




"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


			"""simple docstring"""



			if divisor % 5 == 0 or divisor % 2 == 0:
						return 0
			__UpperCAmelCase	: List[str]		=					1
			__UpperCAmelCase	: Optional[Any]		=					1
			while repunit:
						__UpperCAmelCase	: Dict		=					(10 * repunit + 1) % divisor
						repunit_index += 1
			return repunit_index

def 					_UpperCamelCase    (			UpperCamelCase = 100_0000							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Any		=					limit - 1
			if divisor % 2 == 0:
						divisor += 1
			while least_divisible_repunit(UpperCamelCase							) <= limit:
						divisor += 2
			return divisor


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








"""simple docstring"""





import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class      a__				(	__magic_name__							,    unittest.TestCase   ):
			lowercase_    =	LDMTextToImagePipeline
			lowercase_    =	TEXT_TO_IMAGE_PARAMS - {
			    "negative_prompt",
			    "negative_prompt_embeds",
			    "cross_attention_kwargs",
			    "prompt_embeds",
			}
			lowercase_    =	PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			lowercase_    =	TEXT_TO_IMAGE_BATCH_PARAMS
			lowercase_    =	False


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						torch.manual_seed(0)
						__UpperCAmelCase	: str		=					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      ,  )
						__UpperCAmelCase	: Dict		=					DDIMScheduler(
						    beta_start=0.00085      ,  beta_end=0.012      ,  beta_schedule="scaled_linear"      ,  clip_sample=UpperCamelCase_      ,  set_alpha_to_one=UpperCamelCase_      ,  )
						torch.manual_seed(0)
						__UpperCAmelCase	: Optional[int]		=					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)
						__UpperCAmelCase	: List[str]		=					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=1000      ,  )
						__UpperCAmelCase	: List[str]		=					CLIPTextModel(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

						__UpperCAmelCase	: Optional[int]		=					{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vqvae": vae,
						    "bert": text_encoder,
						    "tokenizer": tokenizer,
						}
						return components


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Tuple=0):


						"""simple docstring"""




						if str(UpperCamelCase_).startswith("mps"):
									__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						else:
									__UpperCAmelCase	: Tuple		=					torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					"cpu"  # ensure determinism for the device-dependent torch.Generator

						__UpperCAmelCase	: Union[str, Any]		=					self.get_dummy_components()
						__UpperCAmelCase	: Union[str, Any]		=					LDMTextToImagePipeline(**UpperCamelCase_)
						pipe.to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: int		=					self.get_dummy_inputs(UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					pipe(**UpperCamelCase_).images
						__UpperCAmelCase	: List[Any]		=					image[0, -3:, -3:, -1]

						assert image.shape == (1, 16, 16, 3)
						__UpperCAmelCase	: List[str]		=					np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3






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


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Dict=0):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Optional[Any]		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: str		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					self.get_inputs(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					pipe(**UpperCamelCase_).images
						__UpperCAmelCase	: Dict		=					image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 256, 256, 3)
						__UpperCAmelCase	: List[Any]		=					np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
						__UpperCAmelCase	: List[Any]		=					np.abs(expected_slice - image_slice).max()
						assert max_diff < 1e-3






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


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




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


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Tuple      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Optional[int]=0):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: Optional[Any]		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 50,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: Union[str, Any]		=					self.get_inputs(UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					pipe(**UpperCamelCase_).images[0]

						__UpperCAmelCase	: int		=					load_numpy(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy")
						__UpperCAmelCase	: int		=					np.abs(expected_image - image).max()
						assert max_diff < 1e-3


