"""simple docstring"""




from typing import Any

import numpy as np





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: np.ndarray			) ->   bool:
  return np.array_equal(snake_case__    ,matrix.conjugate().T			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: np.ndarray    ,_lowerCamelCase		: np.ndarray			) ->   Any:
  _lowerCAmelCase		: Optional[Any]						    =  v.conjugate().T
  _lowerCAmelCase		: Dict						    =  v_star.dot(snake_case__			)
  assert isinstance(snake_case__    ,np.ndarray			)
  return (v_star_dot.dot(snake_case__			)) / (v_star.dot(snake_case__			))





def 							SCREAMING_SNAKE_CASE  (    ) ->   None:
  _lowerCAmelCase		: List[Any]						    =  np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]			)
  _lowerCAmelCase		: List[Any]						    =  np.array([[1], [2], [3]]			)
  assert is_hermitian(snake_case__			), f"{a} is not hermitian."
  print(rayleigh_quotient(snake_case__    ,snake_case__			)			)

  _lowerCAmelCase		: Tuple						    =  np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]			)
  assert is_hermitian(snake_case__			), f"{a} is not hermitian."
  assert rayleigh_quotient(snake_case__    ,snake_case__			) == float(3			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()





"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_a   :	Tuple 					=     logging.get_logger(__name__)
class 		__A							(				SCREAMING_SNAKE_CASE_	):






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




