
import copy
from typing import Dict, Optional

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


A		:   Dict         =    logging.get_logger(__name__)

A		:   Optional[Any]         =    {
    '''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						):
			a  : Optional[int]				      =       '''oneformer'''
			a  : 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 = 12_544							, __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 = 49_408							, __UpperCamelCase = 2							, __UpperCamelCase = 16							, __UpperCamelCase = 256							, __UpperCamelCase = 256							, __UpperCamelCase = 256							, __UpperCamelCase = 1_024							, __UpperCamelCase = "GN"							, __UpperCamelCase = 6							, __UpperCamelCase = 10							, __UpperCamelCase = True							, __UpperCamelCase = 8							, __UpperCamelCase = 0.1							, __UpperCamelCase = 2_048							, __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."""     )
															_lowercase									=				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     ):
															_lowercase									=				backbone_config.get("""model_type"""     )
															_lowercase									=				CONFIG_MAPPING[backbone_model_type]
															_lowercase									=				config_class.from_dict(__UpperCamelCase     )

									_lowercase									=				backbone_config

									_lowercase									=				ignore_value
									_lowercase									=				num_queries
									_lowercase									=				no_object_weight
									_lowercase									=				class_weight
									_lowercase									=				mask_weight
									_lowercase									=				dice_weight
									_lowercase									=				contrastive_weight
									_lowercase									=				contrastive_temperature
									_lowercase									=				train_num_points
									_lowercase									=				oversample_ratio
									_lowercase									=				importance_sample_ratio
									_lowercase									=				init_std
									_lowercase									=				init_xavier_std
									_lowercase									=				layer_norm_eps
									_lowercase									=				is_training
									_lowercase									=				use_auxiliary_loss
									_lowercase									=				output_auxiliary_logits
									_lowercase									=				strides
									_lowercase									=				task_seq_len
									_lowercase									=				text_encoder_width
									_lowercase									=				text_encoder_context_length
									_lowercase									=				text_encoder_num_layers
									_lowercase									=				text_encoder_vocab_size
									_lowercase									=				text_encoder_proj_layers
									_lowercase									=				text_encoder_n_ctx
									_lowercase									=				conv_dim
									_lowercase									=				mask_dim
									_lowercase									=				hidden_dim
									_lowercase									=				encoder_feedforward_dim
									_lowercase									=				norm
									_lowercase									=				encoder_layers
									_lowercase									=				decoder_layers
									_lowercase									=				use_task_norm
									_lowercase									=				num_attention_heads
									_lowercase									=				dropout
									_lowercase									=				dim_feedforward
									_lowercase									=				pre_norm
									_lowercase									=				enforce_input_proj
									_lowercase									=				query_dec_layers
									_lowercase									=				common_stride
									_lowercase									=				decoder_layers

									super().__init__(**__UpperCamelCase     )






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

from math import sqrt




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    list[int]:
						_lowercase									=				[1]
						for i in range(2		,      int(sqrt(SCREAMING_SNAKE_CASE_					)					) + 1		,      1					):
												if number % i == 0:
																		values.append(SCREAMING_SNAKE_CASE_					)
																		if int(number // i					) != i:
																								values.append(int(number // i					)					)
						return sorted(SCREAMING_SNAKE_CASE_					)




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    bool:
						return sum(factors(SCREAMING_SNAKE_CASE_					)					) > n




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    bool:
						_lowercase									=				factors(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				len(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				[[0 for i in range(number + 1					)] for j in range(r + 1					)]
						for i in range(r + 1					):
												_lowercase									=				True

						for i in range(1		,      number + 1					):
												_lowercase									=				False

						for i in range(1		,      r + 1					):
												for j in range(1		,      number + 1					):
																		if j < values[i - 1]:
																								_lowercase									=				subset[i - 1][j]
																		else:
																								_lowercase									=				subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

						return subset[r][number] != 0




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    bool:
						return abundant(SCREAMING_SNAKE_CASE_					) and not semi_perfect(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		for number in (69, 70, 71):
				print(F'{number} is {"" if weird(number) else "not "}weird.')