



"""simple docstring"""







import copy
from typing import Dict, List, Optional

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


A		=						{
    '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
}

A		=						logging.get_logger(__name__)

class       _a  ( __A):
			__magic_name__     					=     """mask2former"""
			__magic_name__     					=     ["""swin"""]
			__magic_name__     					=     {"""hidden_size""": """hidden_dim"""}



			def __init__(			self							:				Dict   ,	_lowercase							:				Optional[Dict] = None   ,	_lowercase							:				int = 256   ,	_lowercase							:				int = 256   ,	_lowercase							:				int = 256   ,	_lowercase							:				int = 1024   ,	_lowercase							:				str = "relu"   ,	_lowercase							:				int = 6   ,	_lowercase							:				int = 10   ,	_lowercase							:				int = 8   ,	_lowercase							:				float = 0.0   ,	_lowercase							:				int = 2048   ,	_lowercase							:				bool = False   ,	_lowercase							:				bool = False   ,	_lowercase							:				int = 4   ,	_lowercase							:				int = 255   ,	_lowercase							:				int = 100   ,	_lowercase							:				float = 0.1   ,	_lowercase							:				float = 2.0   ,	_lowercase							:				float = 5.0   ,	_lowercase							:				float = 5.0   ,	_lowercase							:				int = 12544   ,	_lowercase							:				float = 3.0   ,	_lowercase							:				float = 0.75   ,	_lowercase							:				float = 0.02   ,	_lowercase							:				float = 1.0   ,	_lowercase							:				bool = True   ,	_lowercase							:				List[int] = [4, 8, 16, 32]   ,	_lowercase							:				bool = None   ,	**_lowercase							:				Optional[int]   ,	)							->					List[Any]:

									if backbone_config is None:
															logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."		)
															snake_case   :			Tuple						    = CONFIG_MAPPING["swin"](
															    image_size=224   ,	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=_lowercase   ,	out_features=["stage1", "stage2", "stage3", "stage4"]   ,	)

									if isinstance(_lowercase   ,	_lowercase		):
															snake_case   :			int						    = backbone_config.pop("model_type"		)
															snake_case   :			List[Any]						    = CONFIG_MAPPING[backbone_model_type]
															snake_case   :			Tuple						    = 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 Mask2Former. '''
															    F'''Supported model types: {",".join(self.backbones_supported		)}'''		)

									snake_case   :			Optional[Any]						    = backbone_config
									snake_case   :			int						    = feature_size
									snake_case   :			Dict						    = mask_feature_size
									snake_case   :			Any						    = hidden_dim
									snake_case   :			Any						    = encoder_feedforward_dim
									snake_case   :			Tuple						    = activation_function
									snake_case   :			Tuple						    = encoder_layers
									snake_case   :			Dict						    = decoder_layers
									snake_case   :			List[Any]						    = num_attention_heads
									snake_case   :			List[Any]						    = dropout
									snake_case   :			int						    = dim_feedforward
									snake_case   :			int						    = pre_norm
									snake_case   :			Union[str, Any]						    = enforce_input_projection
									snake_case   :			int						    = common_stride
									snake_case   :			Optional[Any]						    = ignore_value
									snake_case   :			Tuple						    = num_queries
									snake_case   :			List[str]						    = no_object_weight
									snake_case   :			Optional[int]						    = class_weight
									snake_case   :			int						    = mask_weight
									snake_case   :			Tuple						    = dice_weight
									snake_case   :			Union[str, Any]						    = train_num_points
									snake_case   :			Tuple						    = oversample_ratio
									snake_case   :			int						    = importance_sample_ratio
									snake_case   :			Any						    = init_std
									snake_case   :			Union[str, Any]						    = init_xavier_std
									snake_case   :			List[Any]						    = use_auxiliary_loss
									snake_case   :			Optional[int]						    = feature_strides
									snake_case   :			Tuple						    = output_auxiliary_logits
									snake_case   :			str						    = decoder_layers

									super().__init__(**_lowercase		)



			@classmethod
			def 				__lowercase					(			cls							:				Any   ,	_lowercase							:				PretrainedConfig   ,	**_lowercase							:				str		)							->					Optional[int]:
									return cls(
									    backbone_config=_lowercase   ,	**_lowercase   ,	)



			def 				__lowercase					(			self							:				Optional[Any]		)							->					Dict[str, any]:
									snake_case   :			Any						    = copy.deepcopy(self.__dict__		)
									snake_case   :			int						    = self.backbone_config.to_dict()
									snake_case   :			str						    = self.__class__.model_type
									return output






from math import factorial, pi

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_sin() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   int					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_cos() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   str					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(maclaurin_sin(10))
			print(maclaurin_sin(-10))
			print(maclaurin_sin(10, 15))
			print(maclaurin_sin(-10, 15))

			print(maclaurin_cos(5))
			print(maclaurin_cos(-5))
			print(maclaurin_cos(10, 15))
			print(maclaurin_cos(-10, 15))