





def  SCREAMING_SNAKE_CASE   ( )    ->  int:
    return 1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    return 0 if x < 0 else two_pence(x - 2				) + one_pence()


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    return 0 if x < 0 else five_pence(x - 5				) + two_pence(_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    return 0 if x < 0 else ten_pence(x - 10				) + five_pence(_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    return 0 if x < 0 else twenty_pence(x - 20				) + ten_pence(_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    return 0 if x < 0 else fifty_pence(x - 50				) + twenty_pence(_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    return 0 if x < 0 else one_pound(x - 100				) + fifty_pence(_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    return 0 if x < 0 else two_pound(x - 200				) + one_pound(_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 200				)    ->  int:
    return two_pound(_UpperCAmelCase				)


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












from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)

@flax.struct.dataclass
class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	42


@flax_register_to_config
class      lowerCAmelCase							(      nn.Module,						__UpperCamelCase,						__UpperCamelCase    ):
 UpperCAmelCase__									=	32
 UpperCAmelCase__									=	4
 UpperCAmelCase__									=	4
 UpperCAmelCase__									=	(
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "DownBlock2D",
 )
 UpperCAmelCase__									=	("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	(3_20, 6_40, 12_80, 12_80)
 UpperCAmelCase__									=	2
 UpperCAmelCase__									=	8
 UpperCAmelCase__									=	None
 UpperCAmelCase__									=	12_80
 UpperCAmelCase__									=	0.0
 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	jnp.floataa
 UpperCAmelCase__									=	True
 UpperCAmelCase__									=	0
 UpperCAmelCase__									=	False
 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   jax.random.KeyArray			)    ->			FrozenDict:
     # init input tensors
     lowerCamelCase__					:   int      				=	(1, self.in_channels, self.sample_size, self.sample_size)
     lowerCamelCase__					:   List[str]      				=	jnp.zeros(UpperCAmelCase    ,  dtype=jnp.floataa			)
     lowerCamelCase__					:   Tuple      				=	jnp.ones((1,)    ,  dtype=jnp.intaa			)
     lowerCamelCase__					:   Dict      				=	jnp.zeros((1, 1, self.cross_attention_dim)    ,  dtype=jnp.floataa			)

     lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	jax.random.split(UpperCAmelCase			)
     lowerCamelCase__					:   Dict      				=	{'params': params_rng, 'dropout': dropout_rng}

     return self.init(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)["params"]
 def   A_			(   self						:   Tuple			)    ->			Optional[int]:
     lowerCamelCase__					:   Any      				=	self.block_out_channels
     lowerCamelCase__					:   int      				=	block_out_channels[0] * 4

     if self.num_attention_heads is not None:
         raise ValueError(
             'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'			)

     # If `num_attention_heads` is not defined (which is the case for most models)
     # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
     # The reason for this behavior is to correct for incorrectly named variables that were introduced
     # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
     # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
     # which is why we correct for the naming here.
     lowerCamelCase__					:   Tuple      				=	self.num_attention_heads or self.attention_head_dim

     # input
     lowerCamelCase__					:   Optional[Any]      				=	nn.Conv(
         block_out_channels[0]    ,  kernel_size=(3, 3)    ,  strides=(1, 1)    ,  padding=((1, 1), (1, 1))    ,  dtype=self.dtype    ,  )

     # time
     lowerCamelCase__					:   Optional[int]      				=	FlaxTimesteps(
         block_out_channels[0]    ,  flip_sin_to_cos=self.flip_sin_to_cos    ,  freq_shift=self.config.freq_shift			)
     lowerCamelCase__					:   int      				=	FlaxTimestepEmbedding(UpperCAmelCase    ,  dtype=self.dtype			)

     lowerCamelCase__					:   Optional[int]      				=	self.only_cross_attention
     if isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
         lowerCamelCase__					:   str      				=	(only_cross_attention,) * len(self.down_block_types			)

     if isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
         lowerCamelCase__					:   List[Any]      				=	(num_attention_heads,) * len(self.down_block_types			)

