import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     {
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}







class        UpperCAmelCase_					(				__SCREAMING_SNAKE_CASE     ):
    UpperCAmelCase_      :							List[Any]    			=				'vit-hybrid'




    def __init__(       self	: str   ,   lowerCAmelCase	: str=None   ,   lowerCAmelCase	: Dict=768   ,   lowerCAmelCase	: str=12   ,   lowerCAmelCase	: Dict=12   ,   lowerCAmelCase	: Optional[int]=3072   ,   lowerCAmelCase	: List[str]="gelu"   ,   lowerCAmelCase	: Any=0.0   ,   lowerCAmelCase	: Tuple=0.0   ,   lowerCAmelCase	: str=0.02   ,   lowerCAmelCase	: Optional[Any]=1e-1_2   ,   lowerCAmelCase	: Union[str, Any]=224   ,   lowerCAmelCase	: str=1   ,   lowerCAmelCase	: List[Any]=3   ,   lowerCAmelCase	: Tuple=[1, 1024, 24, 24]   ,   lowerCAmelCase	: Tuple=True   ,   **lowerCAmelCase	: int   ,   )						->			Tuple:
      super().__init__(**lowerCamelCase_ )

      if backbone_config is None:
        logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone." )
        a__  		=  {
            "global_padding": "same",
            "layer_type": "bottleneck",
            "depths": [3, 4, 9],
            "out_features": ["stage3"],
            "embedding_dynamic_padding": True,
        }

      if isinstance(lowerCamelCase_   ,   lowerCamelCase_ ):
        if "model_type" in backbone_config:
          a__  		=  CONFIG_MAPPING[backbone_config["model_type"]]
        else:
          logger.info(
              "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class." )
          a__  		=  BitConfig
        a__  		=  backbone_config_class(**lowerCamelCase_ )

      a__  		=  backbone_featmap_shape
      a__  		=  backbone_config
      a__  		=  hidden_size
      a__  		=  num_hidden_layers
      a__  		=  num_attention_heads
      a__  		=  intermediate_size
      a__  		=  hidden_act
      a__  		=  hidden_dropout_prob
      a__  		=  attention_probs_dropout_prob
      a__  		=  initializer_range
      a__  		=  layer_norm_eps
      a__  		=  image_size
      a__  		=  patch_size
      a__  		=  num_channels
      a__  		=  qkv_bias




    def     __lowerCamelCase			(       self	: Any )						->			Union[str, Any]:
      a__  		=  copy.deepcopy(self.__dict__ )
      a__  		=  self.backbone_config.to_dict()
      a__  		=  self.__class__.model_type
      return output





"""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"""

						UpperCAmelCase__												=     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=_UpperCamelCase						,      default=1						,      help='''Number of TPU cores to use (1 or 8).'''			)

						# positional
						parser.add_argument(
						    '''training_script'''						,      type=_UpperCamelCase						,      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=_UpperCamelCase			)

						return parser.parse_args()






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     parse_args()

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

						# Patch sys.argv
						UpperCAmelCase__												=     [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()