

"""simple docstring"""




import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil







class      __lowercase			:

		def __init__(  self		:			str					,A		:			str = "cpu"					,A		:			str = "openai/clip-vit-large-patch14"    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Dict					   =					device
			UpperCAmelCase__					:  List[Any]					   =					CLIPTokenizerFast.from_pretrained(A    )
			UpperCAmelCase__					:  int					   =					[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
			UpperCAmelCase__					:  str					   =					[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
			UpperCAmelCase__					:  Any					   =					torchvision.transforms.Normalize(self.image_mean					,self.image_std    )
			UpperCAmelCase__					:  str					   =					torchvision.transforms.Resize(224    )
			UpperCAmelCase__					:  Optional[Any]					   =					torchvision.transforms.CenterCrop(224    )

		def    __lowercase       (  self		:			Union[str, Any]					,A		:			List[Any]    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Optional[Any]					   =					self.resize(A    )
			UpperCAmelCase__					:  Any					   =					self.center_crop(A    )
			UpperCAmelCase__					:  Optional[Any]					   =					self.normalize(A    )
			return images




		def __call__(  self		:			Optional[int]					,A		:			Optional[int]=None					,A		:			Any=None					,**A		:			Union[str, Any]    ):






			'''simple docstring'''
			UpperCAmelCase__					:  int					   =					self.tokenizer(text=A					,**A    )
			UpperCAmelCase__					:  Union[str, Any]					   =					self.preprocess_img(A    )
			UpperCAmelCase__					:  List[str]					   =					{key: value.to(self.device    ) for (key, value) in encoding.items()}
			return encoding







class      __lowercase			(    nn.Module					):

		def __init__(  self		:			List[str]					,A		:			Union[str, Any]=10					,A		:			Union[str, Any]=0.0_1					,A		:			int=None					,A		:			Any=None					,A		:			Optional[int]=None					,A		:			List[Any]=None					,A		:			List[Any]=None					,A		:			str=None					,A		:			Union[str, Any]=False					,A		:			Union[str, Any]=True					,A		:			Tuple="image"					,A		:			Tuple=True					,A		:			Optional[int]=False					,A		:			Union[str, Any]=False					,A		:			List[Any]=False					,):






			'''simple docstring'''
			super().__init__()
			UpperCAmelCase__					:  Union[str, Any]					   =					None
			UpperCAmelCase__					:  Optional[int]					   =					device if device else get_device()
			if vqgan:
				UpperCAmelCase__					:  Optional[int]					   =					vqgan
			else:
				UpperCAmelCase__					:  Any					   =					load_vqgan(self.device					,conf_path=A					,ckpt_path=A    )
			self.vqgan.eval()
			if clip:
				UpperCAmelCase__					:  Any					   =					clip
			else:
				UpperCAmelCase__					:  Dict					   =					CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""    )
			self.clip.to(self.device    )
			UpperCAmelCase__					:  int					   =					ProcessorGradientFlow(device=self.device    )

			UpperCAmelCase__					:  Dict					   =					iterations
			UpperCAmelCase__					:  Optional[Any]					   =					lr
			UpperCAmelCase__					:  str					   =					log
			UpperCAmelCase__					:  List[Any]					   =					make_grid
			UpperCAmelCase__					:  Tuple					   =					return_val
			UpperCAmelCase__					:  int					   =					quantize
			UpperCAmelCase__					:  Optional[int]					   =					self.vqgan.decoder.z_shape

		def    __lowercase       (  self		:			str					,A		:			str=None					,A		:			Optional[Any]=None					,A		:			Optional[Any]=5					,A		:			Any=True    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Optional[int]					   =					[]
			if output_path is None:
				UpperCAmelCase__					:  List[Any]					   =					"""./animation.gif"""
			if input_path is None:
				UpperCAmelCase__					:  Optional[int]					   =					self.save_path
			UpperCAmelCase__					:  str					   =					sorted(glob(input_path + """/*"""    )    )
			if not len(A    ):
				raise ValueError(
				    """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
				    """ function?)"""    )
			if len(A    ) == 1:
				print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""    )
			UpperCAmelCase__					:  List[str]					   =					total_duration / len(A    )
			UpperCAmelCase__					:  int					   =					[frame_duration] * len(A    )
			if extend_frames:
				UpperCAmelCase__					:  str					   =					1.5
				UpperCAmelCase__					:  List[str]					   =					3
			for file_name in paths:
				if file_name.endswith(""".png"""    ):
					images.append(imageio.imread(A    )    )
			imageio.mimsave(A					,A					,duration=A    )
			print(f"gif saved to {output_path}"    )

		def    __lowercase       (  self		:			Optional[Any]					,A		:			Optional[int]=None					,A		:			List[str]=None    ):






