# Copyright 2025 The SkyReels-V2 Team, The Wan Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import html
import inspect
import math
import re
from copy import deepcopy
from typing import Any, Callable

import torch
from PIL import Image
from transformers import AutoTokenizer, T5EncoderModel, UMT5EncoderModel

from ...callbacks import MultiPipelineCallbacks, PipelineCallback
from ...loaders import SkyReelsV2LoraLoaderMixin
from ...models import AutoencoderKLWan, SkyReelsV2Transformer3DModel
from ...schedulers import UniPCMultistepScheduler
from ...utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
from ...utils.torch_utils import randn_tensor
from ...video_processor import VideoProcessor
from ..pipeline_utils import DiffusionPipeline
from .pipeline_output import SkyReelsV2PipelineOutput


if is_torch_xla_available():
    import torch_xla.core.xla_model as xm

    XLA_AVAILABLE = True
else:
    XLA_AVAILABLE = False

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

if is_ftfy_available():
    import ftfy


EXAMPLE_DOC_STRING = """\
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import (
        ...     SkyReelsV2DiffusionForcingVideoToVideoPipeline,
        ...     UniPCMultistepScheduler,
        ...     AutoencoderKLWan,
        ... )
        >>> from diffusers.utils import export_to_video

        >>> # Load the pipeline
        >>> # Available models:
        >>> # - Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers
        >>> # - Skywork/SkyReels-V2-DF-14B-540P-Diffusers
        >>> # - Skywork/SkyReels-V2-DF-14B-720P-Diffusers
        >>> vae = AutoencoderKLWan.from_pretrained(
        ...     "Skywork/SkyReels-V2-DF-14B-720P-Diffusers",
        ...     subfolder="vae",
        ...     torch_dtype=torch.float32,
        ... )
        >>> pipe = SkyReelsV2DiffusionForcingVideoToVideoPipeline.from_pretrained(
        ...     "Skywork/SkyReels-V2-DF-14B-720P-Diffusers",
        ...     vae=vae,
        ...     torch_dtype=torch.bfloat16,
        ... )
        >>> flow_shift = 8.0  # 8.0 for T2V, 5.0 for I2V
        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
        >>> pipe = pipe.to("cuda")

        >>> prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."

        >>> output = pipe(
        ...     prompt=prompt,
        ...     num_inference_steps=50,
        ...     height=544,
        ...     width=960,
        ...     guidance_scale=6.0,  # 6.0 for T2V, 5.0 for I2V
        ...     num_frames=97,
        ...     ar_step=0,  # Controls asynchronous inference (0 for synchronous mode)
        ...     overlap_history=None,  # Number of frames to overlap for smooth transitions in long videos
        ...     addnoise_condition=20,  # Improves consistency in long video generation
        ... ).frames[0]
        >>> export_to_video(output, "video.mp4", fps=24, quality=8)
        ```
"""


def basic_clean(text):
    text = ftfy.fix_text(text)
    text = html.unescape(html.unescape(text))
    return text.strip()


def whitespace_clean(text):
    text = re.sub(r"\s+", " ", text)
    text = text.strip()
    return text


def prompt_clean(text):
    text = whitespace_clean(basic_clean(text))
    return text


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
    scheduler,
    num_inference_steps: int | None = None,
    device: str | torch.device | None = None,
    timesteps: list[int] | None = None,
    sigmas: list[float] | None = None,
    **kwargs,
):
    r"""
    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.

    Args:
        scheduler (`SchedulerMixin`):
            The scheduler to get timesteps from.
        num_inference_steps (`int`):
            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
            must be `None`.
        device (`str` or `torch.device`, *optional*):
            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
        timesteps (`list[int]`, *optional*):
            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
            `num_inference_steps` and `sigmas` must be `None`.
        sigmas (`list[float]`, *optional*):
            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
            `num_inference_steps` and `timesteps` must be `None`.

