


'''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 	_A  (       A							,A=()							,A=None							,A="no"							,A="29500"							)  ->				Union[str, Any]:
	lowercase						:		int   					=		False
	lowercase						:		Any   					=		False
	if any(key.startswith("KAGGLE"							) for key in os.environ.keys()							):
		lowercase						:		Optional[Any]   					=		True
	elif "IPython" in sys.modules:
		lowercase						:		Any   					=		"google.colab" in str(sys.modules["IPython"].get_ipython()							)

	try:
		lowercase						:		List[str]   					=		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:
			lowercase						:		Dict   					=		8

		lowercase						:		List[Any]   					=		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							):
				lowercase						:		Any   					=		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():
				lowercase						:		Any   					=		"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 	_A  (       A							,A=()							,A=2							)  ->				int:
	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"							,):
			lowercase						:		List[str]   					=		PrepareForLaunch(A							,debug=A							)
			start_processes(A							,args=A							,nprocs=A							,start_method="fork"							)






'''simple docstring'''
def 	_A  (       A							)  ->				list:
	lowercase						:		str   					=		int(A							)
	if n_element < 1:
		lowercase						:		List[Any]   					=		ValueError("a should be a positive number"							)
		raise my_error

	lowercase						:		List[Any]   					=		[1]
	lowercase					,		lowercase					,		lowercase						:		Tuple   					=		(0, 0, 0)
	lowercase						:		Dict   					=		1
	while index < n_element:
		while hamming_list[i] * 2 <= hamming_list[-1]:
			i += 1
		while hamming_list[j] * 3 <= hamming_list[-1]:
			j += 1
		while hamming_list[k] * 5 <= hamming_list[-1]:
			k += 1
		hamming_list.append(
		    min(hamming_list[i] * 2							,hamming_list[j] * 3							,hamming_list[k] * 5							)							)
		index += 1
	return hamming_list


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, Any]             =				input("""Enter the last number (nth term) of the Hamming Number Series: """)
					print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
					lowerCAmelCase   : Tuple             =				hamming(int(n))
					print("""-----------------------------------------------------""")
					print(F'''The list with nth numbers is: {hamming_numbers}''')
					print("""-----------------------------------------------------""")


