

'''simple docstring'''







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

import math
from typing import Union

import torch

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


class    A__ ( _snake_case  ,  _snake_case     ):
    lowercase		   =						1



    @register_to_config
    def __init__(   self						,			UpperCamelCase__=2000						,			UpperCamelCase__=0.1						,			UpperCamelCase__=20						,			UpperCamelCase__=1e-3    )  -> Tuple:





           '''simple docstring'''
           A_		   = None
           A_		   = None
           A_		   = None



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> str:





           '''simple docstring'''
           A_		   = torch.linspace(1						,			self.config.sampling_eps						,			UpperCamelCase__						,			device=UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=None    )  -> Any:





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

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           A_		   = (
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           A_		   = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
           A_		   = std.flatten()
           while len(std.shape    ) < len(score.shape    ):
                  A_		   = std.unsqueeze(-1    )
           A_		   = -score / std

           # compute
           A_		   = -1.0 / len(self.timesteps    )

           A_		   = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           A_		   = beta_t.flatten()
           while len(beta_t.shape    ) < len(x.shape    ):
                  A_		   = beta_t.unsqueeze(-1    )
           A_		   = -0.5 * beta_t * x

           A_		   = torch.sqrt(UpperCamelCase__    )
           A_		   = drift - diffusion**2 * score
           A_		   = x + drift * dt

           # add noise
           A_		   = randn_tensor(x.shape						,			layout=x.layout						,			generator=UpperCamelCase__						,			device=x.device						,			dtype=x.dtype    )
           A_		   = x_mean + diffusion * math.sqrt(-dt    ) * noise

           return x, x_mean







    def __len__(   self    )  -> Optional[Any]:





           '''simple docstring'''
           return self.config.num_train_timesteps



'''simple docstring'''







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    )     ->     list[float]:
       A_   ,     A_		   = coefficient_matrix.shape
       A_   ,     A_		   = constant_matrix.shape

       if rowsa != colsa:
              A_		   = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

       if colsa != 1:
              A_		   = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
              raise ValueError(UpperCAmelCase__			)

       if rowsa != rowsa:
              A_		   = (
                  """Coefficient and constant matrices dimensions must be nxn and nx1 but """
                  F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
              )
              raise ValueError(UpperCAmelCase__			)

       if len(UpperCAmelCase__			) != rowsa:
              A_		   = (
                  """Number of initial values must be equal to number of rows in coefficient """
                  F'''matrix but received {len(UpperCAmelCase__			)} and {rowsa}'''
              )
              raise ValueError(UpperCAmelCase__			)

       if iterations <= 0:
              raise ValueError("""Iterations must be at least 1"""			)

       A_		   = np.concatenate(
           (coefficient_matrix, constant_matrix),    axis=1			)

       A_   ,     A_		   = table.shape

       strictly_diagonally_dominant(UpperCAmelCase__			)

       # Iterates the whole matrix for given number of times
       for _ in range(UpperCAmelCase__			):
              A_		   = []
              for row in range(UpperCAmelCase__			):
                     A_		   = 0
                     for col in range(UpperCAmelCase__			):
                            if col == row:
                                   A_		   = table[row][col]
                            elif col == cols - 1:
                                   A_		   = table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     A_		   = (temp + val) / denom
                     new_val.append(UpperCAmelCase__			)
              A_		   = new_val

       return [float(UpperCAmelCase__			) for i in new_val]
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     bool:
       A_   ,     A_		   = table.shape

       A_		   = True

       for i in range(0,    UpperCAmelCase__			):
              A_		   = 0
              for j in range(0,    cols - 1			):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""			)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
      import doctest

      doctest.testmod()