    Returns:
        `tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
        second element is the number of inference steps.
    """
    if timesteps is not None and sigmas is not None:
        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
    if timesteps is not None:
        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accepts_timesteps:
            raise ValueError(
                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                f" timestep schedules. Please check whether you are using the correct scheduler."
            )
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    elif sigmas is not None:
        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accept_sigmas:
            raise ValueError(
                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                f" sigmas schedules. Please check whether you are using the correct scheduler."
            )
        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
def retrieve_latents(
    encoder_output: torch.Tensor, generator: torch.Generator | None = None, sample_mode: str = "sample"
):
    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
        return encoder_output.latent_dist.sample(generator)
    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
        return encoder_output.latent_dist.mode()
    elif hasattr(encoder_output, "latents"):
        return encoder_output.latents
    else:
        raise AttributeError("Could not access latents of provided encoder_output")


class SkyReelsV2DiffusionForcingVideoToVideoPipeline(DiffusionPipeline, SkyReelsV2LoraLoaderMixin):
    """
    Pipeline for Video-to-Video (v2v) generation using SkyReels-V2 with diffusion forcing.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
    implemented for all pipelines (downloading, saving, running on a specific device, etc.).

    Args:
        tokenizer ([`AutoTokenizer`]):
            Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer),
            specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
        text_encoder ([`UMT5EncoderModel`]):
            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
            the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
        transformer ([`SkyReelsV2Transformer3DModel`]):
            Conditional Transformer to denoise the encoded image latents.
        scheduler ([`UniPCMultistepScheduler`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
        vae ([`AutoencoderKLWan`]):
            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
    """

    model_cpu_offload_seq = "text_encoder->transformer->vae"
    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

    def __init__(
        self,
        tokenizer: AutoTokenizer,
        text_encoder: T5EncoderModel | UMT5EncoderModel,
        transformer: SkyReelsV2Transformer3DModel,
        vae: AutoencoderKLWan,
        scheduler: UniPCMultistepScheduler,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            transformer=transformer,
            scheduler=scheduler,
        )

        self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4
        self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)

    # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline._get_t5_prompt_embeds
    def _get_t5_prompt_embeds(
        self,
        prompt: str | list[str] = None,
        num_videos_per_prompt: int = 1,
        max_sequence_length: int = 226,
        device: torch.device | None = None,
        dtype: torch.dtype | None = None,
    ):
        device = device or self._execution_device
        dtype = dtype or self.text_encoder.dtype

        prompt = [prompt] if isinstance(prompt, str) else prompt
        prompt = [prompt_clean(u) for u in prompt]
        batch_size = len(prompt)

        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            add_special_tokens=True,
            return_attention_mask=True,
            return_tensors="pt",
        )
        text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
        seq_lens = mask.gt(0).sum(dim=1).long()

        prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state
        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
        prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
        prompt_embeds = torch.stack(
            [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0
        )

        # 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)

        return prompt_embeds

    # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline.encode_prompt
    def encode_prompt(
        self,
        prompt: str | list[str],
        negative_prompt: str | list[str] | None = None,
        do_classifier_free_guidance: bool = True,
        num_videos_per_prompt: int = 1,
        prompt_embeds: torch.Tensor | None = None,
        negative_prompt_embeds: torch.Tensor | None = None,
        max_sequence_length: int = 226,
        device: torch.device | None = None,
        dtype: torch.dtype | None = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `list[str]`, *optional*):
                prompt to be encoded
            negative_prompt (`str` or `list[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
                Whether to use classifier free guidance or not.
            num_videos_per_prompt (`int`, *optional*, defaults to 1):
                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
            prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            device: (`torch.device`, *optional*):
                torch device
            dtype: (`torch.dtype`, *optional*):
                torch dtype
        """
        device = device or self._execution_device

        prompt = [prompt] if isinstance(prompt, str) else prompt
        if prompt is not None:
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if prompt_embeds is None:
            prompt_embeds = self._get_t5_prompt_embeds(
                prompt=prompt,
                num_videos_per_prompt=num_videos_per_prompt,
                max_sequence_length=max_sequence_length,
                device=device,
                dtype=dtype,
            )

