
"""simple docstring"""

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


_A						=			{
    '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
}

_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'maskformer'
	lowercase_				=     {'hidden_size': 'mask_feature_size'}
	lowercase_				=     ['resnet', 'swin']
	lowercase_				=     ['detr']




	def __init__(			self		,			UpperCAmelCase_ = 256		,			UpperCAmelCase_ = 256		,			UpperCAmelCase_ = 0.1		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = 0.02		,			UpperCAmelCase_ = 1.0		,			UpperCAmelCase_ = 1.0		,			UpperCAmelCase_ = 1.0		,			UpperCAmelCase_ = 20.0		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Dict:
			if backbone_config is None:
					# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
					lowerCamelCase					:      Tuple          =   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(UpperCAmelCase_		,			UpperCAmelCase_ ):
					lowerCamelCase					:      Union[str, Any]          =   backbone_config.pop('model_type' )
					lowerCamelCase					:      Optional[int]          =   CONFIG_MAPPING[backbone_model_type]
					lowerCamelCase					:      List[str]          =   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 MaskFormer. """
					    F"""Supported model types: {','.join(self.backbones_supported )}""" )

			if decoder_config is None:
					# fall back to https://huggingface.co/facebook/detr-resnet-50
					lowerCamelCase					:      Union[str, Any]          =   DetrConfig()
			else:
					# verify that the decoder is supported
					lowerCamelCase					:      Any          =   (
					    decoder_config.pop('model_type' ) if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) 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(UpperCAmelCase_		,			UpperCAmelCase_ ):
							lowerCamelCase					:      List[str]          =   CONFIG_MAPPING[decoder_type]
							lowerCamelCase					:      List[str]          =   config_class.from_dict(UpperCAmelCase_ )

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

			lowerCamelCase					:      List[str]          =   self.decoder_config.encoder_attention_heads
			lowerCamelCase					:      List[Any]          =   self.decoder_config.num_hidden_layers
			super().__init__(**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Tuple:
			return cls(
			    backbone_config=UpperCAmelCase_		,			decoder_config=UpperCAmelCase_		,			**UpperCAmelCase_		,			)




	def       _UpperCamelCase   (			self )		->							Dict[str, any]:
			lowerCamelCase					:      Dict          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      List[str]          =   self.backbone_config.to_dict()
			lowerCamelCase					:      str          =   self.decoder_config.to_dict()
			lowerCamelCase					:      Union[str, Any]          =   self.__class__.model_type
			return output








"""simple docstring"""

def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return str(a_      ) == str(a_      )[::-1]
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return int(a_      ) + int(str(a_      )[::-1]      )
def 		UpperCAmelCase (		a_ = 1_0000      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[Any]          =   []
		for num in range(1,						a_      ):
				lowerCamelCase					:      List[str]          =   0
				lowerCamelCase					:      Union[str, Any]          =   num
				while iterations < 50:
						lowerCamelCase					:      Optional[int]          =   sum_reverse(a_      )
						iterations += 1
						if is_palindrome(a_      ):
								break
				else:
						lychrel_nums.append(a_      )
		return len(a_      )


if __name__ == "__main__":
					print(F"""{solution() = }""")






