





from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase       :   Optional[Any]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   Optional[Any]				       =							{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """efficientformer"""




       def __init__(  self :    str							,				A :    Dict = [3, 2, 6, 4]							,				A :    Tuple = [48, 96, 224, 448]							,				A :    str = [True, True, True, True]							,				A :    Dict = 448							,				A :    Any = 32							,				A :    str = 4							,				A :    int = 7							,				A :    Tuple = 5							,				A :    Tuple = 8							,				A :    Any = 4							,				A :    int = 0.0							,				A :    int = 16							,				A :    Union[str, Any] = 3							,				A :    Tuple = 3							,				A :    int = 3							,				A :    Tuple = 2							,				A :    str = 1							,				A :    str = 0.0							,				A :    Optional[int] = 1							,				A :    Dict = True							,				A :    Dict = True							,				A :    Tuple = 1E-5							,				A :    List[Any] = "gelu"							,				A :    Optional[int] = 0.02							,				A :    Union[str, Any] = 1E-12							,				A :    List[Any] = 224							,				A :    Optional[int] = 1E-05							,				**A :    str							,				):
         super().__init__(**A		)

         __snake_case:						Optional[Any]   =						hidden_act
         __snake_case:						int   =						hidden_dropout_prob
         __snake_case:						List[Any]   =						hidden_sizes
         __snake_case:						Optional[int]   =						num_hidden_layers
         __snake_case:						Dict   =						num_attention_heads
         __snake_case:						Tuple   =						initializer_range
         __snake_case:						List[Any]   =						layer_norm_eps
         __snake_case:						List[str]   =						patch_size
         __snake_case:						str   =						num_channels
         __snake_case:						Union[str, Any]   =						depths
         __snake_case:						int   =						mlp_expansion_ratio
         __snake_case:						List[Any]   =						downsamples
         __snake_case:						str   =						dim
         __snake_case:						str   =						key_dim
         __snake_case:						Tuple   =						attention_ratio
         __snake_case:						Any   =						resolution
         __snake_case:						Dict   =						pool_size
         __snake_case:						Optional[Any]   =						downsample_patch_size
         __snake_case:						List[str]   =						downsample_stride
         __snake_case:						Dict   =						downsample_pad
         __snake_case:						Dict   =						drop_path_rate
         __snake_case:						Any   =						num_metaad_blocks
         __snake_case:						List[str]   =						distillation
         __snake_case:						Optional[int]   =						use_layer_scale
         __snake_case:						Tuple   =						layer_scale_init_value
         __snake_case:						List[str]   =						image_size
         __snake_case:						Any   =						batch_norm_eps








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							UpperCAmelCase_		(							):
	lowercase       =    ArgumentParser(
	    description=(
	        'PyTorch TPU distributed training launch '
	        'helper utility that will spawn up '
	        'multiple distributed processes'
	    )							)

	# Optional arguments for the launch helper
	parser.add_argument('--num_cores' ,							type=__SCREAMING_SNAKE_CASE ,							default=1 ,							help='Number of TPU cores to use (1 or 8).'							)

	# positional
	parser.add_argument(
	    'training_script' ,							type=__SCREAMING_SNAKE_CASE ,							help=(
	        'The full path to the single TPU training '
	        'program/script to be launched in parallel, '
	        'followed by all the arguments for the '
	        'training script'
	    ) ,							)

	# rest from the training program
	parser.add_argument('training_script_args' ,							nargs=__SCREAMING_SNAKE_CASE							)

	return parser.parse_args()






def 							UpperCAmelCase_		(							):
	lowercase       =    parse_args()

	# Import training_script as a module.
	lowercase       =    Path(args.training_script							)
	sys.path.append(str(script_fpath.parent.resolve()							)							)
	lowercase       =    script_fpath.stem
	lowercase       =    importlib.import_module(__SCREAMING_SNAKE_CASE							)

	# Patch sys.argv
	lowercase       =    [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores							)]
	xmp.spawn(mod._mp_fn ,							args=() ,							nprocs=args.num_cores							)


if __name__ == "__main__":
	main()

