



'''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 			UpperCamelCase_ (							lowercase_			):
	lowercase          = 42




class 			UpperCamelCase_ (							lowercase_      ,     lowercase_			):





	@register_to_config
	def __init__(  self		,		A = 65536		,		A = None		,		A = 2		,		A = 2		,		A = 0		,		A = "fourier"		,		A = True		,		A = False		,		A = 0.0		,		A = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,		A = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,		A = "UNetMidBlock1D"		,		A = None		,		A = (32, 32, 64)		,		A = None		,		A = 8		,		A = 1		,		A = False		,		)			->			List[str]:
								super().__init__()
								UpperCAmelCase     :		List[str]							     =		sample_size

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

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

								UpperCAmelCase     :		Optional[Any]							     =		nn.ModuleList([]    )
								UpperCAmelCase     :		Optional[Any]							     =		None
								UpperCAmelCase     :		Any							     =		nn.ModuleList([]    )
								UpperCAmelCase     :		str							     =		None

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

															if i == 0:
																						input_channel += extra_in_channels

															UpperCAmelCase     :		Dict							     =		i == len(A    ) - 1

															UpperCAmelCase     :		Optional[Any]							     =		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
								UpperCAmelCase     :		Optional[int]							     =		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
								UpperCAmelCase     :		Tuple							     =		list(reversed(A    )    )
								UpperCAmelCase     :		Dict							     =		reversed_block_out_channels[0]
								if out_block_type is None:
															UpperCAmelCase     :		Dict							     =		out_channels
								else:
															UpperCAmelCase     :		int							     =		block_out_channels[0]

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

															UpperCAmelCase     :		Union[str, Any]							     =		i == len(A    ) - 1

															UpperCAmelCase     :		Optional[int]							     =		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    )
															UpperCAmelCase     :		Tuple							     =		output_channel

								# out
								UpperCAmelCase     :		List[str]							     =		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,		32    )
								UpperCAmelCase     :		str							     =		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  _lowercase(  self		,		A		,		A		,		A = True		,		)			->			List[str]:
								UpperCAmelCase     :		Tuple							     =		timestep
								if not torch.is_tensor(A    ):
															UpperCAmelCase     :		Optional[int]							     =		torch.tensor([timesteps]		,		dtype=torch.long		,		device=sample.device    )
								elif torch.is_tensor(A    ) and len(timesteps.shape    ) == 0:
															UpperCAmelCase     :		Union[str, Any]							     =		timesteps[None].to(sample.device    )

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

								# 2. down
								UpperCAmelCase     :		int							     =		()
								for downsample_block in self.down_blocks:
															UpperCAmelCase     ,     UpperCAmelCase     :		Tuple							     =		downsample_block(hidden_states=A		,		temb=A    )
															down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
															UpperCAmelCase     :		Any							     =		self.mid_block(A		,		A    )

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

								# 5. post-process
								if self.out_block:
															UpperCAmelCase     :		Dict							     =		self.out_block(A		,		A    )

								if not return_dict:
															return (sample,)

								return UNetaDOutput(sample=A    )




import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return 1 / (1 + np.exp(-vector      ))





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return vector * sigmoid(UpperCamelCase__      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




