# Adapted from Latte

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# Latte: https://github.com/Vchitect/Latte
# --------------------------------------------------------

import html
import inspect
import re
import urllib.parse as ul
from typing import Callable, List, Optional, Tuple, Union

import einops
import ftfy
import torch
import torch.distributed as dist
import tqdm
from bs4 import BeautifulSoup
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL, AutoencoderKLTemporalDecoder
from diffusers.schedulers import DDIMScheduler
from diffusers.utils.torch_utils import randn_tensor
from transformers import T5EncoderModel, T5Tokenizer

from videosys.core.pab_mgr import PABConfig, set_pab_manager, update_steps
from videosys.core.pipeline import VideoSysPipeline, VideoSysPipelineOutput
from videosys.models.transformers.latte_transformer_3d import LatteT2V
from videosys.utils.logging import logger
from videosys.utils.utils import save_video


class LattePABConfig(PABConfig):
    def __init__(
        self,
        steps: int = 50,
        spatial_broadcast: bool = True,
        spatial_threshold: list = [100, 800],
        spatial_range: int = 2,
        temporal_broadcast: bool = True,
        temporal_threshold: list = [100, 800],
        temporal_range: int = 3,
        cross_broadcast: bool = True,
        cross_threshold: list = [100, 800],
        cross_range: int = 6,
        mlp_broadcast: bool = True,
        mlp_spatial_broadcast_config: dict = {
            720: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
            640: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
            560: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
            480: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
            400: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
        },
        mlp_temporal_broadcast_config: dict = {
            720: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
            640: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
            560: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
            480: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
            400: {"block": [0, 1, 2, 3, 4], "skip_count": 2},
        },
    ):
        super().__init__(
            steps=steps,
            spatial_broadcast=spatial_broadcast,
            spatial_threshold=spatial_threshold,
            spatial_range=spatial_range,
            temporal_broadcast=temporal_broadcast,
            temporal_threshold=temporal_threshold,
            temporal_range=temporal_range,
            cross_broadcast=cross_broadcast,
            cross_threshold=cross_threshold,
            cross_range=cross_range,
            mlp_broadcast=mlp_broadcast,
            mlp_spatial_broadcast_config=mlp_spatial_broadcast_config,
            mlp_temporal_broadcast_config=mlp_temporal_broadcast_config,
        )


class LatteConfig:
    """
    This config is to instantiate a `LattePipeline` class for video generation.

    To be specific, this config will be passed to engine by `VideoSysEngine(config)`.
    In the engine, it will be used to instantiate the corresponding pipeline class.
    And the engine will call the `generate` function of the pipeline to generate the video.
    If you want to explore the detail of generation, please refer to the pipeline class below.

    Args:
        model_path (str):
            A path to the pretrained pipeline. Defaults to "maxin-cn/Latte-1".
        num_gpus (int):
            The number of GPUs to use. Defaults to 1.
        enable_vae_temporal_decoder (bool):
            Whether to enable VAE Temporal Decoder. Defaults to True.
        beta_start (float):
            The initial value of beta for DDIM. Defaults to 0.0001.
        beta_end (float):
            The final value of beta for DDIM. Defaults to 0.02.
        beta_schedule (str):
            The schedule of beta for DDIM. Defaults to "linear".
        variance_type (str):
            The type of variance for DDIM. Defaults to "learned_range".
        enable_pab (bool):
            Whether to enable Pyramid Attention Broadcast. Defaults to False.
        pab_config (CogVideoXPABConfig):
            The configuration for Pyramid Attention Broadcast. Defaults to `LattePABConfig()`.

    Examples:
        ```python
        from videosys import LatteConfig, VideoSysEngine

