


'''simple docstring'''





def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =0
				while len(__snake_case) > 1:
								_SCREAMING_SNAKE_CASE			  =0
								# Consider two files with minimum cost to be merged
								for _ in range(2):
												_SCREAMING_SNAKE_CASE			  =files.index(min(__snake_case))
												temp += files[min_index]
												files.pop(__snake_case)
								files.append(__snake_case)
								optimal_merge_cost += temp
				return optimal_merge_cost


if __name__ == "__main__":
							import doctest

							doctest.testmod()



import os
import sys
import tempfile

import torch

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







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

				try:
								_SCREAMING_SNAKE_CASE			  =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'''	,a__) 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:
												_SCREAMING_SNAKE_CASE			  =8

								_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,distributed_type='''TPU''')
								print(f"Launching a training on {num_processes} TPU cores.")
								xmp.spawn(a__	,args=a__	,nprocs=a__	,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(*a__)
				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=a__	,master_addr='''127.0.01'''	,master_port=a__	,mixed_precision=a__):
																_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,distributed_type='''MULTI_GPU''')
																print(f"Launching training on {num_processes} GPUs.")
																try:
																				start_processes(a__	,args=a__	,nprocs=a__	,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():
																_SCREAMING_SNAKE_CASE			  ='''1'''
																print('''Launching training on MPS.''')
												elif torch.cuda.is_available():
																print('''Launching training on one GPU.''')
												else:
																print('''Launching training on CPU.''')
												function(*a__)







def 						lowerCamelCase(   a__	,a__=()	,a__=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=a__	,master_addr='''127.0.01'''	,master_port='''29500'''	,accelerate_mixed_precision='''no'''	,accelerate_debug_rdv_file=tmp_file.name	,accelerate_use_cpu='''yes'''	,):
												_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,debug=a__)
												start_processes(a__	,args=a__	,nprocs=a__	,start_method='''fork''')