


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

import torch_xla.distributed.xla_multiprocessing as xmp


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    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 _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    parse_args()

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

			# Patch sys.argv
			__lowerCAmelCase       					=    [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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A  :     Optional[int]    					=    logging.get_logger(__name__)

A  :     List[str]    					=    {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          ="""bit"""
							__UpperCAmelCase		:					Optional[int]          =["""preactivation""", """bottleneck"""]
							__UpperCAmelCase		:					List[str]          =["""SAME""", """VALID"""]

							def __init__(  self					,     __a=3					,     __a=64					,     __a=[2_56, 5_12, 10_24, 20_48]					,     __a=[3, 4, 6, 3]					,     __a="preactivation"					,     __a="relu"					,     __a=None					,     __a=32					,     __a=0.0					,     __a=False					,     __a=32					,     __a=1					,     __a=None					,     __a=None					,     **__a					,     ):
										super().__init__(**__a			)
										if layer_type not in self.layer_types:
													raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
										if global_padding is not None:
													if global_padding.upper() in self.supported_padding:
																__lowerCAmelCase       					=    global_padding.upper()
													else:
																raise ValueError(f"Padding strategy {global_padding} not supported"			)
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    embedding_size
										__lowerCAmelCase       					=    hidden_sizes
										__lowerCAmelCase       					=    depths
										__lowerCAmelCase       					=    layer_type
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    global_padding
										__lowerCAmelCase       					=    num_groups
										__lowerCAmelCase       					=    drop_path_rate
										__lowerCAmelCase       					=    embedding_dynamic_padding
										__lowerCAmelCase       					=    output_stride
										__lowerCAmelCase       					=    width_factor

										__lowerCAmelCase       					=    ["stem"] + [f"stage{idx}" for idx in range(1					,     len(__a			) + 1			)]
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(
										    out_features=__a					,     out_indices=__a					,     stage_names=self.stage_names			)




