
"""simple docstring"""




import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()



class      lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				@property
				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       1
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       3
											SCREAMING_SNAKE_CASE__						:	Tuple									=       (32, 32)

											SCREAMING_SNAKE_CASE__						:	List[str]									=       floats_tensor((batch_size, num_channels) + sizes       ,	rng=random.Random(0   )   ).to(SCREAMING_SNAKE_CASE__   )
											return image



				@property
				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											torch.manual_seed(0   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       UNetaDModel(
											    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=6       ,	out_channels=3       ,	down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")       ,	up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")       ,	)
											return model



				@property
				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											torch.manual_seed(0   )
											SCREAMING_SNAKE_CASE__						:	Dict									=       VQModel(
											    block_out_channels=[32, 64]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,	latent_channels=3       ,	)
											return model



				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       """cpu"""
											SCREAMING_SNAKE_CASE__						:	Dict									=       self.dummy_uncond_unet
											SCREAMING_SNAKE_CASE__						:	List[str]									=       DDIMScheduler()
											SCREAMING_SNAKE_CASE__						:	Tuple									=       self.dummy_vq_model

											SCREAMING_SNAKE_CASE__						:	Tuple									=       LDMSuperResolutionPipeline(unet=SCREAMING_SNAKE_CASE__       ,	vqvae=SCREAMING_SNAKE_CASE__       ,	scheduler=SCREAMING_SNAKE_CASE__   )
											ldm.to(SCREAMING_SNAKE_CASE__   )
											ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	int									=       self.dummy_image.to(SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	str									=       torch.Generator(device=SCREAMING_SNAKE_CASE__   ).manual_seed(0   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       ldm(image=SCREAMING_SNAKE_CASE__       ,	generator=SCREAMING_SNAKE_CASE__       ,	num_inference_steps=2       ,	output_type="""numpy"""   ).images

											SCREAMING_SNAKE_CASE__						:	Any									=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											SCREAMING_SNAKE_CASE__						:	Dict									=       np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]   )

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



				@unittest.skipIf(torch_device != """cuda"""       ,	"""This test requires a GPU"""   )
				def      __magic_name__   (self   ) ->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       self.dummy_uncond_unet
											SCREAMING_SNAKE_CASE__						:	str									=       DDIMScheduler()
											SCREAMING_SNAKE_CASE__						:	Any									=       self.dummy_vq_model

											# put models in fp16
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       unet.half()
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       vqvae.half()

											SCREAMING_SNAKE_CASE__						:	List[str]									=       LDMSuperResolutionPipeline(unet=SCREAMING_SNAKE_CASE__       ,	vqvae=SCREAMING_SNAKE_CASE__       ,	scheduler=SCREAMING_SNAKE_CASE__   )
											ldm.to(SCREAMING_SNAKE_CASE__   )
											ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Any									=       self.dummy_image.to(SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Tuple									=       ldm(SCREAMING_SNAKE_CASE__       ,	num_inference_steps=2       ,	output_type="""numpy"""   ).images

											assert image.shape == (1, 64, 64, 3)






@slow
@require_torch
class      lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def      __magic_name__   (self   ) ->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/vq_diffusion/teddy_bear_pool.png"""   )
											SCREAMING_SNAKE_CASE__						:	str									=       init_image.resize((64, 64)       ,	resample=PIL_INTERPOLATION["""lanczos"""]   )

											SCREAMING_SNAKE_CASE__						:	int									=       LDMSuperResolutionPipeline.from_pretrained("""duongna/ldm-super-resolution"""       ,	device_map="""auto"""   )
											ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.manual_seed(0   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       ldm(image=SCREAMING_SNAKE_CASE__       ,	generator=SCREAMING_SNAKE_CASE__       ,	num_inference_steps=20       ,	output_type="""numpy"""   ).images

											SCREAMING_SNAKE_CASE__						:	int									=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_56, 2_56, 3)
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]   )

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






"""simple docstring"""




def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Any									=       index
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       2 * index + 1
							SCREAMING_SNAKE_CASE__						:	Any									=       2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	str									=       left_index

							if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	Any									=       right_index

							if largest != index:
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Tuple									=       unsorted[index], unsorted[largest]
														heapify(_snake_case       ,_snake_case       ,_snake_case						)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       len(_snake_case						)
							for i in range(n // 2 - 1       ,-1       ,-1						):
														heapify(_snake_case       ,_snake_case       ,_snake_case						)
							for i in range(n - 1       ,0       ,-1						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Any									=       unsorted[i], unsorted[0]
														heapify(_snake_case       ,0       ,_snake_case						)
							return unsorted


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




