


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    _a				(							_lowercase):
	_a    :    Union[str, Any]        =				'''oneformer'''
	_a    :    Any        =				{'''hidden_size''': '''hidden_dim'''}




	def __init__(   self		:						Optional[Any]						,   _SCREAMING_SNAKE_CASE		:						Optional[Dict] = None						,   _SCREAMING_SNAKE_CASE		:						int = 255						,   _SCREAMING_SNAKE_CASE		:						int = 150						,   _SCREAMING_SNAKE_CASE		:						int = 0.1						,   _SCREAMING_SNAKE_CASE		:						float = 2.0						,   _SCREAMING_SNAKE_CASE		:						float = 5.0						,   _SCREAMING_SNAKE_CASE		:						float = 5.0						,   _SCREAMING_SNAKE_CASE		:						float = 0.5						,   _SCREAMING_SNAKE_CASE		:						float = 0.07						,   _SCREAMING_SNAKE_CASE		:						int = 1_2544						,   _SCREAMING_SNAKE_CASE		:						float = 3.0						,   _SCREAMING_SNAKE_CASE		:						float = 0.75						,   _SCREAMING_SNAKE_CASE		:						float = 0.02						,   _SCREAMING_SNAKE_CASE		:						float = 1.0						,   _SCREAMING_SNAKE_CASE		:						float = 1E-05						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						Optional[list] = [4, 8, 16, 32]						,   _SCREAMING_SNAKE_CASE		:						int = 77						,   _SCREAMING_SNAKE_CASE		:						int = 256						,   _SCREAMING_SNAKE_CASE		:						int = 77						,   _SCREAMING_SNAKE_CASE		:						int = 6						,   _SCREAMING_SNAKE_CASE		:						int = 4_9408						,   _SCREAMING_SNAKE_CASE		:						int = 2						,   _SCREAMING_SNAKE_CASE		:						int = 16						,   _SCREAMING_SNAKE_CASE		:						int = 256						,   _SCREAMING_SNAKE_CASE		:						int = 256						,   _SCREAMING_SNAKE_CASE		:						int = 256						,   _SCREAMING_SNAKE_CASE		:						int = 1024						,   _SCREAMING_SNAKE_CASE		:						str = "GN"						,   _SCREAMING_SNAKE_CASE		:						int = 6						,   _SCREAMING_SNAKE_CASE		:						int = 10						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						int = 8						,   _SCREAMING_SNAKE_CASE		:						float = 0.1						,   _SCREAMING_SNAKE_CASE		:						int = 2048						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						int = 2						,   _SCREAMING_SNAKE_CASE		:						int = 4						,   **_SCREAMING_SNAKE_CASE		:						Optional[Any]						,   )->			str:
			if backbone_config is None:
					logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''						)
					lowerCAmelCase__  :     int       	=   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=_SCREAMING_SNAKE_CASE						,   out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						,   )
			elif isinstance(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						):
					lowerCAmelCase__  :     Optional[int]       	=   backbone_config.get('''model_type'''						)
					lowerCAmelCase__  :     Any       	=   CONFIG_MAPPING[backbone_model_type]
					lowerCAmelCase__  :     Dict       	=   config_class.from_dict(_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     Optional[Any]       	=   backbone_config

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

			super().__init__(**_SCREAMING_SNAKE_CASE						)




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








import math






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5      ,  int(math.sqrt(_a			) + 1			)      ,  6			):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 	lowerCamelCase_       (			_a = 1_000      ,  _a = 1_000			):




		"""simple docstring"""






		lowerCAmelCase__  :     Union[str, Any]       	=   [0, 0, 0]  # length, a, b
		for a in range((a_limit * -1) + 1      ,  _a			):
				for b in range(2      ,  _a			):
						if is_prime(_a			):
								lowerCAmelCase__  :     str       	=   0
								lowerCAmelCase__  :     int       	=   0
								while is_prime((n**2) + (a * n) + b			):
										count += 1
										n += 1
								if count > longest[0]:
										lowerCAmelCase__  :     Dict       	=   [count, a, b]
		lowerCAmelCase__  :     int       	=   longest[1] * longest[2]
		return ans


if __name__ == "__main__":
		print(solution(1000, 1000))




