


import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
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    UpperCAmelCase_	(							unittest.TestCase):
							'''simple docstring'''






							def  _lowercase   (     self						):




														"""simple docstring"""




														super().tearDown()
														gc.collect()






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	List[Any]   	=		load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/sd2-inpaint/init_image.png'''						)
														UpperCamelCase    :	Union[str, Any]   	=		load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''						)

														UpperCamelCase    :	Union[str, Any]   	=		"""xvjiarui/stable-diffusion-2-inpainting"""
														UpperCamelCase    :	List[Any]   	=		FlaxStableDiffusionInpaintPipeline.from_pretrained(_a ,					safety_checker=_a						)

														UpperCamelCase    :	int   	=		"""Face of a yellow cat, high resolution, sitting on a park bench"""

														UpperCamelCase    :	Tuple   	=		jax.random.PRNGKey(0						)
														UpperCamelCase    :	Optional[int]   	=		50

														UpperCamelCase    :	Union[str, Any]   	=		jax.device_count()
														UpperCamelCase    :	Tuple   	=		num_samples * [prompt]
														UpperCamelCase    :	int   	=		num_samples * [init_image]
														UpperCamelCase    :	Dict   	=		num_samples * [mask_image]
														UpperCamelCase    :	Tuple   	=		pipeline.prepare_inputs(_a ,					_a ,					_a						)

														# shard inputs and rng
														UpperCamelCase    :	str   	=		replicate(_a						)
														UpperCamelCase    :	str   	=		jax.random.split(_a ,					jax.device_count()						)
														UpperCamelCase    :	List[str]   	=		shard(_a						)
														UpperCamelCase    :	int   	=		shard(_a						)
														UpperCamelCase    :	Tuple   	=		shard(_a						)

														UpperCamelCase    :	str   	=		pipeline(
														    _a ,					_a ,					_a ,					_a ,					_a ,					_a ,					jit=_a						)

														UpperCamelCase    :	List[Any]   	=		output.images.reshape(_a ,					512 ,					512 ,					3						)

														UpperCamelCase    :	List[Any]   	=		images[0, 253:256, 253:256, -1]

														UpperCamelCase    :	List[Any]   	=		jnp.asarray(jax.device_get(image_slice.flatten()						)						)
														UpperCamelCase    :	Optional[Any]   	=		jnp.array(
														    [0.3_611_307, 0.37_649_736, 0.3_757_408, 0.38_213_953, 0.39_295_167, 0.3_841_631, 0.41_554_978, 0.4_137_475, 0.4_217_084]						)
														print(f"""output_slice: {output_slice}"""						)
														assert jnp.abs(output_slice - expected_slice						).max() < 1e-2





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		k_size // 2
       UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase    :	Dict   	=		1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE_       ) + square(SCREAMING_SNAKE_CASE_       )) / (2 * square(SCREAMING_SNAKE_CASE_       ))       )
       return g


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase	, UpperCamelCase    :	Tuple   	=		image.shape[0], image.shape[1]
       # dst image height and width
       UpperCamelCase    :	str   	=		height - k_size + 1
       UpperCamelCase    :	Optional[int]   	=		width - k_size + 1

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       UpperCamelCase    :	List[Any]   	=		zeros((dst_height * dst_width, k_size * k_size)       )
       UpperCamelCase    :	Tuple   	=		0
       for i, j in product(range(SCREAMING_SNAKE_CASE_       )							,			range(SCREAMING_SNAKE_CASE_       )       ):
              UpperCamelCase    :	Dict   	=		ravel(image[i : i + k_size, j : j + k_size]       )
              UpperCamelCase    :	Dict   	=		window
              row += 1

       #  turn the kernel into shape(k*k, 1)
       UpperCamelCase    :	Optional[int]   	=		gen_gaussian_kernel(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		ravel(SCREAMING_SNAKE_CASE_       )

       # reshape and get the dst image
       UpperCamelCase    :	Optional[int]   	=		dot(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).reshape(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).astype(SCREAMING_SNAKE_CASE_       )

       return dst


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Union[str, Any] 			=				imread(r"../image_data/lena.jpg")
  # turn image in gray scale value
  __UpperCAmelCase : Optional[Any] 			=				cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  __UpperCAmelCase : Optional[int] 			=				gaussian_filter(gray, 3, sigma=1)
  __UpperCAmelCase : List[Any] 			=				gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow("gaussian filter with 3x3 mask", gaussianaxa)
  imshow("gaussian filter with 5x5 mask", gaussianaxa)
  waitKey()

