# Copyright 2025 The NVIDIA 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.

from typing import Callable, Dict, List, Optional, Union

import numpy as np
import PIL.Image
import torch
from transformers import AutoTokenizer, Qwen2_5_VLForConditionalGeneration

from ...callbacks import MultiPipelineCallbacks, PipelineCallback
from ...image_processor import PipelineImageInput
from ...models import AutoencoderKLWan, CosmosControlNetModel, CosmosTransformer3DModel
from ...schedulers import UniPCMultistepScheduler
from ...utils import is_cosmos_guardrail_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 CosmosPipelineOutput


if is_cosmos_guardrail_available():
    from cosmos_guardrail import CosmosSafetyChecker
else:

    class CosmosSafetyChecker:
        def __init__(self, *args, **kwargs):
            raise ImportError(
                "`cosmos_guardrail` is not installed. Please install it to use the safety checker for Cosmos: `pip install cosmos_guardrail`."
            )


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


def _maybe_pad_or_trim_video(video: torch.Tensor, num_frames: int):
    n_pad_frames = num_frames - video.shape[2]
    if n_pad_frames > 0:
        last_frame = video[:, :, -1:, :, :]
        video = torch.cat((video, last_frame.repeat(1, 1, n_pad_frames, 1, 1)), dim=2)
    elif num_frames < video.shape[2]:
        video = video[:, :, :num_frames, :, :]
    return video


# 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")


DEFAULT_NEGATIVE_PROMPT = "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality."

EXAMPLE_DOC_STRING = """
    Examples:
        ```python
        >>> import cv2
        >>> import numpy as np
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import Cosmos2_5_TransferPipeline, AutoModel
        >>> from diffusers.utils import export_to_video, load_video

        >>> model_id = "nvidia/Cosmos-Transfer2.5-2B"
        >>> # Load a Transfer2.5 controlnet variant (edge, depth, seg, or blur)
        >>> controlnet = AutoModel.from_pretrained(
        ...     model_id, revision="diffusers/controlnet/general/edge", torch_dtype=torch.bfloat16
        ... )
        >>> pipe = Cosmos2_5_TransferPipeline.from_pretrained(
        ...     model_id, controlnet=controlnet, revision="diffusers/general", torch_dtype=torch.bfloat16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> # Video2World with edge control: Generate video guided by edge maps extracted from input video.
        >>> prompt = (
        ...     "The video is a demonstration of robotic manipulation, likely in a laboratory or testing environment. It"
        ...     "features two robotic arms interacting with a piece of blue fabric. The setting is a room with a beige"
        ...     "couch in the background, providing a neutral backdrop for the robotic activity. The robotic arms are"
        ...     "positioned on either side of the fabric, which is placed on a yellow cushion. The left robotic arm is"
        ...     "white with a black gripper, while the right arm is black with a more complex, articulated gripper. At the"
        ...     "beginning, the fabric is laid out on the cushion. The left robotic arm approaches the fabric, its gripper"
        ...     "opening and closing as it positions itself. The right arm remains stationary initially, poised to assist."
        ...     "As the video progresses, the left arm grips the fabric, lifting it slightly off the cushion. The right arm"
        ...     "then moves in, its gripper adjusting to grasp the opposite side of the fabric. Both arms work in"
        ...     "coordination, lifting and holding the fabric between them. The fabric is manipulated with precision,"
        ...     "showcasing the dexterity and control of the robotic arms. The camera remains static throughout, focusing"
        ...     "on the interaction between the robotic arms and the fabric, allowing viewers to observe the detailed"
        ...     "movements and coordination involved in the task."
        ... )
        >>> negative_prompt = (
        ...     "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, "
        ...     "over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, "
        ...     "underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky "
        ...     "movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, "
        ...     "fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. "
        ...     "Overall, the video is of poor quality."
        ... )
        >>> input_video = load_video(
        ...     "https://github.com/nvidia-cosmos/cosmos-transfer2.5/raw/refs/heads/main/assets/robot_example/robot_input.mp4"
        ... )
        >>> num_frames = 93

