:
                        history_latents[:, :, history_start_f:] = video_lat[
                            :, :, video_start_f : video_start_f + copy_count, :, :
                        ]

                    # indices generation (Vanilla): copy from FramePack-F1
                    indices = torch.arange(0, sum([16, 2, 2, latent_window_size])).unsqueeze(0)
                    (
                        clean_latent_4x_indices,
                        clean_latent_2x_indices,
                        clean_latent_indices,
                        latent_indices,
                    ) = indices.split([16, 2, 2, latent_window_size], dim=1)
                    clean_latents_4x, clean_latents_2x, clean_latents = history_latents.split([16, 2, 2], dim=2)

                    all_target_latents.append(target_latents)
                    all_target_latent_indices.append(latent_indices)
                    all_clean_latents.append(clean_latents)
                    all_clean_latent_indices.append(clean_latent_indices)
                    all_clean_latents_2x.append(clean_latents_2x)
                    all_clean_latent_2x_indices.append(clean_latent_2x_indices)
                    all_clean_latents_4x.append(clean_latents_4x)
                    all_clean_latent_4x_indices.append(clean_latent_4x_indices)
                    section_to_video_idx.append(b)
    else:
        pass

    # Stack all sections into batches
    batched_target_latents = torch.cat(all_target_latents, dim=0)
    batched_target_latent_indices = torch.cat(all_target_latent_indices, dim=0)
    batched_clean_latents = torch.cat(all_clean_latents, dim=0)
    batched_clean_latent_indices = torch.cat(all_clean_latent_indices, dim=0)
    batched_clean_latents_2x = torch.cat(all_clean_latents_2x, dim=0)
    batched_clean_latent_2x_indices = torch.cat(all_clean_latent_2x_indices, dim=0)
    batched_clean_latents_4x = torch.cat(all_clean_latents_4x, dim=0)
    batched_clean_latent_4x_indices = torch.cat(all_clean_latent_4x_indices, dim=0)

    return (
        batched_target_latents,
        batched_target_latent_indices,
        batched_clean_latents,
        batched_clean_latent_indices,
        batched_clean_latents_2x,
        batched_clean_latent_2x_indices,
        batched_clean_latents_4x,
        batched_clean_latent_4x_indices,
        section_to_video_idx,
    )


def get_framepack_input_i2v(
    vae,
    pixel_values,  # [-1, 1], (B, C, F, H, W)
    latent_window_size: int = 9,
    vanilla_sampling: bool = False,
    dtype: Optional[torch.dtype] = None,
):
    # calculate latent frame count from original frame count (4n+1)
    latent_f = (pixel_values.shape[2] - 1) // 4 + 1

    # calculate the total number of sections (excluding the first frame, divided by window size)
    total_latent_sections = math.floor((latent_f - 1) / latent_window_size)  # 2.0
    if total_latent_sections < 1:
        min_frames_needed = latent_window_size * 4 + 1
        raise ValueError(
            f"Not enough frames for FramePack: {pixel_values.shape[2]} frames ({latent_f} latent frames), minimum required: {min_frames_needed} frames ({latent_window_size + 1} latent frames)"
        )

    # actual latent frame count (aligned to section boundaries)
    latent_f_aligned = total_latent_sections * latent_window_size + 1

    # actual video frame count
    frame_count_aligned = (latent_f_aligned - 1) * 4 + 1  # 73
    if frame_count_aligned != pixel_values.shape[2]:  # 73 != 89
        print(
            f"Frame count mismatch: required={frame_count_aligned} != actual={pixel_values.shape[2]}, trimming to {frame_count_aligned}"
        )
        pixel_values = pixel_values[
            :, :, :frame_count_aligned, :, :
        ]  # torch.Size([1, 3, 89, 480, 832]) -> torch.Size([1, 3, 73, 480, 832])

    latent_f = latent_f_aligned  # Update to the aligned value

    # VAE encode
    pixel_values = pixel_values.to(device=vae.device, dtype=vae.dtype)
    latents = vae.encode(pixel_values).latent_dist.sample()
    latents = latents * vae.config.scaling_factor
    latents = latents.to(dtype=dtype)

    all_target_latents = []
    all_target_latent_indices = []
    all_clean_latents = []
    all_clean_latent_indices = []
    all_clean_latents_2x = []
    all_clean_latent_2x_indices = []
    all_clean_latents_4x = []
    all_clean_latent_4x_indices = []
    section_to_video_idx = []

    if vanilla_sampling:
        # Vanilla Sampling Logic
        for b in range(latents.shape[0]):
            video_lat = latents[b : b + 1]  # Keep batch dim: 1, C, F_aligned, H, W

            for section_index in range(total_latent_sections):
                target_start_f = section_index * latent_window_size + 1
                target_end_f = target_start_f + latent_window_size
                target_latents = video_lat[:, :, target_start_f:target_end_f, :, :]
                start_latent = video_lat[:, :, 0:1, :, :]

                # Clean latents preparation (Vanilla)
                clean_latents_total_count = 1 + 2 + 16
                history_latents = torch.zeros(
                    size=(
                        1,
                        16,
                        clean_latents_total_count,
                        video_lat.shape[-2],
                        video_lat.shape[-1],
                    ),
                    device=video_lat.device,
                    dtype=video_lat.dtype,
                )

                history_start_f = 0
                video_start_f = target_start_f - clean_latents_total_count
                copy_count = clean_latents_total_count

                if video_start_f < 0:
                    history_start_f = -video_start_f
                    copy_count = clean_latents_total_count - history_start_f
                    video_start_f = 0
                if copy_count > 0:
                    history_latents[:, :, history_start_f:] = video_lat[
                        :, :, video_start_f : video_start_f + copy_count, :, :
                    ]

                # indices generation (Vanilla): copy from FramePack-F1
                indices = torch.arange(0, sum([1, 16, 2, 1, latent_window_size])).unsqueeze(0)
                (
                    clean_latent_indices_start,
                    clean_latent_4x_indices,
                    clean_latent_2x_indices,
                    clean_latent_1x_indices,
                    latent_indices,
                ) = indices.split([1, 16, 2, 1, latent_window_size], dim=1)
                clean_latent_indices = torch.cat([clean_latent_indices_start, clean_latent_1x_indices], dim=1)

                clean_latents_4x, clean_latents_2x, clean_latents_1x = history_latents.split([16, 2, 1], dim=2)
                clean_latents = torch.cat([start_latent, clean_latents_1x], dim=2)

                all_target_latents.append(target_latents)
                all_target_latent_indices.append(latent_indices)
                all_clean_latents.append(clean_latents)
                all_clean_latent_indices.append(clean_latent_indices)
                all_clean_latents_2x.append(clean_latents_2x)
                all_clean_latent_2x_indices.append(clean_latent_2x_indices)
                all_clean_latents_4x.append(clean_latents_4x)
                all_clean_latent_4x_indices.append(clean_latent_4x_indices)
                section_to_video_idx.append(b)
    else:
        # padding is reversed for inference (future to past)
        latent_paddings = list(reversed(range(total_latent_sections)))  # [1, 0]
        # Note: The padding trick for inference. See the paper for details.
        if total_latent_sections > 4:
            latent_paddings = [3] + [2] * (total_latent_sections - 3) + [1, 0]

        for b in range(latents.shape[0]):
            video_lat = latents[
                b : b + 1
            ]  # keep batch dim, (1, C, F, H, W)           # torch.Size([1, 16, 19, 60, 104])

            # emulate inference step (history latents)
            # Note: In inference, history_latents stores *generated* future latents.
            # Here, for caching, we just need its shape and type for clean_* tensors.
            # The actual content doesn't matter much as clean_* will be overwritten.
            history_latents = torch.zeros(
                (
                    1,
                    video_lat.shape[1],
                    1 + 2 + 16,
                    video_lat.shape[3],
                    video_lat.shape[4],
                ),
                dtype=video_lat.dtype,
            ).to(video_lat.device)  # torch.Size([1, 16, 19, 60, 104])

            latent_f_index = latent_f - latent_window_size  # Start from the last section     # 19 - 9 = 10
            section_index = total_latent_sections - 1  # 2 - 1 = 1

            for latent_padding in latent_paddings:
                is_last_section = (
                    section_index == 0
                )  # the last section in inference order == the first section in time
                latent_padding_size = latent_padding * latent_window_size
                if is_last_section:
                    assert latent_f_index == 1, "Last section should be starting from frame 1"

                # indices generation (same as inference)
                indices = torch.arange(0, sum([1, latent_padding_size, latent_window_size, 1, 2, 16])).unsqueeze(0)
                (
                    clean_latent_indices_pre,  # Index for start_latent
                    blank_indices,  # Indices for padding (future context in inference)
                    latent_indices,  # Indices for the target latents to predict
                    clean_latent_indices_post,  # Index for the most recent history frame
                    clean_latent_2x_indices,  # Indices for the next 2 history frames
                    clean_latent_4x_indices,  # Indices for the next 16 history frames
                ) = indices.split([1, latent_padding_size, latent_window_size, 1, 2, 16], dim=1)

                # Indices for clean_latents (start + recent history)
                clean_latent_indices = torch.cat([clean_latent_indices_pre, clean_latent_indices_post], dim=1)

                # clean latents preparation (emulating inference)
                clean_latents_pre = video_lat[:, :, 0:1, :, :]  # Always the first frame (start_latent)
                clean_latents_post, clean_latents_2x, clean_latents_4x = history_latents[
                    :, :, : 1 + 2 + 16, :, :
                ].split([1, 2, 16], dim=2)
                clean_latents = torch.cat(
                    [clean_latents_pre, clean_latents_post], dim=2
                )  # Combine start frame + placeholder