        # change num_gpus for multi-gpu inference
        config = LatteConfig("maxin-cn/Latte-1", num_gpus=1)
        engine = VideoSysEngine(config)

        prompt = "Sunset over the sea."
        # video size is fixed to 16 frames, 512x512.
        video = engine.generate(
            prompt=prompt,
            guidance_scale=7.5,
            num_inference_steps=50,
        ).video[0]
        engine.save_video(video, f"./outputs/{prompt}.mp4")
        ```
    """

    def __init__(
        self,
        model_path: str = "maxin-cn/Latte-1",
        # ======= distributed =======
        num_gpus: int = 1,
        # ======= vae ========
        enable_vae_temporal_decoder: bool = True,
        # ======= scheduler ========
        beta_start: float = 0.0001,
        beta_end: float = 0.02,
        beta_schedule: str = "linear",
        variance_type: str = "learned_range",
        # ======= pab ========
        enable_pab: bool = False,
        pab_config: PABConfig = LattePABConfig(),
    ):
        self.model_path = model_path
        self.pipeline_cls = LattePipeline
        # ======= distributed =======
        self.num_gpus = num_gpus
        # ======= vae ========
        self.enable_vae_temporal_decoder = enable_vae_temporal_decoder
        # ======= scheduler ========
        self.beta_start = beta_start
        self.beta_end = beta_end
        self.beta_schedule = beta_schedule
        self.variance_type = variance_type
        # ======= pab ========
        self.enable_pab = enable_pab
        self.pab_config = pab_config


class LattePipeline(VideoSysPipeline):
    r"""
    Pipeline for text-to-image generation using PixArt-Alpha.

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

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`T5EncoderModel`]):
            Frozen text-encoder. PixArt-Alpha uses
            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
        tokenizer (`T5Tokenizer`):
            Tokenizer of class
            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
        transformer ([`Transformer2DModel`]):
            A text conditioned `Transformer2DModel` to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
    """
    bad_punct_regex = re.compile(
        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
    )  # noqa

    _optional_components = ["tokenizer", "text_encoder"]
    model_cpu_offload_seq = "text_encoder->transformer->vae"

    def __init__(
        self,
        config: LatteConfig,
        tokenizer: Optional[T5Tokenizer] = None,
        text_encoder: Optional[T5EncoderModel] = None,
        vae: Optional[AutoencoderKL] = None,
        transformer: Optional[LatteT2V] = None,
        scheduler: Optional[DDIMScheduler] = None,
        device: torch.device = torch.device("cuda"),
        dtype: torch.dtype = torch.float16,
    ):
        super().__init__()
        self._config = config

        # initialize the model if not provided
        if transformer is None:
            transformer = LatteT2V.from_pretrained(config.model_path, subfolder="transformer", video_length=16).to(
                dtype=dtype
            )
        if vae is None:
            if config.enable_vae_temporal_decoder:
                vae = AutoencoderKLTemporalDecoder.from_pretrained(
                    config.model_path, subfolder="vae_temporal_decoder", torch_dtype=dtype
                )
            else:
                vae = AutoencoderKL.from_pretrained(config.model_path, subfolder="vae", torch_dtype=dtype)
        if tokenizer is None:
            tokenizer = T5Tokenizer.from_pretrained(config.model_path, subfolder="tokenizer")
        if text_encoder is None:
            text_encoder = T5EncoderModel.from_pretrained(
                config.model_path, subfolder="text_encoder", torch_dtype=dtype
            )
        if scheduler is None:
            scheduler = DDIMScheduler.from_pretrained(
                config.model_path,
                subfolder="scheduler",
                beta_start=config.beta_start,
                beta_end=config.beta_end,
                beta_schedule=config.beta_schedule,
                variance_type=config.variance_type,
                clip_sample=False,
            )

        # pab
        if config.enable_pab:
            set_pab_manager(config.pab_config)

        # set eval and device
        self.set_eval_and_device(device, text_encoder, vae, transformer)

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

        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    # Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/utils.py
    def mask_text_embeddings(self, emb, mask):
        if emb.shape[0] == 1:
            keep_index = mask.sum().item()
            return emb[:, :, :keep_index, :], keep_index  # 1, 120, 4096 -> 1 7 4096
        else:
            masked_feature = emb * mask[:, None, :, None]  # 1 120 4096
            return masked_feature, emb.shape[2]

    # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
    def encode_prompt(
        self,
        prompt: Union[str, List[str]],
        do_classifier_free_guidance: bool = True,
        negative_prompt: str = "",
        num_images_per_prompt: int = 1,
        device: Optional[torch.device] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        clean_caption: bool = False,
        mask_feature: bool = True,
    ):
        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 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`). For
                PixArt-Alpha, this should be "".
            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
                whether to use classifier free guidance or not
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                number of images that should be generated per prompt
            device: (`torch.device`, *optional*):
                torch device to place the resulting embeddings on
            prompt_embeds (`torch.FloatTensor`, *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-Alpha, it's should be the embeddings of the ""
                string.
            clean_caption (bool, defaults to `False`):
                If `True`, the function will preprocess and clean the provided caption before encoding.
            mask_feature: (bool, defaults to `True`):
                If `True`, the function will mask the text embeddings.
        """
        embeds_initially_provided = prompt_embeds is not None and negative_prompt_embeds is not None

        if device is None:
            device = self._execution_device

        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]

        # See Section 3.1. of the paper.
        max_length = 120

        if prompt_embeds is None:
            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_attention_mask=True,
                add_special_tokens=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {max_length} tokens: {removed_text}"
                )

            attention_mask = text_inputs.attention_mask.to(device)
            prompt_embeds_attention_mask = attention_mask

            prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
            prompt_embeds = prompt_embeds[0]
        else:
            prompt_embeds_attention_mask = torch.ones_like(prompt_embeds)

        if self.text_encoder is not None:
            dtype = self.text_encoder.dtype
        elif self.transformer is not None:
            dtype = self.transformer.dtype
        else:
            dtype = None

        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
        prompt_embeds_attention_mask = prompt_embeds_attention_mask.view(bs_embed, -1)
        prompt_embeds_attention_mask = prompt_embeds_attention_mask.repeat(num_images_per_prompt, 1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens = [negative_prompt] * batch_size
            uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_attention_mask=True,
                add_special_tokens=True,
                return_tensors="pt",
            )
            attention_mask = uncond_input.attention_mask.to(device)

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
        else:
            negative_prompt_embeds = None

        # Perform additional masking.
        if mask_feature and not embeds_initially_provided:
            prompt_embeds = prompt_embeds.unsqueeze(1)
            masked_prompt_embeds, keep_indices = self.mask_text_embeddings(prompt_embeds, prompt_embeds_attention_mask)
            masked_prompt_embeds = masked_prompt_embeds.squeeze(1)
            masked_negative_prompt_embeds = (
                negative_prompt_embeds[:, :keep_indices, :] if negative_prompt_embeds is not None else None
            )

            # import torch.nn.functional as F

            # padding = (0, 0, 0, 113)  # (左, 右, 下, 上)
            # masked_prompt_embeds_ = F.pad(masked_prompt_embeds, padding, "constant", 0)
            # masked_negative_prompt_embeds_ = F.pad(masked_negative_prompt_embeds, padding, "constant", 0)

            # print(masked_prompt_embeds == masked_prompt_embeds_[:, :masked_negative_prompt_embeds.shape[1], ...])

            return masked_prompt_embeds, masked_negative_prompt_embeds
            # return masked_prompt_embeds_, masked_negative_prompt_embeds_

        return prompt_embeds, negative_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def check_inputs(
        self,
        prompt,
        height,
        width,
        negative_prompt,
        callback_steps,
        prompt_embeds=None,
        negative_prompt_embeds=None,
    ):
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        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 prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if 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`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
    def _text_preprocessing(self, text, clean_caption=False):
        if not isinstance(text, (tuple, list)):
            text = [text]

        def process(text: str):
            if clean_caption:
                text = self._clean_caption(text)
                text = self._clean_caption(text)
            else:
                text = text.lower().strip()
            return text

        return [process(t) for t in text]

    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
    def _clean_caption(self, caption):
        caption = str(caption)
        caption = ul.unquote_plus(caption)
        caption = caption.strip().lower()
        caption = re.sub("<person>", "person", caption)
        # urls:
        caption = re.sub(
            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
            "",
            caption,
        )  # regex for urls
        caption = re.sub(
            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
            "",
            caption,
        )  # regex for urls
        # html:
        caption = BeautifulSoup(caption, features="html.parser").text

        # @<nickname>
        caption = re.sub(r"@[\w\d]+\b", "", caption)

        # 31C0—31EF CJK Strokes
        # 31F0—31FF Katakana Phonetic Extensions
        # 3200—32FF Enclosed CJK Letters and Months
        # 3300—33FF CJK Compatibility
        # 3400—4DBF CJK Unified Ideographs Extension A
        # 4DC0—4DFF Yijing Hexagram Symbols
        # 4E00—9FFF CJK Unified Ideographs
        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
        #######################################################

