class_docstr: Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
class_docstr_tok: ["Inspired","by",":","https",":","\/\/github.com\/haofanwang\/ControlNet-for-Diffusers\/"]
docstr: Encodes the prompt into text encoder hidden states.
  
  Args:
       prompt (`str` or `List[str]`, *optional*):
          prompt to be encoded
      device: (`torch.device`):
          torch device
      num_images_per_prompt (`int`):
          number of images that should be generated per prompt
      do_classifier_free_guidance (`bool`):
          whether to use classifier free guidance or not
      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`).
      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. 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.
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code: def _encode_prompt(
      self,
      prompt,
      device,
      num_images_per_prompt,
      do_classifier_free_guidance,
      negative_prompt=None,
      prompt_embeds: Optional[torch.FloatTensor] = None,
      negative_prompt_embeds: Optional[torch.FloatTensor] = None,
  ):
      r"""
      Encodes the prompt into text encoder hidden states.
  
      Args:
           prompt (`str` or `List[str]`, *optional*):
              prompt to be encoded
          device: (`torch.device`):
              torch device
          num_images_per_prompt (`int`):
              number of images that should be generated per prompt
          do_classifier_free_guidance (`bool`):
              whether to use classifier free guidance or not
          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`).
          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. 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.
      """
      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]
  
      if prompt_embeds is None:
          text_inputs = self.tokenizer(
              prompt,
              padding="max_length",
              max_length=self.tokenizer.model_max_length,
              truncation=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[
                      :, self.tokenizer.model_max_length - 1 : -1
                  ]
              )
              logger.warning(
                  "The following part of your input was truncated because CLIP can only handle sequences up to"
                  f" {self.tokenizer.model_max_length} tokens: {removed_text}"
              )
  
          if (
              hasattr(self.text_encoder.config, "use_attention_mask")
              and self.text_encoder.config.use_attention_mask
          ):
              attention_mask = text_inputs.attention_mask.to(device)
          else:
              attention_mask = None
  
          prompt_embeds = self.text_encoder(
              text_input_ids.to(device),
              attention_mask=attention_mask,
          )
          prompt_embeds = prompt_embeds[0]
  
      prompt_embeds = prompt_embeds.to(
          dtype=self.text_encoder.dtype, device=device
      )
  
      bs_embed, seq_len, _ = prompt_embeds.shape
      # duplicate text embeddings 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
      )
  
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance and negative_prompt_embeds is None:
          uncond_tokens: List[str]
          if negative_prompt is None:
              uncond_tokens = [""] * batch_size
          elif 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 isinstance(negative_prompt, str):
              uncond_tokens = [negative_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`."
              )
          else:
              uncond_tokens = negative_prompt
  
          max_length = prompt_embeds.shape[1]
          uncond_input = self.tokenizer(
              uncond_tokens,
              padding="max_length",
              max_length=max_length,
              truncation=True,
              return_tensors="pt",
          )
  
          if (
              hasattr(self.text_encoder.config, "use_attention_mask")
              and self.text_encoder.config.use_attention_mask
          ):
              attention_mask = uncond_input.attention_mask.to(device)
          else:
              attention_mask = None
  
          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=self.text_encoder.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
          prompt_embeds = torch.cat(
              [negative_prompt_embeds, prompt_embeds]
          )
  
      return prompt_embeds
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, UNet2DConditionModel, logging
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
  from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, replace_example_docstring
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 7 for calling the StableDiffusionControlNetInpaintPipeline obj's underlying member method code with example usage: obj._encode_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds, negative_prompt_embeds) and returns: prompt_embeds