



'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class        UpperCAmelCase__						(		UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							unittest.TestCase			):
					a   :  int   =				StableDiffusionModelEditingPipeline
					a   :  Union[str, Any]   =				TEXT_TO_IMAGE_PARAMS
					a   :  Dict   =				TEXT_TO_IMAGE_BATCH_PARAMS
					a   :  int   =				TEXT_TO_IMAGE_IMAGE_PARAMS
					a   :  Any   =				TEXT_TO_IMAGE_IMAGE_PARAMS



					def 							UpperCAmelCase_			(     self  )  ->      int:
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	UNetaDConditionModel(
						    block_out_channels=(32, 64)      ,					layers_per_block=2      ,					sample_size=32      ,					in_channels=4      ,					out_channels=4      ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,					cross_attention_dim=32      ,					)
						__lowerCAmelCase       				=	DDIMScheduler()
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	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      ,					)
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	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      ,					)
						__lowerCAmelCase       				=	CLIPTextModel(UpperCamelCase  )
						__lowerCAmelCase       				=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

						__lowerCAmelCase       				=	{
						    "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  )  ->      List[Any]:
						__lowerCAmelCase       				=	torch.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	{
						    "prompt": "A field of roses",
						    "generator": generator,
						    # Setting height and width to None to prevent OOMs on CPU.
						    "height": None,
						    "width": None,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase       				=	self.get_dummy_components()
						__lowerCAmelCase       				=	StableDiffusionModelEditingPipeline(**UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	self.get_dummy_inputs(UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe(**UpperCamelCase  ).images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						assert image.shape == (1, 64, 64, 3)

						__lowerCAmelCase       				=	np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]  )

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



					def 							UpperCAmelCase_			(     self  )  ->      List[str]:
						__lowerCAmelCase       				=	"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase       				=	self.get_dummy_components()
						__lowerCAmelCase       				=	StableDiffusionModelEditingPipeline(**UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	self.get_dummy_inputs(UpperCamelCase  )
						__lowerCAmelCase       				=	"french fries"
						__lowerCAmelCase       				=	sd_pipe(**UpperCamelCase      ,					negative_prompt=UpperCamelCase  )
						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]

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

						__lowerCAmelCase       				=	np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]  )

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



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						__lowerCAmelCase       				=	"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase       				=	self.get_dummy_components()
						__lowerCAmelCase       				=	EulerAncestralDiscreteScheduler(
						    beta_start=0.0_00_85      ,					beta_end=0.0_12      ,					beta_schedule="scaled_linear"  )
						__lowerCAmelCase       				=	StableDiffusionModelEditingPipeline(**UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

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

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

						__lowerCAmelCase       				=	np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]  )

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



					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						__lowerCAmelCase       				=	"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase       				=	self.get_dummy_components()
						__lowerCAmelCase       				=	PNDMScheduler()
						__lowerCAmelCase       				=	StableDiffusionModelEditingPipeline(**UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	self.get_dummy_inputs(UpperCamelCase  )
						# the pipeline does not expect pndm so test if it raises error.
						with self.assertRaises(UpperCamelCase  ):
							__lowerCAmelCase       				=	sd_pipe(**UpperCamelCase  ).images



					def 							UpperCAmelCase_			(     self  )  ->      str:
						super().test_inference_batch_single_identical(expected_max_diff=5E-3  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						super().test_attention_slicing_forward_pass(expected_max_diff=5E-3  )






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



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase=0  )  ->      Optional[int]:
						__lowerCAmelCase       				=	torch.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	{
						    "prompt": "A field of roses",
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 7.5,
						    "output_type": "numpy",
						}
						return inputs



					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						__lowerCAmelCase       				=	"CompVis/stable-diffusion-v1-4"
						__lowerCAmelCase       				=	StableDiffusionModelEditingPipeline.from_pretrained(UpperCamelCase      ,					safety_checker=UpperCamelCase  )
						pipe.to(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )
						pipe.enable_attention_slicing()

						__lowerCAmelCase       				=	self.get_inputs()
						__lowerCAmelCase       				=	pipe(**UpperCamelCase  ).images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)

						__lowerCAmelCase       				=	np.array(
						    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]  )

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

						# make sure image changes after editing
						pipe.edit_model("A pack of roses"      ,					"A pack of blue roses"  )

						__lowerCAmelCase       				=	pipe(**UpperCamelCase  ).images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)

						assert np.abs(expected_slice - image_slice  ).max() > 1E-1



					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						__lowerCAmelCase       				=	"CompVis/stable-diffusion-v1-4"
						__lowerCAmelCase       				=	DDIMScheduler.from_pretrained(UpperCamelCase      ,					subfolder="scheduler"  )
						__lowerCAmelCase       				=	StableDiffusionModelEditingPipeline.from_pretrained(
						    UpperCamelCase      ,					scheduler=UpperCamelCase      ,					safety_checker=UpperCamelCase  )
						__lowerCAmelCase       				=	pipe.to(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )
						pipe.enable_attention_slicing(1  )
						pipe.enable_sequential_cpu_offload()

						__lowerCAmelCase       				=	self.get_inputs()
						__lowerCAmelCase       				=	pipe(**UpperCamelCase  )

						__lowerCAmelCase       				=	torch.cuda.max_memory_allocated()
						# make sure that less than 4.4 GB is allocated
						assert mem_bytes < 4.4 * 10**9




'''simple docstring'''



from __future__ import annotations

from random import randrange



def 	__lowerCAmelCase       (				lowerCamelCase :    list       ):






	'''simple docstring'''




	if len(lowerCamelCase       ) < 2:
		return collection
	__lowerCAmelCase       				=	randrange(len(lowerCamelCase       )       )  # Use random element as pivot
	__lowerCAmelCase       				=	collection[pivot_index]
	__lowerCAmelCase       				=	[]  # All elements greater than pivot
	__lowerCAmelCase       				=	[]  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	return [*quick_sort(lowerCamelCase       ), pivot, *quick_sort(lowerCamelCase       )]


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