
"""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"""



				__UpperCamelCase   :		torch.FloatTensor






class 			lowerCAmelCase_		(a__				,      a__						):


				"""simple docstring"""



				@register_to_config
				def __init__(self     , SCREAMING_SNAKE_CASE__ = 6_55_36     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = 2     , SCREAMING_SNAKE_CASE__ = 2     , SCREAMING_SNAKE_CASE__ = 0     , SCREAMING_SNAKE_CASE__ = "fourier"     , SCREAMING_SNAKE_CASE__ = True     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = 0.0     , SCREAMING_SNAKE_CASE__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")     , SCREAMING_SNAKE_CASE__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")     , SCREAMING_SNAKE_CASE__ = "UNetMidBlock1D"     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = (32, 32, 64)     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = 8     , SCREAMING_SNAKE_CASE__ = 1     , SCREAMING_SNAKE_CASE__ = False     , )      ->	List[Any]:



											"""simple docstring"""




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

											# time
											if time_embedding_type == "fourier":
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     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__		: List[str]     =     2 * block_out_channels[0]
											elif time_embedding_type == "positional":
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     Timesteps(
																		    block_out_channels[0]     , flip_sin_to_cos=SCREAMING_SNAKE_CASE__     , downscale_freq_shift=SCREAMING_SNAKE_CASE__					)
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     block_out_channels[0]

											if use_timestep_embedding:
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     block_out_channels[0] * 4
																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     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__		: Union[str, Any]     =     nn.ModuleList([]					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     None
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     nn.ModuleList([]					)
											SCREAMING_SNAKE_CASE__		: Dict     =     None

											# down
											SCREAMING_SNAKE_CASE__		: List[str]     =     in_channels
											for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     output_channel
																		SCREAMING_SNAKE_CASE__		: Tuple     =     block_out_channels[i]

																		if i == 0:
																									input_channel += extra_in_channels

																		SCREAMING_SNAKE_CASE__		: Dict     =     i == len(SCREAMING_SNAKE_CASE__					) - 1

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

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

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

																		SCREAMING_SNAKE_CASE__		: List[str]     =     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__		: Any     =     output_channel

											# out
											SCREAMING_SNAKE_CASE__		: List[Any]     =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     , 32					)
											SCREAMING_SNAKE_CASE__		: Any     =     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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = True     , )      ->	Union[UNetaDOutput, Tuple]:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.time_proj(SCREAMING_SNAKE_CASE__					)
											if self.config.use_timestep_embedding:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     self.time_mlp(SCREAMING_SNAKE_CASE__					)
											else:
																		SCREAMING_SNAKE_CASE__		: int     =     timestep_embed[..., None]
																		SCREAMING_SNAKE_CASE__		: List[str]     =     timestep_embed.repeat([1, 1, sample.shape[2]]					).to(sample.dtype					)
																		SCREAMING_SNAKE_CASE__		: int     =     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__   ,   SCREAMING_SNAKE_CASE__		: Optional[Any]     =     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__		: Union[str, Any]     =     self.mid_block(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											# 4. up
											for i, upsample_block in enumerate(self.up_blocks					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     down_block_res_samples[-1:]
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     down_block_res_samples[:-1]
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     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__		: List[str]     =     self.out_block(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											if not return_dict:
																		return (sample,)

											return UNetaDOutput(sample=SCREAMING_SNAKE_CASE__					)






"""simple docstring"""




import torch

def lowercase_						(     ):
							if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.cuda.device_count()
							else:
														SCREAMING_SNAKE_CASE__		: int     =     0
							print(f'''Successfully ran on {num_gpus} GPUs'''		)


if __name__ == "__main__":
					main()




