code: 
  
  
  
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMParallelScheduler,
      DDPMParallelScheduler,
      StableDiffusionParadigmsPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      require_torch_gpu,
  )
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  class 					snake_case_							(       lowercase_      ,			lowercase_      ,			unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						StableDiffusionParadigmsPipeline
     __lowerCAmelCase      =						TEXT_TO_IMAGE_PARAMS
     __lowerCAmelCase      =						TEXT_TO_IMAGE_BATCH_PARAMS
     __lowerCAmelCase      =						TEXT_TO_IMAGE_IMAGE_PARAMS
     __lowerCAmelCase      =						TEXT_TO_IMAGE_IMAGE_PARAMS
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         torch.manual_seed(0	)
         _lowercase     =	UNetaDConditionModel(
             block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,       cross_attention_dim=32   ,       attention_head_dim=(2, 4)   ,       use_linear_projection=snake_case__   ,       )
         _lowercase     =	DDIMParallelScheduler(
             beta_start=0.0_00_85   ,       beta_end=0.0_12   ,       beta_schedule='scaled_linear'   ,       clip_sample=snake_case__   ,       set_alpha_to_one=snake_case__   ,       )
         torch.manual_seed(0	)
         _lowercase     =	AutoencoderKL(
             block_out_channels=[32, 64]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,       latent_channels=4   ,       sample_size=128   ,       )
         torch.manual_seed(0	)
         _lowercase     =	CLIPTextConfig(
             bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       intermediate_size=37   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1_000   ,       hidden_act='gelu'   ,       projection_dim=512   ,       )
         _lowercase     =	CLIPTextModel(snake_case__	)
         _lowercase     =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
  
         _lowercase     =	{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Any=0	)  ->		str:
  
  
         """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     =	{
             'prompt': 'a photograph of an astronaut riding a horse',
             'generator': generator,
             'num_inference_steps': 10,
             'guidance_scale': 6.0,
             'output_type': 'numpy',
             'parallel': 3,
             'debug': True,
         }
         return inputs
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
         _lowercase     =	self.get_dummy_components()
         _lowercase     =	StableDiffusionParadigmsPipeline(**snake_case__	)
         _lowercase     =	sd_pipe.to(snake_case__	)
         sd_pipe.set_progress_bar_config(disable=snake_case__	)
  
         _lowercase     =	self.get_dummy_inputs(snake_case__	)
         _lowercase     =	sd_pipe(**snake_case__	).images
         _lowercase     =	image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)
  
         _lowercase     =	np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]	)
  
         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
         _lowercase     =	self.get_dummy_components()
         torch.manual_seed(0	)
         _lowercase     =	DDPMParallelScheduler()
         torch.manual_seed(0	)
         _lowercase     =	StableDiffusionParadigmsPipeline(**snake_case__	)
         _lowercase     =	sd_pipe.to(snake_case__	)
         sd_pipe.set_progress_bar_config(disable=snake_case__	)
  
         _lowercase     =	self.get_dummy_inputs(snake_case__	)
         _lowercase     =	sd_pipe(**snake_case__	).images
         _lowercase     =	image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)
  
         _lowercase     =	np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]	)
  
         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         super().test_inference_batch_consistent(batch_sizes=[1, 2]	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         super().test_inference_batch_single_identical(batch_size=2   ,       expected_max_diff=3E-3	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'cpu'  # ensure determinism for the device-dependent torch.Generator
         _lowercase     =	self.get_dummy_components()
         _lowercase     =	StableDiffusionParadigmsPipeline(**snake_case__	)
         _lowercase     =	sd_pipe.to(snake_case__	)
         sd_pipe.set_progress_bar_config(disable=snake_case__	)
  
         _lowercase     =	self.get_dummy_inputs(snake_case__	)
         _lowercase     =	'french fries'
         _lowercase     =	sd_pipe(**snake_case__   ,       negative_prompt=snake_case__	)
         _lowercase     =	output.images
         _lowercase     =	image[0, -3:, -3:, -1]
  
         assert image.shape == (1, 64, 64, 3)
  
         _lowercase     =	np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]	)
  
         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 					snake_case_							(       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	Tuple=0	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	torch.Generator(device=snake_case__	).manual_seed(snake_case__	)
         _lowercase     =	{
             'prompt': 'a photograph of an astronaut riding a horse',
             'generator': generator,
             'num_inference_steps': 10,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
             'parallel': 3,
             'debug': True,
         }
         return inputs
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	'stabilityai/stable-diffusion-2-base'
         _lowercase     =	DDIMParallelScheduler.from_pretrained(snake_case__   ,       subfolder='scheduler'	)
         _lowercase     =	StableDiffusionParadigmsPipeline.from_pretrained(snake_case__   ,       scheduler=snake_case__   ,       safety_checker=snake_case__	)
         pipe.to(snake_case__	)
         pipe.set_progress_bar_config(disable=snake_case__	)
         pipe.enable_attention_slicing()
  
         _lowercase     =	self.get_inputs()
         _lowercase     =	pipe(**snake_case__	).images
         _lowercase     =	image[0, -3:, -3:, -1].flatten()
  
         assert image.shape == (1, 512, 512, 3)
  
         _lowercase     =	np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]	)
  
         assert np.abs(expected_slice - image_slice	).max() < 1E-2
  
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])
  