



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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


__snake_case       :	Union[str, 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 			lowerCAmelCase__	(					):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any=None):





				'''simple docstring'''

				if subparsers is not None:
								lowerCAmelCase__  :       Any				=  subparsers.add_parser('''config''' ,description=lowerCamelCase_)
				else:
								lowerCAmelCase__  :       Optional[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 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  get_user_input()
				if args.config_file is not None:
								lowerCAmelCase__  :       Any				=  args.config_file
				else:
								if not os.path.isdir(lowerCamelCase_):
												os.makedirs(lowerCamelCase_)
								lowerCAmelCase__  :       int				=  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 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  config_command_parser()
				lowerCAmelCase__  :       int				=  parser.parse_args()
				config_command(lowerCamelCase_)


if __name__ == "__main__":
							main()






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  [0 for i in range(r + 1)]
				# nc0 = 1
				lowerCAmelCase__  :       Tuple				=  1
				for i in range(1 ,n + 1):
								# to compute current row from previous row.
								lowerCAmelCase__  :       Optional[int]				=  min(lowerCamelCase_ ,lowerCamelCase_)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]

print(binomial_coefficient(n=1_0, r=5))

