'''simple docstring'''


import copy
from typing import Dict, Optional

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


snake_case__	:     int      =    logging.get_logger(__name__)

snake_case__	:     Union[str, 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 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:List[Any]		       =						'''oneformer'''
  lowerCamelCase_						:Optional[Any]		       =						{'''hidden_size''': '''hidden_dim'''}


  def __init__(					self ,						snake_case_ = None ,						snake_case_ = 2_5_5 ,						snake_case_ = 1_5_0 ,						snake_case_ = 0.1 ,						snake_case_ = 2.0 ,						snake_case_ = 5.0 ,						snake_case_ = 5.0 ,						snake_case_ = 0.5 ,						snake_case_ = 0.07 ,						snake_case_ = 1_2_5_4_4 ,						snake_case_ = 3.0 ,						snake_case_ = 0.75 ,						snake_case_ = 0.02 ,						snake_case_ = 1.0 ,						snake_case_ = 1E-05 ,						snake_case_ = False ,						snake_case_ = True ,						snake_case_ = True ,						snake_case_ = [4, 8, 1_6, 3_2] ,						snake_case_ = 7_7 ,						snake_case_ = 2_5_6 ,						snake_case_ = 7_7 ,						snake_case_ = 6 ,						snake_case_ = 4_9_4_0_8 ,						snake_case_ = 2 ,						snake_case_ = 1_6 ,						snake_case_ = 2_5_6 ,						snake_case_ = 2_5_6 ,						snake_case_ = 2_5_6 ,						snake_case_ = 1_0_2_4 ,						snake_case_ = "GN" ,						snake_case_ = 6 ,						snake_case_ = 1_0 ,						snake_case_ = True ,						snake_case_ = 8 ,						snake_case_ = 0.1 ,						snake_case_ = 2_0_4_8 ,						snake_case_ = False ,						snake_case_ = False ,						snake_case_ = 2 ,						snake_case_ = 4 ,						**snake_case_ ,						):




       '''simple docstring'''

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

       UpperCAmelCase_				:  List[str]  = backbone_config

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

       super().__init__(**snake_case_		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = copy.deepcopy(self.__dict__		)
       UpperCAmelCase_				:  Optional[Any]  = self.backbone_config.to_dict()
       UpperCAmelCase_				:  Tuple  = self.__class__.model_type
       return output









'''simple docstring'''

def      __magic_name__							(			A				)      ->		tuple[int, int]:
	# base case
	if len(A				) == 1:
		return a[0], a[0]
	else:
		# split A into half.
		snake_case							=					a[: len(A				) // 2]
		snake_case							=					a[len(A				) // 2 :]

		# 2 sub problems, 1/2 of original size.
		snake_case			,			snake_case							=					max_difference(A				)
		snake_case			,			snake_case							=					max_difference(A				)

		# get min of first and max of second
		# linear time
		snake_case							=					min(A				)
		snake_case							=					max(A				)

		# 3 cases, either (small1, big1),
		# (min_first, max_second), (small2, big2)
		# constant comparisons
		if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
			return smalla, biga
		elif biga - smalla > max_second - min_first:
			return smalla, biga
		else:
			return min_first, max_second


if __name__ == "__main__":
						import doctest

						doctest.testmod()





