





'''simple docstring'''







import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput


@dataclass
class    __a (lowerCamelCase       ):
							__a   :				Union[List[PIL.Image.Image], np.ndarray]





class    __a (unittest.TestCase       ):

							def       UpperCAmelCase__		(							self							:       Dict				)   ->    Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					CustomOutput(images=np.random.rand(1    ,       3    ,       4    ,       4				)				)

													# check every way of getting the attribute
													assert isinstance(outputs.images    ,       np.ndarray				)
													assert outputs.images.shape == (1, 3, 4, 4)
													assert isinstance(outputs['''images''']    ,       np.ndarray				)
													assert outputs["images"].shape == (1, 3, 4, 4)
													assert isinstance(outputs[0]    ,       np.ndarray				)
													assert outputs[0].shape == (1, 3, 4, 4)

													# test with a non-tensor attribute
													UpperCAmelCase_     : List[Any]         =					CustomOutput(images=[PIL.Image.new('''RGB'''    ,       (4, 4)				)]				)

													# check every way of getting the attribute
													assert isinstance(outputs.images    ,       __magic_name__				)
													assert isinstance(outputs.images[0]    ,       PIL.Image.Image				)
													assert isinstance(outputs['''images''']    ,       __magic_name__				)
													assert isinstance(outputs['''images'''][0]    ,       PIL.Image.Image				)
													assert isinstance(outputs[0]    ,       __magic_name__				)
													assert isinstance(outputs[0][0]    ,       PIL.Image.Image				)



							def       UpperCAmelCase__		(							self							:       Optional[Any]				)   ->    Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					CustomOutput({'''images''': np.random.rand(1    ,       3    ,       4    ,       4				)}				)

													# check every way of getting the attribute
													assert isinstance(outputs.images    ,       np.ndarray				)
													assert outputs.images.shape == (1, 3, 4, 4)
													assert isinstance(outputs['''images''']    ,       np.ndarray				)
													assert outputs["images"].shape == (1, 3, 4, 4)
													assert isinstance(outputs[0]    ,       np.ndarray				)
													assert outputs[0].shape == (1, 3, 4, 4)

													# test with a non-tensor attribute
													UpperCAmelCase_     : Any         =					CustomOutput({'''images''': [PIL.Image.new('''RGB'''    ,       (4, 4)				)]}				)

													# check every way of getting the attribute
													assert isinstance(outputs.images    ,       __magic_name__				)
													assert isinstance(outputs.images[0]    ,       PIL.Image.Image				)
													assert isinstance(outputs['''images''']    ,       __magic_name__				)
													assert isinstance(outputs['''images'''][0]    ,       PIL.Image.Image				)
													assert isinstance(outputs[0]    ,       __magic_name__				)
													assert isinstance(outputs[0][0]    ,       PIL.Image.Image				)







'''simple docstring'''







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





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     NDArray[floataa],     SCREAMING_SNAKE_CASE__ :     NDArray[floataa]				)	->		NDArray[floataa]:
						UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					np.shape(SCREAMING_SNAKE_CASE__				)

						UpperCAmelCase_     : NDArray[floataa]         =					np.zeros((rows, 1),     dtype=SCREAMING_SNAKE_CASE__				)
						for row in reversed(range(SCREAMING_SNAKE_CASE__				)				):
												UpperCAmelCase_     : Union[str, Any]         =					0
												for col in range(row + 1,     SCREAMING_SNAKE_CASE__				):
																		total += coefficients[row, col] * x[col]

												UpperCAmelCase_     : str         =					(vector[row] - total) / coefficients[row, row]

						return x





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     NDArray[floataa],     SCREAMING_SNAKE_CASE__ :     NDArray[floataa]				)	->		NDArray[floataa]:
						UpperCAmelCase_  ,    UpperCAmelCase_     : List[Any]         =					np.shape(SCREAMING_SNAKE_CASE__				)
						if rows != columns:
												return np.array((),     dtype=SCREAMING_SNAKE_CASE__				)

						# augmented matrix
						UpperCAmelCase_     : NDArray[floataa]         =					np.concatenate((coefficients, vector),     axis=1				)
						UpperCAmelCase_     : Optional[Any]         =					augmented_mat.astype('''float64'''				)

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

						UpperCAmelCase_     : Optional[Any]         =					retroactive_resolution(
						    augmented_mat[:, 0:columns],     augmented_mat[:, columns : columns + 1]				)

						return x


if __name__ == "__main__":
					import doctest

					doctest.testmod()





