






"""simple docstring"""



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        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Union[str, Any]             =			KandinskyVaaInpaintPipeline
			_UpperCAmelCase		:List[Any]             =			["image_embeds", "negative_image_embeds", "image", "mask_image"]
			_UpperCAmelCase		:int             =			[
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			    "mask_image",
			]
			_UpperCAmelCase		:Dict             =			[
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			_UpperCAmelCase		:Dict             =			False


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


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


			@property
			def      UpperCAmelCase__						(				self					:  Any ):
										return self.time_input_dim


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


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


			@property
			def      UpperCAmelCase__						(				self					:  Any ):
										torch.manual_seed(0 )

										lowerCamelCase_	:							List[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,
										}

										lowerCamelCase_	:							Optional[int]									=UNetaDConditionModel(**snake_case__ )
										return model


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


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Dict									=self.dummy_unet
										lowerCamelCase_	:							List[Any]									=self.dummy_movq

										lowerCamelCase_	:							Tuple									=DDIMScheduler(
										    num_train_timesteps=1000       ,	beta_schedule="linear"       ,	beta_start=0.00_085       ,	beta_end=0.012       ,	clip_sample=snake_case__       ,	set_alpha_to_one=snake_case__       ,	steps_offset=1       ,	prediction_type="epsilon"       ,	thresholding=snake_case__       ,	)

										lowerCamelCase_	:							int									={
										    "unet": unet,
										    "scheduler": scheduler,
										    "movq": movq,
										}

										return components


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

										if str(snake_case__ ).startswith("mps" ):
																	lowerCamelCase_	:							Tuple									=torch.manual_seed(snake_case__ )
										else:
																	lowerCamelCase_	:							Tuple									=torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
										lowerCamelCase_	:							Union[str, Any]									={
										    "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					:  int ):
										lowerCamelCase_	:							Optional[Any]									="cpu"

										lowerCamelCase_	:							Dict									=self.get_dummy_components()

										lowerCamelCase_	:							Union[str, Any]									=self.pipeline_class(**snake_case__ )
										lowerCamelCase_	:							Any									=pipe.to(snake_case__ )

										pipe.set_progress_bar_config(disable=snake_case__ )

										lowerCamelCase_	:							Any									=pipe(**self.get_dummy_inputs(snake_case__ ) )
										lowerCamelCase_	:							Any									=output.images

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

										lowerCamelCase_	:							Dict									=image[0, -3:, -3:, -1]
										lowerCamelCase_	:							Union[str, Any]									=image_from_tuple[0, -3:, -3:, -1]

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

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

										lowerCamelCase_	:							List[Any]									=np.array(
										    [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848] )

										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					:  Tuple ):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







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


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


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							str									=load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy" )

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

										lowerCamelCase_	:							Optional[int]									="a hat"

										lowerCamelCase_	:							Union[str, Any]									=KandinskyVaaPriorPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-prior"       ,	torch_dtype=torch.floataa )
										pipe_prior.to(snake_case__ )

										lowerCamelCase_	:							Dict									=KandinskyVaaInpaintPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-decoder-inpaint"       ,	torch_dtype=torch.floataa )
										lowerCamelCase_	:							Dict									=pipeline.to(snake_case__ )
										pipeline.set_progress_bar_config(disable=snake_case__ )

										lowerCamelCase_	:							Optional[int]									=torch.Generator(device="cpu" ).manual_seed(0 )
										lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=pipe_prior(
										    snake_case__       ,	generator=snake_case__       ,	num_inference_steps=5       ,	negative_prompt=""       ,	).to_tuple()

										lowerCamelCase_	:							List[Any]									=pipeline(
										    image=snake_case__       ,	mask_image=snake_case__       ,	image_embeds=snake_case__       ,	negative_image_embeds=snake_case__       ,	generator=snake_case__       ,	num_inference_steps=100       ,	height=768       ,	width=768       ,	output_type="np"       ,	)

										lowerCamelCase_	:							str									=output.images[0]

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

										assert_mean_pixel_difference(snake_case__       ,	snake_case__ )











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   list[int]  )    ->     bool:

							# 1. Validate that path exists between current and next vertices
							if graph[path[curr_ind - 1]][next_ver] == 0:
														return False

							# 2. Validate that next vertex is not already in path
							return not any(vertex == next_ver for vertex in path  )

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   list[int]    ,						lowerCamelCase__     :   int  )    ->     bool:

							# Base Case
							if curr_ind == len(lowerCamelCase__  ):
														# return whether path exists between current and starting vertices
														return graph[path[curr_ind - 1]][path[0]] == 1

							# Recursive Step
							for next_ver in range(0    ,						len(lowerCamelCase__  )  ):
														if valid_connection(lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__  ):
																					# Insert current vertex  into path as next transition
																					lowerCamelCase_	:							Tuple									=next_ver
																					# Validate created path
																					if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						curr_ind + 1  ):
																												return True
																					# Backtrack
																					lowerCamelCase_	:							int									=-1
							return False

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int = 0  )    ->     list[int]:
							lowerCamelCase_	:							Optional[Any]									=[-1] * (len(lowerCamelCase__  ) + 1)
							# initialize start and end of path with starting index
							lowerCamelCase_	:							Optional[int]									=start_index
							# evaluate and if we find answer return path either return empty array
							return path if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						1  ) else []



