





"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					"dpt"

  def __init__(		self   :       Optional[Any]					,   __a   :       Union[str, Any]=768					,   __a   :       Tuple=12					,   __a   :       Any=12					,   __a   :       Any=3072					,   __a   :       Dict="gelu"					,   __a   :       int=0.0					,   __a   :       int=0.0					,   __a   :       Tuple=0.02					,   __a   :       str=1e-1_2					,   __a   :       Dict=384					,   __a   :       Optional[Any]=16					,   __a   :       List[str]=3					,   __a   :       List[str]=False					,   __a   :       Optional[Any]=True					,   __a   :       str=[2, 5, 8, 11]					,   __a   :       int="project"					,   __a   :       Union[str, Any]=[4, 2, 1, 0.5]					,   __a   :       Optional[int]=[96, 192, 384, 768]					,   __a   :       List[str]=256					,   __a   :       Tuple=-1					,   __a   :       Union[str, Any]=False					,   __a   :       Dict=True					,   __a   :       int=0.4					,   __a   :       List[str]=255					,   __a   :       Optional[Any]=0.1					,   __a   :       Any=[1, 1024, 24, 24]					,   __a   :       Optional[Any]=[0, 1]					,   __a   :       Any=None					,   **__a   :       str					,   )		->				List[str]:
        super().__init__(**__a	)

        _UpperCamelCase      :	Union[str, Any]						=       hidden_size
        _UpperCamelCase      :	List[str]						=       is_hybrid

        if self.is_hybrid:
              if backbone_config is None:
                    logger.info("Initializing the config with a `BiT` backbone."	)
                    _UpperCamelCase      :	int						=       {
                        "global_padding": "same",
                        "layer_type": "bottleneck",
                        "depths": [3, 4, 9],
                        "out_features": ["stage1", "stage2", "stage3"],
                        "embedding_dynamic_padding": True,
                    }
                    _UpperCamelCase      :	Union[str, Any]						=       BitConfig(**__a	)
              elif isinstance(__a					,   __a	):
                    logger.info("Initializing the config with a `BiT` backbone."	)
                    _UpperCamelCase      :	int						=       BitConfig(**__a	)
              elif isinstance(__a					,   __a	):
                    _UpperCamelCase      :	List[str]						=       backbone_config
              else:
                    raise ValueError(
                        F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''	)

              _UpperCamelCase      :	str						=       backbone_featmap_shape
              _UpperCamelCase      :	Tuple						=       neck_ignore_stages

              if readout_type != "project":
                    raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."	)
        else:
              _UpperCamelCase      :	Dict						=       None
              _UpperCamelCase      :	Tuple						=       None
              _UpperCamelCase      :	Optional[int]						=       []

        _UpperCamelCase      :	str						=       num_hidden_layers
        _UpperCamelCase      :	Optional[int]						=       num_attention_heads
        _UpperCamelCase      :	str						=       intermediate_size
        _UpperCamelCase      :	Optional[Any]						=       hidden_act
        _UpperCamelCase      :	Union[str, Any]						=       hidden_dropout_prob
        _UpperCamelCase      :	Optional[int]						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Tuple						=       initializer_range
        _UpperCamelCase      :	Any						=       layer_norm_eps
        _UpperCamelCase      :	Optional[Any]						=       image_size
        _UpperCamelCase      :	Optional[Any]						=       patch_size
        _UpperCamelCase      :	Any						=       num_channels
        _UpperCamelCase      :	List[Any]						=       qkv_bias
        _UpperCamelCase      :	str						=       backbone_out_indices
        if readout_type not in ["ignore", "add", "project"]:
              raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"	)
        _UpperCamelCase      :	Tuple						=       readout_type
        _UpperCamelCase      :	Optional[Any]						=       reassemble_factors
        _UpperCamelCase      :	Union[str, Any]						=       neck_hidden_sizes
        _UpperCamelCase      :	int						=       fusion_hidden_size
        _UpperCamelCase      :	List[str]						=       head_in_index
        _UpperCamelCase      :	Tuple						=       use_batch_norm_in_fusion_residual
        # auxiliary head attributes (semantic segmentation)
        _UpperCamelCase      :	Union[str, Any]						=       use_auxiliary_head
        _UpperCamelCase      :	List[Any]						=       auxiliary_loss_weight
        _UpperCamelCase      :	int						=       semantic_loss_ignore_index
        _UpperCamelCase      :	Dict						=       semantic_classifier_dropout


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[int]:
        _UpperCamelCase      :	List[Any]						=       copy.deepcopy(self.__dict__	)

        if output["backbone_config"] is not None:
              _UpperCamelCase      :	Any						=       self.backbone_config.to_dict()

        _UpperCamelCase      :	Any						=       self.__class__.model_type
        return output







"""simple docstring"""


import math



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       list[list[float]]					,   __a   :       list[int]	)		->				int:
        _UpperCamelCase      :	List[Any]						=       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    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       list[list[int | float]]					,   __a   :       list[int]					,   __a   :       int					,   __a   :       float	)		->				list[list[int | float]]:
        for i in range(len(__a	)	):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights





def 					lowercase__    (					)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

      # training
      _UpperCamelCase      :	List[Any]						=       SelfOrganizingMap()
      _UpperCamelCase      :	int						=       3
      _UpperCamelCase      :	List[Any]						=       0.5

      for _ in range(lowercase_			):
            for j in range(len(lowercase_			)			):
                  # training sample
                  _UpperCamelCase      :	int						=       training_samples[j]

                  # Compute the winning vector
                  _UpperCamelCase      :	Tuple						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

                  # Update the winning vector
                  _UpperCamelCase      :	int						=       self_organizing_map.update(lowercase_		,lowercase_		,lowercase_		,lowercase_			)

    # classify test sample
      _UpperCamelCase      :	Optional[int]						=       [0, 0, 0, 1]
      _UpperCamelCase      :	Dict						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

      # 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()
