

'''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


A_							    =				"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  A_	(			):
  SCREAMING_SNAKE_CASE:int       =       _ask_options(
      "In which compute environment are you running?"   ,							["This machine", "AWS (Amazon SageMaker)"]   ,							_convert_compute_environment   ,							)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    SCREAMING_SNAKE_CASE:Optional[int]       =       get_sagemaker_input()
  else:
    SCREAMING_SNAKE_CASE:int       =       get_cluster_input()
  return config





def  A_	(			snake_case=None			):
  if subparsers is not None:
    SCREAMING_SNAKE_CASE:List[str]       =       subparsers.add_parser("config"   ,							description=snake_case			)
  else:
    SCREAMING_SNAKE_CASE:int       =       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  A_	(			snake_case			):
  SCREAMING_SNAKE_CASE:Optional[int]       =       get_user_input()
  if args.config_file is not None:
    SCREAMING_SNAKE_CASE:int       =       args.config_file
  else:
    if not os.path.isdir(snake_case			):
      os.makedirs(snake_case			)
    SCREAMING_SNAKE_CASE:List[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  A_	(			):
  SCREAMING_SNAKE_CASE:Optional[int]       =       config_command_parser()
  SCREAMING_SNAKE_CASE:List[str]       =       parser.parse_args()
  config_command(snake_case			)


if __name__ == "__main__":
    main()







"""simple docstring"""



from __future__ import annotations

def  _A(		lowerCAmelCase							):
							if len(lowerCAmelCase							) == 0:
														return []
							A__				,							A__       :     Dict								=			min(lowerCAmelCase							), max(lowerCAmelCase							)
							A__       :     List[Any]								=			int(max_value - min_value							) + 1
							A__       :     list[list]								=			[[] for _ in range(lowerCAmelCase							)]

							for i in my_list:
														buckets[int(i - min_value							)].append(lowerCAmelCase							)

							return [v for bucket in buckets for v in sorted(lowerCAmelCase							)]


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





