
"""simple docstring"""

def 				A		(							snake_case :Any							,   snake_case :int							,   snake_case :Optional[Any]							,   snake_case :Tuple						) ->      Optional[int]:
 if start == end:
  return start if lst[start] > item else start + 1
 if start > end:
  return start

 __UpperCamelCase							    =			(start + end) // 2
 if lst[mid] < item:
  return binary_search(snake_case							,   snake_case							,   mid + 1							,   snake_case						)
 elif lst[mid] > item:
  return binary_search(snake_case							,   snake_case							,   snake_case							,   mid - 1						)
 else:
  return mid






def 				A		(							snake_case :List[Any]						) ->      Optional[Any]:
 __UpperCamelCase							    =			len(snake_case						)

 for index in range(1							,   snake_case						):
  __UpperCamelCase							    =			lst[index]
  __UpperCamelCase							    =			binary_search(snake_case							,   snake_case							,   0							,   index - 1						)
  __UpperCamelCase							    =			lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

 return lst






def 				A		(							snake_case :Tuple							,   snake_case :List[Any]						) ->      List[Any]:
 if not left:
  return right

 if not right:
  return left

 if left[0] < right[0]:
  return [left[0], *merge(left[1:]							,   snake_case						)]

 return [right[0], *merge(snake_case							,   right[1:]						)]






def 				A		(							snake_case :str						) ->      Union[str, Any]:
 __UpperCamelCase							    =			len(snake_case						)
 __UpperCamelCase    ,      __UpperCamelCase							    =			[], []
 __UpperCamelCase							    =			[lst[0]]
 __UpperCamelCase							    =			[]
 __UpperCamelCase							    =			1
 while i < length:
  if lst[i] < lst[i - 1]:
   runs.append(snake_case						)
   __UpperCamelCase							    =			[lst[i]]
  else:
   new_run.append(lst[i]						)
  i += 1
 runs.append(snake_case						)

 for run in runs:
  sorted_runs.append(insertion_sort(snake_case						)						)
 for run in sorted_runs:
  __UpperCamelCase							    =			merge(snake_case							,   snake_case						)

 return sorted_array






def 				A		(							) ->      List[str]:
 __UpperCamelCase							    =			[5, 9, 1_0, 3, -4, 5, 1_7_8, 9_2, 4_6, -1_8, 0, 7]
 __UpperCamelCase							    =			tim_sort(snake_case						)
 print(snake_case						)


if __name__ == "__main__":
      main()





"""simple docstring"""

import copy
from typing import Dict, Optional

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


UpperCamelCase      :							List[str]          =					logging.get_logger(__name__)

UpperCamelCase      :							Dict          =					{
    "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						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"oneformer"
     lowercase		  =		{"hidden_size": "hidden_dim"}





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

      '''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=__UpperCAmelCase      ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,	)
      elif isinstance(__UpperCAmelCase      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			backbone_config.get('model_type' )
       __UpperCamelCase							    =			CONFIG_MAPPING[backbone_model_type]
       __UpperCamelCase							    =			config_class.from_dict(__UpperCAmelCase )

      __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__(**__UpperCAmelCase )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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



