





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    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			42



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 @register_to_config
 def __init__(self				:   str       , UpperCAmelCase_				:   int = 65_536       , UpperCAmelCase_				:   Optional[int] = None       , UpperCAmelCase_				:   int = 2       , UpperCAmelCase_				:   int = 2       , UpperCAmelCase_				:   int = 0       , UpperCAmelCase_				:   str = "fourier"       , UpperCAmelCase_				:   bool = True       , UpperCAmelCase_				:   bool = False       , UpperCAmelCase_				:   float = 0.0       , UpperCAmelCase_				:   Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       , UpperCAmelCase_				:   Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       , UpperCAmelCase_				:   Tuple[str] = "UNetMidBlock1D"       , UpperCAmelCase_				:   str = None       , UpperCAmelCase_				:   Tuple[int] = (32, 32, 64)       , UpperCAmelCase_				:   str = None       , UpperCAmelCase_				:   int = 8       , UpperCAmelCase_				:   int = 1       , UpperCAmelCase_				:   bool = False       , )       ->Tuple:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:      Tuple			  =sample_size

   # time
   if time_embedding_type == "fourier":
     lowerCamelCase__:      Optional[int]			  =GaussianFourierProjection(
         embedding_size=8       , set_W_to_weight=UpperCAmelCase_       , log=UpperCAmelCase_       , flip_sin_to_cos=UpperCAmelCase_)
     lowerCamelCase__:      int			  =2 * block_out_channels[0]
   elif time_embedding_type == "positional":
     lowerCamelCase__:      str			  =Timesteps(
         block_out_channels[0]       , flip_sin_to_cos=UpperCAmelCase_       , downscale_freq_shift=UpperCAmelCase_)
     lowerCamelCase__:      Union[str, Any]			  =block_out_channels[0]

   if use_timestep_embedding:
     lowerCamelCase__:      List[Any]			  =block_out_channels[0] * 4
     lowerCamelCase__:      Tuple			  =TimestepEmbedding(
         in_channels=UpperCAmelCase_       , time_embed_dim=UpperCAmelCase_       , act_fn=UpperCAmelCase_       , out_dim=block_out_channels[0]       , )

   lowerCamelCase__:      List[str]			  =nn.ModuleList([])
   lowerCamelCase__:      List[str]			  =None
   lowerCamelCase__:      List[str]			  =nn.ModuleList([])
   lowerCamelCase__:      str			  =None

   # down
   lowerCamelCase__:      Optional[Any]			  =in_channels
   for i, down_block_type in enumerate(UpperCAmelCase_):
     lowerCamelCase__:      Optional[Any]			  =output_channel
     lowerCamelCase__:      List[str]			  =block_out_channels[i]

     if i == 0:
       input_channel += extra_in_channels

     lowerCamelCase__:      Optional[Any]			  =i == len(UpperCAmelCase_) - 1

     lowerCamelCase__:      int			  =get_down_block(
         UpperCAmelCase_       , num_layers=UpperCAmelCase_       , in_channels=UpperCAmelCase_       , out_channels=UpperCAmelCase_       , temb_channels=block_out_channels[0]       , add_downsample=not is_final_block or downsample_each_block       , )
     self.down_blocks.append(UpperCAmelCase_)

   # mid
   lowerCamelCase__:      Any			  =get_mid_block(
       UpperCAmelCase_       , 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=UpperCAmelCase_       , add_downsample=UpperCAmelCase_       , )

   # up
   lowerCamelCase__:      Optional[Any]			  =list(reversed(UpperCAmelCase_))
   lowerCamelCase__:      Optional[Any]			  =reversed_block_out_channels[0]
   if out_block_type is None:
     lowerCamelCase__:      Tuple			  =out_channels
   else:
     lowerCamelCase__:      Optional[int]			  =block_out_channels[0]

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

     lowerCamelCase__:      Union[str, Any]			  =i == len(UpperCAmelCase_) - 1

     lowerCamelCase__:      Any			  =get_up_block(
         UpperCAmelCase_       , num_layers=UpperCAmelCase_       , in_channels=UpperCAmelCase_       , out_channels=UpperCAmelCase_       , temb_channels=block_out_channels[0]       , add_upsample=not is_final_block       , )
     self.up_blocks.append(UpperCAmelCase_)
     lowerCamelCase__:      Optional[int]			  =output_channel

