



'''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 UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class 			__magic_name__		(					a__     ):
							lowerCAmelCase :					Optional[int]            =      4_2

class 			__magic_name__		(					a__    ,		a__     ):







							@register_to_config
							def __init__(							self      : Any			,_UpperCAmelCase      : Dict = None			,_UpperCAmelCase      : Any = 3			,_UpperCAmelCase      : int = 3			,_UpperCAmelCase      : Union[str, Any] = False			,_UpperCAmelCase      : List[Any] = "positional"			,_UpperCAmelCase      : int = 0			,_UpperCAmelCase      : Optional[Any] = True			,_UpperCAmelCase      : Any = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")			,_UpperCAmelCase      : str = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")			,_UpperCAmelCase      : Optional[int] = (224, 448, 672, 896)			,_UpperCAmelCase      : int = 2			,_UpperCAmelCase      : List[str] = 1			,_UpperCAmelCase      : Any = 1			,_UpperCAmelCase      : Union[str, Any] = "conv"			,_UpperCAmelCase      : str = "conv"			,_UpperCAmelCase      : Any = "silu"			,_UpperCAmelCase      : str = 8			,_UpperCAmelCase      : List[str] = 32			,_UpperCAmelCase      : Optional[int] = 1E-5			,_UpperCAmelCase      : Dict = "default"			,_UpperCAmelCase      : Optional[int] = True			,_UpperCAmelCase      : Union[str, Any] = None			,_UpperCAmelCase      : str = None			,):
									super().__init__()

									_a  :   Union[str, Any]				 =							sample_size
									_a  :   Any				 =							block_out_channels[0] * 4

									# Check inputs
									if len(__A	) != len(__A	):
											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(__A	) != len(__A	):
											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
									_a  :   Optional[Any]				 =							nn.Convad(__A			,block_out_channels[0]			,kernel_size=3			,padding=(1, 1)	)

									# time
									if time_embedding_type == "fourier":
											_a  :   str				 =							GaussianFourierProjection(embedding_size=block_out_channels[0]			,scale=16	)
											_a  :   Optional[Any]				 =							2 * block_out_channels[0]
									elif time_embedding_type == "positional":
											_a  :   Dict				 =							Timesteps(block_out_channels[0]			,__A			,__A	)
											_a  :   str				 =							block_out_channels[0]

									_a  :   Optional[Any]				 =							TimestepEmbedding(__A			,__A	)

									# class embedding
									if class_embed_type is None and num_class_embeds is not None:
											_a  :   Tuple				 =							nn.Embedding(__A			,__A	)
									elif class_embed_type == "timestep":
											_a  :   List[Any]				 =							TimestepEmbedding(__A			,__A	)
									elif class_embed_type == "identity":
											_a  :   Any				 =							nn.Identity(__A			,__A	)
									else:
											_a  :   List[str]				 =							None

									_a  :   Dict				 =							nn.ModuleList([]	)
									_a  :   Union[str, Any]				 =							None
									_a  :   str				 =							nn.ModuleList([]	)

									# down
									_a  :   Optional[int]				 =							block_out_channels[0]
									for i, down_block_type in enumerate(__A	):
											_a  :   List[Any]				 =							output_channel
											_a  :   Any				 =							block_out_channels[i]
											_a  :   Optional[int]				 =							i == len(__A	) - 1

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

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

									# up
									_a  :   List[str]				 =							list(reversed(__A	)	)
									_a  :   Optional[int]				 =							reversed_block_out_channels[0]
									for i, up_block_type in enumerate(__A	):
											_a  :   List[Any]				 =							output_channel
											_a  :   Tuple				 =							reversed_block_out_channels[i]
											_a  :   Union[str, Any]				 =							reversed_block_out_channels[min(i + 1			,len(__A	) - 1	)]

											_a  :   List[Any]				 =							i == len(__A	) - 1

											_a  :   Dict				 =							get_up_block(
											    __A			,num_layers=layers_per_block + 1			,in_channels=__A			,out_channels=__A			,prev_output_channel=__A			,temb_channels=__A			,add_upsample=not is_final_block			,resnet_eps=__A			,resnet_act_fn=__A			,resnet_groups=__A			,attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,resnet_time_scale_shift=__A			,upsample_type=__A			,)
											self.up_blocks.append(__A	)
											_a  :   Optional[Any]				 =							output_channel

