



"""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    lowercase__     (					SCREAMING_SNAKE_CASE       ,      SCREAMING_SNAKE_CASE       ,      SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







							'''simple docstring'''


							UpperCamelCase		=  StableDiffusionInpaintPipeline
							UpperCamelCase		=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							UpperCamelCase		=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							UpperCamelCase		=  frozenset(
							    []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							UpperCamelCase		=  frozenset([]   )



							def   lowercase__       (							self    :						Optional[Any]					)				->		Any:


													'''simple docstring'''




													torch.manual_seed(0					)
													UpperCAmelCase_    				=							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=_UpperCAmelCase	,       )
													UpperCAmelCase_    				=							PNDMScheduler(skip_prk_steps=_UpperCAmelCase					)
													torch.manual_seed(0					)
													UpperCAmelCase_    				=							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_    				=							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=1000	,       hidden_act="gelu"	,       projection_dim=512	,       )
													UpperCAmelCase_    				=							CLIPTextModel(_UpperCAmelCase					)
													UpperCAmelCase_    				=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

													UpperCAmelCase_    				=							{
													    "unet": unet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components



							def   lowercase__       (							self    :						Dict	,       _UpperCAmelCase    :						Dict	,       _UpperCAmelCase    :						Union[str, Any]=0					)				->		Union[str, Any]:


													'''simple docstring'''




													UpperCAmelCase_    				=							floats_tensor((1, 3, 32, 32)	,       rng=random.Random(_UpperCAmelCase					)					).to(_UpperCAmelCase					)
													UpperCAmelCase_    				=							image.cpu().permute(0	,       2	,       3	,       1					)[0]
													UpperCAmelCase_    				=							Image.fromarray(np.uinta(_UpperCAmelCase					)					).convert("RGB"					).resize((64, 64)					)
													UpperCAmelCase_    				=							Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((64, 64)					)
													if str(_UpperCAmelCase					).startswith("mps"					):
																			UpperCAmelCase_    				=							torch.manual_seed(_UpperCAmelCase					)
													else:
																			UpperCAmelCase_    				=							torch.Generator(device=_UpperCAmelCase					).manual_seed(_UpperCAmelCase					)
													UpperCAmelCase_    				=							{
													    "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    :						Any					)				->		int:


													'''simple docstring'''




													UpperCAmelCase_    				=							"cpu"  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_    				=							self.get_dummy_components()
													UpperCAmelCase_    				=							StableDiffusionInpaintPipeline(**_UpperCAmelCase					)
													UpperCAmelCase_    				=							sd_pipe.to(_UpperCAmelCase					)
													sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

													UpperCAmelCase_    				=							self.get_dummy_inputs(_UpperCAmelCase					)
													UpperCAmelCase_    				=							sd_pipe(**_UpperCAmelCase					).images
													UpperCAmelCase_    				=							image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													UpperCAmelCase_    				=							np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]					)

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







							def   lowercase__       (							self    :						Optional[Any]					)				->		str:


													'''simple docstring'''




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



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







							'''simple docstring'''


							def   lowercase__       (							self    :						Tuple					)				->		Optional[int]:


													'''simple docstring'''




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



							def   lowercase__       (							self    :						Tuple					)				->		str:


													'''simple docstring'''




													UpperCAmelCase_    				=							load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/sd2-inpaint/init_image.png"					)
													UpperCAmelCase_    				=							load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"					)
													UpperCAmelCase_    				=							load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
													    "/yellow_cat_sitting_on_a_park_bench.npy"					)

													UpperCAmelCase_    				=							"stabilityai/stable-diffusion-2-inpainting"
													UpperCAmelCase_    				=							StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase	,       safety_checker=_UpperCAmelCase					)
													pipe.to(_UpperCAmelCase					)
													pipe.set_progress_bar_config(disable=_UpperCAmelCase					)
													pipe.enable_attention_slicing()

													UpperCAmelCase_    				=							"Face of a yellow cat, high resolution, sitting on a park bench"

