





"""simple docstring"""


import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=()							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE="no"							,     SCREAMING_SNAKE_CASE="29500"     ):
						UpperCamelCase				:		Any				   =			False
						UpperCamelCase				:		List[Any]				   =			False
						if any(key.startswith("""KAGGLE"""     ) for key in os.environ.keys()     ):
												UpperCamelCase				:		List[str]				   =			True
						elif "IPython" in sys.modules:
												UpperCamelCase				:		Any				   =			"""google.colab""" in str(sys.modules["""IPython"""].get_ipython()     )

						try:
												UpperCamelCase				:		Union[str, Any]				   =			PrecisionType(mixed_precision.lower()     )
						except ValueError:
												raise ValueError(
												    f"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""     )

						if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME"""							,     SCREAMING_SNAKE_CASE     ) is not None):
												# TPU launch
												import torch_xla.distributed.xla_multiprocessing as xmp

												if len(AcceleratorState._shared_state     ) > 0:
																		raise ValueError(
																		    """To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
																		    """your training function. Restart your notebook and make sure no cells initializes an """
																		    """`Accelerator`."""     )
												if num_processes is None:
																		UpperCamelCase				:		List[str]				   =			8

												UpperCamelCase				:		Any				   =			PrepareForLaunch(SCREAMING_SNAKE_CASE							,     distributed_type="""TPU"""     )
												print(f"""Launching a training on {num_processes} TPU cores."""     )
												xmp.spawn(SCREAMING_SNAKE_CASE							,     args=SCREAMING_SNAKE_CASE							,     nprocs=SCREAMING_SNAKE_CASE							,     start_method="""fork"""     )
						elif in_colab:
												# No need for a distributed launch otherwise as it's either CPU or one GPU.
												if torch.cuda.is_available():
																		print("""Launching training on one GPU."""     )
												else:
																		print("""Launching training on one CPU."""     )
												function(*SCREAMING_SNAKE_CASE     )
						else:
												if num_processes is None:
																		raise ValueError(
																		    """You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."""     )

												if num_processes > 1:
																		# Multi-GPU launch
																		from torch.multiprocessing import start_processes
																		from torch.multiprocessing.spawn import ProcessRaisedException

																		if len(AcceleratorState._shared_state     ) > 0:
																								raise ValueError(
																								    """To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
																								    """inside your training function. Restart your notebook and make sure no cells initializes an """
																								    """`Accelerator`."""     )

																		if torch.cuda.is_initialized():
																								raise ValueError(
																								    """To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
																								    """using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
																								    """function."""     )

																		# torch.distributed will expect a few environment variable to be here. We set the ones common to each
																		# process here (the other ones will be set be the launcher).
																		with patch_environment(
																		    world_size=SCREAMING_SNAKE_CASE							,     master_addr="""127.0.01"""							,     master_port=SCREAMING_SNAKE_CASE							,     mixed_precision=SCREAMING_SNAKE_CASE     ):
																								UpperCamelCase				:		Optional[int]				   =			PrepareForLaunch(SCREAMING_SNAKE_CASE							,     distributed_type="""MULTI_GPU"""     )
																								print(f"""Launching training on {num_processes} GPUs."""     )
																								try:
																														start_processes(SCREAMING_SNAKE_CASE							,     args=SCREAMING_SNAKE_CASE							,     nprocs=SCREAMING_SNAKE_CASE							,     start_method="""fork"""     )
																								except ProcessRaisedException as e:
																														if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																																				raise RuntimeError(
																																				    """CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
																																				    """This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
																																				    """Please review your imports and test them when running the `notebook_launcher()` to identify """
																																				    """which one is problematic."""     ) from e

												else:
																		# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
																		if is_mps_available():
																								UpperCamelCase				:		List[str]				   =			"""1"""
																								print("""Launching training on MPS."""     )
																		elif torch.cuda.is_available():
																								print("""Launching training on one GPU."""     )
																		else:
																								print("""Launching training on CPU."""     )
																		function(*SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=()							,     SCREAMING_SNAKE_CASE=2     ):
						from torch.multiprocessing import start_processes

						with tempfile.NamedTemporaryFile() as tmp_file:
												# torch.distributed will expect a few environment variable to be here. We set the ones common to each
												# process here (the other ones will be set be the launcher).
												with patch_environment(
												    world_size=SCREAMING_SNAKE_CASE							,     master_addr="""127.0.01"""							,     master_port="""29500"""							,     accelerate_mixed_precision="""no"""							,     accelerate_debug_rdv_file=tmp_file.name							,     accelerate_use_cpu="""yes"""							,     ):
																		UpperCamelCase				:		Union[str, Any]				   =			PrepareForLaunch(SCREAMING_SNAKE_CASE							,     debug=SCREAMING_SNAKE_CASE     )
																		start_processes(SCREAMING_SNAKE_CASE							,     args=SCREAMING_SNAKE_CASE							,     nprocs=SCREAMING_SNAKE_CASE							,     start_method="""fork"""     )







'''simple docstring'''

from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class     __lowerCAmelCase		:





      '''simple docstring'''






      lowerCAmelCase__   :	int
      lowerCAmelCase__   :	TreeNode | None       =				None
      lowerCAmelCase__   :	TreeNode | None       =				None






lowerCamelCase				:  Optional[Any]    		=      namedtuple('CoinsDistribResult', 'moves excess')

def _SCREAMING_SNAKE_CASE (A			)			->			int:






       """simple docstring"""







       if root is None:
              return 0

       # Validation
       def count_nodes(A			) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left			) + count_nodes(node.right			) + 1

       def count_coins(A			) -> int:
              if node is None:
                     return 0

              return count_coins(node.left			) + count_coins(node.right			) + node.data

       if count_nodes(A			) != count_coins(A			):
              raise ValueError('''The nodes number should be same as the number of coins'''			)

       # Main calculation
       def get_distrib(A			) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0    ,					1			)

              lowercase__     ,lowercase__      =					get_distrib(node.left			)
              lowercase__     ,lowercase__      =					get_distrib(node.right			)

              lowercase__      =					1 - left_distrib_excess
              lowercase__      =					1 - right_distrib_excess

              lowercase__      =					(
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(A			)
                  + abs(A			)
              )
              lowercase__      =					node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(A    ,					A			)

       return get_distrib(A			)[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




