code: 
  
  
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from ...models import UNetaDConditionModel, VQModel
  from ...pipelines import DiffusionPipeline
  from ...pipelines.pipeline_utils import ImagePipelineOutput
  from ...schedulers import DDPMScheduler
  from ...utils import (
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  
  
  __snake_case             =     logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  __snake_case             =     '''
      Examples:
          ```py
          >>> import torch
          >>> import numpy as np
  
          >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
          >>> from transformers import pipeline
          >>> from diffusers.utils import load_image
  
  
          >>> def make_hint(image, depth_estimator):
          ...     image = depth_estimator(image)["depth"]
          ...     image = np.array(image)
          ...     image = image[:, :, None]
          ...     image = np.concatenate([image, image, image], axis=2)
          ...     detected_map = torch.from_numpy(image).float() / 255.0
          ...     hint = detected_map.permute(2, 0, 1)
          ...     return hint
  
  
          >>> depth_estimator = pipeline("depth-estimation")
  
          >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
          ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
          ... )
          >>> pipe_prior = pipe_prior.to("cuda")
  
          >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
          ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
          ... )
          >>> pipe = pipe.to("cuda")
  
          >>> img = load_image(
          ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          ...     "/kandinsky/cat.png"
          ... ).resize((768, 768))
  
  
          >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
  
          >>> prompt = "A robot, 4k photo"
          >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
  
          >>> generator = torch.Generator(device="cuda").manual_seed(43)
  
          >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
          >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)
  
          >>> images = pipe(
          ...     image=img,
          ...     strength=0.5,
          ...     image_embeds=img_emb.image_embeds,
          ...     negative_image_embeds=negative_emb.image_embeds,
          ...     hint=hint,
          ...     num_inference_steps=50,
          ...     generator=generator,
          ...     height=768,
          ...     width=768,
          ... ).images
  
          >>> images[0].save("robot_cat.png")
          ```
  '''
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase=8   )				->   List[Any]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = height // scale_factor**2
       if height % scale_factor**2 != 0:
            new_height += 1
       __UpperCamelCase   = width // scale_factor**2
       if width % scale_factor**2 != 0:
            new_width += 1
       return new_height * scale_factor, new_width * scale_factor
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase=5_12				,     _lowercase=5_12   )				->   Tuple:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = pil_image.resize((w, h)				,     resample=Image.BICUBIC				,     reducing_gap=1   )
       __UpperCamelCase   = np.array(pil_image.convert('RGB'   )   )
       __UpperCamelCase   = arr.astype(np.floataa   ) / 1_27.5 - 1
       __UpperCamelCase   = np.transpose(_lowercase				,     [2, 0, 1]   )
       __UpperCamelCase   = torch.from_numpy(_lowercase   ).unsqueeze(0   )
       return image
  
  
  
  class   __lowerCamelCase (_a	):
  
  
  
  
  
  
  
         def __init__( self:   Union[str, Any],A_:   UNetaDConditionModel,A_:   DDPMScheduler,A_:   VQModel,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              super().__init__()
  
              self.register_modules(
                  unet=A_,scheduler=A_,movq=A_,)
              __UpperCamelCase   = 2 ** (len(self.movq.config.block_out_channels				) - 1)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   int,A_:   List[Any],A_:   Optional[Any],A_:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = min(int(num_inference_steps * strength				),A_				)
  
              __UpperCamelCase   = max(num_inference_steps - init_timestep,0				)
              __UpperCamelCase   = self.scheduler.timesteps[t_start:]
  
              return timesteps, num_inference_steps - t_start
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Optional[int],A_:   Optional[int],A_:   Tuple,A_:   Optional[Any],A_:   Any,A_:   Union[str, Any],A_:   int,A_:   Optional[Any]=None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if not isinstance(A_,(torch.Tensor, PIL.Image.Image, list)				):
                   raise ValueError(
                       F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A_				)}'''				)
  
              __UpperCamelCase   = image.to(device=A_,dtype=A_				)
  
              __UpperCamelCase   = batch_size * num_images_per_prompt
  
              if image.shape[1] == 4:
                   __UpperCamelCase   = image
  
              else:
                   if isinstance(A_,A_				) and len(A_				) != batch_size:
                        raise ValueError(
                            F'''You have passed a list of generators of length {len(A_				)}, but requested an effective batch'''
                            F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)
  
                   elif isinstance(A_,A_				):
                        __UpperCamelCase   = [
                            self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(A_				)
                        ]
                        __UpperCamelCase   = torch.cat(A_,dim=0				)
                   else:
                        __UpperCamelCase   = self.movq.encode(A_				).latent_dist.sample(A_				)
  
                   __UpperCamelCase   = self.movq.config.scaling_factor * init_latents
  
              __UpperCamelCase   = torch.cat([init_latents],dim=0				)
  
              __UpperCamelCase   = init_latents.shape
              __UpperCamelCase   = randn_tensor(A_,generator=A_,device=A_,dtype=A_				)
  
              # get latents
              __UpperCamelCase   = self.scheduler.add_noise(A_,A_,A_				)
  
              __UpperCamelCase   = init_latents
  
              return latents
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[str],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   = torch.device(F'''cuda:{gpu_id}'''				)
  
