






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


UpperCAmelCase__							  =						{
    "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
}

UpperCAmelCase__							  =						logging.get_logger(__name__)






class 						lowercase_   (      SCREAMING_SNAKE_CASE__  ):






      '''simple docstring'''




      __snake_case	    =    '''maskformer'''
      __snake_case	    =    {'''hidden_size''': '''mask_feature_size'''}
      __snake_case	    =    ['''resnet''', '''swin''']
      __snake_case	    =    ['''detr''']







      def __init__( self :		Any					,   __UpperCAmelCase :		Optional[Any] = 256					,   __UpperCAmelCase :		Union[str, Any] = 256					,   __UpperCAmelCase :		Tuple = 0.1					,   __UpperCAmelCase :		List[Any] = False					,   __UpperCAmelCase :		List[Any] = None					,   __UpperCAmelCase :		Optional[Any] = None					,   __UpperCAmelCase :		Optional[Any] = 0.02					,   __UpperCAmelCase :		List[str] = 1.0					,   __UpperCAmelCase :		Optional[Any] = 1.0					,   __UpperCAmelCase :		Optional[Any] = 1.0					,   __UpperCAmelCase :		List[str] = 20.0					,   __UpperCAmelCase :		Any = None					,   **__UpperCAmelCase :		int					,   )       ->Optional[int]:




       """simple docstring"""


       if backbone_config is None:
        # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
        a						    = 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(__UpperCAmelCase					,   __UpperCAmelCase    ):
        a						    = backbone_config.pop('''model_type'''    )
        a						    = CONFIG_MAPPING[backbone_model_type]
        a						    = config_class.from_dict(__UpperCAmelCase    )

       # 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
        a						    = DetrConfig()
       else:
        # verify that the decoder is supported
        a						    = (
            decoder_config.pop('''model_type'''    ) if isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ) 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(__UpperCAmelCase					,   __UpperCAmelCase    ):
         a						    = CONFIG_MAPPING[decoder_type]
         a						    = config_class.from_dict(__UpperCAmelCase    )

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

       a						    = self.decoder_config.encoder_attention_heads
       a						    = self.decoder_config.num_hidden_layers
       super().__init__(**__UpperCAmelCase    )







      @classmethod
      def  __lowerCAmelCase   ( cls :		List[Any]					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		str					,   **__UpperCAmelCase :		List[str]    )       ->Optional[int]:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Dict[str, any]:




       """simple docstring"""


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








def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			if not numbers:
						return 0

			if not isinstance(SCREAMING_SNAKE_CASE       ,    (list, tuple) ) or not all(
			    isinstance(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ) for number in numbers ):
						raise ValueError('numbers must be an iterable of integers' )

			SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						numbers[0]

			for i in range(1       ,    len(SCREAMING_SNAKE_CASE ) ):
						# update the maximum and minimum subarray products
						SCREAMING_SNAKE_CASE_								=						numbers[i]
						if number < 0:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						min_till_now, max_till_now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    max_till_now * number )
						SCREAMING_SNAKE_CASE_								=						min(SCREAMING_SNAKE_CASE       ,    min_till_now * number )

						# update the maximum product found till now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			return max_prod
