
'''simple docstring'''





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 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







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






     @require_multi_gpu
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           print(F'''Found {torch.cuda.device_count()} devices.'''  )
           UpperCamelCase__    :   Union[str, Any]					=				['''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  )  ->   int:




           """simple docstring"""







           print(F'''Found {torch.cuda.device_count()} devices.'''  )
           UpperCamelCase__    :   Optional[Any]					=				['''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  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   int					=				['''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  )  ->   Union[str, Any]:




           """simple docstring"""







           print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''  )
           UpperCamelCase__    :   Optional[Any]					=				['''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__":
    lowerCamelCase      =Accelerator()
    lowerCamelCase      =(accelerator.state.process_index + 2, 1_0)
    lowerCamelCase      =torch.randint(0, 1_0, shape).to(accelerator.device)

    lowerCamelCase      =""

    lowerCamelCase      =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)."

    lowerCamelCase      =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."
    lowerCamelCase      =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)




lowerCamelCase      ={"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowerCamelCase      =["a", "b", "c", "d", "e"]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				start
      # add current to visited
      visited.append(UpperCamelCase__		)
      UpperCamelCase__    :   int					=				edges[current]
      for neighbor in neighbors:
            # if neighbor not in visited, visit
            if neighbor not in visited:
                  UpperCamelCase__    :   int					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # if all neighbors visited add current to sort
      sort.append(UpperCamelCase__		)
      # if all vertices haven't been visited select a new one to visit
      if len(UpperCamelCase__		) != len(UpperCamelCase__		):
            for vertice in vertices:
                  if vertice not in visited:
                        UpperCamelCase__    :   Optional[int]					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # return sort
      return sort


if __name__ == "__main__":
    lowerCamelCase      =topological_sort("a", [], [])
    print(sort)


