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,
  )
  
  
  UpperCAmelCase					=				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  UpperCAmelCase					=				'''
      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 SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	List[str]		,						lowercase_:	Optional[Any]=8				):
         _UpperCamelCase     :       Optional[int]  						=  height // scale_factor**2
         if height % scale_factor**2 != 0:
                new_height += 1
         _UpperCamelCase     :       Union[str, Any]  						=  width // scale_factor**2
         if width % scale_factor**2 != 0:
                new_width += 1
         return new_height * scale_factor, new_width * scale_factor
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	Optional[int]=512		,						lowercase_:	str=512				):
         _UpperCamelCase     :       str  						=  pil_image.resize((w, h)		,						resample=Image.BICUBIC		,						reducing_gap=1				)
         _UpperCamelCase     :       List[str]  						=  np.array(pil_image.convert('RGB'				)				)
         _UpperCamelCase     :       Tuple  						=  arr.astype(np.floataa				) / 127.5 - 1
         _UpperCamelCase     :       List[Any]  						=  np.transpose(lowercase_		,						[2, 0, 1]				)
         _UpperCamelCase     :       Optional[int]  						=  torch.from_numpy(lowercase_				).unsqueeze(0				)
         return image
  
  
  
  
  
  class    __lowerCAmelCase  (       a_						):
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Tuple       ,			lowerCamelCase       :		UNetaDConditionModel       ,			lowerCamelCase       :		DDPMScheduler       ,			lowerCamelCase       :		VQModel       ,			) ->							str:
          super().__init__()
  
          self.register_modules(
              unet=lowerCamelCase       ,			scheduler=lowerCamelCase       ,			movq=lowerCamelCase       ,			)
          _UpperCamelCase     :       Dict  						=  2 ** (len(self.movq.config.block_out_channels  ) - 1)
  
   def 					__snake_case					(			self       :		List[str]       ,			lowerCamelCase       :		Optional[int]       ,			lowerCamelCase       :		Any       ,			lowerCamelCase       :		Union[str, Any]  ) ->							int:
          # get the original timestep using init_timestep
          _UpperCamelCase     :       Dict  						=  min(int(num_inference_steps * strength  )       ,			lowerCamelCase  )
  
          _UpperCamelCase     :       Tuple  						=  max(num_inference_steps - init_timestep       ,			0  )
          _UpperCamelCase     :       List[Any]  						=  self.scheduler.timesteps[t_start:]
  
          return timesteps, num_inference_steps - t_start
  
   def 					__snake_case					(			self       :		Dict       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Any       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Dict       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		str=None  ) ->							Any:
          if not isinstance(lowerCamelCase       ,			(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(lowerCamelCase  )}"  )
  
          _UpperCamelCase     :       Union[str, Any]  						=  image.to(device=lowerCamelCase       ,			dtype=lowerCamelCase  )
  
          _UpperCamelCase     :       Dict  						=  batch_size * num_images_per_prompt
  
          if image.shape[1] == 4:
                 _UpperCamelCase     :       int  						=  image
  
          else:
                 if isinstance(lowerCamelCase       ,			lowerCamelCase  ) and len(lowerCamelCase  ) != batch_size:
                        raise ValueError(
                            f"You have passed a list of generators of length {len(lowerCamelCase  )}, but requested an effective batch"
                            f" size of {batch_size}. Make sure the batch size matches the length of the generators."  )
  
                 elif isinstance(lowerCamelCase       ,			lowerCamelCase  ):
                        _UpperCamelCase     :       Union[str, Any]  						=  [
                            self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(lowerCamelCase  )
                        ]
                        _UpperCamelCase     :       Optional[int]  						=  torch.cat(lowerCamelCase       ,			dim=0  )
                 else:
                        _UpperCamelCase     :       int  						=  self.movq.encode(lowerCamelCase  ).latent_dist.sample(lowerCamelCase  )
  
                 _UpperCamelCase     :       Any  						=  self.movq.config.scaling_factor * init_latents
  
          _UpperCamelCase     :       List[str]  						=  torch.cat([init_latents]       ,			dim=0  )
  
          _UpperCamelCase     :       Union[str, Any]  						=  init_latents.shape
          _UpperCamelCase     :       Any  						=  randn_tensor(lowerCamelCase       ,			generator=lowerCamelCase       ,			device=lowerCamelCase       ,			dtype=lowerCamelCase  )
  
          # get latents
          _UpperCamelCase     :       Tuple  						=  self.scheduler.add_noise(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
  
          _UpperCamelCase     :       Tuple  						=  init_latents
  
          return latents
  
   def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		int=0  ) ->							Any:
          if is_accelerate_available():
                 from accelerate import cpu_offload
          else:
                 raise ImportError('Please install accelerate via `pip install accelerate`'  )
  
