


"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowerCamelCase_   :   str									=  TypeVar("""T""")






class       __A				(       Generic[T]			):






 """simple docstring"""




 def __init__(		self ,    __A ,    __A						)					->     None:
     a 		=None

     a 		=len(__lowercase						)
     a 		=[any_type for _ in range(self.N						)] + arr
     a 		=fnc
     self.build()


 def       SCREAMING_SNAKE_CASE							(		self						)					->     None:
     for p in range(self.N - 1 ,    0 ,    -1						):
         a 		=self.fn(self.st[p * 2] ,    self.st[p * 2 + 1]						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A						)					->     None:
     p += self.N
     a 		=v
     while p > 1:
         a 		=p // 2
         a 		=self.fn(self.st[p * 2] ,    self.st[p * 2 + 1]						)





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A						)					->     T | None:  # noqa: E741
     a 		=l + self.N, r + self.N

     a 		=None
     while l <= r:
         if l % 2 == 1:
             a 		=self.st[l] if res is None else self.fn(__lowercase ,    self.st[l]						)
         if r % 2 == 0:
             a 		=self.st[r] if res is None else self.fn(__lowercase ,    self.st[r]						)
         a 		=(l + 1) // 2, (r - 1) // 2
     return res


if __name__ == "__main__":
       from functools import reduce

       lowerCamelCase_   :   int									=  [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

       lowerCamelCase_   :   Union[str, Any]									=  {
           0: 7,
           1: 2,
           2: 6,
           3: -1_4,
           4: 5,
           5: 4,
           6: 7,
           7: -1_0,
           8: 9,
           9: 1_0,
           1_0: 1_2,
           1_1: 1,
       }

       lowerCamelCase_   :   Optional[int]									=  SegmentTree(test_array, min)
       lowerCamelCase_   :   Any									=  SegmentTree(test_array, max)
       lowerCamelCase_   :   Union[str, Any]									=  SegmentTree(test_array, lambda a, b: a + b)
       def _A						(						):







           """simple docstring"""







           for i in range(len(lowercase		)		):
               for j in range(lowercase       ,    len(lowercase		)		):
                   a 		=reduce(lowercase       ,    test_array[i : j + 1]		)
                   a 		=reduce(lowercase       ,    test_array[i : j + 1]		)
                   a 		=reduce(lambda lowercase       ,    lowercase		: a + b       ,    test_array[i : j + 1]		)
                   assert min_range == min_segment_tree.query(lowercase       ,    lowercase		)
                   assert max_range == max_segment_tree.query(lowercase       ,    lowercase		)
                   assert sum_range == sum_segment_tree.query(lowercase       ,    lowercase		)

       test_all_segments()

       for index, value in test_updates.items():
              lowerCamelCase_   :   str									=  value
              min_segment_tree.update(index, value)
              max_segment_tree.update(index, value)
              sum_segment_tree.update(index, value)
              test_all_segments()
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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						List[str]						=      """deformable_detr"""
    a__	:						Union[str, Any]						=      {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }







    def __init__(	self , __lowercase=True , __lowercase=None , __lowercase=3 , __lowercase=300 , __lowercase=1_024 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=0.0 , __lowercase=True , __lowercase="relu" , __lowercase=256 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1.0 , __lowercase=True , __lowercase=False , __lowercase="sine" , __lowercase="resnet50" , __lowercase=True , __lowercase=False , __lowercase=4 , __lowercase=4 , __lowercase=4 , __lowercase=False , __lowercase=300 , __lowercase=False , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=1 , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=0.1 , __lowercase=0.25 , __lowercase=False , **__lowercase , )       ->	int:
        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.''')
                __UpperCamelCase     :str			  =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
            elif isinstance(__lowercase , __lowercase):
                __UpperCamelCase     :str			  =		backbone_config.get('''model_type''')
                __UpperCamelCase     :Tuple			  =		CONFIG_MAPPING[backbone_model_type]
                __UpperCamelCase     :Any			  =		config_class.from_dict(__lowercase)
        __UpperCamelCase     :int			  =		use_timm_backbone
        __UpperCamelCase     :Dict			  =		backbone_config
        __UpperCamelCase     :Any			  =		num_channels
        __UpperCamelCase     :Optional[int]			  =		num_queries
        __UpperCamelCase     :Any			  =		max_position_embeddings
        __UpperCamelCase     :str			  =		d_model
        __UpperCamelCase     :Tuple			  =		encoder_ffn_dim
        __UpperCamelCase     :Union[str, Any]			  =		encoder_layers
        __UpperCamelCase     :List[Any]			  =		encoder_attention_heads
        __UpperCamelCase     :Any			  =		decoder_ffn_dim
        __UpperCamelCase     :List[str]			  =		decoder_layers
        __UpperCamelCase     :int			  =		decoder_attention_heads
        __UpperCamelCase     :str			  =		dropout
        __UpperCamelCase     :Any			  =		attention_dropout
        __UpperCamelCase     :int			  =		activation_dropout
        __UpperCamelCase     :List[Any]			  =		activation_function
        __UpperCamelCase     :List[Any]			  =		init_std
        __UpperCamelCase     :List[Any]			  =		init_xavier_std
        __UpperCamelCase     :int			  =		encoder_layerdrop
        __UpperCamelCase     :str			  =		auxiliary_loss
        __UpperCamelCase     :Optional[Any]			  =		position_embedding_type
        __UpperCamelCase     :Union[str, Any]			  =		backbone
        __UpperCamelCase     :Any			  =		use_pretrained_backbone
        __UpperCamelCase     :str			  =		dilation
        # deformable attributes
        __UpperCamelCase     :Optional[Any]			  =		num_feature_levels
        __UpperCamelCase     :str			  =		encoder_n_points
        __UpperCamelCase     :int			  =		decoder_n_points
        __UpperCamelCase     :Union[str, Any]			  =		two_stage
        __UpperCamelCase     :Optional[Any]			  =		two_stage_num_proposals
        __UpperCamelCase     :Dict			  =		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
        __UpperCamelCase     :Optional[int]			  =		class_cost
        __UpperCamelCase     :List[Any]			  =		bbox_cost
        __UpperCamelCase     :str			  =		giou_cost
        # Loss coefficients
        __UpperCamelCase     :Tuple			  =		mask_loss_coefficient
        __UpperCamelCase     :Tuple			  =		dice_loss_coefficient
        __UpperCamelCase     :int			  =		bbox_loss_coefficient
        __UpperCamelCase     :Any			  =		giou_loss_coefficient
        __UpperCamelCase     :Dict			  =		eos_coefficient
        __UpperCamelCase     :Optional[Any]			  =		focal_alpha
        __UpperCamelCase     :Optional[Any]			  =		disable_custom_kernels
        super().__init__(is_encoder_decoder=__lowercase , **__lowercase)







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.encoder_attention_heads







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.d_model







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Dict			  =		copy.deepcopy(self.__dict__)
        if self.backbone_config is not None:
            __UpperCamelCase     :Tuple			  =		self.backbone_config.to_dict()
        __UpperCamelCase     :List[Any]			  =		self.__class__.model_type
        return output





