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 __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							42







class __snake_case       (		lowerCamelCase_     ,	lowerCamelCase_	):




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



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__    							=   sample_size

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

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

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

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

       if i == 0:
         input_channel += extra_in_channels

       SCREAMING_SNAKE_CASE__    							=   i == len(_lowercase   ) - 1

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

     # mid
     SCREAMING_SNAKE_CASE__    							=   get_mid_block(
         _lowercase ,			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=_lowercase ,			add_downsample=_lowercase ,			)

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

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

       SCREAMING_SNAKE_CASE__    							=   i == len(_lowercase   ) - 1

       SCREAMING_SNAKE_CASE__    							=   get_up_block(
           _lowercase ,			num_layers=_lowercase ,			in_channels=_lowercase ,			out_channels=_lowercase ,			temb_channels=block_out_channels[0] ,			add_upsample=not is_final_block ,			)
       self.up_blocks.append(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   output_channel

     # out
     SCREAMING_SNAKE_CASE__    							=   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,			32   )
     SCREAMING_SNAKE_CASE__    							=   get_out_block(
         out_block_type=_lowercase ,			num_groups_out=_lowercase ,			embed_dim=block_out_channels[0] ,			out_channels=_lowercase ,			act_fn=_lowercase ,			fc_dim=block_out_channels[-1] // 4 ,			)




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       torch.FloatTensor ,			_lowercase    :       Union[torch.Tensor, float, int] ,			_lowercase    :       bool = True ,			):



     """simple docstring"""






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

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

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

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

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

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

     if not return_dict:
       return (sample,)

     return UNetaDOutput(sample=_lowercase   )

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


if __name__ == "__main__":
   from doctest import testmod

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
