

'''simple docstring'''

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


lowerCAmelCase_     =      logging.get_logger(__name__)

lowerCAmelCase_     =      {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''vilt'''

				def __init__(       self,						lowercase_=30522,						lowercase_=2,						lowercase_=2,						lowercase_=40,						lowercase_=768,						lowercase_=12,						lowercase_=12,						lowercase_=3072,						lowercase_="gelu",						lowercase_=0.0,						lowercase_=0.0,						lowercase_=0.02,						lowercase_=1E-12,						lowercase_=384,						lowercase_=32,						lowercase_=3,						lowercase_=True,						lowercase_=-1,						lowercase_=False,						lowercase_=-1,						**lowercase_,						)    ->							int:
					super().__init__(tie_word_embeddings=lowercase_,						**lowercase_ )

					snake_case							=					vocab_size
					snake_case							=					type_vocab_size
					snake_case							=					modality_type_vocab_size
					snake_case							=					max_position_embeddings

					snake_case							=					hidden_size
					snake_case							=					num_hidden_layers
					snake_case							=					num_attention_heads
					snake_case							=					intermediate_size
					snake_case							=					hidden_act
					snake_case							=					hidden_dropout_prob
					snake_case							=					attention_probs_dropout_prob
					snake_case							=					initializer_range
					snake_case							=					layer_norm_eps

					snake_case							=					image_size
					snake_case							=					patch_size
					snake_case							=					num_channels
					snake_case							=					qkv_bias
					snake_case							=					max_image_length
					snake_case							=					num_images










"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:str		=     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=_lowercase,   default=1,   help="""Number of TPU cores to use (1 or 8)."""      )

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

						return parser.parse_args()
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     parse_args()

						# Import training_script as a module.
						snake_case_					:str		=     Path(args.training_script      )
						sys.path.append(str(script_fpath.parent.resolve()      )      )
						snake_case_					:Dict		=     script_fpath.stem
						snake_case_					:int		=     importlib.import_module(_lowercase      )

						# Patch sys.argv
						snake_case_					:Optional[Any]		=     [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()
