

'''simple docstring'''






import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowerCAmelCase__   		=      logging.get_logger(__name__)

lowerCAmelCase__   		=      {
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Dict							     =     'detr'
  SCREAMING_SNAKE_CASE       :    List[str]							     =     ['past_key_values']
  SCREAMING_SNAKE_CASE       :    Any							     =     {
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }






  def __init__( self  :	int   ,lowercase__  :	Dict=True   ,lowercase__  :	Optional[Any]=None   ,lowercase__  :	str=3   ,lowercase__  :	Optional[int]=1_0_0   ,lowercase__  :	Optional[int]=6   ,lowercase__  :	Optional[Any]=2_0_4_8   ,lowercase__  :	Any=8   ,lowercase__  :	int=6   ,lowercase__  :	Any=2_0_4_8   ,lowercase__  :	Any=8   ,lowercase__  :	List[str]=0.0   ,lowercase__  :	Union[str, Any]=0.0   ,lowercase__  :	Union[str, Any]=True   ,lowercase__  :	str="relu"   ,lowercase__  :	int=2_5_6   ,lowercase__  :	int=0.1   ,lowercase__  :	Tuple=0.0   ,lowercase__  :	Tuple=0.0   ,lowercase__  :	int=0.0_2   ,lowercase__  :	Dict=1.0   ,lowercase__  :	str=False   ,lowercase__  :	Union[str, Any]="sine"   ,lowercase__  :	List[str]="resnet50"   ,lowercase__  :	Dict=True   ,lowercase__  :	List[Any]=False   ,lowercase__  :	Union[str, Any]=1   ,lowercase__  :	Union[str, Any]=5   ,lowercase__  :	Tuple=2   ,lowercase__  :	str=1   ,lowercase__  :	List[Any]=1   ,lowercase__  :	Dict=5   ,lowercase__  :	Optional[Any]=2   ,lowercase__  :	int=0.1   ,**lowercase__  :	Dict   ,):
   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.'''     )
     __lowercase				    =					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']     )
    elif isinstance(lowercase__   ,lowercase__     ):
     __lowercase				    =					backbone_config.get('''model_type'''     )
     __lowercase				    =					CONFIG_MAPPING[backbone_model_type]
     __lowercase				    =					config_class.from_dict(lowercase__     )
    # set timm attributes to None
    __lowercase    ,      __lowercase    ,      __lowercase				    =					None, None, None

   __lowercase				    =					use_timm_backbone
   __lowercase				    =					backbone_config
   __lowercase				    =					num_channels
   __lowercase				    =					num_queries
   __lowercase				    =					d_model
   __lowercase				    =					encoder_ffn_dim
   __lowercase				    =					encoder_layers
   __lowercase				    =					encoder_attention_heads
   __lowercase				    =					decoder_ffn_dim
   __lowercase				    =					decoder_layers
   __lowercase				    =					decoder_attention_heads
   __lowercase				    =					dropout
   __lowercase				    =					attention_dropout
   __lowercase				    =					activation_dropout
   __lowercase				    =					activation_function
   __lowercase				    =					init_std
   __lowercase				    =					init_xavier_std
   __lowercase				    =					encoder_layerdrop
   __lowercase				    =					decoder_layerdrop
   __lowercase				    =					encoder_layers
   __lowercase				    =					auxiliary_loss
   __lowercase				    =					position_embedding_type
   __lowercase				    =					backbone
   __lowercase				    =					use_pretrained_backbone
   __lowercase				    =					dilation
   # Hungarian matcher
   __lowercase				    =					class_cost
   __lowercase				    =					bbox_cost
   __lowercase				    =					giou_cost
   # Loss coefficients
   __lowercase				    =					mask_loss_coefficient
   __lowercase				    =					dice_loss_coefficient
   __lowercase				    =					bbox_loss_coefficient
   __lowercase				    =					giou_loss_coefficient
   __lowercase				    =					eos_coefficient
   super().__init__(is_encoder_decoder=lowercase__   ,**lowercase__     )






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   return self.encoder_attention_heads






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   return self.d_model






  @classmethod
  def    SCREAMING_SNAKE_CASE ( cls  :	int   ,lowercase__  :	PretrainedConfig   ,**lowercase__  :	Any     ):
   return cls(backbone_config=lowercase__   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   __lowercase				    =					copy.deepcopy(self.__dict__     )
   if output["backbone_config"] is not None:
    __lowercase				    =					self.backbone_config.to_dict()
   __lowercase				    =					self.__class__.model_type
   return output






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    str							     =     version.parse('1.11' )






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   return OrderedDict(
       [
           ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ('''pixel_mask''', {0: '''batch'''}),
       ]     )






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   return 1e-5






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   return 1_2





from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]    )
			else:
				wrong_args.append(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



