



import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE__   			=   {
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Any      							=     "mask2former"
					A__					:		int      							=     ["swin"]
					A__					:		Union[str, Any]      							=     {"hidden_size": "hidden_dim"}



					def __init__(  self       :							Union[str, Any]					,       _snake_case       :							Optional[Dict] = None					,       _snake_case       :							int = 2_56					,       _snake_case       :							int = 2_56					,       _snake_case       :							int = 2_56					,       _snake_case       :							int = 10_24					,       _snake_case       :							str = "relu"					,       _snake_case       :							int = 6					,       _snake_case       :							int = 10					,       _snake_case       :							int = 8					,       _snake_case       :							float = 0.0					,       _snake_case       :							int = 20_48					,       _snake_case       :							bool = False					,       _snake_case       :							bool = False					,       _snake_case       :							int = 4					,       _snake_case       :							int = 2_55					,       _snake_case       :							int = 1_00					,       _snake_case       :							float = 0.1					,       _snake_case       :							float = 2.0					,       _snake_case       :							float = 5.0					,       _snake_case       :							float = 5.0					,       _snake_case       :							int = 1_25_44					,       _snake_case       :							float = 3.0					,       _snake_case       :							float = 0.75					,       _snake_case       :							float = 0.02					,       _snake_case       :							float = 1.0					,       _snake_case       :							bool = True					,       _snake_case       :							List[int] = [4, 8, 16, 32]					,       _snake_case       :							bool = None					,       **_snake_case       :							str					,       ):






								"""simple docstring"""


								if backbone_config is None:
											logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'       )
											A__	  =     CONFIG_MAPPING['swin'](
											    image_size=2_24					,       in_channels=3					,       patch_size=4					,       embed_dim=96					,       depths=[2, 2, 18, 2]					,       num_heads=[3, 6, 12, 24]					,       window_size=7					,       drop_path_rate=0.3					,       use_absolute_embeddings=_snake_case					,       out_features=['stage1', 'stage2', 'stage3', 'stage4']					,       )

								if isinstance(_snake_case					,       _snake_case       ):
											A__	  =     backbone_config.pop('model_type'       )
											A__	  =     CONFIG_MAPPING[backbone_model_type]
											A__	  =     config_class.from_dict(_snake_case       )

								# 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 Mask2Former. '''
											    F'''Supported model types: {",".join(self.backbones_supported       )}'''       )

								A__	  =     backbone_config
								A__	  =     feature_size
								A__	  =     mask_feature_size
								A__	  =     hidden_dim
								A__	  =     encoder_feedforward_dim
								A__	  =     activation_function
								A__	  =     encoder_layers
								A__	  =     decoder_layers
								A__	  =     num_attention_heads
								A__	  =     dropout
								A__	  =     dim_feedforward
								A__	  =     pre_norm
								A__	  =     enforce_input_projection
								A__	  =     common_stride
								A__	  =     ignore_value
								A__	  =     num_queries
								A__	  =     no_object_weight
								A__	  =     class_weight
								A__	  =     mask_weight
								A__	  =     dice_weight
								A__	  =     train_num_points
								A__	  =     oversample_ratio
								A__	  =     importance_sample_ratio
								A__	  =     init_std
								A__	  =     init_xavier_std
								A__	  =     use_auxiliary_loss
								A__	  =     feature_strides
								A__	  =     output_auxiliary_logits
								A__	  =     decoder_layers

								super().__init__(**_snake_case       )



					@classmethod
					def      _a				(  cls       :							Union[str, Any]					,       _snake_case       :							PretrainedConfig					,       **_snake_case       :							Tuple       ):






								"""simple docstring"""


								return cls(
								    backbone_config=_snake_case					,       **_snake_case					,       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     copy.deepcopy(self.__dict__       )
								A__	  =     self.backbone_config.to_dict()
								A__	  =     self.__class__.model_type
								return output









def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     0
			A__	  =     len(__UpperCamelCase       ) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(__UpperCamelCase       ):
									return None

						A__	  =     sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												A__	  =     left
												A__	  =     point
									elif point > right:
												A__	  =     right
												A__	  =     point
									else:
												if item < current_item:
															A__	  =     point - 1
												else:
															A__	  =     point + 1
			return None


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			# avoid divided by 0 during interpolation
			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(__UpperCamelCase       ):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			elif point > right:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						point - 1       )
						else:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						point + 1			,						__UpperCamelCase       )


def A    (							__UpperCamelCase       )    ->   List[str]:
			if collection != sorted(__UpperCamelCase       ):
						raise ValueError('Collection must be ascending sorted'       )
			return True


if __name__ == "__main__":
							import sys
							SCREAMING_SNAKE_CASE__   			=   0
							if debug == 1:
														SCREAMING_SNAKE_CASE__   			=   [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
														try:
																					__assert_sorted(collection)
														except ValueError:
																					sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
														SCREAMING_SNAKE_CASE__   			=   6_7

							SCREAMING_SNAKE_CASE__   			=   interpolation_search(collection, target)
							if result is not None:
														print(f'{target} found at positions: {result}')
							else:
														print('''Not found''')




