






def     a_   (__A				,       __A      )				->						tuple[float, float]:



					"""simple docstring"""

					# Check if the input is valid
					if not len(__A      ) == len(__A      ) == 3:
										raise ValueError("Please enter a valid equation."      )
					if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
										raise ValueError("Both a & b of two equations can't be zero."      )

					# Extract the coefficients
					__a      ,			__a      ,			__a					:						Any      =					equationa
					__a      ,			__a      ,			__a					:						Optional[int]      =					equationa

					# Calculate the determinants of the matrices
					__a					:						Any      =					aa * ba - aa * ba
					__a					:						Optional[Any]      =					ca * ba - ca * ba
					__a					:						Dict      =					aa * ca - aa * ca

					# Check if the system of linear equations has a solution (using Cramer's rule)
					if determinant == 0:
										if determinant_x == determinant_y == 0:
															raise ValueError("Infinite solutions. (Consistent system)"      )
										else:
															raise ValueError("No solution. (Inconsistent system)"      )
					else:
										if determinant_x == determinant_y == 0:
															# Trivial solution (Inconsistent system)
															return (0.0, 0.0)
										else:
															__a					:						Union[str, Any]      =					determinant_x / determinant
															__a					:						Any      =					determinant_y / determinant
															# Non-Trivial Solution (Consistent system)
															return (x, y)













import copy
from typing import Dict, List, Optional

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


UpperCAmelCase__      			=						{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase__      			=						logging.get_logger(__name__)




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """mask2former"""
						snake_case__        =      ["""swin"""]
						snake_case__        =      {"""hidden_size""": """hidden_dim"""}



						def __init__(self:					Any ,					__UpperCAmelCase:					Optional[Dict] = None ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 1024 ,					__UpperCAmelCase:					str = "relu" ,					__UpperCAmelCase:					int = 6 ,					__UpperCAmelCase:					int = 10 ,					__UpperCAmelCase:					int = 8 ,					__UpperCAmelCase:					float = 0.0 ,					__UpperCAmelCase:					int = 2048 ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					int = 4 ,					__UpperCAmelCase:					int = 255 ,					__UpperCAmelCase:					int = 100 ,					__UpperCAmelCase:					float = 0.1 ,					__UpperCAmelCase:					float = 2.0 ,					__UpperCAmelCase:					float = 5.0 ,					__UpperCAmelCase:					float = 5.0 ,					__UpperCAmelCase:					int = 12544 ,					__UpperCAmelCase:					float = 3.0 ,					__UpperCAmelCase:					float = 0.75 ,					__UpperCAmelCase:					float = 0.02 ,					__UpperCAmelCase:					float = 1.0 ,					__UpperCAmelCase:					bool = True ,					__UpperCAmelCase:					List[int] = [4, 8, 16, 32] ,					__UpperCAmelCase:					bool = None ,					**__UpperCAmelCase:					Any ,					)    ->     int:




											'''simple docstring'''



											if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."   )
																__a					:						str      =					CONFIG_MAPPING["swin"](
																    image_size=224 ,					in_channels=3 ,					patch_size=4 ,					embed_dim=96 ,					depths=[2, 2, 18, 2] ,					num_heads=[3, 6, 12, 24] ,					window_size=7 ,					drop_path_rate=0.3 ,					use_absolute_embeddings=__UpperCAmelCase ,					out_features=["stage1", "stage2", "stage3", "stage4"] ,					)

											if isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ):
																__a					:						List[Any]      =					backbone_config.pop("model_type"   )
																__a					:						List[str]      =					CONFIG_MAPPING[backbone_model_type]
																__a					:						Dict      =					config_class.from_dict(__UpperCAmelCase   )

											# verify that the backbone is supported
											if backbone_config.model_type not in self.backbones_supported:
																logger.warning_once(
																    f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
																    f'Supported model types: {",".join(self.backbones_supported   )}'   )

											__a					:						Tuple      =					backbone_config
											__a					:						Any      =					feature_size
											__a					:						int      =					mask_feature_size
											__a					:						Optional[int]      =					hidden_dim
											__a					:						int      =					encoder_feedforward_dim
											__a					:						List[str]      =					activation_function
											__a					:						Optional[Any]      =					encoder_layers
											__a					:						List[Any]      =					decoder_layers
											__a					:						Union[str, Any]      =					num_attention_heads
											__a					:						Optional[Any]      =					dropout
											__a					:						Tuple      =					dim_feedforward
											__a					:						List[str]      =					pre_norm
											__a					:						int      =					enforce_input_projection
											__a					:						List[str]      =					common_stride
											__a					:						Any      =					ignore_value
											__a					:						Union[str, Any]      =					num_queries
											__a					:						int      =					no_object_weight
											__a					:						Tuple      =					class_weight
											__a					:						int      =					mask_weight
											__a					:						Union[str, Any]      =					dice_weight
											__a					:						int      =					train_num_points
											__a					:						Any      =					oversample_ratio
											__a					:						Any      =					importance_sample_ratio
											__a					:						List[str]      =					init_std
											__a					:						List[str]      =					init_xavier_std
											__a					:						Dict      =					use_auxiliary_loss
											__a					:						List[Any]      =					feature_strides
											__a					:						Tuple      =					output_auxiliary_logits
											__a					:						List[Any]      =					decoder_layers

											super().__init__(**__UpperCAmelCase   )



						@classmethod
						def        UpperCAmelCase__					(cls:					Tuple ,					__UpperCAmelCase:					PretrainedConfig ,					**__UpperCAmelCase:					List[str]   )    ->     Any:




											'''simple docstring'''



											return cls(
											    backbone_config=__UpperCAmelCase ,					**__UpperCAmelCase ,					)



						def        UpperCAmelCase__					(self:					str   )    ->     Dict[str, any]:




											'''simple docstring'''



											__a					:						Tuple      =					copy.deepcopy(self.__dict__   )
											__a					:						List[str]      =					self.backbone_config.to_dict()
											__a					:						Any      =					self.__class__.model_type
											return output





