



import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput


class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Dict		=		1







						@register_to_config
						def __init__(  self    :				Optional[Any]       ,	snake_case    :				int = 1000       ,	snake_case    :				Optional[Union[np.ndarray, List[float]]] = None	):



											'''simple docstring'''


											self.set_timesteps(snake_case	)

											# standard deviation of the initial noise distribution
											SCREAMING_SNAKE_CASE							:     List[str]						=				1.0

											# For now we only support F-PNDM, i.e. the runge-kutta method
											# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
											# mainly at formula (9), (12), (13) and the Algorithm 2.
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				4

											# running values
											SCREAMING_SNAKE_CASE							:     List[str]						=				[]







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				int       ,	snake_case    :				Union[str, torch.device] = None	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				num_inference_steps
											SCREAMING_SNAKE_CASE							:     Tuple						=				torch.linspace(1       ,	0       ,	num_inference_steps + 1	)[:-1]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.cat([steps, torch.tensor([0.0]	)]	)

											if self.config.trained_betas is not None:
																SCREAMING_SNAKE_CASE							:     Any						=				torch.tensor(self.config.trained_betas       ,	dtype=torch.floataa	)
											else:
																SCREAMING_SNAKE_CASE							:     Any						=				torch.sin(steps * math.pi / 2	) ** 2

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(1.0 - self.betas**2) ** 0.5

											SCREAMING_SNAKE_CASE							:     Dict						=				(torch.atana(self.betas       ,	self.alphas	) / math.pi * 2)[:-1]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				timesteps.to(snake_case	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[]







						def       lowerCamelCase_				(  self    :				Optional[int]       ,	snake_case    :				torch.FloatTensor       ,	snake_case    :				int       ,	snake_case    :				torch.FloatTensor       ,	snake_case    :				bool = True       ,	):



											'''simple docstring'''


											if self.num_inference_steps is None:
																raise ValueError(
																    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'	)

											SCREAMING_SNAKE_CASE							:     int						=				(self.timesteps == timestep).nonzero().item()
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				timestep_index + 1

											SCREAMING_SNAKE_CASE							:     Dict						=				sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
											self.ets.append(snake_case	)

											if len(self.ets	) == 1:
																SCREAMING_SNAKE_CASE							:     Any						=				self.ets[-1]
											elif len(self.ets	) == 2:
																SCREAMING_SNAKE_CASE							:     int						=				(3 * self.ets[-1] - self.ets[-2]) / 2
											elif len(self.ets	) == 3:
																SCREAMING_SNAKE_CASE							:     Any						=				(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
											else:
																SCREAMING_SNAKE_CASE							:     Dict						=				(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

											SCREAMING_SNAKE_CASE							:     Tuple						=				self._get_prev_sample(snake_case       ,	snake_case       ,	snake_case       ,	snake_case	)

											if not return_dict:
																return (prev_sample,)

											return SchedulerOutput(prev_sample=snake_case	)







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				torch.FloatTensor       ,	*snake_case    :				Optional[Any]       ,	**snake_case    :				Optional[int]	):



											'''simple docstring'''


											return sample







						def       lowerCamelCase_				(  self    :				Optional[Any]       ,	snake_case    :				List[str]       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Any       ,	snake_case    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.alphas[timestep_index]
											SCREAMING_SNAKE_CASE							:     str						=				self.betas[timestep_index]

											SCREAMING_SNAKE_CASE							:     str						=				self.alphas[prev_timestep_index]
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.betas[prev_timestep_index]

											SCREAMING_SNAKE_CASE							:     List[Any]						=				(sample - sigma * ets) / max(snake_case       ,	1E-8	)
											SCREAMING_SNAKE_CASE							:     int						=				next_alpha * pred + ets * next_sigma

											return prev_sample







						def __len__(  self    :				Optional[Any]	):



											'''simple docstring'''


											return self.config.num_train_timesteps




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        __a			(   __lowerCAmelCase   )     ->     tuple:
					return (data["data"], data["target"])

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

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Dict						=				fetch_california_housing()
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				data_handling(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				train_test_split(
					    __lowerCAmelCase      ,	__lowerCAmelCase      ,	test_size=0.25      ,	random_state=1   )
					SCREAMING_SNAKE_CASE							:     int						=				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()