                # Target latents for this section (ground truth)
                target_latents = video_lat[:, :, latent_f_index : latent_f_index + latent_window_size, :, :]

                all_target_latents.append(target_latents)
                all_target_latent_indices.append(latent_indices)
                all_clean_latents.append(clean_latents)
                all_clean_latent_indices.append(clean_latent_indices)
                all_clean_latents_2x.append(clean_latents_2x)
                all_clean_latent_2x_indices.append(clean_latent_2x_indices)
                all_clean_latents_4x.append(clean_latents_4x)
                all_clean_latent_4x_indices.append(clean_latent_4x_indices)
                section_to_video_idx.append(b)

                if is_last_section:  # If this was the first section generated in inference (time=0)
                    # History gets the start frame + the generated first section
                    generated_latents_for_history = video_lat[:, :, : latent_window_size + 1, :, :]
                else:
                    # History gets the generated current section
                    generated_latents_for_history = target_latents  # Use true latents as stand-in for generated

                history_latents = torch.cat([generated_latents_for_history, history_latents], dim=2)

                section_index -= 1
                latent_f_index -= latent_window_size

    # Stack all sections into batches
    batched_target_latents = torch.cat(all_target_latents, dim=0)
    batched_target_latent_indices = torch.cat(all_target_latent_indices, dim=0)
    batched_clean_latents = torch.cat(all_clean_latents, dim=0)
    batched_clean_latent_indices = torch.cat(all_clean_latent_indices, dim=0)
    batched_clean_latents_2x = torch.cat(all_clean_latents_2x, dim=0)
    batched_clean_latent_2x_indices = torch.cat(all_clean_latent_2x_indices, dim=0)
    batched_clean_latents_4x = torch.cat(all_clean_latents_4x, dim=0)
    batched_clean_latent_4x_indices = torch.cat(all_clean_latent_4x_indices, dim=0)

    return (
        batched_target_latents,
        batched_target_latent_indices,
        batched_clean_latents,
        batched_clean_latent_indices,
        batched_clean_latents_2x,
        batched_clean_latent_2x_indices,
        batched_clean_latents_4x,
        batched_clean_latent_4x_indices,
        section_to_video_idx,
    )


def get_pyramid_input(
    args,
    scheduler,
    latents,  # [b c t h w]
    pyramid_stage_num=3,
    pyramid_sample_ratios=[1, 2, 1],
    pyramid_sample_mode="efficient",  # ["efficient", "full", "diffusion_forcing", "stream_sample"]
    pyramid_stream_inference_steps=[10, 10, 10],
    stream_chunk_size=5,
):
    assert pyramid_stage_num == len(pyramid_sample_ratios)
    if pyramid_sample_mode not in ["efficient", "full", "diffusion_forcing", "stream_sample"]:
        raise ValueError(
            f"Invalid pyramid_sample_mode: {pyramid_sample_mode}. Must be one of ['efficient', 'full', 'diffusion_forcing', 'dance_forcing']."
        )

    # Get clen pyramid latent list
    pyramid_latent_list = []
    pyramid_latent_list.append(latents)
    num_frames, height, width = latents.shape[-3], latents.shape[-2], latents.shape[-1]
    for _ in range(pyramid_stage_num - 1):
        height //= 2
        width //= 2
        latents = rearrange(latents, "b c t h w -> (b t) c h w")
        latents = torch.nn.functional.interpolate(latents, size=(height, width), mode="bilinear")
        latents = rearrange(latents, "(b t) c h w -> b c t h w", t=num_frames)
        pyramid_latent_list.append(latents)
    pyramid_latent_list = list(reversed(pyramid_latent_list))

    # Get pyramid noise list
    noise = torch.randn_like(pyramid_latent_list[-1])
    device = noise.device
    dtype = pyramid_latent_list[-1].dtype
    latent_frame_num = noise.shape[2]
    input_video_num = noise.shape[0]

    height, width = noise.shape[-2], noise.shape[-1]
    noise_list = [noise]
    cur_noise = noise
    for i_s in range(pyramid_stage_num - 1):
        height //= 2
        width //= 2
        cur_noise = rearrange(cur_noise, "b c t h w -> (b t) c h w")
        cur_noise = F.interpolate(cur_noise, size=(height, width), mode="bilinear") * 2
        cur_noise = rearrange(cur_noise, "(b t) c h w -> b c t h w", t=latent_frame_num)
        noise_list.append(cur_noise)
    noise_list = list(reversed(noise_list))  # make sure from low res to high res

    # Get pyramid target list
    if pyramid_sample_mode == "efficient":
        assert input_video_num % (int(sum(pyramid_sample_ratios))) == 0
        # To calculate the padding batchsize and column size
        bsz = input_video_num // int(sum(pyramid_sample_ratios))
        column_size = int(sum(pyramid_sample_ratios))
        column_to_stage = {}
        i_sum = 0
        for i_s, column_num in enumerate(pyramid_sample_ratios):
            for index in range(i_sum, i_sum + column_num):
                column_to_stage[index] = i_s
            i_sum += column_num

        # from low resolution to high resolution
        noisy_latents_list = []
        sigmas_list = []
        targets_list = []
        timesteps_list = []
        training_steps = scheduler.config.num_train_timesteps
        for index in range(column_size):
            i_s = column_to_stage[index]
            clean_latent = pyramid_latent_list[i_s][index::column_size]  # [bs, c, t, h, w]
            last_clean_latent = None if i_s == 0 else pyramid_latent_list[i_s - 1][index::column_size]
            start_sigma = scheduler.start_sigmas[i_s]
            end_sigma = scheduler.end_sigmas[i_s]

            if i_s == 0:
                start_point = noise_list[i_s][index::column_size]
            else:
                # Get the upsampled latent
                last_clean_latent = rearrange(last_clean_latent, "b c t h w -> (b t) c h w")
                last_clean_latent = F.interpolate(
                    last_clean_latent,
                    size=(
                        last_clean_latent.shape[-2] * 2,
                        last_clean_latent.shape[-1] * 2,
                    ),
                    mode="nearest",
                )
                last_clean_latent = rearrange(last_clean_latent, "(b t) c h w -> b c t h w", t=latent_frame_num)
                start_point = start_sigma * noise_list[i_s][index::column_size] + (1 - start_sigma) * last_clean_latent

            if i_s == pyramid_stage_num - 1:
                end_point = clean_latent
            else:
                end_point = end_sigma * noise_list[i_s][index::column_size] + (1 - end_sigma) * clean_latent