													UpperCAmelCase_    				=							torch.manual_seed(0					)
													UpperCAmelCase_    				=							pipe(
													    prompt=_UpperCAmelCase	,       image=_UpperCAmelCase	,       mask_image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       output_type="np"	,       )
													UpperCAmelCase_    				=							output.images[0]

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



							def   lowercase__       (							self    :						str					)				->		List[Any]:


													'''simple docstring'''




													UpperCAmelCase_    				=							load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/sd2-inpaint/init_image.png"					)
													UpperCAmelCase_    				=							load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"					)
													UpperCAmelCase_    				=							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_    				=							"stabilityai/stable-diffusion-2-inpainting"
													UpperCAmelCase_    				=							StableDiffusionInpaintPipeline.from_pretrained(
													    _UpperCAmelCase	,       torch_dtype=torch.floataa	,       safety_checker=_UpperCAmelCase	,       )
													pipe.to(_UpperCAmelCase					)
													pipe.set_progress_bar_config(disable=_UpperCAmelCase					)
													pipe.enable_attention_slicing()

													UpperCAmelCase_    				=							"Face of a yellow cat, high resolution, sitting on a park bench"

													UpperCAmelCase_    				=							torch.manual_seed(0					)
													UpperCAmelCase_    				=							pipe(
													    prompt=_UpperCAmelCase	,       image=_UpperCAmelCase	,       mask_image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       output_type="np"	,       )
													UpperCAmelCase_    				=							output.images[0]

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







							def   lowercase__       (							self    :						Dict					)				->		Dict:


													'''simple docstring'''




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

													UpperCAmelCase_    				=							load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/sd2-inpaint/init_image.png"					)
													UpperCAmelCase_    				=							load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"					)

													UpperCAmelCase_    				=							"stabilityai/stable-diffusion-2-inpainting"
													UpperCAmelCase_    				=							PNDMScheduler.from_pretrained(_UpperCAmelCase	,       subfolder="scheduler"					)
													UpperCAmelCase_    				=							StableDiffusionInpaintPipeline.from_pretrained(
													    _UpperCAmelCase	,       safety_checker=_UpperCAmelCase	,       scheduler=_UpperCAmelCase	,       torch_dtype=torch.floataa	,       )
													pipe.to(_UpperCAmelCase					)
													pipe.set_progress_bar_config(disable=_UpperCAmelCase					)
													pipe.enable_attention_slicing(1					)
													pipe.enable_sequential_cpu_offload()

													UpperCAmelCase_    				=							"Face of a yellow cat, high resolution, sitting on a park bench"

													UpperCAmelCase_    				=							torch.manual_seed(0					)
													UpperCAmelCase_    				=							pipe(
													    prompt=_UpperCAmelCase	,       image=_UpperCAmelCase	,       mask_image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       num_inference_steps=2	,       output_type="np"	,       )

													UpperCAmelCase_    				=							torch.cuda.max_memory_allocated()
													# make sure that less than 2.65 GB is allocated
													assert mem_bytes < 2.65 * 10**9









"""simple docstring"""





from __future__ import annotations

import math
def      a__			(   lowerCAmelCase__   ):

      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(math.sqrt(lowerCAmelCase__   ) + 1   )			,    6   ):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True


lowerCamelCase      =		[num for num in range(3, 100_001, 2) if not is_prime(num)]
def      a__			(   lowerCAmelCase__   ):
      if not isinstance(lowerCAmelCase__			,    lowerCAmelCase__   ):
            raise ValueError("n must be an integer"   )
      if n <= 0:
            raise ValueError("n must be >= 0"   )

      UpperCAmelCase_    				=							[]
      for num in range(len(lowerCAmelCase__   )   ):
            UpperCAmelCase_    				=							0
            while 2 * i * i <= odd_composites[num]:
                  UpperCAmelCase_    				=							odd_composites[num] - 2 * i * i
                  if is_prime(lowerCAmelCase__   ):
                        break
                  i += 1
            else:
                  list_nums.append(odd_composites[num]   )
                  if len(lowerCAmelCase__   ) == n:
                        return list_nums

      return []
def      a__			(   ):
      return compute_nums(1   )[0]


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




