

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros





def    _snake_case		(snake_case_				:    Optional[int]						,					snake_case_				:    Tuple     )       ->		Optional[int]:
		__UpperCAmelCase, __UpperCAmelCase  :     str		=    image.shape
		__UpperCAmelCase  :     str		=    cols - block_size[1] + 1
		__UpperCAmelCase  :     Any		=    rows - block_size[0] + 1
		__UpperCAmelCase  :     List[Any]		=    zeros((dst_height * dst_width, block_size[1] * block_size[0])     )
		__UpperCAmelCase  :     Union[str, Any]		=    0
		for i in range(0						,					__lowerCamelCase     ):
				for j in range(0						,					__lowerCamelCase     ):
						__UpperCAmelCase  :     List[str]		=    ravel(image[i : i + block_size[0], j : j + block_size[1]]     )
						__UpperCAmelCase  :     List[str]		=    window
						row += 1

		return image_array





def    _snake_case		(snake_case_				:    Union[str, Any]						,					snake_case_				:    Optional[int]     )       ->		Optional[Any]:
		__UpperCAmelCase, __UpperCAmelCase  :     Tuple		=    image.shape[0], image.shape[1]
		__UpperCAmelCase  :     List[Any]		=    filter_kernel.shape[0]
		__UpperCAmelCase  :     Union[str, Any]		=    k_size // 2
		# Pads image with the edge values of array.
		__UpperCAmelCase  :     List[str]		=    pad(__lowerCamelCase						,					__lowerCamelCase						,					mode='edge'     )

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		__UpperCAmelCase  :     List[Any]		=    imacol(__lowerCamelCase						,					(k_size, k_size)     )

		#  turn the kernel into shape(k*k, 1)
		__UpperCAmelCase  :     Union[str, Any]		=    ravel(__lowerCamelCase     )
		# reshape and get the dst image
		__UpperCAmelCase  :     str		=    dot(__lowerCamelCase						,					__lowerCamelCase     ).reshape(__lowerCamelCase						,					__lowerCamelCase     )
		return dst


if __name__ == "__main__":
						# read original image
						_SCREAMING_SNAKE_CASE          = imread(r'''../image_data/lena.jpg''')
						# turn image in gray scale value
						_SCREAMING_SNAKE_CASE          = cvtColor(img, COLOR_BGR2GRAY)
						# Laplace operator
						_SCREAMING_SNAKE_CASE          = array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
						_SCREAMING_SNAKE_CASE          = img_convolve(gray, Laplace_kernel).astype(uinta)
						imshow('''Laplacian''', out)
						waitKey(0)








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_SCREAMING_SNAKE_CASE          = False


@nightly
@require_torch_gpu
class     lowerCamelCase_   ( unittest.TestCase):




 def 						a					( self       ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   # remove text_unet
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Any		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )

   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     Union[str, Any]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(_UpperCAmelCase       )
     __UpperCAmelCase  :     List[str]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_UpperCAmelCase       )

   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     List[str]		=    generator.manual_seed(0       )
   __UpperCAmelCase  :     Optional[int]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   assert np.abs(image - new_image       ).sum() < 1E-5, "Models don't have the same forward pass"




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    'cyberpunk 2077'
   __UpperCAmelCase  :     Dict		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     List[Any]		=    pipe(
       prompt=_UpperCAmelCase     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=50     ,     output_type='numpy'     ,     ).images

   __UpperCAmelCase  :     Tuple		=    image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   __UpperCAmelCase  :     Tuple		=    np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]       )

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2





