





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 UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						42






class  __A   (   lowerCAmelCase      ,    lowerCAmelCase			):

			'''simple docstring'''







			@register_to_config
			def __init__(   self	,  __lowerCAmelCase = None	,  __lowerCAmelCase = 3	,  __lowerCAmelCase = 3	,  __lowerCAmelCase = False	,  __lowerCAmelCase = "positional"	,  __lowerCAmelCase = 0	,  __lowerCAmelCase = True	,  __lowerCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")	,  __lowerCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")	,  __lowerCAmelCase = (2_2_4, 4_4_8, 6_7_2, 8_9_6)	,  __lowerCAmelCase = 2	,  __lowerCAmelCase = 1	,  __lowerCAmelCase = 1	,  __lowerCAmelCase = "conv"	,  __lowerCAmelCase = "conv"	,  __lowerCAmelCase = "silu"	,  __lowerCAmelCase = 8	,  __lowerCAmelCase = 3_2	,  __lowerCAmelCase = 1E-5	,  __lowerCAmelCase = "default"	,  __lowerCAmelCase = True	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				super().__init__()

				lowerCamelCase__        =		sample_size
				lowerCamelCase__        =		block_out_channels[0] * 4

				# Check inputs
				if len(__lowerCAmelCase							) != len(__lowerCAmelCase							):
					raise ValueError(
					    F'Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'							)

				if len(__lowerCAmelCase							) != len(__lowerCAmelCase							):
					raise ValueError(
					    F'Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'							)

				# input
				lowerCamelCase__        =		nn.Convad(__lowerCAmelCase	,  block_out_channels[0]	,  kernel_size=3	,  padding=(1, 1)							)

				# time
				if time_embedding_type == "fourier":
					lowerCamelCase__        =		GaussianFourierProjection(embedding_size=block_out_channels[0]	,  scale=1_6							)
					lowerCamelCase__        =		2 * block_out_channels[0]
				elif time_embedding_type == "positional":
					lowerCamelCase__        =		Timesteps(block_out_channels[0]	,  __lowerCAmelCase	,  __lowerCAmelCase							)
					lowerCamelCase__        =		block_out_channels[0]

				lowerCamelCase__        =		TimestepEmbedding(__lowerCAmelCase	,  __lowerCAmelCase							)

				# class embedding
				if class_embed_type is None and num_class_embeds is not None:
					lowerCamelCase__        =		nn.Embedding(__lowerCAmelCase	,  __lowerCAmelCase							)
				elif class_embed_type == "timestep":
					lowerCamelCase__        =		TimestepEmbedding(__lowerCAmelCase	,  __lowerCAmelCase							)
				elif class_embed_type == "identity":
					lowerCamelCase__        =		nn.Identity(__lowerCAmelCase	,  __lowerCAmelCase							)
				else:
					lowerCamelCase__        =		None

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

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

					lowerCamelCase__        =		get_down_block(
					    __lowerCAmelCase	,  num_layers=__lowerCAmelCase	,  in_channels=__lowerCAmelCase	,  out_channels=__lowerCAmelCase	,  temb_channels=__lowerCAmelCase	,  add_downsample=not is_final_block	,  resnet_eps=__lowerCAmelCase	,  resnet_act_fn=__lowerCAmelCase	,  resnet_groups=__lowerCAmelCase	,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel	,  downsample_padding=__lowerCAmelCase	,  resnet_time_scale_shift=__lowerCAmelCase	,  downsample_type=__lowerCAmelCase	,  )
					self.down_blocks.append(__lowerCAmelCase							)

				# mid
				lowerCamelCase__        =		UNetMidBlockaD(
				    in_channels=block_out_channels[-1]	,  temb_channels=__lowerCAmelCase	,  resnet_eps=__lowerCAmelCase	,  resnet_act_fn=__lowerCAmelCase	,  output_scale_factor=__lowerCAmelCase	,  resnet_time_scale_shift=__lowerCAmelCase	,  attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]	,  resnet_groups=__lowerCAmelCase	,  add_attention=__lowerCAmelCase	,  )

				# up
				lowerCamelCase__        =		list(reversed(__lowerCAmelCase							)							)
				lowerCamelCase__        =		reversed_block_out_channels[0]
				for i, up_block_type in enumerate(__lowerCAmelCase							):
					lowerCamelCase__        =		output_channel
					lowerCamelCase__        =		reversed_block_out_channels[i]
					lowerCamelCase__        =		reversed_block_out_channels[min(i + 1	,  len(__lowerCAmelCase							) - 1							)]

					lowerCamelCase__        =		i == len(__lowerCAmelCase							) - 1

					lowerCamelCase__        =		get_up_block(
					    __lowerCAmelCase	,  num_layers=layers_per_block + 1	,  in_channels=__lowerCAmelCase	,  out_channels=__lowerCAmelCase	,  prev_output_channel=__lowerCAmelCase	,  temb_channels=__lowerCAmelCase	,  add_upsample=not is_final_block	,  resnet_eps=__lowerCAmelCase	,  resnet_act_fn=__lowerCAmelCase	,  resnet_groups=__lowerCAmelCase	,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel	,  resnet_time_scale_shift=__lowerCAmelCase	,  upsample_type=__lowerCAmelCase	,  )
					self.up_blocks.append(__lowerCAmelCase							)
					lowerCamelCase__        =		output_channel

