



"""simple docstring"""
import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


SCREAMING_SNAKE_CASE_				= """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""



def        lowerCAmelCase_						(							)			->      Any:
	a_     :      Tuple 						=     _ask_options(
	    "In which compute environment are you running?", ["This machine", "AWS (Amazon SageMaker)"], _convert_compute_environment, )
	if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
		a_     :      str 						=     get_sagemaker_input()
	else:
		a_     :      Optional[int] 						=     get_cluster_input()
	return config



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__=None	)			->      List[Any]:





	if subparsers is not None:
		a_     :      str 						=     subparsers.add_parser("config", description=__snake_case	)
	else:
		a_     :      Dict 						=     argparse.ArgumentParser("Accelerate config command", description=__snake_case	)

	parser.add_argument(
	    "--config_file", default=__snake_case, help=(
	        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
	        "location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have "
	        "such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed "
	        "with \'huggingface\'."
	    ), )

	if subparsers is not None:
		parser.set_defaults(func=__snake_case	)
	return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
	a_     :      Tuple 						=     get_user_input()
	if args.config_file is not None:
		a_     :      Tuple 						=     args.config_file
	else:
		if not os.path.isdir(__snake_case	):
			os.makedirs(__snake_case	)
		a_     :      Union[str, Any] 						=     default_yaml_config_file

	if config_file.endswith(".json"	):
		config.to_json_file(__snake_case	)
	else:
		config.to_yaml_file(__snake_case	)
	print(F"""accelerate configuration saved at {config_file}"""	)



def        lowerCAmelCase_						(							)			->      Optional[int]:
	a_     :      Optional[Any] 						=     config_command_parser()
	a_     :      Dict 						=     parser.parse_args()
	config_command(__snake_case	)


if __name__ == "__main__":
					main()




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(a - b	) for a, b in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(x - y	) for x, y in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()