            # Sample a random timestep for each image
            # for weighting schemes where we sample timesteps non-uniformly
            u = compute_density_for_timestep_sampling(
                weighting_scheme=get_config_value(args, 'weighting_scheme'),
                batch_size=bsz,
                logit_mean=get_config_value(args, 'logit_mean'),
                logit_std=get_config_value(args, 'logit_std'),
                mode_scale=get_config_value(args, 'mode_scale'),
            )
            indices = (u * training_steps).long()  # Totally 1000 training steps per stage
            indices = indices.clamp(0, training_steps - 1)
            timesteps = scheduler.timesteps_per_stage[i_s][indices].to(device=device)

            # Add noise according to flow matching.
            # zt = (1 - texp) * x + texp * z1
            sigmas = scheduler.sigmas_per_stage[i_s][indices].to(device=device)
            while len(sigmas.shape) < start_point.ndim:
                sigmas = sigmas.unsqueeze(-1)

            noisy_latents = sigmas * start_point + (1 - sigmas) * end_point

            # [stage1_latent, stage2_latent, ..., stagen_latent], which will be concat after patching
            noisy_latents_list.append([noisy_latents.to(dtype)])
            sigmas_list.append(sigmas.to(dtype))
            timesteps_list.append(timesteps.to(dtype))
            targets_list.append(start_point - end_point)  # The standard rectified flow matching objective
    elif pyramid_sample_mode == "full":
        # To calculate the batchsize
        bsz = input_video_num

        # from low resolution to high resolution
        noisy_latents_list = []
        sigmas_list = []
        targets_list = []
        timesteps_list = []
        training_steps = scheduler.config.num_train_timesteps
        for i_s, cur_sample_ratio in zip(range(pyramid_stage_num), pyramid_sample_ratios):
            clean_latent = pyramid_latent_list[i_s]  # [bs, c, t, h, w]
            last_clean_latent = None if i_s == 0 else pyramid_latent_list[i_s - 1]
            start_sigma = scheduler.start_sigmas[i_s]
            end_sigma = scheduler.end_sigmas[i_s]

            if i_s == 0:
                start_point = noise_list[i_s]
            else:
                # Get the upsampled latent
                last_clean_latent = rearrange(last_clean_latent, "b c t h w -> (b t) c h w")
                last_clean_latent = F.interpolate(
                    last_clean_latent,
                    size=(
                        last_clean_latent.shape[-2] * 2,
                        last_clean_latent.shape[-1] * 2,
                    ),
                    mode="nearest",
                )
                last_clean_latent = rearrange(last_clean_latent, "(b t) c h w -> b c t h w", t=latent_frame_num)
                start_point = start_sigma * noise_list[i_s] + (1 - start_sigma) * last_clean_latent

            if i_s == pyramid_stage_num - 1:
                end_point = clean_latent
            else:
                end_point = end_sigma * noise_list[i_s] + (1 - end_sigma) * clean_latent

            for _ in range(cur_sample_ratio):
                # Sample a random timestep for each image
                # for weighting schemes where we sample timesteps non-uniformly
                u = compute_density_for_timestep_sampling(
                    weighting_scheme=get_config_value(args, 'weighting_scheme'),
                    batch_size=bsz,
                    logit_mean=get_config_value(args, 'logit_mean'),
                    logit_std=get_config_value(args, 'logit_std'),
                    mode_scale=get_config_value(args, 'mode_scale'),
                )
                indices = (u * training_steps).long()  # Totally 1000 training steps per stage
                indices = indices.clamp(0, training_steps - 1)
                timesteps = scheduler.timesteps_per_stage[i_s][indices].to(device=device)

                # Add noise according to flow matching.
                # zt = (1 - texp) * x + texp * z1
                sigmas = scheduler.sigmas_per_stage[i_s][indices].to(device=device)
                while len(sigmas.shape) < start_point.ndim:
                    sigmas = sigmas.unsqueeze(-1)

                noisy_latents = sigmas * start_point + (1 - sigmas) * end_point

                # [stage1_latent, stage2_latent, ..., stagen_latent]
                noisy_latents_list.append(noisy_latents.to(dtype))
                sigmas_list.append(sigmas.to(dtype))
                timesteps_list.append(timesteps.to(dtype))
                targets_list.append(start_point - end_point)  # The standard rectified flow matching objective
    elif pyramid_sample_mode == "diffusion_forcing":
        # To calculate the batchsize
        bsz = input_video_num
        latent_chunk_num = latent_frame_num // stream_chunk_size
        assert latent_frame_num % stream_chunk_size == 0

        # from low resolution to high resolution
        noisy_latents_list = []
        sigmas_list = []
        targets_list = []
        timesteps_list = []
        training_steps = scheduler.config.num_train_timesteps
        for i_s, cur_sample_ratio in zip(range(pyramid_stage_num), pyramid_sample_ratios):
            clean_latent = pyramid_latent_list[i_s]  # [bs, c, t, h, w]
            last_clean_latent = None if i_s == 0 else pyramid_latent_list[i_s - 1]
            start_sigma = scheduler.start_sigmas[i_s]
            end_sigma = scheduler.end_sigmas[i_s]

            if i_s == 0:
                start_point = noise_list[i_s]
            else:
                # Get the upsampled latent
                last_clean_latent = rearrange(last_clean_latent, "b c t h w -> (b t) c h w")
                last_clean_latent = F.interpolate(
                    last_clean_latent,
                    size=(
                        last_clean_latent.shape[-2] * 2,
                        last_clean_latent.shape[-1] * 2,
                    ),
                    mode="nearest",
                )
                last_clean_latent = rearrange(last_clean_latent, "(b t) c h w -> b c t h w", t=latent_frame_num)
                start_point = start_sigma * noise_list[i_s] + (1 - start_sigma) * last_clean_latent

            if i_s == pyramid_stage_num - 1:
                end_point = clean_latent
            else:
                end_point = end_sigma * noise_list[i_s] + (1 - end_sigma) * clean_latent

            for _ in range(cur_sample_ratio):
                # Sample a random timestep for each image
                # for weighting schemes where we sample timesteps non-uniformly
                u = compute_density_for_timestep_sampling(
                    weighting_scheme=get_config_value(args, 'weighting_scheme'),
                    batch_size=bsz * latent_chunk_num,
                    logit_mean=get_config_value(args, 'logit_mean'),
                    logit_std=get_config_value(args, 'logit_std'),
                    mode_scale=get_config_value(args, 'mode_scale'),
                )
                indices = (u * training_steps).long()  # Totally 1000 training steps per stage
                indices = indices.clamp(0, training_steps - 1)

                timesteps = scheduler.timesteps_per_stage[i_s][indices].to(device=device)
                timesteps = timesteps.view(bsz, latent_chunk_num)  # [bsz, latent_chunk_num]
                sigmas = scheduler.sigmas_per_stage[i_s][indices].to(device=device)
                sigmas = sigmas.view(bsz, latent_chunk_num)  # [bsz, latent_chunk_num]

                chunk_index = (
                    torch.arange(latent_frame_num, device=device).unsqueeze(0).expand(bsz, -1) // stream_chunk_size
                )
                chunk_index = chunk_index.clamp(max=latent_chunk_num - 1)
                sigmas = torch.gather(sigmas, 1, chunk_index)  # [bsz, t]
                timesteps = torch.gather(timesteps, 1, chunk_index)

                # Add noise according to flow matching.
                # zt = (1 - texp) * x + texp * z1
                sigmas = (
                    sigmas.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
                )  # reshape to [bsz, 1, t, 1, 1] for broadcasting
                noisy_latents = sigmas * start_point + (1 - sigmas) * end_point

                # [stage1_latent, stage2_latent, ..., stagen_latent]
                noisy_latents_list.append(noisy_latents.to(dtype))  # torch.Size([2, 16, 10, 12, 20])
                sigmas_list.append(sigmas.to(dtype))  # torch.Size([2, 1, 10, 1, 1])
                timesteps_list.append(timesteps.to(dtype))  # torch.Size([2, 10])
                targets_list.append(start_point - end_point)  # The standard rectified flow matching objective
    elif pyramid_sample_mode == "stream_sample":
        # training_all_progressive_timesteps
        # skip 0. (1, max_inference_steps):[1.3850,   44.1200,   86.8550,  129.5900,  172.3250,
        #                                       215.0600,  257.7950,  300.5300,  343.2650,  386.0000,
        #                                       386.3580,  426.0960,  465.8340,  505.5720,  545.3100,
        #                                       585.0480,  624.7860,  664.5240,  704.2620,  744.0000,
        #                                       744.2560,  772.6720,  801.0880,  829.5040,  857.9200,
        #                                       886.3360,  914.7520,  943.1680,  971.5840, 1000.0000]

        # progressive_timesteps_stages
        # stream_chunk_size=3:
        # [    386.,     386.,     386.,     744.,     744.,     744.,    1000.,    1000.,    1000.]  high, mid, low
        # [343.2650, 343.2650, 343.2650, 704.2620, 704.2620, 704.2620, 971.5840, 971.5840, 971.5840]  high, mid, low
        # [300.5300, 300.5300, 300.5300, 664.5240, 664.5240, 664.5240, 943.1680, 943.1680, 943.1680]  high, mid, low
        # [257.7950, 257.7950, 257.7950, 624.7860, 624.7860, 624.7860, 914.7520, 914.7520, 914.7520]  high, mid, low
        # [215.0600, 215.0600, 215.0600, 585.0480, 585.0480, 585.0480, 886.3360, 886.3360, 886.3360]  high, mid, low
        # [172.3250, 172.3250, 172.3250, 545.3100, 545.3100, 545.3100, 857.9200, 857.9200, 857.9200]  high, mid, low
        # [129.5900, 129.5900, 129.5900, 505.5720, 505.5720, 505.5720, 829.5040, 829.5040, 829.5040]  high, mid, low
        # [ 86.8550,  86.8550,  86.8550, 465.8340, 465.8340, 465.8340, 801.0880, 801.0880, 801.0880]  high, mid, low
        # [ 44.1200,  44.1200,  44.1200, 426.0960, 426.0960, 426.0960, 772.6720, 772.6720, 772.6720]  high, mid, low
        # [  1.3850,   1.3850,   1.3850, 386.3580, 386.3580, 386.3580, 744.2560, 744.2560, 744.2560]  high, mid, low

        # stream_chunk_size=5, shape = (training_num_steps_to_be_saved, latent_frame_num):
        # [545.3100, 545.3100, 545.3100, 545.3100, 545.3100, 1000.0000, 1000.0000, 1000.0000, 1000.0000, 1000.0000]  mid,  low
        # [505.5720, 505.5720, 505.5720, 505.5720, 505.5720,  971.5840,  971.5840,  971.5840,  971.5840,  971.5840]  mid,  low
        # [465.8340, 465.8340, 465.8340, 465.8340, 465.8340,  943.1680,  943.1680,  943.1680,  943.1680,  943.1680]  mid,  low
        # [426.0960, 426.0960, 426.0960, 426.0960, 426.0960,  914.7520,  914.7520,  914.7520,  914.7520,  914.7520]  mid,  low
        # [386.3580, 386.3580, 386.3580, 386.3580, 386.3580,  886.3360,  886.3360,  886.3360,  886.3360,  886.3360]  mid,  low
        # [386.0000, 386.0000, 386.0000, 386.0000, 386.0000,  857.9200,  857.9200,  857.9200,  857.9200,  857.9200]  high, low
        # [343.2650, 343.2650, 343.2650, 343.2650, 343.2650,  829.5040,  829.5040,  829.5040,  829.5040,  829.5040]  high, low
        # [300.5300, 300.5300, 300.5300, 300.5300, 300.5300,  801.0880,  801.0880,  801.0880,  801.0880,  801.0880]  high, low
        # [257.7950, 257.7950, 257.7950, 257.7950, 257.7950,  772.6720,  772.6720,  772.6720,  772.6720,  772.6720]  high, low
        # [215.0600, 215.0600, 215.0600, 215.0600, 215.0600,  744.2560,  744.2560,  744.2560,  744.2560,  744.2560]  high, low
        # [172.3250, 172.3250, 172.3250, 172.3250, 172.3250,  744.0000,  744.0000,  744.0000,  744.0000,  744.0000]  high, mid
        # [129.5900, 129.5900, 129.5900, 129.5900, 129.5900,  704.2620,  704.2620,  704.2620,  704.2620,  704.2620]  high, mid
        # [ 86.8550,  86.8550,  86.8550,  86.8550,  86.8550,  664.5240,  664.5240,  664.5240,  664.5240,  664.5240]  high, mid
        # [ 44.1200,  44.1200,  44.1200,  44.1200,  44.1200,  624.7860,  624.7860,  624.7860,  624.7860,  624.7860]  high, mid
        # [  1.3850,   1.3850,   1.3850,   1.3850,   1.3850,  585.0480,  585.0480,  585.0480,  585.0480,  585.0480]  high, mid

        # To calculate the batchsize
        bsz = input_video_num

        # Get multi stage timesteps for streamgen
        (
            training_num_steps_to_be_saved,
            training_all_timesteps_stage_ids,
            training_all_progressive_timesteps,
            progressive_timesteps_stages,
        ) = get_stream_sample(
            scheduler=scheduler,
            max_latent_frame_num=latent_frame_num,
            stream_chunk_size=stream_chunk_size,
            pyramid_stage_num=pyramid_stage_num,
            pyramid_stream_inference_steps=pyramid_stream_inference_steps,
        )
        timestep_to_stage = {
            float(t.item()): int(stage.item())
            for t, stage in zip(training_all_progressive_timesteps[0], training_all_timesteps_stage_ids[0])
        }

        while True:
            initialization = random.choice([True, False])
            termination = random.choice([True, False])
            if not (initialization and termination):  # Make sure not both are True
                break

        stage_i = random.randint(0, training_num_steps_to_be_saved - 1)
        timesteps = progressive_timesteps_stages[stage_i].clone().repeat(bsz, 1)  # (b, f)
        if initialization:  # get the ending timesteps, [999]x5 from [91, 192, ..., 999]x5
            timesteps = timesteps[:, -latent_frame_num:]
        elif termination:  # get the starting timesteps, [91]x5 from [91, ..., 999]x5
            timesteps = timesteps[:, :latent_frame_num]

        # For stage mapping / Get sigmas
        sigmas, stage_latent_mapping = get_sigmas_from_pyramid_timesteps(scheduler, timesteps, timestep_to_stage)

