


'''simple docstring'''






import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def 						_A					(lowerCAmelCase__	:NDArray[floataa]					,    lowerCAmelCase__	:NDArray[floataa]		) ->	NDArray[floataa]:




     '''simple docstring'''



     _a   , _a         =				np.shape(lowerCAmelCase__		)

     _a         =				np.zeros((rows, 1)					,    dtype=lowerCAmelCase__		)
     for row in reversed(range(lowerCAmelCase__		)		):
          _a         =				0
          for col in range(row + 1					,    lowerCAmelCase__		):
               total += coefficients[row, col] * x[col]

          _a         =				(vector[row] - total) / coefficients[row, row]

     return x






def 						_A					(lowerCAmelCase__	:NDArray[floataa]					,    lowerCAmelCase__	:NDArray[floataa]		) ->	NDArray[floataa]:




     '''simple docstring'''



     _a   , _a         =				np.shape(lowerCAmelCase__		)
     if rows != columns:
          return np.array(()					,    dtype=lowerCAmelCase__		)

     # augmented matrix
     _a         =				np.concatenate((coefficients, vector)					,    axis=1		)
     _a         =				augmented_mat.astype('float64'		)

     # scale the matrix leaving it triangular
     for row in range(rows - 1		):
          _a         =				augmented_mat[row, row]
          for col in range(row + 1					,    lowerCAmelCase__		):
               _a         =				augmented_mat[col, row] / pivot
               augmented_mat[col, :] -= factor * augmented_mat[row, :]

     _a         =				retroactive_resolution(
         augmented_mat[:, 0:columns]					,    augmented_mat[:, columns : columns + 1]		)

     return x


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''simple docstring'''






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_5,
     """sld_guidance_scale""": 2_0,
     """sld_threshold""": 0.0,
     """sld_momentum_scale""": 0.0,
     """sld_mom_beta""": 0.0,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_0,
     """sld_guidance_scale""": 1_0_0_0,
     """sld_threshold""": 0.01,
     """sld_momentum_scale""": 0.3,
     """sld_mom_beta""": 0.4,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 7,
     """sld_guidance_scale""": 2_0_0_0,
     """sld_threshold""": 0.025,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 0,
     """sld_guidance_scale""": 5_0_0_0,
     """sld_threshold""": 1.0,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }





@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42


if is_transformers_available() and is_torch_available():
 from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
 from .safety_checker import SafeStableDiffusionSafetyChecker

