
'''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


lowerCAmelCase					:    str           = {
    "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
}

lowerCAmelCase					:    str           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""maskformer"""
				lowerCAmelCase_     			=							{"""hidden_size""": """mask_feature_size"""}
				lowerCAmelCase_     			=							["""resnet""", """swin"""]
				lowerCAmelCase_     			=							["""detr"""]







				def __init__(       self						,				A_ = 256						,				A_ = 256						,				A_ = 0.1						,				A_ = False						,				A_ = None						,				A_ = None						,				A_ = 0.02						,				A_ = 1.0						,				A_ = 1.0						,				A_ = 1.0						,				A_ = 20.0						,				A_ = None						,				**A_						,				)->		int:







											'''simple docstring'''






											if backbone_config is None:
																		# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																		UpperCamelCase   			=     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(A_						,				A_     ):
																		UpperCamelCase   			=     backbone_config.pop('model_type'     )
																		UpperCamelCase   			=     CONFIG_MAPPING[backbone_model_type]
																		UpperCamelCase   			=     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 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   			=     DetrConfig()
											else:
																		# verify that the decoder is supported
																		UpperCamelCase   			=     (
																		    decoder_config.pop('model_type'     ) if isinstance(A_						,				A_     ) 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(A_						,				A_     ):
																									UpperCamelCase   			=     CONFIG_MAPPING[decoder_type]
																									UpperCamelCase   			=     config_class.from_dict(A_     )

											UpperCamelCase   			=     backbone_config
											UpperCamelCase   			=     decoder_config
											# main feature dimension for the model
											UpperCamelCase   			=     fpn_feature_size
											UpperCamelCase   			=     mask_feature_size
											# initializer
											UpperCamelCase   			=     init_std
											UpperCamelCase   			=     init_xavier_std
											# Hungarian matcher && loss
											UpperCamelCase   			=     cross_entropy_weight
											UpperCamelCase   			=     dice_weight
											UpperCamelCase   			=     mask_weight
											UpperCamelCase   			=     use_auxiliary_loss
											UpperCamelCase   			=     no_object_weight
											UpperCamelCase   			=     output_auxiliary_logits

											UpperCamelCase   			=     self.decoder_config.encoder_attention_heads
											UpperCamelCase   			=     self.decoder_config.num_hidden_layers
											super().__init__(**A_     )







				@classmethod
				def 						UpperCAmelCase_    (       cls						,				A_						,				A_						,				**A_     )->		Optional[int]:







											'''simple docstring'''






											return cls(
											    backbone_config=A_						,				decoder_config=A_						,				**A_						,				)







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






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










from math import factorial, radians





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase = 18				,     __lowerCamelCase = 10       ) ->			float:
	a									=				angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

	# Converting from degrees to radians
	a									=				radians(__lowerCamelCase       )

	a									=				angle_in_radians
	a									=				3
	a									=				-1

	for _ in range(__lowerCamelCase       ):
		result += (b * (angle_in_radians**a)) / factorial(__lowerCamelCase       )

		a									=				-b  # One positive term and the next will be negative and so on...
		a += 2  # Increased by 2 for every term.

	return round(__lowerCamelCase				,     __lowerCamelCase       )


if __name__ == "__main__":
		__import__("doctest").testmod()

