

import copy
from typing import Dict, Optional

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


lowerCAmelCase_ 			=			logging.get_logger(__name__)

lowerCAmelCase_ 			=			{
    '''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 			snake_case_     (	a__      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     str		    =       "oneformer"
			SCREAMING_SNAKE_CASE  :     Union[str, Any]		    =       {"hidden_size": "hidden_dim"}







			def __init__(  self				:       Optional[int]							,     _UpperCamelCase				:       Optional[int] = None							,     _UpperCamelCase				:       Union[str, Any] = 2_5_5							,     _UpperCamelCase				:       Optional[int] = 1_5_0							,     _UpperCamelCase				:       Tuple = 0.1							,     _UpperCamelCase				:       List[Any] = 2.0							,     _UpperCamelCase				:       Any = 5.0							,     _UpperCamelCase				:       str = 5.0							,     _UpperCamelCase				:       int = 0.5							,     _UpperCamelCase				:       Union[str, Any] = 0.07							,     _UpperCamelCase				:       List[str] = 1_2_5_4_4							,     _UpperCamelCase				:       str = 3.0							,     _UpperCamelCase				:       str = 0.75							,     _UpperCamelCase				:       List[Any] = 0.02							,     _UpperCamelCase				:       int = 1.0							,     _UpperCamelCase				:       Optional[Any] = 1e-05							,     _UpperCamelCase				:       List[Any] = False							,     _UpperCamelCase				:       str = True							,     _UpperCamelCase				:       Optional[int] = True							,     _UpperCamelCase				:       List[str] = [4, 8, 1_6, 3_2]							,     _UpperCamelCase				:       List[str] = 7_7							,     _UpperCamelCase				:       Dict = 2_5_6							,     _UpperCamelCase				:       Tuple = 7_7							,     _UpperCamelCase				:       Union[str, Any] = 6							,     _UpperCamelCase				:       List[str] = 4_9_4_0_8							,     _UpperCamelCase				:       Tuple = 2							,     _UpperCamelCase				:       str = 1_6							,     _UpperCamelCase				:       str = 2_5_6							,     _UpperCamelCase				:       str = 2_5_6							,     _UpperCamelCase				:       List[Any] = 2_5_6							,     _UpperCamelCase				:       Union[str, Any] = 1_0_2_4							,     _UpperCamelCase				:       List[Any] = "GN"							,     _UpperCamelCase				:       List[str] = 6							,     _UpperCamelCase				:       Optional[int] = 1_0							,     _UpperCamelCase				:       str = True							,     _UpperCamelCase				:       Any = 8							,     _UpperCamelCase				:       Tuple = 0.1							,     _UpperCamelCase				:       Union[str, Any] = 2_0_4_8							,     _UpperCamelCase				:       str = False							,     _UpperCamelCase				:       Any = False							,     _UpperCamelCase				:       int = 2							,     _UpperCamelCase				:       List[str] = 4							,     **_UpperCamelCase				:       str							,     )   ->Optional[int]:
									if backbone_config is None:
															logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''							)
															snake_case_     =			CONFIG_MAPPING['swin'](
															    image_size=2_2_4							,     in_channels=3							,     patch_size=4							,     embed_dim=9_6							,     depths=[2, 2, 6, 2]							,     num_heads=[3, 6, 1_2, 2_4]							,     window_size=7							,     drop_path_rate=0.3							,     use_absolute_embeddings=SCREAMING_SNAKE_CASE_							,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							,     )
									elif isinstance(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							):
															snake_case_     =			backbone_config.get('''model_type'''							)
															snake_case_     =			CONFIG_MAPPING[backbone_model_type]
															snake_case_     =			config_class.from_dict(SCREAMING_SNAKE_CASE_							)

									snake_case_     =			backbone_config

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

									super().__init__(**SCREAMING_SNAKE_CASE_							)







			def        snake_case__(  self				:       Optional[Any]							)   ->Dict[str, any]:
									snake_case_     =			copy.deepcopy(self.__dict__							)
									snake_case_     =			self.backbone_config.to_dict()
									snake_case_     =			self.__class__.model_type
									return output


lowerCAmelCase_ 			=			(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCAmelCase_ 			=			(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Initialization of sum and number of digits.
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						# Calculation of digits of the number
						while temp > 0:
												number_of_digits += 1
												temp //= 10
						# Dividing number into separate digits and find Armstrong number
						snake_case_     =			n
						while temp > 0:
												snake_case_     =			temp % 10
												total += rem**number_of_digits
												temp //= 10
						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Init a "histogram" of the digits
						snake_case_     =			[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						while temp > 0:
												snake_case_,					snake_case_     =			divmod(SCREAMING_SNAKE_CASE__						,	10 )
												digit_histogram[rem] += 1
												digit_total += 1

						for cnt, i in zip(SCREAMING_SNAKE_CASE__						,	range(len(SCREAMING_SNAKE_CASE__ ) ) ):
												total += cnt * i**digit_total

						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False
						snake_case_     =			len(str(SCREAMING_SNAKE_CASE__ ) )  # the power that all digits will be raised to
						# check if sum of each digit multiplied expo times is equal to number
						return n == sum(int(SCREAMING_SNAKE_CASE__ ) ** expo for i in str(SCREAMING_SNAKE_CASE__ ) )


def 						__SCREAMING_SNAKE_CASE      ():
						snake_case_     =			int(input('''Enter an integer to see if it is an Armstrong number: ''' ).strip() )
						print(F'''{num} is {'' if armstrong_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if narcissistic_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if pluperfect_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()