





'''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       a_   (				snake_case_							):
     __A      =			42







class       a_   (				snake_case_	,						snake_case_							):





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





        """simple docstring"""




        super().__init__()
        lowercase_			:List[Any]    			= sample_size

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

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

        lowercase_			:Optional[int]    			= nn.ModuleList([]						)
        lowercase_			:Dict    			= None
        lowercase_			:Tuple    			= nn.ModuleList([]						)
        lowercase_			:int    			= None

        # down
        lowercase_			:Any    			= in_channels
        for i, down_block_type in enumerate(UpperCamelCase__						):
           lowercase_			:Dict    			= output_channel
           lowercase_			:int    			= block_out_channels[i]

           if i == 0:
              input_channel += extra_in_channels

           lowercase_			:Any    			= i == len(UpperCamelCase__						) - 1

           lowercase_			:Tuple    			= 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
        lowercase_			:str    			= 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
        lowercase_			:Optional[int]    			= list(reversed(UpperCamelCase__						)						)
        lowercase_			:int    			= reversed_block_out_channels[0]
        if out_block_type is None:
           lowercase_			:List[Any]    			= out_channels
        else:
           lowercase_			:int    			= block_out_channels[0]

        for i, up_block_type in enumerate(UpperCamelCase__						):
           lowercase_			:Optional[int]    			= output_channel
           lowercase_			:List[Any]    			= (
               reversed_block_out_channels[i + 1] if i < len(UpperCamelCase__						) - 1 else final_upsample_channels
           )

           lowercase_			:List[str]    			= i == len(UpperCamelCase__						) - 1

           lowercase_			:Union[str, 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__						)
           lowercase_			:Any    			= output_channel

        # out
        lowercase_			:str    			= norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,      32						)
        lowercase_			:Dict    			= 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 							lowercase__      ( self		:					str	,      lowercase		:					Tuple	,      lowercase		:					Tuple	,      lowercase		:					Dict = True	,      ):





        """simple docstring"""




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

        lowercase_			:str    			= self.time_proj(UpperCamelCase__						)
        if self.config.use_timestep_embedding:
           lowercase_			:Tuple    			= self.time_mlp(UpperCamelCase__						)
        else:
           lowercase_			:int    			= timestep_embed[..., None]
           lowercase_			:Optional[Any]    			= timestep_embed.repeat([1, 1, sample.shape[2]]						).to(sample.dtype						)
           lowercase_			:Optional[int]    			= timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])						)

        # 2. down
        lowercase_			:List[str]    			= ()
        for downsample_block in self.down_blocks:
           lowercase_			:Dict    			= downsample_block(hidden_states=UpperCamelCase__	,      temb=UpperCamelCase__						)
           down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
           lowercase_			:int    			= self.mid_block(UpperCamelCase__	,      UpperCamelCase__						)

        # 4. up
        for i, upsample_block in enumerate(self.up_blocks						):
           lowercase_			:Any    			= down_block_res_samples[-1:]
           lowercase_			:int    			= down_block_res_samples[:-1]
           lowercase_			:Optional[int]    			= upsample_block(UpperCamelCase__	,      res_hidden_states_tuple=UpperCamelCase__	,      temb=UpperCamelCase__						)

        # 5. post-process
        if self.out_block:
           lowercase_			:Any    			= self.out_block(UpperCamelCase__	,      UpperCamelCase__						)

        if not return_dict:
           return (sample,)

        return UNetaDOutput(sample=UpperCamelCase__						)






"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  list[int] )	->						float:


    """simple docstring"""





    if not nums:  # Makes sure that the list is not empty
        raise ValueError("List is empty" )

    snake_case    :      List[str]      					=    sum(lowercase ) / len(lowercase )  # Calculate the average
    return sum(abs(x - average ) for x in nums ) / len(lowercase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





