






"""simple docstring"""

import copy

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


lowercase    			=       logging.get_logger(__name__)

lowercase    			=       {
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''deformable_detr'''
				UpperCAmelCase__        =				{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}







				def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Union[str, Any]=True						,    __lowerCamelCase							:						str=None						,    __lowerCamelCase							:						List[Any]=3						,    __lowerCamelCase							:						Any=3_0_0						,    __lowerCamelCase							:						Union[str, Any]=1_0_2_4						,    __lowerCamelCase							:						int=6						,    __lowerCamelCase							:						List[str]=1_0_2_4						,    __lowerCamelCase							:						Tuple=8						,    __lowerCamelCase							:						Tuple=6						,    __lowerCamelCase							:						Tuple=1_0_2_4						,    __lowerCamelCase							:						Optional[Any]=8						,    __lowerCamelCase							:						Any=0.0						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						Union[str, Any]="relu"						,    __lowerCamelCase							:						Optional[int]=2_5_6						,    __lowerCamelCase							:						Dict=0.1						,    __lowerCamelCase							:						Union[str, Any]=0.0						,    __lowerCamelCase							:						Optional[int]=0.0						,    __lowerCamelCase							:						str=0.02						,    __lowerCamelCase							:						Dict=1.0						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Union[str, Any]=False						,    __lowerCamelCase							:						Union[str, Any]="sine"						,    __lowerCamelCase							:						str="resnet50"						,    __lowerCamelCase							:						List[Any]=True						,    __lowerCamelCase							:						Any=False						,    __lowerCamelCase							:						Any=4						,    __lowerCamelCase							:						List[str]=4						,    __lowerCamelCase							:						Dict=4						,    __lowerCamelCase							:						List[str]=False						,    __lowerCamelCase							:						Tuple=3_0_0						,    __lowerCamelCase							:						Union[str, Any]=False						,    __lowerCamelCase							:						Any=1						,    __lowerCamelCase							:						Optional[int]=5						,    __lowerCamelCase							:						Any=2						,    __lowerCamelCase							:						int=1						,    __lowerCamelCase							:						Union[str, Any]=1						,    __lowerCamelCase							:						Dict=5						,    __lowerCamelCase							:						int=2						,    __lowerCamelCase							:						int=0.1						,    __lowerCamelCase							:						Optional[Any]=0.25						,    __lowerCamelCase							:						Union[str, Any]=False						,    **__lowerCamelCase							:						int						,    )				->					Optional[int]:
										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."	)
																						__magic_name__            =     CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
																elif isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																						__magic_name__            =     backbone_config.get("model_type"	)
																						__magic_name__            =     CONFIG_MAPPING[backbone_model_type]
																						__magic_name__            =     config_class.from_dict(__lowerCamelCase	)
										__magic_name__            =     use_timm_backbone
										__magic_name__            =     backbone_config
										__magic_name__            =     num_channels
										__magic_name__            =     num_queries
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     d_model
										__magic_name__            =     encoder_ffn_dim
										__magic_name__            =     encoder_layers
										__magic_name__            =     encoder_attention_heads
										__magic_name__            =     decoder_ffn_dim
										__magic_name__            =     decoder_layers
										__magic_name__            =     decoder_attention_heads
										__magic_name__            =     dropout
										__magic_name__            =     attention_dropout
										__magic_name__            =     activation_dropout
										__magic_name__            =     activation_function
										__magic_name__            =     init_std
										__magic_name__            =     init_xavier_std
										__magic_name__            =     encoder_layerdrop
										__magic_name__            =     auxiliary_loss
										__magic_name__            =     position_embedding_type
										__magic_name__            =     backbone
										__magic_name__            =     use_pretrained_backbone
										__magic_name__            =     dilation
										# deformable attributes
										__magic_name__            =     num_feature_levels
										__magic_name__            =     encoder_n_points
										__magic_name__            =     decoder_n_points
										__magic_name__            =     two_stage
										__magic_name__            =     two_stage_num_proposals
										__magic_name__            =     with_box_refine
										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
										__magic_name__            =     class_cost
										__magic_name__            =     bbox_cost
										__magic_name__            =     giou_cost
										# Loss coefficients
										__magic_name__            =     mask_loss_coefficient
										__magic_name__            =     dice_loss_coefficient
										__magic_name__            =     bbox_loss_coefficient
										__magic_name__            =     giou_loss_coefficient
										__magic_name__            =     eos_coefficient
										__magic_name__            =     focal_alpha
										__magic_name__            =     disable_custom_kernels
										super().__init__(is_encoder_decoder=__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				def     _snake_case					(							self							:						List[Any]	)				->					int:
										return self.encoder_attention_heads







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







				def     _snake_case					(							self							:						List[str]	)				->					int:
										__magic_name__            =     copy.deepcopy(self.__dict__	)
										if self.backbone_config is not None:
																__magic_name__            =     self.backbone_config.to_dict()
										__magic_name__            =     self.__class__.model_type
										return output











"""simple docstring"""

import colorsys

from PIL import Image  # type: ignore






def _lowerCAmelCase	(  __lowerCamelCase:float ,						__lowerCamelCase:float ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     x
						__magic_name__            =     y
						for step in range(__lowerCamelCase   ):  # noqa: B007
												__magic_name__            =     a * a - b * b + x
												__magic_name__            =     2 * a * b + y
												__magic_name__            =     a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return (2_5_5, 2_5_5, 2_5_5)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 2_5_5   ) for i in colorsys.hsv_to_rgb(__lowerCamelCase ,						1 ,						1   )   )






def _lowerCAmelCase	(  __lowerCamelCase:int = 8_0_0 ,						__lowerCamelCase:int = 6_0_0 ,						__lowerCamelCase:float = -0.6 ,						__lowerCamelCase:float = 0 ,						__lowerCamelCase:float = 3.2 ,						__lowerCamelCase:int = 5_0 ,						__lowerCamelCase:bool = True ,						):

						'''simple docstring'''






						__magic_name__            =     Image.new("RGB" ,						(image_width, image_height)   )
						__magic_name__            =     img.load()

						# loop through the image-coordinates
						for image_x in range(__lowerCamelCase   ):
												for image_y in range(__lowerCamelCase   ):
																		# determine the figure-coordinates based on the image-coordinates
																		__magic_name__            =     figure_width / image_width * image_height
																		__magic_name__            =     figure_center_x + (image_x / image_width - 0.5) * figure_width
																		__magic_name__            =     figure_center_y + (image_y / image_height - 0.5) * figure_height

																		__magic_name__            =     get_distance(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								__magic_name__            =     get_color_coded_rgb(__lowerCamelCase   )
																		else:
																								__magic_name__            =     get_black_and_white_rgb(__lowerCamelCase   )

						return img


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# colored version, full figure
			lowercase    			=       get_image()

			# uncomment for colored version, different section, zoomed in
			# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
			# figure_width = 0.8)

			# uncomment for black and white version, full figure
			# img = get_image(use_distance_color_coding = False)

			# uncomment to save the image
			# img.save("mandelbrot.png")

			img.show()



