



"""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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





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



					def 	snake_case ( self							:						int  )->      int:
								torch.manual_seed(0  )
								lowerCamelCase__	:	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=lowerCamelCase,						)
								lowerCamelCase__	:	Tuple				=PNDMScheduler(skip_prk_steps=lowerCamelCase  )
								torch.manual_seed(0  )
								lowerCamelCase__	:	Optional[int]				=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  )
								lowerCamelCase__	:	Optional[int]				=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,						)
								lowerCamelCase__	:	Union[str, Any]				=CLIPTextModel(lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								lowerCamelCase__	:	Optional[int]				={
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components



					def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						str,						lowerCamelCase							:						List[Any]=0  )->      List[Any]:
								# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
								lowerCamelCase__	:	Any				=floats_tensor((1, 3, 32, 32),						rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )
								lowerCamelCase__	:	Any				=image.cpu().permute(0,						2,						3,						1  )[0]
								lowerCamelCase__	:	Union[str, Any]				=Image.fromarray(np.uinta(lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )
								lowerCamelCase__	:	List[str]				=Image.fromarray(np.uinta(image + 4  )  ).convert('''RGB'''  ).resize((64, 64)  )
								if str(lowerCamelCase  ).startswith('''mps'''  ):
											lowerCamelCase__	:	List[Any]				=torch.manual_seed(lowerCamelCase  )
								else:
											lowerCamelCase__	:	Optional[Any]				=torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	str				={
								    '''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 	snake_case ( self							:						Optional[Any]  )->      int:
								lowerCamelCase__	:	List[str]				='''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase__	:	Any				=self.get_dummy_components()
								lowerCamelCase__	:	Tuple				=StableDiffusionInpaintPipeline(**lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=self.get_dummy_inputs(lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=sd_pipe(**lowerCamelCase  ).images
								lowerCamelCase__	:	str				=image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								lowerCamelCase__	:	Tuple				=np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]  )

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




					def 	snake_case ( self							:						str  )->      Optional[int]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3  )





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


					'''simple docstring'''





					def 	snake_case ( self							:						int  )->      Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



					def 	snake_case ( self							:						Dict  )->      Any:
								lowerCamelCase__	:	Tuple				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/sd2-inpaint/init_image.png'''  )
								lowerCamelCase__	:	str				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''  )
								lowerCamelCase__	:	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'''  )

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

								lowerCamelCase__	:	List[Any]				='''Face of a yellow cat, high resolution, sitting on a park bench'''

								lowerCamelCase__	:	Dict				=torch.manual_seed(0  )
								lowerCamelCase__	:	Any				=pipe(
								    prompt=lowerCamelCase,						image=lowerCamelCase,						mask_image=lowerCamelCase,						generator=lowerCamelCase,						output_type='''np''',						)
								lowerCamelCase__	:	Tuple				=output.images[0]

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



					def 	snake_case ( self							:						Any  )->      int:
								lowerCamelCase__	:	List[Any]				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/sd2-inpaint/init_image.png'''  )
								lowerCamelCase__	:	int				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''  )
								lowerCamelCase__	:	List[str]				=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
								    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''  )

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

								lowerCamelCase__	:	Union[str, Any]				='''Face of a yellow cat, high resolution, sitting on a park bench'''

								lowerCamelCase__	:	Any				=torch.manual_seed(0  )
								lowerCamelCase__	:	Optional[int]				=pipe(
								    prompt=lowerCamelCase,						image=lowerCamelCase,						mask_image=lowerCamelCase,						generator=lowerCamelCase,						output_type='''np''',						)
								lowerCamelCase__	:	List[Any]				=output.images[0]

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




					def 	snake_case ( self							:						Any  )->      Any:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

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

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

								lowerCamelCase__	:	Dict				='''Face of a yellow cat, high resolution, sitting on a park bench'''

								lowerCamelCase__	:	Tuple				=torch.manual_seed(0  )
								lowerCamelCase__	:	str				=pipe(
								    prompt=lowerCamelCase,						image=lowerCamelCase,						mask_image=lowerCamelCase,						generator=lowerCamelCase,						num_inference_steps=2,						output_type='''np''',						)

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









"""simple docstring"""






_lowercase				:				str   		=		0  # The first color of the flag.
_lowercase				:				Dict   		=		1  # The second color of the flag.
_lowercase				:				Tuple   		=		2  # The third color of the flag.
_lowercase				:				Optional[int]   		=		(red, white, blue)






def   snake_case__     (	__lowerCamelCase       :					list			):
			"""simple docstring"""
			if not sequence:
						return []
			if len(__lowerCamelCase			) == 1:
						return list(__lowerCamelCase			)
			lowerCamelCase__	:	List[Any]				=0
			lowerCamelCase__	:	Dict				=len(__lowerCamelCase			) - 1
			lowerCamelCase__	:	Tuple				=0
			while mid <= high:
						if sequence[mid] == colors[0]:
									lowerCamelCase__			,     lowerCamelCase__	:	Optional[Any]				=sequence[mid], sequence[low]
									low += 1
									mid += 1
						elif sequence[mid] == colors[1]:
									mid += 1
						elif sequence[mid] == colors[2]:
									lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=sequence[high], sequence[mid]
									high -= 1
						else:
									lowerCamelCase__	:	Dict				=f'''The elements inside the sequence must contains only {colors} values'''
									raise ValueError(__lowerCamelCase			)
			return sequence


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_lowercase				:				Optional[Any]   		=		input("Enter numbers separated by commas:\n").strip()
		_lowercase				:				int   		=		[int(item.strip()) for item in user_input.split(",")]
		print(f'{dutch_national_flag_sort(unsorted)}')




