


"""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


UpperCamelCase    :					List[str]							   =   logging.get_logger(__name__)

UpperCamelCase    :					Optional[int]							   =   {
    "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       lowerCamelCase__    ( lowerCAmelCase__      ):
						lowerCAmelCase  	=  '''detr'''
						lowerCAmelCase  	=  ['''past_key_values''']
						lowerCAmelCase  	=  {
						    '''hidden_size''': '''d_model''',
						    '''num_attention_heads''': '''encoder_attention_heads''',
						}







						def __init__(				self :     Union[str, Any]      ,      _lowercase :     List[str]=True      ,      _lowercase :     Dict=None      ,      _lowercase :     List[Any]=3      ,      _lowercase :     int=100      ,      _lowercase :     str=6      ,      _lowercase :     str=2_048      ,      _lowercase :     List[str]=8      ,      _lowercase :     int=6      ,      _lowercase :     Any=2_048      ,      _lowercase :     Union[str, Any]=8      ,      _lowercase :     Union[str, Any]=0.0      ,      _lowercase :     Tuple=0.0      ,      _lowercase :     Optional[int]=True      ,      _lowercase :     Any="relu"      ,      _lowercase :     Tuple=256      ,      _lowercase :     List[Any]=0.1      ,      _lowercase :     Tuple=0.0      ,      _lowercase :     Union[str, Any]=0.0      ,      _lowercase :     Union[str, Any]=0.0_2      ,      _lowercase :     Tuple=1.0      ,      _lowercase :     Union[str, Any]=False      ,      _lowercase :     List[Any]="sine"      ,      _lowercase :     Optional[Any]="resnet50"      ,      _lowercase :     Dict=True      ,      _lowercase :     Any=False      ,      _lowercase :     Optional[Any]=1      ,      _lowercase :     List[Any]=5      ,      _lowercase :     str=2      ,      _lowercase :     Optional[int]=1      ,      _lowercase :     Dict=1      ,      _lowercase :     Dict=5      ,      _lowercase :     Optional[int]=2      ,      _lowercase :     Tuple=0.1      ,      **_lowercase :     int      ,      ):


								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.'		)
												A       							=			CONFIG_MAPPING['resnet'](out_features=['stage4']		)
										elif isinstance(__lowerCAmelCase      ,      __lowerCAmelCase		):
												A       							=			backbone_config.get('model_type'		)
												A       							=			CONFIG_MAPPING[backbone_model_type]
												A       							=			config_class.from_dict(__lowerCAmelCase		)
										# set timm attributes to None
										A							,			A							,			A       							=			None, None, None

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







						@property
						def __a    (				self :     Optional[Any]		):
								return self.encoder_attention_heads







						@property
						def __a    (				self :     Tuple		):
								return self.d_model







						@classmethod
						def __a    (				cls :     List[Any]      ,      _lowercase :     Tuple      ,      **_lowercase :     List[Any]		):
								return cls(backbone_config=__lowerCAmelCase      ,      **__lowerCAmelCase		)







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



class       lowerCamelCase__    ( lowerCAmelCase__      ):
						lowerCAmelCase  	=  version.parse("""1.11"""      )







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







						@property
						def __a    (				self :     Any		):
								return 1e-5







						@property
						def __a    (				self :     List[Any]		):
								return 12






'''simple docstring'''




from functools import lru_cache






@lru_cache
def 							snake_case__				(     _A:   int					)       ->      int:

		'''simple docstring'''


		if num < 0:
				raise ValueError("""Number should not be negative."""					)

		return 1 if num in (0, 1) else num * factorial(num - 1					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