     # down
     lowerCamelCase__					:   List[Any]      				=	[]
     lowerCamelCase__					:   Dict      				=	block_out_channels[0]
     for i, down_block_type in enumerate(self.down_block_types			):
         lowerCamelCase__					:   Dict      				=	output_channel
         lowerCamelCase__					:   Optional[int]      				=	block_out_channels[i]
         lowerCamelCase__					:   List[Any]      				=	i == len(UpperCAmelCase			) - 1

         if down_block_type == "CrossAttnDownBlock2D":
             lowerCamelCase__					:   Tuple      				=	FlaxCrossAttnDownBlockaD(
                 in_channels=UpperCAmelCase    ,  out_channels=UpperCAmelCase    ,  dropout=self.dropout    ,  num_layers=self.layers_per_block    ,  num_attention_heads=num_attention_heads[i]    ,  add_downsample=not is_final_block    ,  use_linear_projection=self.use_linear_projection    ,  only_cross_attention=only_cross_attention[i]    ,  use_memory_efficient_attention=self.use_memory_efficient_attention    ,  dtype=self.dtype    ,  )
         else:
             lowerCamelCase__					:   str      				=	FlaxDownBlockaD(
                 in_channels=UpperCAmelCase    ,  out_channels=UpperCAmelCase    ,  dropout=self.dropout    ,  num_layers=self.layers_per_block    ,  add_downsample=not is_final_block    ,  dtype=self.dtype    ,  )

         down_blocks.append(UpperCAmelCase			)
     lowerCamelCase__					:   List[Any]      				=	down_blocks

     # mid
     lowerCamelCase__					:   Dict      				=	FlaxUNetMidBlockaDCrossAttn(
         in_channels=block_out_channels[-1]    ,  dropout=self.dropout    ,  num_attention_heads=num_attention_heads[-1]    ,  use_linear_projection=self.use_linear_projection    ,  use_memory_efficient_attention=self.use_memory_efficient_attention    ,  dtype=self.dtype    ,  )

     # up
     lowerCamelCase__					:   Any      				=	[]
     lowerCamelCase__					:   Optional[int]      				=	list(reversed(UpperCAmelCase			)			)
     lowerCamelCase__					:   Any      				=	list(reversed(UpperCAmelCase			)			)
     lowerCamelCase__					:   int      				=	list(reversed(UpperCAmelCase			)			)
     lowerCamelCase__					:   Tuple      				=	reversed_block_out_channels[0]
     for i, up_block_type in enumerate(self.up_block_types			):
         lowerCamelCase__					:   str      				=	output_channel
         lowerCamelCase__					:   int      				=	reversed_block_out_channels[i]
         lowerCamelCase__					:   int      				=	reversed_block_out_channels[min(i + 1    ,  len(UpperCAmelCase			) - 1			)]

         lowerCamelCase__					:   Optional[Any]      				=	i == len(UpperCAmelCase			) - 1

         if up_block_type == "CrossAttnUpBlock2D":
             lowerCamelCase__					:   Tuple      				=	FlaxCrossAttnUpBlockaD(
                 in_channels=UpperCAmelCase    ,  out_channels=UpperCAmelCase    ,  prev_output_channel=UpperCAmelCase    ,  num_layers=self.layers_per_block + 1    ,  num_attention_heads=reversed_num_attention_heads[i]    ,  add_upsample=not is_final_block    ,  dropout=self.dropout    ,  use_linear_projection=self.use_linear_projection    ,  only_cross_attention=only_cross_attention[i]    ,  use_memory_efficient_attention=self.use_memory_efficient_attention    ,  dtype=self.dtype    ,  )
         else:
             lowerCamelCase__					:   Optional[Any]      				=	FlaxUpBlockaD(
                 in_channels=UpperCAmelCase    ,  out_channels=UpperCAmelCase    ,  prev_output_channel=UpperCAmelCase    ,  num_layers=self.layers_per_block + 1    ,  add_upsample=not is_final_block    ,  dropout=self.dropout    ,  dtype=self.dtype    ,  )

         up_blocks.append(UpperCAmelCase			)
         lowerCamelCase__					:   Tuple      				=	output_channel
     lowerCamelCase__					:   Tuple      				=	up_blocks

