






"""simple docstring"""
from __future__ import annotations

from collections.abc import MutableSequence



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		if len(lowerCAmelCase_							) != degree + 1:
			raise ValueError(
			    """The number of coefficients should be equal to the degree + 1."""							)

		A_		:						list[float]	    =   list(lowerCAmelCase_							)
		A_		:						List[str]	    =   degree





	def __add__(self     ,     lowerCAmelCase_							):

		if self.degree > polynomial_a.degree:
			A_		:						Optional[int]	    =   self.coefficients[:]
			for i in range(polynomial_a.degree + 1							):
				coefficients[i] += polynomial_a.coefficients[i]
			return Polynomial(self.degree     ,     lowerCAmelCase_							)
		else:
			A_		:						Union[str, Any]	    =   polynomial_a.coefficients[:]
			for i in range(self.degree + 1							):
				coefficients[i] += self.coefficients[i]
			return Polynomial(polynomial_a.degree     ,     lowerCAmelCase_							)





	def __sub__(self     ,     lowerCAmelCase_							):
		return self + polynomial_a * Polynomial(0     ,     [-1]							)





	def __neg__(self							):
		return Polynomial(self.degree     ,     [-c for c in self.coefficients]							)





	def __mul__(self     ,     lowerCAmelCase_							):
		A_		:						list[float]	    =   [0] * (self.degree + polynomial_a.degree + 1)
		for i in range(self.degree + 1							):
			for j in range(polynomial_a.degree + 1							):
				coefficients[i + j] += (
				    self.coefficients[i] * polynomial_a.coefficients[j]
				)

		return Polynomial(self.degree + polynomial_a.degree     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						int | float	    =   0
		for i in range(self.degree + 1							):
			result += self.coefficients[i] * (substitution**i)
		return result





	def __str__(self							):
		A_		:						Optional[Any]	    =   """"""
		for i in range(self.degree     ,     -1     ,     -1							):
			if self.coefficients[i] == 0:
				continue
			elif self.coefficients[i] > 0:
				if polynomial:
					polynomial += " + "
			else:
				polynomial += " - "

			if i == 0:
				polynomial += str(abs(self.coefficients[i]							)							)
			elif i == 1:
				polynomial += str(abs(self.coefficients[i]							)							) + "x"
			else:
				polynomial += str(abs(self.coefficients[i]							)							) + "x^" + str(lowerCAmelCase_							)

		return polynomial





	def __repr__(self							):
		return self.__str__()





	def    lowerCamelCase(self							):
		A_		:						list[float]	    =   [0] * self.degree
		for i in range(self.degree							):
			A_		:						Tuple	    =   self.coefficients[i + 1] * (i + 1)
		return Polynomial(self.degree - 1     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_ = 0							):
		A_		:						list[float]	    =   [0] * (self.degree + 2)
		A_		:						Optional[int]	    =   constant
		for i in range(self.degree + 1							):
			A_		:						str	    =   self.coefficients[i] / (i + 1)
		return Polynomial(self.degree + 1     ,     lowerCAmelCase_							)





	def __eq__(self     ,     lowerCAmelCase_							):
		if not isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
			return False

		if self.degree != polynomial_a.degree:
			return False

		for i in range(self.degree + 1							):
			if self.coefficients[i] != polynomial_a.coefficients[i]:
				return False

		return True





	def __ne__(self     ,     lowerCAmelCase_							):
		return not self.__eq__(lowerCAmelCase_							)









"""simple docstring"""
import copy

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


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				"""deta"""
	_A       :				Tuple    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=900     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=1024     ,     lowerCAmelCase_=8     ,     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_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_="sine"     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=300     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.25     ,     **lowerCAmelCase_     ,     ):
		if backbone_config is None:
			logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
			A_		:						Any	    =   CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
		else:
			if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						List[str]	    =   backbone_config.pop("""model_type"""							)
				A_		:						List[str]	    =   CONFIG_MAPPING[backbone_model_type]
				A_		:						Tuple	    =   config_class.from_dict(lowerCAmelCase_							)

		A_		:						List[Any]	    =   backbone_config
		A_		:						List[str]	    =   num_queries
		A_		:						List[Any]	    =   max_position_embeddings
		A_		:						int	    =   d_model
		A_		:						Optional[Any]	    =   encoder_ffn_dim
		A_		:						Optional[Any]	    =   encoder_layers
		A_		:						List[Any]	    =   encoder_attention_heads
		A_		:						int	    =   decoder_ffn_dim
		A_		:						Dict	    =   decoder_layers
		A_		:						Dict	    =   decoder_attention_heads
		A_		:						Optional[Any]	    =   dropout
		A_		:						str	    =   attention_dropout
		A_		:						Any	    =   activation_dropout
		A_		:						Union[str, Any]	    =   activation_function
		A_		:						int	    =   init_std
		A_		:						List[Any]	    =   init_xavier_std
		A_		:						List[Any]	    =   encoder_layerdrop
		A_		:						Any	    =   auxiliary_loss
		A_		:						Tuple	    =   position_embedding_type
		# deformable attributes
		A_		:						List[str]	    =   num_feature_levels
		A_		:						Tuple	    =   encoder_n_points
		A_		:						str	    =   decoder_n_points
		A_		:						List[Any]	    =   two_stage
		A_		:						Optional[Any]	    =   two_stage_num_proposals
		A_		:						Union[str, Any]	    =   with_box_refine
		A_		:						Optional[int]	    =   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_		:						List[Any]	    =   class_cost
		A_		:						str	    =   bbox_cost
		A_		:						Tuple	    =   giou_cost
		# Loss coefficients
		A_		:						int	    =   mask_loss_coefficient
		A_		:						Union[str, Any]	    =   dice_loss_coefficient
		A_		:						Union[str, Any]	    =   bbox_loss_coefficient
		A_		:						Optional[Any]	    =   giou_loss_coefficient
		A_		:						List[str]	    =   eos_coefficient
		A_		:						Optional[Any]	    =   focal_alpha
		super().__init__(is_encoder_decoder=lowerCAmelCase_     ,     **lowerCAmelCase_							)





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





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





	def    lowerCamelCase(self							):
		A_		:						str	    =   copy.deepcopy(self.__dict__							)
		A_		:						int	    =   self.backbone_config.to_dict()
		A_		:						Dict	    =   self.__class__.model_type
		return output

