class_docstr: 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.
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code: 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()
  
imports: import html
  import inspect
  import re
  import urllib.parse
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from transformers import T5EncoderModel, T5Tokenizer
  from ...image_processor import VaeImageProcessor
  from ...models import AutoencoderKL, Transformer2DModel
  from ...schedulers import DPMSolverMultistepScheduler
  from ...utils import BACKENDS_MAPPING, deprecate, is_bs4_available, is_ftfy_available, logging, replace_example_docstring
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 7 for calling the PixArtAlphaPipeline obj's underlying member method code with example usage: obj._clean_caption(caption) and returns: caption