

'''simple docstring'''
import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 							A__					( A__  ,     unittest.TestCase						):
  A__					=       PaintByExamplePipeline
  A__					=       IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  A__					=       IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  A__					=       frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess





  def 		A						(	self   :			str				)			->					Any:





       '''simple docstring'''






       torch.manual_seed(0				)
       _SCREAMING_SNAKE_CASE												=UNetaDConditionModel(
           block_out_channels=(32, 64)							,      layers_per_block=2							,      sample_size=32							,      in_channels=9							,      out_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,      cross_attention_dim=32							,      )
       _SCREAMING_SNAKE_CASE												=PNDMScheduler(skip_prk_steps=_a				)
       torch.manual_seed(0				)
       _SCREAMING_SNAKE_CASE												=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				)
       _SCREAMING_SNAKE_CASE												=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							,      )
       _SCREAMING_SNAKE_CASE												=PaintByExampleImageEncoder(_a							,      proj_size=32				)
       _SCREAMING_SNAKE_CASE												=CLIPImageProcessor(crop_size=32							,      size=32				)

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





  def 		A						(	self   :			str							,      _a   :			Optional[int]				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=np.array(image.convert('RGB'				)				)
       _SCREAMING_SNAKE_CASE												=image[None].transpose(0							,      3							,      1							,      2				)
       _SCREAMING_SNAKE_CASE												=torch.from_numpy(_a				).to(dtype=torch.floataa				) / 127.5 - 1.0
       return image





  def 		A						(	self   :			Union[str, Any]							,      _a   :			List[Any]="cpu"							,      _a   :			Tuple=0				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=floats_tensor((1, 3, 32, 32)							,      rng=random.Random(_a				)				).to(_a				)
       _SCREAMING_SNAKE_CASE												=image.cpu().permute(0							,      2							,      3							,      1				)[0]
       _SCREAMING_SNAKE_CASE												=Image.fromarray(np.uinta(_a				)				).convert('RGB'				).resize((64, 64)				)
       _SCREAMING_SNAKE_CASE												=Image.fromarray(np.uinta(image + 4				)				).convert('RGB'				).resize((64, 64)				)
       _SCREAMING_SNAKE_CASE												=Image.fromarray(np.uinta(_a				)				).convert('RGB'				).resize((32, 32)				)

       if str(_a				).startswith('mps'				):
            _SCREAMING_SNAKE_CASE												=torch.manual_seed(_a				)
       else:
            _SCREAMING_SNAKE_CASE												=torch.Generator(device=_a				).manual_seed(_a				)
       _SCREAMING_SNAKE_CASE												={
           'example_image': example_image,
           'image': init_image,
           'mask_image': mask_image,
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
       }
       return inputs





  def 		A						(	self   :			Any				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.get_dummy_components()

       # make sure here that pndm scheduler skips prk
       _SCREAMING_SNAKE_CASE												=PaintByExamplePipeline(**_a				)
       _SCREAMING_SNAKE_CASE												=pipe.to('cpu'				)
       pipe.set_progress_bar_config(disable=_a				)

       _SCREAMING_SNAKE_CASE												=self.get_dummy_inputs()
       _SCREAMING_SNAKE_CASE												=pipe(**_a				)
       _SCREAMING_SNAKE_CASE												=output.images

       _SCREAMING_SNAKE_CASE												=image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       _SCREAMING_SNAKE_CASE												=np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]				)

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





  def 		A						(	self   :			Union[str, Any]				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												='cpu'
       _SCREAMING_SNAKE_CASE												=self.get_dummy_inputs()
       inputs.pop('mask_image'				)
       _SCREAMING_SNAKE_CASE												=self.convert_to_pt(inputs.pop('image'				)				)
       _SCREAMING_SNAKE_CASE												=image.clamp(0							,      1				) / 2

       # make sure here that pndm scheduler skips prk
       _SCREAMING_SNAKE_CASE												=PaintByExamplePipeline(**self.get_dummy_components()				)
       _SCREAMING_SNAKE_CASE												=pipe.to(_a				)
       pipe.set_progress_bar_config(disable=_a				)

       _SCREAMING_SNAKE_CASE												=pipe(image=_a							,      mask_image=mask_image[:, 0]							,      **_a				)
       _SCREAMING_SNAKE_CASE												=output.images

       _SCREAMING_SNAKE_CASE												=image.cpu().permute(0							,      2							,      3							,      1				)[0]
       _SCREAMING_SNAKE_CASE												=mask_image.cpu().permute(0							,      2							,      3							,      1				)[0]

       _SCREAMING_SNAKE_CASE												=Image.fromarray(np.uinta(_a				)				).convert('RGB'				)
       _SCREAMING_SNAKE_CASE												=Image.fromarray(np.uinta(_a				)				).convert('RGB'				)

       _SCREAMING_SNAKE_CASE												=pipe(**self.get_dummy_inputs()				)
       _SCREAMING_SNAKE_CASE												=output.images

       assert out_a.shape == (1, 64, 64, 3)
       assert np.abs(out_a.flatten() - out_a.flatten()				).max() < 5e-2





  def 		A						(	self   :			int				)			->					str:





       '''simple docstring'''






       super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







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





  def 		A						(	self   :			Tuple				)			->					Optional[Any]:





       '''simple docstring'''






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





  def 		A						(	self   :			str				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/paint_by_example/dog_in_bucket.png'				)
       _SCREAMING_SNAKE_CASE												=load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/paint_by_example/mask.png'				)
       _SCREAMING_SNAKE_CASE												=load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/paint_by_example/panda.jpg'				)

       _SCREAMING_SNAKE_CASE												=PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'				)
       _SCREAMING_SNAKE_CASE												=pipe.to(_a				)
       pipe.set_progress_bar_config(disable=_a				)

       _SCREAMING_SNAKE_CASE												=torch.manual_seed(321				)
       _SCREAMING_SNAKE_CASE												=pipe(
           image=_a							,      mask_image=_a							,      example_image=_a							,      generator=_a							,      guidance_scale=5.0							,      num_inference_steps=50							,      output_type='np'							,      )

       _SCREAMING_SNAKE_CASE												=output.images

       _SCREAMING_SNAKE_CASE												=image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE												=np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]				)

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



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  list       )   ->    list:
     """simple docstring"""




     for i, _ in enumerate(_UpperCamelCase       ):
          if (i % 2 == 1) == (nums[i - 1] > nums[i]):
               _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=nums[i], nums[i - 1]

     return nums


if __name__ == "__main__":
     print("Enter the array elements:")
     lowerCamelCase				:   Tuple      				=  list(map(int, input().split()))
     print("The unsorted array is:")
     print(array)
     print("Array after Wiggle sort:")
     print(wiggle_sort(array))
