


'''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__			(			UpperCAmelCase__	):
			lowerCAmelCase :	torch.FloatTensor




class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__	):




			@register_to_config
			def __init__(		self  :     str	,  lowerCamelCase__  :     int = 6_55_36	,  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	,  )   ->Optional[Any]:



					'''simple docstring'''







					super().__init__()
					_UpperCAmelCase		:       Optional[int]								=      sample_size

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

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

					_UpperCAmelCase		:       List[str]								=      nn.ModuleList([] )
					_UpperCAmelCase		:       List[Any]								=      None
					_UpperCAmelCase		:       Union[str, Any]								=      nn.ModuleList([] )
					_UpperCAmelCase		:       Dict								=      None

					# down
					_UpperCAmelCase		:       List[str]								=      in_channels
					for i, down_block_type in enumerate(lowerCamelCase__ ):
							_UpperCAmelCase		:       str								=      output_channel
							_UpperCAmelCase		:       Any								=      block_out_channels[i]

							if i == 0:
									input_channel += extra_in_channels

							_UpperCAmelCase		:       Optional[int]								=      i == len(lowerCamelCase__ ) - 1

							_UpperCAmelCase		:       List[str]								=      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		:       List[Any]								=      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		:       Optional[Any]								=      list(reversed(lowerCamelCase__ ) )
					_UpperCAmelCase		:       Optional[Any]								=      reversed_block_out_channels[0]
					if out_block_type is None:
							_UpperCAmelCase		:       List[str]								=      out_channels
					else:
							_UpperCAmelCase		:       List[str]								=      block_out_channels[0]

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

							_UpperCAmelCase		:       Tuple								=      i == len(lowerCamelCase__ ) - 1

							_UpperCAmelCase		:       Dict								=      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		:       Tuple								=      output_channel

					# out
					_UpperCAmelCase		:       Union[str, Any]								=      norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,  32 )
					_UpperCAmelCase		:       Optional[Any]								=      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		:       Any								=      timestep
					if not torch.is_tensor(lowerCamelCase__ ):
							_UpperCAmelCase		:       List[str]								=      torch.tensor([timesteps]	,  dtype=torch.long	,  device=sample.device )
					elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
							_UpperCAmelCase		:       List[Any]								=      timesteps[None].to(sample.device )

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

					# 2. down
					_UpperCAmelCase		:       List[Any]								=      ()
					for downsample_block in self.down_blocks:
							_UpperCAmelCase		:       Tuple								=      downsample_block(hidden_states=lowerCamelCase__	,  temb=lowerCamelCase__ )
							down_block_res_samples += res_samples

					# 3. mid
					if self.mid_block:
							_UpperCAmelCase		:       List[Any]								=      self.mid_block(lowerCamelCase__	,  lowerCamelCase__ )

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

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

					if not return_dict:
							return (sample,)

					return UNetaDOutput(sample=lowerCamelCase__ )







'''simple docstring'''


def  __lowerCAmelCase ():
		_UpperCAmelCase		:       str								=      0
		for i in range(1      , 1_001   ):
				total += i**i
		return str(__lowerCAmelCase   )[-10:]


if __name__ == "__main__":
						print(solution())