          _UpperCamelCase     :       Tuple  						=  torch.device(f"cuda:{gpu_id}"  )
  
          _UpperCamelCase     :       int  						=  [
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                        cpu_offload(lowerCamelCase       ,			lowerCamelCase  )
  
   def 					__snake_case					(			self       :		int       ,			lowerCamelCase       :		Any=0  ) ->							Union[str, Any]:
          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     :       Union[str, Any]  						=  torch.device(f"cuda:{gpu_id}"  )
  
          if self.device.type != "cpu":
                 self.to('cpu'       ,			silence_dtype_warnings=lowerCamelCase  )
                 torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
          _UpperCamelCase     :       Union[str, Any]  						=  None
          for cpu_offloaded_model in [self.unet, self.movq]:
                 _UpperCamelCase     :       Dict  						=  cpu_offload_with_hook(lowerCamelCase       ,			lowerCamelCase       ,			prev_module_hook=lowerCamelCase  )
  
          # We'll offload the last model manually.
          _UpperCamelCase     :       List[str]  						=  hook
  
   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 					__snake_case					(			self       :		List[str]  ) ->							Any:
          if not hasattr(self.unet       ,			'_hf_hook'  ):
                 return self.device
          for module in self.unet.modules():
                 if (
                     hasattr(lowerCamelCase       ,			'_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(lowerCamelCase  )
   def __call__(			self       :		Optional[Any]       ,			lowerCamelCase       :		Union[torch.FloatTensor, List[torch.FloatTensor]]       ,			lowerCamelCase       :		Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]       ,			lowerCamelCase       :		Union[torch.FloatTensor, List[torch.FloatTensor]]       ,			lowerCamelCase       :		torch.FloatTensor       ,			lowerCamelCase       :		int = 5_1_2       ,			lowerCamelCase       :		int = 5_1_2       ,			lowerCamelCase       :		int = 1_0_0       ,			lowerCamelCase       :		float = 4.0       ,			lowerCamelCase       :		float = 0.3       ,			lowerCamelCase       :		int = 1       ,			lowerCamelCase       :		Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,			lowerCamelCase       :		Optional[str] = "pil"       ,			lowerCamelCase       :		bool = True       ,			) ->							Optional[int]:
          _UpperCamelCase     :       Optional[Any]  						=  self._execution_device
  
          _UpperCamelCase     :       Optional[Any]  						=  guidance_scale > 1.0
  
          if isinstance(lowerCamelCase       ,			lowerCamelCase  ):
                 _UpperCamelCase     :       List[Any]  						=  torch.cat(lowerCamelCase       ,			dim=0  )
          if isinstance(lowerCamelCase       ,			lowerCamelCase  ):
                 _UpperCamelCase     :       List[str]  						=  torch.cat(lowerCamelCase       ,			dim=0  )
          if isinstance(lowerCamelCase       ,			lowerCamelCase  ):
                 _UpperCamelCase     :       Optional[Any]  						=  torch.cat(lowerCamelCase       ,			dim=0  )
  
          _UpperCamelCase     :       Optional[int]  						=  image_embeds.shape[0]
  
          if do_classifier_free_guidance:
                 _UpperCamelCase     :       List[str]  						=  image_embeds.repeat_interleave(lowerCamelCase       ,			dim=0  )
                 _UpperCamelCase     :       List[Any]  						=  negative_image_embeds.repeat_interleave(lowerCamelCase       ,			dim=0  )
                 _UpperCamelCase     :       Union[str, Any]  						=  hint.repeat_interleave(lowerCamelCase       ,			dim=0  )
  
          _UpperCamelCase     :       List[Any]  						=  torch.cat([negative_image_embeds, image_embeds]       ,			dim=0  ).to(dtype=self.unet.dtype       ,			device=lowerCamelCase  )
          _UpperCamelCase     :       int  						=  torch.cat([hint, hint]       ,			dim=0  ).to(dtype=self.unet.dtype       ,			device=lowerCamelCase  )
  
          if not isinstance(lowerCamelCase       ,			lowerCamelCase  ):
                 _UpperCamelCase     :       int  						=  [image]
          if not all(isinstance(lowerCamelCase       ,			(PIL.Image.Image, torch.Tensor)  ) for i in image  ):
                 raise ValueError(
                     f"Input is in incorrect format: {[type(lowerCamelCase  ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"  )
  
          _UpperCamelCase     :       Any  						=  torch.cat([prepare_image(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  ) for i in image]       ,			dim=0  )
          _UpperCamelCase     :       Any  						=  image.to(dtype=image_embeds.dtype       ,			device=lowerCamelCase  )
  
