




'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 	__lowercase				(  UpperCamelCase_      ,     UpperCamelCase_      ,     UpperCamelCase_      ,     unittest.TestCase    ):
			lowerCamelCase			:		Optional[int]	      =			StableDiffusionInpaintPipeline
			lowerCamelCase			:		Optional[int]	      =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase			:		List[Any]	      =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase			:		List[str]	      =			frozenset(
			    []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			lowerCamelCase			:		int	      =			frozenset([]    )

			def        UpperCAmelCase__ (self      ):
								torch.manual_seed(0      )
								lowerCamelCase_					:      Optional[Any]     =		UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)			,   layers_per_block=2			,   sample_size=3_2			,   in_channels=9			,   out_channels=4			,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,   cross_attention_dim=3_2			,   attention_head_dim=(2, 4)			,   use_linear_projection=__A			,   )
								lowerCamelCase_					:      int     =		PNDMScheduler(skip_prk_steps=__A      )
								torch.manual_seed(0      )
								lowerCamelCase_					:      Union[str, Any]     =		AutoencoderKL(
								    block_out_channels=[3_2, 6_4]			,   in_channels=3			,   out_channels=3			,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,   latent_channels=4			,   sample_size=1_2_8			,   )
								torch.manual_seed(0      )
								lowerCamelCase_					:      List[Any]     =		CLIPTextConfig(
								    bos_token_id=0			,   eos_token_id=2			,   hidden_size=3_2			,   intermediate_size=3_7			,   layer_norm_eps=1E-05			,   num_attention_heads=4			,   num_hidden_layers=5			,   pad_token_id=1			,   vocab_size=1_0_0_0			,   hidden_act='''gelu'''			,   projection_dim=5_1_2			,   )
								lowerCamelCase_					:      Tuple     =		CLIPTextModel(__A      )
								lowerCamelCase_					:      Optional[int]     =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

								lowerCamelCase_					:      Dict     =		{
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								return components

			def        UpperCAmelCase__ (self			,   A			,   A=0      ):
								# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
								lowerCamelCase_					:      str     =		floats_tensor((1, 3, 3_2, 3_2)			,   rng=random.Random(__A      )      ).to(__A      )
								lowerCamelCase_					:      str     =		image.cpu().permute(0			,   2			,   3			,   1      )[0]
								lowerCamelCase_					:      int     =		Image.fromarray(np.uinta(__A      )      ).convert('''RGB'''      ).resize((6_4, 6_4)      )
								lowerCamelCase_					:      Optional[int]     =		Image.fromarray(np.uinta(image + 4      )      ).convert('''RGB'''      ).resize((6_4, 6_4)      )
								if str(__A      ).startswith('''mps'''      ):
													lowerCamelCase_					:      str     =		torch.manual_seed(__A      )
								else:
													lowerCamelCase_					:      Any     =		torch.Generator(device=__A      ).manual_seed(__A      )
								lowerCamelCase_					:      List[Any]     =		{
								    "prompt": "A painting of a squirrel eating a burger",
								    "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      ):
								lowerCamelCase_					:      str     =		"cpu"  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase_					:      Tuple     =		self.get_dummy_components()
								lowerCamelCase_					:      str     =		StableDiffusionInpaintPipeline(**__A      )
								lowerCamelCase_					:      int     =		sd_pipe.to(__A      )
								sd_pipe.set_progress_bar_config(disable=__A      )

								lowerCamelCase_					:      str     =		self.get_dummy_inputs(__A      )
								lowerCamelCase_					:      int     =		sd_pipe(**__A      ).images
								lowerCamelCase_					:      List[Any]     =		image[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)
								lowerCamelCase_					:      Dict     =		np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]      )

