


'''simple docstring'''



import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__		:					str												=  imread(R"""digital_image_processing/image_data/lena_small.jpg""")
A__		:					Dict												=  cvtColor(img, COLOR_BGR2GRAY)



def 			UpperCAmelCase__					(	)     ->		str:
     __lowerCamelCase   :	Optional[Any]								=		cn.convert_to_negative(UpperCAmelCase_							)
     # assert negative_img array for at least one True
     assert negative_img.any()



def 			UpperCAmelCase__					(	)     ->		List[Any]:
     with Image.open('digital_image_processing/image_data/lena_small.jpg'							) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(UpperCAmelCase_      ,    1_10							)							).startswith(
              '<PIL.Image.Image image mode=RGB size=100x100 at'							)



def 			UpperCAmelCase__					(	)     ->		List[Any]:
     __lowerCamelCase   :	Optional[int]								=		canny.gen_gaussian_kernel(9      ,    sigma=1.4							)
     # Assert ambiguous array
     assert resp.all()



def 			UpperCAmelCase__					(	)     ->		int:
     __lowerCamelCase   :	Optional[int]								=		imread('digital_image_processing/image_data/lena_small.jpg'      ,    0							)
     # assert ambiguous array for all == True
     assert canny_img.all()
     __lowerCamelCase   :	Dict								=		canny.canny(UpperCAmelCase_							)
     # assert canny array for at least one True
     assert canny_array.any()



def 			UpperCAmelCase__					(	)     ->		Optional[int]:
     assert gg.gaussian_filter(UpperCAmelCase_      ,    5      ,    sigma=0.9							).all()



def 			UpperCAmelCase__					(	)     ->		List[str]:
     # laplace diagonals
     __lowerCamelCase   :	Optional[Any]								=		array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]							)
     __lowerCamelCase   :	str								=		conv.img_convolve(UpperCAmelCase_      ,    UpperCAmelCase_							).astype(UpperCAmelCase_							)
     assert res.any()



def 			UpperCAmelCase__					(	)     ->		Union[str, Any]:
     assert med.median_filter(UpperCAmelCase_      ,    3							).any()



def 			UpperCAmelCase__					(	)     ->		Union[str, Any]:
     __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		sob.sobel_filter(UpperCAmelCase_							)
     assert grad.any() and theta.any()



def 			UpperCAmelCase__					(	)     ->		List[str]:
     __lowerCamelCase   :	str								=		sp.make_sepia(UpperCAmelCase_      ,    20							)
     assert sepia.all()



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str = "digital_image_processing/image_data/lena_small.jpg"							)     ->		Dict:
     __lowerCamelCase   :	Dict								=		bs.Burkes(imread(UpperCAmelCase_      ,    1							)      ,    1_20							)
     burkes.process()
     assert burkes.output_img.any()



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str = "digital_image_processing/image_data/lena_small.jpg"      ,    )     ->		Any:
     __lowerCamelCase   :	List[Any]								=		rs.NearestNeighbour(imread(UpperCAmelCase_      ,    1							)      ,    4_00      ,    2_00							)
     nn.process()
     assert nn.output.any()



def 			UpperCAmelCase__					(	)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[Any]								=		'digital_image_processing/image_data/lena.jpg'

     # Reading the image and converting it to grayscale.
     __lowerCamelCase   :	Optional[int]								=		imread(UpperCAmelCase_      ,    0							)

     # Test for get_neighbors_pixel function() return not None
     __lowerCamelCase   :	Dict								=		0
     __lowerCamelCase   :	Optional[int]								=		0
     __lowerCamelCase   :	Any								=		image[x_coordinate][y_coordinate]

     __lowerCamelCase   :	List[Any]								=		lbp.get_neighbors_pixel(
         UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)

     assert neighbors_pixels is not None

     # Test for local_binary_pattern function()
     # Create a numpy array as the same height and width of read image
     __lowerCamelCase   :	Optional[int]								=		np.zeros((image.shape[0], image.shape[1])							)

     # Iterating through the image and calculating the local binary pattern value
     # for each pixel.
     for i in range(0      ,    image.shape[0]							):
          for j in range(0      ,    image.shape[1]							):
               __lowerCamelCase   :	Any								=		lbp.local_binary_value(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)

     assert lbp_image.any()










'''simple docstring'''



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Union[str, Any]							     =			VQModel
    lowerCamelCase  :					Union[str, Any]							     =			'sample'







    @property
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=(32, 32)	)				->     Any:
         __lowerCamelCase   :	Tuple								=		4
         __lowerCamelCase   :	Union[str, Any]								=		3

         __lowerCamelCase   :	List[Any]								=		floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)

         return {"sample": image}







    @property
    def       lowercase_				(							self	)				->     Optional[int]:
         return (3, 32, 32)







    @property
    def       lowercase_				(							self	)				->     List[Any]:
         return (3, 32, 32)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Optional[Any]								=		{
             'block_out_channels': [32, 64],
             'in_channels': 3,
             'out_channels': 3,
             'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
             'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
             'latent_channels': 3,
         }
         __lowerCamelCase   :	Union[str, Any]								=		self.dummy_input
         return init_dict, inputs_dict







    def       lowercase_				(							self	)				->     str:
         pass







    def       lowercase_				(							self	)				->     Optional[int]:
         pass







    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		VQModel.from_pretrained('fusing/vqgan-dummy'			,     output_loading_info=SCREAMING_SNAKE_CASE_	)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(len(loading_info['missing_keys']	)			,     0	)

         model.to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		model(**self.dummy_input	)

         assert image is not None, "Make sure output is not None"







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	List[Any]								=		VQModel.from_pretrained('fusing/vqgan-dummy'	)
         model.to(SCREAMING_SNAKE_CASE_	).eval()

         torch.manual_seed(0	)
         if torch.cuda.is_available():
              torch.cuda.manual_seed_all(0	)

         __lowerCamelCase   :	List[str]								=		torch.randn(1			,     model.config.in_channels			,     model.config.sample_size			,     model.config.sample_size	)
         __lowerCamelCase   :	Optional[int]								=		image.to(SCREAMING_SNAKE_CASE_	)
         with torch.no_grad():
              __lowerCamelCase   :	int								=		model(SCREAMING_SNAKE_CASE_	).sample

         __lowerCamelCase   :	List[str]								=		output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         __lowerCamelCase   :	Union[str, Any]								=		torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]	)
         # fmt: on
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-3	)	)






