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 UpperCAmelCase						(					__A					,       unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    =					KandinskyVaaInpaintPipeline
				lowerCamelCase_    =					["image_embeds", "negative_image_embeds", "image", "mask_image"]
				lowerCamelCase_    =					[
				    "image_embeds",
				    "negative_image_embeds",
				    "image",
				    "mask_image",
				]
				lowerCamelCase_    =					[
				    "generator",
				    "height",
				    "width",
				    "latents",
				    "guidance_scale",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				lowerCamelCase_    =					False
				@property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return 3_2
				@property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return 3_2
				@property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





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



						"""simple docstring"""





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



						"""simple docstring"""





						return 1_0_0
				@property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						torch.manual_seed(0					)

						A_		:				Dict     =      {
						    "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,
						}

						A_		:				Any     =      UNetaDConditionModel(**__A					)
						return model
				@property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return {
						    "block_out_channels": [3_2, 6_4],
						    "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": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}
				@property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						torch.manual_seed(0					)
						A_		:				Any     =      VQModel(**self.dummy_movq_kwargs					)
						return model
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Union[str, Any]     =      self.dummy_unet
						A_		:				Any     =      self.dummy_movq

						A_		:				Dict     =      DDIMScheduler(
						    num_train_timesteps=1_0_0_0		,	beta_schedule='linear'		,	beta_start=0.0_0085		,	beta_end=0.012		,	clip_sample=__A		,	set_alpha_to_one=__A		,	steps_offset=1		,	prediction_type='epsilon'		,	thresholding=__A		,	)

						A_		:				Optional[Any]     =      {
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}

						return components
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase=0					):



						"""simple docstring"""





						A_		:				Union[str, Any]     =      floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(__A					)					).to(__A					)
						A_		:				Optional[Any]     =      floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(seed + 1					)					).to(
						    __A					)
						# create init_image
						A_		:				Any     =      floats_tensor((1, 3, 6_4, 6_4)		,	rng=random.Random(__A					)					).to(__A					)
						A_		:				int     =      image.cpu().permute(0		,	2		,	3		,	1					)[0]
						A_		:				int     =      Image.fromarray(np.uinta(__A					)					).convert('RGB'					).resize((2_5_6, 2_5_6)					)
						# create mask
						A_		:				int     =      np.ones((6_4, 6_4)		,	dtype=np.floataa					)
						A_		:				Optional[int]     =      0

						if str(__A					).startswith('mps'					):
								A_		:				Optional[Any]     =      torch.manual_seed(__A					)
						else:
								A_		:				List[Any]     =      torch.Generator(device=__A					).manual_seed(__A					)
						A_		:				Optional[Any]     =      {
						    "image": init_image,
						    "mask_image": mask,
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "generator": generator,
						    "height": 6_4,
						    "width": 6_4,
						    "num_inference_steps": 2,
						    "guidance_scale": 4.0,
						    "output_type": "np",
						}
						return inputs
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Any     =      "cpu"

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

						A_		:				str     =      self.pipeline_class(**__A					)
						A_		:				Tuple     =      pipe.to(__A					)

						pipe.set_progress_bar_config(disable=__A					)

						A_		:				List[str]     =      pipe(**self.get_dummy_inputs(__A					)					)
						A_		:				List[str]     =      output.images

						A_		:				Optional[Any]     =      pipe(
						    **self.get_dummy_inputs(__A					)		,	return_dict=__A		,	)[0]

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

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

						assert image.shape == (1, 6_4, 6_4, 3)

						A_		:				Optional[int]     =      np.array(
						    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]					)

						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   lowerCAmelCase_   (						self					):



						"""simple docstring"""





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





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


				'''simple docstring'''






				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				str     =      load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'					)

						A_		:				List[Any]     =      load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
						A_		:				Union[str, Any]     =      np.ones((7_6_8, 7_6_8)		,	dtype=np.floataa					)
						A_		:				str     =      0

						A_		:				Dict     =      "a hat"

						A_		:				str     =      KandinskyVaaPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'		,	torch_dtype=torch.floataa					)
						pipe_prior.to(__A					)

						A_		:				Any     =      KandinskyVaaInpaintPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-decoder-inpaint'		,	torch_dtype=torch.floataa					)
						A_		:				Dict     =      pipeline.to(__A					)
						pipeline.set_progress_bar_config(disable=__A					)

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

						A_		:				Optional[int]     =      pipeline(
						    image=__A		,	mask_image=__A		,	image_embeds=__A		,	negative_image_embeds=__A		,	generator=__A		,	num_inference_steps=1_0_0		,	height=7_6_8		,	width=7_6_8		,	output_type='np'		,	)

						A_		:				Any     =      output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(__A		,	__A					)










def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if divisor % 5 == 0 or divisor % 2 == 0:
    return 0
  lowerCamelCase :					List[Any]    =				1
  lowerCamelCase :					Union[str, Any]    =				1
  while repunit:
    lowerCamelCase :					Union[str, Any]    =				(10 * repunit + 1) % divisor
    repunit_index += 1
  return repunit_index






def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 1000000	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				limit - 1
  if divisor % 2 == 0:
    divisor += 1
  while least_divisible_repunit(SCREAMING_SNAKE_CASE_	) <= limit:
    divisor += 2
  return divisor


if __name__ == "__main__":
   print(f'''{solution() = }''')



