






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 (      snake_case__     ,       snake_case__     ,       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        UpperCamelCase    (							self    ):
							"""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        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=0    ):
							"""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        UpperCamelCase    (							self    ):
							"""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([0.47_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        UpperCamelCase    (							self    ):
							"""simple docstring"""







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





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





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""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        UpperCamelCase    (							self    ):
							"""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        UpperCamelCase    (							self    ):
							"""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








def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) < 2:
												return collection

						def circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	) -> bool:
												_UpperCAmelCase									=			False

												if low == high:
																		return swapped

												_UpperCAmelCase									=			low
												_UpperCAmelCase									=			high

												while left < right:
																		if collection[left] > collection[right]:
																								_UpperCAmelCase					, _UpperCAmelCase									=			(
																								    collection[right],
																								    collection[left],
																								)
																								_UpperCAmelCase									=			True

																		left += 1
																		right -= 1

												if left == right and collection[left] > collection[right + 1]:
																		_UpperCAmelCase					, _UpperCAmelCase									=			(
																		    collection[right + 1],
																		    collection[left],
																		)

																		_UpperCAmelCase									=			True

												_UpperCAmelCase									=			low + int((high - low) / 2	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		mid + 1 ,		__lowerCAmelCase	)

												return swapped or left_swap or right_swap

						_UpperCAmelCase									=			True

						while is_not_sorted is True:
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		0 ,		len(__lowerCAmelCase	) - 1	)

						return collection


if __name__ == "__main__":
		_a      	=  input('''Enter numbers separated by a comma:\n''').strip()
		_a      	=  [int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))