              __UpperCamelCase   = [
                  self.unet,
                  self.movq,
              ]
              for cpu_offloaded_model in models:
                   if cpu_offloaded_model is not None:
                        cpu_offload(A_,A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict,A_:   Tuple=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   = 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   = None
              for cpu_offloaded_model in [self.unet, self.movq]:
                   __UpperCamelCase,						__UpperCamelCase   = cpu_offload_with_hook(A_,A_,prev_module_hook=A_				)
  
              # We'll offload the last model manually.
              __UpperCamelCase   = hook
  
  
  
  
  
  
  
         @property
         # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
         def 		snake_case_  ( 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:   Any,A_:   Union[torch.FloatTensor, List[torch.FloatTensor]],A_:   Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],A_:   Union[torch.FloatTensor, List[torch.FloatTensor]],A_:   torch.FloatTensor,A_:   int = 512,A_:   int = 512,A_:   int = 100,A_:   float = 4.0,A_:   float = 0.3,A_:   int = 1,A_:   Optional[Union[torch.Generator, List[torch.Generator]]] = None,A_:   Optional[str] = "pil",A_:   bool = True,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self._execution_device
  
              __UpperCamelCase   = guidance_scale > 1.0
  
              if isinstance(A_,A_				):
                   __UpperCamelCase   = torch.cat(A_,dim=0				)
              if isinstance(A_,A_				):
                   __UpperCamelCase   = torch.cat(A_,dim=0				)
              if isinstance(A_,A_				):
                   __UpperCamelCase   = torch.cat(A_,dim=0				)
  
              __UpperCamelCase   = image_embeds.shape[0]
  
              if do_classifier_free_guidance:
                   __UpperCamelCase   = image_embeds.repeat_interleave(A_,dim=0				)
                   __UpperCamelCase   = negative_image_embeds.repeat_interleave(A_,dim=0				)
                   __UpperCamelCase   = hint.repeat_interleave(A_,dim=0				)
  
              __UpperCamelCase   = torch.cat([negative_image_embeds, image_embeds],dim=0				).to(dtype=self.unet.dtype,device=A_				)
              __UpperCamelCase   = torch.cat([hint, hint],dim=0				).to(dtype=self.unet.dtype,device=A_				)
  
              if not isinstance(A_,A_				):
                   __UpperCamelCase   = [image]
              if not all(isinstance(A_,(PIL.Image.Image, torch.Tensor)				) for i in image				):
                   raise ValueError(
                       F'''Input is in incorrect format: {[type(A_				) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''				)
  
              __UpperCamelCase   = torch.cat([prepare_image(A_,A_,A_				) for i in image],dim=0				)
              __UpperCamelCase   = image.to(dtype=image_embeds.dtype,device=A_				)
  
              __UpperCamelCase   = self.movq.encode(A_				)['latents']
              __UpperCamelCase   = latents.repeat_interleave(A_,dim=0				)
              self.scheduler.set_timesteps(A_,device=A_				)
              __UpperCamelCase,						__UpperCamelCase   = self.get_timesteps(A_,A_,A_				)
              __UpperCamelCase   = timesteps[:1].repeat(batch_size * num_images_per_prompt				)
              __UpperCamelCase,						__UpperCamelCase   = downscale_height_and_width(A_,A_,self.movq_scale_factor				)
              __UpperCamelCase   = self.prepare_latents(
                  A_,A_,A_,A_,image_embeds.dtype,A_,A_				)
              for i, t in enumerate(self.progress_bar(A_				)				):
                   # expand the latents if we are doing classifier free guidance
                   __UpperCamelCase   = torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
  
                   __UpperCamelCase   = {'image_embeds': image_embeds, 'hint': hint}
                   __UpperCamelCase   = 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   = noise_pred.split(latents.shape[1],dim=1				)
                        __UpperCamelCase,						__UpperCamelCase   = noise_pred.chunk(2				)
                        __UpperCamelCase,						__UpperCamelCase   = variance_pred.chunk(2				)
                        __UpperCamelCase   = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                        __UpperCamelCase   = 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   = noise_pred.split(latents.shape[1],dim=1				)
  
                   # compute the previous noisy sample x_t -> x_t-1
  
                   __UpperCamelCase   = self.scheduler.step(
                       A_,A_,A_,generator=A_,)[0]
  
              # post-processing
              __UpperCamelCase   = 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   = image * 0.5 + 0.5
                   __UpperCamelCase   = image.clamp(0,1				)
                   __UpperCamelCase   = image.cpu().permute(0,2,3,1				).float().numpy()
  
              if output_type == "pil":
                   __UpperCamelCase   = self.numpy_to_pil(A_				)
  
              if not return_dict:
                   return (image,)
  
              return ImagePipelineOutput(images=A_				)
  
  
  
style_context: 
  
  
  from math import cos, sin
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = float(_lowercase   )
       return [[scaling_factor * int(x == y   ) for x in range(2   )] for y in range(2   )]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       return [[c, -s], [s, c]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[c * c, cs], [cs, s * s]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]
  
  
  print(f"""    {scaling(5) = }""")
  print(f"""  {rotation(4_5) = }""")
  print(f"""{projection(4_5) = }""")
  
  
  print(f"""{reflection(4_5) = }""")
  
  
  