
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 UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     42



class     _SCREAMING_SNAKE_CASE							(		snake_case,    snake_case							):





				@register_to_config
				def __init__(		self :		str					,      snake_case_ :		Optional[Union[int, Tuple[int, int]]] = None					,      snake_case_ :		int = 3					,      snake_case_ :		int = 3					,      snake_case_ :		bool = False					,      snake_case_ :		str = "positional"					,      snake_case_ :		int = 0					,      snake_case_ :		bool = True					,      snake_case_ :		Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")					,      snake_case_ :		Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")					,      snake_case_ :		Tuple[int] = (224, 448, 672, 896)					,      snake_case_ :		int = 2					,      snake_case_ :		float = 1					,      snake_case_ :		int = 1					,      snake_case_ :		str = "conv"					,      snake_case_ :		str = "conv"					,      snake_case_ :		str = "silu"					,      snake_case_ :		Optional[int] = 8					,      snake_case_ :		int = 32					,      snake_case_ :		float = 1E-5					,      snake_case_ :		str = "default"					,      snake_case_ :		bool = True					,      snake_case_ :		Optional[str] = None					,      snake_case_ :		Optional[int] = None					,      ):


								"""simple docstring"""

								super().__init__()

								A     :  Dict   =				sample_size
								A     :  List[Any]   =				block_out_channels[0] * 4

								# Check inputs
								if len(snake_case_							) != len(snake_case_							):
												raise ValueError(
												    f"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""							)

								if len(snake_case_							) != len(snake_case_							):
												raise ValueError(
												    f"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""							)

								# input
								A     :  int   =				nn.Convad(snake_case_					,      block_out_channels[0]					,      kernel_size=3					,      padding=(1, 1)							)

								# time
								if time_embedding_type == "fourier":
												A     :  Optional[int]   =				GaussianFourierProjection(embedding_size=block_out_channels[0]					,      scale=16							)
												A     :  List[Any]   =				2 * block_out_channels[0]
								elif time_embedding_type == "positional":
												A     :  List[Any]   =				Timesteps(block_out_channels[0]					,      snake_case_					,      snake_case_							)
												A     :  Tuple   =				block_out_channels[0]

								A     :  Dict   =				TimestepEmbedding(snake_case_					,      snake_case_							)

								# class embedding
								if class_embed_type is None and num_class_embeds is not None:
												A     :  Union[str, Any]   =				nn.Embedding(snake_case_					,      snake_case_							)
								elif class_embed_type == "timestep":
												A     :  Any   =				TimestepEmbedding(snake_case_					,      snake_case_							)
								elif class_embed_type == "identity":
												A     :  Optional[Any]   =				nn.Identity(snake_case_					,      snake_case_							)
								else:
												A     :  Union[str, Any]   =				None

								A     :  Dict   =				nn.ModuleList([]							)
								A     :  Optional[Any]   =				None
								A     :  Optional[int]   =				nn.ModuleList([]							)

								# down
								A     :  Tuple   =				block_out_channels[0]
								for i, down_block_type in enumerate(snake_case_							):
												A     :  Union[str, Any]   =				output_channel
												A     :  Optional[Any]   =				block_out_channels[i]
												A     :  Optional[int]   =				i == len(snake_case_							) - 1

												A     :  Any   =				get_down_block(
												    snake_case_					,      num_layers=snake_case_					,      in_channels=snake_case_					,      out_channels=snake_case_					,      temb_channels=snake_case_					,      add_downsample=not is_final_block					,      resnet_eps=snake_case_					,      resnet_act_fn=snake_case_					,      resnet_groups=snake_case_					,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel					,      downsample_padding=snake_case_					,      resnet_time_scale_shift=snake_case_					,      downsample_type=snake_case_					,      )
												self.down_blocks.append(snake_case_							)

								# mid
								A     :  Tuple   =				UNetMidBlockaD(
								    in_channels=block_out_channels[-1]					,      temb_channels=snake_case_					,      resnet_eps=snake_case_					,      resnet_act_fn=snake_case_					,      output_scale_factor=snake_case_					,      resnet_time_scale_shift=snake_case_					,      attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]					,      resnet_groups=snake_case_					,      add_attention=snake_case_					,      )

								# up
								A     :  Optional[Any]   =				list(reversed(snake_case_							)							)
								A     :  Dict   =				reversed_block_out_channels[0]
								for i, up_block_type in enumerate(snake_case_							):
												A     :  List[str]   =				output_channel
												A     :  List[str]   =				reversed_block_out_channels[i]
												A     :  Optional[int]   =				reversed_block_out_channels[min(i + 1					,      len(snake_case_							) - 1							)]

												A     :  Any   =				i == len(snake_case_							) - 1

												A     :  str   =				get_up_block(
												    snake_case_					,      num_layers=layers_per_block + 1					,      in_channels=snake_case_					,      out_channels=snake_case_					,      prev_output_channel=snake_case_					,      temb_channels=snake_case_					,      add_upsample=not is_final_block					,      resnet_eps=snake_case_					,      resnet_act_fn=snake_case_					,      resnet_groups=snake_case_					,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel					,      resnet_time_scale_shift=snake_case_					,      upsample_type=snake_case_					,      )
												self.up_blocks.append(snake_case_							)
												A     :  Union[str, Any]   =				output_channel