			'''simple docstring'''
			if not (path or img):
				raise ValueError("""Input either path or tensor"""    )
			if img is not None:
				raise NotImplementedError
			UpperCAmelCase__					:  Dict					   =					preprocess(Image.open(A    )					,target_image_size=256    ).to(self.device    )
			UpperCAmelCase__					:  Union[str, Any]					   =					preprocess_vqgan(A    )
			UpperCAmelCase__					:  Union[str, Any]					   =					self.vqgan.encode(A    )
			return z

		def    __lowercase       (  self		:			Dict					,A		:			List[Any]    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Any					   =					self.latent.detach().requires_grad_()
			UpperCAmelCase__					:  List[str]					   =					base_latent + transform_vector
			if self.quantize:
				UpperCAmelCase__					:  Optional[int]					   =					self.vqgan.quantize(A    )
			else:
				UpperCAmelCase__					:  int					   =					trans_latent
			return self.vqgan.decode(A    )

		def    __lowercase       (  self		:			List[Any]					,A		:			Optional[Any]					,A		:			str					,A		:			Any=None    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Optional[int]					   =					self.clip_preprocessor(text=A					,images=A					,return_tensors="""pt"""					,padding=A    )
			UpperCAmelCase__					:  Tuple					   =					self.clip(**A    )
			UpperCAmelCase__					:  Optional[int]					   =					clip_outputs.logits_per_image
			if weights is not None:
				UpperCAmelCase__					:  Optional[int]					   =					similarity_logits * weights
			return similarity_logits.sum()

		def    __lowercase       (  self		:			List[Any]					,A		:			Tuple					,A		:			List[Any]					,A		:			Any    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Tuple					   =					self._get_clip_similarity(pos_prompts["""prompts"""]					,A					,weights=(1 / pos_prompts["""weights"""])    )
			if neg_prompts:
				UpperCAmelCase__					:  Any					   =					self._get_clip_similarity(neg_prompts["""prompts"""]					,A					,weights=neg_prompts["""weights"""]    )
			else:
				UpperCAmelCase__					:  Dict					   =					torch.tensor([1]					,device=self.device    )
			UpperCAmelCase__					:  Dict					   =					-torch.log(A    ) + torch.log(A    )
			return loss

		def    __lowercase       (  self		:			Union[str, Any]					,A		:			Optional[Any]					,A		:			Dict					,A		:			List[Any]    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Union[str, Any]					   =					torch.randn_like(self.latent					,requires_grad=A					,device=self.device    )
			UpperCAmelCase__					:  str					   =					torch.optim.Adam([vector]					,lr=self.lr    )

			for i in range(self.iterations    ):
				optim.zero_grad()
				UpperCAmelCase__					:  Dict					   =					self._add_vector(A    )
				UpperCAmelCase__					:  Optional[int]					   =					loop_post_process(A    )
				UpperCAmelCase__					:  Optional[int]					   =					self._get_CLIP_loss(A					,A					,A    )
				print("""CLIP loss"""					,A    )
				if self.log:
					wandb.log({"""CLIP Loss""": clip_loss}    )
				clip_loss.backward(retain_graph=A    )
				optim.step()
				if self.return_val == "image":
					yield custom_to_pil(transformed_img[0]    )
				else:
					yield vector

		def    __lowercase       (  self		:			Dict					,A		:			str					,A		:			str					,A		:			Optional[Any]    ):






			'''simple docstring'''
			wandb.init(reinit=A					,project="""face-editor"""    )
			wandb.config.update({"""Positive Prompts""": positive_prompts}    )
			wandb.config.update({"""Negative Prompts""": negative_prompts}    )
			wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations}    )
			if image_path:
				UpperCAmelCase__					:  Any					   =					Image.open(A    )
				UpperCAmelCase__					:  List[Any]					   =					image.resize((256, 256)    )
				wandb.log("""Original Image"""					,wandb.Image(A    )    )

		def    __lowercase       (  self		:			int					,A		:			List[str]    ):






