



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 		UpperCAmelCase    (    snake_case_							,snake_case_							,snake_case_							,unittest.TestCase					):
   SCREAMING_SNAKE_CASE__					      =  StableDiffusionImageVariationPipeline
   SCREAMING_SNAKE_CASE__					      =  IMAGE_VARIATION_PARAMS
   SCREAMING_SNAKE_CASE__					      =  IMAGE_VARIATION_BATCH_PARAMS
   SCREAMING_SNAKE_CASE__					      =  frozenset([]					)
   # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   SCREAMING_SNAKE_CASE__					      =  frozenset([]					)





   def 						__lowerCAmelCase				(						self      ):
         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			      =	PNDMScheduler(skip_prk_steps=_lowerCAmelCase      )
         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			      =	CLIPVisionConfig(
             hidden_size=32							,  projection_dim=32							,  intermediate_size=37							,  layer_norm_eps=1E-05							,  num_attention_heads=4							,  num_hidden_layers=5							,  image_size=32							,  patch_size=4							,  )
         _lowerCAmelCase			      =	CLIPVisionModelWithProjection(_lowerCAmelCase      )
         _lowerCAmelCase			      =	CLIPImageProcessor(crop_size=32							,  size=32      )

         _lowerCAmelCase			      =	{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''image_encoder''': image_encoder,
             '''feature_extractor''': feature_extractor,
             '''safety_checker''': None,
         }
         return components





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase=0      ):
         _lowerCAmelCase			      =	floats_tensor((1, 3, 32, 32)							,  rng=random.Random(_lowerCAmelCase      )      )
         _lowerCAmelCase			      =	image.cpu().permute(0							,  2							,  3							,  1      )[0]
         _lowerCAmelCase			      =	Image.fromarray(np.uinta(_lowerCAmelCase      )      ).convert('''RGB'''      ).resize((32, 32)      )
         if str(_lowerCAmelCase      ).startswith('''mps'''      ):
               _lowerCAmelCase			      =	torch.manual_seed(_lowerCAmelCase      )
         else:
               _lowerCAmelCase			      =	torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
         _lowerCAmelCase			      =	{
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowerCAmelCase			      =	self.get_dummy_components()
         _lowerCAmelCase			      =	StableDiffusionImageVariationPipeline(**_lowerCAmelCase      )
         _lowerCAmelCase			      =	sd_pipe.to(_lowerCAmelCase      )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

         assert image.shape == (1, 64, 64, 3)
         _lowerCAmelCase			      =	np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowerCAmelCase			      =	self.get_dummy_components()
         _lowerCAmelCase			      =	StableDiffusionImageVariationPipeline(**_lowerCAmelCase      )
         _lowerCAmelCase			      =	sd_pipe.to(_lowerCAmelCase      )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

         _lowerCAmelCase			      =	self.get_dummy_inputs(_lowerCAmelCase      )
         _lowerCAmelCase			      =	2 * [inputs['''image''']]
         _lowerCAmelCase			      =	sd_pipe(**_lowerCAmelCase      )

         _lowerCAmelCase			      =	output.images

         _lowerCAmelCase			      =	image[-1, -3:, -3:, -1]

         assert image.shape == (2, 64, 64, 3)
         _lowerCAmelCase			      =	np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3





   def 						__lowerCAmelCase				(						self      ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






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





   def 						__lowerCAmelCase				(						self      ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase="cpu"							,  _lowerCAmelCase=torch.floataa							,  _lowerCAmelCase=0      ):
         _lowerCAmelCase			      =	torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
         _lowerCAmelCase			      =	load_image(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
             '''/stable_diffusion_imgvar/input_image_vermeer.png'''      )
         _lowerCAmelCase			      =	np.random.RandomState(_lowerCAmelCase      ).standard_normal((1, 4, 64, 64)      )
         _lowerCAmelCase			      =	torch.from_numpy(_lowerCAmelCase      ).to(device=_lowerCAmelCase							,  dtype=_lowerCAmelCase      )
         _lowerCAmelCase			      =	{
             '''image''': init_image,
             '''latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	StableDiffusionImageVariationPipeline.from_pretrained(
             '''lambdalabs/sd-image-variations-diffusers'''							,  safety_checker=_lowerCAmelCase      )
         _lowerCAmelCase			      =	sd_pipe.to(_lowerCAmelCase      )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

         _lowerCAmelCase			      =	self.get_inputs(_lowerCAmelCase      )
         _lowerCAmelCase			      =	sd_pipe(**_lowerCAmelCase      ).images
         _lowerCAmelCase			      =	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         _lowerCAmelCase			      =	np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379]      )
         assert np.abs(image_slice - expected_slice      ).max() < 6E-3





