





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"""




							UpperCAmelCase_      						=   42


class 						lowerCamelCase       (_a							,     _a      ):







							"""simple docstring"""




							@register_to_config
							def __init__( self     :					str,			_UpperCAmelCase     :					int = 6_5_5_3_6,			_UpperCAmelCase     :					Optional[int] = None,			_UpperCAmelCase     :					int = 2,			_UpperCAmelCase     :					int = 2,			_UpperCAmelCase     :					int = 0,			_UpperCAmelCase     :					str = "fourier",			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					float = 0.0,			_UpperCAmelCase     :					Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"),			_UpperCAmelCase     :					Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"),			_UpperCAmelCase     :					Tuple[str] = "UNetMidBlock1D",			_UpperCAmelCase     :					str = None,			_UpperCAmelCase     :					Tuple[int] = (3_2, 3_2, 6_4),			_UpperCAmelCase     :					str = None,			_UpperCAmelCase     :					int = 8,			_UpperCAmelCase     :					int = 1,			_UpperCAmelCase     :					bool = False,			)				->			List[Any]:





														"""simple docstring"""
														super().__init__()
														SCREAMING_SNAKE_CASE__       :      List[str]     =						sample_size

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

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

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						nn.ModuleList([]						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						None
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						nn.ModuleList([]						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						None

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

																					if i == 0:
																												input_channel += extra_in_channels

																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						i == len(_a						) - 1

																					SCREAMING_SNAKE_CASE__       :      Tuple     =						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
														SCREAMING_SNAKE_CASE__       :      Optional[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
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						list(reversed(_a						)						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						reversed_block_out_channels[0]
														if out_block_type is None:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						out_channels
														else:
																					SCREAMING_SNAKE_CASE__       :      str     =						block_out_channels[0]

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

																					SCREAMING_SNAKE_CASE__       :      Tuple     =						i == len(_a						) - 1

																					SCREAMING_SNAKE_CASE__       :      Optional[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						)
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						output_channel

														# out
														SCREAMING_SNAKE_CASE__       :      int     =						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4,			3_2						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						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     :					Optional[Any],			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					Union[torch.Tensor, float, int],			_UpperCAmelCase     :					bool = True,			)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						timestep
														if not torch.is_tensor(_a						):
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.tensor([timesteps],			dtype=torch.long,			device=sample.device						)
														elif torch.is_tensor(_a						) and len(timesteps.shape						) == 0:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						timesteps[None].to(sample.device						)

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

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

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

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

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

														if not return_dict:
																					return (sample,)

														return UNetaDOutput(sample=_a						)













def _a (  SCREAMING_SNAKE_CASE__      :  int = 50_00_00_00       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						set()
							SCREAMING_SNAKE_CASE__       :      Dict     =						int((limit - 24) ** (1 / 2)       )

							SCREAMING_SNAKE_CASE__       :      Any     =						set(range(3			,				prime_square_limit + 1			,				2       )       )
							primes.add(2       )
							for p in range(3			,				prime_square_limit + 1			,				2       ):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p			,				prime_square_limit + 1			,				SCREAMING_SNAKE_CASE__       )       )       )

							for primea in primes:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						primea * primea
														for primea in primes:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						primea * primea * primea * primea
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(SCREAMING_SNAKE_CASE__       )

							return len(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
		print(f"{solution() = }")