        if do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ""
            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt

            if prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )

            negative_prompt_embeds = self._get_t5_prompt_embeds(
                prompt=negative_prompt,
                num_videos_per_prompt=num_videos_per_prompt,
                max_sequence_length=max_sequence_length,
                device=device,
                dtype=dtype,
            )

        return prompt_embeds, negative_prompt_embeds

    def check_inputs(
        self,
        prompt,
        negative_prompt,
        height,
        width,
        video=None,
        latents=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
        overlap_history=None,
        num_frames=None,
        base_num_frames=None,
    ):
        if height % 16 != 0 or width % 16 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")

        if callback_on_step_end_tensor_inputs is not None and not all(
            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
        ):
            raise ValueError(
                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
        elif negative_prompt is not None and (
            not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
        ):
            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")

        if video is not None and latents is not None:
            raise ValueError("Only one of `video` or `latents` should be provided")

        if num_frames > base_num_frames and overlap_history is None:
            raise ValueError(
                "`overlap_history` is required when `num_frames` exceeds `base_num_frames` to ensure smooth transitions in long video generation. "
                "Please specify a value for `overlap_history`. Recommended values are 17 or 37."
            )

    def prepare_latents(
        self,
        video: torch.Tensor,
        batch_size: int = 1,
        num_channels_latents: int = 16,
        height: int = 480,
        width: int = 832,
        num_frames: int = 97,
        dtype: torch.dtype | None = None,
        device: torch.device | None = None,
        generator: torch.Generator | list[torch.Generator] | None = None,
        latents: torch.Tensor | None = None,
        video_latents: torch.Tensor | None = None,
        base_latent_num_frames: int | None = None,
        overlap_history: int | None = None,
        causal_block_size: int | None = None,
        overlap_history_latent_frames: int | None = None,
        long_video_iter: int | None = None,
    ) -> torch.Tensor:
        if latents is not None:
            return latents.to(device=device, dtype=dtype)

        num_latent_frames = (
            (num_frames - 1) // self.vae_scale_factor_temporal + 1 if latents is None else latents.shape[2]
        )
        latent_height = height // self.vae_scale_factor_spatial
        latent_width = width // self.vae_scale_factor_spatial

        if long_video_iter == 0:
            prefix_video_latents = [
                retrieve_latents(
                    self.vae.encode(
                        vid.unsqueeze(0)[:, :, -overlap_history:] if vid.dim() == 4 else vid[:, :, -overlap_history:]
                    ),
                    sample_mode="argmax",
                )
                for vid in video
            ]
            prefix_video_latents = torch.cat(prefix_video_latents, dim=0).to(dtype)

            latents_mean = (
                torch.tensor(self.vae.config.latents_mean)
                .view(1, self.vae.config.z_dim, 1, 1, 1)
                .to(device, self.vae.dtype)
            )
            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
                device, self.vae.dtype
            )
            prefix_video_latents = (prefix_video_latents - latents_mean) * latents_std
        else:
            prefix_video_latents = video_latents[:, :, -overlap_history_latent_frames:]

        if prefix_video_latents.shape[2] % causal_block_size != 0:
            truncate_len_latents = prefix_video_latents.shape[2] % causal_block_size
            logger.warning(
                f"The length of prefix video latents is truncated by {truncate_len_latents} frames for the causal block size alignment. "
                f"This truncation ensures compatibility with the causal block size, which is required for proper processing. "
                f"However, it may slightly affect the continuity of the generated video at the truncation boundary."
            )
            prefix_video_latents = prefix_video_latents[:, :, :-truncate_len_latents]
        prefix_video_latents_frames = prefix_video_latents.shape[2]

        finished_frame_num = (
            long_video_iter * (base_latent_num_frames - overlap_history_latent_frames) + overlap_history_latent_frames
        )
        left_frame_num = num_latent_frames - finished_frame_num
        num_latent_frames = min(left_frame_num + overlap_history_latent_frames, base_latent_num_frames)

        shape = (
            batch_size,
            num_channels_latents,
            num_latent_frames,
            latent_height,
            latent_width,
        )
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)

        return latents, num_latent_frames, prefix_video_latents, prefix_video_latents_frames

    # Copied from diffusers.pipelines.skyreels_v2.pipeline_skyreels_v2_diffusion_forcing.SkyReelsV2DiffusionForcingPipeline.generate_timestep_matrix
    def generate_timestep_matrix(
        self,
        num_latent_frames: int,
        step_template: torch.Tensor,
        base_num_latent_frames: int,
        ar_step: int = 5,
        num_pre_ready: int = 0,
        causal_block_size: int = 1,
        shrink_interval_with_mask: bool = False,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, list[tuple]]:
        """
        This function implements the core diffusion forcing algorithm that creates a coordinated denoising schedule
        across temporal frames. It supports both synchronous and asynchronous generation modes:

        **Synchronous Mode** (ar_step=0, causal_block_size=1):
        - All frames are denoised simultaneously at each timestep
        - Each frame follows the same denoising trajectory: [1000, 800, 600, ..., 0]
        - Simpler but may have less temporal consistency for long videos

        **Asynchronous Mode** (ar_step>0, causal_block_size>1):
        - Frames are grouped into causal blocks and processed block/chunk-wise
        - Each block is denoised in a staggered pattern creating a "denoising wave"
        - Earlier blocks are more denoised, later blocks lag behind by ar_step timesteps
        - Creates stronger temporal dependencies and better consistency

        Args:
            num_latent_frames (int): Total number of latent frames to generate
            step_template (torch.Tensor): Base timestep schedule (e.g., [1000, 800, 600, ..., 0])
            base_num_latent_frames (int): Maximum frames the model can process in one forward pass
            ar_step (int, optional): Autoregressive step size for temporal lag.
                                   0 = synchronous, >0 = asynchronous. Defaults to 5.
            num_pre_ready (int, optional):
                                         Number of frames already denoised (e.g., from prefix in a video2video task).
                                         Defaults to 0.
            causal_block_size (int, optional): Number of frames processed as a causal block.
                                             Defaults to 1.
            shrink_interval_with_mask (bool, optional): Whether to optimize processing intervals.
                                                      Defaults to False.

        Returns:
            tuple containing:
                - step_matrix (torch.Tensor): Matrix of timesteps for each frame at each iteration Shape:
                  [num_iterations, num_latent_frames]
                - step_index (torch.Tensor): Index matrix for timestep lookup Shape: [num_iterations,
                  num_latent_frames]
                - step_update_mask (torch.Tensor): Boolean mask indicating which frames to update Shape:
                  [num_iterations, num_latent_frames]
                - valid_interval (list[tuple]): list of (start, end) intervals for each iteration

        Raises:
            ValueError: If ar_step is too small for the given configuration
        """
        # Initialize lists to store the scheduling matrices and metadata
        step_matrix, step_index = [], []  # Will store timestep values and indices for each iteration
        update_mask, valid_interval = [], []  # Will store update masks and processing intervals

        # Calculate total number of denoising iterations (add 1 for initial noise state)
        num_iterations = len(step_template) + 1

        # Convert frame counts to block counts for causal processing
        # Each block contains causal_block_size frames that are processed together
        # E.g.: 25 frames ÷ 5 = 5 blocks total
        num_blocks = num_latent_frames // causal_block_size
        base_num_blocks = base_num_latent_frames // causal_block_size

        # Validate ar_step is sufficient for the given configuration
        # In asynchronous mode, we need enough timesteps to create the staggered pattern
        if base_num_blocks < num_blocks:
            min_ar_step = len(step_template) / base_num_blocks
            if ar_step < min_ar_step:
                raise ValueError(f"`ar_step` should be at least {math.ceil(min_ar_step)} in your setting")

