import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCamelCase      			=     False


@nightly
@require_torch_gpu
class _a       (	unittest.TestCase ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	def 					__UpperCAmelCase(       self   ):
			__A							:   Tuple	  =				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
			# remove text_unet
			pipe.remove_unused_weights()
			pipe.to(__UpperCAmelCase   )
			pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A							:   int	  =				load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )

			__A							:   List[str]	  =				torch.manual_seed(0   )
			__A							:   List[str]	  =				pipe(
			    prompt="first prompt"							,			image=__UpperCAmelCase							,			text_to_image_strength=0.75							,			generator=__UpperCAmelCase							,			guidance_scale=7.5							,			num_inference_steps=2							,			output_type="numpy"							,			).images

			with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(__UpperCAmelCase   )
					__A							:   Dict	  =				VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCAmelCase   )

			pipe.to(__UpperCAmelCase   )
			pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A							:   List[str]	  =				generator.manual_seed(0   )
			__A							:   str	  =				pipe(
			    prompt="first prompt"							,			image=__UpperCAmelCase							,			text_to_image_strength=0.75							,			generator=__UpperCAmelCase							,			guidance_scale=7.5							,			num_inference_steps=2							,			output_type="numpy"							,			).images

			assert np.abs(image - new_image   ).sum() < 1e-5, "Models don't have the same forward pass"







	def 					__UpperCAmelCase(       self   ):
			__A							:   List[str]	  =				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
			pipe.remove_unused_weights()
			pipe.to(__UpperCAmelCase   )
			pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A							:   Tuple	  =				"cyberpunk 2077"
			__A							:   Dict	  =				load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
			__A							:   Dict	  =				torch.manual_seed(0   )
			__A							:   Tuple	  =				pipe(
			    prompt=__UpperCAmelCase							,			image=__UpperCAmelCase							,			text_to_image_strength=0.75							,			generator=__UpperCAmelCase							,			guidance_scale=7.5							,			num_inference_steps=50							,			output_type="numpy"							,			).images

			__A							:   Optional[int]	  =				image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			__A							:   Any	  =				np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]   )

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






def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Tuple:
		__A							:   Union[str, Any]	  =				index
		__A							:   int	  =				2 * index + 1
		__A							:   Dict	  =				2 * index + 2
		if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
				__A							:   Dict	  =				left_index

		if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
				__A							:   int	  =				right_index

		if largest != index:
				__A  ,       __A							:   Dict	  =				unsorted[index], unsorted[largest]
				heapify(_lowercase ,     _lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase    )							->	Tuple:
		__A							:   Dict	  =				len(_lowercase    )
		for i in range(n // 2 - 1 ,     -1 ,     -1    ):
				heapify(_lowercase ,     _lowercase ,     _lowercase    )
		for i in range(n - 1 ,     0 ,     -1    ):
				__A  ,       __A							:   Dict	  =				unsorted[i], unsorted[0]
				heapify(_lowercase ,     0 ,     _lowercase    )
		return unsorted


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