        # To device
        timesteps = timesteps.to(device)
        sigmas = sigmas.to(device)

        # Get pyramid stage points
        stage_point_list = []
        for i_s in range(pyramid_stage_num):
            clean_latent = pyramid_latent_list[i_s]  # [bs, c, t, h, w]
            last_clean_latent = None if i_s == 0 else pyramid_latent_list[i_s - 1]
            start_sigma = scheduler.start_sigmas[i_s]
            end_sigma = scheduler.end_sigmas[i_s]

            if i_s == 0:
                start_point = noise_list[i_s]
            else:
                # Get the upsampled latent
                last_clean_latent = rearrange(last_clean_latent, "b c t h w -> (b t) c h w")
                last_clean_latent = F.interpolate(
                    last_clean_latent,
                    size=(
                        last_clean_latent.shape[-2] * 2,
                        last_clean_latent.shape[-1] * 2,
                    ),
                    mode="nearest",
                )
                last_clean_latent = rearrange(last_clean_latent, "(b t) c h w -> b c t h w", t=latent_frame_num)
                start_point = start_sigma * noise_list[i_s] + (1 - start_sigma) * last_clean_latent

            if i_s == pyramid_stage_num - 1:
                end_point = clean_latent
            else:
                end_point = end_sigma * noise_list[i_s] + (1 - end_sigma) * clean_latent

            stage_point_list.append((start_point, end_point))

        noisy_latents_list = []  # torch.Size([2, 16, 10, 12, 20])
        targets_list = []  # torch.Size([2, 16, 10, 12, 20])
        sigmas_list = []  # torch.Size([2, 1, 10, 1, 1])
        timesteps_list = []  # torch.Size([2, 10])
        temp_noisy_latents_list = []
        temp_targets_list = []

        unique_elements = list(map(int, torch.unique(stage_latent_mapping)))
        for cur_stage in reversed(unique_elements):
            stage_indices = torch.nonzero(stage_latent_mapping == cur_stage, as_tuple=True)
            start_index = stage_indices[1][0].item()
            end_index = start_index + stream_chunk_size

            start_point, end_point = stage_point_list[cur_stage]
            start_point_slice = start_point[:, :, start_index:end_index, :, :]
            end_point_slice = end_point[:, :, start_index:end_index, :, :]

            sigmas_slice = sigmas[:, :, start_index:end_index, :, :]
            noisy_latents = sigmas_slice * start_point_slice + (1 - sigmas_slice) * end_point_slice
            target = start_point_slice - end_point_slice

            temp_noisy_latents_list.append(noisy_latents.to(dtype))
            temp_targets_list.append(target)

        noisy_latents_list.append(temp_noisy_latents_list)
        targets_list.append(temp_targets_list)
        sigmas_list.append(sigmas.to(dtype))
        timesteps_list.append(timesteps.to(dtype=dtype))

    return noisy_latents_list, sigmas_list, timesteps_list, targets_list


def get_sigmas_from_pyramid_timesteps(scheduler, timesteps, timestep_to_stage):
    # For stage mapping
    flat_timesteps = timesteps.flatten()
    stage_latent_mapping = torch.tensor(
        [timestep_to_stage.get(float(t.item()), -1) for t in flat_timesteps],
        device=timesteps.device,
    ).view(timesteps.shape)

    # Get sigmas
    sigmas = torch.full_like(timesteps, -1.0)
    for i in range(timesteps.shape[0]):
        for j in range(timesteps.shape[1]):
            temp_stage_mapping = int(stage_latent_mapping[i, j])
            target_value = timesteps[i, j]
            temp_indice = (
                (
                    torch.isclose(
                        scheduler.timesteps_per_stage[temp_stage_mapping],
                        target_value.clone().detach().to(scheduler.timesteps_per_stage[temp_stage_mapping].dtype),
                    )
                )
                .nonzero(as_tuple=True)[0]
                .item()
            )
            sigmas[i, j] = scheduler.sigmas_per_stage[temp_stage_mapping][temp_indice]
    sigmas = sigmas.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)

    return sigmas, stage_latent_mapping


def get_stream_sample(
    scheduler,
    max_latent_frame_num,
    stream_chunk_size,
    pyramid_stage_num=3,
    pyramid_stream_inference_steps=[10, 10, 10],
):
    max_inference_steps = sum(pyramid_stream_inference_steps)

    # Set training all progressive timesteps and stage mapping
    all_progressive_timesteps_list = []
    timestep_stage_list = []
    for stage_idx in range(pyramid_stage_num):
        scheduler.set_timesteps(pyramid_stream_inference_steps[stage_idx], stage_idx)
        temp_timesteps = scheduler.timesteps  # shape: (n_i,)
        all_progressive_timesteps_list.append(temp_timesteps)
        timestep_stage_list.append(
            torch.full_like(temp_timesteps, fill_value=stage_idx)
        )  # same shape, filled with stage_idx
    all_progressive_timesteps = torch.cat(all_progressive_timesteps_list).unsqueeze(0).flip(1)  # (1, T)
    all_timesteps_stage_ids = torch.cat(timestep_stage_list).unsqueeze(0).flip(1)

    # Set training progressive timesteps stages
    # every stream_chunk_size frames is treated as one, using the same noise level. f' = f / c
    assert max_latent_frame_num % stream_chunk_size == 0, (
        f"num_frames should be multiple of stream_chunk_size, {max_latent_frame_num} % {stream_chunk_size} != 0"
    )
    assert max_inference_steps % (max_latent_frame_num // stream_chunk_size) == 0, (
        f"max_inference_steps should be multiple of max_latent_frame_num // stream_chunk_size, {max_inference_steps} % {max_latent_frame_num // stream_chunk_size} != 0"
    )
    num_steps_to_be_saved = max_inference_steps // (
        max_latent_frame_num // stream_chunk_size
    )  # every m steps, save stream_chunk_size frames. m = t / f' = t / (f / c) = c * (t / f)