        # Extend step_template with boundary values for easier indexing
        # 999: dummy value for counter starting from 1
        # 0: final timestep (completely denoised)
        step_template = torch.cat(
            [
                torch.tensor([999], dtype=torch.int64, device=step_template.device),
                step_template.long(),
                torch.tensor([0], dtype=torch.int64, device=step_template.device),
            ]
        )

        # Initialize the previous row state (tracks denoising progress for each block)
        # 0 means not started, num_iterations means fully denoised
        pre_row = torch.zeros(num_blocks, dtype=torch.long)

        # Mark pre-ready frames (e.g., from prefix video for a video2video task) as already at final denoising state
        if num_pre_ready > 0:
            pre_row[: num_pre_ready // causal_block_size] = num_iterations

        # Main loop: Generate denoising schedule until all frames are fully denoised
        while not torch.all(pre_row >= (num_iterations - 1)):
            # Create new row representing the next denoising step
            new_row = torch.zeros(num_blocks, dtype=torch.long)

            # Apply diffusion forcing logic for each block
            for i in range(num_blocks):
                if i == 0 or pre_row[i - 1] >= (
                    num_iterations - 1
                ):  # the first frame or the last frame is completely denoised
                    new_row[i] = pre_row[i] + 1
                else:
                    # Asynchronous mode: lag behind previous block by ar_step timesteps
                    # This creates the "diffusion forcing" staggered pattern
                    new_row[i] = new_row[i - 1] - ar_step

            # Clamp values to valid range [0, num_iterations]
            new_row = new_row.clamp(0, num_iterations)

            # Create update mask: True for blocks that need denoising update at this iteration
            # Exclude blocks that haven't started (new_row != pre_row) or are finished (new_row != num_iterations)
            # Final state example: [False, ..., False, True, True, True, True, True]
            # where first 20 frames are done (False) and last 5 frames still need updates (True)
            update_mask.append((new_row != pre_row) & (new_row != num_iterations))

            # Store the iteration state
            step_index.append(new_row)  # Index into step_template
            step_matrix.append(step_template[new_row])  # Actual timestep values
            pre_row = new_row  # Update for next iteration

        # For videos longer than model capacity, we process in sliding windows
        terminal_flag = base_num_blocks

        # Optional optimization: shrink interval based on first update mask
        if shrink_interval_with_mask:
            idx_sequence = torch.arange(num_blocks, dtype=torch.int64)
            update_mask = update_mask[0]
            update_mask_idx = idx_sequence[update_mask]
            last_update_idx = update_mask_idx[-1].item()
            terminal_flag = last_update_idx + 1

        # Each interval defines which frames to process in the current forward pass
        for curr_mask in update_mask:
            # Extend terminal flag if current mask has updates beyond current terminal
            if terminal_flag < num_blocks and curr_mask[terminal_flag]:
                terminal_flag += 1
            # Create interval: [start, end) where start ensures we don't exceed model capacity
            valid_interval.append((max(terminal_flag - base_num_blocks, 0), terminal_flag))

        # Convert lists to tensors for efficient processing
        step_update_mask = torch.stack(update_mask, dim=0)
        step_index = torch.stack(step_index, dim=0)
        step_matrix = torch.stack(step_matrix, dim=0)

        # Each block's schedule is replicated to all frames within that block
        if causal_block_size > 1:
            # Expand each block to causal_block_size frames
            step_update_mask = step_update_mask.unsqueeze(-1).repeat(1, 1, causal_block_size).flatten(1).contiguous()
            step_index = step_index.unsqueeze(-1).repeat(1, 1, causal_block_size).flatten(1).contiguous()
            step_matrix = step_matrix.unsqueeze(-1).repeat(1, 1, causal_block_size).flatten(1).contiguous()
            # Scale intervals from block-level to frame-level
            valid_interval = [(s * causal_block_size, e * causal_block_size) for s, e in valid_interval]

        return step_matrix, step_index, step_update_mask, valid_interval

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1.0

    @property
    def num_timesteps(self):
        return self._num_timesteps

    @property
    def current_timestep(self):
        return self._current_timestep

    @property
    def interrupt(self):
        return self._interrupt

    @property
    def attention_kwargs(self):
        return self._attention_kwargs

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        video: list[Image.Image],
        prompt: str | list[str] = None,
        negative_prompt: str | list[str] = None,
        height: int = 544,
        width: int = 960,
        num_frames: int = 120,
        num_inference_steps: int = 50,
        guidance_scale: float = 6.0,
        num_videos_per_prompt: int | None = 1,
        generator: torch.Generator | list[torch.Generator] | None = None,
        latents: torch.Tensor | None = None,
        prompt_embeds: torch.Tensor | None = None,
        negative_prompt_embeds: torch.Tensor | None = None,
        output_type: str | None = "np",
        return_dict: bool = True,
        attention_kwargs: dict[str, Any] | None = None,
        callback_on_step_end: Callable[[int, int], None] | PipelineCallback | MultiPipelineCallbacks | None = None,
        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
        max_sequence_length: int = 512,
        overlap_history: int | None = None,
        addnoise_condition: float = 0,
        base_num_frames: int = 97,
        ar_step: int = 0,
        causal_block_size: int | None = None,
        fps: int = 24,
    ):
        r"""
        The call function to the pipeline for generation.

        Args:
            video (`list[Image.Image]`):
                The video to guide the video generation.
            prompt (`str` or `list[str]`, *optional*):
                The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            negative_prompt (`str` or `list[str]`, *optional*):
                The prompt or prompts not to guide the video generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            height (`int`, defaults to `544`):
                The height of the generated video.
            width (`int`, defaults to `960`):
                The width of the generated video.
            num_frames (`int`, defaults to `120`):
                The number of frames in the generated video.
            num_inference_steps (`int`, defaults to `50`):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, defaults to `6.0`):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality. (**6.0 for T2V**, **5.0 for I2V**)
            num_videos_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            generator (`torch.Generator` or `list[torch.Generator]`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            latents (`torch.Tensor`, *optional*):
                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor is generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
                provided, text embeddings are generated from the `prompt` input argument.
            negative_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
                provided, text embeddings are generated from the `negative_prompt` input argument.
            output_type (`str`, *optional*, defaults to `"np"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`SkyReelsV2PipelineOutput`] instead of a plain tuple.
            attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`list`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.
            max_sequence_length (`int`, *optional*, defaults to `512`):
                The maximum sequence length of the prompt.
            overlap_history (`int`, *optional*, defaults to `None`):
                Number of frames to overlap for smooth transitions in long videos. If `None`, the pipeline assumes
                short video generation mode, and no overlap is applied. 17 and 37 are recommended to set.
            addnoise_condition (`float`, *optional*, defaults to `0`):
                This is used to help smooth the long video generation by adding some noise to the clean condition. Too
                large noise can cause the inconsistency as well. 20 is a recommended value, and you may try larger
                ones, but it is recommended to not exceed 50.
            base_num_frames (`int`, *optional*, defaults to `97`):
                97 or 121 | Base frame count (**97 for 540P**, **121 for 720P**)
            ar_step (`int`, *optional*, defaults to `0`):
                Controls asynchronous inference (0 for synchronous mode) You can set `ar_step=5` to enable asynchronous
                inference. When asynchronous inference, `causal_block_size=5` is recommended while it is not supposed
                to be set for synchronous generation. Asynchronous inference will take more steps to diffuse the whole
                sequence which means it will be SLOWER than synchronous mode. In our experiments, asynchronous
                inference may improve the instruction following and visual consistent performance.
            causal_block_size (`int`, *optional*, defaults to `None`):
                The number of frames in each block/chunk. Recommended when using asynchronous inference (when ar_step >
                0)
            fps (`int`, *optional*, defaults to `24`):
                Frame rate of the generated video

        Examples:

        Returns:
            [`~SkyReelsV2PipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`SkyReelsV2PipelineOutput`] is returned, otherwise a `tuple` is returned
                where the first element is a list with the generated images and the second element is a list of `bool`s
                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
        """

        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs

        height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial
        width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial
        num_videos_per_prompt = 1

