code: 
  
  
  
  
  
  
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from torch import nn
  from transformers import (
      CLIPImageProcessor,
      CLIPTextConfig,
      CLIPTextModelWithProjection,
      CLIPTokenizer,
      CLIPVisionConfig,
      CLIPVisionModelWithProjection,
  )
  
  from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
  from diffusers.utils import floats_tensor, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
  
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  class 					snake_case_							(       lowercase_      ,			unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						KandinskyVaaPriorEmbaEmbPipeline
     __lowerCAmelCase      =						["""prompt""", """image"""]
     __lowerCAmelCase      =						["""prompt""", """image"""]
     __lowerCAmelCase      =						[
         """num_images_per_prompt""",
         """strength""",
         """generator""",
         """num_inference_steps""",
         """latents""",
         """negative_prompt""",
         """guidance_scale""",
         """output_type""",
         """return_dict""",
     ]
     __lowerCAmelCase      =						False
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         return 32
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         return 32
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         return self.time_input_dim
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         return self.time_input_dim * 4
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         return 100
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
         return tokenizer
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         torch.manual_seed(0	)
         _lowercase     =	CLIPTextConfig(
             bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=self.text_embedder_hidden_size   ,       projection_dim=self.text_embedder_hidden_size   ,       intermediate_size=37   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1_000   ,       )
         return CLIPTextModelWithProjection(snake_case__	)
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         torch.manual_seed(0	)
  
         _lowercase     =	{
             'num_attention_heads': 2,
             'attention_head_dim': 12,
             'embedding_dim': self.text_embedder_hidden_size,
             'num_layers': 1,
         }
  
         _lowercase     =	PriorTransformer(**snake_case__	)
         # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
         _lowercase     =	nn.Parameter(torch.ones(model.clip_std.shape	)	)
         return model
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         torch.manual_seed(0	)
         _lowercase     =	CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size   ,       image_size=224   ,       projection_dim=self.text_embedder_hidden_size   ,       intermediate_size=37   ,       num_attention_heads=4   ,       num_channels=3   ,       num_hidden_layers=5   ,       patch_size=14   ,       )
  
         _lowercase     =	CLIPVisionModelWithProjection(snake_case__	)
         return model
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	CLIPImageProcessor(
             crop_size=224   ,       do_center_crop=snake_case__   ,       do_normalize=snake_case__   ,       do_resize=snake_case__   ,       image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]   ,       image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]   ,       resample=3   ,       size=224   ,       )
  
         return image_processor
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.dummy_prior
         _lowercase     =	self.dummy_image_encoder
         _lowercase     =	self.dummy_text_encoder
         _lowercase     =	self.dummy_tokenizer
         _lowercase     =	self.dummy_image_processor
  
         _lowercase     =	UnCLIPScheduler(
             variance_type='fixed_small_log'   ,       prediction_type='sample'   ,       num_train_timesteps=1_000   ,       clip_sample=snake_case__   ,       clip_sample_range=10.0   ,       )
  
         _lowercase     =	{
             'prior': prior,
             'image_encoder': image_encoder,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'scheduler': scheduler,
             'image_processor': image_processor,
         }
  
         return components
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int   ,       snake_case__:	Any   ,       snake_case__:	Optional[Any]=0	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         if str(snake_case__	).startswith('mps'	):
             _lowercase     =	torch.manual_seed(snake_case__	)
         else:
             _lowercase     =	torch.Generator(device=snake_case__	).manual_seed(snake_case__	)
  
         _lowercase     =	floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(snake_case__	)	).to(snake_case__	)
         _lowercase     =	image.cpu().permute(0   ,       2   ,       3   ,       1	)[0]
         _lowercase     =	Image.fromarray(np.uinta(snake_case__	)	).convert('RGB'	).resize((256, 256)	)
  
         _lowercase     =	{
             'prompt': 'horse',
             'image': init_image,
             'strength': 0.5,
             'generator': generator,
             'guidance_scale': 4.0,
             'num_inference_steps': 2,
             'output_type': 'np',
         }
         return inputs
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'cpu'
  
         _lowercase     =	self.get_dummy_components()
  
         _lowercase     =	self.pipeline_class(**snake_case__	)
         _lowercase     =	pipe.to(snake_case__	)
  
         pipe.set_progress_bar_config(disable=snake_case__	)
  
         _lowercase     =	pipe(**self.get_dummy_inputs(snake_case__	)	)
         _lowercase     =	output.image_embeds
  
         _lowercase     =	pipe(
             **self.get_dummy_inputs(snake_case__	)   ,       return_dict=snake_case__   ,       )[0]
  
         _lowercase     =	image[0, -10:]
         _lowercase     =	image_from_tuple[0, -10:]
  
         assert image.shape == (1, 32)
  
         _lowercase     =	np.array(
             [
                 0.1_07_12_84,
                 1.3_33_02_71,
                 0.61_26_02_23,
                 -0.6_69_10_65,
                 -0.3_84_68_52,
                 -1.0_30_36_61,
                 0.22_71_61_11,
                 0.03_34_89_01,
                 0.30_04_06_75,
                 -0.24_80_50_29,
             ]	)
  
         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
  
     @skip_mps
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	torch_device == 'cpu'
         _lowercase     =	True
         _lowercase     =	False
  
         self._test_inference_batch_single_identical(
             test_max_difference=snake_case__   ,       relax_max_difference=snake_case__   ,       test_mean_pixel_difference=snake_case__   ,       )
  
     @skip_mps
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	torch_device == 'cpu'
         _lowercase     =	False
  
         self._test_attention_slicing_forward_pass(
             test_max_difference=snake_case__   ,       test_mean_pixel_difference=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])
  