			'''simple docstring'''
			if not prompts:
				return []
			UpperCAmelCase__					:  Any					   =					[]
			UpperCAmelCase__					:  int					   =					[]
			if isinstance(A					,A    ):
				UpperCAmelCase__					:  Tuple					   =					[prompt.strip() for prompt in prompts.split("""|"""    )]
			for prompt in prompts:
				if isinstance(A					,(tuple, list)    ):
					UpperCAmelCase__					:  str					   =					prompt[0]
					UpperCAmelCase__					:  Optional[int]					   =					float(prompt[1]    )
				elif ":" in prompt:
					UpperCAmelCase__					:  List[Any]					   =					prompt.split(""":"""    )
					UpperCAmelCase__					:  str					   =					float(A    )
				else:
					UpperCAmelCase__					:  List[str]					   =					prompt
					UpperCAmelCase__					:  str					   =					1.0
				processed_prompts.append(A    )
				weights.append(A    )
			return {
			    "prompts": processed_prompts,
			    "weights": torch.tensor(A					,device=self.device    ),
			}




		def    __lowercase       (  self		:			Union[str, Any]					,A		:			Union[str, Any]					,A		:			int=None					,A		:			List[str]=None					,A		:			Optional[Any]=True					,A		:			Any=False					,A		:			List[str]=True					,A		:			Dict=True					,A		:			List[Any]=None					,):






			'''simple docstring'''
			if image_path:
				UpperCAmelCase__					:  int					   =					self._get_latent(A    )
			else:
				UpperCAmelCase__					:  Dict					   =					torch.randn(self.latent_dim					,device=self.device    )
			if self.log:
				self._init_logging(A					,A					,A    )

			assert pos_prompts, "You must provide at least one positive prompt."
			UpperCAmelCase__					:  int					   =					self.process_prompts(A    )
			UpperCAmelCase__					:  Dict					   =					self.process_prompts(A    )
			if save_final and save_path is None:
				UpperCAmelCase__					:  Optional[int]					   =					os.path.join("""./outputs/"""					,"""_""".join(pos_prompts["""prompts"""]    )    )
			if not os.path.exists(A    ):
				os.makedirs(A    )
			else:
				UpperCAmelCase__					:  Tuple					   =					save_path + """_""" + get_timestamp()
				os.makedirs(A    )
			UpperCAmelCase__					:  Tuple					   =					save_path

			UpperCAmelCase__					:  int					   =					self.vqgan.decode(self.latent    )[0]
			if show_intermediate:
				print("""Original Image"""    )
				show_pil(custom_to_pil(A    )    )

			UpperCAmelCase__					:  int					   =					loop_post_process(A    )
			for iter, transformed_img in enumerate(self._optimize_CLIP(A					,A					,A    )    ):
				if show_intermediate:
					show_pil(A    )
				if save_intermediate:
					transformed_img.save(os.path.join(self.save_path					,f"iter_{iter:03d}.png"    )    )
				if self.log:
					wandb.log({"""Image""": wandb.Image(A    )}    )
			if show_final:
				show_pil(A    )
			if save_final:
				transformed_img.save(os.path.join(self.save_path					,f"iter_{iter:03d}_final.png"    )    )








"""simple docstring"""




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

__UpperCAmelCase =				'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=8      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					h // scale_factor**2
 if h % scale_factor**2 != 0:
  new_h += 1
 UpperCAmelCase__					:  Any					   =					w // scale_factor**2
 if w % scale_factor**2 != 0:
  new_w += 1
 return new_h * scale_factor, new_w * scale_factor







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			str					,A		:			MultilingualCLIP					,A		:			XLMRobertaTokenizer					,A		:			UNetaDConditionModel					,A		:			Union[DDIMScheduler, DDPMScheduler]					,A		:			VQModel					,):






   '''simple docstring'''
   super().__init__()

   self.register_modules(
       text_encoder=A					,tokenizer=A					,unet=A					,scheduler=A					,movq=A					,)
   UpperCAmelCase__					:  Optional[Any]					   =					2 ** (len(self.movq.config.block_out_channels    ) - 1)

  def    __lowercase       (  self		:			Dict					,A		:			Any					,A		:			Tuple					,A		:			Dict					,A		:			int					,A		:			str					,A		:			List[str]    ):






   '''simple docstring'''
   if latents is None:
    UpperCAmelCase__					:  Any					   =					randn_tensor(A					,generator=A					,device=A					,dtype=A    )
   else:
    if latents.shape != shape:
     raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"    )
    UpperCAmelCase__					:  int					   =					latents.to(A    )

   UpperCAmelCase__					:  Any					   =					latents * scheduler.init_noise_sigma
   return latents

