

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 PoolFormerImageProcessor


class      lowerCAmelCase					(     unittest.TestCase					):


       '''simple docstring'''







       def __init__(						self       :	str    ,						__snake_case       :	Dict    ,						__snake_case       :	List[Any]=7    ,						__snake_case       :	Any=3    ,						__snake_case       :	Any=30    ,						__snake_case       :	Any=400    ,						__snake_case       :	Union[str, Any]=True    ,						__snake_case       :	List[Any]=None    ,						__snake_case       :	Optional[int]=0.9    ,						__snake_case       :	Dict=None    ,						__snake_case       :	Dict=True    ,						__snake_case       :	List[Any]=[0.5, 0.5, 0.5]    ,						__snake_case       :	Dict=[0.5, 0.5, 0.5]    ,						) ->			str:







        '''simple docstring'''

        lowerCamelCase       =     size if size is not None else {'shortest_edge': 30}
        lowerCamelCase       =     crop_size if crop_size is not None else {'height': 30, 'width': 30}
        lowerCamelCase       =     parent
        lowerCamelCase       =     batch_size
        lowerCamelCase       =     num_channels
        lowerCamelCase       =     min_resolution
        lowerCamelCase       =     max_resolution
        lowerCamelCase       =     do_resize_and_center_crop
        lowerCamelCase       =     size
        lowerCamelCase       =     crop_pct
        lowerCamelCase       =     crop_size
        lowerCamelCase       =     do_normalize
        lowerCamelCase       =     image_mean
        lowerCamelCase       =     image_std





       def 	lowerCamelCase__    (						self       :	Dict  ) ->			Dict:







        '''simple docstring'''

        return {
            "size": self.size,
            "do_resize_and_center_crop": self.do_resize_and_center_crop,
            "crop_pct": self.crop_pct,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }


@require_torch
@require_vision
class      lowerCAmelCase					(     __snake_case ,					unittest.TestCase					):


       '''simple docstring'''







       snake_case     				=						PoolFormerImageProcessor if is_vision_available() else None

       def 	lowerCamelCase__    (						self       :	List[Any]  ) ->			List[Any]:







        '''simple docstring'''

        lowerCamelCase       =     PoolFormerImageProcessingTester(self  )

       @property
       def 	lowerCamelCase__    (						self       :	Optional[int]  ) ->			List[Any]:







        '''simple docstring'''

        return self.image_processor_tester.prepare_image_processor_dict()

       def 	lowerCamelCase__    (						self       :	Union[str, Any]  ) ->			Tuple:







        '''simple docstring'''

        lowerCamelCase       =     self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(__lowerCamelCase    ,						'do_resize_and_center_crop'  )  )
        self.assertTrue(hasattr(__lowerCamelCase    ,						'size'  )  )
        self.assertTrue(hasattr(__lowerCamelCase    ,						'crop_pct'  )  )
        self.assertTrue(hasattr(__lowerCamelCase    ,						'do_normalize'  )  )
        self.assertTrue(hasattr(__lowerCamelCase    ,						'image_mean'  )  )
        self.assertTrue(hasattr(__lowerCamelCase    ,						'image_std'  )  )

       def 	lowerCamelCase__    (						self       :	Union[str, Any]  ) ->			List[Any]:







        '''simple docstring'''

        lowerCamelCase       =     self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size    ,						{'shortest_edge': 30}  )
        self.assertEqual(image_processor.crop_size    ,						{'height': 30, 'width': 30}  )

        lowerCamelCase       =     self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42    ,						crop_size=84  )
        self.assertEqual(image_processor.size    ,						{'shortest_edge': 42}  )
        self.assertEqual(image_processor.crop_size    ,						{'height': 84, 'width': 84}  )

       def 	lowerCamelCase__    (						self       :	List[str]  ) ->			Union[str, Any]:







        '''simple docstring'''

        pass

       def 	lowerCamelCase__    (						self       :	List[Any]  ) ->			Tuple:







        '''simple docstring'''

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

        # Test not batched input
        lowerCamelCase       =     image_processing(image_inputs[0]    ,						return_tensors='pt'  ).pixel_values

        self.assertEqual(
            encoded_images.shape    ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )    ,						)

        # Test batched
        lowerCamelCase       =     image_processing(__lowerCamelCase    ,						return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )    ,						)

       def 	lowerCamelCase__    (						self       :	Optional[int]  ) ->			List[str]:







        '''simple docstring'''

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

        # Test not batched input
        lowerCamelCase       =     image_processing(image_inputs[0]    ,						return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )    ,						)

        # Test batched
        lowerCamelCase       =     image_processing(__lowerCamelCase    ,						return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )    ,						)





       def 	lowerCamelCase__    (						self       :	str  ) ->			Optional[int]:







        '''simple docstring'''

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

        # Test not batched input
        lowerCamelCase       =     image_processing(image_inputs[0]    ,						return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )    ,						)

        # Test batched
        lowerCamelCase       =     image_processing(__lowerCamelCase    ,						return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )    ,						)







from typing import Any

import numpy as np





def 	__a     (      A__			:  np.ndarray       ):
    return np.array_equal(A__			,  matrix.conjugate().T       )





def 	__a     (      A__			:  np.ndarray			,  A__			:  np.ndarray       ):
    SCREAMING_SNAKE_CASE										=   v.conjugate().T
    SCREAMING_SNAKE_CASE										=   v_star.dot(A__       )
    assert isinstance(A__			,  np.ndarray       )
    return (v_star_dot.dot(A__       )) / (v_star.dot(A__       ))





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
    SCREAMING_SNAKE_CASE										=   np.array([[1], [2], [3]]       )
    assert is_hermitian(A__       ), F"{a} is not hermitian."
    print(rayleigh_quotient(A__			,  A__       )       )

    SCREAMING_SNAKE_CASE										=   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()