




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	(	__magic_name__ ):
							UpperCamelCase__					     =  42







class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__ ):
							@register_to_config
							def __init__(						self						:Union[str, Any]		,      __magic_name__						:int = 6_5536		,      __magic_name__						:Optional[int] = None		,      __magic_name__						:int = 2		,      __magic_name__						:int = 2		,      __magic_name__						:int = 0		,      __magic_name__						:str = "fourier"		,      __magic_name__						:bool = True		,      __magic_name__						:bool = False		,      __magic_name__						:float = 0.0		,      __magic_name__						:Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,      __magic_name__						:Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,      __magic_name__						:Tuple[str] = "UNetMidBlock1D"		,      __magic_name__						:str = None		,      __magic_name__						:Tuple[int] = (32, 32, 64)		,      __magic_name__						:str = None		,      __magic_name__						:int = 8		,      __magic_name__						:int = 1		,      __magic_name__						:bool = False		,      ):







								'''simple docstring'''







								super().__init__()
								a										=							sample_size

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

								if use_timestep_embedding:
									a										=							block_out_channels[0] * 4
									a										=							TimestepEmbedding(
									    in_channels=__magic_name__		,      time_embed_dim=__magic_name__		,      act_fn=__magic_name__		,      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(__magic_name__				):
									a										=							output_channel
									a										=							block_out_channels[i]

									if i == 0:
										input_channel += extra_in_channels

									a										=							i == len(__magic_name__				) - 1

									a										=							get_down_block(
									    __magic_name__		,      num_layers=__magic_name__		,      in_channels=__magic_name__		,      out_channels=__magic_name__		,      temb_channels=block_out_channels[0]		,      add_downsample=not is_final_block or downsample_each_block		,      )
									self.down_blocks.append(__magic_name__				)

								# mid
								a										=							get_mid_block(
								    __magic_name__		,      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=__magic_name__		,      add_downsample=__magic_name__		,      )

								# up
								a										=							list(reversed(__magic_name__				)				)
								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(__magic_name__				):
									a										=							output_channel
									a										=							(
									    reversed_block_out_channels[i + 1] if i < len(__magic_name__				) - 1 else final_upsample_channels
									)

									a										=							i == len(__magic_name__				) - 1

									a										=							get_up_block(
									    __magic_name__		,      num_layers=__magic_name__		,      in_channels=__magic_name__		,      out_channels=__magic_name__		,      temb_channels=block_out_channels[0]		,      add_upsample=not is_final_block		,      )
									self.up_blocks.append(__magic_name__				)
									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=__magic_name__		,      num_groups_out=__magic_name__		,      embed_dim=block_out_channels[0]		,      out_channels=__magic_name__		,      act_fn=__magic_name__		,      fc_dim=block_out_channels[-1] // 4		,      )





							def       lowerCamelCase__							(						self						:Tuple		,      __magic_name__						:torch.FloatTensor		,      __magic_name__						:Union[torch.Tensor, float, int]		,      __magic_name__						:bool = True		,      ):







								'''simple docstring'''







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

								a										=							self.time_proj(__magic_name__				)
								if self.config.use_timestep_embedding:
									a										=							self.time_mlp(__magic_name__				)
								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=__magic_name__		,      temb=__magic_name__				)
									down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
									a										=							self.mid_block(__magic_name__		,      __magic_name__				)

								# 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(__magic_name__		,      res_hidden_states_tuple=__magic_name__		,      temb=__magic_name__				)

								# 5. post-process
								if self.out_block:
									a										=							self.out_block(__magic_name__		,      __magic_name__				)

								if not return_dict:
									return (sample,)

								return UNetaDOutput(sample=__magic_name__				)







def 		__A		(	__lowerCamelCase    )      ->       int:
	a										=							hex_num.strip()
	if not hex_num:
		raise ValueError("""No value was passed to the function"""    )

	a										=							hex_num[0] == """-"""
	if is_negative:
		a										=							hex_num[1:]

	try:
		a										=							int(__lowerCamelCase							,			16    )
	except ValueError:
		raise ValueError("""Invalid value was passed to the function"""    )

	a										=							""""""
	while int_num > 0:
		a										=							str(int_num % 2    ) + bin_str
		int_num >>= 1

	return int(("""-""" + bin_str) if is_negative else bin_str    )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