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[Any]					,A		:			Optional[Any]					,A		:			str					,A		:			Optional[Any]					,A		:			str=None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					len(A    ) if isinstance(A					,A    ) else 1
   # get prompt text embeddings
   UpperCAmelCase__					:  List[Any]					   =					self.tokenizer(
       A					,padding="""max_length"""					,truncation=A					,max_length=77					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)

   UpperCAmelCase__					:  List[str]					   =					text_inputs.input_ids
   UpperCAmelCase__					:  Any					   =					self.tokenizer(A					,padding="""longest"""					,return_tensors="""pt"""    ).input_ids

   if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A					,A    ):
    UpperCAmelCase__					:  List[str]					   =					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}"    )

   UpperCAmelCase__					:  str					   =					text_input_ids.to(A    )
   UpperCAmelCase__					:  Optional[Any]					   =					text_inputs.attention_mask.to(A    )

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self.text_encoder(
       input_ids=A					,attention_mask=A    )

   UpperCAmelCase__					:  Optional[int]					   =					prompt_embeds.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  Optional[int]					   =					text_encoder_hidden_states.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  List[str]					   =					text_mask.repeat_interleave(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  List[str]
    if negative_prompt is None:
     UpperCAmelCase__					:  List[Any]					   =					[""""""] * batch_size
    elif type(A    ) is not type(A    ):
     raise TypeError(
         f"`negative_prompt` should be the same type to `prompt`, but got {type(A    )} !="
         f" {type(A    )}."    )
    elif isinstance(A					,A    ):
     UpperCAmelCase__					:  Any					   =					[negative_prompt]
    elif batch_size != len(A    ):
     raise ValueError(
         f"`negative_prompt`: {negative_prompt} has batch size {len(A    )}, but `prompt`:"
         f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
         """ the batch size of `prompt`."""    )
    else:
     UpperCAmelCase__					:  List[Any]					   =					negative_prompt

    UpperCAmelCase__					:  Any					   =					self.tokenizer(
        A					,padding="""max_length"""					,max_length=77					,truncation=A					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)
    UpperCAmelCase__					:  Optional[int]					   =					uncond_input.input_ids.to(A    )
    UpperCAmelCase__					:  str					   =					uncond_input.attention_mask.to(A    )

    UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					self.text_encoder(
        input_ids=A					,attention_mask=A    )

    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.shape[1]
    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.repeat(1					,A    )
    UpperCAmelCase__					:  str					   =					negative_prompt_embeds.view(batch_size * num_images_per_prompt					,A    )

    UpperCAmelCase__					:  Dict					   =					uncond_text_encoder_hidden_states.shape[1]
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.repeat(1					,A					,1    )
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.view(
        batch_size * num_images_per_prompt					,A					,-1    )
    UpperCAmelCase__					:  List[Any]					   =					uncond_text_mask.repeat_interleave(A					,dim=0    )

    # done duplicates

    # 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
    UpperCAmelCase__					:  Any					   =					torch.cat([negative_prompt_embeds, prompt_embeds]    )
    UpperCAmelCase__					:  int					   =					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]    )

    UpperCAmelCase__					:  str					   =					torch.cat([uncond_text_mask, text_mask]    )

   return prompt_embeds, text_encoder_hidden_states, text_mask

  def    __lowercase       (  self		:			Tuple					,A		:			Dict=0    ):






   '''simple docstring'''
   if is_accelerate_available():
    from accelerate import cpu_offload
   else:
    raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

   UpperCAmelCase__					:  str					   =					torch.device(f"cuda:{gpu_id}"    )

   UpperCAmelCase__					:  Tuple					   =					[
       self.unet,
       self.text_encoder,
       self.movq,
   ]
   for cpu_offloaded_model in models:
    if cpu_offloaded_model is not None:
     cpu_offload(A					,A    )

  def    __lowercase       (  self		:			int					,A		:			Optional[Any]=0    ):






   '''simple docstring'''
   if is_accelerate_available() and is_accelerate_version(""">="""					,"""0.17.0.dev0"""    ):
    from accelerate import cpu_offload_with_hook
   else:
    raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""    )

   UpperCAmelCase__					:  List[str]					   =					torch.device(f"cuda:{gpu_id}"    )

   if self.device.type != "cpu":
    self.to("""cpu"""					,silence_dtype_warnings=A    )
    torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

   UpperCAmelCase__					:  List[str]					   =					None
   for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					cpu_offload_with_hook(A					,A					,prev_module_hook=A    )

   if self.safety_checker is not None:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					cpu_offload_with_hook(self.safety_checker					,A					,prev_module_hook=A    )

