




from collections.abc import Sequence




def        _SCREAMING_SNAKE_CASE      (		snake_case = None       )						->				int:
						if nums is None or not nums:
												raise ValueError("""Input sequence should not be empty"""       )

						_UpperCAmelCase      =			nums[0]
						for i in range(1			,     len(snake_case       )       ):
												_UpperCAmelCase      =			nums[i]
												_UpperCAmelCase      =			max(snake_case			,     ans + num			,     snake_case       )

						return ans


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Try on a sample input from the user
				a													=			int(input("Enter number of elements : ").strip())
				a													=			list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
				print(max_subsequence_sum(array))





from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block




@dataclass
class       _A				(		__lowercase     ):
		__a					    =       42




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",)						,		_SCREAMING_SNAKE_CASE=(64,)						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE="silu"						,		_SCREAMING_SNAKE_CASE=True						,		):
								super().__init__()
								_UpperCAmelCase      =			layers_per_block

								_UpperCAmelCase      =			torch.nn.Convad(
								    _SCREAMING_SNAKE_CASE						,		block_out_channels[0]						,		kernel_size=3						,		stride=1						,		padding=1						,		)

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

								# down
								_UpperCAmelCase      =			block_out_channels[0]
								for i, down_block_type in enumerate(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			output_channel
														_UpperCAmelCase      =			block_out_channels[i]
														_UpperCAmelCase      =			i == len(_SCREAMING_SNAKE_CASE			) - 1

														_UpperCAmelCase      =			get_down_block(
														    _SCREAMING_SNAKE_CASE						,		num_layers=self.layers_per_block						,		in_channels=_SCREAMING_SNAKE_CASE						,		out_channels=_SCREAMING_SNAKE_CASE						,		add_downsample=not is_final_block						,		resnet_eps=1e-6						,		downsample_padding=0						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		attention_head_dim=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)
														self.down_blocks.append(_SCREAMING_SNAKE_CASE			)

								# mid
								_UpperCAmelCase      =			UNetMidBlockaD(
								    in_channels=block_out_channels[-1]						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		output_scale_factor=1						,		resnet_time_scale_shift="""default"""						,		attention_head_dim=block_out_channels[-1]						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)

								# out
								_UpperCAmelCase      =			nn.GroupNorm(num_channels=block_out_channels[-1]						,		num_groups=_SCREAMING_SNAKE_CASE						,		eps=1e-6			)
								_UpperCAmelCase      =			nn.SiLU()

								_UpperCAmelCase      =			2 * out_channels if double_z else out_channels
								_UpperCAmelCase      =			nn.Convad(block_out_channels[-1]						,		_SCREAMING_SNAKE_CASE						,		3						,		padding=1			)

								_UpperCAmelCase      =			False






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			x
								_UpperCAmelCase      =			self.conv_in(_SCREAMING_SNAKE_CASE			)

								if self.training and self.gradient_checkpointing:

														def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																				def custom_forward(*_SCREAMING_SNAKE_CASE			):
																										return module(*_SCREAMING_SNAKE_CASE			)

																				return custom_forward

														# down
														if is_torch_version(""">="""						,		"""1.11.0"""			):
																				for down_block in self.down_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
														else:
																				for down_block in self.down_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE			)
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE			)

								else:
														# down
														for down_block in self.down_blocks:
																				_UpperCAmelCase      =			down_block(_SCREAMING_SNAKE_CASE			)

														# middle
														_UpperCAmelCase      =			self.mid_block(_SCREAMING_SNAKE_CASE			)

								# post-process
								_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_act(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_out(_SCREAMING_SNAKE_CASE			)

								return sample




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",)						,		_SCREAMING_SNAKE_CASE=(64,)						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE="silu"						,		_SCREAMING_SNAKE_CASE="group"						,		):
								super().__init__()
								_UpperCAmelCase      =			layers_per_block

								_UpperCAmelCase      =			nn.Convad(
								    _SCREAMING_SNAKE_CASE						,		block_out_channels[-1]						,		kernel_size=3						,		stride=1						,		padding=1						,		)

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

								_UpperCAmelCase      =			in_channels if norm_type == """spatial""" else None

								# mid
								_UpperCAmelCase      =			UNetMidBlockaD(
								    in_channels=block_out_channels[-1]						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		output_scale_factor=1						,		resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type						,		attention_head_dim=block_out_channels[-1]						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)

