

import os
import sys
import tempfile

import torch

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

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

	try:
		A__							:   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"""   ,				lowercase_ ) 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:
			A__							:   List[Any]					       =		8

		A__							:   Union[str, Any]					       =		PrepareForLaunch(lowercase_   ,				distributed_type="""TPU""" )
		print(f"""Launching a training on {num_processes} TPU cores.""" )
		xmp.spawn(lowercase_   ,				args=lowercase_   ,				nprocs=lowercase_   ,				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(*lowercase_ )
	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=lowercase_   ,				master_addr="""127.0.01"""   ,				master_port=lowercase_   ,				mixed_precision=lowercase_ ):
				A__							:   Optional[Any]					       =		PrepareForLaunch(lowercase_   ,				distributed_type="""MULTI_GPU""" )
				print(f"""Launching training on {num_processes} GPUs.""" )
				try:
					start_processes(lowercase_   ,				args=lowercase_   ,				nprocs=lowercase_   ,				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():
				A__							:   List[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(*lowercase_ )


def 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Dict=()   ,				lowercase_:			Optional[int]=2 )					->				Union[str, Any]:
	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=lowercase_   ,				master_addr="""127.0.01"""   ,				master_port="""29500"""   ,				accelerate_mixed_precision="""no"""   ,				accelerate_debug_rdv_file=tmp_file.name   ,				accelerate_use_cpu="""yes"""   ,				):
			A__							:   List[Any]					       =		PrepareForLaunch(lowercase_   ,				debug=lowercase_ )
			start_processes(lowercase_   ,				args=lowercase_   ,				nprocs=lowercase_   ,				start_method="""fork""" )






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



