



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase						:		Any			= logging.get_logger(__name__)

UpperCAmelCase						:		Any			= {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):
    lowercase__      =   "swin"

    lowercase__      =   {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }

    def __init__(				self			:				List[Any] ,  lowerCAmelCase_			:				Union[str, Any]=2_2_4 ,  lowerCAmelCase_			:				Union[str, Any]=4 ,  lowerCAmelCase_			:				str=3 ,  lowerCAmelCase_			:				Optional[int]=9_6 ,  lowerCAmelCase_			:				List[str]=[2, 2, 6, 2] ,  lowerCAmelCase_			:				Dict=[3, 6, 1_2, 2_4] ,  lowerCAmelCase_			:				Dict=7 ,  lowerCAmelCase_			:				Union[str, Any]=4.0 ,  lowerCAmelCase_			:				Optional[int]=True ,  lowerCAmelCase_			:				Dict=0.0 ,  lowerCAmelCase_			:				Dict=0.0 ,  lowerCAmelCase_			:				Dict=0.1 ,  lowerCAmelCase_			:				List[Any]="gelu" ,  lowerCAmelCase_			:				List[Any]=False ,  lowerCAmelCase_			:				List[Any]=0.02 ,  lowerCAmelCase_			:				List[str]=1E-5 ,  lowerCAmelCase_			:				Optional[Any]=3_2 ,  lowerCAmelCase_			:				List[str]=None ,  lowerCAmelCase_			:				Optional[Any]=None ,  **lowerCAmelCase_			:				int ,  ):







      """simple docstring"""







      super().__init__(**lowerCAmelCase_)

      lowercase_								= image_size
      lowercase_								= patch_size
      lowercase_								= num_channels
      lowercase_								= embed_dim
      lowercase_								= depths
      lowercase_								= len(lowerCAmelCase_)
      lowercase_								= num_heads
      lowercase_								= window_size
      lowercase_								= mlp_ratio
      lowercase_								= qkv_bias
      lowercase_								= hidden_dropout_prob
      lowercase_								= attention_probs_dropout_prob
      lowercase_								= drop_path_rate
      lowercase_								= hidden_act
      lowercase_								= use_absolute_embeddings
      lowercase_								= layer_norm_eps
      lowercase_								= initializer_range
      lowercase_								= encoder_stride
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      lowercase_								= int(embed_dim * 2 ** (len(lowerCAmelCase_) - 1))
      lowercase_								= ["""stem"""] + [F'''stage{idx}''' for idx in range(1 ,  len(lowerCAmelCase_) + 1)]
      lowercase_					,    lowercase_								= get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase_ ,  out_indices=lowerCAmelCase_ ,  stage_names=self.stage_names)







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   version.parse("1.11"   )

    @property
    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ])




    @property
    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      return 1E-4






"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple:







  '''simple docstring'''


  return (data["data"], data["target"])


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						np.ndarray:







  '''simple docstring'''


  lowercase_								= XGBRegressor(verbosity=0 ,  random_state=42							)
  xgb.fit(__lowerCAmelCase ,  __lowerCAmelCase							)
  # Predict target for test data
  lowercase_								= xgb.predict(__lowerCAmelCase							)
  lowercase_								= predictions.reshape(len(__lowerCAmelCase							) ,  1							)
  return predictions


def 			_SCREAMING_SNAKE_CASE						() ->						None:







  '''simple docstring'''


  lowercase_								= fetch_california_housing()
  lowercase_					,    lowercase_								= data_handling(__lowerCAmelCase							)
  lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= train_test_split(
      __lowerCAmelCase ,  __lowerCAmelCase ,  test_size=0.25 ,  random_state=1							)
  lowercase_								= xgboost(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)
  print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

