
"""simple docstring"""

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCamelCase			:       Union[str, Any]									=				logging.get_logger(__name__)

UpperCamelCase			:       Any									=				{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"conditional_detr"
     lowercase		     =		["past_key_values"]
     lowercase		     =		{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }





     def __init__(			self			,  __UpperCAmelCase=True			,  __UpperCAmelCase=None			,  __UpperCAmelCase=3			,  __UpperCAmelCase=300			,  __UpperCAmelCase=6			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=8			,  __UpperCAmelCase=6			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=8			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=True			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=256			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=1.0			,  __UpperCAmelCase=False			,  __UpperCAmelCase="sine"			,  __UpperCAmelCase="resnet50"			,  __UpperCAmelCase=True			,  __UpperCAmelCase=False			,  __UpperCAmelCase=2			,  __UpperCAmelCase=5			,  __UpperCAmelCase=2			,  __UpperCAmelCase=1			,  __UpperCAmelCase=1			,  __UpperCAmelCase=2			,  __UpperCAmelCase=5			,  __UpperCAmelCase=2			,  __UpperCAmelCase=0.2_5			,  **__UpperCAmelCase			,  ):

      '''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(__UpperCAmelCase			,  __UpperCAmelCase   ):
        __UpperCamelCase       						= backbone_config.get('model_type'   )
        __UpperCamelCase       						= CONFIG_MAPPING[backbone_model_type]
        __UpperCamelCase       						= config_class.from_dict(__UpperCAmelCase   )

      __UpperCamelCase       						= use_timm_backbone
      __UpperCamelCase       						= backbone_config
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= num_queries
      __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       						= decoder_layerdrop
      __UpperCamelCase       						= encoder_layers
      __UpperCamelCase       						= auxiliary_loss
      __UpperCamelCase       						= position_embedding_type
      __UpperCamelCase       						= backbone
      __UpperCamelCase       						= use_pretrained_backbone
      __UpperCamelCase       						= dilation
      # Hungarian matcher
      __UpperCamelCase       						= class_cost
      __UpperCamelCase       						= bbox_cost
      __UpperCamelCase       						= giou_cost
      # Loss coefficients
      __UpperCamelCase       						= mask_loss_coefficient
      __UpperCamelCase       						= dice_loss_coefficient
      __UpperCamelCase       						= cls_loss_coefficient
      __UpperCamelCase       						= bbox_loss_coefficient
      __UpperCamelCase       						= giou_loss_coefficient
      __UpperCamelCase       						= focal_alpha
      super().__init__(is_encoder_decoder=__UpperCAmelCase			,  **__UpperCAmelCase   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.encoder_attention_heads





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.d_model







     def 		UpperCAmelCase      (			self   ):

      '''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


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		version.parse("1.11"    )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ('pixel_mask', {0: 'batch'}),
          ]   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 1E-5







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 12





"""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		(       )   ->			List[Any]:
 __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=snake_case       ,      default=1       ,      help='Number of TPU cores to use (1 or 8).'	)

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

 return parser.parse_args()






def 							A		(       )   ->			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(snake_case	)

 # 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()



