


'''simple docstring'''





import math






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int   )     ->   Optional[Any]:






			"""simple docstring"""




			__A=  math.log(2,10   ) * number
			__A=  round((log_number - int(lowercase__   )),15   )

			return difference






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int = 67_8910   )     ->   Any:






			"""simple docstring"""




			__A=  90
			__A=  0

			__A=  math.log(1.23,10   )
			__A=  math.log(1.24,10   )
			__A=  0

			while position < number:
						__A=  log_difference(lowercase__   )

						if difference >= upper_limit:
									power_iterator += 93

						elif difference < lower_limit:
									power_iterator += 196

						else:
									__A=  power_iterator
									power_iterator += 196
									position += 1

			return previous_power


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					print(F"""{solution() = }""")







'''simple docstring'''





# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class 	a__				(				a_			,     a_			,     a_			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Optional[int]							= StableDiffusionControlNetInpaintPipeline
			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							List[Any]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A	:							Any							= frozenset({'''control_image'''}      )  # skip `image` and `mask` for now, only test for control_image
			A	:							List[Any]							= TEXT_TO_IMAGE_IMAGE_PARAMS
			def 				lowerCAmelCase    (     self       : Tuple  )			->		List[str]:
						torch.manual_seed(0  )
						__A=  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 ,						)
						torch.manual_seed(0  )
						__A=  ControlNetModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
						torch.manual_seed(0  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						torch.manual_seed(0  )
						__A=  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  )
						__A=  CLIPTextConfig(
						    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_000 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

						__A=  {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components
			def 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : int=0  )			->		str:
						if str(lowerCAmelCase_  ).startswith('mps'  ):
									__A=  torch.manual_seed(lowerCAmelCase_  )
						else:
									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )

						__A=  2
						__A=  randn_tensor(
						    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						)
						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]

						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )

						__A=  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'image': image,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Optional[int]:
						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )
			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available() ,						reason='XFormers attention is only available with CUDA and `xformers` installed' ,						)
			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		str:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )

			def 				lowerCAmelCase    (     self       : List[Any]  )			->		str:
						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )


class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= StableDiffusionControlNetInpaintPipeline
			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							Tuple							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A	:							int							= frozenset([]      )
			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
						torch.manual_seed(0  )
						__A=  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 ,						)
						torch.manual_seed(0  )
						__A=  ControlNetModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
						torch.manual_seed(0  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						torch.manual_seed(0  )
						__A=  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  )
						__A=  CLIPTextConfig(
						    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_000 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

						__A=  {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components


class 	a__				(				a_			,     a_			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Tuple							= StableDiffusionControlNetInpaintPipeline
			A	:							int							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							Optional[int]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			def 				lowerCAmelCase    (     self       : List[Any]  )			->		int:
						torch.manual_seed(0  )
						__A=  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 ,						)
						torch.manual_seed(0  )

						def init_weights(lowerCAmelCase_       : Optional[int]  ):
									if isinstance(lowerCAmelCase_ ,						torch.nn.Convad  ):
												torch.nn.init.normal(m.weight  )
												m.bias.data.fill_(1.0  )

						__A=  ControlNetModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
						controlneta.controlnet_down_blocks.apply(lowerCAmelCase_  )

						torch.manual_seed(0  )
						__A=  ControlNetModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						in_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						cross_attention_dim=32 ,						conditioning_embedding_out_channels=(16, 32) ,						)
						controlneta.controlnet_down_blocks.apply(lowerCAmelCase_  )

						torch.manual_seed(0  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						torch.manual_seed(0  )
						__A=  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  )
						__A=  CLIPTextConfig(
						    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_000 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__A=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

						__A=  MultiControlNetModel([controlneta, controlneta]  )

						__A=  {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components
			def 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[Any]=0  )			->		List[Any]:
						if str(lowerCAmelCase_  ).startswith('mps'  ):
									__A=  torch.manual_seed(lowerCAmelCase_  )
						else:
									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )

						__A=  2

						__A=  [
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						),
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						),
						]
						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]

						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )

						__A=  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'image': image,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Any:
						__A=  self.get_dummy_components()
						__A=  self.pipeline_class(**lowerCAmelCase_  )
						pipe.to(lowerCAmelCase_  )

						__A=  10.0
						__A=  4

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_  )[0]

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=0.1 ,						control_guidance_end=0.2  )[0]

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=[0.1, 0.3] ,						control_guidance_end=[0.2, 0.7]  )[0]

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_ ,						control_guidance_start=0.4 ,						control_guidance_end=[0.5, 0.8]  )[0]

						# make sure that all outputs are different
						assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
						assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
						assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
			def 				lowerCAmelCase    (     self       : str  )			->		Any:
						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )
			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available() ,						reason='XFormers attention is only available with CUDA and `xformers` installed' ,						)
			def 				lowerCAmelCase    (     self       : Any  )			->		int:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )
			def 				lowerCAmelCase    (     self       : Dict  )			->		Dict:
						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )

			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Optional[int]:
						__A=  self.get_dummy_components()
						__A=  self.pipeline_class(**lowerCAmelCase_  )
						pipe.to(lowerCAmelCase_  )
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
						with tempfile.TemporaryDirectory() as tmpdir:
									try:
												# save_pretrained is not implemented for Multi-ControlNet
												pipe.save_pretrained(lowerCAmelCase_  )
									except NotImplementedError:
												pass


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





			'''simple docstring'''

			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Tuple:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Tuple:
						__A=  ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'  )

						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

						__A=  torch.Generator(device='cpu'  ).manual_seed(0  )
						__A=  load_image(
						    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'  ).resize((512, 512)  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
						    '/stable_diffusion_inpaint/input_bench_mask.png'  ).resize((512, 512)  )

						__A=  'pitch black hole'

						__A=  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'  ).resize((512, 512)  )

						__A=  pipe(
						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						output_type='np' ,						num_inference_steps=3 ,						)

						__A=  output.images[0]

						assert image.shape == (512, 512, 3)

						__A=  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'  )

						assert np.abs(expected_image - image  ).max() < 9E-2

			def 				lowerCAmelCase    (     self       : str  )			->		Tuple:
						__A=  ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'  )

						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-v1-5' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
						__A=  DDIMScheduler.from_config(pipe.scheduler.config  )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

						__A=  torch.Generator(device='cpu'  ).manual_seed(33  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'  )
						__A=  init_image.resize((512, 512)  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'  )
						__A=  mask_image.resize((512, 512)  )

						__A=  'a handsome man with ray-ban sunglasses'

						def make_inpaint_condition(lowerCAmelCase_       : int ,						lowerCAmelCase_       : str  ):
									__A=  np.array(image.convert('RGB'  )  ).astype(np.floataa  ) / 2_55.0
									__A=  np.array(image_mask.convert('L'  )  ).astype(np.floataa  ) / 2_55.0

									assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
									__A=  -1.0  # set as masked pixel
									__A=  np.expand_dims(lowerCAmelCase_ ,						0  ).transpose(0 ,						3 ,						1 ,						2  )
									__A=  torch.from_numpy(lowerCAmelCase_  )
									return image

						__A=  make_inpaint_condition(lowerCAmelCase_ ,						lowerCAmelCase_  )

						__A=  pipe(
						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						guidance_scale=9.0 ,						eta=1.0 ,						generator=lowerCAmelCase_ ,						num_inference_steps=20 ,						output_type='np' ,						)
						__A=  output.images[0]

						assert image.shape == (512, 512, 3)

						__A=  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'  )

						assert np.abs(expected_image - image  ).max() < 9E-2



