

'''simple docstring'''
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment







@require_huggingface_suite
class 							A__    (       unittest.TestCase  ):





  def 						A  (		self      :	Optional[Any] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =inspect.getfile(accelerate.test_utils )
       _SCREAMING_SNAKE_CASE							    =os.path.sep.join(
           mod_file.split(os.path.sep )[:-1] + ['scripts', 'external_deps', 'test_metrics.py'] )

       from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

       _SCREAMING_SNAKE_CASE							    =test_metrics





  @require_cpu
  def 						A  (		self      :	Optional[int] )	->    str:





       '''simple docstring'''






       debug_launcher(self.test_metrics.main							,					num_processes=1 )





  @require_cpu
  def 						A  (		self      :	Optional[Any] )	->    Union[str, Any]:





       '''simple docstring'''






       debug_launcher(self.test_metrics.main )





  @require_single_gpu
  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       self.test_metrics.main()





  @require_multi_gpu
  def 						A  (		self      :	List[Any] )	->    Union[str, Any]:





       '''simple docstring'''






       print(f"Found {torch.cuda.device_count()} devices." )
       _SCREAMING_SNAKE_CASE							    =['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
       with patch_environment(omp_num_threads=1 ):
            execute_subprocess_async(_a							,					env=os.environ.copy() )




'''simple docstring'''
from PIL import Image


def 			a      (			__a ,					__a   )  ->	Image:



					'''simple docstring'''






					def brightness(__a   ) -> float:
										return 128 + level + (c - 128)

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
					return img.point(__a   )


if __name__ == "__main__":
		# Load image
		with Image.open('''image_data/lena.jpg''') as img:
				# Change brightness to 100
				__snake_case							      =     change_brightness(img, 100)
				brigt_img.save('''image_data/lena_brightness.png''', format='''png''')