






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment





class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  inspect.getfile(accelerate.test_utils )
           UpperCamelCase					       =  os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_script.py'] )
           UpperCamelCase					       =  os.path.sep.join(
               mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_distributed_data_loop.py'] )
           UpperCamelCase					       =  os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_ops.py'] )







     @require_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




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







     @require_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




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







     @require_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
           with patch_environment(omp_num_threads=1 ):
                 execute_subprocess_async(_SCREAMING_SNAKE_CASE   ,						env=os.environ.copy() )







     @require_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           print(f'Found {torch.cuda.device_count()} devices, using 2 devices only' )
           UpperCamelCase					       =  ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
           with patch_environment(omp_num_threads=1   ,						cuda_visible_devices='0,1' ):
                 execute_subprocess_async(_SCREAMING_SNAKE_CASE   ,						env=os.environ.copy() )


if __name__ == "__main__":
       __magic_name__   :  Optional[Any]        =							Accelerator()
       __magic_name__   :  str        =							(accelerator.state.process_index + 2, 10)
       __magic_name__   :  List[str]        =							torch.randint(0, 10, shape).to(accelerator.device)

       __magic_name__   :  Optional[Any]        =							''''''

       __magic_name__   :  List[str]        =							accelerator.pad_across_processes(tensor)
       if tensora.shape[0] != accelerator.state.num_processes + 1:
              error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
       if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
              error_msg += "Tensors have different values."
       if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
              error_msg += "Padding was not done with the right value (0)."

       __magic_name__   :  int        =							accelerator.pad_across_processes(tensor, pad_first=True)
       if tensora.shape[0] != accelerator.state.num_processes + 1:
              error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
       __magic_name__   :  Optional[Any]        =							accelerator.state.num_processes - accelerator.state.process_index - 1
       if not torch.equal(tensora[index:], tensor):
              error_msg += "Tensors have different values."
       if not torch.all(tensora[:index] == 0):
              error_msg += "Padding was not done with the right value (0)."

       # Raise error at the end to make sure we don't stop at the first failure.
       if len(error_msg) > 0:
              raise ValueError(error_msg)











__magic_name__   :  Optional[Any]        =							0  # The first color of the flag.
__magic_name__   :  List[str]        =							1  # The second color of the flag.
__magic_name__   :  Dict        =							2  # The third color of the flag.
__magic_name__   :  Union[str, Any]        =							(red, white, blue)
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if not sequence:
            return []
      if len(_UpperCamelCase) == 1:
            return list(_UpperCamelCase)
      UpperCamelCase					       =  0
      UpperCamelCase					       =  len(_UpperCamelCase) - 1
      UpperCamelCase					       =  0
      while mid <= high:
            if sequence[mid] == colors[0]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[mid], sequence[low]
                  low += 1
                  mid += 1
            elif sequence[mid] == colors[1]:
                  mid += 1
            elif sequence[mid] == colors[2]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[high], sequence[mid]
                  high -= 1
            else:
                  UpperCamelCase					       =  F'The elements inside the sequence must contains only {colors} values'
                  raise ValueError(_UpperCamelCase)
      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __magic_name__   :  str        =							input('''Enter numbers separated by commas:\n''').strip()
       __magic_name__   :  Union[str, Any]        =							[int(item.strip()) for item in user_input.split(''',''')]
       print(F'{dutch_national_flag_sort(unsorted)}')



