



from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


__UpperCAmelCase    	=  logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class 						lowerCamelCase__			(		_a ):
						_lowerCAmelCase             =			42


class 						lowerCamelCase__			(		_a					,       _a ):
						_lowerCAmelCase             =			1


						@register_to_config
						def __init__(			self    :	Union[str, Any]   ,							_a    :	int = 4_0   ,							_a    :	float = 0.0_0_2   ,							_a    :	float = 8_0.0   ,							_a    :	float = 0.5   ,							_a    :	float = 1.0   ,							_a    :	float = 7.0   ,							_a    :	bool = True   ,							):
													# standard deviation of the initial noise distribution
													a__:							int									=sigma_max

													a__:							Optional[Any]									=np.linspace(0   ,							1   ,							_a      )
													a__:							int									=self._convert_to_karras(_a      )
													a__:							List[Any]									=self.sigma_to_t(_a      )

													# setable values
													a__:							List[Any]									=None
													a__:							Optional[int]									=torch.from_numpy(_a      )
													a__:							Tuple									=torch.from_numpy(_a      )
													a__:							Optional[int]									=False
													a__:							Any									=False


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	List[str]   ,							_a    :	Any=None      ):
													if schedule_timesteps is None:
																				a__:							Dict									=self.timesteps

													a__:							Optional[Any]									=(schedule_timesteps == timestep).nonzero()
													return indices.item()


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	torch.FloatTensor   ,							_a    :	Union[float, torch.FloatTensor]      ):
													# Get sigma corresponding to timestep
													if isinstance(_a   ,							torch.Tensor      ):
																				a__:							Tuple									=timestep.to(self.timesteps.device      )
													a__:							Optional[Any]									=self.index_for_timestep(_a      )
													a__:							Union[str, Any]									=self.sigmas[step_idx]

													a__:							str									=sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

													a__:							str									=True
													return sample


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	Union[float, np.ndarray]      ):
													if not isinstance(_a   ,							np.ndarray      ):
																				a__:							Dict									=np.array(_a   ,							dtype=np.floataa      )

													a__:							str									=1_0_0_0 * 0.2_5 * np.log(sigmas + 1e-44      )

													return timesteps


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	Optional[int] = None   ,							_a    :	Union[str, torch.device] = None   ,							_a    :	Optional[List[int]] = None   ,							):
													if num_inference_steps is None and timesteps is None:
																				raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."      )

													if num_inference_steps is not None and timesteps is not None:
																				raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."      )

													# Follow DDPMScheduler custom timesteps logic
													if timesteps is not None:
																				for i in range(1   ,							len(_a      )      ):
																											if timesteps[i] >= timesteps[i - 1]:
																																		raise ValueError("`timesteps` must be in descending order."      )

																				if timesteps[0] >= self.config.num_train_timesteps:
																											raise ValueError(
																											    F"`timesteps` must start before `self.config.train_timesteps`:"
																											    F" {self.config.num_train_timesteps}."      )

																				a__:							Optional[Any]									=np.array(_a   ,							dtype=np.intaa      )
																				a__:							List[Any]									=True
													else:
																				if num_inference_steps > self.config.num_train_timesteps:
																											raise ValueError(
																											    F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																											    F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																											    F" maximal {self.config.num_train_timesteps} timesteps."      )

																				a__:							Optional[int]									=num_inference_steps

																				a__:							Optional[int]									=self.config.num_train_timesteps // self.num_inference_steps
																				a__:							Optional[int]									=(np.arange(0   ,							_a      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
																				a__:							Union[str, Any]									=False

													# Map timesteps to Karras sigmas directly for multistep sampling
													# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
													a__:							Optional[Any]									=self.config.num_train_timesteps
													a__:							Optional[Any]									=timesteps[::-1].copy()
													a__:							Optional[int]									=ramp / (num_train_timesteps - 1)
													a__:							Tuple									=self._convert_to_karras(_a      )
													a__:							Any									=self.sigma_to_t(_a      )

													a__:							Union[str, Any]									=np.concatenate([sigmas, [self.sigma_min]]      ).astype(np.floataa      )
													a__:							Optional[Any]									=torch.from_numpy(_a      ).to(device=_a      )

													if str(_a      ).startswith("mps"      ):
																				# mps does not support float64
																				a__:							str									=torch.from_numpy(_a      ).to(_a   ,							dtype=torch.floataa      )
													else:
																				a__:							Any									=torch.from_numpy(_a      ).to(device=_a      )


						def 	_lowerCamelCase      (			self    :	int   ,							_a    :	List[Any]      ):
													a__:							float									=self.config.sigma_min
													a__:							float									=self.config.sigma_max

													a__:							str									=self.config.rho
													a__:							int									=sigma_min ** (1 / rho)
													a__:							str									=sigma_max ** (1 / rho)
													a__:							str									=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
													return sigmas


