

'''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 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      42







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



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


      """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=__SCREAMING_SNAKE_CASE					,							log=__SCREAMING_SNAKE_CASE					,							flip_sin_to_cos=__SCREAMING_SNAKE_CASE			)
           __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=__SCREAMING_SNAKE_CASE					,							downscale_freq_shift=__SCREAMING_SNAKE_CASE			)
           __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=__SCREAMING_SNAKE_CASE					,							time_embed_dim=__SCREAMING_SNAKE_CASE					,							act_fn=__SCREAMING_SNAKE_CASE					,							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(__SCREAMING_SNAKE_CASE			):
           __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(__SCREAMING_SNAKE_CASE			) - 1

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

      # mid
      __SCREAMING_SNAKE_CASE     =      get_mid_block(
          __SCREAMING_SNAKE_CASE					,							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=__SCREAMING_SNAKE_CASE					,							add_downsample=__SCREAMING_SNAKE_CASE					,							)

      # up
      __SCREAMING_SNAKE_CASE     =      list(reversed(__SCREAMING_SNAKE_CASE			)			)
      __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(__SCREAMING_SNAKE_CASE			):
           __SCREAMING_SNAKE_CASE     =      output_channel
           __SCREAMING_SNAKE_CASE     =      (
               reversed_block_out_channels[i + 1] if i < len(__SCREAMING_SNAKE_CASE			) - 1 else final_upsample_channels
           )

           __SCREAMING_SNAKE_CASE     =      i == len(__SCREAMING_SNAKE_CASE			) - 1

           __SCREAMING_SNAKE_CASE     =      get_up_block(
               __SCREAMING_SNAKE_CASE					,							num_layers=__SCREAMING_SNAKE_CASE					,							in_channels=__SCREAMING_SNAKE_CASE					,							out_channels=__SCREAMING_SNAKE_CASE					,							temb_channels=block_out_channels[0]					,							add_upsample=not is_final_block					,							)
           self.up_blocks.append(__SCREAMING_SNAKE_CASE			)
           __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=__SCREAMING_SNAKE_CASE					,							num_groups_out=__SCREAMING_SNAKE_CASE					,							embed_dim=block_out_channels[0]					,							out_channels=__SCREAMING_SNAKE_CASE					,							act_fn=__SCREAMING_SNAKE_CASE					,							fc_dim=block_out_channels[-1] // 4					,							)


 def       UpperCAmelCase__		(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       torch.FloatTensor					,							__SCREAMING_SNAKE_CASE					:       Union[torch.Tensor, float, int]					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							)  -> Union[UNetaDOutput, Tuple]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      self.time_proj(__SCREAMING_SNAKE_CASE			)
      if self.config.use_timestep_embedding:
           __SCREAMING_SNAKE_CASE     =      self.time_mlp(__SCREAMING_SNAKE_CASE			)
      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=__SCREAMING_SNAKE_CASE					,							temb=__SCREAMING_SNAKE_CASE			)
           down_block_res_samples += res_samples

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

      # 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(__SCREAMING_SNAKE_CASE					,							res_hidden_states_tuple=__SCREAMING_SNAKE_CASE					,							temb=__SCREAMING_SNAKE_CASE			)

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

      if not return_dict:
           return (sample,)

      return UNetaDOutput(sample=__SCREAMING_SNAKE_CASE			)









'''simple docstring'''



def    a__       (  a__						):




     """simple docstring"""


     if isinstance(a__      ,    a__						):
          raise TypeError("""'float' object cannot be interpreted as an integer"""						)
     if isinstance(a__      ,    a__						):
          raise TypeError("""'str' object cannot be interpreted as an integer"""						)

     if num == 0:
          return "0b0"

     __SCREAMING_SNAKE_CASE     =      False

     if num < 0:
          __SCREAMING_SNAKE_CASE     =      True
          __SCREAMING_SNAKE_CASE     =      -num

     __SCREAMING_SNAKE_CASE     =      []
     while num > 0:
          binary.insert(0      ,    num % 2						)
          num >>= 1

     if negative:
          return "-0b" + "".join(str(a__						) for e in binary						)

     return "0b" + "".join(str(a__						) for e in binary						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






