code: 
  
  
  
  
  
  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   _UpperCAmelCase	(   _lowercase					):
  					__A          =   42
  
  
  
  
  class   _UpperCAmelCase	(   _lowercase ,       _lowercase					):
  
  
  
  
  					@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 = (224, 448, 672, 896)    ,      __lowerCAmelCase = 2    ,      __lowerCAmelCase = 1    ,      __lowerCAmelCase = 1    ,      __lowerCAmelCase = "conv"    ,      __lowerCAmelCase = "conv"    ,      __lowerCAmelCase = "silu"    ,      __lowerCAmelCase = 8    ,      __lowerCAmelCase = 32    ,      __lowerCAmelCase = 1E-5    ,      __lowerCAmelCase = "default"    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      )						->  Any:
  							super().__init__()
  
  							UpperCamelCase__         =	sample_size
  							UpperCamelCase__         =	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
  							UpperCamelCase__         =	nn.Convad(__lowerCAmelCase    ,      block_out_channels[0]    ,      kernel_size=3    ,      padding=(1, 1)       )
  
  							# time
  							if time_embedding_type == "fourier":
  									UpperCamelCase__         =	GaussianFourierProjection(embedding_size=block_out_channels[0]    ,      scale=16       )
  									UpperCamelCase__         =	2 * block_out_channels[0]
  							elif time_embedding_type == "positional":
  									UpperCamelCase__         =	Timesteps(block_out_channels[0]    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  									UpperCamelCase__         =	block_out_channels[0]
  
  							UpperCamelCase__         =	TimestepEmbedding(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
  							# class embedding
  							if class_embed_type is None and num_class_embeds is not None:
  									UpperCamelCase__         =	nn.Embedding(__lowerCAmelCase    ,      __lowerCAmelCase       )
  							elif class_embed_type == "timestep":
  									UpperCamelCase__         =	TimestepEmbedding(__lowerCAmelCase    ,      __lowerCAmelCase       )
  							elif class_embed_type == "identity":
  									UpperCamelCase__         =	nn.Identity(__lowerCAmelCase    ,      __lowerCAmelCase       )
  							else:
  									UpperCamelCase__         =	None
  
  							UpperCamelCase__         =	nn.ModuleList([]       )
  							UpperCamelCase__         =	None
  							UpperCamelCase__         =	nn.ModuleList([]       )
  
  							# down
  							UpperCamelCase__         =	block_out_channels[0]
  							for i, down_block_type in enumerate(__lowerCAmelCase       ):
  									UpperCamelCase__         =	output_channel
  									UpperCamelCase__         =	block_out_channels[i]
  									UpperCamelCase__         =	i == len(__lowerCAmelCase       ) - 1
  
  									UpperCamelCase__         =	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
  							UpperCamelCase__         =	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
  							UpperCamelCase__         =	list(reversed(__lowerCAmelCase       )       )
  							UpperCamelCase__         =	reversed_block_out_channels[0]
  							for i, up_block_type in enumerate(__lowerCAmelCase       ):
  									UpperCamelCase__         =	output_channel
  									UpperCamelCase__         =	reversed_block_out_channels[i]
  									UpperCamelCase__         =	reversed_block_out_channels[min(i + 1    ,      len(__lowerCAmelCase       ) - 1       )]
  
  									UpperCamelCase__         =	i == len(__lowerCAmelCase       ) - 1
  
  									UpperCamelCase__         =	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       )
  									UpperCamelCase__         =	output_channel
  
  							# out
  							UpperCamelCase__         =	norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,      32       )
  							UpperCamelCase__         =	nn.GroupNorm(num_channels=block_out_channels[0]    ,      num_groups=__lowerCAmelCase    ,      eps=__lowerCAmelCase       )
  							UpperCamelCase__         =	nn.SiLU()
  							UpperCamelCase__         =	nn.Convad(block_out_channels[0]    ,      __lowerCAmelCase    ,      kernel_size=3    ,      padding=1       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = True    ,      )						->  Union[UNetaDOutput, Tuple]:
  							# 0. center input if necessary
  							if self.config.center_input_sample:
  									UpperCamelCase__         =	2 * sample - 1.0
  
  							# 1. time
  							UpperCamelCase__         =	timestep
  							if not torch.is_tensor(__lowerCAmelCase       ):
  									UpperCamelCase__         =	torch.tensor([timesteps]    ,      dtype=torch.long    ,      device=sample.device       )
  							elif torch.is_tensor(__lowerCAmelCase       ) and len(timesteps.shape       ) == 0:
  									UpperCamelCase__         =	timesteps[None].to(sample.device       )
  
