


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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  _a							(    lowerCamelCase__					,						unittest.TestCase):






	"""simple docstring"""




	_a  :  List[Any]				   =							KandinskyInpaintPipeline
	_a  :  Optional[int]				   =							['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
	_a  :  List[Any]				   =							[
	    'prompt',
	    'negative_prompt',
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	    'mask_image',
	]
	_a  :  Dict				   =							[
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'negative_prompt',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	_a  :  Union[str, Any]				   =							False




	@property
	def 							UpperCAmelCase__( self						:		str		)->	Optional[int]:
			return 32




	@property
	def 							UpperCAmelCase__( self						:		str		)->	Any:
			return 32




	@property
	def 							UpperCAmelCase__( self						:		Optional[int]		)->	int:
			return self.time_input_dim




	@property
	def 							UpperCAmelCase__( self						:		List[str]		)->	List[Any]:
			return self.time_input_dim * 4




	@property
	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	str:
			return 100




	@property
	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Union[str, Any]:
			lowerCAmelCase__       :      List[str]   					= XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
			return tokenizer




	@property
	def 							UpperCAmelCase__( self						:		Optional[int]		)->	Any:
			torch.manual_seed(0		)
			lowerCAmelCase__       :      Optional[Any]   					= MCLIPConfig(
			    numDims=self.cross_attention_dim		, transformerDimensions=self.text_embedder_hidden_size		, hidden_size=self.text_embedder_hidden_size		, intermediate_size=37		, num_attention_heads=4		, num_hidden_layers=5		, vocab_size=1005		, )

			lowerCAmelCase__       :      Any   					= MultilingualCLIP(lowercase__		)
			lowerCAmelCase__       :      Any   					= text_encoder.eval()

			return text_encoder




	@property
	def 							UpperCAmelCase__( self						:		Optional[int]		)->	List[str]:
			torch.manual_seed(0		)

			lowerCAmelCase__       :      int   					= {
			    '''in_channels''': 9,
			    # Out channels is double in channels because predicts mean and variance
			    '''out_channels''': 8,
			    '''addition_embed_type''': '''text_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''': '''text_image_proj''',
			    '''cross_attention_dim''': self.cross_attention_dim,
			    '''attention_head_dim''': 4,
			    '''resnet_time_scale_shift''': '''scale_shift''',
			    '''class_embed_type''': None,
			}

			lowerCAmelCase__       :      Optional[int]   					= UNetaDConditionModel(**lowercase__		)
			return model




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Any:
			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 							UpperCAmelCase__( self						:		int		)->	Union[str, Any]:
			torch.manual_seed(0		)
			lowerCAmelCase__       :      List[Any]   					= VQModel(**self.dummy_movq_kwargs		)
			return model




	def 							UpperCAmelCase__( self						:		Dict		)->	Optional[int]:
			lowerCAmelCase__       :      Dict   					= self.dummy_text_encoder
			lowerCAmelCase__       :      List[Any]   					= self.dummy_tokenizer
			lowerCAmelCase__       :      Any   					= self.dummy_unet
			lowerCAmelCase__       :      Dict   					= self.dummy_movq

			lowerCAmelCase__       :      Dict   					= DDIMScheduler(
			    num_train_timesteps=1000		, beta_schedule='''linear'''		, beta_start=0.0_0085		, beta_end=0.012		, clip_sample=lowercase__		, set_alpha_to_one=lowercase__		, steps_offset=1		, prediction_type='''epsilon'''		, thresholding=lowercase__		, )

			lowerCAmelCase__       :      int   					= {
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''unet''': unet,
			    '''scheduler''': scheduler,
			    '''movq''': movq,
			}

			return components




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		List[Any]=0		)->	List[str]:
			lowerCAmelCase__       :      Union[str, Any]   					= floats_tensor((1, self.cross_attention_dim)		, rng=random.Random(lowercase__		)		).to(lowercase__		)
			lowerCAmelCase__       :      List[str]   					= floats_tensor((1, self.cross_attention_dim)		, rng=random.Random(seed + 1		)		).to(lowercase__		)
			# create init_image
			lowerCAmelCase__       :      List[str]   					= floats_tensor((1, 3, 64, 64)		, rng=random.Random(lowercase__		)		).to(lowercase__		)
			lowerCAmelCase__       :      Optional[Any]   					= image.cpu().permute(0		, 2		, 3		, 1		)[0]
			lowerCAmelCase__       :      List[Any]   					= Image.fromarray(np.uinta(lowercase__		)		).convert('''RGB'''		).resize((256, 256)		)
			# create mask
			lowerCAmelCase__       :      int   					= np.ones((64, 64)		, dtype=np.floataa		)
			lowerCAmelCase__       :      str   					= 0

