



"""simple docstring"""

from typing import Any

import numpy as np




def __lowercase    (							_a					):
    return np.array_equal(_a	,    matrix.conjugate().T					)




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Dict       =			v.conjugate().T
    snake_case_				:  int       =			v_star.dot(_a					)
    assert isinstance(_a	,    np.ndarray					)
    return (v_star_dot.dot(_a					)) / (v_star.dot(_a					))




def __lowercase    (							):
    snake_case_				:  List[str]       =			np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]					)
    snake_case_				:  int       =			np.array([[1], [2], [3]]					)
    assert is_hermitian(_a					), f"{a} is not hermitian."
    print(rayleigh_quotient(_a	,    _a					)					)

    snake_case_				:  Union[str, Any]       =			np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
    assert is_hermitian(_a					), f"{a} is not hermitian."
    assert rayleigh_quotient(_a	,    _a					) == float(3					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()










"""simple docstring"""

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor




@require_torch
@require_vision
class  _UpperCAmelCase (    unittest.TestCase):

 @slow
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[Any]       =			AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     snake_case_				:  List[Any]       =			AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     model.to(lowercase_       )

     from datasets import load_dataset

     snake_case_				:  str       =			load_dataset('''nielsr/rvlcdip-demo'''       )

     snake_case_				:  Tuple       =			dataset['''train'''][0]['''image'''].convert('''RGB'''       )

     snake_case_				:  Union[str, Any]       =			image_processor(lowercase_						,  return_tensors='''pt'''       ).to(lowercase_       )

     # forward pass
     with torch.no_grad():
         snake_case_				:  List[Any]       =			model(**lowercase_       )
         snake_case_				:  int       =			outputs.logits

     snake_case_				:  Any       =			torch.Size((1, 16)       )
     self.assertEqual(logits.shape						,  lowercase_       )

     snake_case_				:  Any       =			torch.tensor(
         [-0.41_58, -0.40_92, -0.43_47]						,  device=lowercase_						,  dtype=torch.float						,  )
     self.assertTrue(torch.allclose(logits[0, :3]						,  lowercase_						,  atol=1E-4       )       )