  							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
  							UpperCamelCase__         =	timesteps * torch.ones(sample.shape[0]    ,      dtype=timesteps.dtype    ,      device=timesteps.device       )
  
  							UpperCamelCase__         =	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.
  							UpperCamelCase__         =	t_emb.to(dtype=self.dtype       )
  							UpperCamelCase__         =	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":
  											UpperCamelCase__         =	self.time_proj(__lowerCAmelCase       )
  
  									UpperCamelCase__         =	self.class_embedding(__lowerCAmelCase       ).to(dtype=self.dtype       )
  									UpperCamelCase__         =	emb + class_emb
  
  							# 2. pre-process
  							UpperCamelCase__         =	sample
  							UpperCamelCase__         =	self.conv_in(__lowerCAmelCase       )
  
  							# 3. down
  							UpperCamelCase__         =	(sample,)
  							for downsample_block in self.down_blocks:
  									if hasattr(__lowerCAmelCase    ,      """skip_conv"""       ):
  											UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__         =	downsample_block(
  											    hidden_states=__lowerCAmelCase    ,      temb=__lowerCAmelCase    ,      skip_sample=__lowerCAmelCase       )
  									else:
  											UpperCamelCase__  ,			UpperCamelCase__         =	downsample_block(hidden_states=__lowerCAmelCase    ,      temb=__lowerCAmelCase       )
  
  									down_block_res_samples += res_samples
  
  							# 4. mid
  							UpperCamelCase__         =	self.mid_block(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
  							# 5. up
  							UpperCamelCase__         =	None
  							for upsample_block in self.up_blocks:
  									UpperCamelCase__         =	down_block_res_samples[-len(upsample_block.resnets       ) :]
  									UpperCamelCase__         =	down_block_res_samples[: -len(upsample_block.resnets       )]
  
  									if hasattr(__lowerCAmelCase    ,      """skip_conv"""       ):
  											UpperCamelCase__  ,			UpperCamelCase__         =	upsample_block(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  									else:
  											UpperCamelCase__         =	upsample_block(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  
          # 6. post-process
  							UpperCamelCase__         =	self.conv_norm_out(__lowerCAmelCase       )
  							UpperCamelCase__         =	self.conv_act(__lowerCAmelCase       )
  							UpperCamelCase__         =	self.conv_out(__lowerCAmelCase       )
  
  							if skip_sample is not None:
  									sample += skip_sample
  
  							if self.config.time_embedding_type == "fourier":
  									UpperCamelCase__         =	timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]       )))       )
  									UpperCamelCase__         =	sample / timesteps
  
  							if not return_dict:
  									return (sample,)
  
  							return UNetaDOutput(sample=__lowerCAmelCase       )
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  __lowerCAmelCase     =			{
      "0": 1,
      "1": 1,
      "2": 2,
      "3": 6,
      "4": 24,
      "5": 120,
      "6": 720,
      "7": 5_040,
      "8": 40_320,
      "9": 362_880,
  }
  
  __lowerCAmelCase     =			{145: 145}
  
  __lowerCAmelCase     =			{
      145: 0,
      169: 3,
      36_301: 3,
      1_454: 3,
      871: 2,
      45_361: 2,
      872: 2,
  }
  
  def     a						(      _lowercase   :     int       )     ->int:
  		if n in CACHE_SUM_DIGIT_FACTORIALS:
  				return CACHE_SUM_DIGIT_FACTORIALS[n]
  		UpperCamelCase__         =	sum(DIGIT_FACTORIALS[let] for let in str(_lowercase       )       )
  		UpperCamelCase__         =	ret
  		return ret
  
  def     a						(      _lowercase   :     int ,						_lowercase   :     set | None = None       )     ->int:
  		UpperCamelCase__         =	previous or set()
  		if n in CHAIN_LENGTH_CACHE:
  				return CHAIN_LENGTH_CACHE[n]
  		UpperCamelCase__         =	sum_digit_factorials(_lowercase       )
  		if next_number in previous:
  				UpperCamelCase__         =	0
  				return 0
  		else:
  				previous.add(_lowercase       )
  				UpperCamelCase__         =	1 + chain_length(_lowercase ,						_lowercase       )
  				UpperCamelCase__         =	ret
  				return ret
  
  def     a						(      _lowercase   :     int = 6_0 ,						_lowercase   :     int = 1_0_0_0_0_0_0       )     ->int:
  		return sum(1 for i in range(1 ,						_lowercase       ) if chain_length(_lowercase       ) == num_terms       )
  
  
  if __name__ == "__main__":
  				print(f"""{solution() = }""")
  
  
  
  
  
  