     # out
     lowerCamelCase__					:   Optional[int]      				=	nn.GroupNorm(num_groups=32    ,  epsilon=1e-5			)
     lowerCamelCase__					:   Any      				=	nn.Conv(
         self.out_channels    ,  kernel_size=(3, 3)    ,  strides=(1, 1)    ,  padding=((1, 1), (1, 1))    ,  dtype=self.dtype    ,  )







 def __call__(   self						:   int    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   Optional[Any]=None    ,  UpperCAmelCase						:   Dict=None    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   bool = False    ,  )    ->			Union[FlaxUNetaDConditionOutput, Tuple]:
     # 1. time
     if not isinstance(UpperCAmelCase    ,  jnp.ndarray			):
         lowerCamelCase__					:   List[str]      				=	jnp.array([timesteps]    ,  dtype=jnp.intaa			)
     elif isinstance(UpperCAmelCase    ,  jnp.ndarray			) and len(timesteps.shape			) == 0:
         lowerCamelCase__					:   List[Any]      				=	timesteps.astype(dtype=jnp.floataa			)
         lowerCamelCase__					:   Any      				=	jnp.expand_dims(UpperCAmelCase    ,  0			)

     lowerCamelCase__					:   List[str]      				=	self.time_proj(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	self.time_embedding(UpperCAmelCase			)

     # 2. pre-process
     lowerCamelCase__					:   Dict      				=	jnp.transpose(UpperCAmelCase    ,  (0, 2, 3, 1)			)
     lowerCamelCase__					:   Optional[Any]      				=	self.conv_in(UpperCAmelCase			)

     # 3. down
     lowerCamelCase__					:   Any      				=	(sample,)
     for down_block in self.down_blocks:
         if isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
             lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	down_block(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  deterministic=not train			)
         else:
             lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	down_block(UpperCAmelCase    ,  UpperCAmelCase    ,  deterministic=not train			)
         down_block_res_samples += res_samples

     if down_block_additional_residuals is not None:
         lowerCamelCase__					:   Union[str, Any]      				=	()

         for down_block_res_sample, down_block_additional_residual in zip(
             UpperCAmelCase    ,  UpperCAmelCase			):
             down_block_res_sample += down_block_additional_residual
             new_down_block_res_samples += (down_block_res_sample,)

         lowerCamelCase__					:   str      				=	new_down_block_res_samples

     # 4. mid
     lowerCamelCase__					:   List[Any]      				=	self.mid_block(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  deterministic=not train			)

     if mid_block_additional_residual is not None:
         sample += mid_block_additional_residual

     # 5. up
     for up_block in self.up_blocks:
         lowerCamelCase__					:   str      				=	down_block_res_samples[-(self.layers_per_block + 1) :]
         lowerCamelCase__					:   List[str]      				=	down_block_res_samples[: -(self.layers_per_block + 1)]
         if isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
             lowerCamelCase__					:   List[Any]      				=	up_block(
                 UpperCAmelCase    ,  temb=UpperCAmelCase    ,  encoder_hidden_states=UpperCAmelCase    ,  res_hidden_states_tuple=UpperCAmelCase    ,  deterministic=not train    ,  )
         else:
             lowerCamelCase__					:   int      				=	up_block(UpperCAmelCase    ,  temb=UpperCAmelCase    ,  res_hidden_states_tuple=UpperCAmelCase    ,  deterministic=not train			)

        # 6. post-process
     lowerCamelCase__					:   str      				=	self.conv_norm_out(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	nn.silu(UpperCAmelCase			)
     lowerCamelCase__					:   Any      				=	self.conv_out(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	jnp.transpose(UpperCAmelCase    ,  (0, 3, 1, 2)			)

     if not return_dict:
         return (sample,)

     return FlaxUNetaDConditionOutput(sample=UpperCAmelCase			)





