





'''simple docstring'''






from __future__ import annotations

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

A__							:  List[Any]    =TypeVar('''T''')




class 					UpperCAmelCase	(     Generic[T]  ):




      def __init__(     self     :					str     ,     __snake_case     :					list[T]     ,     __snake_case     :					Callable[[T, T], T]    )							-> Optional[int]:
            _lowerCAmelCase					=				None

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




      def 							lowercase__       (     self     :					Dict    )							-> Optional[int]:
            for p in range(self.N - 1     ,     0     ,     -1    ):
                  _lowerCAmelCase					=				self.fn(self.st[p * 2]     ,     self.st[p * 2 + 1]    )




      def 							lowercase__       (     self     :					Any     ,     __snake_case     :					int     ,     __snake_case     :					T    )							-> str:
            p += self.N
            _lowerCAmelCase					=				v
            while p > 1:
                  _lowerCAmelCase					=				p // 2
                  _lowerCAmelCase					=				self.fn(self.st[p * 2]     ,     self.st[p * 2 + 1]    )




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					int     ,     __snake_case     :					int    )							-> Optional[int]:  # noqa: E741
            _lowerCAmelCase	,				_lowerCAmelCase					=				l + self.N, r + self.N

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


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

      A__							:  Dict    =[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

      A__							:  List[str]    ={
          0: 7,
          1: 2,
          2: 6,
          3: -14,
          4: 5,
          5: 4,
          6: 7,
          7: -10,
          8: 9,
          9: 10,
          10: 12,
          11: 1,
      }

      A__							:  Dict    =SegmentTree(test_array, min)
      A__							:  int    =SegmentTree(test_array, max)
      A__							:  str    =SegmentTree(test_array, lambda a, b: a + b)
      def   UpperCamelCase__ (						):





            """simple docstring"""


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

      test_all_segments()

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


