
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


A		: Optional[int]								=     logging.get_logger(__name__)

A		: Union[str, Any]								=     {
    '''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    a_ (			_a    ):
			a	:			int					   =							'''detr'''
			a	:			Dict					   =							['''past_key_values''']
			a	:			Tuple					   =							{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}






			def __init__(      self				,			__UpperCamelCase=True				,			__UpperCamelCase=None				,			__UpperCamelCase=3				,			__UpperCamelCase=100				,			__UpperCamelCase=6				,			__UpperCamelCase=2_048				,			__UpperCamelCase=8				,			__UpperCamelCase=6				,			__UpperCamelCase=2_048				,			__UpperCamelCase=8				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.0				,			__UpperCamelCase=True				,			__UpperCamelCase="relu"				,			__UpperCamelCase=256				,			__UpperCamelCase=0.1				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.02				,			__UpperCamelCase=1.0				,			__UpperCamelCase=False				,			__UpperCamelCase="sine"				,			__UpperCamelCase="resnet50"				,			__UpperCamelCase=True				,			__UpperCamelCase=False				,			__UpperCamelCase=1				,			__UpperCamelCase=5				,			__UpperCamelCase=2				,			__UpperCamelCase=1				,			__UpperCamelCase=1				,			__UpperCamelCase=5				,			__UpperCamelCase=2				,			__UpperCamelCase=0.1				,			**__UpperCamelCase				,			):
									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(__UpperCamelCase				,			__UpperCamelCase  ):
																					_lowercase					       =					backbone_config.get("""model_type"""  )
																					_lowercase					       =					CONFIG_MAPPING[backbone_model_type]
																					_lowercase					       =					config_class.from_dict(__UpperCamelCase  )
															# 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=__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									return self.encoder_attention_heads






			@property
			def    UpperCamelCase_				(      self  ):
									return self.d_model






			@classmethod
			def    UpperCamelCase_				(      cls				,			__UpperCamelCase				,			**__UpperCamelCase  ):
									return cls(backbone_config=__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_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    a_ (			_a    ):
			a	:			Optional[Any]					   =							version.parse('''1.11'''    )






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






			@property
			def    UpperCamelCase_				(      self  ):
									return 1E-5






			@property
			def    UpperCamelCase_				(      self  ):
									return 12

from __future__ import annotations




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	float		,   SCREAMING_SNAKE_CASE_	:	float		,   SCREAMING_SNAKE_CASE_	:	float		,   )  ->     tuple:
						if (electron_conc, hole_conc, intrinsic_conc).count(0       ) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""       )
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative in a semiconductor"""       )
						elif hole_conc < 0:
												raise ValueError("""Hole concentration cannot be negative in a semiconductor"""       )
						elif intrinsic_conc < 0:
												raise ValueError(
												    """Intrinsic concentration cannot be negative in a semiconductor"""       )
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    intrinsic_conc**2 / hole_conc,
												)
						elif hole_conc == 0:
												return (
												    "hole_conc",
												    intrinsic_conc**2 / electron_conc,
												)
						elif intrinsic_conc == 0:
												return (
												    "intrinsic_conc",
												    (electron_conc * hole_conc) ** 0.5,
												)
						else:
												return (-1, -1)


if __name__ == "__main__":
		import doctest

		doctest.testmod()