





'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Optional[int]      	=			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='deta'
				__a            ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}




				def __init__(  self     :					List[str]					,				__a     :					List[str]=None					,				__a     :					Dict=9_00					,				__a     :					str=20_48					,				__a     :					Tuple=6					,				__a     :					List[str]=20_48					,				__a     :					str=8					,				__a     :					Union[str, Any]=6					,				__a     :					int=10_24					,				__a     :					List[Any]=8					,				__a     :					Dict=0.0					,				__a     :					Tuple=True					,				__a     :					Optional[Any]="relu"					,				__a     :					Tuple=2_56					,				__a     :					Optional[Any]=0.1					,				__a     :					int=0.0					,				__a     :					List[Any]=0.0					,				__a     :					Optional[int]=0.02					,				__a     :					str=1.0					,				__a     :					Dict=True					,				__a     :					Dict=False					,				__a     :					Optional[int]="sine"					,				__a     :					Any=5					,				__a     :					List[str]=4					,				__a     :					Optional[int]=4					,				__a     :					List[str]=True					,				__a     :					str=3_00					,				__a     :					int=True					,				__a     :					int=True					,				__a     :					Tuple=1					,				__a     :					Optional[int]=5					,				__a     :					Tuple=2					,				__a     :					Dict=1					,				__a     :					Optional[int]=1					,				__a     :					Any=5					,				__a     :					Optional[int]=2					,				__a     :					Dict=0.1					,				__a     :					str=0.25					,				**__a     :					Tuple					,				):
								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=["stage2", "stage3", "stage4"]					)
								else:
												if isinstance(__a					,				__a					):
																_a								= backbone_config.pop("model_type"					)
																_a								= CONFIG_MAPPING[backbone_model_type]
																_a								= config_class.from_dict(__a					)

								_a								= backbone_config
								_a								= num_queries
								_a								= max_position_embeddings
								_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								= auxiliary_loss
								_a								= position_embedding_type
								# deformable attributes
								_a								= num_feature_levels
								_a								= encoder_n_points
								_a								= decoder_n_points
								_a								= two_stage
								_a								= two_stage_num_proposals
								_a								= with_box_refine
								_a								= assign_first_stage
								if two_stage is True and with_box_refine is False:
												raise ValueError("If two_stage is True, with_box_refine must be True."					)
								# 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
								_a								= focal_alpha
								super().__init__(is_encoder_decoder=__a					,				**__a					)




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




				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								return self.d_model




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= copy.deepcopy(self.__dict__					)
								_a								= self.backbone_config.to_dict()
								_a								= self.__class__.model_type
								return output







from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			complex		,   __UpperCAmelCase:			str = "x"		,   __UpperCAmelCase:			float = 10**-10		,   __UpperCAmelCase:			int = 1		,   )      ->	complex:
    UpperCamelCase__							:				Optional[int]          =      symbols(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      lambdify(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				List[str]          =      lambdify(__UpperCAmelCase		,   diff(__UpperCAmelCase		,   __UpperCAmelCase      )      )

    UpperCamelCase__							:				int          =      starting_point

    while True:
        if diff_function(__UpperCAmelCase      ) != 0:
            UpperCamelCase__							:				Optional[int]          =      prev_guess - multiplicity * func(__UpperCAmelCase      ) / diff_function(
                __UpperCAmelCase      )
        else:
            raise ZeroDivisionError('''Could not find root'''      ) from None

        # Precision is checked by comparing the difference of consecutive guesses
        if abs(next_guess - prev_guess      ) < precision:
            return next_guess

        UpperCamelCase__							:				Tuple          =      next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')

  # Find root of polynomial
  # Find fourth Root of 5
  print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}''')

  # Find value of e
  print(
      'The root of log(y) - 1 = 0 is ',
      F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
  )

  # Exponential Roots
  print(
      'The root of exp(x) - 1 = 0 is',
      F'''{newton_raphson("exp(x) - 1", 10, precision=0.005)}''',
  )

  # Find root of cos(x)
  print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')

