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,
  )
  
  
  lowerCamelCase										=  logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  lowerCamelCase										=  '''
      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        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase=8      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	height // scale_factor**2
      if height % scale_factor**2 != 0:
          new_height += 1
      _lowercase     =	width // scale_factor**2
      if width % scale_factor**2 != 0:
          new_width += 1
      return new_height * scale_factor, new_width * scale_factor
  def        UpperCAmelCase      (   _lowercase,      _lowercase=512,      _lowercase=512      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	pil_image.resize((w, h),      resample=Image.BICUBIC,      reducing_gap=1      )
      _lowercase     =	np.array(pil_image.convert('RGB'      )      )
      _lowercase     =	arr.astype(np.floataa      ) / 127.5 - 1
      _lowercase     =	np.transpose(_lowercase,      [2, 0, 1]      )
      _lowercase     =	torch.from_numpy(_lowercase      ).unsqueeze(0      )
      return image
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Optional[Any]   ,       snake_case__:	UNetaDConditionModel   ,       snake_case__:	DDPMScheduler   ,       snake_case__:	VQModel   ,       )  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         super().__init__()
  
         self.register_modules(
             unet=snake_case__   ,       scheduler=snake_case__   ,       movq=snake_case__   ,       )
         _lowercase     =	2 ** (len(self.movq.config.block_out_channels	) - 1)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]   ,       snake_case__:	int   ,       snake_case__:	Optional[Any]   ,       snake_case__:	int	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	min(int(num_inference_steps * strength	)   ,       snake_case__	)
  
         _lowercase     =	max(num_inference_steps - init_timestep   ,       0	)
         _lowercase     =	self.scheduler.timesteps[t_start:]
  
         return timesteps, num_inference_steps - t_start
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	Tuple   ,       snake_case__:	int   ,       snake_case__:	Optional[int]   ,       snake_case__:	List[Any]   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	Optional[Any]   ,       snake_case__:	int=None	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         if not isinstance(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(snake_case__	)}'''	)
  
         _lowercase     =	image.to(device=snake_case__   ,       dtype=snake_case__	)
  
         _lowercase     =	batch_size * num_images_per_prompt
  
         if image.shape[1] == 4:
             _lowercase     =	image
  
         else:
             if isinstance(snake_case__   ,       snake_case__	) and len(snake_case__	) != batch_size:
                 raise ValueError(
                     f'''You have passed a list of generators of length {len(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(snake_case__   ,       snake_case__	):
                 _lowercase     =	[
                     self.movq.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(snake_case__	)
                 ]
                 _lowercase     =	torch.cat(snake_case__   ,       dim=0	)
             else:
                 _lowercase     =	self.movq.encode(snake_case__	).latent_dist.sample(snake_case__	)
  
             _lowercase     =	self.movq.config.scaling_factor * init_latents
  
         _lowercase     =	torch.cat([init_latents]   ,       dim=0	)
  
         _lowercase     =	init_latents.shape
         _lowercase     =	randn_tensor(snake_case__   ,       generator=snake_case__   ,       device=snake_case__   ,       dtype=snake_case__	)
  
         # get latents
         _lowercase     =	self.scheduler.add_noise(snake_case__   ,       snake_case__   ,       snake_case__	)
  
         _lowercase     =	init_latents
  
         return latents
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	int=0	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError('Please install accelerate via `pip install accelerate`'	)
  
         _lowercase     =	torch.device(f'''cuda:{gpu_id}'''	)
  
         _lowercase     =	[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                 cpu_offload(snake_case__   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]   ,       snake_case__:	Tuple=0	)  ->		List[Any]:
  
  
         """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.'	)
  
         _lowercase     =	torch.device(f'''cuda:{gpu_id}'''	)
  
         if self.device.type != "cpu":
             self.to('cpu'   ,       silence_dtype_warnings=snake_case__	)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
         _lowercase     =	None
         for cpu_offloaded_model in [self.unet, self.movq]:
             _lowercase ,    _lowercase     =	cpu_offload_with_hook(snake_case__   ,       snake_case__   ,       prev_module_hook=snake_case__	)
  
         # We'll offload the last model manually.
         _lowercase     =	hook
  
     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         if not hasattr(self.unet   ,       '_hf_hook'	):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(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(snake_case__	)
     def __call__(    self:	Dict   ,       snake_case__:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,       snake_case__:	Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]   ,       snake_case__:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,       snake_case__:	torch.FloatTensor   ,       snake_case__:	int = 512   ,       snake_case__:	int = 512   ,       snake_case__:	int = 100   ,       snake_case__:	float = 4.0   ,       snake_case__:	float = 0.3   ,       snake_case__:	int = 1   ,       snake_case__:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,       snake_case__:	Optional[str] = "pil"   ,       snake_case__:	bool = True   ,       )  ->		str:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self._execution_device
  
         _lowercase     =	guidance_scale > 1.0
  
         if isinstance(snake_case__   ,       snake_case__	):
             _lowercase     =	torch.cat(snake_case__   ,       dim=0	)
         if isinstance(snake_case__   ,       snake_case__	):
             _lowercase     =	torch.cat(snake_case__   ,       dim=0	)
         if isinstance(snake_case__   ,       snake_case__	):
             _lowercase     =	torch.cat(snake_case__   ,       dim=0	)
  
         _lowercase     =	image_embeds.shape[0]
  
         if do_classifier_free_guidance:
             _lowercase     =	image_embeds.repeat_interleave(snake_case__   ,       dim=0	)
             _lowercase     =	negative_image_embeds.repeat_interleave(snake_case__   ,       dim=0	)
             _lowercase     =	hint.repeat_interleave(snake_case__   ,       dim=0	)
  
