



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


a__      :				List[Any]   	=			{
    "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
}

a__      :				List[Any]   	=			logging.get_logger(__name__)



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Any       				=    "maskformer"
   A    :  str       				=    {"hidden_size": "mask_feature_size"}
   A    :  Optional[int]       				=    ["resnet", "swin"]
   A    :  Dict       				=    ["detr"]


   def __init__(						self  :			Tuple			,				lowerCAmelCase  :			int = 2_56			,				lowerCAmelCase  :			int = 2_56			,				lowerCAmelCase  :			float = 0.1			,				lowerCAmelCase  :			bool = False			,				lowerCAmelCase  :			Optional[Dict] = None			,				lowerCAmelCase  :			Optional[Dict] = None			,				lowerCAmelCase  :			float = 0.02			,				lowerCAmelCase  :			float = 1.0			,				lowerCAmelCase  :			float = 1.0			,				lowerCAmelCase  :			float = 1.0			,				lowerCAmelCase  :			float = 20.0			,				lowerCAmelCase  :			Optional[bool] = None			,				**lowerCAmelCase  :			List[Any]			,				)	->				Tuple:




         """simple docstring"""

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

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

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

         lowercase__     =  self.decoder_config.encoder_attention_heads
         lowercase__     =  self.decoder_config.num_hidden_layers
         super().__init__(**lowerCAmelCase)


   @classmethod
   def 			UpperCAmelCase		(						cls  :			Optional[Any]			,				lowerCAmelCase  :			PretrainedConfig			,				lowerCAmelCase  :			PretrainedConfig			,				**lowerCAmelCase  :			List[Any])	->				Dict:




         """simple docstring"""

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



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




         """simple docstring"""

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





from __future__ import annotations

def   _lowerCAmelCase   (						A__						,  A__	):
      if b == 0:
            return (1, 0)
      ((lowercase__),	(lowercase__))     =  extended_euclid(A__						,  a % b	)
      lowercase__     =  a // b
      return (y, x - k * y)

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      ((lowercase__),	(lowercase__))     =  extended_euclid(A__						,  A__	)
      lowercase__     =  na * na
      lowercase__     =  ra * x * na + ra * y * na
      return (n % m + m) % m

def   _lowerCAmelCase   (						A__						,  A__	):
      ((lowercase__),	(lowercase__))     =  extended_euclid(A__						,  A__	)
      if b < 0:
            lowercase__     =  (b % n + n) % n
      return b

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      lowercase__,	lowercase__     =  invert_modulo(A__						,  A__	), invert_modulo(A__						,  A__	)
      lowercase__     =  na * na
      lowercase__     =  ra * x * na + ra * y * na
      return (n % m + m) % m


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

       testmod(name="chinese_remainder_theorem", verbose=True)
       testmod(name="chinese_remainder_theorem2", verbose=True)
       testmod(name="invert_modulo", verbose=True)
       testmod(name="extended_euclid", verbose=True)
