
def 							__A()		->				int:

		"""simple docstring"""






		return 1


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		return 0 if x < 0 else two_pence(x - 2 ) + one_pence()


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(lowerCAmelCase )


def 							__A(lowerCAmelCase = 2_0_0 )		->				int:

		"""simple docstring"""






		return two_pound(lowerCAmelCase )


if __name__ == "__main__":
		print(solution(int(input().strip())))





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

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


								'''simple docstring'''

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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       ,							attention_head_dim=(2, 4)       ,							use_linear_projection=a       ,							)
								_UpperCamelCase						    =	PNDMScheduler(skip_prk_steps=a				)
								torch.manual_seed(0				)
								_UpperCamelCase						    =	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       ,							sample_size=1_28       ,							)
								torch.manual_seed(0				)
								_UpperCamelCase						    =	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=10_00       ,							hidden_act="""gelu"""       ,							projection_dim=5_12       ,							)
								_UpperCamelCase						    =	CLIPTextModel(a				)
								_UpperCamelCase						    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

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

						def  A_				(			self       ,							a       ,							a=0				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(a				)				).to(a				)
								_UpperCamelCase						    =	image.cpu().permute(0       ,							2       ,							3       ,							1				)[0]
								_UpperCamelCase						    =	Image.fromarray(np.uinta(a				)				).convert("""RGB"""				).resize((64, 64)				)
								_UpperCamelCase						    =	Image.fromarray(np.uinta(image + 4				)				).convert("""RGB"""				).resize((64, 64)				)
								if str(a				).startswith("""mps"""				):
										_UpperCamelCase						    =	torch.manual_seed(a				)
								else:
										_UpperCamelCase						    =	torch.Generator(device=a				).manual_seed(a				)
								_UpperCamelCase						    =	{
								    """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  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline(**a				)
								_UpperCamelCase						    =	sd_pipe.to(a				)
								sd_pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	sd_pipe(**a				).images
								_UpperCamelCase						    =	image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								_UpperCamelCase						    =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]				)

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







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







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

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


								'''simple docstring'''

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

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)
								_UpperCamelCase						    =	load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench.npy"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline.from_pretrained(a       ,							safety_checker=a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)
								pipe.enable_attention_slicing()

								_UpperCamelCase						    =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							output_type="""np"""       ,							)
								_UpperCamelCase						    =	output.images[0]

								assert image.shape == (5_12, 5_12, 3)
								assert np.abs(expected_image - image				).max() < 9e-3

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)
								_UpperCamelCase						    =	load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	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()

								_UpperCamelCase						    =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							output_type="""np"""       ,							)
								_UpperCamelCase						    =	output.images[0]

								assert image.shape == (5_12, 5_12, 3)
								assert np.abs(expected_image - image				).max() < 5e-1







						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	PNDMScheduler.from_pretrained(a       ,							subfolder="""scheduler"""				)
								_UpperCamelCase						    =	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()

								_UpperCamelCase						    =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							num_inference_steps=2       ,							output_type="""np"""       ,							)

								_UpperCamelCase						    =	torch.cuda.max_memory_allocated()
								# make sure that less than 2.65 GB is allocated
								assert mem_bytes < 2.65 * 10**9