									# out
									_a  :   Tuple				 =							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,32	)
									_a  :   Union[str, Any]				 =							nn.GroupNorm(num_channels=block_out_channels[0]			,num_groups=__A			,eps=__A	)
									_a  :   Dict				 =							nn.SiLU()
									_a  :   str				 =							nn.Convad(block_out_channels[0]			,__A			,kernel_size=3			,padding=1	)







							def 							__lowercase    (							self      : Union[str, Any]			,_UpperCAmelCase      : Optional[Any]			,_UpperCAmelCase      : List[Any]			,_UpperCAmelCase      : int = None			,_UpperCAmelCase      : Optional[int] = True			,):







									if self.config.center_input_sample:
											_a  :   List[str]				 =							2 * sample - 1.0

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

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

									_a  :   Tuple				 =							self.time_proj(__A	)

									# 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.
									_a  :   List[Any]				 =							t_emb.to(dtype=self.dtype	)
									_a  :   List[str]				 =							self.time_embedding(__A	)

									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":
													_a  :   Tuple				 =							self.time_proj(__A	)

											_a  :   int				 =							self.class_embedding(__A	).to(dtype=self.dtype	)
											_a  :   Optional[Any]				 =							emb + class_emb

									# 2. pre-process
									_a  :   str				 =							sample
									_a  :   int				 =							self.conv_in(__A	)

									# 3. down
									_a  :   Any				 =							(sample,)
									for downsample_block in self.down_blocks:
											if hasattr(__A			,'skip_conv'	):
													_a  :   Optional[int]				 =							downsample_block(
													    hidden_states=__A			,temb=__A			,skip_sample=__A	)
											else:
													_a  :   int				 =							downsample_block(hidden_states=__A			,temb=__A	)

											down_block_res_samples += res_samples

									# 4. mid
									_a  :   List[str]				 =							self.mid_block(__A			,__A	)

									# 5. up
									_a  :   str				 =							None
									for upsample_block in self.up_blocks:
											_a  :   List[Any]				 =							down_block_res_samples[-len(upsample_block.resnets	) :]
											_a  :   Dict				 =							down_block_res_samples[: -len(upsample_block.resnets	)]

											if hasattr(__A			,'skip_conv'	):
													_a  :   Optional[Any]				 =							upsample_block(__A			,__A			,__A			,__A	)
											else:
													_a  :   Tuple				 =							upsample_block(__A			,__A			,__A	)

        # 6. post-process
									_a  :   List[Any]				 =							self.conv_norm_out(__A	)
									_a  :   Any				 =							self.conv_act(__A	)
									_a  :   Any				 =							self.conv_out(__A	)

									if skip_sample is not None:
											sample += skip_sample

									if self.config.time_embedding_type == "fourier":
											_a  :   Optional[Any]				 =							timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]	)))	)
											_a  :   List[str]				 =							sample / timesteps

									if not return_dict:
											return (sample,)

									return UNetaDOutput(sample=__A	)









'''simple docstring'''

from __future__ import annotations

class 			__magic_name__		:







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : int	):
									_a  :   Tuple				 =							value
									_a  :   Node | None				 =							None
									_a  :   Node | None				 =							None

class 			__magic_name__		:
							lowerCAmelCase :					int







							def __init__(							self      : Union[str, Any]	):
									_a  :   Tuple				 =							0







							def 							__lowercase    (							self      : Tuple			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int	):
									if node is None:
											return

									if path_sum == self.target:
											self.paths += 1

									if node.left:
											self.depth_first_search(node.left			,path_sum + node.left.value	)
									if node.right:
											self.depth_first_search(node.right			,path_sum + node.right.value	)







							def 							__lowercase    (							self      : Union[str, Any]			,_UpperCAmelCase      : Node | None			,_UpperCAmelCase      : int | None = None	):
									if node is None:
											return 0
									if target is not None:
											_a  :   Any				 =							target

									self.depth_first_search(_UpperCAmelCase			,node.value	)
									self.path_sum(node.left	)
									self.path_sum(node.right	)

									return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()




