


import copy

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


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """deformable_detr"""
      __snake_case 	=       {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }




      def __init__(    self:	Optional[Any]						,				a:	Union[str, Any]=True						,				a:	Optional[int]=None						,				a:	List[str]=3						,				a:	Union[str, Any]=300						,				a:	List[str]=1024						,				a:	Dict=6						,				a:	Optional[Any]=1024						,				a:	Union[str, Any]=8						,				a:	Any=6						,				a:	Tuple=1024						,				a:	Union[str, Any]=8						,				a:	Optional[Any]=0.0						,				a:	Tuple=True						,				a:	str="relu"						,				a:	Union[str, Any]=256						,				a:	List[Any]=0.1						,				a:	Any=0.0						,				a:	str=0.0						,				a:	Optional[Any]=0.0_2						,				a:	str=1.0						,				a:	List[Any]=True						,				a:	Union[str, Any]=False						,				a:	List[str]="sine"						,				a:	Optional[int]="resnet50"						,				a:	List[str]=True						,				a:	int=False						,				a:	Any=4						,				a:	List[str]=4						,				a:	List[Any]=4						,				a:	int=False						,				a:	List[Any]=300						,				a:	Optional[int]=False						,				a:	List[str]=1						,				a:	Any=5						,				a:	Any=2						,				a:	Optional[int]=1						,				a:	Dict=1						,				a:	List[Any]=5						,				a:	Union[str, Any]=2						,				a:	Union[str, Any]=0.1						,				a:	List[Any]=0.2_5						,				a:	Any=False						,				**a:	Optional[Any]						,				):
       if backbone_config is not None and use_timm_backbone:
        raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'    )

       if not use_timm_backbone:
        if backbone_config is None:
         logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'    )
         __lowerCamelCase		:							Any		 =						CONFIG_MAPPING['resnet'](out_features=['stage4']    )
        elif isinstance(a						,				a    ):
         __lowerCamelCase		:							Optional[Any]		 =						backbone_config.get('model_type'    )
         __lowerCamelCase		:							Any		 =						CONFIG_MAPPING[backbone_model_type]
         __lowerCamelCase		:							Union[str, Any]		 =						config_class.from_dict(a    )
       __lowerCamelCase		:							List[str]		 =						use_timm_backbone
       __lowerCamelCase		:							List[Any]		 =						backbone_config
       __lowerCamelCase		:							List[Any]		 =						num_channels
       __lowerCamelCase		:							int		 =						num_queries
       __lowerCamelCase		:							Tuple		 =						max_position_embeddings
       __lowerCamelCase		:							Optional[int]		 =						d_model
       __lowerCamelCase		:							List[Any]		 =						encoder_ffn_dim
       __lowerCamelCase		:							List[Any]		 =						encoder_layers
       __lowerCamelCase		:							str		 =						encoder_attention_heads
       __lowerCamelCase		:							Optional[Any]		 =						decoder_ffn_dim
       __lowerCamelCase		:							Any		 =						decoder_layers
       __lowerCamelCase		:							Tuple		 =						decoder_attention_heads
       __lowerCamelCase		:							Optional[Any]		 =						dropout
       __lowerCamelCase		:							Optional[int]		 =						attention_dropout
       __lowerCamelCase		:							Dict		 =						activation_dropout
       __lowerCamelCase		:							Dict		 =						activation_function
       __lowerCamelCase		:							List[Any]		 =						init_std
       __lowerCamelCase		:							Optional[Any]		 =						init_xavier_std
       __lowerCamelCase		:							Any		 =						encoder_layerdrop
       __lowerCamelCase		:							Any		 =						auxiliary_loss
       __lowerCamelCase		:							Dict		 =						position_embedding_type
       __lowerCamelCase		:							str		 =						backbone
       __lowerCamelCase		:							Dict		 =						use_pretrained_backbone
       __lowerCamelCase		:							Dict		 =						dilation
       # deformable attributes
       __lowerCamelCase		:							int		 =						num_feature_levels
       __lowerCamelCase		:							Tuple		 =						encoder_n_points
       __lowerCamelCase		:							List[str]		 =						decoder_n_points
       __lowerCamelCase		:							List[Any]		 =						two_stage
       __lowerCamelCase		:							Tuple		 =						two_stage_num_proposals
       __lowerCamelCase		:							str		 =						with_box_refine
       if two_stage is True and with_box_refine is False:
        raise ValueError('If two_stage is True, with_box_refine must be True.'    )
       # Hungarian matcher
       __lowerCamelCase		:							Optional[Any]		 =						class_cost
       __lowerCamelCase		:							Union[str, Any]		 =						bbox_cost
       __lowerCamelCase		:							Tuple		 =						giou_cost
       # Loss coefficients
       __lowerCamelCase		:							Dict		 =						mask_loss_coefficient
       __lowerCamelCase		:							Dict		 =						dice_loss_coefficient
       __lowerCamelCase		:							Optional[int]		 =						bbox_loss_coefficient
       __lowerCamelCase		:							Any		 =						giou_loss_coefficient
       __lowerCamelCase		:							int		 =						eos_coefficient
       __lowerCamelCase		:							Optional[int]		 =						focal_alpha
       __lowerCamelCase		:							Optional[int]		 =						disable_custom_kernels
       super().__init__(is_encoder_decoder=a						,				**a    )




