


"""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					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			torch.FloatTensor







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		@register_to_config
		def __init__(  self							, _a = 65_536							, _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							, ):
				super().__init__()
				__a           =			sample_size

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

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

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

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

						if i == 0:
								input_channel += extra_in_channels

						__a           =			i == len(_a      ) - 1

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

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

						__a           =			i == len(_a      ) - 1

						__a           =			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      )
						__a           =			output_channel

				# out
				__a           =			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4							, 32      )
				__a           =			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 			__UpperCAmelCase       (  self							, _a							, _a							, _a = True							, ):
				__a           =			timestep
				if not torch.is_tensor(_a      ):
						__a           =			torch.tensor([timesteps]							, dtype=torch.long							, device=sample.device      )
				elif torch.is_tensor(_a      ) and len(timesteps.shape      ) == 0:
						__a           =			timesteps[None].to(sample.device      )

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

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

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

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

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

				if not return_dict:
						return (sample,)

				return UNetaDOutput(sample=_a      )










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     int			) ->      str:
		if number > 0:
				raise ValueError('''input must be a negative integer'''			)
		__a           =			len(bin(lowerCAmelCase__			)[3:]			)
		__a           =			bin(abs(lowerCAmelCase__			) - (1 << binary_number_length)			)[3:]
		__a           =			(
		    (
		        '''1'''
		        + '''0''' * (binary_number_length - len(lowerCAmelCase__			))
		        + twos_complement_number
		    )
		    if number < 0
		    else '''0'''
		)
		return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()






