


"""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 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Tuple				=      inspect.getfile(accelerate.test_utils )
           lowerCAmelCase    :		Optional[Any]				=      os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
           lowerCAmelCase    :		Optional[int]				=      os.path.sep.join(
               mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
           lowerCAmelCase    :		Union[str, Any]				=      os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )






     @require_multi_gpu
     def    lowerCamelCase__   (		self     :    int ):
           print(F'''Found {torch.cuda.device_count()} devices.''' )
           lowerCAmelCase    :		str				=      ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
           with patch_environment(omp_num_threads=1 ):
                 execute_subprocess_async(UpperCamelCase_    , env=os.environ.copy() )






     @require_multi_gpu
     def    lowerCamelCase__   (		self     :    List[Any] ):
           print(F'''Found {torch.cuda.device_count()} devices.''' )
           lowerCAmelCase    :		List[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(UpperCamelCase_    , env=os.environ.copy() )






     @require_multi_gpu
     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Optional[int]				=      ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
           with patch_environment(omp_num_threads=1 ):
                 execute_subprocess_async(UpperCamelCase_    , env=os.environ.copy() )






     @require_multi_gpu
     def    lowerCamelCase__   (		self     :    Dict ):
           print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
           lowerCAmelCase    :		Tuple				=      ['''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(UpperCamelCase_    , env=os.environ.copy() )


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

 snake_case__						:   Any			 = ''''''

 snake_case__						:   Tuple			 = 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)."

 snake_case__						:   Dict			 = 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."
 snake_case__						:   int			 = 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"""
from __future__ import annotations







class 			UpperCamelCase_       :





	def __init__(      self					: Any	,	lowerCAmelCase_					: int						)						->			None:
				UpperCAmelCase_     : Any			     =					data
				UpperCAmelCase_     : Node | None			     =					None
				UpperCAmelCase_     : Node | None			     =					None





def 				snake_case      (   A__						):  # In Order traversal of the tree
			if tree:
						display(tree.left						)
						print(tree.data						)
						display(tree.right						)

def 				snake_case      (   A__						):
			return 1 + max(depth_of_tree(tree.left						)	,depth_of_tree(tree.right						)						) if tree else 0

def 				snake_case      (   A__						):
			if not tree:
						return True
			if tree.left and tree.right:
						return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
			else:
						return not tree.left and not tree.right

def 				snake_case      (   ):  # Main function for testing.
			UpperCAmelCase_     : List[str]			     =					Node(1						)
			UpperCAmelCase_     : Any			     =					Node(2						)
			UpperCAmelCase_     : Optional[Any]			     =					Node(3						)
			UpperCAmelCase_     : Union[str, Any]			     =					Node(4						)
			UpperCAmelCase_     : int			     =					Node(5						)
			UpperCAmelCase_     : Optional[int]			     =					Node(6						)
			UpperCAmelCase_     : Any			     =					Node(7						)
			UpperCAmelCase_     : List[str]			     =					Node(8						)
			UpperCAmelCase_     : List[Any]			     =					Node(9						)

			print(is_full_binary_tree(A__						)						)
			print(depth_of_tree(A__						)						)
			print("Tree is: "						)
			display(A__						)


if __name__ == "__main__":
			main()



