


import copy
from typing import Dict, Optional

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


__snake_case     =		logging.get_logger(__name__)

__snake_case     =		{
    '''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 					__lowerCamelCase	(_a		):
       _lowercase    							=      """oneformer"""
       _lowercase    							=      {"""hidden_size""": """hidden_dim"""}







       def __init__(	self:				List[Any],A_:				Optional[Dict] = None,A_:				int = 255,A_:				int = 150,A_:				int = 0.1,A_:				float = 2.0,A_:				float = 5.0,A_:				float = 5.0,A_:				float = 0.5,A_:				float = 0.0_7,A_:				int = 1_2544,A_:				float = 3.0,A_:				float = 0.7_5,A_:				float = 0.0_2,A_:				float = 1.0,A_:				float = 1E-05,A_:				bool = False,A_:				bool = True,A_:				bool = True,A_:				Optional[list] = [4, 8, 16, 32],A_:				int = 77,A_:				int = 256,A_:				int = 77,A_:				int = 6,A_:				int = 4_9408,A_:				int = 2,A_:				int = 16,A_:				int = 256,A_:				int = 256,A_:				int = 256,A_:				int = 1024,A_:				str = "GN",A_:				int = 6,A_:				int = 10,A_:				bool = True,A_:				int = 8,A_:				float = 0.1,A_:				int = 2048,A_:				bool = False,A_:				bool = False,A_:				int = 2,A_:				int = 4,**A_:				Any,):






            '''simple docstring'''




            if backbone_config is None:
                 logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'			)
                 __UpperCamelCase				=	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=A_,out_features=['stage1', 'stage2', 'stage3', 'stage4'],)
            elif isinstance(A_,A_			):
                 __UpperCamelCase				=	backbone_config.get('model_type'			)
                 __UpperCamelCase				=	CONFIG_MAPPING[backbone_model_type]
                 __UpperCamelCase				=	config_class.from_dict(A_			)

            __UpperCamelCase				=	backbone_config

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

            super().__init__(**A_			)







       def 						snake_case_					(	self:				Union[str, Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	copy.deepcopy(self.__dict__			)
            __UpperCamelCase				=	self.backbone_config.to_dict()
            __UpperCamelCase				=	self.__class__.model_type
            return output






import math






def 						_A						(						_lowercase				)			->				list:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive integers have prime factors'				)
     __UpperCamelCase				=	[]
     while n % 2 == 0:
          pf.append(2				)
          __UpperCamelCase				=	int(n / 2				)
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          while n % i == 0:
               pf.append(_lowercase				)
               __UpperCamelCase				=	int(n / i				)
     if n > 2:
          pf.append(_lowercase				)
     return pf






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive numbers are accepted'				)
     __UpperCamelCase				=	1
     __UpperCamelCase				=	1
     while n % 2 == 0:
          temp += 1
          __UpperCamelCase				=	int(n / 2				)
     div *= temp
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          __UpperCamelCase				=	1
          while n % i == 0:
               temp += 1
               __UpperCamelCase				=	int(n / i				)
          div *= temp
     if n > 1:
          div *= 2
     return div






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive numbers are accepted'				)
     __UpperCamelCase				=	1
     __UpperCamelCase				=	1
     while n % 2 == 0:
          temp += 1
          __UpperCamelCase				=	int(n / 2				)
     if temp > 1:
          s *= (2**temp - 1) / (2 - 1)
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          __UpperCamelCase				=	1
          while n % i == 0:
               temp += 1
               __UpperCamelCase				=	int(n / i				)
          if temp > 1:
               s *= (i**temp - 1) / (i - 1)
     return int(_lowercase				)






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	n
     for x in set(prime_factors(_lowercase				)				):
          s *= (x - 1) / x
     return int(_lowercase				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


