




'''simple docstring'''




import copy
from typing import Dict, Optional

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


snake_case__          =						logging.get_logger(__name__)

snake_case__          =						{
    '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_     (a__     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					'oneformer'
						_lowerCAmelCase    						=					{'hidden_size': 'hidden_dim'}







						def __init__(     self      :       List[Any]							, _lowerCamelCase      :       Optional[Dict] = None							, _lowerCamelCase      :       int = 255							, _lowerCamelCase      :       int = 150							, _lowerCamelCase      :       int = 0.1							, _lowerCamelCase      :       float = 2.0							, _lowerCamelCase      :       float = 5.0							, _lowerCamelCase      :       float = 5.0							, _lowerCamelCase      :       float = 0.5							, _lowerCamelCase      :       float = 0.07							, _lowerCamelCase      :       int = 12544							, _lowerCamelCase      :       float = 3.0							, _lowerCamelCase      :       float = 0.75							, _lowerCamelCase      :       float = 0.02							, _lowerCamelCase      :       float = 1.0							, _lowerCamelCase      :       float = 1E-05							, _lowerCamelCase      :       bool = False							, _lowerCamelCase      :       bool = True							, _lowerCamelCase      :       bool = True							, _lowerCamelCase      :       Optional[list] = [4, 8, 16, 32]							, _lowerCamelCase      :       int = 77							, _lowerCamelCase      :       int = 256							, _lowerCamelCase      :       int = 77							, _lowerCamelCase      :       int = 6							, _lowerCamelCase      :       int = 49408							, _lowerCamelCase      :       int = 2							, _lowerCamelCase      :       int = 16							, _lowerCamelCase      :       int = 256							, _lowerCamelCase      :       int = 256							, _lowerCamelCase      :       int = 256							, _lowerCamelCase      :       int = 1024							, _lowerCamelCase      :       str = "GN"							, _lowerCamelCase      :       int = 6							, _lowerCamelCase      :       int = 10							, _lowerCamelCase      :       bool = True							, _lowerCamelCase      :       int = 8							, _lowerCamelCase      :       float = 0.1							, _lowerCamelCase      :       int = 2048							, _lowerCamelCase      :       bool = False							, _lowerCamelCase      :       bool = False							, _lowerCamelCase      :       int = 2							, _lowerCamelCase      :       int = 4							, **_lowerCamelCase      :       List[Any]							, ):





											"""simple docstring"""







											if backbone_config is None:
																logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''    )
																A_ :     Any =							CONFIG_MAPPING['''swin'''](
																    image_size=224							, in_channels=3							, patch_size=4							, embed_dim=96							, depths=[2, 2, 6, 2]							, num_heads=[3, 6, 12, 24]							, window_size=7							, drop_path_rate=0.3							, use_absolute_embeddings=A_							, out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							, )
											elif isinstance(A_							, A_    ):
																A_ :     int =							backbone_config.get('''model_type'''    )
																A_ :     Dict =							CONFIG_MAPPING[backbone_model_type]
																A_ :     List[Any] =							config_class.from_dict(A_    )

											A_ :     int =							backbone_config

											A_ :     List[str] =							ignore_value
											A_ :     List[str] =							num_queries
											A_ :     Union[str, Any] =							no_object_weight
											A_ :     List[Any] =							class_weight
											A_ :     Optional[int] =							mask_weight
											A_ :     Tuple =							dice_weight
											A_ :     Any =							contrastive_weight
											A_ :     List[str] =							contrastive_temperature
											A_ :     Any =							train_num_points
											A_ :     Tuple =							oversample_ratio
											A_ :     Optional[int] =							importance_sample_ratio
											A_ :     Dict =							init_std
											A_ :     List[str] =							init_xavier_std
											A_ :     Any =							layer_norm_eps
											A_ :     Optional[int] =							is_training
											A_ :     List[Any] =							use_auxiliary_loss
											A_ :     Optional[Any] =							output_auxiliary_logits
											A_ :     List[Any] =							strides
											A_ :     Tuple =							task_seq_len
											A_ :     Dict =							text_encoder_width
											A_ :     int =							text_encoder_context_length
											A_ :     str =							text_encoder_num_layers
											A_ :     Optional[int] =							text_encoder_vocab_size
											A_ :     List[str] =							text_encoder_proj_layers
											A_ :     Dict =							text_encoder_n_ctx
											A_ :     Dict =							conv_dim
											A_ :     Any =							mask_dim
											A_ :     List[Any] =							hidden_dim
											A_ :     str =							encoder_feedforward_dim
											A_ :     Any =							norm
											A_ :     str =							encoder_layers
											A_ :     Tuple =							decoder_layers
											A_ :     Tuple =							use_task_norm
											A_ :     Union[str, Any] =							num_attention_heads
											A_ :     str =							dropout
											A_ :     List[Any] =							dim_feedforward
											A_ :     Dict =							pre_norm
											A_ :     List[str] =							enforce_input_proj
											A_ :     Optional[int] =							query_dec_layers
											A_ :     Optional[Any] =							common_stride
											A_ :     Optional[Any] =							decoder_layers