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






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

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

			def        UpperCAmelCase__ (self      ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/sd2-inpaint/init_image.png'''      )
								lowerCamelCase_					:      Tuple     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
								lowerCamelCase_					:      List[Any]     =		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
								    '''/yellow_cat_sitting_on_a_park_bench.npy'''      )

								lowerCamelCase_					:      str     =		"stabilityai/stable-diffusion-2-inpainting"
								lowerCamelCase_					:      Union[str, Any]     =		StableDiffusionInpaintPipeline.from_pretrained(__A			,   safety_checker=__A      )
								pipe.to(__A      )
								pipe.set_progress_bar_config(disable=__A      )
								pipe.enable_attention_slicing()

								lowerCamelCase_					:      Union[str, Any]     =		"Face of a yellow cat, high resolution, sitting on a park bench"

								lowerCamelCase_					:      Tuple     =		torch.manual_seed(0      )
								lowerCamelCase_					:      int     =		pipe(
								    prompt=__A			,   image=__A			,   mask_image=__A			,   generator=__A			,   output_type='''np'''			,   )
								lowerCamelCase_					:      Dict     =		output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image      ).max() < 9E-3

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/sd2-inpaint/init_image.png'''      )
								lowerCamelCase_					:      List[str]     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
								lowerCamelCase_					:      str     =		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
								    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''      )

								lowerCamelCase_					:      int     =		"stabilityai/stable-diffusion-2-inpainting"
								lowerCamelCase_					:      List[Any]     =		StableDiffusionInpaintPipeline.from_pretrained(
								    __A			,   torch_dtype=torch.floataa			,   safety_checker=__A			,   )
								pipe.to(__A      )
								pipe.set_progress_bar_config(disable=__A      )
								pipe.enable_attention_slicing()

								lowerCamelCase_					:      List[str]     =		"Face of a yellow cat, high resolution, sitting on a park bench"

								lowerCamelCase_					:      Optional[Any]     =		torch.manual_seed(0      )
								lowerCamelCase_					:      Union[str, Any]     =		pipe(
								    prompt=__A			,   image=__A			,   mask_image=__A			,   generator=__A			,   output_type='''np'''			,   )
								lowerCamelCase_					:      Any     =		output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image      ).max() < 5E-1






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

								lowerCamelCase_					:      Optional[int]     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/sd2-inpaint/init_image.png'''      )
								lowerCamelCase_					:      Any     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )

								lowerCamelCase_					:      Union[str, Any]     =		"stabilityai/stable-diffusion-2-inpainting"
								lowerCamelCase_					:      List[str]     =		PNDMScheduler.from_pretrained(__A			,   subfolder='''scheduler'''      )
								lowerCamelCase_					:      Tuple     =		StableDiffusionInpaintPipeline.from_pretrained(
								    __A			,   safety_checker=__A			,   scheduler=__A			,   torch_dtype=torch.floataa			,   )
								pipe.to(__A      )
								pipe.set_progress_bar_config(disable=__A      )
								pipe.enable_attention_slicing(1      )
								pipe.enable_sequential_cpu_offload()

								lowerCamelCase_					:      Union[str, Any]     =		"Face of a yellow cat, high resolution, sitting on a park bench"

								lowerCamelCase_					:      Tuple     =		torch.manual_seed(0      )
								lowerCamelCase_					:      List[Any]     =		pipe(
								    prompt=__A			,   image=__A			,   mask_image=__A			,   generator=__A			,   num_inference_steps=2			,   output_type='''np'''			,   )

								lowerCamelCase_					:      Tuple     =		torch.cuda.max_memory_allocated()
								# make sure that less than 2.65 GB is allocated
								assert mem_bytes < 2.65 * 1_0**9




'''simple docstring'''


def lowercase_	(   _lowercase = 1_000				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Dict     =		2**power
					lowerCamelCase_					:      Union[str, Any]     =		str(_lowercase				)
					lowerCamelCase_					:      Union[str, Any]     =		list(_lowercase				)
					lowerCamelCase_					:      Dict     =		0

					for i in list_num:
										sum_of_num += int(_lowercase				)

					return sum_of_num


if __name__ == "__main__":
					__lowercase							:   Optional[Any]					=     int(input('''Enter the power of 2: ''').strip())
					print('''2 ^ ''', power, ''' = ''', 2**power)
					__lowercase							:   Tuple					=     solution(power)
					print('''Sum of the digits is: ''', result)


