


'''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 __a						(					self     :							Any						):





       '''simple docstring'''

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

  @require_multi_gpu
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       print(f'''Found {torch.cuda.device_count()} devices.'''						)
       __a       =   ["""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 __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       print(f'''Found {torch.cuda.device_count()} devices.'''						)
       __a       =   ["""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 __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   ["""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 __a						(					self     :							str						):





       '''simple docstring'''

       print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''						)
       __a       =   ["""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__":
 SCREAMING_SNAKE_CASE_      =			Accelerator()
 SCREAMING_SNAKE_CASE_      =			(accelerator.state.process_index + 2, 10)
 SCREAMING_SNAKE_CASE_      =			torch.randint(0, 10, shape).to(accelerator.device)

 SCREAMING_SNAKE_CASE_      =			''

 SCREAMING_SNAKE_CASE_      =			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)."

 SCREAMING_SNAKE_CASE_      =			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."
 SCREAMING_SNAKE_CASE_      =			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)










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   []
     __a       =   set({"""(""", """[""", """{"""}		)
     __a       =   set({""")""", """]""", """}"""}		)
     __a       =   {"""{""": """}""", """[""": """]""", """(""": """)"""}

     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          if s[i] in open_brackets:
               stack.append(s[i]		)

          elif s[i] in closed_brackets and (
              len(__SCREAMING_SNAKE_CASE		) == 0 or (len(__SCREAMING_SNAKE_CASE		) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(__SCREAMING_SNAKE_CASE		) == 0




def        __lowercase						(      )				->					List[str]:
     """simple docstring"""




     __a       =   input("""Enter sequence of brackets: """		)
     if is_balanced(__SCREAMING_SNAKE_CASE		):
          print(__SCREAMING_SNAKE_CASE				,  """is balanced"""		)
     else:
          print(__SCREAMING_SNAKE_CASE				,  """is not balanced"""		)


if __name__ == "__main__":
 main()






