
import numpy as np
class       A__  :





							def __init__(					self    :  Optional[int]	,					_UpperCAmelCase    :  Optional[int]=5	,					_UpperCAmelCase    :  Optional[int]=5							)		->						List[str]:



											"""simple docstring"""


											__lowercase								=   depth
											__lowercase								=   0
											__lowercase								=   None
											__lowercase								=   None
											__lowercase								=   min_leaf_size
											__lowercase								=   None





							def      a__					(					self    :  List[Any]	,					_UpperCAmelCase    :  Dict	,					_UpperCAmelCase    :  Dict							)		->						str:



											"""simple docstring"""


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

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





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  Dict	,					_UpperCAmelCase    :  List[Any]							)		->						List[Any]:



											"""simple docstring"""


											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:
															__lowercase								=   np.mean(_UpperCAmelCase							)
															return

											if self.depth == 1:
															__lowercase								=   np.mean(_UpperCAmelCase							)
															return

											__lowercase								=   0
											__lowercase								=   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:
																			__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(_UpperCAmelCase	,					_UpperCAmelCase							)
															self.right.train(_UpperCAmelCase	,					_UpperCAmelCase							)
											else:
															__lowercase								=   np.mean(_UpperCAmelCase							)

											return





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  int							)		->						str:



											"""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(_UpperCAmelCase							)
															else:
																			return self.left.predict(_UpperCAmelCase							)
											else:
															print('Error: Decision tree not yet trained'							)
															return None






class       A__  :





							@staticmethod
							def      a__					(					_UpperCAmelCase    :  int	,					_UpperCAmelCase    :  Dict							)		->						Union[str, Any]:



											"""simple docstring"""


											__lowercase								=   float(0							)
											for label in labels:
															squared_error_sum += (label - prediction) ** 2

											return float(squared_error_sum / labels.size							)






def 				__SCREAMING_SNAKE_CASE  (						)	->		List[Any]:
				__lowercase								=   np.arange(-1.0       ,				1.0       ,				0.005     )
				__lowercase								=   np.sin(SCREAMING_SNAKE_CASE     )

				__lowercase								=   DecisionTree(depth=10       ,				min_leaf_size=10     )
				tree.train(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )

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







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				c_float





							@property
							def      a__					(					self    :  List[str]							)		->						int:



											"""simple docstring"""


											return sizeof(self.value							)






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int = 0     )	->		int:
				# < 0 is possible if using a dynamic axis
				if dimension <= 0:
								__lowercase								=   fixed_dimension

				dimension -= num_token_to_add
				return dimension




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: ParameterFormat     )	->		int:
				return num_parameters * dtype.size




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str     )	->		Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

				try:
								return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE     )
				except (ValueError, OSError, KeyError):
								__lowercase						,   __lowercase								=   None, None
								try:
												__lowercase								=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass
								try:
												__lowercase								=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass

								if tokenizer is not None and feature_extractor is not None:
												raise ValueError(
												    F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""     )
								elif tokenizer is None and feature_extractor is None:
												return None
								elif tokenizer is not None:
												return tokenizer
								else:
												return feature_extractor





