
"""simple docstring"""






import argparse
import os

from accelerate.test_utils import execute_subprocess_async





def __A						(   a_ :							Union[str, Any]=None     )->							Any:




		'''simple docstring'''






		if subparsers is not None:
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= subparsers.add_parser('''test'''     )
		else:
				SCREAMING_SNAKE_CASE       :     List[Any]				= argparse.ArgumentParser('''Accelerate test command'''     )

		parser.add_argument(
		    '''--config_file'''		,		default=a_		,		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=a_     )
		return parser





def __A						(   a_ :							Optional[int]     )->							Optional[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= os.path.sep.join(__file__.split(os.path.sep     )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']     )

		if args.config_file is None:
				SCREAMING_SNAKE_CASE       :     List[Any]				= script_name
		else:
				SCREAMING_SNAKE_CASE       :     Dict				= F"--config_file={args.config_file} {script_name}"

		SCREAMING_SNAKE_CASE       :     Optional[Any]				= ['''accelerate-launch'''] + test_args.split()
		SCREAMING_SNAKE_CASE       :     List[Any]				= execute_subprocess_async(a_		,		env=os.environ.copy()     )
		if result.returncode == 0:
				print('''Test is a success! You are ready for your distributed training!'''     )





def __A						(   )->							Dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= test_command_parser()
		SCREAMING_SNAKE_CASE       :     Dict				= parser.parse_args()
		test_command(a_     )


if __name__ == "__main__":
					main()







"""simple docstring"""






import os
import sys

lowerCamelCase__ :       List[Any]						     =			os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__ :       str						     =			[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Union[str, Any]     )->							Dict:




		'''simple docstring'''






		return AutoConfig.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def __A						(   *a_ :							str		,		**a_ :							Union[str, Any]     )->							Union[str, Any]:




		'''simple docstring'''






		return AutoTokenizer.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModel.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							Dict:




		'''simple docstring'''






		return AutoModel.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Tuple     )->							Dict:




		'''simple docstring'''






		return AutoModelForCausalLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def __A						(   *a_ :							Dict		,		**a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForMaskedLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def __A						(   *a_ :							Optional[int]		,		**a_ :							str     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForSequenceClassification.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							List[Any]:




		'''simple docstring'''






		return AutoModelForQuestionAnswering.from_pretrained(*a_		,		**a_     )





