
'''simple docstring'''


import copy

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


__lowercase							:   Optional[Any]					=     logging.get_logger(__name__)

__lowercase							:   Tuple					=     {
    '''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 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		List[str]	      =			"deformable_detr"
			lowerCamelCase			:		Dict	      =			{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}

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

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

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






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		copy.deepcopy(self.__dict__      )
								if self.backbone_config is not None:
													lowerCamelCase_					:      List[str]     =		self.backbone_config.to_dict()
								lowerCamelCase_					:      Dict     =		self.__class__.model_type
								return output




'''simple docstring'''


from __future__ import annotations

from decimal import Decimal

from numpy import array




def lowercase_	(   _lowercase				)     ->							list[list[float]]:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		Decimal

					# Check if the provided matrix has 2 rows and 2 columns
					# since this implementation only works for 2x2 matrices
					if len(_lowercase				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
										# Calculate the determinant of the matrix
										lowerCamelCase_					:      Union[str, Any]     =		float(
										    d(matrix[0][0]				) * d(matrix[1][1]				) - d(matrix[1][0]				) * d(matrix[0][1]				)				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creates a copy of the matrix with swapped positions of the elements
										lowerCamelCase_					:      Optional[Any]     =		[[0.0, 0.0], [0.0, 0.0]]
										lowerCamelCase_,      lowerCamelCase_					:      int     =		matrix[1][1], matrix[0][0]
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		-matrix[1][0], -matrix[0][1]

										# Calculate the inverse of the matrix
										return [
										    [(float(d(_lowercase				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
										]
					elif (
					    len(_lowercase				) == 3
					    and len(matrix[0]				) == 3
					    and len(matrix[1]				) == 3
					    and len(matrix[2]				) == 3
					):
										# Calculate the determinant of the matrix using Sarrus rule
										lowerCamelCase_					:      Tuple     =		float(
										    (
										        (d(matrix[0][0]				) * d(matrix[1][1]				) * d(matrix[2][2]				))
										        + (d(matrix[0][1]				) * d(matrix[1][2]				) * d(matrix[2][0]				))
										        + (d(matrix[0][2]				) * d(matrix[1][0]				) * d(matrix[2][1]				))
										    )
										    - (
										        (d(matrix[0][2]				) * d(matrix[1][1]				) * d(matrix[2][0]				))
										        + (d(matrix[0][1]				) * d(matrix[1][0]				) * d(matrix[2][2]				))
										        + (d(matrix[0][0]				) * d(matrix[1][2]				) * d(matrix[2][1]				))
										    )				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creating cofactor matrix
										lowerCamelCase_					:      Any     =		[
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										]
										lowerCamelCase_					:      Union[str, Any]     =		(d(matrix[1][1]				) * d(matrix[2][2]				)) - (
										    d(matrix[1][2]				) * d(matrix[2][1]				)
										)
										lowerCamelCase_					:      Any     =		-(
										    (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[1][0]				) * d(matrix[2][1]				)) - (
										    d(matrix[1][1]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      Optional[Any]     =		-(
										    (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[0][0]				) * d(matrix[2][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      List[Any]     =		-(
										    (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Any     =		(d(matrix[0][1]				) * d(matrix[1][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[1][1]				)
										)
										lowerCamelCase_					:      Tuple     =		-(
										    (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
										)
										lowerCamelCase_					:      str     =		(d(matrix[0][0]				) * d(matrix[1][1]				)) - (
										    d(matrix[0][1]				) * d(matrix[1][0]				)
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										lowerCamelCase_					:      str     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				lowerCamelCase_					:      int     =		cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										lowerCamelCase_					:      int     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				inverse_matrix[i][j] /= d(_lowercase				)

        # Calculate the inverse of the matrix
										return [[float(d(_lowercase				)				) or 0.0 for n in row] for row in inverse_matrix]
					raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''				)


