



"""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 SCREAMING_SNAKE_CASE__					(		__snake_case     ,					__snake_case      ):
						_lowerCAmelCase							=					1



						@register_to_config
						def __init__(self						,     _lowercase = 1000						,     _lowercase = None							):





													'''simple docstring'''

													self.set_timesteps(_lowercase							)

													# standard deviation of the initial noise distribution
													__a		:		Union[str, Any]									=       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.
													__a		:		Union[str, Any]									=       4

													# running values
													__a		:		int									=       []



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





													'''simple docstring'''

													__a		:		Dict									=       num_inference_steps
													__a		:		List[str]									=       torch.linspace(1						,     0						,     num_inference_steps + 1							)[:-1]
													__a		:		Dict									=       torch.cat([steps, torch.tensor([0.0]							)]							)

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

													__a		:		Any									=       (1.0 - self.betas**2) ** 0.5

													__a		:		Optional[int]									=       (torch.atana(self.betas						,     self.alphas							) / math.pi * 2)[:-1]
													__a		:		List[str]									=       timesteps.to(_lowercase							)

													__a		:		Union[str, Any]									=       []



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase = 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"""							)

													__a		:		Dict									=       (self.timesteps == timestep).nonzero().item()
													__a		:		Union[str, Any]									=       timestep_index + 1

													__a		:		Any									=       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
													self.ets.append(_lowercase							)

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

													__a		:		Dict									=       self._get_prev_sample(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)

													if not return_dict:
																				return (prev_sample,)

													return SchedulerOutput(prev_sample=_lowercase							)



						def     lowerCAmelCase__(self						,     _lowercase						,     *_lowercase						,     **_lowercase							):





													'''simple docstring'''

													return sample



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Any									=       self.alphas[timestep_index]
													__a		:		Dict									=       self.betas[timestep_index]

													__a		:		Any									=       self.alphas[prev_timestep_index]
													__a		:		Any									=       self.betas[prev_timestep_index]

													__a		:		Dict									=       (sample - sigma * ets) / max(_lowercase						,     1e-8							)
													__a		:		List[str]									=       next_alpha * pred + ets * next_sigma

													return prev_sample





						def __len__(self							):





													'''simple docstring'''

													return self.config.num_train_timesteps





"""simple docstring"""

import torch

from diffusers import DiffusionPipeline




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													super().__init__()

													self.register_modules(unet=_lowercase						,     scheduler=_lowercase							)





						def __call__(self							):





													'''simple docstring'''

													__a		:		Dict									=       torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     )
													__a		:		Optional[Any]									=       1

													__a		:		List[str]									=       self.unet(_lowercase						,     _lowercase							).sample
													__a		:		Union[str, Any]									=       self.scheduler.step(_lowercase						,     _lowercase						,     _lowercase							).prev_sample

													__a		:		Optional[int]									=       scheduler_output - scheduler_output + torch.ones_like(_lowercase							)

													return result

