



'''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

SCREAMING_SNAKE_CASE__          =  imread(R"digital_image_processing/image_data/lena_small.jpg")
SCREAMING_SNAKE_CASE__          =  cvtColor(img, COLOR_BGR2GRAY)




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    cn.convert_to_negative(_snake_case							)
    # assert negative_img array for at least one True
    assert negative_img.any()




def 							lowerCamelCase	(					):





    '''simple docstring'''


    with Image.open("digital_image_processing/image_data/lena_small.jpg"							) as img:
        # Work around assertion for response
        assert str(cc.change_contrast(_snake_case  ,110							)							).startswith(
            "<PIL.Image.Image image mode=RGB size=100x100 at"							)




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    canny.gen_gaussian_kernel(9  ,sigma=1.4							)
    # Assert ambiguous array
    assert resp.all()




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    imread("digital_image_processing/image_data/lena_small.jpg"  ,0							)
    # assert ambiguous array for all == True
    assert canny_img.all()
    lowercase__									=    canny.canny(_snake_case							)
    # assert canny array for at least one True
    assert canny_array.any()




def 							lowerCamelCase	(					):





    '''simple docstring'''


    assert gg.gaussian_filter(_snake_case  ,5  ,sigma=0.9							).all()




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]							)
    lowercase__									=    conv.img_convolve(_snake_case  ,_snake_case							).astype(_snake_case							)
    assert res.any()




def 							lowerCamelCase	(					):





    '''simple docstring'''


    assert med.median_filter(_snake_case  ,3							).any()




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__							,							lowercase__									=    sob.sobel_filter(_snake_case							)
    assert grad.any() and theta.any()




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    sp.make_sepia(_snake_case  ,20							)
    assert sepia.all()




def 							lowerCamelCase	(					_snake_case	:    str = "digital_image_processing/image_data/lena_small.jpg"							):





    '''simple docstring'''


    lowercase__									=    bs.Burkes(imread(_snake_case  ,1							)  ,120							)
    burkes.process()
    assert burkes.output_img.any()




def 							lowerCamelCase	(					_snake_case	:    str = "digital_image_processing/image_data/lena_small.jpg"  ,):





    '''simple docstring'''


    lowercase__									=    rs.NearestNeighbour(imread(_snake_case  ,1							)  ,400  ,200							)
    nn.process()
    assert nn.output.any()




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    "digital_image_processing/image_data/lena.jpg"

    # Reading the image and converting it to grayscale.
    lowercase__									=    imread(_snake_case  ,0							)

    # Test for get_neighbors_pixel function() return not None
    lowercase__									=    0
    lowercase__									=    0
    lowercase__									=    image[x_coordinate][y_coordinate]

    lowercase__									=    lbp.get_neighbors_pixel(
        _snake_case  ,_snake_case  ,_snake_case  ,_snake_case							)

    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
    lowercase__									=    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]							):
            lowercase__									=    lbp.local_binary_value(_snake_case  ,_snake_case  ,_snake_case							)

    assert lbp_image.any()









'''simple docstring'''





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard


@slow
@require_flax
class 					snake_case			(unittest.TestCase ):



       def 				_a   (					self   )	->				Tuple:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()



       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-canny"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "bird"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"   )
           lowercase__									=    pipe.prepare_image_inputs([canny_image] * num_samples   )

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    jnp.array(
               [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]   )
           print(F'''output_slice: {output_slice}'''   )
           assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2







       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-openpose"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "Chef in the kitchen"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"   )
           lowercase__									=    pipe.prepare_image_inputs([pose_image] * num_samples   )

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    jnp.array(
               [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]   )
           print(F'''output_slice: {output_slice}'''   )
           assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2




