



import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_=5	,	a_=5		):
											a__					   =		depth
											a__					   =		0
											a__					   =		None
											a__					   =		None
											a__					   =		min_leaf_size
											a__					   =		None


							def _a   (				self	,	a_	,	a_		):
											if labels.ndim != 1:
															print("""Error: Input labels must be one dimensional"""		)

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


							def _a   (				self	,	a_	,	a_		):
											if x.ndim != 1:
															print("""Error: Input data set must be one dimensional"""		)
															return
											if len(a_		) != len(a_		):
															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(a_		) < 2 * self.min_leaf_size:
															a__					   =		np.mean(a_		)
															return

											if self.depth == 1:
															a__					   =		np.mean(a_		)
															return

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

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

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

											return







							def _a   (				self	,	a_		):
											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(a_		)
															else:
																			return self.left.predict(a_		)
											else:
															print("""Error: Decision tree not yet trained"""		)
															return None




class 			__snake_case   :







							'''simple docstring'''






							@staticmethod
							def _a   (				a_	,	a_		):
											a__					   =		float(0		)
											for label in labels:
															squared_error_sum += (label - prediction) ** 2

											return float(squared_error_sum / labels.size		)




def       A_   (						):

				"""simple docstring"""
				a__					   =		np.arange(-1.0			,		1.0			,		0.0_0_5		)
				a__					   =		np.sin(__a		)

				a__					   =		DecisionTree(depth=10			,		min_leaf_size=10		)
				tree.train(__a			,		__a		)

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

				print("""Test values: """ + str(__a		)		)
				print("""Predictions: """ + str(__a		)		)
				print("""Average error: """ + str(__a		)		)


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

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








from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

													# set step values
													self.scheduler.set_timesteps(a     )

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )