import copy

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


SCREAMING_SNAKE_CASE		:     int         =	logging.get_logger(__name__)

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

class        UpperCamelCase						(				UpperCamelCase_				):
						a__		:int      =			'deta'
						a__		:Optional[int]      =			{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						}





						def __init__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=900			,     __UpperCamelCase=2_048			,     __UpperCamelCase=6			,     __UpperCamelCase=2_048			,     __UpperCamelCase=8			,     __UpperCamelCase=6			,     __UpperCamelCase=1_024			,     __UpperCamelCase=8			,     __UpperCamelCase=0.0			,     __UpperCamelCase=True			,     __UpperCamelCase="relu"			,     __UpperCamelCase=256			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.0			,     __UpperCamelCase=0.0			,     __UpperCamelCase=0.02			,     __UpperCamelCase=1.0			,     __UpperCamelCase=True			,     __UpperCamelCase=False			,     __UpperCamelCase="sine"			,     __UpperCamelCase=5			,     __UpperCamelCase=4			,     __UpperCamelCase=4			,     __UpperCamelCase=True			,     __UpperCamelCase=300			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=1			,     __UpperCamelCase=5			,     __UpperCamelCase=2			,     __UpperCamelCase=1			,     __UpperCamelCase=1			,     __UpperCamelCase=5			,     __UpperCamelCase=2			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.25			,     **__UpperCamelCase			,     )				->				Optional[Any]:




												if backbone_config is None:
																		logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""						)
																		UpperCamelCase_		:							int  =		CONFIG_MAPPING['resnet'](out_features=["""stage2""", """stage3""", """stage4"""]						)
												else:
																		if isinstance(lowercase_			,     lowercase_						):
																								UpperCamelCase_		:							List[Any]  =		backbone_config.pop("""model_type"""						)
																								UpperCamelCase_		:							int  =		CONFIG_MAPPING[backbone_model_type]
																								UpperCamelCase_		:							List[str]  =		config_class.from_dict(lowercase_						)

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





						@property
						def        A_							(self						)				->				int:
												return self.encoder_attention_heads





						@property
						def        A_							(self						)				->				int:
												return self.d_model





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							List[str]  =		copy.deepcopy(self.__dict__						)
												UpperCamelCase_		:							Optional[Any]  =		self.backbone_config.to_dict()
												UpperCamelCase_		:							str  =		self.__class__.model_type
												return output










from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCamelCase		   =			numpy.array([0, 0])
lowerCamelCase		   =			numpy.array([0.5, 0.866_0254])
lowerCamelCase		   =			numpy.array([1, 0])
lowerCamelCase		   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]						,     SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						List[Any]            =initial_vectors
       for _ in range(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						List[str]            =iteration_step(SCREAMING_SNAKE_CASE__	)
       return vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Dict            =[]
       for i, start_vector in enumerate(vectors[:-1]	):
              _lowerCamelCase			:						Tuple            =vectors[i + 1]
              new_vectors.append(SCREAMING_SNAKE_CASE__	)
              _lowerCamelCase			:						Tuple            =end_vector - start_vector
              new_vectors.append(start_vector + difference_vector / 3	)
              new_vectors.append(
                  start_vector + difference_vector / 3 + rotate(difference_vector / 3						,     60	)	)
              new_vectors.append(start_vector + difference_vector * 2 / 3	)
       new_vectors.append(vectors[-1]	)
       return new_vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : numpy.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =numpy.radians(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =numpy.cos(SCREAMING_SNAKE_CASE__	), numpy.sin(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						str            =numpy.array(((c, -s), (s, c))	)
       return numpy.dot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[Any]            =plt.gca()
       axes.set_aspect('equal'	)

       # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
       # y-coordinates as inputs, which are constructed from the vector-list using
       # zip()
       _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =zip(*SCREAMING_SNAKE_CASE__	)
       plt.plot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase		   =			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)


