





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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  torch.FloatTensor





class 						_A(					snake_case__				, snake_case__   ):


				"""simple docstring"""


				@register_to_config
				def __init__(			self     ,					_A = 3     ,					_A = 3     ,					_A = ("DownEncoderBlock2D",)     ,					_A = ("UpDecoderBlock2D",)     ,					_A = (64,)     ,					_A = 1     ,					_A = "silu"     ,					_A = 3     ,					_A = 32     ,					_A = 256     ,					_A = 32     ,					_A = None     ,					_A = 0.1_8_2_1_5     ,					_A = "group"     ,					):
									super().__init__()

									# pass init params to Encoder
									__A	:						Optional[Any]       =				Encoder(
									    in_channels=_A     ,					out_channels=_A     ,					down_block_types=_A     ,					block_out_channels=_A     ,					layers_per_block=_A     ,					act_fn=_A     ,					norm_num_groups=_A     ,					double_z=_A     ,					)

									__A	:						List[Any]       =				vq_embed_dim if vq_embed_dim is not None else latent_channels

									__A	:						Union[str, Any]       =				nn.Convad(_A     ,					_A     ,					1     )
									__A	:						Optional[Any]       =				VectorQuantizer(_A     ,					_A     ,					beta=0.2_5     ,					remap=_A     ,					sane_index_shape=_A     )
									__A	:						int       =				nn.Convad(_A     ,					_A     ,					1     )

									# pass init params to Decoder
									__A	:						Union[str, Any]       =				Decoder(
									    in_channels=_A     ,					out_channels=_A     ,					up_block_types=_A     ,					block_out_channels=_A     ,					layers_per_block=_A     ,					act_fn=_A     ,					norm_num_groups=_A     ,					norm_type=_A     ,					)



				@apply_forward_hook
				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = True     ):
									__A	:						List[str]       =				self.encoder(_A     )
									__A	:						Tuple       =				self.quant_conv(_A     )

									if not return_dict:
														return (h,)

									return VQEncoderOutput(latents=_A     )



				@apply_forward_hook
				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = False     ,					_A = True     ):
									# also go through quantization layer
									if not force_not_quantize:
														__A    ,				__A    ,				__A	:						Dict       =				self.quantize(_A     )
									else:
														__A	:						List[str]       =				h
									__A	:						int       =				self.post_quant_conv(_A     )
									__A	:						Optional[Any]       =				self.decoder(_A     ,					quant if self.config.norm_type == 'spatial' else None     )

									if not return_dict:
														return (dec,)

									return DecoderOutput(sample=_A     )





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = True     ):
									__A	:						Tuple       =				sample
									__A	:						List[str]       =				self.encode(_A     ).latents
									__A	:						Tuple       =				self.decode(_A     ).sample

									if not return_dict:
														return (dec,)

									return DecoderOutput(sample=_A     )











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Tuple:
					__A    ,				__A	:						Optional[Any]       =				[], []
					while len(a  ) > 1:
										__A    ,				__A	:						Any       =				min(a  ), max(a  )
										start.append(a  )
										end.append(a  )
										collection.remove(a  )
										collection.remove(a  )
					end.reverse()
					return start + collection + end


if __name__ == "__main__":
				UpperCAmelCase						:   int			  =     input('''Enter numbers separated by a comma:\n''').strip()
				UpperCAmelCase						:   Dict			  =     [int(item) for item in user_input.split(''',''')]
				print(*merge_sort(unsorted), sep=''',''')




