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,
  )
  
  
  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 							_snake_case			(      __snake_case       :			str	,   __snake_case       :			Any	,   __snake_case       :			Dict=8      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				List[str]      =		height // scale_factor**2
     if height % scale_factor**2 != 0:
        new_height += 1
     _lowerCamelCase  :				Optional[int]      =		width // scale_factor**2
     if width % scale_factor**2 != 0:
        new_width += 1
     return new_height * scale_factor, new_width * scale_factor
  
  def 							_snake_case			(      __snake_case       :			Dict	,   __snake_case       :			Tuple=512	,   __snake_case       :			List[Any]=512      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Union[str, Any]      =		pil_image.resize((w, h)	,   resample=Image.BICUBIC	,   reducing_gap=1      )
     _lowerCamelCase  :				Dict      =		np.array(pil_image.convert("""RGB"""      )      )
     _lowerCamelCase  :				Any      =		arr.astype(np.floataa      ) / 127.5 - 1
     _lowerCamelCase  :				Tuple      =		np.transpose(__snake_case	,   [2, 0, 1]      )
     _lowerCamelCase  :				Any      =		torch.from_numpy(__snake_case      ).unsqueeze(0      )
     return image
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      )     ->     int:
        super().__init__()
  
        self.register_modules(
            unet=SCREAMING_SNAKE_CASE			,      scheduler=SCREAMING_SNAKE_CASE			,      movq=SCREAMING_SNAKE_CASE			,      )
        _lowerCamelCase  :				Optional[int]      =		2 ** (len(self.movq.config.block_out_channels) - 1)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     int:
        # get the original timestep using init_timestep
        _lowerCamelCase  :				List[str]      =		min(int(num_inference_steps * strength)			,      SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Tuple      =		max(num_inference_steps - init_timestep			,      0)
        _lowerCamelCase  :				Dict      =		self.scheduler.timesteps[t_start:]
  
        return timesteps, num_inference_steps - t_start
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=None)     ->     Optional[Any]:
        if not isinstance(SCREAMING_SNAKE_CASE			,      (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(SCREAMING_SNAKE_CASE)}')
  
        _lowerCamelCase  :				Optional[int]      =		image.to(device=SCREAMING_SNAKE_CASE			,      dtype=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				List[str]      =		batch_size * num_images_per_prompt
  
        if image.shape[1] == 4:
           _lowerCamelCase  :				List[str]      =		image
  
        else:
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE) and len(SCREAMING_SNAKE_CASE) != batch_size:
              raise ValueError(
                  F'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE)}, but requested an effective batch'
                  F' size of {batch_size}. Make sure the batch size matches the length of the generators.')
  
           elif isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				Union[str, Any]      =		[
                  self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(SCREAMING_SNAKE_CASE)
              ]
              _lowerCamelCase  :				Union[str, Any]      =		torch.cat(SCREAMING_SNAKE_CASE			,      dim=0)
           else:
              _lowerCamelCase  :				Tuple      =		self.movq.encode(SCREAMING_SNAKE_CASE).latent_dist.sample(SCREAMING_SNAKE_CASE)
  
           _lowerCamelCase  :				Optional[int]      =		self.movq.config.scaling_factor * init_latents
  
        _lowerCamelCase  :				int      =		torch.cat([init_latents]			,      dim=0)
  
        _lowerCamelCase  :				Any      =		init_latents.shape
        _lowerCamelCase  :				Dict      =		randn_tensor(SCREAMING_SNAKE_CASE			,      generator=SCREAMING_SNAKE_CASE			,      device=SCREAMING_SNAKE_CASE			,      dtype=SCREAMING_SNAKE_CASE)
  
        # get latents
        _lowerCamelCase  :				List[str]      =		self.scheduler.add_noise(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				int      =		init_latents
  
        return latents
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE=0)     ->     int:
        if is_accelerate_available():
           from accelerate import cpu_offload
        else:
           raise ImportError("""Please install accelerate via `pip install accelerate`""")
  
        _lowerCamelCase  :				Any      =		torch.device(F'cuda:{gpu_id}')
  
        _lowerCamelCase  :				List[str]      =		[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
              cpu_offload(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE=0)     ->     List[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.""")
  
        _lowerCamelCase  :				str      =		torch.device(F'cuda:{gpu_id}')
  
        if self.device.type != "cpu":
           self.to("""cpu"""			,      silence_dtype_warnings=SCREAMING_SNAKE_CASE)
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
        _lowerCamelCase  :				str      =		None
        for cpu_offloaded_model in [self.unet, self.movq]:
           _lowerCamelCase      ,						_lowerCamelCase  :				Any      =		cpu_offload_with_hook(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      prev_module_hook=SCREAMING_SNAKE_CASE)
  
        # We'll offload the last model manually.
        _lowerCamelCase  :				Tuple      =		hook
  
  
  
  
  
     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 		UpperCamelCase_     (       self)     ->     str:
        if not hasattr(self.unet			,      """_hf_hook"""):
           return self.device
        for module in self.unet.modules():
           if (
               hasattr(SCREAMING_SNAKE_CASE			,      """_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(SCREAMING_SNAKE_CASE)
     def __call__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = 512			,      SCREAMING_SNAKE_CASE = 512			,      SCREAMING_SNAKE_CASE = 100			,      SCREAMING_SNAKE_CASE = 4.0			,      SCREAMING_SNAKE_CASE = 0.3			,      SCREAMING_SNAKE_CASE = 1			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = "pil"			,      SCREAMING_SNAKE_CASE = True			,      )     ->     str:
        _lowerCamelCase  :				List[Any]      =		self._execution_device
  
        _lowerCamelCase  :				List[str]      =		guidance_scale > 1.0
  
        if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Union[str, Any]      =		torch.cat(SCREAMING_SNAKE_CASE			,      dim=0)
        if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Any      =		torch.cat(SCREAMING_SNAKE_CASE			,      dim=0)
        if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				List[Any]      =		torch.cat(SCREAMING_SNAKE_CASE			,      dim=0)
  
        _lowerCamelCase  :				Optional[int]      =		image_embeds.shape[0]
  
        if do_classifier_free_guidance:
           _lowerCamelCase  :				Union[str, Any]      =		image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE			,      dim=0)
           _lowerCamelCase  :				Dict      =		negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE			,      dim=0)
           _lowerCamelCase  :				int      =		hint.repeat_interleave(SCREAMING_SNAKE_CASE			,      dim=0)
  
        _lowerCamelCase  :				Dict      =		torch.cat([negative_image_embeds, image_embeds]			,      dim=0).to(dtype=self.unet.dtype			,      device=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Union[str, Any]      =		torch.cat([hint, hint]			,      dim=0).to(dtype=self.unet.dtype			,      device=SCREAMING_SNAKE_CASE)
  
        if not isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				str      =		[image]
        if not all(isinstance(SCREAMING_SNAKE_CASE			,      (PIL.Image.Image, torch.Tensor)) for i in image):
           raise ValueError(
               F'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE) for i in image]}. Currently, we only support  PIL image and pytorch tensor')
  
        _lowerCamelCase  :				Any      =		torch.cat([prepare_image(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE) for i in image]			,      dim=0)
        _lowerCamelCase  :				Optional[int]      =		image.to(dtype=image_embeds.dtype			,      device=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Tuple      =		self.movq.encode(SCREAMING_SNAKE_CASE)["""latents"""]
        _lowerCamelCase  :				Tuple      =		latents.repeat_interleave(SCREAMING_SNAKE_CASE			,      dim=0)
        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE			,      device=SCREAMING_SNAKE_CASE)
        _lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		self.get_timesteps(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		timesteps[:1].repeat(batch_size * num_images_per_prompt)
        _lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		downscale_height_and_width(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      self.movq_scale_factor)
        _lowerCamelCase  :				Optional[Any]      =		self.prepare_latents(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      image_embeds.dtype			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
        for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE)):
           # expand the latents if we are doing classifier free guidance
           _lowerCamelCase  :				List[str]      =		torch.cat([latents] * 2) if do_classifier_free_guidance else latents
  
           _lowerCamelCase  :				List[Any]      =		{"""image_embeds""": image_embeds, """hint""": hint}
           _lowerCamelCase  :				Dict      =		self.unet(
               sample=SCREAMING_SNAKE_CASE			,      timestep=SCREAMING_SNAKE_CASE			,      encoder_hidden_states=SCREAMING_SNAKE_CASE			,      added_cond_kwargs=SCREAMING_SNAKE_CASE			,      return_dict=SCREAMING_SNAKE_CASE			,      )[0]
  
           if do_classifier_free_guidance:
              _lowerCamelCase      ,						_lowerCamelCase  :				Dict      =		noise_pred.split(latents.shape[1]			,      dim=1)
              _lowerCamelCase      ,						_lowerCamelCase  :				str      =		noise_pred.chunk(2)
              _lowerCamelCase      ,						_lowerCamelCase  :				str      =		variance_pred.chunk(2)
              _lowerCamelCase  :				int      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
              _lowerCamelCase  :				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"]
           ):
              _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		noise_pred.split(latents.shape[1]			,      dim=1)
  