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            negative_prompt,
            height,
            width,
            video,
            latents,
            prompt_embeds,
            negative_prompt_embeds,
            callback_on_step_end_tensor_inputs,
            overlap_history,
            num_frames,
            base_num_frames,
        )

        if addnoise_condition > 60:
            logger.warning(
                f"The value of 'addnoise_condition' is too large ({addnoise_condition}) and may cause inconsistencies in long video generation. A value of 20 is recommended."
            )

        if num_frames % self.vae_scale_factor_temporal != 1:
            logger.warning(
                f"`num_frames - 1` has to be divisible by {self.vae_scale_factor_temporal}. Rounding to the nearest number."
            )
            num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
        num_frames = max(num_frames, 1)

        self._guidance_scale = guidance_scale
        self._attention_kwargs = attention_kwargs
        self._current_timestep = None
        self._interrupt = False

        device = self._execution_device

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        # 3. Encode input prompt
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
            prompt=prompt,
            negative_prompt=negative_prompt,
            do_classifier_free_guidance=self.do_classifier_free_guidance,
            num_videos_per_prompt=num_videos_per_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            max_sequence_length=max_sequence_length,
            device=device,
        )

        transformer_dtype = self.transformer.dtype
        prompt_embeds = prompt_embeds.to(transformer_dtype)
        if negative_prompt_embeds is not None:
            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        if latents is None:
            video_original = self.video_processor.preprocess_video(video, height=height, width=width).to(
                device, dtype=torch.float32
            )

        if causal_block_size is None:
            causal_block_size = self.transformer.config.num_frame_per_block
        else:
            self.transformer._set_ar_attention(causal_block_size)

        fps_embeds = [fps] * prompt_embeds.shape[0]
        fps_embeds = [0 if i == 16 else 1 for i in fps_embeds]

        # Long video generation
        accumulated_latents = None
        overlap_history_latent_frames = (overlap_history - 1) // self.vae_scale_factor_temporal + 1
        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
        base_latent_num_frames = (
            (base_num_frames - 1) // self.vae_scale_factor_temporal + 1
            if base_num_frames is not None
            else num_latent_frames
        )
        n_iter = (
            1
            + (num_latent_frames - base_latent_num_frames - 1)
            // (base_latent_num_frames - overlap_history_latent_frames)
            + 1
        )
        for long_video_iter in range(n_iter):
            logger.debug(f"Processing iteration {long_video_iter + 1}/{n_iter} for long video generation...")

            # 5. Prepare latent variables
            num_channels_latents = self.transformer.config.in_channels
            latents, current_num_latent_frames, prefix_video_latents, prefix_video_latents_frames = (
                self.prepare_latents(
                    video_original,
                    batch_size * num_videos_per_prompt,
                    num_channels_latents,
                    height,
                    width,
                    num_frames,
                    torch.float32,
                    device,
                    generator,
                    latents if long_video_iter == 0 else None,
                    video_latents=accumulated_latents,  # Pass latents directly instead of decoded video
                    overlap_history=overlap_history,
                    base_latent_num_frames=base_latent_num_frames,
                    causal_block_size=causal_block_size,
                    overlap_history_latent_frames=overlap_history_latent_frames,
                    long_video_iter=long_video_iter,
                )
            )

            if prefix_video_latents_frames > 0:
                latents[:, :, :prefix_video_latents_frames, :, :] = prefix_video_latents.to(transformer_dtype)

            # 4. Prepare sample schedulers and timestep matrix
            sample_schedulers = []
            for _ in range(current_num_latent_frames):
                sample_scheduler = deepcopy(self.scheduler)
                sample_scheduler.set_timesteps(num_inference_steps, device=device)
                sample_schedulers.append(sample_scheduler)
            step_matrix, _, step_update_mask, valid_interval = self.generate_timestep_matrix(
                current_num_latent_frames,
                timesteps,
                current_num_latent_frames,
                ar_step,
                prefix_video_latents_frames,
                causal_block_size,
            )