        >>> # Extract edge maps from the input video using Canny edge detection
        >>> edge_maps = [
        ...     cv2.Canny(cv2.cvtColor(np.array(frame.convert("RGB")), cv2.COLOR_RGB2BGR), 100, 200)
        ...     for frame in input_video[:num_frames]
        ... ]
        >>> edge_maps = np.stack(edge_maps)[None]  # (T, H, W) -> (1, T, H, W)
        >>> controls = torch.from_numpy(edge_maps).expand(3, -1, -1, -1)  # (1, T, H, W) -> (3, T, H, W)
        >>> controls = [Image.fromarray(x.numpy()) for x in controls.permute(1, 2, 3, 0)]
        >>> export_to_video(controls, "edge_controlled_video_edge.mp4", fps=30)

        >>> # Transfer inference with controls.
        >>> video = pipe(
        ...     controls=controls,
        ...     controls_conditioning_scale=1.0,
        ...     prompt=prompt,
        ...     negative_prompt=negative_prompt,
        ...     num_frames=num_frames,
        ... ).frames[0]
        >>> export_to_video(video, "edge_controlled_video.mp4", fps=30)
        ```
"""


class Cosmos2_5_TransferPipeline(DiffusionPipeline):
    r"""
    Pipeline for Cosmos Transfer2.5, supporting auto-regressive inference.

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

    Args:
        text_encoder ([`Qwen2_5_VLForConditionalGeneration`]):
            Frozen text-encoder. Cosmos Transfer2.5 uses the [Qwen2.5
            VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) encoder.
        tokenizer (`AutoTokenizer`):
            Tokenizer associated with the Qwen2.5 VL encoder.
        transformer ([`CosmosTransformer3DModel`]):
            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.
        controlnet ([`CosmosControlNetModel`]):
            ControlNet used to condition generation on control inputs.
    """

    model_cpu_offload_seq = "text_encoder->transformer->controlnet->vae"
    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
    # We mark safety_checker as optional here to get around some test failures, but it is not really optional
    _optional_components = ["safety_checker"]
    _exclude_from_cpu_offload = ["safety_checker"]

    def __init__(
        self,
        text_encoder: Qwen2_5_VLForConditionalGeneration,
        tokenizer: AutoTokenizer,
        transformer: CosmosTransformer3DModel,
        vae: AutoencoderKLWan,
        scheduler: UniPCMultistepScheduler,
        controlnet: CosmosControlNetModel,
        safety_checker: Optional[CosmosSafetyChecker] = None,
    ):
        super().__init__()

        if safety_checker is None:
            safety_checker = CosmosSafetyChecker()

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

        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)

        latents_mean = (
            torch.tensor(self.vae.config.latents_mean).view(1, self.vae.config.z_dim, 1, 1, 1).float()
            if getattr(self.vae.config, "latents_mean", None) is not None
            else None
        )
        latents_std = (
            torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).float()
            if getattr(self.vae.config, "latents_std", None) is not None
            else None
        )
        self.latents_mean = latents_mean
        self.latents_std = latents_std

        if self.latents_mean is None or self.latents_std is None:
            raise ValueError("VAE configuration must define both `latents_mean` and `latents_std`.")

    def _get_prompt_embeds(
        self,
        prompt: Union[str, List[str]] = None,
        max_sequence_length: int = 512,
        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

        input_ids_batch = []

        for sample_idx in range(len(prompt)):
            conversations = [
                {
                    "role": "system",
                    "content": [
                        {
                            "type": "text",
                            "text": "You are a helpful assistant who will provide prompts to an image generator.",
                        }
                    ],
                },
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": prompt[sample_idx],
                        }
                    ],
                },
            ]
            input_ids = self.tokenizer.apply_chat_template(
                conversations,
                tokenize=True,
                add_generation_prompt=False,
                add_vision_id=False,
                max_length=max_sequence_length,
                truncation=True,
                padding="max_length",
            )
            input_ids = (
                input_ids["input_ids"] if not isinstance(input_ids, list) and "input_ids" in input_ids else input_ids
            )
            input_ids = torch.LongTensor(input_ids)
            input_ids_batch.append(input_ids)

        input_ids_batch = torch.stack(input_ids_batch, dim=0)

        outputs = self.text_encoder(
            input_ids_batch.to(device),
            output_hidden_states=True,
        )
        hidden_states = outputs.hidden_states

        normalized_hidden_states = []
        for layer_idx in range(1, len(hidden_states)):
            normalized_state = (hidden_states[layer_idx] - hidden_states[layer_idx].mean(dim=-1, keepdim=True)) / (
                hidden_states[layer_idx].std(dim=-1, keepdim=True) + 1e-8
            )
            normalized_hidden_states.append(normalized_state)

        prompt_embeds = torch.cat(normalized_hidden_states, dim=-1)
        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)

        return prompt_embeds

    # Modified from diffusers.pipelines.cosmos.pipeline_cosmos_text2world.CosmosTextToWorldPipeline.encode_prompt
    def encode_prompt(
        self,
        prompt: Union[str, List[str]],
        negative_prompt: Optional[Union[str, List[str]]] = 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 = 512,
        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_prompt_embeds(
                prompt=prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype
            )

