





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


UpperCAmelCase						:   Any			  =     {
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

UpperCAmelCase						:   Tuple			  =     logging.get_logger(__name__)





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  str					    =	'''maskformer'''
				UpperCamelCase    :  Any					    =	{'''hidden_size''': '''mask_feature_size'''}
				UpperCamelCase    :  List[Any]					    =	['''resnet''', '''swin''']
				UpperCamelCase    :  Any					    =	['''detr''']



				def __init__(			self     ,					_A = 256     ,					_A = 256     ,					_A = 0.1     ,					_A = False     ,					_A = None     ,					_A = None     ,					_A = 0.0_2     ,					_A = 1.0     ,					_A = 1.0     ,					_A = 1.0     ,					_A = 2_0.0     ,					_A = None     ,					**_A     ,					):
									if backbone_config is None:
														# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
														__A	:						Union[str, Any]       =				SwinConfig(
														    image_size=384     ,					in_channels=3     ,					patch_size=4     ,					embed_dim=128     ,					depths=[2, 2, 18, 2]     ,					num_heads=[4, 8, 16, 32]     ,					window_size=12     ,					drop_path_rate=0.3     ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,					)

									if isinstance(_A     ,					_A     ):
														__A	:						Tuple       =				backbone_config.pop('model_type'     )
														__A	:						Optional[int]       =				CONFIG_MAPPING[backbone_model_type]
														__A	:						Dict       =				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 MaskFormer. """
														    F"""Supported model types: {",".join(self.backbones_supported     )}"""     )

									if decoder_config is None:
														# fall back to https://huggingface.co/facebook/detr-resnet-50
														__A	:						int       =				DetrConfig()
									else:
														# verify that the decoder is supported
														__A	:						Dict       =				(
														    decoder_config.pop('model_type'     ) if isinstance(_A     ,					_A     ) else decoder_config.model_type
														)
														if decoder_type not in self.decoders_supported:
																			raise ValueError(
																			    F"""Transformer Decoder {decoder_type} not supported, please use one of"""
																			    F""" {",".join(self.decoders_supported     )}"""     )
														if isinstance(_A     ,					_A     ):
																			__A	:						Tuple       =				CONFIG_MAPPING[decoder_type]
																			__A	:						Optional[int]       =				config_class.from_dict(_A     )

									__A	:						Dict       =				backbone_config
									__A	:						List[str]       =				decoder_config
									# main feature dimension for the model
									__A	:						Optional[Any]       =				fpn_feature_size
									__A	:						int       =				mask_feature_size
									# initializer
									__A	:						Any       =				init_std
									__A	:						Union[str, Any]       =				init_xavier_std
									# Hungarian matcher && loss
									__A	:						Any       =				cross_entropy_weight
									__A	:						Any       =				dice_weight
									__A	:						List[str]       =				mask_weight
									__A	:						Dict       =				use_auxiliary_loss
									__A	:						int       =				no_object_weight
									__A	:						Any       =				output_auxiliary_logits

									__A	:						Any       =				self.decoder_config.encoder_attention_heads
									__A	:						Optional[int]       =				self.decoder_config.num_hidden_layers
									super().__init__(**_A     )



				@classmethod
				def 					UpperCAmelCase_		(			cls     ,					_A     ,					_A     ,					**_A     ):
									return cls(
									    backbone_config=_A     ,					decoder_config=_A     ,					**_A     ,					)





				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				copy.deepcopy(self.__dict__     )
									__A	:						Dict       =				self.backbone_config.to_dict()
									__A	:						List[Any]       =				self.decoder_config.to_dict()
									__A	:						Optional[Any]       =				self.__class__.model_type
									return output











from __future__ import annotations







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      float:
					if days_between_payments <= 0:
										raise ValueError('days_between_payments must be > 0'  )
					if daily_interest_rate < 0:
										raise ValueError('daily_interest_rate must be >= 0'  )
					if principal <= 0:
										raise ValueError('principal must be > 0'  )
					return principal * daily_interest_rate * days_between_payments







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						)      ->      float:
					if number_of_compounding_periods <= 0:
										raise ValueError('number_of_compounding_periods must be > 0'  )
					if nominal_annual_interest_rate_percentage < 0:
										raise ValueError('nominal_annual_interest_rate_percentage must be >= 0'  )
					if principal <= 0:
										raise ValueError('principal must be > 0'  )

					return principal * (
					    (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
					    - 1
					)







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						)      ->      float:
					if number_of_years <= 0:
										raise ValueError('number_of_years must be > 0'  )
					if nominal_annual_percentage_rate < 0:
										raise ValueError('nominal_annual_percentage_rate must be >= 0'  )
					if principal <= 0:
										raise ValueError('principal must be > 0'  )

					return compound_interest(
					    a					,						nominal_annual_percentage_rate / 3_65					,						number_of_years * 3_65  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