          _UpperCamelCase     :       int  						=  self.movq.encode(lowerCamelCase  )['latents']
          _UpperCamelCase     :       Optional[Any]  						=  latents.repeat_interleave(lowerCamelCase       ,			dim=0  )
          self.scheduler.set_timesteps(lowerCamelCase       ,			device=lowerCamelCase  )
          _UpperCamelCase     :       Tuple  						=  self.get_timesteps(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
          _UpperCamelCase     :       Optional[int]  						=  timesteps[:1].repeat(batch_size * num_images_per_prompt  )
          _UpperCamelCase     :       List[Any]  						=  downscale_height_and_width(lowerCamelCase       ,			lowerCamelCase       ,			self.movq_scale_factor  )
          _UpperCamelCase     :       Any  						=  self.prepare_latents(
              lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			image_embeds.dtype       ,			lowerCamelCase       ,			lowerCamelCase  )
          for i, t in enumerate(self.progress_bar(lowerCamelCase  )  ):
                 # expand the latents if we are doing classifier free guidance
                 _UpperCamelCase     :       List[Any]  						=  torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
  
                 _UpperCamelCase     :       str  						=  {'image_embeds': image_embeds, 'hint': hint}
                 _UpperCamelCase     :       Any  						=  self.unet(
                     sample=lowerCamelCase       ,			timestep=lowerCamelCase       ,			encoder_hidden_states=lowerCamelCase       ,			added_cond_kwargs=lowerCamelCase       ,			return_dict=lowerCamelCase       ,			)[0]
  
                 if do_classifier_free_guidance:
                        _UpperCamelCase     :       Optional[int]  						=  noise_pred.split(latents.shape[1]       ,			dim=1  )
                        _UpperCamelCase     :       Any  						=  noise_pred.chunk(2  )
                        _UpperCamelCase     :       int  						=  variance_pred.chunk(2  )
                        _UpperCamelCase     :       int  						=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                        _UpperCamelCase     :       Tuple  						=  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     :       List[Any]  						=  noise_pred.split(latents.shape[1]       ,			dim=1  )
  
                 # compute the previous noisy sample x_t -> x_t-1
  
                 _UpperCamelCase     :       List[Any]  						=  self.scheduler.step(
                     lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			generator=lowerCamelCase       ,			)[0]
  
          # post-processing
          _UpperCamelCase     :       Optional[int]  						=  self.movq.decode(lowerCamelCase       ,			force_not_quantize=lowerCamelCase  )['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     :       Any  						=  image * 0.5 + 0.5
                 _UpperCamelCase     :       List[str]  						=  image.clamp(0       ,			1  )
                 _UpperCamelCase     :       str  						=  image.cpu().permute(0       ,			2       ,			3       ,			1  ).float().numpy()
  
          if output_type == "pil":
                 _UpperCamelCase     :       Optional[Any]  						=  self.numpy_to_pil(lowerCamelCase  )
  
          if not return_dict:
                 return (image,)
  
          return ImagePipelineOutput(images=lowerCamelCase  )
  
  
  
  
style_context: from __future__ import annotations
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int				):
         assert (
             c % greatest_common_divisor(lowercase_		,						lowercase_				) == 0
         )  # greatest_common_divisor(a,b) function implemented below
         ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       int  						=  extended_gcd(lowercase_		,						lowercase_				)  # extended_gcd(a,b) function implemented below
         _UpperCamelCase     :       List[Any]  						=  c / d
         return (r * x, r * y)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int = 2				):
         ((_UpperCamelCase)						,      (_UpperCamelCase))     :       Tuple  						=  diophantine(lowercase_		,						lowercase_		,						lowercase_				)  # Initial value
         _UpperCamelCase     :       List[Any]  						=  greatest_common_divisor(lowercase_		,						lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  a // d
         _UpperCamelCase     :       Optional[Any]  						=  b // d
  
         for i in range(lowercase_				):
                _UpperCamelCase     :       Union[str, Any]  						=  xa + i * q
                _UpperCamelCase     :       List[Any]  						=  ya - i * p
                print(lowercase_		,						lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
         if a < b:
                _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  b, a
  
         while a % b != 0:
                _UpperCamelCase						,      _UpperCamelCase     :       Union[str, Any]  						=  b, a % b
  
         return b
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
         assert a >= 0 and b >= 0
  
         if b == 0:
                _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  a, 1, 0
         else:
                ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       Any  						=  extended_gcd(lowercase_		,						a % b				)
                _UpperCamelCase     :       Any  						=  q
                _UpperCamelCase     :       str  						=  p - q * (a // b)
  
         assert a % d == 0 and b % d == 0
         assert d == a * x + b * y
  
         return (d, x, y)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod(name='''diophantine''', verbose=True)
   testmod(name='''diophantine_all_soln''', verbose=True)
   testmod(name='''extended_gcd''', verbose=True)
   testmod(name='''greatest_common_divisor''', verbose=True)
  
  
  
  