								# out
								A     :  Optional[int]   =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,      32							)
								A     :  List[Any]   =				nn.GroupNorm(num_channels=block_out_channels[0]					,      num_groups=snake_case_					,      eps=snake_case_							)
								A     :  Dict   =				nn.SiLU()
								A     :  Optional[int]   =				nn.Convad(block_out_channels[0]					,      snake_case_					,      kernel_size=3					,      padding=1							)






				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		Union[torch.Tensor, float, int]					,      snake_case_ :		Optional[torch.Tensor] = None					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								if self.config.center_input_sample:
												A     :  List[Any]   =				2 * sample - 1.0

								# 1. time
								A     :  Dict   =				timestep
								if not torch.is_tensor(snake_case_							):
												A     :  Union[str, Any]   =				torch.tensor([timesteps]					,      dtype=torch.long					,      device=sample.device							)
								elif torch.is_tensor(snake_case_							) and len(timesteps.shape							) == 0:
												A     :  Optional[Any]   =				timesteps[None].to(sample.device							)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								A     :  List[Any]   =				timesteps * torch.ones(sample.shape[0]					,      dtype=timesteps.dtype					,      device=timesteps.device							)

								A     :  str   =				self.time_proj(snake_case_							)

								# timesteps does not contain any weights and will always return f32 tensors
								# but time_embedding might actually be running in fp16. so we need to cast here.
								# there might be better ways to encapsulate this.
								A     :  Optional[Any]   =				t_emb.to(dtype=self.dtype							)
								A     :  List[Any]   =				self.time_embedding(snake_case_							)

								if self.class_embedding is not None:
												if class_labels is None:
																raise ValueError('''class_labels should be provided when doing class conditioning'''							)

												if self.config.class_embed_type == "timestep":
																A     :  Union[str, Any]   =				self.time_proj(snake_case_							)

												A     :  Dict   =				self.class_embedding(snake_case_							).to(dtype=self.dtype							)
												A     :  Optional[Any]   =				emb + class_emb

								# 2. pre-process
								A     :  Tuple   =				sample
								A     :  Union[str, Any]   =				self.conv_in(snake_case_							)

								# 3. down
								A     :  int   =				(sample,)
								for downsample_block in self.down_blocks:
												if hasattr(snake_case_					,      '''skip_conv'''							):
																A ,       A ,       A     :  Dict   =				downsample_block(
																    hidden_states=snake_case_					,      temb=snake_case_					,      skip_sample=snake_case_							)
												else:
																A ,       A     :  int   =				downsample_block(hidden_states=snake_case_					,      temb=snake_case_							)

												down_block_res_samples += res_samples

								# 4. mid
								A     :  List[Any]   =				self.mid_block(snake_case_					,      snake_case_							)

								# 5. up
								A     :  List[str]   =				None
								for upsample_block in self.up_blocks:
												A     :  List[Any]   =				down_block_res_samples[-len(upsample_block.resnets							) :]
												A     :  Dict   =				down_block_res_samples[: -len(upsample_block.resnets							)]

												if hasattr(snake_case_					,      '''skip_conv'''							):
																A ,       A     :  Dict   =				upsample_block(snake_case_					,      snake_case_					,      snake_case_					,      snake_case_							)
												else:
																A     :  Optional[Any]   =				upsample_block(snake_case_					,      snake_case_					,      snake_case_							)

        # 6. post-process
								A     :  Dict   =				self.conv_norm_out(snake_case_							)
								A     :  Any   =				self.conv_act(snake_case_							)
								A     :  Union[str, Any]   =				self.conv_out(snake_case_							)

								if skip_sample is not None:
												sample += skip_sample

								if self.config.time_embedding_type == "fourier":
												A     :  Optional[int]   =				timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]							)))							)
												A     :  Optional[int]   =				sample / timesteps

								if not return_dict:
												return (sample,)

								return UNetaDOutput(sample=snake_case_							)

def 							_lowerCamelCase			(					lowerCamelCase_:  list		):



				'''simple docstring'''







				A     :  Optional[int]   =				1.3
				A     :  List[str]   =				len(lowerCamelCase_		)
				A     :  Optional[Any]   =				False

				while not completed:
								# Update the gap value for a next comb
								A     :  str   =				int(gap / shrink_factor		)
								if gap <= 1:
												A     :  Tuple   =				True

								A     :  List[Any]   =				0
								while index + gap < len(lowerCamelCase_		):
												if data[index] > data[index + gap]:
																# Swap values
																A ,       A     :  int   =				data[index + gap], data[index]
																A     :  Dict   =				False
												index += 1

				return data


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					UpperCamelCase_  =	input("Enter numbers separated by a comma:\n").strip()
					UpperCamelCase_  =	[int(item) for item in user_input.split(",")]
					print(comb_sort(unsorted))