											super().__init__(**A_    )







						def       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											A_ :     Any =							copy.deepcopy(self.__dict__    )
											A_ :     List[str] =							self.backbone_config.to_dict()
											A_ :     Any =							self.__class__.model_type
											return output









'''simple docstring'''




from __future__ import annotations

snake_case__          =						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class     UpperCamelCase_     :







						"""simple docstring"""


						def __init__(     self      :       Dict							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											A_ :     int =							-1
											A_ :     int =							-1
											A_ :     list[int] =							[-1] * length
											A_ :     Node | None =							None
											A_ :     Node | None =							None







						def __repr__(     self      :       List[str]    ):





											"""simple docstring"""







											return f'Node(min_value={self.minn} max_value={self.maxx})'






def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   Node | None:
					A_ :     Optional[Any] =							Node(len(lowerCamelCase__			)			)
					A_ ,A_ :     str =							min(lowerCamelCase__			), max(lowerCamelCase__			)
					# Leaf node case where the node contains only one unique value
					if root.minn == root.maxx:
										return root
					A_ :     Optional[int] =							(root.minn + root.maxx) // 2

					A_ :     list[int] =							[]
					A_ :     list[int] =							[]

					for index, num in enumerate(lowerCamelCase__			):
										if num <= pivot:
															left_arr.append(lowerCamelCase__			)
										else:
															right_arr.append(lowerCamelCase__			)
										A_ :     Union[str, Any] =							len(lowerCamelCase__			)
					A_ :     Optional[int] =							build_tree(lowerCamelCase__			)
					A_ :     Optional[int] =							build_tree(lowerCamelCase__			)
					return root




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if index < 0 or node is None:
										return 0
					# Leaf node cases
					if node.minn == node.maxx:
										return index + 1 if node.minn == num else 0
					A_ :     List[Any] =							(node.minn + node.maxx) // 2
					if num <= pivot:
										# go the left subtree and map index to the left subtree
										return rank_till_index(node.left			,		lowerCamelCase__			,		node.map_left[index] - 1			)
					else:
										# go to the right subtree and map index to the right subtree
										return rank_till_index(node.right			,		lowerCamelCase__			,		index - node.map_left[index]			)




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if start > end:
										return 0
					A_ :     Union[str, Any] =							rank_till_index(lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			)
					A_ :     Union[str, Any] =							rank_till_index(lowerCamelCase__			,		lowerCamelCase__			,		start - 1			)
					return rank_till_end - rank_before_start




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if index > (end - start) or start > end or node is None:
										return -1
					# Leaf node case
					if node.minn == node.maxx:
										return node.minn
					# Number of elements in the left subtree in interval [start, end]
					A_ :     List[str] =							node.map_left[end] - (
					    node.map_left[start - 1] if start else 0
					)
					if num_elements_in_left_tree > index:
										return quantile(
										    node.left			,		lowerCamelCase__			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		)
					else:
										return quantile(
										    node.right			,		index - num_elements_in_left_tree			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		)




def 						snake_case__    ( lowerCamelCase__				:   Node | None			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			,		lowerCamelCase__				:   int			)   ->   int:
					if (
					    start > end
					    or node is None
					    or start_num > end_num
					    or node.minn > end_num
					    or node.maxx < start_num
					):
										return 0
					if start_num <= node.minn and node.maxx <= end_num:
										return end - start + 1
					A_ :     int =							range_counting(
					    node.left			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		lowerCamelCase__			,		lowerCamelCase__			,		)
					A_ :     List[str] =							range_counting(
					    node.right			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		lowerCamelCase__			,		lowerCamelCase__			,		)
					return left + right


if __name__ == "__main__":
							import doctest

							doctest.testmod()



