
"""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 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'detr'
		A_		: Union[str, Any]            =			['past_key_values']
		A_		: Any            =			{
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=1_00 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1.0 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE="sine" ,	_SCREAMING_SNAKE_CASE="resnet50" ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.1 ,	**_SCREAMING_SNAKE_CASE ,	):
			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.'				)
					__lowerCAmelCase    :		str		    =   CONFIG_MAPPING['resnet'](out_features=['stage4']				)
				elif isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		str		    =   backbone_config.get('model_type'				)
					__lowerCAmelCase    :		str		    =   CONFIG_MAPPING[backbone_model_type]
					__lowerCAmelCase    :		str		    =   config_class.from_dict(_SCREAMING_SNAKE_CASE				)
				# set timm attributes to None
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   None, None, None

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

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

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

		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return cls(backbone_config=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   copy.deepcopy(self.__dict__				)
			if output["backbone_config"] is not None:
				__lowerCAmelCase    :		Dict		    =   self.backbone_config.to_dict()
			__lowerCAmelCase    :		List[str]		    =   self.__class__.model_type
			return output




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

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

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







		@property
		def 							__lowerCamelCase							(   self				):
			return 12

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   [[0] * n for i in range(_UpperCamelCase	)]
	for i in range(_UpperCamelCase	):
		__lowerCAmelCase    :		Any		    =   y_points[i]

	for i in range(2 ,						_UpperCamelCase	):
		for j in range(_UpperCamelCase ,						_UpperCamelCase	):
			__lowerCAmelCase    :		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()