      @property
      def 					_snake_case     (    self:	Optional[int]    ):
       return self.encoder_attention_heads




      @property
      def 					_snake_case     (    self:	Dict    ):
       return self.d_model




      def 					_snake_case     (    self:	int    ):
       __lowerCamelCase		:							Dict		 =						copy.deepcopy(self.__dict__    )
       if self.backbone_config is not None:
        __lowerCamelCase		:							Dict		 =						self.backbone_config.to_dict()
       __lowerCamelCase		:							List[Any]		 =						self.__class__.model_type
       return output





def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						0
 __lowerCamelCase		:							Tuple		 =						len(SCREAMING_SNAKE_CASE__						)

 for i in range(n - 1						):
  for j in range(i + 1      ,	SCREAMING_SNAKE_CASE__						):
   if arr[i] > arr[j]:
    num_inversions += 1

 return num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if len(SCREAMING_SNAKE_CASE__						) <= 1:
  return arr, 0
 __lowerCamelCase		:							Optional[int]		 =						len(SCREAMING_SNAKE_CASE__						) // 2
 __lowerCamelCase		:							Union[str, Any]		 =						arr[0:mid]
 __lowerCamelCase		:							List[Any]		 =						arr[mid:]

 __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						_count_cross_inversions(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							List[str]		 =						inversion_p + inversions_q + cross_inversions
 return c, num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						[]
 __lowerCamelCase		:							List[Any]		 =						0
 while i < len(SCREAMING_SNAKE_CASE__						) and j < len(SCREAMING_SNAKE_CASE__						):
  if p[i] > q[j]:
   # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
   # These are all inversions. The claim emerges from the
   # property that P is sorted.
   num_inversion += len(SCREAMING_SNAKE_CASE__						) - i
   r.append(q[j]						)
   j += 1
  else:
   r.append(p[i]						)
   i += 1

 if i < len(SCREAMING_SNAKE_CASE__						):
  r.extend(p[i:]						)
 else:
  r.extend(q[j:]						)

 return r, num_inversion
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Optional[int]		 =						[10, 2, 1, 5, 5, 2, 11]

 # this arr has 8 inversions:
 # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 8

 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # testing an array with zero inversion (a sorted arr_1)

 arr_a.sort()
 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							int		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # an empty list should also have zero inversions
 __lowerCamelCase		:							List[str]		 =						[]
 __lowerCamelCase		:							Dict		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       main()

