



"""simple docstring"""


import numpy as np







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Optional[Any]  ,    lowercase_					:Dict=5  ,    lowercase_					:Tuple=5   )							->     str:
												UpperCAmelCase			 =					depth
												UpperCAmelCase			 =					0
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					min_leaf_size
												UpperCAmelCase			 =					None






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:List[Any]   )							->     Any:
												if labels.ndim != 1:
																		print('Error: Input labels must be one dimensional'   )

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






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:str  ,    lowercase_					:Union[str, Any]   )							->     Optional[Any]:
												if x.ndim != 1:
																		print('Error: Input data set must be one dimensional'   )
																		return
												if len(lowercase_   ) != len(lowercase_   ):
																		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(lowercase_   ) < 2 * self.min_leaf_size:
																		UpperCAmelCase			 =					np.mean(lowercase_   )
																		return

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

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

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

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

												return






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:str   )							->     int:
												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(lowercase_   )
																		else:
																								return self.left.predict(lowercase_   )
												else:
																		print('Error: Decision tree not yet trained'   )
																		return None







class  A_     :



						"""simple docstring"""






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:int  ,    lowercase_					:List[Any]   )							->     Any:
												UpperCAmelCase			 =					float(0   )
												for label in labels:
																		squared_error_sum += (label - prediction) ** 2

												return float(squared_error_sum / labels.size   )


def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					np.arange(-1.0						,      1.0						,      0.0_0_5		)
						UpperCAmelCase			 =					np.sin(lowercase_		)

						UpperCAmelCase			 =					DecisionTree(depth=10						,      min_leaf_size=10		)
						tree.train(lowercase_						,      lowercase_		)

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

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


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

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












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


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