   # out
   lowerCamelCase__:      Optional[Any]			  =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       , 32)
   lowerCamelCase__:      Union[str, Any]			  =get_out_block(
       out_block_type=UpperCAmelCase_       , num_groups_out=UpperCAmelCase_       , embed_dim=block_out_channels[0]       , out_channels=UpperCAmelCase_       , act_fn=UpperCAmelCase_       , fc_dim=block_out_channels[-1] // 4       , )







 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   torch.FloatTensor       , UpperCAmelCase_				:   Union[torch.Tensor, float, int]       , UpperCAmelCase_				:   bool = True       , )       ->Union[UNetaDOutput, Tuple]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =timestep
   if not torch.is_tensor(UpperCAmelCase_):
     lowerCamelCase__:      Any			  =torch.tensor([timesteps]       , dtype=torch.long       , device=sample.device)
   elif torch.is_tensor(UpperCAmelCase_) and len(timesteps.shape) == 0:
     lowerCamelCase__:      Optional[Any]			  =timesteps[None].to(sample.device)

   lowerCamelCase__:      Union[str, Any]			  =self.time_proj(UpperCAmelCase_)
   if self.config.use_timestep_embedding:
     lowerCamelCase__:      Dict			  =self.time_mlp(UpperCAmelCase_)
   else:
     lowerCamelCase__:      Optional[Any]			  =timestep_embed[..., None]
     lowerCamelCase__:      Union[str, Any]			  =timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
     lowerCamelCase__:      List[str]			  =timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

   # 2. down
   lowerCamelCase__:      List[str]			  =()
   for downsample_block in self.down_blocks:
     lowerCamelCase__  ,					lowerCamelCase__:      Any			  =downsample_block(hidden_states=UpperCAmelCase_       , temb=UpperCAmelCase_)
     down_block_res_samples += res_samples

   # 3. mid
   if self.mid_block:
     lowerCamelCase__:      int			  =self.mid_block(UpperCAmelCase_       , UpperCAmelCase_)

   # 4. up
   for i, upsample_block in enumerate(self.up_blocks):
     lowerCamelCase__:      Union[str, Any]			  =down_block_res_samples[-1:]
     lowerCamelCase__:      Optional[int]			  =down_block_res_samples[:-1]
     lowerCamelCase__:      List[str]			  =upsample_block(UpperCAmelCase_       , res_hidden_states_tuple=UpperCAmelCase_       , temb=UpperCAmelCase_)

   # 5. post-process
   if self.out_block:
     lowerCamelCase__:      List[Any]			  =self.out_block(UpperCAmelCase_       , UpperCAmelCase_)

   if not return_dict:
     return (sample,)

   return UNetaDOutput(sample=UpperCAmelCase_)






'''simple docstring'''


import math

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]=0 ):  # a graph with Node 0,1,...,N-1


       """simple docstring"""







       UpperCamelCase					=  n
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # adjacency matrix for weight
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # dp[i][j] stores minimum distance from i to j






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  w






    def  A       (  self      :					str ):


       """simple docstring"""







       for k in range(0  ,						self.n ):
          for i in range(0  ,						self.n ):
             for j in range(0  ,						self.n ):
                UpperCamelCase					=  min(self.dp[i][j]  ,						self.dp[i][k] + self.dp[k][j] )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       return self.dp[u][v]


if __name__ == "__main__":
   _lowerCamelCase	:       List[str]      	=	Graph(5)
   graph.add_edge(0, 2, 9)
   graph.add_edge(0, 4, 10)
   graph.add_edge(1, 3, 5)
   graph.add_edge(2, 3, 7)
   graph.add_edge(3, 0, 10)
   graph.add_edge(3, 1, 2)
   graph.add_edge(3, 2, 1)
   graph.add_edge(3, 4, 6)
   graph.add_edge(4, 1, 3)
   graph.add_edge(4, 2, 4)
   graph.add_edge(4, 3, 9)
   graph.floyd_warshall()
   graph.show_min(1, 4)
   graph.show_min(0, 3)

