


'''simple docstring'''





import numpy as np
class 	a__				:





			'''simple docstring'''

			def __init__(     self       : List[str] ,						lowerCAmelCase_       : Optional[Any]=5 ,						lowerCAmelCase_       : List[str]=5  )			->		List[str]:
						__A=  depth
						__A=  0
						__A=  None
						__A=  None
						__A=  min_leaf_size
						__A=  None
			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : str  )			->		int:
						if labels.ndim != 1:
									print('Error: Input labels must be one dimensional'  )

						return np.mean((labels - prediction) ** 2  )
			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : List[Any]  )			->		Dict:
						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:
									__A=  np.mean(lowerCAmelCase_  )
									return

						if self.depth == 1:
									__A=  np.mean(lowerCAmelCase_  )
									return

						__A=  0
						__A=  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:
												__A=  self.mean_squared_error(x[:i] ,						np.mean(y[:i]  )  )
												__A=  self.mean_squared_error(x[i:] ,						np.mean(y[i:]  )  )
												__A=  error_left + error_right
												if error < min_error:
															__A=  i
															__A=  error

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

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

						return

			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : List[Any]  )			->		Dict:
						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 	a__				:





			'''simple docstring'''

			@staticmethod
			def 				lowerCAmelCase    (     lowerCAmelCase_       : int ,						lowerCAmelCase_       : int  )			->		List[str]:
						__A=  float(0  )
						for label in labels:
									squared_error_sum += (label - prediction) ** 2

						return float(squared_error_sum / labels.size  )






def 	UpperCAmelCase__(							):






			"""simple docstring"""




			__A=  np.arange(-1.0,1.0,0.0_05   )
			__A=  np.sin(_SCREAMING_SNAKE_CASE   )

			__A=  DecisionTree(depth=10,min_leaf_size=10   )
			tree.train(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )

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

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


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

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







'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__      =							logging.get_logger(__name__)

UpperCAmelCase__      =							{
    '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
}
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							Dict							= '''vilt'''
			def __init__(     self       : str ,						lowerCAmelCase_       : Tuple=30_522 ,						lowerCAmelCase_       : List[Any]=2 ,						lowerCAmelCase_       : Optional[Any]=2 ,						lowerCAmelCase_       : Union[str, Any]=40 ,						lowerCAmelCase_       : str=768 ,						lowerCAmelCase_       : Tuple=12 ,						lowerCAmelCase_       : List[Any]=12 ,						lowerCAmelCase_       : int=3_072 ,						lowerCAmelCase_       : List[Any]="gelu" ,						lowerCAmelCase_       : int=0.0 ,						lowerCAmelCase_       : str=0.0 ,						lowerCAmelCase_       : Optional[Any]=0.02 ,						lowerCAmelCase_       : Union[str, Any]=1E-12 ,						lowerCAmelCase_       : Dict=384 ,						lowerCAmelCase_       : Optional[int]=32 ,						lowerCAmelCase_       : Optional[Any]=3 ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : List[Any]=-1 ,						lowerCAmelCase_       : Union[str, Any]=False ,						lowerCAmelCase_       : str=-1 ,						**lowerCAmelCase_       : Any ,						)			->		Tuple:
						super().__init__(tie_word_embeddings=lowerCAmelCase_ ,						**lowerCAmelCase_  )

						__A=  vocab_size
						__A=  type_vocab_size
						__A=  modality_type_vocab_size
						__A=  max_position_embeddings

						__A=  hidden_size
						__A=  num_hidden_layers
						__A=  num_attention_heads
						__A=  intermediate_size
						__A=  hidden_act
						__A=  hidden_dropout_prob
						__A=  attention_probs_dropout_prob
						__A=  initializer_range
						__A=  layer_norm_eps

						__A=  image_size
						__A=  patch_size
						__A=  num_channels
						__A=  qkv_bias
						__A=  max_image_length
						__A=  num_images



