





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				KandinskyVaaInpaintPipeline
	_A													=				["image_embeds", "negative_image_embeds", "image", "mask_image"]
	_A													=				[
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	_A													=				[
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	_A													=				False

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			return 32

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			return 32

	@property
	def    snake_case__(						self:						Optional[int]			):
			return self.time_input_dim

	@property
	def    snake_case__(						self:						List[str]			):
			return self.time_input_dim * 4

	@property
	def    snake_case__(						self:						List[Any]			):
			return 100

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			torch.manual_seed(0			)

			lowercase__					:      Any      =		{
			    'in_channels': 9,
			    # 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,
			}

			lowercase__					:      Any      =		UNetaDConditionModel(**lowerCamelCase_			)
			return model

	@property
	def    snake_case__(						self:						str			):
			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    snake_case__(						self:						List[Any]			):
			torch.manual_seed(0			)
			lowercase__					:      Optional[Any]      =		VQModel(**self.dummy_movq_kwargs			)
			return model

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[Any]      =		self.dummy_unet
			lowercase__					:      Tuple      =		self.dummy_movq

			lowercase__					:      int      =		DDIMScheduler(
			    num_train_timesteps=1000,					beta_schedule='linear',					beta_start=0.0_0_0_8_5,					beta_end=0.0_1_2,					clip_sample=lowerCamelCase_,					set_alpha_to_one=lowerCamelCase_,					steps_offset=1,					prediction_type='epsilon',					thresholding=lowerCamelCase_,					)

			lowercase__					:      List[Any]      =		{
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components

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

			if str(lowerCamelCase_			).startswith('mps'			):
					lowercase__					:      List[str]      =		torch.manual_seed(lowerCamelCase_			)
			else:
					lowercase__					:      Union[str, Any]      =		torch.Generator(device=lowerCamelCase_			).manual_seed(lowerCamelCase_			)
			lowercase__					:      Dict      =		{
			    '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    snake_case__(						self:						List[Any]			):
			lowercase__					:      Tuple      =		'cpu'

			lowercase__					:      int      =		self.get_dummy_components()

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

			pipe.set_progress_bar_config(disable=lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		pipe(**self.get_dummy_inputs(lowerCamelCase_			)			)
			lowercase__					:      Tuple      =		output.images

			lowercase__					:      Dict      =		pipe(
			    **self.get_dummy_inputs(lowerCamelCase_			),					return_dict=lowerCamelCase_,					)[0]

			lowercase__					:      int      =		image[0, -3:, -3:, -1]
			lowercase__					:      int      =		image_from_tuple[0, -3:, -3:, -1]

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

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

			lowercase__					:      Tuple      =		np.array(
			    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]			)

			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    snake_case__(						self:						List[str]			):
			super().test_inference_batch_single_identical(expected_max_diff=3E-3			)


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


	'''simple docstring'''





	def    snake_case__(						self:						Optional[int]			):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	def    snake_case__(						self:						Dict			):
			lowercase__					:      int      =		load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'			)

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

			lowercase__					:      str      =		'a hat'

			lowercase__					:      Any      =		KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior',					torch_dtype=torch.floataa			)
			pipe_prior.to(lowerCamelCase_			)

			lowercase__					:      List[str]      =		KandinskyVaaInpaintPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder-inpaint',					torch_dtype=torch.floataa			)
			lowercase__					:      Optional[Any]      =		pipeline.to(lowerCamelCase_			)
			pipeline.set_progress_bar_config(disable=lowerCamelCase_			)

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

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

			lowercase__					:      str      =		output.images[0]

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

			assert_mean_pixel_difference(lowerCamelCase_,					lowerCamelCase_			)









__UpperCamelCase:	str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__UpperCamelCase:	Tuple = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      str      =		True
		lowercase__					:      int      =		[]

		for neighbour in graph[vert]:
				if not visited[neighbour]:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		order.append(_lowercase			)

		return order


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		True
		lowercase__					:      Optional[Any]      =		[vert]

		for neighbour in reversed_graph[vert]:
				if not visited[neighbour]:
						component += find_components(_lowercase   ,    _lowercase   ,    _lowercase			)

		return component





def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]			)   ->							list[list[int]]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		len(_lowercase			) * [False]
		lowercase__					:      dict[int, list[int]]      =		{vert: [] for vert in range(len(_lowercase			)			)}

		for vert, neighbours in graph.items():
				for neighbour in neighbours:
						reversed_graph[neighbour].append(_lowercase			)

		lowercase__					:      Dict      =		[]
		for i, was_visited in enumerate(_lowercase			):
				if not was_visited:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		lowercase__					:      Dict      =		[]
		lowercase__					:      Optional[Any]      =		len(_lowercase			) * [False]

		for i in range(len(_lowercase			)			):
				lowercase__					:      List[Any]      =		order[len(_lowercase			) - i - 1]
				if not visited[vert]:
						lowercase__					:      Tuple      =		find_components(_lowercase   ,    _lowercase   ,    _lowercase			)
						components_list.append(_lowercase			)

		return components_list