				# out
				lowerCamelCase__        =		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,  3_2							)
				lowerCamelCase__        =		nn.GroupNorm(num_channels=block_out_channels[0]	,  num_groups=__lowerCAmelCase	,  eps=__lowerCAmelCase							)
				lowerCamelCase__        =		nn.SiLU()
				lowerCamelCase__        =		nn.Convad(block_out_channels[0]	,  __lowerCAmelCase	,  kernel_size=3	,  padding=1							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







				if self.config.center_input_sample:
					lowerCamelCase__        =		2 * sample - 1.0

				# 1. time
				lowerCamelCase__        =		timestep
				if not torch.is_tensor(__lowerCAmelCase							):
					lowerCamelCase__        =		torch.tensor([timesteps]	,  dtype=torch.long	,  device=sample.device							)
				elif torch.is_tensor(__lowerCAmelCase							) and len(timesteps.shape							) == 0:
					lowerCamelCase__        =		timesteps[None].to(sample.device							)

				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
				lowerCamelCase__        =		timesteps * torch.ones(sample.shape[0]	,  dtype=timesteps.dtype	,  device=timesteps.device							)

				lowerCamelCase__        =		self.time_proj(__lowerCAmelCase							)

				# timesteps does not contain any weights and will always return f32 tensors
				# but time_embedding might actually be running in fp16. so we need to cast here.
				# there might be better ways to encapsulate this.
				lowerCamelCase__        =		t_emb.to(dtype=self.dtype							)
				lowerCamelCase__        =		self.time_embedding(__lowerCAmelCase							)

				if self.class_embedding is not None:
					if class_labels is None:
						raise ValueError('''class_labels should be provided when doing class conditioning'''							)

					if self.config.class_embed_type == "timestep":
						lowerCamelCase__        =		self.time_proj(__lowerCAmelCase							)

					lowerCamelCase__        =		self.class_embedding(__lowerCAmelCase							).to(dtype=self.dtype							)
					lowerCamelCase__        =		emb + class_emb

				# 2. pre-process
				lowerCamelCase__        =		sample
				lowerCamelCase__        =		self.conv_in(__lowerCAmelCase							)

				# 3. down
				lowerCamelCase__        =		(sample,)
				for downsample_block in self.down_blocks:
					if hasattr(__lowerCAmelCase	,  '''skip_conv'''							):
						lowerCamelCase__    ,     lowerCamelCase__    ,     lowerCamelCase__        =		downsample_block(
						    hidden_states=__lowerCAmelCase	,  temb=__lowerCAmelCase	,  skip_sample=__lowerCAmelCase							)
					else:
						lowerCamelCase__    ,     lowerCamelCase__        =		downsample_block(hidden_states=__lowerCAmelCase	,  temb=__lowerCAmelCase							)

					down_block_res_samples += res_samples

				# 4. mid
				lowerCamelCase__        =		self.mid_block(__lowerCAmelCase	,  __lowerCAmelCase							)

				# 5. up
				lowerCamelCase__        =		None
				for upsample_block in self.up_blocks:
					lowerCamelCase__        =		down_block_res_samples[-len(upsample_block.resnets							) :]
					lowerCamelCase__        =		down_block_res_samples[: -len(upsample_block.resnets							)]

					if hasattr(__lowerCAmelCase	,  '''skip_conv'''							):
						lowerCamelCase__    ,     lowerCamelCase__        =		upsample_block(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
					else:
						lowerCamelCase__        =		upsample_block(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)

        # 6. post-process
				lowerCamelCase__        =		self.conv_norm_out(__lowerCAmelCase							)
				lowerCamelCase__        =		self.conv_act(__lowerCAmelCase							)
				lowerCamelCase__        =		self.conv_out(__lowerCAmelCase							)

				if skip_sample is not None:
					sample += skip_sample

				if self.config.time_embedding_type == "fourier":
					lowerCamelCase__        =		timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]							)))							)
					lowerCamelCase__        =		sample / timesteps

				if not return_dict:
					return (sample,)

				return UNetaDOutput(sample=__lowerCAmelCase							)










import os





def lowerCAmelCase__()    ->				int:







	'''simple docstring'''







	lowerCamelCase__        =		os.path.dirname(os.path.realpath(__snake_case							)							)
	lowerCamelCase__        =		os.path.join(__snake_case	,'''triangle.txt'''							)

	with open(__snake_case							) as in_file:
		lowerCamelCase__        =		[[int(__snake_case							) for i in line.split()] for line in in_file]

	while len(__snake_case							) != 1:
		lowerCamelCase__        =		triangle.pop()
		lowerCamelCase__        =		triangle[-1]
		for j in range(len(__snake_case							) - 1							):
			curr_row[j] += max(last_row[j]	,last_row[j + 1]							)
	return triangle[0][0]


if __name__ == "__main__":
			print(solution())



