

"""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 get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class 					a						(       _lowerCamelCase				):



				"""simple docstring"""



				UpperCAmelCase						 =					42



class 					a						(       _lowerCamelCase,	_lowerCamelCase				):



				"""simple docstring"""



				@register_to_config
				def __init__(     self: Optional[Any]    ,						UpperCamelCase: Tuple = 6_55_36    ,						UpperCamelCase: str = None    ,						UpperCamelCase: Any = 2    ,						UpperCamelCase: Union[str, Any] = 2    ,						UpperCamelCase: Optional[Any] = 0    ,						UpperCamelCase: List[Any] = "fourier"    ,						UpperCamelCase: Union[str, Any] = True    ,						UpperCamelCase: Optional[int] = False    ,						UpperCamelCase: Optional[Any] = 0.0    ,						UpperCamelCase: str = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")    ,						UpperCamelCase: Tuple = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")    ,						UpperCamelCase: Optional[int] = "UNetMidBlock1D"    ,						UpperCamelCase: List[Any] = None    ,						UpperCamelCase: List[str] = (32, 32, 64)    ,						UpperCamelCase: Tuple = None    ,						UpperCamelCase: List[str] = 8    ,						UpperCamelCase: Union[str, Any] = 1    ,						UpperCamelCase: List[Any] = False    ,						):






									"""simple docstring"""


									super().__init__()
									A__										=  sample_size

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

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

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

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

														if i == 0:
																			input_channel += extra_in_channels

														A__										=  i == len(A__       ) - 1

														A__										=  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
									A__										=  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
									A__										=  list(reversed(A__       )       )
									A__										=  reversed_block_out_channels[0]
									if out_block_type is None:
														A__										=  out_channels
									else:
														A__										=  block_out_channels[0]

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

														A__										=  i == len(A__       ) - 1

														A__										=  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__       )
														A__										=  output_channel

									# out
									A__										=  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,						32       )
									A__										=  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 				UpperCamelCase	(     self: str    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Tuple = True    ,						):






									"""simple docstring"""


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

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

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

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

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

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

									if not return_dict:
														return (sample,)

									return UNetaDOutput(sample=A__       )









"""simple docstring"""



from collections import defaultdict





def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  1
     A__										=  True
     for v in tree[start]:
          if v not in visited:
               ret += dfs(UpperCAmelCase_			)
     if ret % 2 == 0:
          cuts.append(UpperCAmelCase_			)
     return ret





def  _snake_case     (    ):
     dfs(1			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_					:	Any							  =						1_0, 9
 SCREAMING_SNAKE_CASE_					:	Any							  =						defaultdict(list)
 SCREAMING_SNAKE_CASE_					:	dict[int, bool]							  =						{}
 SCREAMING_SNAKE_CASE_					:	list[int]							  =						[]
 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						0
 SCREAMING_SNAKE_CASE_					:	Any							  =						[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
 for u, v in edges:
  tree[u].append(v)
  tree[v].append(u)
 even_tree()
 print(len(cuts) - 1)