            # 6. Denoising loop
            num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
            self._num_timesteps = len(step_matrix)

            with self.progress_bar(total=len(step_matrix)) as progress_bar:
                for i, t in enumerate(step_matrix):
                    if self.interrupt:
                        continue

                    self._current_timestep = t
                    valid_interval_start, valid_interval_end = valid_interval[i]
                    latent_model_input = (
                        latents[:, :, valid_interval_start:valid_interval_end, :, :].to(transformer_dtype).clone()
                    )
                    timestep = t.expand(latents.shape[0], -1)[:, valid_interval_start:valid_interval_end].clone()

                    if addnoise_condition > 0 and valid_interval_start < prefix_video_latents_frames:
                        noise_factor = 0.001 * addnoise_condition
                        latent_model_input[:, :, valid_interval_start:prefix_video_latents_frames, :, :] = (
                            latent_model_input[:, :, valid_interval_start:prefix_video_latents_frames, :, :]
                            * (1.0 - noise_factor)
                            + torch.randn_like(
                                latent_model_input[:, :, valid_interval_start:prefix_video_latents_frames, :, :]
                            )
                            * noise_factor
                        )
                        timestep[:, valid_interval_start:prefix_video_latents_frames] = addnoise_condition

                    with self.transformer.cache_context("cond"):
                        noise_pred = self.transformer(
                            hidden_states=latent_model_input,
                            timestep=timestep,
                            encoder_hidden_states=prompt_embeds,
                            enable_diffusion_forcing=True,
                            fps=fps_embeds,
                            attention_kwargs=attention_kwargs,
                            return_dict=False,
                        )[0]

                    if self.do_classifier_free_guidance:
                        with self.transformer.cache_context("uncond"):
                            noise_uncond = self.transformer(
                                hidden_states=latent_model_input,
                                timestep=timestep,
                                encoder_hidden_states=negative_prompt_embeds,
                                enable_diffusion_forcing=True,
                                fps=fps_embeds,
                                attention_kwargs=attention_kwargs,
                                return_dict=False,
                            )[0]
                        noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)

                    update_mask_i = step_update_mask[i]
                    for idx in range(valid_interval_start, valid_interval_end):
                        if update_mask_i[idx].item():
                            latents[:, :, idx, :, :] = sample_schedulers[idx].step(
                                noise_pred[:, :, idx - valid_interval_start, :, :],
                                t[idx],
                                latents[:, :, idx, :, :],
                                return_dict=False,
                            )[0]

                    if callback_on_step_end is not None:
                        callback_kwargs = {}
                        for k in callback_on_step_end_tensor_inputs:
                            callback_kwargs[k] = locals()[k]
                        callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                        latents = callback_outputs.pop("latents", latents)
                        prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                        negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)

                    # call the callback, if provided
                    if i == len(step_matrix) - 1 or (
                        (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
                    ):
                        progress_bar.update()

                    if XLA_AVAILABLE:
                        xm.mark_step()

            if accumulated_latents is None:
                accumulated_latents = latents
            else:
                # Keep overlap frames for conditioning but don't include them in final output
                accumulated_latents = torch.cat(
                    [accumulated_latents, latents[:, :, overlap_history_latent_frames:]], dim=2
                )

        latents = accumulated_latents

        self._current_timestep = None

        # Final decoding step - convert latents to pixels
        if not output_type == "latent":
            latents = latents.to(self.vae.dtype)
            latents_mean = (
                torch.tensor(self.vae.config.latents_mean)
                .view(1, self.vae.config.z_dim, 1, 1, 1)
                .to(latents.device, latents.dtype)
            )
            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
                latents.device, latents.dtype
            )
            latents = latents / latents_std + latents_mean
            video_generated = self.vae.decode(latents, return_dict=False)[0]
            video = torch.cat([video_original, video_generated], dim=2)
            video = self.video_processor.postprocess_video(video, output_type=output_type)
        else:
            video = latents

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (video,)

        return SkyReelsV2PipelineOutput(frames=video)
