

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

import torch_xla.distributed.xla_multiprocessing as xmp
def 						_snake_case(    )							->      Optional[Any]:





    '''simple docstring'''






    A__          =   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 						_snake_case(    )							->      Union[str, Any]:





    '''simple docstring'''






    A__          =   parse_args()

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

    # Patch sys.argv
    A__          =   [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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



