'''simple docstring'''





def 	__lowerCamelCase							():
					return 1





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					return 0 if x < 0 else two_pence(x - 2  ) + one_pence()





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					return 0 if x < 0 else five_pence(x - 5  ) + two_pence(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					return 0 if x < 0 else ten_pence(x - 1_0  ) + five_pence(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					return 0 if x < 0 else twenty_pence(x - 2_0  ) + ten_pence(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					return 0 if x < 0 else fifty_pence(x - 5_0  ) + twenty_pence(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					return 0 if x < 0 else one_pound(x - 1_0_0  ) + fifty_pence(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					return 0 if x < 0 else two_pound(x - 2_0_0  ) + one_pound(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int = 2_0_0  ):
					return two_pound(UpperCAmelCase__  )


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





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image


class 						lowercase    :




							def __init__(						self   :					Any	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Dict=13	,							_UpperCamelCase   :					List[Any]=64	,							_UpperCamelCase   :					Union[str, Any]=2	,							_UpperCamelCase   :					int=3	,							_UpperCamelCase   :					Union[str, Any]=True	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Tuple=32	,							_UpperCamelCase   :					str=5	,							_UpperCamelCase   :					Tuple=4	,							_UpperCamelCase   :					Any=37	,							_UpperCamelCase   :					List[str]="gelu"	,							_UpperCamelCase   :					int=0.1	,							_UpperCamelCase   :					int=0.1	,							_UpperCamelCase   :					Optional[int]=10	,							_UpperCamelCase   :					Tuple=0.0_2	,							_UpperCamelCase   :					Union[str, Any]=[1, 16, 4, 4]	,							_UpperCamelCase   :					Optional[Any]=None	,							)					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       patch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       is_training
												SCREAMING_SNAKE_CASE    			=       use_labels
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       type_sequence_label_size
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       scope
												SCREAMING_SNAKE_CASE    			=       backbone_featmap_shape

												# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
												# the number of patches is based on the feature map of the backbone, which by default uses an output stride
												# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
												SCREAMING_SNAKE_CASE    			=       (self.image_size // 32) ** 2
												SCREAMING_SNAKE_CASE    			=       num_patches + 1




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

												SCREAMING_SNAKE_CASE    			=       None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.type_sequence_label_size      )

												SCREAMING_SNAKE_CASE    			=       self.get_config()

												return config, pixel_values, labels




							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "global_padding": "same",
												    "layer_type": "bottleneck",
												    "depths": [3, 4, 9],
												    "out_features": ["stage1", "stage2", "stage3"],
												    "embedding_dynamic_padding": True,
												    "hidden_sizes": [4, 8, 16, 32],
												    "num_groups": 2,
												}

												return ViTHybridConfig(
												    image_size=self.image_size	,							patch_size=self.patch_size	,							num_channels=self.num_channels	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							is_decoder=_UpperCamelCase	,							initializer_range=self.initializer_range	,							backbone_featmap_shape=self.backbone_featmap_shape	,							backbone_config=_UpperCamelCase	,							)




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Optional[int]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridModel(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )
												self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)      )




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.type_sequence_label_size
												SCREAMING_SNAKE_CASE    			=       ViTHybridForImageClassification(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.type_sequence_label_size)      )





							def 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       a				,       unittest.TestCase     ):
							lowercase__						:       Optional[int]								=							(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
							lowercase__						:       List[Any]								=							(
							    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
							    if is_torch_available()
							    else {}
							)
							lowercase__						:       int								=							False
							lowercase__						:       Any								=							False
							lowercase__						:       Optional[int]								=							False




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							has_text_modality=_UpperCamelCase	,							hidden_size=37      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				Any:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason="ViT does not use inputs_embeds"      )
							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	self.assertIsInstance(model.get_input_embeddings()	,							(nn.Module)      )
																	SCREAMING_SNAKE_CASE    			=       model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_UpperCamelCase	,							nn.Linear      )      )




							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       inspect.signature(model.forward      )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	SCREAMING_SNAKE_CASE    			=       [*signature.parameters.keys()]

																	SCREAMING_SNAKE_CASE    			=       ["pixel_values"]
																	self.assertListEqual(arg_names[:1]	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					List[str]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[int]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												SCREAMING_SNAKE_CASE    			=       _config_zero_init(_UpperCamelCase      )
												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(config=_UpperCamelCase      )
																	# Skip the check for the backbone
																	for name, module in model.named_modules():
																						if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																											SCREAMING_SNAKE_CASE    			=       [F"{name}.{key}" for key in module.state_dict().keys()]
																											break

																	for name, param in model.named_parameters():
																						if param.requires_grad:
																											if name in backbone_params:
																																continue
																											self.assertIn(
																											    ((param.data.mean() * 1e9).round() / 1e9).item()	,							[0.0, 1.0]	,							msg=F"Parameter {name} of model {model_class} seems not properly initialized"	,							)





							@slow
							def 	__snake_case(						self   :					Any      )					->				List[Any]:



												'''simple docstring'''

												for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE    			=       ViTHybridModel.from_pretrained(_UpperCamelCase      )
																	self.assertIsNotNone(_UpperCamelCase      )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
					return image


@require_torch
@require_vision
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												return (
												    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
												    if is_vision_available()
												    else None
												)




							@slow
							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(
												    _UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.default_image_processor
												SCREAMING_SNAKE_CASE    			=       prepare_img()
												SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      ).to(_UpperCamelCase      )

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      )

												# verify the logits
												SCREAMING_SNAKE_CASE    			=       torch.Size((1, 1_000)      )
												self.assertEqual(outputs.logits.shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]      ).to(_UpperCamelCase      )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]	,							_UpperCamelCase	,							atol=1e-4      )      )





							@slow
							@require_accelerate
							def 	__snake_case(						self   :					Optional[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"      )
												SCREAMING_SNAKE_CASE    			=       ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"	,							device_map="auto"      )

												SCREAMING_SNAKE_CASE    			=       prepare_img()

												SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      )
												SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       outputs.logits
												# model predicts one of the 1000 ImageNet classes
												SCREAMING_SNAKE_CASE    			=       logits.argmax(-1      ).item()

												self.assertTrue(model.config.idalabel[predicted_class_idx]	,							"tabby, tabby cat"      )




