





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


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

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





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     '''conditional_detr'''
				SCREAMING_SNAKE_CASE__     :   Optional[Any]		      =     ['''past_key_values''']
				SCREAMING_SNAKE_CASE__     :   Optional[Any]		      =     {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}






				def __init__(  self			, _lowerCAmelCase=True			, _lowerCAmelCase=None			, _lowerCAmelCase=3			, _lowerCAmelCase=300			, _lowerCAmelCase=6			, _lowerCAmelCase=2048			, _lowerCAmelCase=8			, _lowerCAmelCase=6			, _lowerCAmelCase=2048			, _lowerCAmelCase=8			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.0			, _lowerCAmelCase=True			, _lowerCAmelCase="relu"			, _lowerCAmelCase=256			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1.0			, _lowerCAmelCase=False			, _lowerCAmelCase="sine"			, _lowerCAmelCase="resnet50"			, _lowerCAmelCase=True			, _lowerCAmelCase=False			, _lowerCAmelCase=2			, _lowerCAmelCase=5			, _lowerCAmelCase=2			, _lowerCAmelCase=1			, _lowerCAmelCase=1			, _lowerCAmelCase=2			, _lowerCAmelCase=5			, _lowerCAmelCase=2			, _lowerCAmelCase=0.25			, **_lowerCAmelCase			, ):

										"""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.'''					)
																						__SCREAMING_SNAKE_CASE:				int           =   CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
																elif isinstance(_lowerCAmelCase			, _lowerCAmelCase					):
																						__SCREAMING_SNAKE_CASE:				Tuple           =   backbone_config.get('''model_type'''					)
																						__SCREAMING_SNAKE_CASE:				List[Any]           =   CONFIG_MAPPING[backbone_model_type]
																						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   config_class.from_dict(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   use_timm_backbone
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   backbone_config
										__SCREAMING_SNAKE_CASE:				str           =   num_channels
										__SCREAMING_SNAKE_CASE:				int           =   num_queries
										__SCREAMING_SNAKE_CASE:				str           =   d_model
										__SCREAMING_SNAKE_CASE:				Any           =   encoder_ffn_dim
										__SCREAMING_SNAKE_CASE:				List[str]           =   encoder_layers
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   encoder_attention_heads
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   decoder_ffn_dim
										__SCREAMING_SNAKE_CASE:				int           =   decoder_layers
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   decoder_attention_heads
										__SCREAMING_SNAKE_CASE:				Dict           =   dropout
										__SCREAMING_SNAKE_CASE:				int           =   attention_dropout
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   activation_dropout
										__SCREAMING_SNAKE_CASE:				int           =   activation_function
										__SCREAMING_SNAKE_CASE:				Tuple           =   init_std
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   init_xavier_std
										__SCREAMING_SNAKE_CASE:				Dict           =   encoder_layerdrop
										__SCREAMING_SNAKE_CASE:				int           =   decoder_layerdrop
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   encoder_layers
										__SCREAMING_SNAKE_CASE:				str           =   auxiliary_loss
										__SCREAMING_SNAKE_CASE:				Tuple           =   position_embedding_type
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   backbone
										__SCREAMING_SNAKE_CASE:				Tuple           =   use_pretrained_backbone
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   dilation
										# Hungarian matcher
										__SCREAMING_SNAKE_CASE:				Tuple           =   class_cost
										__SCREAMING_SNAKE_CASE:				str           =   bbox_cost
										__SCREAMING_SNAKE_CASE:				Dict           =   giou_cost
										# Loss coefficients
										__SCREAMING_SNAKE_CASE:				List[str]           =   mask_loss_coefficient
										__SCREAMING_SNAKE_CASE:				Dict           =   dice_loss_coefficient
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   cls_loss_coefficient
										__SCREAMING_SNAKE_CASE:				int           =   bbox_loss_coefficient
										__SCREAMING_SNAKE_CASE:				Tuple           =   giou_loss_coefficient
										__SCREAMING_SNAKE_CASE:				Dict           =   focal_alpha
										super().__init__(is_encoder_decoder=_lowerCAmelCase			, **_lowerCAmelCase					)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self.encoder_attention_heads






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self.d_model






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   copy.deepcopy(self.__dict__					)
										if self.backbone_config is not None:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.backbone_config.to_dict()
										__SCREAMING_SNAKE_CASE:				Dict           =   self.__class__.model_type
										return output





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     version.parse('''1.11'''			)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 1e-5






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 12










def 		lowerCAmelCase  (	UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   int					)      ->    list:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   len(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   [[0] * n for i in range(UpperCamelCase__					)]
						for i in range(UpperCamelCase__					):
												__SCREAMING_SNAKE_CASE:				str           =   y_points[i]

						for i in range(2       ,			UpperCamelCase__					):
												for j in range(UpperCamelCase__       ,			UpperCamelCase__					):
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   (
																		    (xa - x_points[j - i + 1]) * q[j][i - 1]
																		    - (xa - x_points[j]) * q[j - 1][i - 1]
																		) / (x_points[j] - x_points[j - i + 1])

						return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()



