



import math
from datetime import datetime, timedelta







def    _snake_case (       __snake_case    )     ->     datetime:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		year % 1_9
      UpperCAmelCase_  :	List[str]				      =		year % 4
      UpperCAmelCase_  :	str				      =		year % 7
      UpperCAmelCase_  :	Dict				      =		math.floor(year / 1_0_0    )
      UpperCAmelCase_  :	int				      =		math.floor((1_3 + 8 * leap_day_inhibits) / 2_5    )
      UpperCAmelCase_  :	Tuple				      =		leap_day_inhibits / 4
      UpperCAmelCase_  :	Union[str, Any]				      =		(
          1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
      ) % 3_0
      UpperCAmelCase_  :	Union[str, Any]				      =		(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

      # days to be added to March 21
      UpperCAmelCase_  :	Tuple				      =		(1_9 * metonic_cycle + secular_moon_shift) % 3_0

      # PHM -> Paschal Full Moon
      UpperCAmelCase_  :	Union[str, Any]				      =		(
          2 * julian_leap_year
          + 4 * non_leap_year
          + 6 * days_to_add
          + century_starting_point
      ) % 7

      if days_to_add == 2_9 and days_from_phm_to_sunday == 6:
            return datetime(__snake_case					,  4					,  1_9    )
      elif days_to_add == 2_8 and days_from_phm_to_sunday == 6:
            return datetime(__snake_case					,  4					,  1_8    )
      else:
            return datetime(__snake_case					,  3					,  2_2    ) + timedelta(
                days=int(days_to_add + days_from_phm_to_sunday    )    )


if __name__ == "__main__":
   for year in (1994, 2000, 2010, 2021, 2023):
      __lowerCamelCase         =			'''will be''' if year > datetime.now().year else '''was'''
      print(F'''Easter in {year} {tense} {gauss_easter(year)}''')





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				1



       @register_to_config
       def __init__(     self     ,lowercase = 2000     ,lowercase = 0.15     ,lowercase = 0.01     ,lowercase = 1348.0     ,lowercase = 1E-5     ,lowercase = 1     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		sigma_max

             # setable values
             UpperCAmelCase_  :	Optional[int]				      =		None

             self.set_sigmas(lowercase     ,lowercase     ,lowercase     ,lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             return sample



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

             UpperCAmelCase_  :	List[Any]				      =		torch.linspace(1     ,lowercase     ,lowercase     ,device=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		sigma_min if sigma_min is not None else self.config.sigma_min
             UpperCAmelCase_  :	int				      =		sigma_max if sigma_max is not None else self.config.sigma_max
             UpperCAmelCase_  :	Union[str, Any]				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
             if self.timesteps is None:
                   self.set_timesteps(lowercase     ,lowercase)

             UpperCAmelCase_  :	Union[str, Any]				      =		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
             UpperCAmelCase_  :	Optional[int]				      =		torch.exp(torch.linspace(math.log(lowercase)     ,math.log(lowercase)     ,lowercase))
             UpperCAmelCase_  :	Any				      =		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





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



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = 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")

             UpperCAmelCase_  :	Optional[int]				      =		timestep * torch.ones(
                 sample.shape[0]     ,device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
             UpperCAmelCase_  :	Tuple				      =		(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
             UpperCAmelCase_  :	Optional[int]				      =		timesteps.to(self.discrete_sigmas.device)

             UpperCAmelCase_  :	Optional[Any]				      =		self.discrete_sigmas[timesteps].to(sample.device)
             UpperCAmelCase_  :	Optional[Any]				      =		self.get_adjacent_sigma(lowercase     ,lowercase).to(sample.device)
             UpperCAmelCase_  :	Any				      =		torch.zeros_like(lowercase)
             UpperCAmelCase_  :	Dict				      =		(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
             UpperCAmelCase_  :	Dict				      =		diffusion.flatten()
             while len(diffusion.shape) < len(sample.shape):
                   UpperCAmelCase_  :	List[str]				      =		diffusion.unsqueeze(-1)
             UpperCAmelCase_  :	List[Any]				      =		drift - diffusion**2 * model_output

             #  equation 6: sample noise for the diffusion term of
             UpperCAmelCase_  :	Union[str, Any]				      =		randn_tensor(
                 sample.shape     ,layout=sample.layout     ,generator=lowercase     ,device=sample.device     ,dtype=sample.dtype)
             UpperCAmelCase_  :	Any				      =		sample - drift  # subtract because `dt` is a small negative timestep
             # TODO is the variable diffusion the correct scaling term for the noise?
             UpperCAmelCase_  :	Tuple				      =		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=lowercase     ,prev_sample_mean=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = 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
             UpperCAmelCase_  :	int				      =		randn_tensor(sample.shape     ,layout=sample.layout     ,generator=lowercase).to(sample.device)

             # compute step size from the model_output, the noise, and the snr
             UpperCAmelCase_  :	Union[str, Any]				      =		torch.norm(model_output.reshape(model_output.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	Optional[Any]				      =		torch.norm(noise.reshape(noise.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	List[Any]				      =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
             UpperCAmelCase_  :	Optional[Any]				      =		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
             UpperCAmelCase_  :	Any				      =		step_size.flatten()
             while len(step_size.shape) < len(sample.shape):
                   UpperCAmelCase_  :	Tuple				      =		step_size.unsqueeze(-1)
             UpperCAmelCase_  :	Dict				      =		sample + step_size * model_output
             UpperCAmelCase_  :	int				      =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

             if not return_dict:
                   return (prev_sample,)

             return SchedulerOutput(prev_sample=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		timesteps.to(original_samples.device)
             UpperCAmelCase_  :	List[str]				      =		self.discrete_sigmas.to(original_samples.device)[timesteps]
             UpperCAmelCase_  :	Tuple				      =		(
                 noise * sigmas[:, None, None, None]
                 if noise is not None
                 else torch.randn_like(lowercase) * sigmas[:, None, None, None]
             )
             UpperCAmelCase_  :	Tuple				      =		noise + original_samples
             return noisy_samples



       def __len__(     self):

             """simple docstring"""





             return self.config.num_train_timesteps
