


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 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      StableDiffusionInpaintPipeline
					UpperCAmelCase__ :   Optional[int]   				=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					UpperCAmelCase__ :   int   				=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCAmelCase__ :   Optional[int]   				=      frozenset(
					    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					UpperCAmelCase__ :   Tuple   				=      frozenset([]				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Tuple:
												torch.manual_seed(0				)
												a__					=					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=__snake_case      ,)
												a__					=					PNDMScheduler(skip_prk_steps=__snake_case				)
												torch.manual_seed(0				)
												a__					=					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=1_28      ,)
												torch.manual_seed(0				)
												a__					=					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=10_00      ,hidden_act='gelu'      ,projection_dim=5_12      ,)
												a__					=					CLIPTextModel(__snake_case				)
												a__					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

												a__					=					{
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}
												return components
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :Union[str, Any]      ,__snake_case       :int=0				) ->   List[Any]:
												# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
												a__					=					floats_tensor((1, 3, 32, 32)      ,rng=random.Random(__snake_case				)				).to(__snake_case				)
												a__					=					image.cpu().permute(0      ,2      ,3      ,1				)[0]
												a__					=					Image.fromarray(np.uinta(__snake_case				)				).convert('RGB'				).resize((64, 64)				)
												a__					=					Image.fromarray(np.uinta(image + 4				)				).convert('RGB'				).resize((64, 64)				)
												if str(__snake_case				).startswith('mps'				):
																			a__					=					torch.manual_seed(__snake_case				)
												else:
																			a__					=					torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
												a__					=					{
												    '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 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												a__					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
												a__					=					self.get_dummy_components()
												a__					=					StableDiffusionInpaintPipeline(**__snake_case				)
												a__					=					sd_pipe.to(__snake_case				)
												sd_pipe.set_progress_bar_config(disable=__snake_case				)

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

												assert image.shape == (1, 64, 64, 3)
												a__					=					np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]				)

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



					def 			lowerCamelCase__(				self       :List[Any]				) ->   Union[str, Any]:
												super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







@slow
@require_torch_gpu
class 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :Dict				) ->   Optional[Any]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Optional[int]:
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-inpaint/init_image.png'				)
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)
												a__					=					load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
												    '/yellow_cat_sitting_on_a_park_bench.npy'				)

												a__					=					'stabilityai/stable-diffusion-2-inpainting'
												a__					=					StableDiffusionInpaintPipeline.from_pretrained(__snake_case      ,safety_checker=__snake_case				)
												pipe.to(__snake_case				)
												pipe.set_progress_bar_config(disable=__snake_case				)
												pipe.enable_attention_slicing()

												a__					=					'Face of a yellow cat, high resolution, sitting on a park bench'

												a__					=					torch.manual_seed(0				)
												a__					=					pipe(
												    prompt=__snake_case      ,image=__snake_case      ,mask_image=__snake_case      ,generator=__snake_case      ,output_type='np'      ,)
												a__					=					output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image				).max() < 9E-3
					def 			lowerCamelCase__(				self       :List[Any]				) ->   List[Any]:
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-inpaint/init_image.png'				)
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)
												a__					=					load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
												    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'				)

												a__					=					'stabilityai/stable-diffusion-2-inpainting'
												a__					=					StableDiffusionInpaintPipeline.from_pretrained(
												    __snake_case      ,torch_dtype=torch.floataa      ,safety_checker=__snake_case      ,)
												pipe.to(__snake_case				)
												pipe.set_progress_bar_config(disable=__snake_case				)
												pipe.enable_attention_slicing()

												a__					=					'Face of a yellow cat, high resolution, sitting on a park bench'

												a__					=					torch.manual_seed(0				)
												a__					=					pipe(
												    prompt=__snake_case      ,image=__snake_case      ,mask_image=__snake_case      ,generator=__snake_case      ,output_type='np'      ,)
												a__					=					output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image				).max() < 5E-1



					def 			lowerCamelCase__(				self       :List[str]				) ->   Union[str, Any]:
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

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

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

												a__					=					'Face of a yellow cat, high resolution, sitting on a park bench'

												a__					=					torch.manual_seed(0				)
												a__					=					pipe(
												    prompt=__snake_case      ,image=__snake_case      ,mask_image=__snake_case      ,generator=__snake_case      ,num_inference_steps=2      ,output_type='np'      ,)

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








def 							__lowercase       (			__lowerCAmelCase							:    int       ):

							if num <= 0:
														raise ValueError('Input must be a positive integer'       )

							a__					=					[True] * (num + 1)

							a__					=					2
							while p * p <= num:
														if primes[p]:
																					for i in range(p * p ,   num + 1 ,   __lowerCAmelCase       ):
																												a__					=					False
														p += 1

							return [prime for prime in range(2 ,   num + 1       ) if primes[prime]]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						snake_case							:	Optional[Any]					      =				int(input('''Enter a positive integer: ''').strip())
						print(prime_sieve_eratosthenes(user_num))




