





"""simple docstring"""






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 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	"""oneformer"""
       lowerCAmelCase__    			=	{"""hidden_size""": """hidden_dim"""}






       def __init__( self     ,	a__ = None     ,	a__ = 2_55     ,	a__ = 1_50     ,	a__ = 0.1     ,	a__ = 2.0     ,	a__ = 5.0     ,	a__ = 5.0     ,	a__ = 0.5     ,	a__ = 0.07     ,	a__ = 1_25_44     ,	a__ = 3.0     ,	a__ = 0.75     ,	a__ = 0.02     ,	a__ = 1.0     ,	a__ = 1E-05     ,	a__ = False     ,	a__ = True     ,	a__ = True     ,	a__ = [4, 8, 16, 32]     ,	a__ = 77     ,	a__ = 2_56     ,	a__ = 77     ,	a__ = 6     ,	a__ = 4_94_08     ,	a__ = 2     ,	a__ = 16     ,	a__ = 2_56     ,	a__ = 2_56     ,	a__ = 2_56     ,	a__ = 10_24     ,	a__ = "GN"     ,	a__ = 6     ,	a__ = 10     ,	a__ = True     ,	a__ = 8     ,	a__ = 0.1     ,	a__ = 20_48     ,	a__ = False     ,	a__ = False     ,	a__ = 2     ,	a__ = 4     ,	**a__     ,	):
              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=2_24     ,	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 				__A			( self  ):
              _UpperCAmelCase			     =  copy.deepcopy(self.__dict__  )
              _UpperCAmelCase			     =  self.backbone_config.to_dict()
              _UpperCAmelCase			     =  self.__class__.model_type
              return output













"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Dict:






       """simple docstring"""




       _UpperCAmelCase			     =  [701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(SCREAMING_SNAKE_CASE,len(SCREAMING_SNAKE_CASE				)				):
                     _UpperCAmelCase			     =  collection[i]
                     _UpperCAmelCase			     =  i
                     while j >= gap and collection[j - gap] > insert_value:
                            _UpperCAmelCase			     =  collection[j - gap]
                            j -= gap
                     if j != i:
                            _UpperCAmelCase			     =  insert_value
       return collection


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    lowerCAmelCase_      =				input('''Enter numbers separated by a comma:\n''').strip()
    lowerCAmelCase_      =				[int(item) for item in user_input.split(''',''')]
    print(shell_sort(unsorted))






