"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor






class    a__    (   unittest.TestCase	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase=7,				_UpperCAmelCase=3,				_UpperCAmelCase=18,				_UpperCAmelCase=30,				_UpperCAmelCase=400,				_UpperCAmelCase=True,				_UpperCAmelCase=32,				_UpperCAmelCase=True,				):
        '''simple docstring'''



        lowercase__				 =							parent
        lowercase__				 =							batch_size
        lowercase__				 =							num_channels
        lowercase__				 =							image_size
        lowercase__				 =							min_resolution
        lowercase__				 =							max_resolution
        lowercase__				 =							do_resize
        lowercase__				 =							size_divisor
        lowercase__				 =							do_rescale


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }






@require_torch
@require_vision
class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      GLPNImageProcessor if is_vision_available() else None


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							GLPNImageProcessingTester(self  )


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"do_resize"  )  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"size_divisor"  )  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"resample"  )  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"do_rescale"  )  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowercase__				 =							prepare_image_inputs(self.image_processor_tester,				equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,				Image.Image  )

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        lowercase__				 =							image_processing(image_inputs[0],				return_tensors="pt"  ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowercase__				 =							prepare_image_inputs(self.image_processor_tester,				equal_resolution=_UpperCAmelCase,				numpify=_UpperCAmelCase  )
        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,				np.ndarray  )

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        lowercase__				 =							image_processing(image_inputs[0],				return_tensors="pt"  ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowercase__				 =							prepare_image_inputs(self.image_processor_tester,				equal_resolution=_UpperCAmelCase,				torchify=_UpperCAmelCase  )
        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,				torch.Tensor  )

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        lowercase__				 =							image_processing(image_inputs[0],				return_tensors="pt"  ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )





"""simple docstring"""


from typing import Any

import numpy as np







def 					__a   (		A      ):






     '''simple docstring'''



     return np.array_equal(A    ,				matrix.conjugate().T      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							v.conjugate().T
     lowercase__				 =							v_star.dot(A      )
     assert isinstance(A    ,				np.ndarray      )
     return (v_star_dot.dot(A      )) / (v_star.dot(A      ))







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]      )
     lowercase__				 =							np.array([[1], [2], [3]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     print(rayleigh_quotient(A    ,				A      )      )

     lowercase__				 =							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()




