






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   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = "oneformer"
		snake_case			 = {"hidden_size": "hidden_dim"}
		def __init__(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							Tuple = None    ,_SCREAMING_SNAKE_CASE		:							int = 2_5_5    ,_SCREAMING_SNAKE_CASE		:							Any = 1_5_0    ,_SCREAMING_SNAKE_CASE		:							Dict = 0.1    ,_SCREAMING_SNAKE_CASE		:							Optional[Any] = 2.0    ,_SCREAMING_SNAKE_CASE		:							Any = 5.0    ,_SCREAMING_SNAKE_CASE		:							Dict = 5.0    ,_SCREAMING_SNAKE_CASE		:							Tuple = 0.5    ,_SCREAMING_SNAKE_CASE		:							List[str] = 0.07    ,_SCREAMING_SNAKE_CASE		:							List[str] = 1_2_5_4_4    ,_SCREAMING_SNAKE_CASE		:							Any = 3.0    ,_SCREAMING_SNAKE_CASE		:							List[str] = 0.75    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any] = 0.02    ,_SCREAMING_SNAKE_CASE		:							Any = 1.0    ,_SCREAMING_SNAKE_CASE		:							List[str] = 1E-05    ,_SCREAMING_SNAKE_CASE		:							List[str] = False    ,_SCREAMING_SNAKE_CASE		:							Dict = True    ,_SCREAMING_SNAKE_CASE		:							Tuple = True    ,_SCREAMING_SNAKE_CASE		:							List[Any] = [4, 8, 1_6, 3_2]    ,_SCREAMING_SNAKE_CASE		:							Tuple = 7_7    ,_SCREAMING_SNAKE_CASE		:							Dict = 2_5_6    ,_SCREAMING_SNAKE_CASE		:							Any = 7_7    ,_SCREAMING_SNAKE_CASE		:							Dict = 6    ,_SCREAMING_SNAKE_CASE		:							Dict = 4_9_4_0_8    ,_SCREAMING_SNAKE_CASE		:							List[str] = 2    ,_SCREAMING_SNAKE_CASE		:							str = 1_6    ,_SCREAMING_SNAKE_CASE		:							Dict = 2_5_6    ,_SCREAMING_SNAKE_CASE		:							Dict = 2_5_6    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any] = 2_5_6    ,_SCREAMING_SNAKE_CASE		:							List[str] = 1_0_2_4    ,_SCREAMING_SNAKE_CASE		:							str = "GN"    ,_SCREAMING_SNAKE_CASE		:							int = 6    ,_SCREAMING_SNAKE_CASE		:							Tuple = 1_0    ,_SCREAMING_SNAKE_CASE		:							List[str] = True    ,_SCREAMING_SNAKE_CASE		:							Optional[int] = 8    ,_SCREAMING_SNAKE_CASE		:							Any = 0.1    ,_SCREAMING_SNAKE_CASE		:							Tuple = 2_0_4_8    ,_SCREAMING_SNAKE_CASE		:							int = False    ,_SCREAMING_SNAKE_CASE		:							List[Any] = False    ,_SCREAMING_SNAKE_CASE		:							Tuple = 2    ,_SCREAMING_SNAKE_CASE		:							Dict = 4    ,**_SCREAMING_SNAKE_CASE		:							str    ,)     -> Optional[int]:


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

						A									=   backbone_config

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

						super().__init__(**a__		)







		def    A(	self		:							Dict		)     -> Any:


						'''simple docstring'''
						A									=   copy.deepcopy(self.__dict__		)
						A									=   self.backbone_config.to_dict()
						A									=   self.__class__.model_type
						return output












"""simple docstring"""






from __future__ import annotations

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							list:






       """simple docstring"""




       _UpperCAmelCase			     =  [None] * (len(SCREAMING_SNAKE_CASE				) + len(SCREAMING_SNAKE_CASE				))

       _UpperCAmelCase			     =  0  # pointer to current index for left Half
       _UpperCAmelCase			     =  0  # pointer to current index for the right Half
       _UpperCAmelCase			     =  0  # pointer to current index for the sorted array Half

       while pointera < len(SCREAMING_SNAKE_CASE				) and pointera < len(SCREAMING_SNAKE_CASE				):
              if left_half[pointera] < right_half[pointera]:
                     _UpperCAmelCase			     =  left_half[pointera]
                     pointera += 1
                     index += 1
              else:
                     _UpperCAmelCase			     =  right_half[pointera]
                     pointera += 1
                     index += 1
       while pointera < len(SCREAMING_SNAKE_CASE				):
              _UpperCAmelCase			     =  left_half[pointera]
              pointera += 1
              index += 1

       while pointera < len(SCREAMING_SNAKE_CASE				):
              _UpperCAmelCase			     =  right_half[pointera]
              pointera += 1
              index += 1

       return sorted_array

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							list:






       """simple docstring"""




       if len(SCREAMING_SNAKE_CASE				) <= 1:
              return array
       # the actual formula to calculate the middle element = left + (right - left) // 2
       # this avoids integer overflow in case of large N
       _UpperCAmelCase			     =  0 + (len(SCREAMING_SNAKE_CASE				) - 0) // 2

       # Split the array into halves till the array length becomes equal to One
       # merge the arrays of single length returned by mergeSort function and
       # pass them into the merge arrays function which merges the array
       _UpperCAmelCase			     =  array[:middle]
       _UpperCAmelCase			     =  array[middle:]

       return merge(merge_sort(SCREAMING_SNAKE_CASE				),merge_sort(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






