


"""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__):





							'''simple docstring'''

							__lowercase:		torch.FloatTensor





class      __A     (snake_case__						,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(				self							:      Tuple		,       UpperCAmelCase_							:      int = 65_536		,       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] = (32, 32, 64)		,       UpperCAmelCase_							:      str = None		,       UpperCAmelCase_							:      int = 8		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      bool = False		,       )					->Dict:



														"""simple docstring"""



														super().__init__()
														snake_case_           =		sample_size

														# time
														if time_embedding_type == "fourier":
																					snake_case_           =		GaussianFourierProjection(
																					    embedding_size=8		,       set_W_to_weight=UpperCAmelCase_		,       log=UpperCAmelCase_		,       flip_sin_to_cos=UpperCAmelCase_       )
																					snake_case_           =		2 * block_out_channels[0]
														elif time_embedding_type == "positional":
																					snake_case_           =		Timesteps(
																					    block_out_channels[0]		,       flip_sin_to_cos=UpperCAmelCase_		,       downscale_freq_shift=UpperCAmelCase_       )
																					snake_case_           =		block_out_channels[0]

														if use_timestep_embedding:
																					snake_case_           =		block_out_channels[0] * 4
																					snake_case_           =		TimestepEmbedding(
																					    in_channels=UpperCAmelCase_		,       time_embed_dim=UpperCAmelCase_		,       act_fn=UpperCAmelCase_		,       out_dim=block_out_channels[0]		,       )

														snake_case_           =		nn.ModuleList([]       )
														snake_case_           =		None
														snake_case_           =		nn.ModuleList([]       )
														snake_case_           =		None

														# down
														snake_case_           =		in_channels
														for i, down_block_type in enumerate(UpperCAmelCase_       ):
																					snake_case_           =		output_channel
																					snake_case_           =		block_out_channels[i]

																					if i == 0:
																												input_channel += extra_in_channels

																					snake_case_           =		i == len(UpperCAmelCase_       ) - 1

																					snake_case_           =		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
														snake_case_           =		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
														snake_case_           =		list(reversed(UpperCAmelCase_       )       )
														snake_case_           =		reversed_block_out_channels[0]
														if out_block_type is None:
																					snake_case_           =		out_channels
														else:
																					snake_case_           =		block_out_channels[0]

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

																					snake_case_           =		i == len(UpperCAmelCase_       ) - 1

																					snake_case_           =		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_       )
																					snake_case_           =		output_channel

														# out
														snake_case_           =		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,       32       )
														snake_case_           =		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 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      torch.FloatTensor		,       UpperCAmelCase_							:      Union[torch.Tensor, float, int]		,       UpperCAmelCase_							:      bool = True		,       )					->Union[UNetaDOutput, Tuple]:



														"""simple docstring"""



														snake_case_           =		timestep
														if not torch.is_tensor(UpperCAmelCase_       ):
																					snake_case_           =		torch.tensor([timesteps]		,       dtype=torch.long		,       device=sample.device       )
														elif torch.is_tensor(UpperCAmelCase_       ) and len(timesteps.shape       ) == 0:
																					snake_case_           =		timesteps[None].to(sample.device       )

														snake_case_           =		self.time_proj(UpperCAmelCase_       )
														if self.config.use_timestep_embedding:
																					snake_case_           =		self.time_mlp(UpperCAmelCase_       )
														else:
																					snake_case_           =		timestep_embed[..., None]
																					snake_case_           =		timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
																					snake_case_           =		timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

														# 2. down
														snake_case_           =		()
														for downsample_block in self.down_blocks:
																					snake_case_       ,						snake_case_           =		downsample_block(hidden_states=UpperCAmelCase_		,       temb=UpperCAmelCase_       )
																					down_block_res_samples += res_samples

														# 3. mid
														if self.mid_block:
																					snake_case_           =		self.mid_block(UpperCAmelCase_		,       UpperCAmelCase_       )

														# 4. up
														for i, upsample_block in enumerate(self.up_blocks       ):
																					snake_case_           =		down_block_res_samples[-1:]
																					snake_case_           =		down_block_res_samples[:-1]
																					snake_case_           =		upsample_block(UpperCAmelCase_		,       res_hidden_states_tuple=UpperCAmelCase_		,       temb=UpperCAmelCase_       )

														# 5. post-process
														if self.out_block:
																					snake_case_           =		self.out_block(UpperCAmelCase_		,       UpperCAmelCase_       )

														if not return_dict:
																					return (sample,)

														return UNetaDOutput(sample=UpperCAmelCase_       )







"""simple docstring"""
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				int:
							if index == number_of_items:
														return 0
							snake_case_           =		0
							snake_case_           =		0
							snake_case_           =		knapsack(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, index + 1				)
							if weights[index] <= max_weight:
														snake_case_           =		values[index] + knapsack(
														    _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, max_weight - weights[index]				, index + 1				)
							return max(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



