






__snake_case      :      int             =       6_55_21







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  1
						lowerCAmelCase__        =  0
						for plain_chr in plain_text:
												lowerCAmelCase__        =  (a + ord(UpperCamelCase_						)) % MOD_ADLER
												lowerCAmelCase__        =  (b + a) % MOD_ADLER
						return (b << 16) | a













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       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    int						=   KandinskyVaaInpaintPipeline
						_SCREAMING_SNAKE_CASE		:    int						=   ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   [
						    '''image_embeds''',
						    '''negative_image_embeds''',
						    '''image''',
						    '''mask_image''',
						]
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''latents''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   False






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim * 4






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 1_00






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)

												lowerCAmelCase__        =  {
												    '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__        =  UNetaDConditionModel(**_UpperCamelCase				)
												return model






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  VQModel(**self.dummy_movq_kwargs				)
												return model






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.dummy_unet
												lowerCAmelCase__        =  self.dummy_movq

												lowerCAmelCase__        =  DDIMScheduler(
												    num_train_timesteps=10_00       ,  beta_schedule='linear'       ,  beta_start=0.0_00_85       ,  beta_end=0.0_12       ,  clip_sample=_UpperCamelCase       ,  set_alpha_to_one=_UpperCamelCase       ,  steps_offset=1       ,  prediction_type='epsilon'       ,  thresholding=_UpperCamelCase       ,  )

												lowerCAmelCase__        =  {
												    'unet': unet,
												    'scheduler': scheduler,
												    'movq': movq,
												}

												return components






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=0				):






												"""simple docstring"""
												lowerCAmelCase__        =  floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1				)				).to(
												    _UpperCamelCase				)
												# create init_image
												lowerCAmelCase__        =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
												lowerCAmelCase__        =  Image.fromarray(np.uinta(_UpperCamelCase				)				).convert('RGB'				).resize((2_56, 2_56)				)
												# create mask
												lowerCAmelCase__        =  np.ones((64, 64)       ,  dtype=np.floataa				)
												lowerCAmelCase__        =  0

												if str(_UpperCamelCase				).startswith('mps'				):
																		lowerCAmelCase__        =  torch.manual_seed(_UpperCamelCase				)
												else:
																		lowerCAmelCase__        =  torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)
												lowerCAmelCase__        =  {
												    '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				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'

												lowerCAmelCase__        =  self.get_dummy_components()

												lowerCAmelCase__        =  self.pipeline_class(**_UpperCamelCase				)
												lowerCAmelCase__        =  pipe.to(_UpperCamelCase				)

												pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  pipe(**self.get_dummy_inputs(_UpperCamelCase				)				)
												lowerCAmelCase__        =  output.images

												lowerCAmelCase__        =  pipe(
												    **self.get_dummy_inputs(_UpperCamelCase				)       ,  return_dict=_UpperCamelCase       ,  )[0]

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

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

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

												lowerCAmelCase__        =  np.array(
												    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]				)

												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				):






												"""simple docstring"""
												super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'				)

												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
												lowerCAmelCase__        =  np.ones((7_68, 7_68)       ,  dtype=np.floataa				)
												lowerCAmelCase__        =  0

												lowerCAmelCase__        =  'a hat'

												lowerCAmelCase__        =  KandinskyVaaPriorPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-prior'       ,  torch_dtype=torch.floataa				)
												pipe_prior.to(_UpperCamelCase				)

												lowerCAmelCase__        =  KandinskyVaaInpaintPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-decoder-inpaint'       ,  torch_dtype=torch.floataa				)
												lowerCAmelCase__        =  pipeline.to(_UpperCamelCase				)
												pipeline.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  torch.Generator(device='cpu'				).manual_seed(0				)
												lowerCAmelCase__  ,    lowerCAmelCase__        =  pipe_prior(
												    _UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_inference_steps=5       ,  negative_prompt=''       ,  ).to_tuple()

												lowerCAmelCase__        =  pipeline(
												    image=_UpperCamelCase       ,  mask_image=_UpperCamelCase       ,  image_embeds=_UpperCamelCase       ,  negative_image_embeds=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_inference_steps=1_00       ,  height=7_68       ,  width=7_68       ,  output_type='np'       ,  )

												lowerCAmelCase__        =  output.images[0]

												assert image.shape == (7_68, 7_68, 3)

												assert_mean_pixel_difference(_UpperCamelCase       ,  _UpperCamelCase				)





