


"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class 			lowerCamelCase__						(   snake_case__			):
						lowerCAmelCase       =     42



class 			lowerCamelCase__						(   snake_case__							,    snake_case__			):







						@register_to_config
						def __init__(							self   :    Tuple			,			_lowercase   :    int = 65_536			,			_lowercase   :    Optional[int] = None			,			_lowercase   :    int = 2			,			_lowercase   :    int = 2			,			_lowercase   :    int = 0			,			_lowercase   :    str = "fourier"			,			_lowercase   :    bool = True			,			_lowercase   :    bool = False			,			_lowercase   :    float = 0.0			,			_lowercase   :    Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,			_lowercase   :    Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,			_lowercase   :    Tuple[str] = "UNetMidBlock1D"			,			_lowercase   :    str = None			,			_lowercase   :    Tuple[int] = (32, 32, 64)			,			_lowercase   :    str = None			,			_lowercase   :    int = 8			,			_lowercase   :    int = 1			,			_lowercase   :    bool = False			,			):
								super().__init__()
								A					       = sample_size

								# time
								if time_embedding_type == "fourier":
										A					       = GaussianFourierProjection(
										    embedding_size=8			,			set_W_to_weight=UpperCAmelCase_			,			log=UpperCAmelCase_			,			flip_sin_to_cos=UpperCAmelCase_		)
										A					       = 2 * block_out_channels[0]
								elif time_embedding_type == "positional":
										A					       = Timesteps(
										    block_out_channels[0]			,			flip_sin_to_cos=UpperCAmelCase_			,			downscale_freq_shift=UpperCAmelCase_		)
										A					       = block_out_channels[0]

								if use_timestep_embedding:
										A					       = block_out_channels[0] * 4
										A					       = TimestepEmbedding(
										    in_channels=UpperCAmelCase_			,			time_embed_dim=UpperCAmelCase_			,			act_fn=UpperCAmelCase_			,			out_dim=block_out_channels[0]			,			)

								A					       = nn.ModuleList([]		)
								A					       = None
								A					       = nn.ModuleList([]		)
								A					       = None

								# down
								A					       = in_channels
								for i, down_block_type in enumerate(UpperCAmelCase_		):
										A					       = output_channel
										A					       = block_out_channels[i]

										if i == 0:
												input_channel += extra_in_channels

										A					       = i == len(UpperCAmelCase_		) - 1

										A					       = get_down_block(
										    UpperCAmelCase_			,			num_layers=UpperCAmelCase_			,			in_channels=UpperCAmelCase_			,			out_channels=UpperCAmelCase_			,			temb_channels=block_out_channels[0]			,			add_downsample=not is_final_block or downsample_each_block			,			)
										self.down_blocks.append(UpperCAmelCase_		)

								# mid
								A					       = get_mid_block(
								    UpperCAmelCase_			,			in_channels=block_out_channels[-1]			,			mid_channels=block_out_channels[-1]			,			out_channels=block_out_channels[-1]			,			embed_dim=block_out_channels[0]			,			num_layers=UpperCAmelCase_			,			add_downsample=UpperCAmelCase_			,			)

								# up
								A					       = list(reversed(UpperCAmelCase_		)		)
								A					       = reversed_block_out_channels[0]
								if out_block_type is None:
										A					       = out_channels
								else:
										A					       = block_out_channels[0]

								for i, up_block_type in enumerate(UpperCAmelCase_		):
										A					       = output_channel
										A					       = (
										    reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase_		) - 1 else final_upsample_channels
										)

										A					       = i == len(UpperCAmelCase_		) - 1

										A					       = get_up_block(
										    UpperCAmelCase_			,			num_layers=UpperCAmelCase_			,			in_channels=UpperCAmelCase_			,			out_channels=UpperCAmelCase_			,			temb_channels=block_out_channels[0]			,			add_upsample=not is_final_block			,			)
										self.up_blocks.append(UpperCAmelCase_		)
										A					       = output_channel

								# out
								A					       = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,			32		)
								A					       = get_out_block(
								    out_block_type=UpperCAmelCase_			,			num_groups_out=UpperCAmelCase_			,			embed_dim=block_out_channels[0]			,			out_channels=UpperCAmelCase_			,			act_fn=UpperCAmelCase_			,			fc_dim=block_out_channels[-1] // 4			,			)







						def __a						(							self   :    Any			,			_lowercase   :    torch.FloatTensor			,			_lowercase   :    Union[torch.Tensor, float, int]			,			_lowercase   :    bool = True			,			):
								A					       = timestep
								if not torch.is_tensor(UpperCAmelCase_		):
										A					       = torch.tensor([timesteps]			,			dtype=torch.long			,			device=sample.device		)
								elif torch.is_tensor(UpperCAmelCase_		) and len(timesteps.shape		) == 0:
										A					       = timesteps[None].to(sample.device		)

								A					       = self.time_proj(UpperCAmelCase_		)
								if self.config.use_timestep_embedding:
										A					       = self.time_mlp(UpperCAmelCase_		)
								else:
										A					       = timestep_embed[..., None]
										A					       = timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
										A					       = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

								# 2. down
								A					       = ()
								for downsample_block in self.down_blocks:
										A       , A					       = downsample_block(hidden_states=UpperCAmelCase_			,			temb=UpperCAmelCase_		)
										down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
										A					       = self.mid_block(UpperCAmelCase_			,			UpperCAmelCase_		)

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks		):
										A					       = down_block_res_samples[-1:]
										A					       = down_block_res_samples[:-1]
										A					       = upsample_block(UpperCAmelCase_			,			res_hidden_states_tuple=UpperCAmelCase_			,			temb=UpperCAmelCase_		)

								# 5. post-process
								if self.out_block:
										A					       = self.out_block(UpperCAmelCase_			,			UpperCAmelCase_		)

								if not return_dict:
										return (sample,)

								return UNetaDOutput(sample=UpperCAmelCase_		)






"""simple docstring"""


def  __snake_case       (   )      ->		int:







		"""simple docstring"""





		return 1







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





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







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





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







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else ten_pence(x - 10	) + five_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else twenty_pence(x - 20	) + ten_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else fifty_pence(x - 50	) + twenty_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else one_pound(x - 100	) + fifty_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else two_pound(x - 200	) + one_pound(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__ = 200	)      ->		int:







		"""simple docstring"""





		return two_pound(UpperCamelCase__	)


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