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	Optional[Any]      ):
													a__:							Any									=self.config.sigma_data

													a__:							List[str]									=sigma_data**2 / (sigma**2 + sigma_data**2)
													a__:							str									=sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
													return c_skip, c_out


						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	Union[str, Any]      ):
													a__:							Any									=self.config.sigma_min
													a__:							Union[str, Any]									=self.config.sigma_data

													a__:							Union[str, Any]									=sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
													a__:							Optional[int]									=(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
													return c_skip, c_out


						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	torch.FloatTensor   ,							_a    :	Union[float, torch.FloatTensor]   ,							_a    :	torch.FloatTensor   ,							_a    :	Optional[torch.Generator] = None   ,							_a    :	bool = True   ,							):

													if (
													    isinstance(_a   ,							_a      )
													    or isinstance(_a   ,							torch.IntTensor      )
													    or isinstance(_a   ,							torch.LongTensor      )
													):
																				raise ValueError(
																				    (
																				        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
																				        F" `{self.__class__}.step()` is not supported. Make sure to pass"
																				        " one of the `scheduler.timesteps` as a timestep."
																				    )   ,							)

													if not self.is_scale_input_called:
																				logger.warning(
																				    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
																				    "See `StableDiffusionPipeline` for a usage example."      )

													if isinstance(_a   ,							torch.Tensor      ):
																				a__:							Dict									=timestep.to(self.timesteps.device      )

													a__:							List[str]									=self.config.sigma_min
													a__:							List[Any]									=self.config.sigma_max

													a__:							int									=self.index_for_timestep(_a      )

													# sigma_next corresponds to next_t in original implementation
													a__:							List[str]									=self.sigmas[step_index]
													if step_index + 1 < self.config.num_train_timesteps:
																				a__:							str									=self.sigmas[step_index + 1]
													else:
																				# Set sigma_next to sigma_min
																				a__:							int									=self.sigmas[-1]

													# Get scalings for boundary conditions
													a__				,      a__:							Union[str, Any]									=self.get_scalings_for_boundary_condition(_a      )

													# 1. Denoise model output using boundary conditions
													a__:							List[str]									=c_out * model_output + c_skip * sample
													if self.config.clip_denoised:
																				a__:							Union[str, Any]									=denoised.clamp(-1   ,							1      )

													# 2. Sample z ~ N(0, s_noise^2 * I)
													# Noise is not used for onestep sampling.
													if len(self.timesteps      ) > 1:
																				a__:							int									=randn_tensor(
																				    model_output.shape   ,							dtype=model_output.dtype   ,							device=model_output.device   ,							generator=_a      )
													else:
																				a__:							int									=torch.zeros_like(_a      )
													a__:							int									=noise * self.config.s_noise

													a__:							Dict									=sigma_next.clamp(min=_a   ,							max=_a      )

													# 3. Return noisy sample
													# tau = sigma_hat, eps = sigma_min
													a__:							Union[str, Any]									=denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

													if not return_dict:
																				return (prev_sample,)

													return CMStochasticIterativeSchedulerOutput(prev_sample=_a      )


						def 	_lowerCamelCase      (			self    :	Optional[Any]   ,							_a    :	torch.FloatTensor   ,							_a    :	torch.FloatTensor   ,							_a    :	torch.FloatTensor   ,							):
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													a__:							str									=self.sigmas.to(device=original_samples.device   ,							dtype=original_samples.dtype      )
													if original_samples.device.type == "mps" and torch.is_floating_point(_a      ):
																				# mps does not support float64
																				a__:							Optional[int]									=self.timesteps.to(original_samples.device   ,							dtype=torch.floataa      )
																				a__:							str									=timesteps.to(original_samples.device   ,							dtype=torch.floataa      )
													else:
																				a__:							Optional[int]									=self.timesteps.to(original_samples.device      )
																				a__:							Optional[Any]									=timesteps.to(original_samples.device      )

													a__:							Optional[Any]									=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

													a__:							int									=sigmas[step_indices].flatten()
													while len(sigma.shape      ) < len(original_samples.shape      ):
																				a__:							List[str]									=sigma.unsqueeze(-1      )

													a__:							Dict									=original_samples + noise * sigma
													return noisy_samples







						def __len__(			self    :	Tuple      ):
													return self.config.num_train_timesteps










import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

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


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													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__:							List[str]									=np.mean(_a      )
																				return

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

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

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

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

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													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 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

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


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

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