								# up
								_UpperCAmelCase      =			list(reversed(_SCREAMING_SNAKE_CASE			)			)
								_UpperCAmelCase      =			reversed_block_out_channels[0]
								for i, up_block_type in enumerate(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			output_channel
														_UpperCAmelCase      =			reversed_block_out_channels[i]

														_UpperCAmelCase      =			i == len(_SCREAMING_SNAKE_CASE			) - 1

														_UpperCAmelCase      =			get_up_block(
														    _SCREAMING_SNAKE_CASE						,		num_layers=self.layers_per_block + 1						,		in_channels=_SCREAMING_SNAKE_CASE						,		out_channels=_SCREAMING_SNAKE_CASE						,		prev_output_channel=_SCREAMING_SNAKE_CASE						,		add_upsample=not is_final_block						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		attention_head_dim=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		resnet_time_scale_shift=_SCREAMING_SNAKE_CASE						,		)
														self.up_blocks.append(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			output_channel

								# out
								if norm_type == "spatial":
														_UpperCAmelCase      =			SpatialNorm(block_out_channels[0]						,		_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			nn.GroupNorm(num_channels=block_out_channels[0]						,		num_groups=_SCREAMING_SNAKE_CASE						,		eps=1e-6			)
								_UpperCAmelCase      =			nn.SiLU()
								_UpperCAmelCase      =			nn.Convad(block_out_channels[0]						,		_SCREAMING_SNAKE_CASE						,		3						,		padding=1			)

								_UpperCAmelCase      =			False






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_UpperCAmelCase      =			z
								_UpperCAmelCase      =			self.conv_in(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			next(iter(self.up_blocks.parameters()			)			).dtype
								if self.training and self.gradient_checkpointing:

														def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																				def custom_forward(*_SCREAMING_SNAKE_CASE			):
																										return module(*_SCREAMING_SNAKE_CASE			)

																				return custom_forward

														if is_torch_version(""">="""						,		"""1.11.0"""			):
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

																				# up
																				for up_block in self.up_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
														else:
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

																				# up
																				for up_block in self.up_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								else:
														# middle
														_UpperCAmelCase      =			self.mid_block(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

														# up
														for up_block in self.up_blocks:
																				_UpperCAmelCase      =			up_block(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

        # post-process
								if latent_embeds is None:
														_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_act(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_out(_SCREAMING_SNAKE_CASE			)

								return sample




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE="random"						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=True			):
								super().__init__()
								_UpperCAmelCase      =			n_e
								_UpperCAmelCase      =			vq_embed_dim
								_UpperCAmelCase      =			beta
								_UpperCAmelCase      =			legacy

								_UpperCAmelCase      =			nn.Embedding(self.n_e						,		self.vq_embed_dim			)
								self.embedding.weight.data.uniform_(-1.0 / self.n_e						,		1.0 / self.n_e			)

								_UpperCAmelCase      =			remap
								if self.remap is not None:
														self.register_buffer("""used"""						,		torch.tensor(np.load(self.remap			)			)			)
														_UpperCAmelCase      =			self.used.shape[0]
														_UpperCAmelCase      =			unknown_index  # "random" or "extra" or integer
														if self.unknown_index == "extra":
																				_UpperCAmelCase      =			self.re_embed
																				_UpperCAmelCase      =			self.re_embed + 1
														print(
														    F"Remapping {self.n_e} indices to {self.re_embed} indices. "
														    F"Using {self.unknown_index} for unknown indices."			)
								else:
														_UpperCAmelCase      =			n_e

								_UpperCAmelCase      =			sane_index_shape






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			inds.shape
								assert len(_SCREAMING_SNAKE_CASE			) > 1
								_UpperCAmelCase      =			inds.reshape(ishape[0]						,		-1			)
								_UpperCAmelCase      =			self.used.to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			(inds[:, :, None] == used[None, None, ...]).long()
								_UpperCAmelCase      =			match.argmax(-1			)
								_UpperCAmelCase      =			match.sum(2			) < 1
								if self.unknown_index == "random":
														_UpperCAmelCase      =			torch.randint(0						,		self.re_embed						,		size=new[unknown].shape			).to(device=new.device			)
								else:
														_UpperCAmelCase      =			self.unknown_index
								return new.reshape(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			inds.shape
								assert len(_SCREAMING_SNAKE_CASE			) > 1
								_UpperCAmelCase      =			inds.reshape(ishape[0]						,		-1			)
								_UpperCAmelCase      =			self.used.to(_SCREAMING_SNAKE_CASE			)
								if self.re_embed > self.used.shape[0]:  # extra token
														_UpperCAmelCase      =			0  # simply set to zero
								_UpperCAmelCase      =			torch.gather(used[None, :][inds.shape[0] * [0], :]						,		1						,		_SCREAMING_SNAKE_CASE			)
								return back.reshape(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								# reshape z -> (batch, height, width, channel) and flatten
								_UpperCAmelCase      =			z.permute(0						,		2						,		3						,		1			).contiguous()
								_UpperCAmelCase      =			z.view(-1						,		self.vq_embed_dim			)

								# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
								_UpperCAmelCase      =			torch.argmin(torch.cdist(_SCREAMING_SNAKE_CASE						,		self.embedding.weight			)						,		dim=1			)

								_UpperCAmelCase      =			self.embedding(_SCREAMING_SNAKE_CASE			).view(z.shape			)
								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			None

								# compute loss for embedding
								if not self.legacy:
														_UpperCAmelCase      =			self.beta * torch.mean((z_q.detach() - z) ** 2			) + torch.mean((z_q - z.detach()) ** 2			)
								else:
														_UpperCAmelCase      =			torch.mean((z_q.detach() - z) ** 2			) + self.beta * torch.mean((z_q - z.detach()) ** 2			)

								# preserve gradients
								_UpperCAmelCase      =			z + (z_q - z).detach()

								# reshape back to match original input shape
								_UpperCAmelCase      =			z_q.permute(0						,		3						,		1						,		2			).contiguous()

								if self.remap is not None:
														_UpperCAmelCase      =			min_encoding_indices.reshape(z.shape[0]						,		-1			)  # add batch axis
														_UpperCAmelCase      =			self.remap_to_used(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			min_encoding_indices.reshape(-1						,		1			)  # flatten

								if self.sane_index_shape:
														_UpperCAmelCase      =			min_encoding_indices.reshape(z_q.shape[0]						,		z_q.shape[2]						,		z_q.shape[3]			)

								return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								# shape specifying (batch, height, width, channel)
								if self.remap is not None:
														_UpperCAmelCase      =			indices.reshape(shape[0]						,		-1			)  # add batch axis
														_UpperCAmelCase      =			self.unmap_to_all(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			indices.reshape(-1			)  # flatten again

								# get quantized latent vectors
								_UpperCAmelCase      =			self.embedding(_SCREAMING_SNAKE_CASE			)

								if shape is not None:
														_UpperCAmelCase      =			z_q.view(_SCREAMING_SNAKE_CASE			)
														# reshape back to match original input shape
														_UpperCAmelCase      =			z_q.permute(0						,		3						,		1						,		2			).contiguous()

								return z_q




class       _A				(		__lowercase     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								_UpperCAmelCase      =			parameters
								_UpperCAmelCase    ,					_UpperCAmelCase      =			torch.chunk(_SCREAMING_SNAKE_CASE						,		2						,		dim=1			)
								_UpperCAmelCase      =			torch.clamp(self.logvar						,		-30.0						,		20.0			)
								_UpperCAmelCase      =			deterministic
								_UpperCAmelCase      =			torch.exp(0.5 * self.logvar			)
								_UpperCAmelCase      =			torch.exp(self.logvar			)
								if self.deterministic:
														_UpperCAmelCase      =			_UpperCAmelCase      =			torch.zeros_like(
														    self.mean						,		device=self.parameters.device						,		dtype=self.parameters.dtype			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE = None			):
								# make sure sample is on the same device as the parameters and has same dtype
								_UpperCAmelCase      =			randn_tensor(
								    self.mean.shape						,		generator=_SCREAMING_SNAKE_CASE						,		device=self.parameters.device						,		dtype=self.parameters.dtype			)
								_UpperCAmelCase      =			self.mean + self.std * sample
								return x






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None			):
								if self.deterministic:
														return torch.Tensor([0.0]			)
								else:
														if other is None:
																				return 0.5 * torch.sum(torch.pow(self.mean						,		2			) + self.var - 1.0 - self.logvar						,		dim=[1, 2, 3]			)
														else:
																				return 0.5 * torch.sum(
																				    torch.pow(self.mean - other.mean						,		2			) / other.var
																				    + self.var / other.var
																				    - 1.0
																				    - self.logvar
																				    + other.logvar						,		dim=[1, 2, 3]						,		)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=[1, 2, 3]			):
								if self.deterministic:
														return torch.Tensor([0.0]			)
								_UpperCAmelCase      =			np.log(2.0 * np.pi			)
								return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean						,		2			) / self.var						,		dim=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								return self.mean