


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor



def 			lowerCAmelCase (		lowerCAmelCase_ )-> tuple:
				return (data["data"], data["target"])



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> np.ndarray:
				lowerCAmelCase_ :      Optional[Any]    				=				XGBRegressor(verbosity=0 ,   random_state=42 )
				xgb.fit(lowerCAmelCase_ ,   lowerCAmelCase_ )
				# Predict target for test data
				lowerCAmelCase_ :      Union[str, Any]    				=				xgb.predict(lowerCAmelCase_ )
				lowerCAmelCase_ :      List[str]    				=				predictions.reshape(len(lowerCAmelCase_ ) ,   1 )
				return predictions



def 			lowerCAmelCase (		)-> None:
				lowerCAmelCase_ :      Any    				=				fetch_california_housing()
				lowerCAmelCase_				,  lowerCAmelCase_ :      Union[str, Any]    				=				data_handling(lowerCAmelCase_ )
				lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      List[Any]    				=				train_test_split(
				    lowerCAmelCase_ ,   lowerCAmelCase_ ,   test_size=0.25 ,   random_state=1 )
				lowerCAmelCase_ :      Dict    				=				xgboost(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
				# Error printing
				print(f"""Mean Absolute Error : {mean_absolute_error(lowerCAmelCase_ ,   lowerCAmelCase_ )}""" )
				print(f"""Mean Square Error  : {mean_squared_error(lowerCAmelCase_ ,   lowerCAmelCase_ )}""" )


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)
						main()



# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = True   ,						__lowercase = False							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				scheduler
										lowerCAmelCase_ :      Dict    				=				optimizers if isinstance(__lowercase   ,						(list, tuple)							) else [optimizers]
										lowerCAmelCase_ :      str    				=				split_batches
										lowerCAmelCase_ :      Any    				=				step_with_optimizer
										lowerCAmelCase_ :      Optional[Any]    				=				GradientState()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    Any:
										if not self.step_with_optimizer:
														# No link between scheduler and optimizer -> just step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
														return

										# Otherwise, first make sure the optimizer was stepped.
										if not self.gradient_state.sync_gradients:
														if self.gradient_state.adjust_scheduler:
																		self.scheduler._step_count += 1
														return

										for opt in self.optimizers:
														if opt.step_was_skipped:
																		return
										if self.split_batches:
														# Split batches -> the training dataloader batch size is not changed so one step per training step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
										else:
														# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
														# num_processes steps per training step
														lowerCAmelCase_ :      Optional[Any]    				=				AcceleratorState().num_processes
														for _ in range(__lowercase							):
																		# Special case when using OneCycle and `drop_last` was not used
																		if hasattr(self.scheduler   ,						'''total_steps'''							):
																						if self.scheduler._step_count <= self.scheduler.total_steps:
																										self.scheduler.step(*__lowercase   ,						**__lowercase							)
																		else:
																						self.scheduler.step(*__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return self.scheduler.get_last_lr()




						def 						lowercase_ (				self							)					->    List[str]:
										return self.scheduler.state_dict()




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										self.scheduler.load_state_dict(__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										return self.scheduler.get_lr()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    int:
										return self.scheduler.print_lr(*__lowercase   ,						**__lowercase							)