



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






def        a( A			:       NDArray[floataa]					,	A			:       NDArray[floataa]			)						->			NDArray[floataa]:
					"""simple docstring"""





					a	,							a   	=  np.shape(A			)

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

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

					return x






def        a( A			:       NDArray[floataa]					,	A			:       NDArray[floataa]			)						->			NDArray[floataa]:
					"""simple docstring"""





					a	,							a   	=  np.shape(A			)
					if rows != columns:
										return np.array(()					,	dtype=A			)

					# 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					,	A			):
															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()





from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline
