






'''simple docstring'''

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

import torch_xla.distributed.xla_multiprocessing as xmp







def 					lowercase__				(   )->				List[str]:
      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 					lowercase__				(   )->				Optional[int]:
      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()








'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__											=     {
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class        a_		(    lowerCamelCase       ):
  lowercase  	=   """deformable_detr"""
  lowercase  	=   {
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=300    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=6    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=6    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE="relu"    ,	_SCREAMING_SNAKE_CASE=256    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=1.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE="sine"    ,	_SCREAMING_SNAKE_CASE="resnet50"    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=300    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.2_5    ,	_SCREAMING_SNAKE_CASE=False    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[str]:



        """simple docstring"""






        if backbone_config is not None and use_timm_backbone:
              raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""   )

        if not use_timm_backbone:
              if backbone_config is None:
                    logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""   )
                    UpperCamelCase       						=      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]   )
              elif isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                    UpperCamelCase       						=      backbone_config.get("""model_type"""   )
                    UpperCamelCase       						=      CONFIG_MAPPING[backbone_model_type]
                    UpperCamelCase       						=      config_class.from_dict(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      use_timm_backbone
        UpperCamelCase       						=      backbone_config
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      num_queries
        UpperCamelCase       						=      max_position_embeddings
        UpperCamelCase       						=      d_model
        UpperCamelCase       						=      encoder_ffn_dim
        UpperCamelCase       						=      encoder_layers
        UpperCamelCase       						=      encoder_attention_heads
        UpperCamelCase       						=      decoder_ffn_dim
        UpperCamelCase       						=      decoder_layers
        UpperCamelCase       						=      decoder_attention_heads
        UpperCamelCase       						=      dropout
        UpperCamelCase       						=      attention_dropout
        UpperCamelCase       						=      activation_dropout
        UpperCamelCase       						=      activation_function
        UpperCamelCase       						=      init_std
        UpperCamelCase       						=      init_xavier_std
        UpperCamelCase       						=      encoder_layerdrop
        UpperCamelCase       						=      auxiliary_loss
        UpperCamelCase       						=      position_embedding_type
        UpperCamelCase       						=      backbone
        UpperCamelCase       						=      use_pretrained_backbone
        UpperCamelCase       						=      dilation
        # deformable attributes
        UpperCamelCase       						=      num_feature_levels
        UpperCamelCase       						=      encoder_n_points
        UpperCamelCase       						=      decoder_n_points
        UpperCamelCase       						=      two_stage
        UpperCamelCase       						=      two_stage_num_proposals
        UpperCamelCase       						=      with_box_refine
        if two_stage is True and with_box_refine is False:
              raise ValueError("""If two_stage is True, with_box_refine must be True."""   )
        # Hungarian matcher
        UpperCamelCase       						=      class_cost
        UpperCamelCase       						=      bbox_cost
        UpperCamelCase       						=      giou_cost
        # Loss coefficients
        UpperCamelCase       						=      mask_loss_coefficient
        UpperCamelCase       						=      dice_loss_coefficient
        UpperCamelCase       						=      bbox_loss_coefficient
        UpperCamelCase       						=      giou_loss_coefficient
        UpperCamelCase       						=      eos_coefficient
        UpperCamelCase       						=      focal_alpha
        UpperCamelCase       						=      disable_custom_kernels
        super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

  @property
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return self.encoder_attention_heads

  @property
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return self.d_model


  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      copy.deepcopy(self.__dict__   )
        if self.backbone_config is not None:
              UpperCamelCase       						=      self.backbone_config.to_dict()
        UpperCamelCase       						=      self.__class__.model_type
        return output
