



from typing import Any

import numpy as np






def 					a(   A :					np.ndarray )	-> bool:
					"""simple docstring"""





					assert np.shape(A )[0] == np.shape(A )[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(A				,  matrix.T ) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					a  ,       a        =     np.linalg.eigh(A )

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0 ) )






def 					a(   A :					int )	-> Any:
					"""simple docstring"""





					a        =     np.random.randn(A				,  A )
					a        =     np.dot(A				,  random_matrix.T )
					assert _is_matrix_spd(A )
					return spd_matrix






def 					a(   A :					np.ndarray				,  A :					np.ndarray				,  A :					int = 1000				,  A :					float = 1e-8				,  )	-> Any:
					"""simple docstring"""





					assert np.shape(A )[0] == np.shape(A )[1]
					assert np.shape(A )[0] == np.shape(A )[0]
					assert _is_matrix_spd(A )

					# Initialize solution guess, residual, search direction.
					a        =     np.zeros((np.shape(A )[0], 1) )
					a        =     np.copy(A )
					a        =     np.copy(A )

					# Set initial errors in solution guess and residual.
					a        =     1e9
					a        =     1e9
					a        =     1e9

					# Set iteration counter to threshold number of iterations.
					a        =     0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										a        =     np.dot(A				,  A )

										# The main algorithm.

										# Update search direction magnitude.
										a        =     np.dot(ra.T				,  A ) / np.dot(pa.T				,  A )
										# Update solution guess.
										a        =     xa + alpha * pa
										# Calculate new residual.
										a        =     ra - alpha * w
										# Calculate new Krylov subspace scale.
										a        =     np.dot(r.T				,  A ) / np.dot(ra.T				,  A )
										# Calculate new A conjuage search direction.
										a        =     r + beta * pa

										# Calculate errors.
										a        =     np.linalg.norm(r - ra )
										a        =     np.linalg.norm(x - xa )
										a        =     np.maximum(A				,  A )

										# Update variables.
										a        =     np.copy(A )
										a        =     np.copy(A )
										a        =     np.copy(A )

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x






def 					a(   )	-> None:
					"""simple docstring"""





					a        =     3
					a        =     _create_spd_matrix(A )
					a        =     np.random.randn(A				,  1 )
					a        =     np.dot(A				,  A )

					# Numpy solution.
					a        =     np.linalg.solve(A				,  A )

					# Our implementation.
					a        =     conjugate_gradient(A				,  A )

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true ) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true ) <= 1e-6


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				test_conjugate_gradient()





import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


_lowercase:				Optional[int]       =	logging.get_logger(__name__)



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	def __init__(self	, *lowerCamelCase_	, **lowerCamelCase_	):
						"""simple docstring"""

						warnings.warn(
						    "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use DetrImageProcessor instead."	, lowerCamelCase_	, )
						super().__init__(*lowerCamelCase_	, **lowerCamelCase_	)
