





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       (	__lowercase						):
      _snake_case							     =42

class      A       (	__lowercase		,      __lowercase						):



      @register_to_config
      def __init__(	self:						List[str]		,  _lowerCAmelCase:						int = 6_5536		,  _lowerCAmelCase:						Optional[int] = None		,  _lowerCAmelCase:						int = 2		,  _lowerCAmelCase:						int = 2		,  _lowerCAmelCase:						int = 0		,  _lowerCAmelCase:						str = "fourier"		,  _lowerCAmelCase:						bool = True		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						float = 0.0		,  _lowerCAmelCase:						Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,  _lowerCAmelCase:						Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,  _lowerCAmelCase:						Tuple[str] = "UNetMidBlock1D"		,  _lowerCAmelCase:						str = None		,  _lowerCAmelCase:						Tuple[int] = (32, 32, 64)		,  _lowerCAmelCase:						str = None		,  _lowerCAmelCase:						int = 8		,  _lowerCAmelCase:						int = 1		,  _lowerCAmelCase:						bool = False		,  )						->    str:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_											=sample_size

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

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

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

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

            if i == 0:
               input_channel += extra_in_channels

            UpperCAmelCase_											=i == len(_lowerCAmelCase       ) - 1

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

         # mid
         UpperCAmelCase_											=get_mid_block(
             _lowerCAmelCase		,  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=_lowerCAmelCase		,  add_downsample=_lowerCAmelCase		,  )

         # up
         UpperCAmelCase_											=list(reversed(_lowerCAmelCase       )       )
         UpperCAmelCase_											=reversed_block_out_channels[0]
         if out_block_type is None:
            UpperCAmelCase_											=out_channels
         else:
            UpperCAmelCase_											=block_out_channels[0]

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

            UpperCAmelCase_											=i == len(_lowerCAmelCase       ) - 1

            UpperCAmelCase_											=get_up_block(
                _lowerCAmelCase		,  num_layers=_lowerCAmelCase		,  in_channels=_lowerCAmelCase		,  out_channels=_lowerCAmelCase		,  temb_channels=block_out_channels[0]		,  add_upsample=not is_final_block		,  )
            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_											=get_out_block(
             out_block_type=_lowerCAmelCase		,  num_groups_out=_lowerCAmelCase		,  embed_dim=block_out_channels[0]		,  out_channels=_lowerCAmelCase		,  act_fn=_lowerCAmelCase		,  fc_dim=block_out_channels[-1] // 4		,  )






      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						torch.FloatTensor		,  _lowerCAmelCase:						Union[torch.Tensor, float, int]		,  _lowerCAmelCase:						bool = True		,  )						->    Union[UNetaDOutput, Tuple]:
         '''simple docstring'''

         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       )

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

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

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

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

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

         if not return_dict:
            return (sample,)

         return UNetaDOutput(sample=_lowerCAmelCase       )











from __future__ import annotations




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_						,       UpperCAmelCase_											=set(lowercase__  ), [start]

   while stack:
      UpperCAmelCase_											=stack.pop()
      explored.add(lowercase__  )
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]  ):
         if adj not in explored:
            stack.append(lowercase__  )
   return explored


__lowercase  :   Dict						      ={
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, """A"""))