            # 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)

        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_prompt_embeds(
                prompt=negative_prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype
            )

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

        return prompt_embeds, negative_prompt_embeds

    # Modified from diffusers.pipelines.cosmos.pipeline_cosmos2_video2world.Cosmos2VideoToWorldPipeline.prepare_latents and
    # diffusers.pipelines.cosmos.pipeline_cosmos2_video2world.Cosmos2TextToImagePipeline.prepare_latents
    def prepare_latents(
        self,
        video: Optional[torch.Tensor],
        batch_size: int,
        num_channels_latents: int = 16,
        height: int = 704,
        width: int = 1280,
        num_frames_in: int = 93,
        num_frames_out: int = 93,
        do_classifier_free_guidance: bool = True,
        dtype: Optional[torch.dtype] = None,
        device: Optional[torch.device] = None,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        num_cond_latent_frames: int = 0,
    ) -> torch.Tensor:
        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."
            )

        B = batch_size
        C = num_channels_latents
        T = (num_frames_out - 1) // self.vae_scale_factor_temporal + 1
        H = height // self.vae_scale_factor_spatial
        W = width // self.vae_scale_factor_spatial
        shape = (B, C, T, H, W)

        if latents is not None:
            if latents.shape[1:] != shape[1:]:
                raise ValueError(f"Unexpected `latents` shape, got {latents.shape}, expected {shape}.")
            latents = latents.to(device=device, dtype=dtype)
        else:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)

        if num_frames_in == 0:
            cond_mask = torch.zeros((B, 1, T, H, W), dtype=latents.dtype, device=latents.device)
            cond_indicator = torch.zeros((B, 1, T, 1, 1), dtype=latents.dtype, device=latents.device)

            cond_latents = torch.zeros_like(latents)

            return (
                latents,
                cond_latents,
                cond_mask,
                cond_indicator,
            )
        else:
            if video is None:
                raise ValueError("`video` must be provided when `num_frames_in` is greater than 0.")
            video = video.to(device=device, dtype=self.vae.dtype)
            if isinstance(generator, list):
                cond_latents = [
                    retrieve_latents(self.vae.encode(video[i].unsqueeze(0)), generator=generator[i])
                    for i in range(batch_size)
                ]
            else:
                cond_latents = [retrieve_latents(self.vae.encode(vid.unsqueeze(0)), generator) for vid in video]

            cond_latents = torch.cat(cond_latents, dim=0).to(dtype)

            latents_mean = self.latents_mean.to(device=device, dtype=dtype)
            latents_std = self.latents_std.to(device=device, dtype=dtype)
            cond_latents = (cond_latents - latents_mean) / latents_std

            padding_shape = (B, 1, T, H, W)
            ones_padding = latents.new_ones(padding_shape)
            zeros_padding = latents.new_zeros(padding_shape)

            cond_indicator = latents.new_zeros(B, 1, latents.size(2), 1, 1)
            cond_indicator[:, :, 0:num_cond_latent_frames, :, :] = 1.0
            cond_mask = cond_indicator * ones_padding + (1 - cond_indicator) * zeros_padding

            return (
                latents,
                cond_latents,
                cond_mask,
                cond_indicator,
            )

    # Modified from diffusers.pipelines.cosmos.pipeline_cosmos_text2world.CosmosTextToWorldPipeline.check_inputs
    def check_inputs(
        self,
        prompt,
        height,
        width,
        prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
        num_ar_conditional_frames=None,
        num_ar_latent_conditional_frames=None,
        num_frames_per_chunk=None,
        num_frames=None,
        conditional_frame_timestep=0.1,
    ):
        if width <= 0 or height <= 0 or height % 16 != 0 or width % 16 != 0:
            raise ValueError(
                f"`height` and `width` have to be divisible by 16 (& positive) but are {height} and {width}."
            )

        if num_frames is not None and num_frames <= 0:
            raise ValueError(f"`num_frames` has to be a positive integer when provided but is {num_frames}.")

