






import copy
from typing import Dict, Optional

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


A										=      logging.get_logger(__name__)

A										=      {
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[Any]					     =    """oneformer"""
		UpperCAmelCase__   :					Optional[Any]					     =    {"""hidden_size""": """hidden_dim"""}


		def __init__(    self			,     UpperCamelCase__ = None			,     UpperCamelCase__ = 255			,     UpperCamelCase__ = 150			,     UpperCamelCase__ = 0.1			,     UpperCamelCase__ = 2.0			,     UpperCamelCase__ = 5.0			,     UpperCamelCase__ = 5.0			,     UpperCamelCase__ = 0.5			,     UpperCamelCase__ = 0.07			,     UpperCamelCase__ = 12544			,     UpperCamelCase__ = 3.0			,     UpperCamelCase__ = 0.75			,     UpperCamelCase__ = 0.02			,     UpperCamelCase__ = 1.0			,     UpperCamelCase__ = 1E-05			,     UpperCamelCase__ = False			,     UpperCamelCase__ = True			,     UpperCamelCase__ = True			,     UpperCamelCase__ = [4, 8, 16, 32]			,     UpperCamelCase__ = 77			,     UpperCamelCase__ = 256			,     UpperCamelCase__ = 77			,     UpperCamelCase__ = 6			,     UpperCamelCase__ = 49408			,     UpperCamelCase__ = 2			,     UpperCamelCase__ = 16			,     UpperCamelCase__ = 256			,     UpperCamelCase__ = 256			,     UpperCamelCase__ = 256			,     UpperCamelCase__ = 1024			,     UpperCamelCase__ = "GN"			,     UpperCamelCase__ = 6			,     UpperCamelCase__ = 10			,     UpperCamelCase__ = True			,     UpperCamelCase__ = 8			,     UpperCamelCase__ = 0.1			,     UpperCamelCase__ = 2048			,     UpperCamelCase__ = False			,     UpperCamelCase__ = False			,     UpperCamelCase__ = 2			,     UpperCamelCase__ = 4			,     **UpperCamelCase__			,     ):
						if backbone_config is None:
										logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= CONFIG_MAPPING['swin'](
										    image_size=224			,     in_channels=3			,     patch_size=4			,     embed_dim=96			,     depths=[2, 2, 6, 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']			,     )
						elif isinstance(UpperCamelCase__			,     UpperCamelCase__							):
										SCREAMING_SNAKE_CASE_ :						str    				= backbone_config.get('model_type'							)
										SCREAMING_SNAKE_CASE_ :						Dict    				= CONFIG_MAPPING[backbone_model_type]
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= config_class.from_dict(UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Dict    				= backbone_config

						SCREAMING_SNAKE_CASE_ :						List[Any]    				= ignore_value
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= num_queries
						SCREAMING_SNAKE_CASE_ :						Any    				= no_object_weight
						SCREAMING_SNAKE_CASE_ :						Dict    				= class_weight
						SCREAMING_SNAKE_CASE_ :						Any    				= mask_weight
						SCREAMING_SNAKE_CASE_ :						List[str]    				= dice_weight
						SCREAMING_SNAKE_CASE_ :						Any    				= contrastive_weight
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= contrastive_temperature
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= train_num_points
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= oversample_ratio
						SCREAMING_SNAKE_CASE_ :						str    				= importance_sample_ratio
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= init_std
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= init_xavier_std
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= layer_norm_eps
						SCREAMING_SNAKE_CASE_ :						str    				= is_training
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= use_auxiliary_loss
						SCREAMING_SNAKE_CASE_ :						Any    				= output_auxiliary_logits
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= strides
						SCREAMING_SNAKE_CASE_ :						str    				= task_seq_len
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= text_encoder_width
						SCREAMING_SNAKE_CASE_ :						str    				= text_encoder_context_length
						SCREAMING_SNAKE_CASE_ :						Tuple    				= text_encoder_num_layers
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= text_encoder_vocab_size
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= text_encoder_proj_layers
						SCREAMING_SNAKE_CASE_ :						Tuple    				= text_encoder_n_ctx
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= conv_dim
						SCREAMING_SNAKE_CASE_ :						int    				= mask_dim
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= hidden_dim
						SCREAMING_SNAKE_CASE_ :						Tuple    				= encoder_feedforward_dim
						SCREAMING_SNAKE_CASE_ :						Tuple    				= norm
						SCREAMING_SNAKE_CASE_ :						str    				= encoder_layers
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= decoder_layers
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= use_task_norm
						SCREAMING_SNAKE_CASE_ :						Dict    				= num_attention_heads
						SCREAMING_SNAKE_CASE_ :						List[str]    				= dropout
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= dim_feedforward
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= pre_norm
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= enforce_input_proj
						SCREAMING_SNAKE_CASE_ :						List[str]    				= query_dec_layers
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= common_stride
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= decoder_layers

						super().__init__(**UpperCamelCase__							)






		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Tuple    				= copy.deepcopy(self.__dict__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= self.backbone_config.to_dict()
						SCREAMING_SNAKE_CASE_ :						Any    				= self.__class__.model_type
						return output







def        _lowerCamelCase(      lowerCAmelCase__					:		int = 99_8001   ):





				'''simple docstring'''







				for number in range(n - 1     ,     9999     ,     -1   ):
								SCREAMING_SNAKE_CASE_ :						int    				= str(lowerCAmelCase__   )

								# checks whether 'str_number' is a palindrome.
								if str_number == str_number[::-1]:
												SCREAMING_SNAKE_CASE_ :						str    				= 999

												# if 'number' is a product of two 3-digit numbers
												# then number is the answer otherwise fetch next number.
												while divisor != 99:
																if (number % divisor == 0) and (len(str(number // divisor   )   ) == 3.0):
																				return number
																divisor -= 1
				raise ValueError('That number is larger than our acceptable range.'   )


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