





'''simple docstring'''

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       UpperCAmelCase (						_lowercase			,       _lowercase  ):
		UpperCAmelCase					:	Optional[Any] 			=		1




		@register_to_config
		def __init__(self      :	List[str]							,			A__      :	int = 1_0_0_0							,			A__      :	Optional[Union[np.ndarray, List[float]]] = None				)					-> Optional[Any]:
						# set `betas`, `alphas`, `timesteps`
						self.set_timesteps(A__				)

						# standard deviation of the initial noise distribution
						lowercase         = 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.
						lowercase         = 4

						# running values
						lowercase         = []




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	int							,			A__      :	Union[str, torch.device] = None				)					-> List[str]:
						lowercase         = num_inference_steps
						lowercase         = torch.linspace(1							,			0							,			num_inference_steps + 1				)[:-1]
						lowercase         = torch.cat([steps, torch.tensor([0.0]				)]				)

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

						lowercase         = (1.0 - self.betas**2) ** 0.5

						lowercase         = (torch.atana(self.betas							,			self.alphas				) / math.pi * 2)[:-1]
						lowercase         = timesteps.to(A__				)

						lowercase         = []




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	torch.FloatTensor							,			A__      :	int							,			A__      :	torch.FloatTensor							,			A__      :	bool = True							,			)					-> Union[SchedulerOutput, Tuple]:
						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"				)

						lowercase         = (self.timesteps == timestep).nonzero().item()
						lowercase         = timestep_index + 1

						lowercase         = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
						self.ets.append(A__				)

						if len(self.ets				) == 1:
										lowercase         = self.ets[-1]
						elif len(self.ets				) == 2:
										lowercase         = (3 * self.ets[-1] - self.ets[-2]) / 2
						elif len(self.ets				) == 3:
										lowercase         = (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
						else:
										lowercase         = (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

						lowercase         = self._get_prev_sample(A__							,			A__							,			A__							,			A__				)

						if not return_dict:
										return (prev_sample,)

						return SchedulerOutput(prev_sample=A__				)




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	torch.FloatTensor							,			*A__      :	List[str]							,			**A__      :	str				)					-> torch.FloatTensor:
						return sample




		def 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	List[Any]							,			A__      :	List[str]							,			A__      :	Union[str, Any]							,			A__      :	Any				)					-> Dict:
						lowercase         = self.alphas[timestep_index]
						lowercase         = self.betas[timestep_index]

						lowercase         = self.alphas[prev_timestep_index]
						lowercase         = self.betas[prev_timestep_index]

						lowercase         = (sample - sigma * ets) / max(A__							,			1e-8				)
						lowercase         = next_alpha * pred + ets * next_sigma

						return prev_sample





		def __len__(self      :	str				)					-> int:
						return self.config.num_train_timesteps











'''simple docstring'''

# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


__lowerCamelCase : Any											=			open  # noqa: we just need to have a builtin inside this module to test it properly




