






"""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						(    _A       ):
						SCREAMING_SNAKE_CASE_:					torch.FloatTensor







class _lowerCAmelCase						(    _A   ,  _A       ):







						@register_to_config
						def __init__(    self   ,				lowerCAmelCase_ = 6_5_5_3_6   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = "fourier"   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = 0.0   ,				lowerCAmelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")   ,				lowerCAmelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")   ,				lowerCAmelCase_ = "UNetMidBlock1D"   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = (3_2, 3_2, 6_4)   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 8   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = False   ,				)		->							Optional[int]:
										super().__init__()
										_SCREAMING_SNAKE_CASE   :		Dict       		=    sample_size

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

										if use_timestep_embedding:
														_SCREAMING_SNAKE_CASE   :		int       		=    block_out_channels[0] * 4
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    TimestepEmbedding(
														    in_channels=__lowerCAmelCase   ,				time_embed_dim=__lowerCAmelCase   ,				act_fn=__lowerCAmelCase   ,				out_dim=block_out_channels[0]   ,				)

										_SCREAMING_SNAKE_CASE   :		Any       		=    nn.ModuleList([]							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    None
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    nn.ModuleList([]							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    None

										# down
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    in_channels
										for i, down_block_type in enumerate(__lowerCAmelCase							):
														_SCREAMING_SNAKE_CASE   :		str       		=    output_channel
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    block_out_channels[i]

														if i == 0:
																		input_channel += extra_in_channels

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    i == len(__lowerCAmelCase							) - 1

														_SCREAMING_SNAKE_CASE   :		Dict       		=    get_down_block(
														    __lowerCAmelCase   ,				num_layers=__lowerCAmelCase   ,				in_channels=__lowerCAmelCase   ,				out_channels=__lowerCAmelCase   ,				temb_channels=block_out_channels[0]   ,				add_downsample=not is_final_block or downsample_each_block   ,				)
														self.down_blocks.append(__lowerCAmelCase							)

										# mid
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    get_mid_block(
										    __lowerCAmelCase   ,				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=__lowerCAmelCase   ,				add_downsample=__lowerCAmelCase   ,				)

										# up
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    list(reversed(__lowerCAmelCase							)							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    reversed_block_out_channels[0]
										if out_block_type is None:
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    out_channels
										else:
														_SCREAMING_SNAKE_CASE   :		Any       		=    block_out_channels[0]

										for i, up_block_type in enumerate(__lowerCAmelCase							):
														_SCREAMING_SNAKE_CASE   :		int       		=    output_channel
														_SCREAMING_SNAKE_CASE   :		str       		=    (
														    reversed_block_out_channels[i + 1] if i < len(__lowerCAmelCase							) - 1 else final_upsample_channels
														)

														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    i == len(__lowerCAmelCase							) - 1

														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    get_up_block(
														    __lowerCAmelCase   ,				num_layers=__lowerCAmelCase   ,				in_channels=__lowerCAmelCase   ,				out_channels=__lowerCAmelCase   ,				temb_channels=block_out_channels[0]   ,				add_upsample=not is_final_block   ,				)
														self.up_blocks.append(__lowerCAmelCase							)
														_SCREAMING_SNAKE_CASE   :		str       		=    output_channel

										# out
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,				3_2							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    get_out_block(
										    out_block_type=__lowerCAmelCase   ,				num_groups_out=__lowerCAmelCase   ,				embed_dim=block_out_channels[0]   ,				out_channels=__lowerCAmelCase   ,				act_fn=__lowerCAmelCase   ,				fc_dim=block_out_channels[-1] // 4   ,				)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = True   ,				)		->							Union[UNetaDOutput, Tuple]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    timestep
										if not torch.is_tensor(__lowerCAmelCase							):
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.tensor([timesteps]   ,				dtype=torch.long   ,				device=sample.device							)
										elif torch.is_tensor(__lowerCAmelCase							) and len(timesteps.shape							) == 0:
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    timesteps[None].to(sample.device							)

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

										# 2. down
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ()
										for downsample_block in self.down_blocks:
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    downsample_block(hidden_states=__lowerCAmelCase   ,				temb=__lowerCAmelCase							)
														down_block_res_samples += res_samples

										# 3. mid
										if self.mid_block:
														_SCREAMING_SNAKE_CASE   :		Any       		=    self.mid_block(__lowerCAmelCase   ,				__lowerCAmelCase							)

										# 4. up
										for i, upsample_block in enumerate(self.up_blocks							):
														_SCREAMING_SNAKE_CASE   :		Dict       		=    down_block_res_samples[-1:]
														_SCREAMING_SNAKE_CASE   :		int       		=    down_block_res_samples[:-1]
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    upsample_block(__lowerCAmelCase   ,				res_hidden_states_tuple=__lowerCAmelCase   ,				temb=__lowerCAmelCase							)

										# 5. post-process
										if self.out_block:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.out_block(__lowerCAmelCase   ,				__lowerCAmelCase							)

										if not return_dict:
														return (sample,)

										return UNetaDOutput(sample=__lowerCAmelCase							)






UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     list:
   if not sequence:
      return []
   if len(_snake_case				) == 1:
      return list(_snake_case				)
   _A      =       0
   _A      =       len(_snake_case				) - 1
   _A      =       0
   while mid <= high:
      if sequence[mid] == colors[0]:
         _A  ,    _A      =       sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         _A  ,    _A      =       sequence[high], sequence[mid]
         high -= 1
      else:
         _A      =       F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(_snake_case				)
   return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_		 =					input("""Enter numbers separated by commas:\n""").strip()
   UpperCAmelCase_		 =					[int(item.strip()) for item in user_input.split(""",""")]
   print(f'{dutch_national_flag_sort(unsorted)}')



