
"""simple docstring"""


import numpy as np




class    __lowerCamelCase     :




				def __init__(							self       ,	UpperCAmelCase=5       ,	UpperCAmelCase=5      ):
							lowerCamelCase_ 		=     depth
							lowerCamelCase_ 		=     0
							lowerCamelCase_ 		=     None
							lowerCamelCase_ 		=     None
							lowerCamelCase_ 		=     min_leaf_size
							lowerCamelCase_ 		=     None




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							if labels.ndim != 1:
										print('''Error: Input labels must be one dimensional'''      )

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




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							if x.ndim != 1:
										print('''Error: Input data set must be one dimensional'''      )
										return
							if len(UpperCAmelCase      ) != len(UpperCAmelCase      ):
										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(UpperCAmelCase      ) < 2 * self.min_leaf_size:
										lowerCamelCase_ 		=     np.mean(UpperCAmelCase      )
										return

							if self.depth == 1:
										lowerCamelCase_ 		=     np.mean(UpperCAmelCase      )
										return

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

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

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

							return





				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							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(UpperCAmelCase      )
										else:
													return self.left.predict(UpperCAmelCase      )
							else:
										print('''Error: Decision tree not yet trained'''      )
										return None




class    __lowerCamelCase     :




				@staticmethod
				def 					UpperCAmelCase__	(							UpperCAmelCase       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     float(0      )
							for label in labels:
										squared_error_sum += (label - prediction) ** 2

							return float(squared_error_sum / labels.size      )






def    lowercase	(  ):
			lowerCamelCase_ 		=     np.arange(-1.0  ,1.0  ,0.005	)
			lowerCamelCase_ 		=     np.sin(lowerCAmelCase__	)

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

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





"""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


A_						       =				logging.get_logger(__name__)

A_						       =				{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Any									=						'swiftformer'




				def __init__(							self       ,	UpperCAmelCase=3       ,	UpperCAmelCase=[3, 3, 6, 4]       ,	UpperCAmelCase=[48, 56, 112, 220]       ,	UpperCAmelCase=4       ,	UpperCAmelCase=[True, True, True, True]       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=3       ,	UpperCAmelCase=2       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0.0       ,	UpperCAmelCase=True       ,	UpperCAmelCase=1e-5       ,	UpperCAmelCase=1e-5       ,	**UpperCAmelCase       ,	):
							super().__init__(**UpperCAmelCase      )
							lowerCamelCase_ 		=     num_channels
							lowerCamelCase_ 		=     depths
							lowerCamelCase_ 		=     embed_dims
							lowerCamelCase_ 		=     mlp_ratio
							lowerCamelCase_ 		=     downsamples
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     down_patch_size
							lowerCamelCase_ 		=     down_stride
							lowerCamelCase_ 		=     down_pad
							lowerCamelCase_ 		=     drop_path_rate
							lowerCamelCase_ 		=     use_layer_scale
							lowerCamelCase_ 		=     layer_scale_init_value
							lowerCamelCase_ 		=     batch_norm_eps




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			str									=						version.parse('1.11' )




				@property
				def 					UpperCAmelCase__	(							self      ):
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]      )





				@property
				def 					UpperCAmelCase__	(							self      ):
							return 1e-4



