

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 _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='mask2former'
			lowerCamelCase__							     =['swin']
			lowerCamelCase__							     ={'hidden_size': 'hidden_dim'}
			def __init__(      self				:			Dict   ,   a				:			Optional[Dict] = None   ,   a				:			int = 256   ,   a				:			int = 256   ,   a				:			int = 256   ,   a				:			int = 1024   ,   a				:			str = "relu"   ,   a				:			int = 6   ,   a				:			int = 10   ,   a				:			int = 8   ,   a				:			float = 0.0   ,   a				:			int = 2048   ,   a				:			bool = False   ,   a				:			bool = False   ,   a				:			int = 4   ,   a				:			int = 255   ,   a				:			int = 100   ,   a				:			float = 0.1   ,   a				:			float = 2.0   ,   a				:			float = 5.0   ,   a				:			float = 5.0   ,   a				:			int = 1_2544   ,   a				:			float = 3.0   ,   a				:			float = 0.75   ,   a				:			float = 0.02   ,   a				:			float = 1.0   ,   a				:			bool = True   ,   a				:			List[int] = [4, 8, 16, 32]   ,   a				:			bool = None   ,   **a				:			Optional[int]   ,   )				->							List[Any]:

				"""simple docstring"""

				if backbone_config is None:
					logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."						)
					SCREAMING_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=a   ,   out_features=["stage1", "stage2", "stage3", "stage4"]   ,   )

				if isinstance(a   ,   a						):
					SCREAMING_SNAKE_CASE :   int					  =							backbone_config.pop("model_type"						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							CONFIG_MAPPING[backbone_model_type]
					SCREAMING_SNAKE_CASE :   Tuple					  =							config_class.from_dict(a						)

				# 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						)}"						)

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

				super().__init__(**a						)
			@classmethod
			def 				__UpperCamelCase			(      cls				:			Any   ,   a				:			PretrainedConfig   ,   **a				:			str						)				->							Optional[int]:

				"""simple docstring"""

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






			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Dict[str, any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							copy.deepcopy(self.__dict__						)
				SCREAMING_SNAKE_CASE :   int					  =							self.backbone_config.to_dict()
				SCREAMING_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))