        if conditional_frame_timestep < 0 or conditional_frame_timestep > 1:
            raise ValueError(
                "`conditional_frame_timestep` has to be a float in the [0, 1] interval but is "
                f"{conditional_frame_timestep}."
            )

        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 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)}")

        if num_ar_latent_conditional_frames is not None and num_ar_conditional_frames is not None:
            raise ValueError(
                "Provide only one of `num_ar_conditional_frames` or `num_ar_latent_conditional_frames`, not both."
            )
        if num_ar_latent_conditional_frames is None and num_ar_conditional_frames is None:
            raise ValueError("Provide either `num_ar_conditional_frames` or `num_ar_latent_conditional_frames`.")
        if num_ar_latent_conditional_frames is not None and num_ar_latent_conditional_frames < 0:
            raise ValueError("`num_ar_latent_conditional_frames` must be >= 0.")
        if num_ar_conditional_frames is not None and num_ar_conditional_frames < 0:
            raise ValueError("`num_ar_conditional_frames` must be >= 0.")

        if num_ar_latent_conditional_frames is not None:
            num_ar_conditional_frames = max(
                0, (num_ar_latent_conditional_frames - 1) * self.vae_scale_factor_temporal + 1
            )

        min_chunk_len = self.vae_scale_factor_temporal + 1
        if num_frames_per_chunk < min_chunk_len:
            logger.warning(f"{num_frames_per_chunk=} must be larger than {min_chunk_len=}, setting to min_chunk_len")
            num_frames_per_chunk = min_chunk_len

        max_frames_by_rope = None
        if getattr(self.transformer.config, "max_size", None) is not None:
            max_frames_by_rope = max(
                size // patch
                for size, patch in zip(self.transformer.config.max_size, self.transformer.config.patch_size)
            )
            if num_frames_per_chunk > max_frames_by_rope:
                raise ValueError(
                    f"{num_frames_per_chunk=} is too large for RoPE setting ({max_frames_by_rope=}). "
                    "Please reduce `num_frames_per_chunk`."
                )

        if num_ar_conditional_frames >= num_frames_per_chunk:
            raise ValueError(
                f"{num_ar_conditional_frames=} must be smaller than {num_frames_per_chunk=} for chunked generation."
            )

        return num_frames_per_chunk

    @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

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        controls: PipelineImageInput | List[PipelineImageInput],
        controls_conditioning_scale: Union[float, List[float]] = 1.0,
        prompt: Union[str, List[str]] | None = None,
        negative_prompt: Union[str, List[str]] = DEFAULT_NEGATIVE_PROMPT,
        height: int = 704,
        width: Optional[int] = None,
        num_frames: Optional[int] = None,
        num_frames_per_chunk: int = 93,
        num_inference_steps: int = 36,
        guidance_scale: float = 3.0,
        num_videos_per_prompt: int = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback_on_step_end: Optional[
            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
        ] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        max_sequence_length: int = 512,
        conditional_frame_timestep: float = 0.1,
        num_ar_conditional_frames: Optional[int] = 1,
        num_ar_latent_conditional_frames: Optional[int] = None,
    ):
        r"""
        `controls` drive the conditioning through ControlNet. Controls are assumed to be pre-processed, e.g. edge maps
        are pre-computed.

        Setting `num_frames` will restrict the total number of frames output, if not provided or assigned to None
        (default) then the number of output frames will match the input `controls`.

        Auto-regressive inference is supported and thus a sliding window of `num_frames_per_chunk` frames are used per
        denoising loop. In addition, when auto-regressive inference is performed, the previous
        `num_ar_latent_conditional_frames` or `num_ar_conditional_frames` are used to condition the following denoising
        inference loops.

        Args:
            controls (`PipelineImageInput`, `List[PipelineImageInput]`):
                Control image or video input used by the ControlNet.
            controls_conditioning_scale (`float` or `List[float]`, *optional*, defaults to `1.0`):
                The scale factor(s) for the ControlNet outputs. A single float is broadcast to all control blocks.
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide generation. Required unless `prompt_embeds` is supplied.
            height (`int`, defaults to `704`):
                The height in pixels of the generated image.
            width (`int`, *optional*):
                The width in pixels of the generated image. If not provided, this will be determined based on the
                aspect ratio of the input and the provided height.
            num_frames (`int`, *optional*):
                Number of output frames. Defaults to `None` to output the same number of frames as the input
                `controls`.
            num_inference_steps (`int`, defaults to `36`):
                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 `3.0`):
                Guidance scale as defined in [Classifier-Free Diffusion
                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
                of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
                `guidance_scale > 1`.
            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. 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, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
                provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                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 [`CosmosPipelineOutput`] instead of a plain tuple.
            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`, defaults to `512`):
                The maximum number of tokens in the prompt. If the prompt exceeds this length, it will be truncated. If
                the prompt is shorter than this length, it will be padded.
            num_ar_conditional_frames (`int`, *optional*, defaults to `1`):
                Number of frames to condition on subsequent inference loops in auto-regressive inference, i.e. for the
                second chunk and onwards. Only used if `num_ar_latent_conditional_frames` is `None`.

