


'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[str]			      = KandinskyVaaImgaImgPipeline
	_lowerCamelCase   :					int			      = ["""image_embeds""", """negative_image_embeds""", """image"""]
	_lowerCamelCase   :					Any			      = [
	    """image_embeds""",
	    """negative_image_embeds""",
	    """image""",
	]
	_lowerCamelCase   :					Any			      = [
	    """generator""",
	    """height""",
	    """width""",
	    """strength""",
	    """guidance_scale""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	_lowerCamelCase   :					Dict			      = False



	@property
	def 				lowercase     (					self    ):
		return 32



	@property
	def 				lowercase     (					self    ):
		return 32



	@property
	def 				lowercase     (					self    ):
		return self.time_input_dim



	@property
	def 				lowercase     (					self    ):
		return self.time_input_dim * 4



	@property
	def 				lowercase     (					self    ):
		return 100



	@property
	def 				lowercase     (					self    ):
		torch.manual_seed(0    )

		lowerCAmelCase					:    List[Any]      							= {
		    'in_channels': 4,
		    # Out channels is double in channels because predicts mean and variance
		    'out_channels': 8,
		    'addition_embed_type': 'image',
		    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
		    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
		    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
		    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
		    'layers_per_block': 1,
		    'encoder_hid_dim': self.text_embedder_hidden_size,
		    'encoder_hid_dim_type': 'image_proj',
		    'cross_attention_dim': self.cross_attention_dim,
		    'attention_head_dim': 4,
		    'resnet_time_scale_shift': 'scale_shift',
		    'class_embed_type': None,
		}

		lowerCAmelCase					:    Union[str, Any]      							= UNetaDConditionModel(**snake_case__    )
		return model



	@property
	def 				lowercase     (					self    ):
		return {
		    "block_out_channels": [32, 64],
		    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
		    "in_channels": 3,
		    "latent_channels": 4,
		    "layers_per_block": 1,
		    "norm_num_groups": 8,
		    "norm_type": "spatial",
		    "num_vq_embeddings": 12,
		    "out_channels": 3,
		    "up_block_types": [
		        "AttnUpDecoderBlock2D",
		        "UpDecoderBlock2D",
		    ],
		    "vq_embed_dim": 4,
		}



	@property
	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    Any      							= VQModel(**self.dummy_movq_kwargs    )
		return model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= self.dummy_unet
		lowerCAmelCase					:    Tuple      							= self.dummy_movq

		lowerCAmelCase					:    List[Any]      							= {
		    'num_train_timesteps': 1000,
		    'beta_schedule': 'linear',
		    'beta_start': 0.0_0_0_8_5,
		    'beta_end': 0.0_1_2,
		    'clip_sample': False,
		    'set_alpha_to_one': False,
		    'steps_offset': 0,
		    'prediction_type': 'epsilon',
		    'thresholding': False,
		}

		lowerCAmelCase					:    List[Any]      							= DDIMScheduler(**snake_case__    )

		lowerCAmelCase					:    Optional[int]      							= {
		    'unet': unet,
		    'scheduler': scheduler,
		    'movq': movq,
		}

		return components



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		lowerCAmelCase					:    Dict      							= floats_tensor((1, self.text_embedder_hidden_size) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= floats_tensor((1, self.text_embedder_hidden_size) ,					rng=random.Random(seed + 1    )    ).to(
		    snake_case__    )
		# create init_image
		lowerCAmelCase					:    Optional[Any]      							= floats_tensor((1, 3, 64, 64) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    Tuple      							= image.cpu().permute(0 ,					2 ,					3 ,					1    )[0]
		lowerCAmelCase					:    Dict      							= Image.fromarray(np.uinta(snake_case__    )    ).convert('RGB'    ).resize((256, 256)    )

		if str(snake_case__    ).startswith('mps'    ):
			lowerCAmelCase					:    Union[str, Any]      							= torch.manual_seed(snake_case__    )
		else:
			lowerCAmelCase					:    Union[str, Any]      							= torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
		lowerCAmelCase					:    str      							= {
		    'image': init_image,
		    'image_embeds': image_embeds,
		    'negative_image_embeds': negative_image_embeds,
		    'generator': generator,
		    'height': 64,
		    'width': 64,
		    'num_inference_steps': 10,
		    'guidance_scale': 7.0,
		    'strength': 0.2,
		    'output_type': 'np',
		}
		return inputs



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= 'cpu'

		lowerCAmelCase					:    Optional[int]      							= self.get_dummy_components()

		lowerCAmelCase					:    Tuple      							= self.pipeline_class(**snake_case__    )
		lowerCAmelCase					:    str      							= pipe.to(snake_case__    )

		pipe.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    Union[str, Any]      							= pipe(**self.get_dummy_inputs(snake_case__    )    )
		lowerCAmelCase					:    int      							= output.images

		lowerCAmelCase					:    Tuple      							= pipe(
		    **self.get_dummy_inputs(snake_case__    ) ,					return_dict=snake_case__ ,					)[0]

		lowerCAmelCase					:    str      							= image[0, -3:, -3:, -1]
		lowerCAmelCase					:    Optional[Any]      							= image_from_tuple[0, -3:, -3:, -1]

		assert image.shape == (1, 64, 64, 3)

		lowerCAmelCase					:    Tuple      							= np.array(
		    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]    )
		assert (
		    np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
		), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
		assert (
		    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
		), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"



