



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      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Dict         =					"""mask2former"""
							UpperCamelCase__				:      List[str]         =					["""swin"""]
							UpperCamelCase__				:      str         =					{"""hidden_size""": """hidden_dim"""}


							def __init__(							self  ,  a_ = None  ,  a_ = 256  ,  a_ = 256  ,  a_ = 256  ,  a_ = 1_024  ,  a_ = "relu"  ,  a_ = 6  ,  a_ = 10  ,  a_ = 8  ,  a_ = 0.0  ,  a_ = 2_048  ,  a_ = False  ,  a_ = False  ,  a_ = 4  ,  a_ = 255  ,  a_ = 100  ,  a_ = 0.1  ,  a_ = 2.0  ,  a_ = 5.0  ,  a_ = 5.0  ,  a_ = 12_544  ,  a_ = 3.0  ,  a_ = 0.75  ,  a_ = 0.02  ,  a_ = 1.0  ,  a_ = True  ,  a_ = [4, 8, 16, 32]  ,  a_ = None  ,  **a_  ,  ):
											if backbone_config is None:
															logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""		)
															a__				      =		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=a_  ,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  ,  )

											if isinstance(a_  ,  a_		):
															a__				      =		backbone_config.pop("""model_type"""		)
															a__				      =		CONFIG_MAPPING[backbone_model_type]
															a__				      =		config_class.from_dict(a_		)

											# 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__				      =		backbone_config
											a__				      =		feature_size
											a__				      =		mask_feature_size
											a__				      =		hidden_dim
											a__				      =		encoder_feedforward_dim
											a__				      =		activation_function
											a__				      =		encoder_layers
											a__				      =		decoder_layers
											a__				      =		num_attention_heads
											a__				      =		dropout
											a__				      =		dim_feedforward
											a__				      =		pre_norm
											a__				      =		enforce_input_projection
											a__				      =		common_stride
											a__				      =		ignore_value
											a__				      =		num_queries
											a__				      =		no_object_weight
											a__				      =		class_weight
											a__				      =		mask_weight
											a__				      =		dice_weight
											a__				      =		train_num_points
											a__				      =		oversample_ratio
											a__				      =		importance_sample_ratio
											a__				      =		init_std
											a__				      =		init_xavier_std
											a__				      =		use_auxiliary_loss
											a__				      =		feature_strides
											a__				      =		output_auxiliary_logits
											a__				      =		decoder_layers

											super().__init__(**a_		)


							@classmethod
							def _a      (							cls  ,  a_  ,  **a_		):
											return cls(
											    backbone_config=a_  ,  **a_  ,  )







							def _a      (							self		):
											a__				      =		copy.deepcopy(self.__dict__		)
											a__				      =		self.backbone_config.to_dict()
											a__				      =		self.__class__.model_type
											return output






from __future__ import annotations

UpperCAmelCase							 =					8.988E9  # units = N * m^s * C^-2

def 			A_      (	__a     :				float ,		__a     :				float ,		__a     :				float ,		__a     :				float		):

				"""simple docstring"""
				a__				      =		abs(chargea * chargea		)

				if (force, chargea, chargea, distance).count(0		) != 1:
								raise ValueError("""One and only one argument must be 0"""		)
				if distance < 0:
								raise ValueError("""Distance cannot be negative"""		)
				if force == 0:
								a__				      =		COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								a__				      =		(COULOMBS_CONSTANT * charge_product / abs(__a		)) ** 0.5
								return {"distance": distance}
				raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