			if str(lowercase__		).startswith('''mps'''		):
					lowerCAmelCase__       :      List[str]   					= torch.manual_seed(lowercase__		)
			else:
					lowerCAmelCase__       :      int   					= torch.Generator(device=lowercase__		).manual_seed(lowercase__		)
			lowerCAmelCase__       :      Union[str, Any]   					= {
			    '''prompt''': '''horse''',
			    '''image''': init_image,
			    '''mask_image''': mask,
			    '''image_embeds''': image_embeds,
			    '''negative_image_embeds''': negative_image_embeds,
			    '''generator''': generator,
			    '''height''': 64,
			    '''width''': 64,
			    '''num_inference_steps''': 2,
			    '''guidance_scale''': 4.0,
			    '''output_type''': '''np''',
			}
			return inputs




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	List[Any]:
			lowerCAmelCase__       :      Dict   					= '''cpu'''

			lowerCAmelCase__       :      List[Any]   					= self.get_dummy_components()

			lowerCAmelCase__       :      List[str]   					= self.pipeline_class(**lowercase__		)
			lowerCAmelCase__       :      List[Any]   					= pipe.to(lowercase__		)

			pipe.set_progress_bar_config(disable=lowercase__		)

			lowerCAmelCase__       :      Dict   					= pipe(**self.get_dummy_inputs(lowercase__		)		)
			lowerCAmelCase__       :      Optional[Any]   					= output.images

			lowerCAmelCase__       :      Any   					= pipe(
			    **self.get_dummy_inputs(lowercase__		)		, return_dict=lowercase__		, )[0]

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

			print(F'image.shape {image.shape}'		)

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

			lowerCAmelCase__       :      str   					= np.array(
			    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]		)

			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()}'




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	Optional[int]:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3		)





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






	"""simple docstring"""




	def 							UpperCAmelCase__( self						:		str		)->	int:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 							UpperCAmelCase__( self						:		Dict		)->	Tuple:
			lowerCAmelCase__       :      Dict   					= load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
			    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''		)

			lowerCAmelCase__       :      List[str]   					= load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
			lowerCAmelCase__       :      Dict   					= np.ones((768, 768)		, dtype=np.floataa		)
			lowerCAmelCase__       :      Optional[int]   					= 0

			lowerCAmelCase__       :      int   					= '''a hat'''

			lowerCAmelCase__       :      Optional[int]   					= KandinskyPriorPipeline.from_pretrained(
			    '''kandinsky-community/kandinsky-2-1-prior'''		, torch_dtype=torch.floataa		)
			pipe_prior.to(lowercase__		)

			lowerCAmelCase__       :      str   					= KandinskyInpaintPipeline.from_pretrained(
			    '''kandinsky-community/kandinsky-2-1-inpaint'''		, torch_dtype=torch.floataa		)
			lowerCAmelCase__       :      Dict   					= pipeline.to(lowercase__		)
			pipeline.set_progress_bar_config(disable=lowercase__		)

			lowerCAmelCase__       :      List[str]   					= torch.Generator(device='''cpu'''		).manual_seed(0		)
			lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= pipe_prior(
			    lowercase__		, generator=lowercase__		, num_inference_steps=5		, negative_prompt=''''''		, ).to_tuple()

			lowerCAmelCase__       :      List[str]   					= pipeline(
			    lowercase__		, image=lowercase__		, mask_image=lowercase__		, image_embeds=lowercase__		, negative_image_embeds=lowercase__		, generator=lowercase__		, num_inference_steps=100		, height=768		, width=768		, output_type='''np'''		, )

			lowerCAmelCase__       :      Optional[Any]   					= output.images[0]

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

			assert_mean_pixel_difference(lowercase__		, lowercase__		)








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 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= k_size // 2
		lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		lowerCAmelCase__       :      str   					= 1 / (2 * pi * sigma) * exp(-(square(_a	) + square(_a	)) / (2 * square(_a	))	)
		return g






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






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

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

		#  turn the kernel into shape(k*k, 1)
		lowerCAmelCase__       :      List[Any]   					= gen_gaussian_kernel(_a     ,       _a	)
		lowerCAmelCase__       :      str   					= ravel(_a	)

		# reshape and get the dst image
		lowerCAmelCase__       :      int   					= dot(_a     ,       _a	).reshape(_a     ,       _a	).astype(_a	)

		return dst


if __name__ == "__main__":
		# read original image
		lowerCamelCase						=		imread(R'''../image_data/lena.jpg''')
		# turn image in gray scale value
		lowerCamelCase						=		cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		lowerCamelCase						=		gaussian_filter(gray, 3, sigma=1)
		lowerCamelCase						=		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()