                This is only used when auto-regressive inference is performed, i.e. when the number of frames in
                controls is > num_frames_per_chunk
            num_ar_latent_conditional_frames (`int`, *optional*):
                Number of latent frames to condition on subsequent inference loops in auto-regressive inference, i.e.
                for the second chunk and onwards. Only used if `num_ar_conditional_frames` is `None`.

                This is only used when auto-regressive inference is performed, i.e. when the number of frames in
                controls is > num_frames_per_chunk
        Examples:

        Returns:
            [`~CosmosPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`CosmosPipelineOutput`] 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 self.safety_checker is None:
            raise ValueError(
                f"You have disabled the safety checker for {self.__class__}. This is in violation of the "
                "[NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). "
                f"Please ensure that you are compliant with the license agreement."
            )

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

        if width is None:
            frame = controls[0] if isinstance(controls, list) else controls
            if isinstance(frame, list):
                frame = frame[0]
            if isinstance(frame, (torch.Tensor, np.ndarray)):
                if frame.ndim == 5:
                    frame = frame[0, 0]
                elif frame.ndim == 4:
                    frame = frame[0]

            if isinstance(frame, PIL.Image.Image):
                width = int((height + 16) * (frame.width / frame.height))
            else:
                if frame.ndim != 3:
                    raise ValueError("`controls` must contain 3D frames in CHW format.")
                width = int((height + 16) * (frame.shape[2] / frame.shape[1]))  # NOTE: assuming C H W

        num_frames_per_chunk = self.check_inputs(
            prompt,
            height,
            width,
            prompt_embeds,
            callback_on_step_end_tensor_inputs,
            num_ar_conditional_frames,
            num_ar_latent_conditional_frames,
            num_frames_per_chunk,
            num_frames,
            conditional_frame_timestep,
        )

        if num_ar_latent_conditional_frames is not None:
            num_cond_latent_frames = num_ar_latent_conditional_frames
            num_ar_conditional_frames = max(0, (num_cond_latent_frames - 1) * self.vae_scale_factor_temporal + 1)
        else:
            num_cond_latent_frames = max(0, (num_ar_conditional_frames - 1) // self.vae_scale_factor_temporal + 1)

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

        device = self._execution_device

        if self.safety_checker is not None:
            self.safety_checker.to(device)
            if prompt is not None:
                prompt_list = [prompt] if isinstance(prompt, str) else prompt
                for p in prompt_list:
                    if not self.safety_checker.check_text_safety(p):
                        raise ValueError(
                            f"Cosmos Guardrail detected unsafe text in the prompt: {p}. Please ensure that the "
                            f"prompt abides by the NVIDIA Open Model License Agreement."
                        )

        # 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]

        # 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,
            device=device,
            max_sequence_length=max_sequence_length,
        )

        vae_dtype = self.vae.dtype
        transformer_dtype = self.transformer.dtype

        if getattr(self.transformer.config, "img_context_dim_in", None):
            img_context = torch.zeros(
                batch_size,
                self.transformer.config.img_context_num_tokens,
                self.transformer.config.img_context_dim_in,
                device=prompt_embeds.device,
                dtype=transformer_dtype,
            )

            if num_videos_per_prompt > 1:
                img_context = img_context.repeat_interleave(num_videos_per_prompt, dim=0)

            encoder_hidden_states = (prompt_embeds, img_context)
            neg_encoder_hidden_states = (negative_prompt_embeds, img_context)
        else:
            encoder_hidden_states = prompt_embeds
            neg_encoder_hidden_states = negative_prompt_embeds

        control_video = self.video_processor.preprocess_video(controls, height, width)
        if control_video.shape[0] != batch_size:
            if control_video.shape[0] == 1:
                control_video = control_video.repeat(batch_size, 1, 1, 1, 1)
            else:
                raise ValueError(
                    f"Expected controls batch size {batch_size} to match prompt batch size, but got {control_video.shape[0]}."
                )

        num_frames_out = control_video.shape[2]
        if num_frames is not None:
            num_frames_out = min(num_frames_out, num_frames)

        control_video = _maybe_pad_or_trim_video(control_video, num_frames_out)

