






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


A__     :      Any             =			logging.get_logger(__name__)

A__     :      Dict             =			{
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}
class 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




				lowercase__    							=	"""swiftformer"""




				def __init__(					self						:   Union[str, Any],  lowerCamelCase						:   Tuple=3,  lowerCamelCase						:   List[str]=[3, 3, 6, 4],  lowerCamelCase						:   List[str]=[48, 56, 112, 220],  lowerCamelCase						:   List[str]=4,  lowerCamelCase						:   Optional[int]=[True, True, True, True],  lowerCamelCase						:   Any="gelu",  lowerCamelCase						:   Dict=3,  lowerCamelCase						:   Dict=2,  lowerCamelCase						:   List[str]=1,  lowerCamelCase						:   str=0.0,  lowerCamelCase						:   int=True,  lowerCamelCase						:   Dict=1E-5,  lowerCamelCase						:   Optional[Any]=1E-5,  **lowerCamelCase						:   str,  ):

								'''simple docstring'''
								super().__init__(**lowerCamelCase  )
								lowercase__       			=      num_channels
								lowercase__       			=      depths
								lowercase__       			=      embed_dims
								lowercase__       			=      mlp_ratio
								lowercase__       			=      downsamples
								lowercase__       			=      hidden_act
								lowercase__       			=      down_patch_size
								lowercase__       			=      down_stride
								lowercase__       			=      down_pad
								lowercase__       			=      drop_path_rate
								lowercase__       			=      use_layer_scale
								lowercase__       			=      layer_scale_init_value
								lowercase__       			=      batch_norm_eps



class 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




				lowercase__    							=	version.parse("""1.11"""     )




				@property
				def     lowercase__       (					self						:   List[str]  ):

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




				@property
				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								return 1E-4












import numpy as np
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   Union[str, Any],  lowerCamelCase						:   int=5,  lowerCamelCase						:   Optional[Any]=5  ):

								'''simple docstring'''
								lowercase__       			=      depth
								lowercase__       			=      0
								lowercase__       			=      None
								lowercase__       			=      None
								lowercase__       			=      min_leaf_size
								lowercase__       			=      None




				def     lowercase__       (					self						:   Union[str, Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								if labels.ndim != 1:
												print('''Error: Input labels must be one dimensional'''  )

								return np.mean((labels - prediction) ** 2  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								if x.ndim != 1:
												print('''Error: Input data set must be one dimensional'''  )
												return
								if len(lowerCamelCase  ) != len(lowerCamelCase  ):
												print('''Error: X and y have different lengths'''  )
												return
								if y.ndim != 1:
												print('''Error: Data set labels must be one dimensional'''  )
												return

								if len(lowerCamelCase  ) < 2 * self.min_leaf_size:
												lowercase__       			=      np.mean(lowerCamelCase  )
												return

								if self.depth == 1:
												lowercase__       			=      np.mean(lowerCamelCase  )
												return

								lowercase__       			=      0
								lowercase__       			=      self.mean_squared_error(lowerCamelCase,  np.mean(lowerCamelCase  )  ) * 2
								for i in range(len(lowerCamelCase  )  ):
												if len(x[:i]  ) < self.min_leaf_size:
																continue
												elif len(x[i:]  ) < self.min_leaf_size:
																continue
												else:
																lowercase__       			=      self.mean_squared_error(x[:i],  np.mean(y[:i]  )  )
																lowercase__       			=      self.mean_squared_error(x[i:],  np.mean(y[i:]  )  )
																lowercase__       			=      error_left + error_right
																if error < min_error:
																				lowercase__       			=      i
																				lowercase__       			=      error

								if best_split != 0:
												lowercase__       			=      x[:best_split]
												lowercase__       			=      y[:best_split]
												lowercase__       			=      x[best_split:]
												lowercase__       			=      y[best_split:]

												lowercase__       			=      x[best_split]
												lowercase__       			=      DecisionTree(
												    depth=self.depth - 1,  min_leaf_size=self.min_leaf_size  )
												lowercase__       			=      DecisionTree(
												    depth=self.depth - 1,  min_leaf_size=self.min_leaf_size  )
												self.left.train(lowerCamelCase,  lowerCamelCase  )
												self.right.train(lowerCamelCase,  lowerCamelCase  )
								else:
												lowercase__       			=      np.mean(lowerCamelCase  )

								return




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								if self.prediction is not None:
												return self.prediction
								elif self.left or self.right is not None:
												if x >= self.decision_boundary:
																return self.right.predict(lowerCamelCase  )
												else:
																return self.left.predict(lowerCamelCase  )
								else:
												print('''Error: Decision tree not yet trained'''  )
												return None



class 				_UpperCAmelCase :

				"""simple docstring"""




				@staticmethod
				def     lowercase__       (					lowerCamelCase						:   int,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      float(0  )
								for label in labels:
												squared_error_sum += (label - prediction) ** 2

								return float(squared_error_sum / labels.size  )






def   a      (						):






				'''simple docstring'''
				lowercase__       			=      np.arange(-1.0		,       1.0		,       0.0_05    )
				lowercase__       			=      np.sin(lowerCamelCase_    )

				lowercase__       			=      DecisionTree(depth=10		,       min_leaf_size=10    )
				tree.train(lowerCamelCase_		,       lowerCamelCase_    )

				lowercase__       			=      (np.random.rand(10    ) * 2) - 1
				lowercase__       			=      np.array([tree.predict(lowerCamelCase_    ) for x in test_cases]    )
				lowercase__       			=      np.mean((predictions - test_cases) ** 2    )

				print('''Test values: ''' + str(lowerCamelCase_    )    )
				print('''Predictions: ''' + str(lowerCamelCase_    )    )
				print('''Average error: ''' + str(lowerCamelCase_    )    )


if __name__ == "__main__":
							main()
							import doctest

							doctest.testmod(name='mean_squarred_error', verbose=True)




