





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 SCREAMING_SNAKE_CASE_    (       snake_case_							,					unittest.TestCase       ):
						__magic_name__:      int			   =							PaintByExamplePipeline
						__magic_name__:      str			   =							IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
						__magic_name__:      List[str]			   =							IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						__magic_name__:      str			   =							frozenset([]       )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






						def UpperCAmelCase_					(   self   :   Optional[int]						)				->     Dict:







							"""simple docstring"""


							torch.manual_seed(0						)
							snake_case_		:      List[str]           =						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		, )
							snake_case_		:      Tuple           =						PNDMScheduler(skip_prk_steps=_A						)
							torch.manual_seed(0						)
							snake_case_		:      List[Any]           =						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						)
							snake_case_		:      List[Any]           =						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		, )
							snake_case_		:      Dict           =						PaintByExampleImageEncoder(_A		, proj_size=32						)
							snake_case_		:      Union[str, Any]           =						CLIPImageProcessor(crop_size=32		, size=32						)

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






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







							"""simple docstring"""


							snake_case_		:      List[str]           =						np.array(image.convert('RGB'						)						)
							snake_case_		:      List[str]           =						image[None].transpose(0		, 3		, 1		, 2						)
							snake_case_		:      Optional[int]           =						torch.from_numpy(_A						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
							return image






						def UpperCAmelCase_					(   self   :   Any		, _A   :   int="cpu"		, _A   :   List[str]=0						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      Tuple           =						floats_tensor((1, 3, 32, 32)		, rng=random.Random(_A						)						).to(_A						)
							snake_case_		:      List[Any]           =						image.cpu().permute(0		, 2		, 3		, 1						)[0]
							snake_case_		:      List[Any]           =						Image.fromarray(np.uinta(_A						)						).convert('RGB'						).resize((64, 64)						)
							snake_case_		:      List[str]           =						Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
							snake_case_		:      Tuple           =						Image.fromarray(np.uinta(_A						)						).convert('RGB'						).resize((32, 32)						)

							if str(_A						).startswith('mps'						):
								snake_case_		:      Any           =						torch.manual_seed(_A						)
							else:
								snake_case_		:      List[Any]           =						torch.Generator(device=_A						).manual_seed(_A						)
							snake_case_		:      Optional[Any]           =						{
							    '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 UpperCAmelCase_					(   self   :   List[Any]						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      List[str]           =						self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							snake_case_		:      Dict           =						PaintByExamplePipeline(**_A						)
							snake_case_		:      List[str]           =						pipe.to('cpu'						)
							pipe.set_progress_bar_config(disable=_A						)

							snake_case_		:      List[str]           =						self.get_dummy_inputs()
							snake_case_		:      str           =						pipe(**_A						)
							snake_case_		:      Optional[int]           =						output.images

							snake_case_		:      List[str]           =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							snake_case_		:      List[Any]           =						np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)

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






						def UpperCAmelCase_					(   self   :   List[str]						)				->     List[str]:







							"""simple docstring"""


							snake_case_		:      int           =						'cpu'
							snake_case_		:      List[Any]           =						self.get_dummy_inputs()
							inputs.pop('mask_image'						)
							snake_case_		:      str           =						self.convert_to_pt(inputs.pop('image'						)						)
							snake_case_		:      Tuple           =						image.clamp(0		, 1						) / 2

							# make sure here that pndm scheduler skips prk
							snake_case_		:      str           =						PaintByExamplePipeline(**self.get_dummy_components()						)
							snake_case_		:      str           =						pipe.to(_A						)
							pipe.set_progress_bar_config(disable=_A						)

							snake_case_		:      List[Any]           =						pipe(image=_A		, mask_image=mask_image[:, 0]		, **_A						)
							snake_case_		:      Tuple           =						output.images

							snake_case_		:      int           =						image.cpu().permute(0		, 2		, 3		, 1						)[0]
							snake_case_		:      Optional[int]           =						mask_image.cpu().permute(0		, 2		, 3		, 1						)[0]

							snake_case_		:      Optional[Any]           =						Image.fromarray(np.uinta(_A						)						).convert('RGB'						)
							snake_case_		:      Optional[Any]           =						Image.fromarray(np.uinta(_A						)						).convert('RGB'						)

							snake_case_		:      Any           =						pipe(**self.get_dummy_inputs()						)
							snake_case_		:      Dict           =						output.images

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






						def UpperCAmelCase_					(   self   :   Optional[int]						)				->     str:







							"""simple docstring"""


							super().test_inference_batch_single_identical(expected_max_diff=3E-3						)
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						def UpperCAmelCase_					(   self   :   List[Any]						)				->     Optional[int]:







							"""simple docstring"""


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






						def UpperCAmelCase_					(   self   :   Tuple						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      List[str]           =						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/paint_by_example/dog_in_bucket.png'						)
							snake_case_		:      List[str]           =						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/paint_by_example/mask.png'						)
							snake_case_		:      Optional[Any]           =						load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/paint_by_example/panda.jpg'						)

							snake_case_		:      Tuple           =						PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'						)
							snake_case_		:      Tuple           =						pipe.to(_A						)
							pipe.set_progress_bar_config(disable=_A						)

							snake_case_		:      Union[str, Any]           =						torch.manual_seed(321						)
							snake_case_		:      str           =						pipe(
							    image=_A		, mask_image=_A		, example_image=_A		, generator=_A		, guidance_scale=5.0		, num_inference_steps=50		, output_type='np'		, )

							snake_case_		:      Tuple           =						output.images

							snake_case_		:      Union[str, Any]           =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							snake_case_		:      Union[str, Any]           =						np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)

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






"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =len(__lowerCamelCase      )
			for i in range(length - 1      ):
						lowerCamelCase__						:						Any						      =False
						for j in range(length - 1 - i      ):
									if collection[j] > collection[j + 1]:
												lowerCamelCase__						:						List[str]						      =True
												lowerCamelCase__      ,  lowerCamelCase__						:						List[str]						      =collection[j + 1], collection[j]
						if not swapped:
									break  # Stop iteration if the collection is sorted.
			return collection


if __name__ == "__main__":
		import doctest
		import time

		doctest.testmod()

		_lowercase					:					str 		=		input("Enter numbers separated by a comma:").strip()
		_lowercase					:					Dict 		=		[int(item) for item in user_input.split(",")]
		_lowercase					:					Optional[Any] 		=		time.process_time()
		print(*bubble_sort(unsorted), sep=",")
		print(f'Processing time: {(time.process_time() - start)%1E9 + 7}')




