




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


__lowerCAmelCase :List[Any]     =    '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				(							):
					_snake_case       :					Any				       =   _ask_options(
					    "In which compute environment are you running?"  ,  ["This machine", "AWS (Amazon SageMaker)"]  ,  _convert_compute_environment  ,  )
					if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
										_snake_case       :					str				       =   get_sagemaker_input()
					else:
										_snake_case       :					Dict				       =   get_cluster_input()
					return config







def    A				(							UpperCAmelCase=None				):
					if subparsers is not None:
										_snake_case       :					Any				       =   subparsers.add_parser("config"  ,  description=UpperCamelCase__				)
					else:
										_snake_case       :					Optional[Any]				       =   argparse.ArgumentParser("Accelerate config command"  ,  description=UpperCamelCase__				)

					parser.add_argument(
					    "--config_file"  ,  default=UpperCamelCase__  ,  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=UpperCamelCase__				)
					return parser







def    A				(							UpperCAmelCase				):
					_snake_case       :					Dict				       =   get_user_input()
					if args.config_file is not None:
										_snake_case       :					Tuple				       =   args.config_file
					else:
										if not os.path.isdir(UpperCamelCase__				):
															os.makedirs(UpperCamelCase__				)
										_snake_case       :					str				       =   default_yaml_config_file

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







def    A				(							):
					_snake_case       :					List[str]				       =   config_command_parser()
					_snake_case       :					List[str]				       =   parser.parse_args()
					config_command(UpperCamelCase__				)


if __name__ == "__main__":
							main()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase :str     =    {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :Optional[int]     =    [
							    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'NllbMoeForConditionalGeneration',
							    'NllbMoeModel',
							    'NllbMoePreTrainedModel',
							    'NllbMoeTop2Router',
							    'NllbMoeSparseMLP',
							]


if TYPE_CHECKING:
							from .configuration_nllb_moe import (
							    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    NllbMoeConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nllb_moe import (
														    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NllbMoeForConditionalGeneration,
														    NllbMoeModel,
														    NllbMoePreTrainedModel,
														    NllbMoeSparseMLP,
														    NllbMoeTopaRouter,
														)


else:
							import sys

							__lowerCAmelCase :int     =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)