






"""simple docstring"""





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 					a						( __snake_case  ,				__snake_case  ,				__snake_case  ,				unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:					Tuple						=			StableDiffusionImageVariationPipeline
    SCREAMING_SNAKE_CASE						:					Dict						=			IMAGE_VARIATION_PARAMS
    SCREAMING_SNAKE_CASE						:					int						=			IMAGE_VARIATION_BATCH_PARAMS
    SCREAMING_SNAKE_CASE						:					List[str]						=			frozenset([]		)
    # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    SCREAMING_SNAKE_CASE						:					int						=			frozenset([]		)






    def        UpperCamelCase	(	self					:							int )					->		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_   					=						PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE )
      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-0_5	,						num_attention_heads=4	,						num_hidden_layers=5	,						image_size=32	,						patch_size=4	,						)
      lowerCamelCase_   					=						CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE )
      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        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict=0 )					->		int:
      lowerCamelCase_   					=						floats_tensor((1, 3, 32, 32)	,						rng=random.Random(__SCREAMING_SNAKE_CASE ) )
      lowerCamelCase_   					=						image.cpu().permute(0	,						2	,						3	,						1 )[0]
      lowerCamelCase_   					=						Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert('RGB' ).resize((32, 32) )
      if str(__SCREAMING_SNAKE_CASE ).startswith('mps' ):
        lowerCamelCase_   					=						torch.manual_seed(__SCREAMING_SNAKE_CASE )
      else:
        lowerCamelCase_   					=						torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						{
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs






    def        UpperCamelCase	(	self					:							Dict )					->		Optional[Any]:
      lowerCamelCase_   					=						'cpu'  # ensure determinism for the device-dependent torch.Generator
      lowerCamelCase_   					=						self.get_dummy_components()
      lowerCamelCase_   					=						StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						sd_pipe.to(__SCREAMING_SNAKE_CASE )
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						sd_pipe(**__SCREAMING_SNAKE_CASE ).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        UpperCamelCase	(	self					:							Dict )					->		Optional[int]:
      lowerCamelCase_   					=						'cpu'  # ensure determinism for the device-dependent torch.Generator
      lowerCamelCase_   					=						self.get_dummy_components()
      lowerCamelCase_   					=						StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						sd_pipe.to(__SCREAMING_SNAKE_CASE )
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

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

      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        UpperCamelCase	(	self					:							Dict )					->		List[Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






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






    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		Any:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






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






    def        UpperCamelCase	(	self					:							int )					->		str:
      lowerCamelCase_   					=						StableDiffusionImageVariationPipeline.from_pretrained(
          'lambdalabs/sd-image-variations-diffusers'	,						safety_checker=__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						sd_pipe.to(__SCREAMING_SNAKE_CASE )
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						self.get_inputs(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						sd_pipe(**__SCREAMING_SNAKE_CASE ).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        UpperCamelCase	(	self					:							Tuple )					->		str:
      lowerCamelCase_   					=						0

      def callback_fn(__SCREAMING_SNAKE_CASE					:							int	,						__SCREAMING_SNAKE_CASE					:							int	,						__SCREAMING_SNAKE_CASE					:							torch.FloatTensor ) -> 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=__SCREAMING_SNAKE_CASE	,						torch_dtype=torch.floataa	,						)
      pipe.to(__SCREAMING_SNAKE_CASE )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
      pipe.enable_attention_slicing()

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






    def        UpperCamelCase	(	self					:							List[Any] )					->		str:
      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(
          __SCREAMING_SNAKE_CASE	,						safety_checker=__SCREAMING_SNAKE_CASE	,						torch_dtype=torch.floataa )
      lowerCamelCase_   					=						pipe.to(__SCREAMING_SNAKE_CASE )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
      pipe.enable_attention_slicing(1 )
      pipe.enable_sequential_cpu_offload()

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

      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 					a						( unittest.TestCase		):






    def        UpperCamelCase	(	self					:							Optional[Any] )					->		Any:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Optional[int]="cpu"	,						__SCREAMING_SNAKE_CASE					:							Tuple=torch.floataa	,						__SCREAMING_SNAKE_CASE					:							List[str]=0 )					->		List[Any]:
      lowerCamelCase_   					=						torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						load_image(
          'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
          '/stable_diffusion_imgvar/input_image_vermeer.png' )
      lowerCamelCase_   					=						np.random.RandomState(__SCREAMING_SNAKE_CASE ).standard_normal((1, 4, 64, 64) )
      lowerCamelCase_   					=						torch.from_numpy(__SCREAMING_SNAKE_CASE ).to(device=__SCREAMING_SNAKE_CASE	,						dtype=__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						{
          'image': init_image,
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 50,
          'guidance_scale': 7.5,
          'output_type': 'numpy',
      }
      return inputs






    def        UpperCamelCase	(	self					:							List[Any] )					->		str:
      lowerCamelCase_   					=						StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers' )
      sd_pipe.to(__SCREAMING_SNAKE_CASE )
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						self.get_inputs(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						sd_pipe(**__SCREAMING_SNAKE_CASE ).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        UpperCamelCase	(	self					:							str )					->		Union[str, Any]:
      lowerCamelCase_   					=						StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers' )
      lowerCamelCase_   					=						DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config )
      sd_pipe.to(__SCREAMING_SNAKE_CASE )
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						self.get_inputs(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						25
      lowerCamelCase_   					=						sd_pipe(**__SCREAMING_SNAKE_CASE ).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












"""simple docstring"""





from __future__ import annotations

from random import randrange







def 					lowerCamelCase__	(							_lowerCamelCase	:		list			)				->    list:
  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__":
  _SCREAMING_SNAKE_CASE :    Dict    =						input('''Enter numbers separated by a comma:\n''').strip()
  _SCREAMING_SNAKE_CASE :    List[str]    =						[int(item) for item in user_input.split(''',''')]
  print(quick_sort(unsorted))




