


'''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


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 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     'maskformer'
      lowerCAmelCase__		     =     {'hidden_size': 'mask_feature_size'}
      lowerCAmelCase__		     =     ['resnet', 'swin']
      lowerCAmelCase__		     =     ['detr']






      def __init__(					self				:    List[str]						,						_A				:    int = 256						,						_A				:    int = 256						,						_A				:    float = 0.1						,						_A				:    bool = False						,						_A				:    Optional[Dict] = None						,						_A				:    Optional[Dict] = None						,						_A				:    float = 0.0_2						,						_A				:    float = 1.0						,						_A				:    float = 1.0						,						_A				:    float = 1.0						,						_A				:    float = 2_0.0						,						_A				:    Optional[bool] = None						,						**_A				:    Tuple						,						):


          '''simple docstring'''


          if backbone_config is None:
              # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
              UpperCAmelCase__			:		List[Any]													=				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__			:		Union[str, Any]													=				backbone_config.pop('''model_type''' )
              UpperCAmelCase__			:		Dict													=				CONFIG_MAPPING[backbone_model_type]
              UpperCAmelCase__			:		Union[str, Any]													=				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__			:		int													=				DetrConfig()
          else:
              # verify that the decoder is supported
              UpperCAmelCase__			:		List[Any]													=				(
                  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__			:		Optional[Any]													=				CONFIG_MAPPING[decoder_type]
                  UpperCAmelCase__			:		Any													=				config_class.from_dict(_A )

          UpperCAmelCase__			:		List[Any]													=				backbone_config
          UpperCAmelCase__			:		Optional[Any]													=				decoder_config
          # main feature dimension for the model
          UpperCAmelCase__			:		Dict													=				fpn_feature_size
          UpperCAmelCase__			:		Optional[int]													=				mask_feature_size
          # initializer
          UpperCAmelCase__			:		List[str]													=				init_std
          UpperCAmelCase__			:		List[Any]													=				init_xavier_std
          # Hungarian matcher && loss
          UpperCAmelCase__			:		Dict													=				cross_entropy_weight
          UpperCAmelCase__			:		str													=				dice_weight
          UpperCAmelCase__			:		int													=				mask_weight
          UpperCAmelCase__			:		Any													=				use_auxiliary_loss
          UpperCAmelCase__			:		int													=				no_object_weight
          UpperCAmelCase__			:		Dict													=				output_auxiliary_logits

          UpperCAmelCase__			:		Union[str, Any]													=				self.decoder_config.encoder_attention_heads
          UpperCAmelCase__			:		Dict													=				self.decoder_config.num_hidden_layers
          super().__init__(**_A )






      @classmethod
      def lowercase_							(					cls				:    str						,						_A				:    PretrainedConfig						,						_A				:    PretrainedConfig						,						**_A				:    List[str] ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				copy.deepcopy(self.__dict__ )
          UpperCAmelCase__			:		Union[str, Any]													=				self.backbone_config.to_dict()
          UpperCAmelCase__			:		Optional[int]													=				self.decoder_config.to_dict()
          UpperCAmelCase__			:		Tuple													=				self.__class__.model_type
          return output




'''simple docstring'''
import math




class 			lowerCamelCase_   :






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    list[list[float]]						,						_A				:    list[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				0.0
          UpperCAmelCase__			:		Union[str, Any]													=				0.0
          for i in range(len(_A ) ):
              da += math.pow((sample[i] - weights[0][i])						,						2 )
              da += math.pow((sample[i] - weights[1][i])						,						2 )
              return 0 if da > da else 1
          return 0






      def lowercase_							(					self				:    Dict						,						_A				:    list[list[int | float]]						,						_A				:    list[int]						,						_A				:    int						,						_A				:    float ):


          '''simple docstring'''


          for i in range(len(_A ) ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
          return weights





def 					a__       (					)    ->	None:
    # Training Examples ( m, n )
    UpperCAmelCase__			:		Optional[Any]													=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    UpperCAmelCase__			:		str													=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    UpperCAmelCase__			:		Optional[Any]													=				SelfOrganizingMap()
    UpperCAmelCase__			:		Tuple													=				3
    UpperCAmelCase__			:		Dict													=				0.5

    for _ in range(lowerCAmelCase__						):
        for j in range(len(lowerCAmelCase__						)						):
            # training sample
            UpperCAmelCase__			:		Optional[int]													=				training_samples[j]

            # Compute the winning vector
            UpperCAmelCase__			:		str													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

            # Update the winning vector
            UpperCAmelCase__			:		int													=				self_organizing_map.update(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

    # classify test sample
    UpperCAmelCase__			:		str													=				[0, 0, 0, 1]
    UpperCAmelCase__			:		int													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

    # results
    print(F"""Clusters that the test sample belongs to : {winner}"""						)
    print(F"""Weights that have been trained : {weights}"""						)


# running the main() function
if __name__ == "__main__":
  main()
