

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

import torch

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

@dataclass
class 				__UpperCamelCase				(					_UpperCAmelCase			):
	_lowercase  :   torch.FloatTensor
	_lowercase  :   torch.FloatTensor




class 				__UpperCamelCase				(					_UpperCAmelCase  ,						_UpperCAmelCase			):
	_lowercase  :   Any       				=  1







	@register_to_config
	def __init__(   self: Tuple  , __UpperCamelCase: int = 20_00  , __UpperCamelCase: float = 0.15  , __UpperCamelCase: float = 0.01  , __UpperCamelCase: float = 13_48.0  , __UpperCamelCase: float = 1E-5  , __UpperCamelCase: int = 1  , ):

						'''simple docstring'''





						__magic_name__							=    sigma_max

						# setable values
						__magic_name__							=    None

						self.set_sigmas(A_  , A_  , A_  , A_      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: torch.FloatTensor  , __UpperCamelCase: Optional[int] = None      ):

						'''simple docstring'''





						return sample







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: int  , __UpperCamelCase: float = None  , __UpperCamelCase: Union[str, torch.device] = None      ):

						'''simple docstring'''





						__magic_name__							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

						__magic_name__							=    torch.linspace(1  , A_  , A_  , device=A_      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: int  , __UpperCamelCase: float = None  , __UpperCamelCase: float = None  , __UpperCamelCase: float = None      ):

						'''simple docstring'''





						__magic_name__							=    sigma_min if sigma_min is not None else self.config.sigma_min
						__magic_name__							=    sigma_max if sigma_max is not None else self.config.sigma_max
						__magic_name__							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if self.timesteps is None:
											self.set_timesteps(A_  , A_      )

						__magic_name__							=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
						__magic_name__							=    torch.exp(torch.linspace(math.log(A_      )  , math.log(A_      )  , A_      )      )
						__magic_name__							=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]      ):

						'''simple docstring'''





						return torch.where(
						    timesteps == 0  , torch.zeros_like(t.to(timesteps.device      )      )  , self.discrete_sigmas[timesteps - 1].to(timesteps.device      )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: torch.FloatTensor  , __UpperCamelCase: int  , __UpperCamelCase: torch.FloatTensor  , __UpperCamelCase: Optional[torch.Generator] = None  , __UpperCamelCase: bool = True  , ):

						'''simple docstring'''





						if self.timesteps is None:
											raise ValueError(
											    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'      )

						__magic_name__							=    timestep * torch.ones(
						    sample.shape[0]  , device=sample.device      )  # torch.repeat_interleave(timestep, sample.shape[0])
						__magic_name__							=    (timestep * (len(self.timesteps      ) - 1)).long()

						# mps requires indices to be in the same device, so we use cpu as is the default with cuda
						__magic_name__							=    timesteps.to(self.discrete_sigmas.device      )

						__magic_name__							=    self.discrete_sigmas[timesteps].to(sample.device      )
						__magic_name__							=    self.get_adjacent_sigma(A_  , A_      ).to(sample.device      )
						__magic_name__							=    torch.zeros_like(A_      )
						__magic_name__							=    (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						__magic_name__							=    diffusion.flatten()
						while len(diffusion.shape      ) < len(sample.shape      ):
											__magic_name__							=    diffusion.unsqueeze(-1      )
						__magic_name__							=    drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						__magic_name__							=    randn_tensor(
						    sample.shape  , layout=sample.layout  , generator=A_  , device=sample.device  , dtype=sample.dtype      )
						__magic_name__							=    sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						__magic_name__							=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean)

						return SdeVeOutput(prev_sample=A_  , prev_sample_mean=A_      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: torch.FloatTensor  , __UpperCamelCase: torch.FloatTensor  , __UpperCamelCase: Optional[torch.Generator] = None  , __UpperCamelCase: bool = True  , ):

						'''simple docstring'''





						if self.timesteps is None:
											raise ValueError(
											    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'      )

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						__magic_name__							=    randn_tensor(sample.shape  , layout=sample.layout  , generator=A_      ).to(sample.device      )

						# compute step size from the model_output, the noise, and the snr
						__magic_name__							=    torch.norm(model_output.reshape(model_output.shape[0]  , -1      )  , dim=-1      ).mean()
						__magic_name__							=    torch.norm(noise.reshape(noise.shape[0]  , -1      )  , dim=-1      ).mean()
						__magic_name__							=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						__magic_name__							=    step_size * torch.ones(sample.shape[0]      ).to(sample.device      )
						# self.repeat_scalar(step_size, sample.shape[0])

						# compute corrected sample: model_output term and noise term
						__magic_name__							=    step_size.flatten()
						while len(step_size.shape      ) < len(sample.shape      ):
											__magic_name__							=    step_size.unsqueeze(-1      )
						__magic_name__							=    sample + step_size * model_output
						__magic_name__							=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample,)

						return SchedulerOutput(prev_sample=A_      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: torch.FloatTensor  , __UpperCamelCase: torch.FloatTensor  , __UpperCamelCase: torch.FloatTensor  , ):

						'''simple docstring'''





						__magic_name__							=    timesteps.to(original_samples.device      )
						__magic_name__							=    self.discrete_sigmas.to(original_samples.device      )[timesteps]
						__magic_name__							=    (
						    noise * sigmas[:, None, None, None]
						    if noise is not None
						    else torch.randn_like(A_      ) * sigmas[:, None, None, None]
						)
						__magic_name__							=    noise + original_samples
						return noisy_samples







	def __len__(   self: Tuple      ):

						'''simple docstring'''





						return self.config.num_train_timesteps







from math import factorial, radians




def 					_lowercase     (				a_				:					float					,a_				:					int = 1_8					,a_				:					int = 1_0				)				->	float:




					'''simple docstring'''







					__magic_name__							=    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

					# Converting from degrees to radians
					__magic_name__							=    radians(a_				)

					__magic_name__							=    angle_in_radians
					__magic_name__							=    3
					__magic_name__							=    -1

					for _ in range(a_				):
										result += (b * (angle_in_radians**a)) / factorial(a_				)

										__magic_name__							=    -b  # One positive term and the next will be negative and so on...
										a += 2  # Increased by 2 for every term.

					return round(a_					,a_				)


if __name__ == "__main__":
						__import__("doctest").testmod()