   # We'll offload the last model manually.
   UpperCAmelCase__					:  Any					   =					hook

  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   if not hasattr(self.unet					,"""_hf_hook"""    ):
    return self.device
   for module in self.unet.modules():
    if (
        hasattr(A					,"""_hf_hook"""    )
        and hasattr(module._hf_hook					,"""execution_device"""    )
        and module._hf_hook.execution_device is not None
    ):
     return torch.device(module._hf_hook.execution_device    )
   return self.device




  @torch.no_grad()
  @replace_example_docstring(A    )
  def __call__(  self		:			Union[str, Any]					,A		:			Union[str, List[str]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Optional[Union[str, List[str]]] = None					,A		:			int = 512					,A		:			int = 512					,A		:			int = 100					,A		:			float = 4.0					,A		:			int = 1					,A		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,A		:			Optional[torch.FloatTensor] = None					,A		:			Optional[str] = "pil"					,A		:			bool = True					,):






   '''simple docstring'''

   if isinstance(A					,A    ):
    UpperCAmelCase__					:  str					   =					1
   elif isinstance(A					,A    ):
    UpperCAmelCase__					:  Tuple					   =					len(A    )
   else:
    raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(A    )}"    )

   UpperCAmelCase__					:  Optional[int]					   =					self._execution_device

   UpperCAmelCase__					:  Dict					   =					batch_size * num_images_per_prompt
   UpperCAmelCase__					:  Union[str, Any]					   =					guidance_scale > 1.0

   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					self._encode_prompt(
       A					,A					,A					,A					,A    )

   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )
   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  Optional[int]					   =					image_embeds.repeat_interleave(A					,dim=0    )
    UpperCAmelCase__					:  Tuple					   =					negative_image_embeds.repeat_interleave(A					,dim=0    )

   UpperCAmelCase__					:  List[str]					   =					torch.cat([negative_image_embeds, image_embeds]					,dim=0    ).to(
       dtype=prompt_embeds.dtype					,device=A    )

   self.scheduler.set_timesteps(A					,device=A    )
   UpperCAmelCase__					:  int					   =					self.scheduler.timesteps

   UpperCAmelCase__					:  Union[str, Any]					   =					self.unet.config.in_channels

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					get_new_h_w(A					,A					,self.movq_scale_factor    )

   # create initial latent
   UpperCAmelCase__					:  Dict					   =					self.prepare_latents(
       (batch_size, num_channels_latents, height, width)					,text_encoder_hidden_states.dtype					,A					,A					,A					,self.scheduler					,)

   for i, t in enumerate(self.progress_bar(A    )    ):
    # expand the latents if we are doing classifier free guidance
    UpperCAmelCase__					:  Optional[Any]					   =					torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

    UpperCAmelCase__					:  Optional[Any]					   =					{"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
    UpperCAmelCase__					:  Any					   =					self.unet(
        sample=A					,timestep=A					,encoder_hidden_states=A					,added_cond_kwargs=A					,return_dict=A					,)[0]

    if do_classifier_free_guidance:
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					noise_pred.split(latents.shape[1]					,dim=1    )
     UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					noise_pred.chunk(2    )
     UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					variance_pred.chunk(2    )
     UpperCAmelCase__					:  int					   =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     UpperCAmelCase__					:  List[Any]					   =					torch.cat([noise_pred, variance_pred_text]					,dim=1    )

    if not (
        hasattr(self.scheduler.config					,"""variance_type"""    )
        and self.scheduler.config.variance_type in ["learned", "learned_range"]
    ):
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					noise_pred.split(latents.shape[1]					,dim=1    )

    # compute the previous noisy sample x_t -> x_t-1
    UpperCAmelCase__					:  Optional[Any]					   =					self.scheduler.step(
        A					,A					,A					,generator=A					,).prev_sample
   # post-processing
   UpperCAmelCase__					:  List[Any]					   =					self.movq.decode(A					,force_not_quantize=A    )["""sample"""]

   if output_type not in ["pt", "np", "pil"]:
    raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"    )

   if output_type in ["np", "pil"]:
    UpperCAmelCase__					:  Union[str, Any]					   =					image * 0.5 + 0.5
    UpperCAmelCase__					:  Optional[Any]					   =					image.clamp(0					,1    )
    UpperCAmelCase__					:  Dict					   =					image.cpu().permute(0					,2					,3					,1    ).float().numpy()

   if output_type == "pil":
    UpperCAmelCase__					:  List[str]					   =					self.numpy_to_pil(A    )

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=A    )





