


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class      lowerCamelCase_  (      __a	,			__a	,			__a	,			unittest.TestCase ):
      lowerCAmelCase__							=	StableDiffusionInpaintPipeline
      lowerCAmelCase__							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      lowerCAmelCase__							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      lowerCAmelCase__							=	frozenset(
          [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      lowerCAmelCase__							=	frozenset([] )






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          torch.manual_seed(0					)
          UpperCAmelCase__     :						List[str]						=							UNetaDConditionModel(
              block_out_channels=(32, 64)		,		layers_per_block=2		,		sample_size=32		,		in_channels=9		,		out_channels=4		,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,		cross_attention_dim=32		,		attention_head_dim=(2, 4)		,		use_linear_projection=_A		,		)
          UpperCAmelCase__     :						int						=							PNDMScheduler(skip_prk_steps=_A					)
          torch.manual_seed(0					)
          UpperCAmelCase__     :						str						=							AutoencoderKL(
              block_out_channels=[32, 64]		,		in_channels=3		,		out_channels=3		,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,		latent_channels=4		,		sample_size=128		,		)
          torch.manual_seed(0					)
          UpperCAmelCase__     :						Optional[Any]						=							CLIPTextConfig(
              bos_token_id=0		,		eos_token_id=2		,		hidden_size=32		,		intermediate_size=37		,		layer_norm_eps=1e-05		,		num_attention_heads=4		,		num_hidden_layers=5		,		pad_token_id=1		,		vocab_size=1_000		,		hidden_act='''gelu'''		,		projection_dim=512		,		)
          UpperCAmelCase__     :						Union[str, Any]						=							CLIPTextModel(_A					)
          UpperCAmelCase__     :						List[Any]						=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

          UpperCAmelCase__     :						str						=							{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''safety_checker''': None,
              '''feature_extractor''': None,
          }
          return components






      def lowercase_				(   self							:       str		,		_A							:       Dict		,		_A							:       Any=0					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							floats_tensor((1, 3, 32, 32)		,		rng=random.Random(_A					)					).to(_A					)
          UpperCAmelCase__     :						Dict						=							image.cpu().permute(0		,		2		,		3		,		1					)[0]
          UpperCAmelCase__     :						Optional[int]						=							Image.fromarray(np.uinta(_A					)					).convert('''RGB'''					).resize((64, 64)					)
          UpperCAmelCase__     :						int						=							Image.fromarray(np.uinta(image + 4					)					).convert('''RGB'''					).resize((64, 64)					)
          if str(_A					).startswith('''mps'''					):
              UpperCAmelCase__     :						List[Any]						=							torch.manual_seed(_A					)
          else:
              UpperCAmelCase__     :						str						=							torch.Generator(device=_A					).manual_seed(_A					)
          UpperCAmelCase__     :						Optional[int]						=							{
              '''prompt''': '''A painting of a squirrel eating a burger''',
              '''image''': init_image,
              '''mask_image''': mask_image,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 6.0,
              '''output_type''': '''numpy''',
          }
          return inputs






      def lowercase_				(   self							:       Union[str, Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase__     :						Tuple						=							self.get_dummy_components()
          UpperCAmelCase__     :						str						=							StableDiffusionInpaintPipeline(**_A					)
          UpperCAmelCase__     :						List[str]						=							sd_pipe.to(_A					)
          sd_pipe.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						Dict						=							self.get_dummy_inputs(_A					)
          UpperCAmelCase__     :						Any						=							sd_pipe(**_A					).images
          UpperCAmelCase__     :						Dict						=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCAmelCase__     :						int						=							np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


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




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






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/sd2-inpaint/init_image.png'''					)
          UpperCAmelCase__     :						Any						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)
          UpperCAmelCase__     :						List[Any]						=							load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
              '''/yellow_cat_sitting_on_a_park_bench.npy'''					)

          UpperCAmelCase__     :						Dict						=							'''stabilityai/stable-diffusion-2-inpainting'''
          UpperCAmelCase__     :						Optional[int]						=							StableDiffusionInpaintPipeline.from_pretrained(_A		,		safety_checker=_A					)
          pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing()

          UpperCAmelCase__     :						Tuple						=							'''Face of a yellow cat, high resolution, sitting on a park bench'''

          UpperCAmelCase__     :						str						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						str						=							pipe(
              prompt=_A		,		image=_A		,		mask_image=_A		,		generator=_A		,		output_type='''np'''		,		)
          UpperCAmelCase__     :						int						=							output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image					).max() < 9e-3






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/sd2-inpaint/init_image.png'''					)
          UpperCAmelCase__     :						Any						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)
          UpperCAmelCase__     :						Union[str, Any]						=							load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
              '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''					)

          UpperCAmelCase__     :						Tuple						=							'''stabilityai/stable-diffusion-2-inpainting'''
          UpperCAmelCase__     :						Any						=							StableDiffusionInpaintPipeline.from_pretrained(
              _A		,		torch_dtype=torch.floataa		,		safety_checker=_A		,		)
          pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing()

          UpperCAmelCase__     :						Tuple						=							'''Face of a yellow cat, high resolution, sitting on a park bench'''

          UpperCAmelCase__     :						Union[str, Any]						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						Optional[Any]						=							pipe(
              prompt=_A		,		image=_A		,		mask_image=_A		,		generator=_A		,		output_type='''np'''		,		)
          UpperCAmelCase__     :						Tuple						=							output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image					).max() < 5e-1






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          UpperCAmelCase__     :						Union[str, Any]						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/sd2-inpaint/init_image.png'''					)
          UpperCAmelCase__     :						Dict						=							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)

          UpperCAmelCase__     :						Optional[Any]						=							'''stabilityai/stable-diffusion-2-inpainting'''
          UpperCAmelCase__     :						str						=							PNDMScheduler.from_pretrained(_A		,		subfolder='''scheduler'''					)
          UpperCAmelCase__     :						Optional[Any]						=							StableDiffusionInpaintPipeline.from_pretrained(
              _A		,		safety_checker=_A		,		scheduler=_A		,		torch_dtype=torch.floataa		,		)
          pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing(1					)
          pipe.enable_sequential_cpu_offload()

          UpperCAmelCase__     :						Optional[int]						=							'''Face of a yellow cat, high resolution, sitting on a park bench'''

          UpperCAmelCase__     :						Tuple						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						Any						=							pipe(
              prompt=_A		,		image=_A		,		mask_image=_A		,		generator=_A		,		num_inference_steps=2		,		output_type='''np'''		,		)

          UpperCAmelCase__     :						int						=							torch.cuda.max_memory_allocated()
          # make sure that less than 2.65 GB is allocated
          assert mem_bytes < 2.6_5 * 10**9







from math import sqrt

def   __snake_case    (       _lowerCAmelCase			:			int						)			->    bool:

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5					,					int(sqrt(_lowerCAmelCase						) + 1						)					,					6						):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True

def   __snake_case    (       _lowerCAmelCase			:			int = 10001						)			->    int:
   A_       :					Any        =      0
   A_       :					Tuple        =      1
   while count != nth and number < 3:
      number += 1
      if is_prime(_lowerCAmelCase						):
         count += 1
   while count != nth:
      number += 2
      if is_prime(_lowerCAmelCase						):
         count += 1
   return number


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