code: 
  
  
  '''simple docstring'''
  
  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,
  )
  
  
  __lowercase:		List[str]							=     logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  __lowercase:		Optional[Any]							=     "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				Any=8   )		->					str:
  
  
  
  
  
     '''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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Optional[int]=5_12    ,		_UpperCamelCase      :				Tuple=5_12   )		->					List[str]:
  
  
  
  
  
     '''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_2_7.5 - 1
     UpperCamelCase__ 						=						np.transpose(_UpperCamelCase    ,		[2, 0, 1]   )
     UpperCamelCase__ 						=						torch.from_numpy(_UpperCamelCase   ).unsqueeze(0   )
     return image
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
  
   def __init__( self :	Optional[int],					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 		lowercase_						( self :	Dict,					a_ :	Tuple,					a_ :	Dict,					a_ :	List[Any]					):
  
  
  
  
  
      """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 		lowercase_						( self :	Tuple,					a_ :	Any,					a_ :	str,					a_ :	Any,					a_ :	str,					a_ :	Optional[int],					a_ :	Tuple,					a_ :	int=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 		lowercase_						( self :	Union[str, Any],					a_ :	List[str]=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 		lowercase_						( self :	Dict,					a_ :	int=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 		lowercase_						( self :	Tuple					):
  
  
  
  
  
      """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 :	Dict,					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: 
  
  
  '''simple docstring'''
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  
  
  
  
  
  @require_vision
  class  UpperCAmelCase (       unittest.TestCase):
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						tempfile.mkdtemp()
  
      UpperCamelCase__ 						=						CLIPImageProcessor()
      UpperCamelCase__ 						=						BertTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-BertModel",					model_input_names=["input_ids", "attention_mask"]					)
  
      UpperCamelCase__ 						=						GitProcessor(a_,					a_					)
  
      processor.save_pretrained(self.tmpdirname					)
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      shutil.rmtree(self.tmpdirname					)
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]
  
      UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]
  
      return image_inputs
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)
  
      UpperCamelCase__ 						=						self.get_tokenizer(bos_token="(BOS)",					eos_token="(EOS)"					)
      UpperCamelCase__ 						=						self.get_image_processor(do_normalize=a_,					padding_value=1.0					)
  
      UpperCamelCase__ 						=						GitProcessor.from_pretrained(
          self.tmpdirname,					bos_token="(BOS)",					eos_token="(EOS)",					do_normalize=a_,					padding_value=1.0					)
  
      self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()					)
      self.assertIsInstance(processor.tokenizer,					a_					)
  
      self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()					)
      self.assertIsInstance(processor.image_processor,					a_					)
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
      UpperCamelCase__ 						=						processor(images=a_,					return_tensors="np"					)
  
      for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
  
      UpperCamelCase__ 						=						processor(text=a_					)
  
      UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)
  
      for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key],					encoded_processor[key]					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)
  
      # test if it raises when no input is passed
      with pytest.raises(a_					):
         processor()
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
      UpperCamelCase__ 						=						processor.batch_decode(a_					)
      UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)
  
      self.assertListEqual(a_,					a_					)
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)