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	0

         def callback_fn(_lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      ) -> None:
               _lowerCAmelCase			      =	True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                     _lowerCAmelCase			      =	latents.detach().cpu().numpy()
                     assert latents.shape == (1, 4, 64, 64)
                     _lowerCAmelCase			      =	latents[0, -3:, -3:, -1]
                     _lowerCAmelCase			      =	np.array(
                         [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309]      )

                     assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2
               elif step == 2:
                     _lowerCAmelCase			      =	latents.detach().cpu().numpy()
                     assert latents.shape == (1, 4, 64, 64)
                     _lowerCAmelCase			      =	latents[0, -3:, -3:, -1]
                     _lowerCAmelCase			      =	np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273]      )

                     assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2

         _lowerCAmelCase			      =	False

         _lowerCAmelCase			      =	StableDiffusionImageVariationPipeline.from_pretrained(
             '''fusing/sd-image-variations-diffusers'''							,  safety_checker=_lowerCAmelCase							,  torch_dtype=torch.floataa							,  )
         pipe.to(_lowerCAmelCase      )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
         pipe.enable_attention_slicing()

         _lowerCAmelCase			      =	self.get_inputs(_lowerCAmelCase							,  dtype=torch.floataa      )
         pipe(**_lowerCAmelCase							,  callback=_lowerCAmelCase							,  callback_steps=1      )
         assert callback_fn.has_been_called
         assert number_of_steps == inputs["num_inference_steps"]





   def 						__lowerCAmelCase				(						self      ):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         _lowerCAmelCase			      =	'''fusing/sd-image-variations-diffusers'''
         _lowerCAmelCase			      =	StableDiffusionImageVariationPipeline.from_pretrained(
             _lowerCAmelCase							,  safety_checker=_lowerCAmelCase							,  torch_dtype=torch.floataa      )
         _lowerCAmelCase			      =	pipe.to(_lowerCAmelCase      )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
         pipe.enable_attention_slicing(1      )
         pipe.enable_sequential_cpu_offload()

         _lowerCAmelCase			      =	self.get_inputs(_lowerCAmelCase							,  dtype=torch.floataa      )
         _lowerCAmelCase			      =	pipe(**_lowerCAmelCase      )

         _lowerCAmelCase			      =	torch.cuda.max_memory_allocated()
         # make sure that less than 2.6 GB is allocated
         assert mem_bytes < 2.6 * 10**9






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





   def 						__lowerCAmelCase				(						self      ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase="cpu"							,  _lowerCAmelCase=torch.floataa							,  _lowerCAmelCase=0      ):
         _lowerCAmelCase			      =	torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
         _lowerCAmelCase			      =	load_image(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
             '''/stable_diffusion_imgvar/input_image_vermeer.png'''      )
         _lowerCAmelCase			      =	np.random.RandomState(_lowerCAmelCase      ).standard_normal((1, 4, 64, 64)      )
         _lowerCAmelCase			      =	torch.from_numpy(_lowerCAmelCase      ).to(device=_lowerCAmelCase							,  dtype=_lowerCAmelCase      )
         _lowerCAmelCase			      =	{
             '''image''': init_image,
             '''latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 50,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers'''      )
         sd_pipe.to(_lowerCAmelCase      )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

         _lowerCAmelCase			      =	self.get_inputs(_lowerCAmelCase      )
         _lowerCAmelCase			      =	sd_pipe(**_lowerCAmelCase      ).images[0]

         _lowerCAmelCase			      =	load_numpy(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
             '''/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'''      )
         _lowerCAmelCase			      =	np.abs(expected_image - image      ).max()
         assert max_diff < 1E-3





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers'''      )
         _lowerCAmelCase			      =	DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config      )
         sd_pipe.to(_lowerCAmelCase      )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

         _lowerCAmelCase			      =	self.get_inputs(_lowerCAmelCase      )
         _lowerCAmelCase			      =	25
         _lowerCAmelCase			      =	sd_pipe(**_lowerCAmelCase      ).images[0]

         _lowerCAmelCase			      =	load_numpy(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
             '''/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'''      )
         _lowerCAmelCase			      =	np.abs(expected_image - image      ).max()
         assert max_diff < 1E-3




from __future__ import annotations

from random import randrange
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						list					)->list:
      if len(_SCREAMING_SNAKE_CASE					) < 2:
            return collection
      _lowerCAmelCase			      =	randrange(len(_SCREAMING_SNAKE_CASE					)					)  # 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(_SCREAMING_SNAKE_CASE					)

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

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


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