




'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_lowerCamelCase				:		Union[str, Any]				      =    logging.get_logger(__name__)





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def __init__(       self,    *__UpperCamelCase,    **__UpperCamelCase					):
							warnings.warn(
							    "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
							    " Please use ConvNextImageProcessor instead.",    __UpperCamelCase,    )
							super().__init__(*__UpperCamelCase,    **__UpperCamelCase					)











'''simple docstring'''







import numpy as np






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case			,       _snake_case      			=			a.shape
		_snake_case      			=			min(lowercase_		,				lowercase_    )
		_snake_case      			=			np.eye(lowercase_    )
		_snake_case      			=			a.copy()

		for k in range(t - 1    ):
				# select a column of modified matrix A':
				_snake_case      			=			r[k:, [k]]
				# construct first basis vector
				_snake_case      			=			np.zeros_like(lowercase_    )
				_snake_case      			=			1.0
				# determine scaling factor
				_snake_case      			=			np.linalg.norm(lowercase_    )
				# construct vector v for Householder reflection
				_snake_case      			=			x + np.sign(x[0]    ) * alpha * ea
				v /= np.linalg.norm(lowercase_    )

				# construct the Householder matrix
				_snake_case      			=			np.eye(m - k    ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				_snake_case      			=			np.block([[np.eye(lowercase_    ), np.zeros((k, m - k)    )], [np.zeros((m - k, k)    ), q_k]]    )

				_snake_case      			=			q @ q_k.T
				_snake_case      			=			q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()