           # compute the previous noisy sample x_t -> x_t-1
  
           _lowerCamelCase  :				List[Any]      =		self.scheduler.step(
               SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      generator=SCREAMING_SNAKE_CASE			,      )[0]
  
        # post-processing
        _lowerCamelCase  :				Any      =		self.movq.decode(SCREAMING_SNAKE_CASE			,      force_not_quantize=SCREAMING_SNAKE_CASE)["""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"]:
           _lowerCamelCase  :				List[Any]      =		image * 0.5 + 0.5
           _lowerCamelCase  :				Tuple      =		image.clamp(0			,      1)
           _lowerCamelCase  :				Dict      =		image.cpu().permute(0			,      2			,      3			,      1).float().numpy()
  
        if output_type == "pil":
           _lowerCamelCase  :				Dict      =		self.numpy_to_pil(SCREAMING_SNAKE_CASE)
  
        if not return_dict:
           return (image,)
  
        return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE)
  
  
style_context: 
  
  
  """simple docstring"""
  from dataclasses import dataclass
  
  UpperCAmelCase   =	"""0.1"""
  UpperCAmelCase   =	"""Lucia Harcekova"""
  
  
  
  
  @dataclass
  class   lowercase__							:
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
  
  
  
  
  
     def __repr__(       self)     ->     str:
        return F'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE = 13			,      SCREAMING_SNAKE_CASE = 6)     ->     None:
        _lowerCamelCase  :				Optional[Any]      =		window_size
        _lowerCamelCase  :				List[str]      =		lookahead_buffer_size
        _lowerCamelCase  :				List[Any]      =		self.window_size - self.lookahead_buffer_size
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     list[Token]:
        _lowerCamelCase  :				Union[str, Any]      =		[]
        _lowerCamelCase  :				Tuple      =		""""""
  
        # while there are still characters in text to compress
        while text:
           # find the next encoding phrase
           # - triplet with offset, length, indicator (the next encoding character)
           _lowerCamelCase  :				List[Any]      =		self._find_encoding_token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
           # update the search buffer:
           # - add new characters from text into it
           # - check if size exceed the max search buffer size, if so, drop the
           #   oldest elements
           search_buffer += text[: token.length + 1]
           if len(SCREAMING_SNAKE_CASE) > self.search_buffer_size:
              _lowerCamelCase  :				int      =		search_buffer[-self.search_buffer_size :]
  
           # update the text
           _lowerCamelCase  :				Tuple      =		text[token.length + 1 :]
  
           # append the token to output
           output.append(SCREAMING_SNAKE_CASE)
  
        return output
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     str:
        _lowerCamelCase  :				str      =		""""""
  
        for token in tokens:
           for _ in range(token.length):
              output += output[-token.offset]
           output += token.indicator
  
        return output
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Token:
  
        if not text:
           raise ValueError("""We need some text to work with.""")
  
        # Initialise result parameters to default values
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		0, 0
  
        if not search_buffer:
           return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])
  
        for i, character in enumerate(SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Tuple      =		len(SCREAMING_SNAKE_CASE) - i
           if character == text[0]:
              _lowerCamelCase  :				Any      =		self._match_length_from_index(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      0			,      SCREAMING_SNAKE_CASE)
              # if the found length is bigger than the current or if it's equal,
              # which means it's offset is smaller: update offset and length
              if found_length >= length:
                 _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		found_offset, found_length
  
        return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     int:
        if not text or text[text_index] != window[window_index]:
           return 0
        return 1 + self._match_length_from_index(
            SCREAMING_SNAKE_CASE			,      window + text[text_index]			,      text_index + 1			,      window_index + 1)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   # Initialize compressor class
   UpperCAmelCase   =	LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
   # Example
   UpperCAmelCase   =	"""cabracadabrarrarrad"""
   UpperCAmelCase   =	lzaa_compressor.compress(TEXT)
   print(lzaa_compressor.compress("""ababcbababaa"""))
   UpperCAmelCase   =	lzaa_compressor.decompress(compressed_text)
   assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  