         _lowercase     =	torch.cat([negative_image_embeds, image_embeds]   ,       dim=0	).to(dtype=self.unet.dtype   ,       device=snake_case__	)
         _lowercase     =	torch.cat([hint, hint]   ,       dim=0	).to(dtype=self.unet.dtype   ,       device=snake_case__	)
  
         if not isinstance(snake_case__   ,       snake_case__	):
             _lowercase     =	[image]
         if not all(isinstance(snake_case__   ,       (PIL.Image.Image, torch.Tensor)	) for i in image	):
             raise ValueError(
                 f'''Input is in incorrect format: {[type(snake_case__	) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''	)
  
         _lowercase     =	torch.cat([prepare_image(snake_case__   ,       snake_case__   ,       snake_case__	) for i in image]   ,       dim=0	)
         _lowercase     =	image.to(dtype=image_embeds.dtype   ,       device=snake_case__	)
  
         _lowercase     =	self.movq.encode(snake_case__	)['latents']
         _lowercase     =	latents.repeat_interleave(snake_case__   ,       dim=0	)
         self.scheduler.set_timesteps(snake_case__   ,       device=snake_case__	)
         _lowercase ,    _lowercase     =	self.get_timesteps(snake_case__   ,       snake_case__   ,       snake_case__	)
         _lowercase     =	timesteps[:1].repeat(batch_size * num_images_per_prompt	)
         _lowercase ,    _lowercase     =	downscale_height_and_width(snake_case__   ,       snake_case__   ,       self.movq_scale_factor	)
         _lowercase     =	self.prepare_latents(
             snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       image_embeds.dtype   ,       snake_case__   ,       snake_case__	)
         for i, t in enumerate(self.progress_bar(snake_case__	)	):
             # expand the latents if we are doing classifier free guidance
             _lowercase     =	torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
  
             _lowercase     =	{'image_embeds': image_embeds, 'hint': hint}
             _lowercase     =	self.unet(
                 sample=snake_case__   ,       timestep=snake_case__   ,       encoder_hidden_states=snake_case__   ,       added_cond_kwargs=snake_case__   ,       return_dict=snake_case__   ,       )[0]
  
             if do_classifier_free_guidance:
                 _lowercase ,    _lowercase     =	noise_pred.split(latents.shape[1]   ,       dim=1	)
                 _lowercase ,    _lowercase     =	noise_pred.chunk(2	)
                 _lowercase ,    _lowercase     =	variance_pred.chunk(2	)
                 _lowercase     =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 _lowercase     =	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"]
             ):
                 _lowercase ,    _lowercase     =	noise_pred.split(latents.shape[1]   ,       dim=1	)
  
             # compute the previous noisy sample x_t -> x_t-1
  
             _lowercase     =	self.scheduler.step(
                 snake_case__   ,       snake_case__   ,       snake_case__   ,       generator=snake_case__   ,       )[0]
  
         # post-processing
         _lowercase     =	self.movq.decode(snake_case__   ,       force_not_quantize=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"]:
             _lowercase     =	image * 0.5 + 0.5
             _lowercase     =	image.clamp(0   ,       1	)
             _lowercase     =	image.cpu().permute(0   ,       2   ,       3   ,       1	).float().numpy()
  
         if output_type == "pil":
             _lowercase     =	self.numpy_to_pil(snake_case__	)
  
         if not return_dict:
             return (image,)
  
         return ImagePipelineOutput(images=snake_case__	)
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import sys
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Any   ,       snake_case__:	str   ,       snake_case__:	int	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	letter
         _lowercase     =	freq
         _lowercase     =	{}
  
     def __repr__(    self:	Optional[int]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         return f'''{self.letter}:{self.freq}'''
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Optional[int]   ,       snake_case__:	int   ,       snake_case__:	Letter | TreeNode   ,       snake_case__:	Letter | TreeNode	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	freq
         _lowercase     =	left
         _lowercase     =	right
  
  
  
  
  
  
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	{}
      with open(_lowercase      ) as f:
          while True:
              _lowercase     =	f.read(1      )
              if not c:
                  break
              _lowercase     =	chars[c] + 1 if c in chars else 1
      return sorted((Letter(_lowercase,      _lowercase      ) for c, f in chars.items()),      key=lambda _lowercase      : x.freq      )
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	letters  # type: ignore
      while len(_lowercase      ) > 1:
          _lowercase     =	response.pop(0      )
          _lowercase     =	response.pop(0      )
          _lowercase     =	left.freq + right.freq
          _lowercase     =	TreeNode(_lowercase,      _lowercase,      _lowercase      )
          response.append(_lowercase      )
          response.sort(key=lambda _lowercase      : x.freq      )
      return response[0]
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      if isinstance(_lowercase,      _lowercase      ):
          _lowercase     =	bitstring
          return [root]
      _lowercase     =	root  # type: ignore
      _lowercase     =	[]
      letters += traverse_tree(treenode.left,      bitstring + '0'      )
      letters += traverse_tree(treenode.right,      bitstring + '1'      )
      return letters
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	parse_file(_lowercase      )
      _lowercase     =	build_tree(_lowercase      )
      _lowercase     =	{
          k: v for letter in traverse_tree(_lowercase,      ''      ) for k, v in letter.bitstring.items()
      }
      print(f'''Huffman Coding  of {file_path}: '''      )
      with open(_lowercase      ) as f:
          while True:
              _lowercase     =	f.read(1      )
              if not c:
                  break
              print(letters[c],      end=' '      )
      print()
  
  
  if __name__ == "__main__":
     # pass the file path to the huffman function
     huffman(sys.argv[1])
  