        # chunk information
        num_latent_frames_per_chunk = (num_frames_per_chunk - 1) // self.vae_scale_factor_temporal + 1
        chunk_stride = num_frames_per_chunk - num_ar_conditional_frames
        chunk_idxs = [
            (start_idx, min(start_idx + num_frames_per_chunk, num_frames_out))
            for start_idx in range(0, num_frames_out - num_ar_conditional_frames, chunk_stride)
        ]

        video_chunks = []
        latents_mean = self.latents_mean.to(dtype=vae_dtype, device=device)
        latents_std = self.latents_std.to(dtype=vae_dtype, device=device)

        def decode_latents(latents):
            latents = latents * latents_std + latents_mean
            video = self.vae.decode(latents.to(dtype=self.vae.dtype, device=device), return_dict=False)[0]
            return video

        latents_arg = latents
        initial_num_cond_latent_frames = 0
        latent_chunks = []
        num_chunks = len(chunk_idxs)
        total_steps = num_inference_steps * num_chunks
        with self.progress_bar(total=total_steps) as progress_bar:
            for chunk_idx, (start_idx, end_idx) in enumerate(chunk_idxs):
                if chunk_idx == 0:
                    prev_output = torch.zeros((batch_size, num_frames_per_chunk, 3, height, width), dtype=vae_dtype)
                    prev_output = self.video_processor.preprocess_video(prev_output, height, width)
                else:
                    prev_output = video_chunks[-1].clone()
                    if num_ar_conditional_frames > 0:
                        prev_output[:, :, :num_ar_conditional_frames] = prev_output[:, :, -num_ar_conditional_frames:]
                        prev_output[:, :, num_ar_conditional_frames:] = -1  # -1 == 0 in processed video space
                    else:
                        prev_output.fill_(-1)

                chunk_video = prev_output.to(device=device, dtype=vae_dtype)
                chunk_video = _maybe_pad_or_trim_video(chunk_video, num_frames_per_chunk)
                latents, cond_latent, cond_mask, cond_indicator = self.prepare_latents(
                    video=chunk_video,
                    batch_size=batch_size * num_videos_per_prompt,
                    num_channels_latents=self.transformer.config.in_channels - 1,
                    height=height,
                    width=width,
                    num_frames_in=chunk_video.shape[2],
                    num_frames_out=num_frames_per_chunk,
                    do_classifier_free_guidance=self.do_classifier_free_guidance,
                    dtype=torch.float32,
                    device=device,
                    generator=generator,
                    num_cond_latent_frames=initial_num_cond_latent_frames
                    if chunk_idx == 0
                    else num_cond_latent_frames,
                    latents=latents_arg,
                )
                cond_mask = cond_mask.to(transformer_dtype)
                cond_timestep = torch.ones_like(cond_indicator) * conditional_frame_timestep
                padding_mask = latents.new_zeros(1, 1, height, width, dtype=transformer_dtype)

                chunk_control_video = control_video[:, :, start_idx:end_idx, ...].to(
                    device=device, dtype=self.vae.dtype
                )
                chunk_control_video = _maybe_pad_or_trim_video(chunk_control_video, num_frames_per_chunk)
                if isinstance(generator, list):
                    controls_latents = [
                        retrieve_latents(self.vae.encode(chunk_control_video[i].unsqueeze(0)), generator=generator[i])
                        for i in range(chunk_control_video.shape[0])
                    ]
                else:
                    controls_latents = [
                        retrieve_latents(self.vae.encode(vid.unsqueeze(0)), generator=generator)
                        for vid in chunk_control_video
                    ]
                controls_latents = torch.cat(controls_latents, dim=0).to(transformer_dtype)

                controls_latents = (controls_latents - latents_mean) / latents_std

                # Denoising loop
                self.scheduler.set_timesteps(num_inference_steps, device=device)
                timesteps = self.scheduler.timesteps
                self._num_timesteps = len(timesteps)

                gt_velocity = (latents - cond_latent) * cond_mask
                for i, t in enumerate(timesteps):
                    if self.interrupt:
                        continue

                    self._current_timestep = t.cpu().item()

