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					:	str       =  '''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       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    _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_  :		Any				      =    get_sagemaker_input()
		else:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    get_cluster_input()
		return config




def       _snake_case	(   lowerCAmelCase							:   Tuple=None	):
		"""simple docstring"""







		if subparsers is not None:
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    subparsers.add_parser("config"   , description=lowerCAmelCase_	)
		else:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    argparse.ArgumentParser("Accelerate config command"   , description=lowerCAmelCase_	)

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




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    get_user_input()
		if args.config_file is not None:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    args.config_file
		else:
				if not os.path.isdir(lowerCAmelCase_	):
						os.makedirs(lowerCAmelCase_	)
				SCREAMING_SNAKE_CASE_  :		str				      =    default_yaml_config_file

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




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    config_command_parser()
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    parser.parse_args()
		config_command(lowerCAmelCase_	)


if __name__ == "__main__":
		main()





'''simple docstring'''

__lowerCAmelCase									=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      List[Any]        =							set()
		# keep track of all the paths to be checked
		_a     :      Any        =							[[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				_a     :      Tuple        =							queue.pop(0  )
				# get the last node from the path
				_a     :      Tuple        =							path[-1]
				if node not in explored:
						_a     :      Optional[Any]        =							graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								_a     :      Any        =							list(lowerCAmelCase_  )
								new_path.append(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(lowerCAmelCase_  )

    # in case there's no path between the 2 nodes
		return []



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		_a     :      Optional[int]        =							[start]
		_a     :      Dict        =							set(lowerCAmelCase_  )
		# Keep tab on distances from `start` node.
		_a     :      Dict        =							{start: 0, target: -1}
		while queue:
				_a     :      List[str]        =							queue.pop(0  )
				if node == target:
						_a     :      Any        =							(
						    dist[node] if dist[target] == -1 else min(dist[target]						,			dist[node]  )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								_a     :      Any        =							dist[node] + 1
		return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




