






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


UpperCamelCase								=		logging.get_logger(__name__)

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

class 				lowerCAmelCase_	(		__snake_case			):
				_UpperCamelCase					:       List[str]							       = """conditional_detr"""
				_UpperCamelCase					:       Optional[int]							       = ["""past_key_values"""]
				_UpperCamelCase					:       Union[str, Any]							       = {
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}


				def __init__(					self ,						_lowerCAmelCase=True ,						_lowerCAmelCase=None ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=3_0_0 ,						_lowerCAmelCase=6 ,						_lowerCAmelCase=2_0_4_8 ,						_lowerCAmelCase=8 ,						_lowerCAmelCase=6 ,						_lowerCAmelCase=2_0_4_8 ,						_lowerCAmelCase=8 ,						_lowerCAmelCase=0.0 ,						_lowerCAmelCase=0.0 ,						_lowerCAmelCase=True ,						_lowerCAmelCase="relu" ,						_lowerCAmelCase=2_5_6 ,						_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 ,						):
											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							:   int 							=   CONFIG_MAPPING["resnet"](out_features=['stage4']      )
																		elif isinstance(_lowerCAmelCase ,						_lowerCAmelCase      ):
																									_lowercase							:   List[str] 							=   backbone_config.get('model_type'      )
																									_lowercase							:   List[Any] 							=   CONFIG_MAPPING[backbone_model_type]
																									_lowercase							:   Union[str, Any] 							=   config_class.from_dict(_lowerCAmelCase      )

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


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


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


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




class 				lowerCAmelCase_	(		__snake_case			):
				_UpperCamelCase					:       Dict							       = version.parse("1.11"			)


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


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


				@property
				def 						__a					(					self      ):
											return 1_2








from __future__ import annotations

import math


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						u
		for i in range(1     , a ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp * (u - i)
		return temp


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						int(input("enter the numbers of values: " ) )
		SCREAMING_SNAKE_CASE_				:list[list[float]]				     =						[]
		for _ in range(a ):
				y.append([] )
		for i in range(a ):
				for j in range(a ):
						y[i].append(a )
						SCREAMING_SNAKE_CASE_				:Any				     =						0

		print("enter the values of parameters in a list: " )
		SCREAMING_SNAKE_CASE_				:Dict				     =						list(map(a     , input().split() ) )

		print("enter the values of corresponding parameters: " )
		for i in range(a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float(input() )

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						int(input("enter the value to interpolate: " ) )
		SCREAMING_SNAKE_CASE_				:str				     =						(value - x[0]) / (x[1] - x[0])

		# for calculating forward difference table

		for i in range(1     , a ):
				for j in range(n - i ):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						y[j + 1][i - 1] - y[j][i - 1]

		SCREAMING_SNAKE_CASE_				:Tuple				     =						y[0][0]
		for i in range(1     , a ):
				summ += (ucal(a     , a ) * y[0][i]) / math.factorial(a )

		print(F"the value at {value} is {summ}" )


if __name__ == "__main__":
		main()






