



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 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 6_5536    ,a_ = None    ,a_ = 2    ,a_ = 2    ,a_ = 0    ,a_ = "fourier"    ,a_ = True    ,a_ = False    ,a_ = 0.0    ,a_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")    ,a_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")    ,a_ = "UNetMidBlock1D"    ,a_ = None    ,a_ = (32, 32, 64)    ,a_ = None    ,a_ = 8    ,a_ = 1    ,a_ = False    ,):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  sample_size

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

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

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

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

										if i == 0:
														input_channel += extra_in_channels

										lowerCAmelCase__		  =  i == len(a_						) - 1

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

						# mid
						lowerCAmelCase__		  =  get_mid_block(
						    a_    ,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=a_    ,add_downsample=a_    ,)

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

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

										lowerCAmelCase__		  =  i == len(a_						) - 1

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

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



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  timestep
						if not torch.is_tensor(a_						):
										lowerCAmelCase__		  =  torch.tensor([timesteps]    ,dtype=torch.long    ,device=sample.device						)
						elif torch.is_tensor(a_						) and len(timesteps.shape						) == 0:
										lowerCAmelCase__		  =  timesteps[None].to(sample.device						)

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

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

						# 3. mid
						if self.mid_block:
										lowerCAmelCase__		  =  self.mid_block(a_    ,a_						)

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

						# 5. post-process
						if self.out_block:
										lowerCAmelCase__		  =  self.out_block(a_    ,a_						)

						if not return_dict:
										return (sample,)

						return UNetaDOutput(sample=a_						)





def     UpperCAmelCase_		(					)	->     int:



				"""simple docstring"""





				return 1







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__ = 200					)	->     int:



				"""simple docstring"""





				return two_pound(snake_case__					)


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