


'''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		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							42







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , __SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @register_to_config
  def __init__(							self :       List[Any]	,_a :       int = 65536	,_a :       Optional[int] = None	,_a :       int = 2	,_a :       int = 2	,_a :       int = 0	,_a :       str = "fourier"	,_a :       bool = True	,_a :       bool = False	,_a :       float = 0.0	,_a :       Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")	,_a :       Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")	,_a :       Tuple[str] = "UNetMidBlock1D"	,_a :       str = None	,_a :       Tuple[int] = (32, 32, 64)	,_a :       str = None	,_a :       int = 8	,_a :       int = 1	,_a :       bool = False	,):

        '''simple docstring'''




        super().__init__()
        A_						:  int          =				sample_size

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

        if use_timestep_embedding:
              A_						:  str          =				block_out_channels[0] * 4
              A_						:  List[str]          =				TimestepEmbedding(
                  in_channels=_a	,time_embed_dim=_a	,act_fn=_a	,out_dim=block_out_channels[0]	,)

        A_						:  List[Any]          =				nn.ModuleList([] )
        A_						:  int          =				None
        A_						:  List[str]          =				nn.ModuleList([] )
        A_						:  Optional[Any]          =				None

        # down
        A_						:  Dict          =				in_channels
        for i, down_block_type in enumerate(_a ):
              A_						:  Optional[int]          =				output_channel
              A_						:  Dict          =				block_out_channels[i]

              if i == 0:
                    input_channel += extra_in_channels

              A_						:  str          =				i == len(_a ) - 1

              A_						:  List[str]          =				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_						:  List[Any]          =				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_						:  Tuple          =				list(reversed(_a ) )
        A_						:  List[Any]          =				reversed_block_out_channels[0]
        if out_block_type is None:
              A_						:  Union[str, Any]          =				out_channels
        else:
              A_						:  Tuple          =				block_out_channels[0]

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

              A_						:  Any          =				i == len(_a ) - 1

              A_						:  Union[str, Any]          =				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_						:  Any          =				output_channel

        # out
        A_						:  str          =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,32 )
        A_						:  Optional[Any]          =				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        _a							(							self :       str	,_a :       torch.FloatTensor	,_a :       Union[torch.Tensor, float, int]	,_a :       bool = True	,):

        '''simple docstring'''




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

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

        # 2. down
        A_						:  List[str]          =				()
        for downsample_block in self.down_blocks:
              A_   ,					A_						:  Tuple          =				downsample_block(hidden_states=_a	,temb=_a )
              down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
              A_						:  int          =				self.mid_block(_a	,_a )

        # 4. up
        for i, upsample_block in enumerate(self.up_blocks ):
              A_						:  List[str]          =				down_block_res_samples[-1:]
              A_						:  List[Any]          =				down_block_res_samples[:-1]
              A_						:  Any          =				upsample_block(_a	,res_hidden_states_tuple=_a	,temb=_a )

        # 5. post-process
        if self.out_block:
              A_						:  Union[str, Any]          =				self.out_block(_a	,_a )

        if not return_dict:
              return (sample,)

        return UNetaDOutput(sample=_a )




'''simple docstring'''


from ..utils import DummyObject, requires_backends



class 	__lowerCAmelCase		(							metaclass=__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""torch""", """torchsde"""]






  def __init__(							self :       Any	,*_a :       Union[str, Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




        requires_backends(self	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       Optional[int]	,*_a :       List[Any]	,**_a :       Any ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       List[Any]	,*_a :       Tuple	,**_a :       Union[str, Any] ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )
