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

import torch_xla.distributed.xla_multiprocessing as xmp







def 						_snake_case(  ) ->    str:
       lowercase							:     Optional[Any]       	=      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=_lowerCamelCase					,	default=1					,	help="""Number of TPU cores to use (1 or 8)."""				)

       # positional
       parser.add_argument(
           """training_script"""					,	type=_lowerCamelCase					,	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=_lowerCamelCase				)

       return parser.parse_args()







def 						_snake_case(  ) ->    Optional[Any]:
       lowercase							:     List[Any]       	=      parse_args()

       # Import training_script as a module.
       lowercase							:     Optional[Any]       	=      Path(args.training_script				)
       sys.path.append(str(script_fpath.parent.resolve()				)				)
       lowercase							:     Union[str, Any]       	=      script_fpath.stem
       lowercase							:     Optional[Any]       	=      importlib.import_module(_lowerCamelCase				)

       # Patch sys.argv
       lowercase							:     Tuple       	=      [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()

"""simple docstring"""




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


_a      :		List[Any]           =   logging.get_logger(__name__)

_a      :		Any           =   {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		"swinv2"

     _UpperCamelCase							:			List[str]							=		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self				,     a__=224				,     a__=4				,     a__=3				,     a__=96				,     a__=[2, 2, 6, 2]				,     a__=[3, 6, 12, 24]				,     a__=7				,     a__=4.0				,     a__=True				,     a__=0.0				,     a__=0.0				,     a__=0.1				,     a__="gelu"				,     a__=False				,     a__=0.0_2				,     a__=1e-5				,     a__=32				,     **a__				,     ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       int  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       Any  			=     num_channels
       _lowerCAmelCase						:       List[Any]  			=     embed_dim
       _lowerCAmelCase						:       Optional[int]  			=     depths
       _lowerCAmelCase						:       List[Any]  			=     len(a__  )
       _lowerCAmelCase						:       Any  			=     num_heads
       _lowerCAmelCase						:       Tuple  			=     window_size
       _lowerCAmelCase						:       Tuple  			=     mlp_ratio
       _lowerCAmelCase						:       Any  			=     qkv_bias
       _lowerCAmelCase						:       Optional[int]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       str  			=     drop_path_rate
       _lowerCAmelCase						:       List[str]  			=     hidden_act
       _lowerCAmelCase						:       List[str]  			=     use_absolute_embeddings
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:       Tuple  			=     int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:       Tuple  			=     (0, 0, 0, 0)




