



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


lowercase_	       =			{
    '''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
}

lowercase_	       =			logging.get_logger(__name__)






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "maskformer"
	SCREAMING_SNAKE_CASE       =       {"hidden_size": "mask_feature_size"}
	SCREAMING_SNAKE_CASE       =       ["resnet", "swin"]
	SCREAMING_SNAKE_CASE       =       ["detr"]



	def __init__(						self							:    Dict				,	snake_case_							:    int = 2_56				,	snake_case_							:    int = 2_56				,	snake_case_							:    float = 0.1				,	snake_case_							:    bool = False				,	snake_case_							:    Optional[Dict] = None				,	snake_case_							:    Optional[Dict] = None				,	snake_case_							:    float = 0.0_2				,	snake_case_							:    float = 1.0				,	snake_case_							:    float = 1.0				,	snake_case_							:    float = 1.0				,	snake_case_							:    float = 2_0.0				,	snake_case_							:    Optional[bool] = None				,	**snake_case_							:    List[str]				,	)->							Tuple:
						if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											__lowerCAmelCase	   =SwinConfig(
											    image_size=3_84				,	in_channels=3				,	patch_size=4				,	embed_dim=1_28				,	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(snake_case_				,	snake_case_):
											__lowerCAmelCase	   =backbone_config.pop("""model_type""")
											__lowerCAmelCase	   =CONFIG_MAPPING[backbone_model_type]
											__lowerCAmelCase	   =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 MaskFormer. """
											    F"""Supported model types: {','.join(self.backbones_supported)}""")

						if decoder_config is None:
											# fall back to https://huggingface.co/facebook/detr-resnet-50
											__lowerCAmelCase	   =DetrConfig()
						else:
											# verify that the decoder is supported
											__lowerCAmelCase	   =(
											    decoder_config.pop("""model_type""") if isinstance(snake_case_				,	snake_case_) 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(snake_case_				,	snake_case_):
																__lowerCAmelCase	   =CONFIG_MAPPING[decoder_type]
																__lowerCAmelCase	   =config_class.from_dict(snake_case_)

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

						__lowerCAmelCase	   =self.decoder_config.encoder_attention_heads
						__lowerCAmelCase	   =self.decoder_config.num_hidden_layers
						super().__init__(**snake_case_)



	@classmethod
	def 		UpperCamelCase							(						cls							:    Union[str, Any]				,	snake_case_							:    PretrainedConfig				,	snake_case_							:    PretrainedConfig				,	**snake_case_							:    Dict)->							Any:
						return cls(
						    backbone_config=snake_case_				,	decoder_config=snake_case_				,	**snake_case_				,	)



	def 		UpperCamelCase							(						self							:    Dict)->							Dict[str, any]:
						__lowerCAmelCase	   =copy.deepcopy(self.__dict__)
						__lowerCAmelCase	   =self.backbone_config.to_dict()
						__lowerCAmelCase	   =self.decoder_config.to_dict()
						__lowerCAmelCase	   =self.__class__.model_type
						return output









import math

from numpy import inf
from scipy.integrate import quad






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float	)  ->						float:
					if num <= 0:
										raise ValueError("""math domain error"""	)

					return quad(__lowerCamelCase				,   0				,   __lowerCamelCase				,   args=(__lowerCamelCase)	)[0]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float				,   __lowerCamelCase	:    float	)  ->						float:
					return math.pow(__lowerCamelCase				,   z - 1	) * math.exp(-x	)


if __name__ == "__main__":
				from doctest import testmod

				testmod()




