



"""simple docstring"""







import numpy as np

class 			_a     :



			def __init__( self       :							Union[str, Any]      ,  _lowercase       :							Optional[int]=5      ,  _lowercase       :							Tuple=5		)       ->				List[Any]:
									snake_case      : List[Any]							=   depth
									snake_case      : str							=   0
									snake_case      : Dict							=   None
									snake_case      : Tuple							=   None
									snake_case      : List[Any]							=   min_leaf_size
									snake_case      : Any							=   None



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Dict      ,  _lowercase       :							str		)       ->				List[Any]:






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

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



			def 							__lowercase				( self       :							Optional[Any]      ,  _lowercase       :							List[str]      ,  _lowercase       :							Optional[Any]		)       ->				Optional[Any]:






									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:
															snake_case      : Tuple							=   np.mean(lowerCAmelCase_		)
															return

									if self.depth == 1:
															snake_case      : Optional[Any]							=   np.mean(lowerCAmelCase_		)
															return

									snake_case      : Optional[Any]							=   0
									snake_case      : Union[str, Any]							=   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:
																					snake_case      : List[Any]							=   self.mean_squared_error(x[:i]      ,  np.mean(y[:i]		)		)
																					snake_case      : Union[str, Any]							=   self.mean_squared_error(x[i:]      ,  np.mean(y[i:]		)		)
																					snake_case      : Any							=   error_left + error_right
																					if error < min_error:
																											snake_case      : Optional[int]							=   i
																											snake_case      : Any							=   error

									if best_split != 0:
															snake_case      : Any							=   x[:best_split]
															snake_case      : Optional[Any]							=   y[:best_split]
															snake_case      : Optional[Any]							=   x[best_split:]
															snake_case      : Optional[int]							=   y[best_split:]

															snake_case      : Union[str, Any]							=   x[best_split]
															snake_case      : List[Any]							=   DecisionTree(
															    depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size		)
															snake_case      : Optional[Any]							=   DecisionTree(
															    depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size		)
															self.left.train(lowerCAmelCase_      ,  lowerCAmelCase_		)
															self.right.train(lowerCAmelCase_      ,  lowerCAmelCase_		)
									else:
															snake_case      : str							=   np.mean(lowerCAmelCase_		)

									return



			def 							__lowercase				( self       :							Any      ,  _lowercase       :							Optional[Any]		)       ->				List[Any]:






									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     :



			@staticmethod
			def 							__lowercase				( _lowercase       :							List[Any]      ,  _lowercase       :							Any		)       ->				str:
									snake_case      : Union[str, Any]							=   float(0		)
									for label in labels:
															squared_error_sum += (label - prediction) ** 2

									return float(squared_error_sum / labels.size		)



def    __SCREAMING_SNAKE_CASE		( ):
						"""simple docstring"""






						snake_case      : int							=   np.arange(-1.0    ,		1.0    ,		0.0_0_5   )
						snake_case      : List[str]							=   np.sin(lowerCamelCase_   )

						snake_case      : List[str]							=   DecisionTree(depth=1_0    ,		min_leaf_size=1_0   )
						tree.train(lowerCamelCase_    ,		lowerCamelCase_   )

						snake_case      : Dict							=   (np.random.rand(1_0   ) * 2) - 1
						snake_case      : Optional[Any]							=   np.array([tree.predict(lowerCamelCase_   ) for x in test_cases]   )
						snake_case      : Dict							=   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"""







import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       int    ,		lowerCamelCase_:       bool = False   ):
						"""simple docstring"""






						if device_specific:
												seed += AcceleratorState().process_index
						random.seed(lowerCamelCase_   )
						np.random.seed(lowerCamelCase_   )
						torch.manual_seed(lowerCamelCase_   )
						if is_xpu_available():
												torch.xpu.manual_seed_all(lowerCamelCase_   )
						elif is_npu_available():
												torch.npu.manual_seed_all(lowerCamelCase_   )
						else:
												torch.cuda.manual_seed_all(lowerCamelCase_   )
						# ^^ safe to call this function even if cuda is not available
						if is_tpu_available():
												xm.set_rng_state(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[RNGType] = None    ,		lowerCamelCase_:       Optional[torch.Generator] = None   ):
						"""simple docstring"""






						if rng_type == RNGType.TORCH:
												snake_case      : List[Any]							=   torch.get_rng_state()
						elif rng_type == RNGType.CUDA:
												snake_case      : Union[str, Any]							=   torch.cuda.get_rng_state()
						elif rng_type == RNGType.XLA:
												assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
												snake_case      : Optional[int]							=   torch.tensor(xm.get_rng_state()   )
						elif rng_type == RNGType.NPU:
												assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
												snake_case      : int							=   torch.npu.get_rng_state()
						elif rng_type == RNGType.XPU:
												assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
												snake_case      : int							=   torch.xpu.get_rng_state()
						elif rng_type == RNGType.GENERATOR:
												assert generator is not None, "Need a generator to synchronize its seed."
												snake_case      : List[Any]							=   generator.get_state()

						# Broadcast the rng state from device 0 to other devices
						snake_case      : Dict							=   AcceleratorState()
						if state.distributed_type == DistributedType.TPU:
												snake_case      : List[str]							=   rng_state.to(xm.xla_device()   )
												xm.collective_broadcast([rng_state]   )
												xm.mark_step()
												snake_case      : List[Any]							=   rng_state.cpu()
						elif (
						    state.distributed_type in CUDA_DISTRIBUTED_TYPES
						    or state.distributed_type == DistributedType.MULTI_NPU
						    or state.distributed_type == DistributedType.MULTI_XPU
						):
												snake_case      : str							=   rng_state.to(state.device   )
												torch.distributed.broadcast(lowerCamelCase_    ,		0   )
												snake_case      : Union[str, Any]							=   rng_state.cpu()
						elif state.distributed_type == DistributedType.MULTI_CPU:
												torch.distributed.broadcast(lowerCamelCase_    ,		0   )

						# Set the broadcast rng state
						if rng_type == RNGType.TORCH:
												torch.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.CUDA:
												torch.cuda.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.NPU:
												torch.npu.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.XPU:
												torch.xpu.set_rng_state(lowerCamelCase_   )
						elif rng_type == RNGType.XLA:
												xm.set_rng_state(rng_state.item()   )
						elif rng_type == RNGType.GENERATOR:
												generator.set_state(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       List[Union[str, RNGType]]    ,		lowerCamelCase_:       Optional[torch.Generator] = None   ):
						"""simple docstring"""






						for rng_type in rng_types:
												synchronize_rng_state(RNGType(lowerCamelCase_   )    ,		generator=lowerCamelCase_   )