        # все виды тире / all types of dash --> "-"
        caption = re.sub(
            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
            "-",
            caption,
        )

        # кавычки к одному стандарту
        caption = re.sub(r"[`´«»“”¨]", '"', caption)
        caption = re.sub(r"[‘’]", "'", caption)

        # &quot;
        caption = re.sub(r"&quot;?", "", caption)
        # &amp
        caption = re.sub(r"&amp", "", caption)

        # ip adresses:
        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)

        # article ids:
        caption = re.sub(r"\d:\d\d\s+$", "", caption)

        # \n
        caption = re.sub(r"\\n", " ", caption)

        # "#123"
        caption = re.sub(r"#\d{1,3}\b", "", caption)
        # "#12345.."
        caption = re.sub(r"#\d{5,}\b", "", caption)
        # "123456.."
        caption = re.sub(r"\b\d{6,}\b", "", caption)
        # filenames:
        caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)

        #
        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""

        caption = re.sub(self.bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "

        # this-is-my-cute-cat / this_is_my_cute_cat
        regex2 = re.compile(r"(?:\-|\_)")
        if len(re.findall(regex2, caption)) > 3:
            caption = re.sub(regex2, " ", caption)

        caption = ftfy.fix_text(caption)
        caption = html.unescape(html.unescape(caption))

        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231

        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
        caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
        caption = re.sub(r"\bpage\s+\d+\b", "", caption)

        caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...

        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)

        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
        caption = re.sub(r"\s+", " ", caption)

        caption.strip()

        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
        caption = re.sub(r"^\.\S+$", "", caption)

        return caption.strip()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(
        self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None
    ):
        shape = (
            batch_size,
            num_channels_latents,
            video_length,
            height // self.vae_scale_factor,
            width // self.vae_scale_factor,
        )
        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."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    @torch.no_grad()
    def generate(
        self,
        prompt: str = None,
        negative_prompt: str = "",
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        clean_caption: bool = True,
        mask_feature: bool = True,
        enable_temporal_attentions: bool = True,
        verbose: bool = True,
    ) -> Union[VideoSysPipelineOutput, Tuple]:
        """
        Function invoked when calling the pipeline for generation.

        Latte can only generate video of 16 frames 512x512.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image 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 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`).
            num_inference_steps (`int`, *optional*, defaults to 100):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            timesteps (`List[int]`, *optional*):
                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
                timesteps are used. Must be in descending order.
            guidance_scale (`float`, *optional*, defaults to 7.0):
                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.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.FloatTensor`, *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 will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.FloatTensor`, *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-Alpha 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 generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            clean_caption (`bool`, *optional*, defaults to `True`):
                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
                be installed. If the dependencies are not installed, the embeddings will be created from the raw
                prompt.
            mask_feature (`bool` defaults to `True`): If set to `True`, the text embeddings will be masked.
            enable_temporal_attentions (`bool`, defaults to `True`):
                If `True`, the model will use temporal attentions to generate the video.
            verbose (`bool`, *optional*, defaults to `True`):
                Whether to print progress bars and other information during inference.

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
                returned where the first element is a list with the generated images
        """
        # 1. Check inputs. Raise error if not correct
        video_length = 16
        height = 512
        width = 512
        update_steps(num_inference_steps)
        self.check_inputs(prompt, height, width, negative_prompt, callback_steps, prompt_embeds, negative_prompt_embeds)

        # 2. Default height and width to transformer
        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]

        device = self.text_encoder.device or self._execution_device

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
            prompt,
            do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            device=device,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            clean_caption=clean_caption,
            mask_feature=mask_feature,
        )
        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)

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

        # 5. Prepare latents.
        latent_channels = self.transformer.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            latent_channels,
            video_length,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6. Prepare extra step kwargs.
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 6.1 Prepare micro-conditions.
        added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
        if self.transformer.config.sample_size == 128:
            resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1)
            aspect_ratio = torch.tensor([float(height / width)]).repeat(batch_size * num_images_per_prompt, 1)
            resolution = resolution.to(dtype=prompt_embeds.dtype, device=device)
            aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device)
            added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio}

        # 7. Denoising loop
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        progress_wrap = tqdm.tqdm if verbose and dist.get_rank() == 0 else (lambda x: x)
        for i, t in progress_wrap(list(enumerate(timesteps))):
            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

            current_timestep = t
            if not torch.is_tensor(current_timestep):
                # This would be a good case for the `match` statement (Python 3.10+)
                is_mps = latent_model_input.device.type == "mps"
                if isinstance(current_timestep, float):
                    dtype = torch.float32 if is_mps else torch.float64
                else:
                    dtype = torch.int32 if is_mps else torch.int64
                current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
            elif len(current_timestep.shape) == 0:
                current_timestep = current_timestep[None].to(latent_model_input.device)
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            current_timestep = current_timestep.expand(latent_model_input.shape[0])

            # predict noise model_output
            noise_pred = self.transformer(
                latent_model_input,
                all_timesteps=timesteps,
                encoder_hidden_states=prompt_embeds,
                timestep=current_timestep,
                added_cond_kwargs=added_cond_kwargs,
                enable_temporal_attentions=enable_temporal_attentions,
                return_dict=False,
            )[0]

            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # learned sigma
            if self.transformer.config.out_channels // 2 == latent_channels:
                noise_pred = noise_pred.chunk(2, dim=1)[0]
            else:
                noise_pred = noise_pred

            # compute previous image: x_t -> x_t-1
            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

            # call the callback, if provided
            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                if callback is not None and i % callback_steps == 0:
                    step_idx = i // getattr(self.scheduler, "order", 1)
                    callback(step_idx, t, latents)

        if not output_type == "latents":
            if latents.shape[2] == 1:  # image
                video = self.decode_latents_image(latents)
            else:  # video
                if self._config.enable_vae_temporal_decoder:
                    video = self.decode_latents_with_temporal_decoder(latents)
                else:
                    video = self.decode_latents(latents)
        else:
            video = latents
            return VideoSysPipelineOutput(video=video)

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (video,)

        return VideoSysPipelineOutput(video=video)

    def decode_latents_image(self, latents):
        video_length = latents.shape[2]
        latents = 1 / self.vae.config.scaling_factor * latents
        latents = einops.rearrange(latents, "b c f h w -> (b f) c h w")
        video = []
        for frame_idx in range(latents.shape[0]):
            video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample)
        video = torch.cat(video)
        video = einops.rearrange(video, "(b f) c h w -> b f c h w", f=video_length)
        video = (video / 2.0 + 0.5).clamp(0, 1)
        return video

    def decode_latents(self, latents):
        video_length = latents.shape[2]
        latents = 1 / self.vae.config.scaling_factor * latents
        latents = einops.rearrange(latents, "b c f h w -> (b f) c h w")
        video = []
        for frame_idx in range(latents.shape[0]):
            video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample)
        video = torch.cat(video)
        video = einops.rearrange(video, "(b f) c h w -> b f h w c", f=video_length)
        video = ((video / 2.0 + 0.5).clamp(0, 1) * 255).to(dtype=torch.uint8).cpu().contiguous()
        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
        return video

    def decode_latents_with_temporal_decoder(self, latents):
        video_length = latents.shape[2]
        latents = 1 / self.vae.config.scaling_factor * latents
        latents = einops.rearrange(latents, "b c f h w -> (b f) c h w")
        video = []

        decode_chunk_size = 14
        for frame_idx in range(0, latents.shape[0], decode_chunk_size):
            num_frames_in = latents[frame_idx : frame_idx + decode_chunk_size].shape[0]

            decode_kwargs = {}
            decode_kwargs["num_frames"] = num_frames_in

            video.append(self.vae.decode(latents[frame_idx : frame_idx + decode_chunk_size], **decode_kwargs).sample)

        video = torch.cat(video)
        video = einops.rearrange(video, "(b f) c h w -> b f h w c", f=video_length)
        video = ((video / 2.0 + 0.5).clamp(0, 1) * 255).to(dtype=torch.uint8).cpu().contiguous()
        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
        return video

    def save_video(self, video, output_path):
        save_video(video, output_path, fps=8)