


from __future__ import annotations

import sys

class   lowerCamelCase						:

       def __init__(      self						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			str							=   letter
        snake_case :			int							=   freq
        snake_case :			dict[str, str]							=   {}



       def __repr__(      self       ):






        '''simple docstring'''






        return f'''{self.letter}:{self.freq}'''







class   lowerCamelCase						:

       def __init__(      self						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			int							=   freq
        snake_case :			Letter | TreeNode							=   left
        snake_case :			Letter | TreeNode							=   right







def  _UpperCAmelCase						( __lowerCamelCase							)		->list[Letter]:


 '''simple docstring'''
 snake_case :			dict[str, int]							=   {}
 with open(__lowerCamelCase							) as f:
  while True:
   snake_case :			str							=   f.read(1							)
   if not c:
    break
   snake_case :			str							=   chars[c] + 1 if c in chars else 1
 return sorted((Letter(__lowerCamelCase			,  __lowerCamelCase							) for c, f in chars.items())			,  key=lambda __lowerCamelCase							: x.freq							)






def  _UpperCAmelCase						( __lowerCamelCase							)		->Letter | TreeNode:


 '''simple docstring'''
 snake_case :			list[Letter | TreeNode]							=   letters  # type: ignore
 while len(__lowerCamelCase							) > 1:
  snake_case :			Any							=   response.pop(0							)
  snake_case :			List[str]							=   response.pop(0							)
  snake_case :			Union[str, Any]							=   left.freq + right.freq
  snake_case :			Dict							=   TreeNode(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)
  response.append(__lowerCamelCase							)
  response.sort(key=lambda __lowerCamelCase							: x.freq							)
 return response[0]






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase							)		->list[Letter]:


 '''simple docstring'''
 if isinstance(__lowerCamelCase			,  __lowerCamelCase							):
  snake_case :			Optional[int]							=   bitstring
  return [root]
 snake_case :			TreeNode							=   root  # type: ignore
 snake_case :			List[str]							=   []
 letters += traverse_tree(treenode.left			,  bitstring + '''0'''							)
 letters += traverse_tree(treenode.right			,  bitstring + '''1'''							)
 return letters






def  _UpperCAmelCase						( __lowerCamelCase							)		->None:


 '''simple docstring'''
 snake_case :			Dict							=   parse_file(__lowerCamelCase							)
 snake_case :			List[Any]							=   build_tree(__lowerCamelCase							)
 snake_case :			str							=   {
     k: v for letter in traverse_tree(__lowerCamelCase			,  ''''''							) for k, v in letter.bitstring.items()
 }
 print(f'''Huffman Coding  of {file_path}: '''							)
 with open(__lowerCamelCase							) as f:
  while True:
   snake_case :			Union[str, Any]							=   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])










import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a					:				Optional[int]       =      False

@nightly
@require_torch_gpu
class   lowerCamelCase						(	unittest.TestCase  ):

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Union[str, Any]							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )

        snake_case :			str							=   torch.manual_seed(0       )
        snake_case :			Tuple							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__       )
         snake_case :			str							=   VersatileDiffusionDualGuidedPipeline.from_pretrained(a__       )

        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Optional[Any]							=   generator.manual_seed(0       )
        snake_case :			List[str]							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        assert np.abs(image - new_image       ).sum() < 1e-5, "Models don't have the same forward pass"



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			int							=   '''cyberpunk 2077'''
        snake_case :			Dict							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )
        snake_case :			Optional[int]							=   torch.manual_seed(0       )
        snake_case :			Union[str, Any]							=   pipe(
            prompt=a__						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=50						,    output_type='''numpy'''						,    ).images

        snake_case :			str							=   image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case :			int							=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]       )

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2