                    # NOTE: assumes sigma(t) \in [0, 1]
                    sigma_t = (
                        torch.tensor(self.scheduler.sigmas[i].item())
                        .unsqueeze(0)
                        .to(device=device, dtype=transformer_dtype)
                    )

                    in_latents = cond_mask * cond_latent + (1 - cond_mask) * latents
                    in_latents = in_latents.to(transformer_dtype)
                    in_timestep = cond_indicator * cond_timestep + (1 - cond_indicator) * sigma_t
                    control_output = self.controlnet(
                        controls_latents=controls_latents,
                        latents=in_latents,
                        timestep=in_timestep,
                        encoder_hidden_states=encoder_hidden_states,
                        condition_mask=cond_mask,
                        conditioning_scale=controls_conditioning_scale,
                        padding_mask=padding_mask,
                        return_dict=False,
                    )
                    control_blocks = control_output[0]

                    noise_pred = self.transformer(
                        hidden_states=in_latents,
                        timestep=in_timestep,
                        encoder_hidden_states=encoder_hidden_states,
                        block_controlnet_hidden_states=control_blocks,
                        condition_mask=cond_mask,
                        padding_mask=padding_mask,
                        return_dict=False,
                    )[0]
                    noise_pred = gt_velocity + noise_pred * (1 - cond_mask)

                    if self.do_classifier_free_guidance:
                        control_output = self.controlnet(
                            controls_latents=controls_latents,
                            latents=in_latents,
                            timestep=in_timestep,
                            encoder_hidden_states=neg_encoder_hidden_states,  # NOTE: negative prompt
                            condition_mask=cond_mask,
                            conditioning_scale=controls_conditioning_scale,
                            padding_mask=padding_mask,
                            return_dict=False,
                        )
                        control_blocks = control_output[0]

                        noise_pred_neg = self.transformer(
                            hidden_states=in_latents,
                            timestep=in_timestep,
                            encoder_hidden_states=neg_encoder_hidden_states,  # NOTE: negative prompt
                            block_controlnet_hidden_states=control_blocks,
                            condition_mask=cond_mask,
                            padding_mask=padding_mask,
                            return_dict=False,
                        )[0]
                        # NOTE: replace velocity (noise_pred_neg) with gt_velocity for conditioning inputs only
                        noise_pred_neg = gt_velocity + noise_pred_neg * (1 - cond_mask)
                        noise_pred = noise_pred + self.guidance_scale * (noise_pred - noise_pred_neg)

                    latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]

                    # call the callback, if provided
                    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)

                    if i == total_steps - 1 or ((i + 1) % self.scheduler.order == 0):
                        progress_bar.update()

                    if XLA_AVAILABLE:
                        xm.mark_step()

                video_chunks.append(decode_latents(latents).detach().cpu())
                latent_chunks.append(latents.detach().cpu())

        self._current_timestep = None

        if not output_type == "latent":
            video_chunks = [
                chunk[:, :, num_ar_conditional_frames:, ...] if chunk_idx != 0 else chunk
                for chunk_idx, chunk in enumerate(video_chunks)
            ]
            video = torch.cat(video_chunks, dim=2)
            video = video[:, :, :num_frames_out, ...]

            assert self.safety_checker is not None
            self.safety_checker.to(device)
            video = self.video_processor.postprocess_video(video, output_type="np")
            video = (video * 255).astype(np.uint8)
            video_batch = []
            for vid in video:
                vid = self.safety_checker.check_video_safety(vid)
                if vid is None:
                    video_batch.append(np.zeros_like(video[0]))
                else:
                    video_batch.append(vid)
            video = np.stack(video_batch).astype(np.float32) / 255.0 * 2 - 1
            video = torch.from_numpy(video).permute(0, 4, 1, 2, 3)
            video = self.video_processor.postprocess_video(video, output_type=output_type)
        else:
            latent_T = (num_frames_out - 1) // self.vae_scale_factor_temporal + 1
            latent_chunks = [
                chunk[:, :, num_cond_latent_frames:, ...] if chunk_idx != 0 else chunk
                for chunk_idx, chunk in enumerate(latent_chunks)
            ]
            video = torch.cat(latent_chunks, dim=2)
            video = video[:, :, :latent_T, ...]

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (video,)

        return CosmosPipelineOutput(frames=video)