@slow
@require_torch_gpu
class  lowerCAmelCase				(				unittest.TestCase							):



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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/kandinskyv22/kandinskyv22_img2img_frog.npy'    )

		lowerCAmelCase					:    Optional[int]      							= load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
		lowerCAmelCase					:    Optional[Any]      							= 'A red cartoon frog, 4k'

		lowerCAmelCase					:    List[str]      							= KandinskyVaaPriorPipeline.from_pretrained(
		    'kandinsky-community/kandinsky-2-2-prior' ,					torch_dtype=torch.floataa    )
		pipe_prior.to(snake_case__    )

		lowerCAmelCase					:    Any      							= KandinskyVaaImgaImgPipeline.from_pretrained(
		    'kandinsky-community/kandinsky-2-2-decoder' ,					torch_dtype=torch.floataa    )
		lowerCAmelCase					:    Optional[int]      							= pipeline.to(snake_case__    )

		pipeline.set_progress_bar_config(disable=snake_case__    )

		lowerCAmelCase					:    int      							= torch.Generator(device='cpu'    ).manual_seed(0    )
		lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= pipe_prior(
		    snake_case__ ,					generator=snake_case__ ,					num_inference_steps=5 ,					negative_prompt='' ,					).to_tuple()

		lowerCAmelCase					:    Optional[int]      							= pipeline(
		    image=snake_case__ ,					image_embeds=snake_case__ ,					negative_image_embeds=snake_case__ ,					generator=snake_case__ ,					num_inference_steps=100 ,					height=768 ,					width=768 ,					strength=0.2 ,					output_type='np' ,					)

		lowerCAmelCase					:    Optional[int]      							= output.images[0]

		assert image.shape == (768, 768, 3)

		assert_mean_pixel_difference(snake_case__ ,					snake_case__    )




'''simple docstring'''




import math
import sys

import cva
import numpy as np

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= math.sqrt(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= 1 / (sigma * math.sqrt(2 * math.pi				))
	return cons * np.exp(-((img / sigma) ** 2) * 0.5				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				int					,   _A    :				int					,   _A    :				int				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= kernel_size // 2
	return img[x - half : x + half + 1, y - half : y + half + 1]

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= np.zeros((kernel_size, kernel_size)				)
	for i in range(0					,   _A				):
		for j in range(0					,   _A				):
			lowerCAmelCase					:    Optional[int]      							= math.sqrt(
			    abs(i - kernel_size // 2				) ** 2 + abs(j - kernel_size // 2				) ** 2				)
	return vec_gaussian(_A					,   _A				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float					,   _A    :				float					,   _A    :				int					,   )    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= np.zeros(img.shape				)
	lowerCAmelCase					:    int      							= get_gauss_kernel(_A					,   _A				)
	lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= img.shape
	for i in range(kernel_size // 2					,   size_x - kernel_size // 2				):
		for j in range(kernel_size // 2					,   size_y - kernel_size // 2				):
			lowerCAmelCase					:    int      							= get_slice(_A					,   _A					,   _A					,   _A				)
			lowerCAmelCase					:    Any      							= img_s - img_s[kernel_size // 2, kernel_size // 2]
			lowerCAmelCase					:    str      							= vec_gaussian(_A					,   _A				)
			lowerCAmelCase					:    Optional[int]      							= np.multiply(_A					,   _A				)
			lowerCAmelCase					:    str      							= np.multiply(_A					,   _A				)
			lowerCAmelCase					:    Union[str, Any]      							= np.sum(_A				) / np.sum(_A				)
			lowerCAmelCase					:    Tuple      							= val
	return imga

def 			__UpperCamelCase    (    _A    :				list				)    ->      tuple:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= args[1] if args[1:] else '../image_data/lena.jpg'
	lowerCAmelCase					:    Any      							= float(args[2]				) if args[2:] else 1.0
	lowerCAmelCase					:    Union[str, Any]      							= float(args[3]				) if args[3:] else 1.0
	if args[4:]:
		lowerCAmelCase					:    int      							= int(args[4]				)
		lowerCAmelCase					:    Optional[Any]      							= kernel_size + abs(kernel_size % 2 - 1				)
	else:
		lowerCAmelCase					:    Optional[int]      							= 5
	return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
				_lowerCAmelCase     ,      _lowerCAmelCase     ,      _lowerCAmelCase     ,      _lowerCAmelCase  :		Any		     =	parse_args(sys.argv)
				_lowerCAmelCase  :		str		     =	cva.imread(filename, 0)
				cva.imshow('input image', img)

				_lowerCAmelCase  :		Union[str, Any]		     =	img / 255
				_lowerCAmelCase  :		List[str]		     =	out.astype('float32')
				_lowerCAmelCase  :		Optional[int]		     =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
				_lowerCAmelCase  :		Union[str, Any]		     =	out * 255
				_lowerCAmelCase  :		Optional[Any]		     =	np.uinta(out)
				cva.imshow('output image', out)
				cva.waitKey(0)
				cva.destroyAllWindows()
