

from __future__ import annotations

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

lowercase__         =      TypeVar("T")




class 	A_			(    Generic[T]							):






     '''simple docstring'''



     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					Tuple					,      lowercase_			:					Dict	)						->     str:
      UpperCAmelCase	:     Any						 =			None

      UpperCAmelCase	:     List[str]						 =			len(lowerCamelCase__	)
      UpperCAmelCase	:     List[Any]						 =			[any_type for _ in range(self.N	)] + arr
      UpperCAmelCase	:     List[Any]						 =			fnc
      self.build()


     def        UpperCAmelCase_	(		self			:					str	)						->     Optional[int]:
      for p in range(self.N - 1					,      0					,      -1	):
       UpperCAmelCase	:     List[Any]						 =			self.fn(self.st[p * 2]					,      self.st[p * 2 + 1]	)


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					List[str]					,      lowercase_			:					str	)						->     int:
      p += self.N
      UpperCAmelCase	:     Any						 =			v
      while p > 1:
       UpperCAmelCase	:     List[str]						 =			p // 2
       UpperCAmelCase	:     Union[str, Any]						 =			self.fn(self.st[p * 2]					,      self.st[p * 2 + 1]	)




     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					List[Any]					,      lowercase_			:					Optional[int]	)						->     Tuple:  # noqa: E741
      UpperCAmelCase ,			UpperCAmelCase	:     int						 =			l + self.N, r + self.N

      UpperCAmelCase	:     str						 =			None
      while l <= r:
       if l % 2 == 1:
        UpperCAmelCase	:     Optional[int]						 =			self.st[l] if res is None else self.fn(lowerCamelCase__					,      self.st[l]	)
       if r % 2 == 0:
        UpperCAmelCase	:     int						 =			self.st[r] if res is None else self.fn(lowerCamelCase__					,      self.st[r]	)
       UpperCAmelCase ,			UpperCAmelCase	:     Tuple						 =			(l + 1) // 2, (r - 1) // 2
      return res


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

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

 lowercase__         =      {
     0: 7,
     1: 2,
     2: 6,
     3: -14,
     4: 5,
     5: 4,
     6: 7,
     7: -10,
     8: 9,
     9: 10,
     10: 12,
     11: 1,
 }

 lowercase__         =      SegmentTree(test_array, min)
 lowercase__         =      SegmentTree(test_array, max)
 lowercase__         =      SegmentTree(test_array, lambda a, b: a + b)




 def 				UpperCamelCase(					):
  for i in range(len(lowercase_     )     ):
   for j in range(lowercase_  ,	len(lowercase_     )     ):
    UpperCAmelCase	:     str						 =			reduce(lowercase_  ,	test_array[i : j + 1]     )
    UpperCAmelCase	:     List[str]						 =			reduce(lowercase_  ,	test_array[i : j + 1]     )
    UpperCAmelCase	:     str						 =			reduce(lambda UpperCAmelCase_  ,	UpperCAmelCase_     : 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():
  lowercase__         =      value
  min_segment_tree.update(index, value)
  max_segment_tree.update(index, value)
  sum_segment_tree.update(index, value)
  test_all_segments()







'''simple docstring'''

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowercase__         =      {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowercase__         =      logging.get_logger(__name__)




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Any  		=  """maskformer"""
     UpperCAmelCase_						:     Union[str, Any]  		=  {"""hidden_size""": """mask_feature_size"""}
     UpperCAmelCase_						:     Union[str, Any]  		=  ["""resnet""", """swin"""]
     UpperCAmelCase_						:     Optional[int]  		=  ["""detr"""]


     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					int = 256					,      lowercase_			:					int = 256					,      lowercase_			:					float = 0.1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					float = 0.02					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 20.0					,      lowercase_			:					Optional[bool] = None					,      **lowercase_			:					int					,      )						->     Optional[Any]:
      if backbone_config is None:
       # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
       UpperCAmelCase	:     List[Any]						 =			SwinConfig(
           image_size=384					,      in_channels=3					,      patch_size=4					,      embed_dim=128					,      depths=[2, 2, 18, 2]					,      num_heads=[4, 8, 16, 32]					,      window_size=12					,      drop_path_rate=0.3					,      out_features=['stage1', 'stage2', 'stage3', 'stage4']					,      )

      if isinstance(lowercase_					,      lowercase_	):
       UpperCAmelCase	:     List[Any]						 =			backbone_config.pop('model_type'	)
       UpperCAmelCase	:     Optional[Any]						 =			CONFIG_MAPPING[backbone_model_type]
       UpperCAmelCase	:     List[Any]						 =			config_class.from_dict(lowercase_	)

      # verify that the backbone is supported
      if backbone_config.model_type not in self.backbones_supported:
       logger.warning_once(
           f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
           f"""Supported model types: {",".join(self.backbones_supported	)}"""	)

      if decoder_config is None:
       # fall back to https://huggingface.co/facebook/detr-resnet-50
       UpperCAmelCase	:     Dict						 =			DetrConfig()
      else:
       # verify that the decoder is supported
       UpperCAmelCase	:     Tuple						 =			(
           decoder_config.pop('model_type'	) if isinstance(lowercase_					,      lowercase_	) else decoder_config.model_type
       )
       if decoder_type not in self.decoders_supported:
        raise ValueError(
            f"""Transformer Decoder {decoder_type} not supported, please use one of"""
            f""" {",".join(self.decoders_supported	)}"""	)
       if isinstance(lowercase_					,      lowercase_	):
        UpperCAmelCase	:     Union[str, Any]						 =			CONFIG_MAPPING[decoder_type]
        UpperCAmelCase	:     str						 =			config_class.from_dict(lowercase_	)

      UpperCAmelCase	:     int						 =			backbone_config
      UpperCAmelCase	:     Tuple						 =			decoder_config
      # main feature dimension for the model
      UpperCAmelCase	:     Union[str, Any]						 =			fpn_feature_size
      UpperCAmelCase	:     Optional[Any]						 =			mask_feature_size
      # initializer
      UpperCAmelCase	:     Union[str, Any]						 =			init_std
      UpperCAmelCase	:     List[str]						 =			init_xavier_std
      # Hungarian matcher && loss
      UpperCAmelCase	:     int						 =			cross_entropy_weight
      UpperCAmelCase	:     Tuple						 =			dice_weight
      UpperCAmelCase	:     int						 =			mask_weight
      UpperCAmelCase	:     Any						 =			use_auxiliary_loss
      UpperCAmelCase	:     Tuple						 =			no_object_weight
      UpperCAmelCase	:     str						 =			output_auxiliary_logits

      UpperCAmelCase	:     Dict						 =			self.decoder_config.encoder_attention_heads
      UpperCAmelCase	:     Union[str, Any]						 =			self.decoder_config.num_hidden_layers
      super().__init__(**lowercase_	)


     @classmethod
     def        UpperCAmelCase_	(		cls			:					Any					,      lowercase_			:					PretrainedConfig					,      lowercase_			:					PretrainedConfig					,      **lowercase_			:					Optional[Any]	)						->     str:
      return cls(
          backbone_config=lowercase_					,      decoder_config=lowercase_					,      **lowercase_					,      )




     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Dict[str, any]:
      UpperCAmelCase	:     Optional[int]						 =			copy.deepcopy(self.__dict__	)
      UpperCAmelCase	:     List[str]						 =			self.backbone_config.to_dict()
      UpperCAmelCase	:     Any						 =			self.decoder_config.to_dict()
      UpperCAmelCase	:     Union[str, Any]						 =			self.__class__.model_type
      return output




