
def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int = 99_80_01					)     ->    int:
						_lowercase									=				0
						for i in range(9_99		,      99		,      -1					):  # 3 digit numbers range from 999 down to 100
												for j in range(9_99		,      99		,      -1					):
																		_lowercase									=				str(i * j					)
																		if product_string == product_string[::-1] and i * j < n:
																								_lowercase									=				max(SCREAMING_SNAKE_CASE_		,      i * j					)
						return answer


if __name__ == "__main__":
		print(F'{solution() = }')

# 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						):
			a  : Optional[int]				      =       StableDiffusionControlNetInpaintPipeline
			a  : Union[str, Any]				      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			a  : List[Any]				      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			a  : int				      =       frozenset({'''control_image'''}						)  # skip `image` and `mask` for now, only test for control_image
			a  : int				      =       TEXT_TO_IMAGE_IMAGE_PARAMS






			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )
									_lowercase									=				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     )
									_lowercase									=				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     )
									_lowercase									=				DDIMScheduler(
									    beta_start=0.0_00_85							, beta_end=0.0_12							, beta_schedule="""scaled_linear"""							, clip_sample=__UpperCamelCase							, set_alpha_to_one=__UpperCamelCase							, )
									torch.manual_seed(0     )
									_lowercase									=				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     )
									_lowercase									=				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							, )
									_lowercase									=				CLIPTextModel(__UpperCamelCase     )
									_lowercase									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

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






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase=0     ):
									if str(__UpperCamelCase     ).startswith("""mps"""     ):
															_lowercase									=				torch.manual_seed(__UpperCamelCase     )
									else:
															_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )

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

									_lowercase									=				Image.fromarray(np.uinta(__UpperCamelCase     )     ).convert("""RGB"""     ).resize((64, 64)     )
									_lowercase									=				Image.fromarray(np.uinta(init_image + 4     )     ).convert("""RGB"""     ).resize((64, 64)     )

									_lowercase									=				{
									    """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       UpperCamelCase_							(			self     ):
									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       UpperCamelCase_							(			self     ):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )






			def       UpperCamelCase_							(			self     ):
									self._test_inference_batch_single_identical(expected_max_diff=2E-3     )







class       a_   ( _a						):
			a  : List[str]				      =       StableDiffusionControlNetInpaintPipeline
			a  : Any				      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			a  : int				      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			a  : Union[str, Any]				      =       frozenset([]						)






			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )
									_lowercase									=				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     )
									_lowercase									=				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     )
									_lowercase									=				DDIMScheduler(
									    beta_start=0.0_00_85							, beta_end=0.0_12							, beta_schedule="""scaled_linear"""							, clip_sample=__UpperCamelCase							, set_alpha_to_one=__UpperCamelCase							, )
									torch.manual_seed(0     )
									_lowercase									=				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     )
									_lowercase									=				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							, )
									_lowercase									=				CLIPTextModel(__UpperCamelCase     )
									_lowercase									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

									_lowercase									=				{
									    """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						):
			a  : str				      =       StableDiffusionControlNetInpaintPipeline
			a  : Any				      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			a  : Optional[Any]				      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )
									_lowercase									=				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(__UpperCamelCase     ):
															if isinstance(__UpperCamelCase							, torch.nn.Convad     ):
																					torch.nn.init.normal(m.weight     )
																					m.bias.data.fill_(1.0     )

									_lowercase									=				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(__UpperCamelCase     )

									torch.manual_seed(0     )
									_lowercase									=				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(__UpperCamelCase     )

									torch.manual_seed(0     )
									_lowercase									=				DDIMScheduler(
									    beta_start=0.0_00_85							, beta_end=0.0_12							, beta_schedule="""scaled_linear"""							, clip_sample=__UpperCamelCase							, set_alpha_to_one=__UpperCamelCase							, )
									torch.manual_seed(0     )
									_lowercase									=				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     )
									_lowercase									=				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							, )
									_lowercase									=				CLIPTextModel(__UpperCamelCase     )
									_lowercase									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

									_lowercase									=				MultiControlNetModel([controlneta, controlneta]     )

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






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase=0     ):
									if str(__UpperCamelCase     ).startswith("""mps"""     ):
															_lowercase									=				torch.manual_seed(__UpperCamelCase     )
									else:
															_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )

									_lowercase									=				2

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

									_lowercase									=				Image.fromarray(np.uinta(__UpperCamelCase     )     ).convert("""RGB"""     ).resize((64, 64)     )
									_lowercase									=				Image.fromarray(np.uinta(init_image + 4     )     ).convert("""RGB"""     ).resize((64, 64)     )

									_lowercase									=				{
									    """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       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_dummy_components()
									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									pipe.to(__UpperCamelCase     )

									_lowercase									=				10.0
									_lowercase									=				4

									_lowercase									=				self.get_dummy_inputs(__UpperCamelCase     )
									_lowercase									=				steps
									_lowercase									=				scale
									_lowercase									=				pipe(**__UpperCamelCase     )[0]

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

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

									_lowercase									=				self.get_dummy_inputs(__UpperCamelCase     )
									_lowercase									=				steps
									_lowercase									=				scale
									_lowercase									=				pipe(**__UpperCamelCase							, 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       UpperCamelCase_							(			self     ):
									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       UpperCamelCase_							(			self     ):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )






			def       UpperCamelCase_							(			self     ):
									self._test_inference_batch_single_identical(expected_max_diff=2E-3     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_dummy_components()
									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									pipe.to(__UpperCamelCase     )
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )
									with tempfile.TemporaryDirectory() as tmpdir:
															try:
																					# save_pretrained is not implemented for Multi-ControlNet
																					pipe.save_pretrained(__UpperCamelCase     )
															except NotImplementedError:
																					pass







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






			def       UpperCamelCase_							(			self     ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""     )

									_lowercase									=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    """runwayml/stable-diffusion-inpainting"""							, safety_checker=__UpperCamelCase							, controlnet=__UpperCamelCase     )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

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

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

									_lowercase									=				"""pitch black hole"""

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

									_lowercase									=				pipe(
									    __UpperCamelCase							, image=__UpperCamelCase							, mask_image=__UpperCamelCase							, control_image=__UpperCamelCase							, generator=__UpperCamelCase							, output_type="""np"""							, num_inference_steps=3							, )

									_lowercase									=				output.images[0]

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

									_lowercase									=				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       UpperCamelCase_							(			self     ):
									_lowercase									=				ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""     )

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

									_lowercase									=				torch.Generator(device="""cpu"""     ).manual_seed(33     )

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

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

									_lowercase									=				"""a handsome man with ray-ban sunglasses"""

									def make_inpaint_condition(__UpperCamelCase							, __UpperCamelCase     ):
															_lowercase									=				np.array(image.convert("""RGB"""     )     ).astype(np.floataa     ) / 2_55.0
															_lowercase									=				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"
															_lowercase									=				-1.0  # set as masked pixel
															_lowercase									=				np.expand_dims(__UpperCamelCase							, 0     ).transpose(0							, 3							, 1							, 2     )
															_lowercase									=				torch.from_numpy(__UpperCamelCase     )
															return image

									_lowercase									=				make_inpaint_condition(__UpperCamelCase							, __UpperCamelCase     )

									_lowercase									=				pipe(
									    __UpperCamelCase							, image=__UpperCamelCase							, mask_image=__UpperCamelCase							, control_image=__UpperCamelCase							, guidance_scale=9.0							, eta=1.0							, generator=__UpperCamelCase							, num_inference_steps=20							, output_type="""np"""							, )
									_lowercase									=				output.images[0]

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

									_lowercase									=				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