    # (b, t) -> [(b, t / m) in reverse range(m)] -> [(b, f) in reverse range(m)]
    progressive_timesteps_stages = [
        repeat(
            all_progressive_timesteps[:, (num_steps_to_be_saved - 1) - s :: num_steps_to_be_saved],
            "b f -> b f c",
            c=stream_chunk_size,
        ).flatten(1, 2)
        for s in range(num_steps_to_be_saved)
    ]

    return num_steps_to_be_saved, all_timesteps_stage_ids, all_progressive_timesteps, progressive_timesteps_stages


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument(
        "--weighting_scheme",
        type=str,
        default="logit_normal",
        choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"],
        help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'),
    )
    parser.add_argument(
        "--logit_mean",
        type=float,
        default=0.0,
        help="mean to use when using the `'logit_normal'` weighting scheme.",
    )
    parser.add_argument(
        "--logit_std",
        type=float,
        default=1.0,
        help="std to use when using the `'logit_normal'` weighting scheme.",
    )
    parser.add_argument(
        "--mode_scale",
        type=float,
        default=1.29,
        help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
    )
    args = parser.parse_args()

    device = "cuda"

    import sys

    sys.path.append("../")
    from scheduler.scheduling_flow_matching_pyramid import PyramidFlowMatchEulerDiscreteScheduler

    stages = [1, 2, 4]
    timestep_shift = 1.0
    stage_range = [0, 1 / 3, 2 / 3, 1]
    scheduler_gamma = 1 / 3
    scheduler = PyramidFlowMatchEulerDiscreteScheduler(
        shift=timestep_shift,
        stages=len(stages),
        stage_range=stage_range,
        gamma=scheduler_gamma,
    )
    print(
        f"The start sigmas and end sigmas of each stage is Start: {scheduler.start_sigmas}, End: {scheduler.end_sigmas}, Ori_start: {scheduler.ori_start_sigmas}"
    )

    # Test get_framepack_input
    from diffusers import AutoencoderKLHunyuanVideo

    # 5:  (21, 41,  61,  81, 101)
    # 6:  (25, 49,  73,  97, 121)
    # 7:  (29, 57,  85, 113, 141)
    # 8:  (33, 65,  97, 129, 161)
    # 9:  (37, 73, 109, 145, 181)
    # 10: (41, 81, 121, 161, 201)
    # 11: (45, 89, 133, 177, 221)
    # 12: (49, 97, 145, 193, 241)

    pixel_values = torch.randn([2, 3, 241, 384, 640], device=device).clamp(-1, 1)
    pixel_values = pixel_values.to(torch.bfloat16)
    vae = AutoencoderKLHunyuanVideo.from_pretrained(
        "/mnt/workspace/checkpoints/hunyuanvideo-community/HunyuanVideo/",
        subfolder="vae",
        weight_dtype=torch.bfloat16,
    ).to(device)
    vae.requires_grad_(False)
    vae.eval()

    (
        model_input,  # torch.Size([2, 16, 9, 60, 104])
        indices_latents,  # torch.Size([2, 9])
        latents_clean,  # torch.Size([2, 16, 2, 60, 104])
        indices_clean_latents,  # torch.Size([2, 2])
        latents_history_2x,  # torch.Size([2, 16, 2, 60, 104])
        indices_latents_history_2x,  # torch.Size([2, 2])
        latents_history_4x,  # torch.Size([2, 16, 16, 60, 104])
        indices_latents_history_4x,  # torch.Size([2, 16])
        section_to_video_idx,
    ) = get_framepack_input_i2v(
        vae=vae,
        pixel_values=pixel_values,  # torch.Size([1, 3, 73, 480, 832])
        latent_window_size=12,
        vanilla_sampling=False,
        dtype=torch.bfloat16,
    )

    print(indices_latents, "\n", indices_clean_latents, "\n", indices_latents_history_2x, "\n", indices_latents_history_4x)

    # print(
    #     indices_latents,
    #     "\n",
    #     indices_clean_latents,
    #     "\n",
    #     indices_latents_history_2x,
    #     "\n",
    #     indices_latents_history_4x,
    # )

    # Test get_pyramid_input
    # model_input = torch.randn([2, 16, 10, 48, 80], device=device)
    # noisy_model_input_list, sigmas_list, timesteps_list, targets_list = get_pyramid_input(
    #     args=args,
    #     scheduler=scheduler,
    #     latents=model_input,
    #     pyramid_stage_num=3,
    #     pyramid_sample_ratios=[1, 2, 1],
    #     pyramid_sample_mode="stream_sample",
    #     stream_chunk_size=3,
    #     pyramid_stream_inference_steps=[10, 10, 10],
    # )

    # if isinstance(noisy_model_input_list[0], list):
    #     total_sample_count = sum(y.shape[0] for x in noisy_model_input_list for y in x)
    # else:
    #     total_sample_count = sum(x.shape[0] for x in noisy_model_input_list)
    # batch_size = model_input.shape[0]