






"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:list   ):

						'''simple docstring'''






						if len(_lowerCamelCase   ) <= 1:
												return [tuple(_lowerCamelCase   )]

						__magic_name__            =     []

						def generate(__lowerCamelCase:int ,						__lowerCamelCase:list   ):
												__magic_name__            =     [0] * n
												res.append(tuple(_lowerCamelCase   )   )

												__magic_name__            =     0
												while i < n:
																		if c[i] < i:
																								if i % 2 == 0:
																														__magic_name__      ,    __magic_name__            =     arr[i], arr[0]
																								else:
																														__magic_name__      ,    __magic_name__            =     arr[i], arr[c[i]]
																								res.append(tuple(_lowerCamelCase   )   )
																								c[i] += 1
																								__magic_name__            =     0
																		else:
																								__magic_name__            =     0
																								i += 1

						generate(len(_lowerCamelCase   ) ,						_lowerCamelCase   )
						return res


if __name__ == "__main__":
			lowercase    			=       input('''Enter numbers separated by a comma:\n''').strip()
			lowercase    			=       [int(item) for item in user_input.split(''',''')]
			print(heaps(arr))











"""simple docstring"""

from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder

@dataclass
class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				42





class 						A_							(      snake_case_					,							snake_case_  ):
				UpperCAmelCase__        =				True







				@register_to_config
				def __init__(							self							:						Any						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						Tuple[str] = ("DownEncoderBlock2D",)						,    __lowerCamelCase							:						Tuple[str] = ("UpDecoderBlock2D",)						,    __lowerCamelCase							:						Tuple[int] = (6_4,)						,    __lowerCamelCase							:						int = 1						,    __lowerCamelCase							:						str = "silu"						,    __lowerCamelCase							:						int = 4						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						float = 0.1_8215						,    )				->					Any:
										super().__init__()

										# pass init params to Encoder
										__magic_name__            =     Encoder(
										    in_channels=__lowerCamelCase						,    out_channels=__lowerCamelCase						,    down_block_types=__lowerCamelCase						,    block_out_channels=__lowerCamelCase						,    layers_per_block=__lowerCamelCase						,    act_fn=__lowerCamelCase						,    norm_num_groups=__lowerCamelCase						,    double_z=__lowerCamelCase						,    )

										# pass init params to Decoder
										__magic_name__            =     Decoder(
										    in_channels=__lowerCamelCase						,    out_channels=__lowerCamelCase						,    up_block_types=__lowerCamelCase						,    block_out_channels=__lowerCamelCase						,    layers_per_block=__lowerCamelCase						,    norm_num_groups=__lowerCamelCase						,    act_fn=__lowerCamelCase						,    )

										__magic_name__            =     nn.Convad(2 * latent_channels						,    2 * latent_channels						,    1	)
										__magic_name__            =     nn.Convad(__lowerCamelCase						,    __lowerCamelCase						,    1	)

										__magic_name__            =     False
										__magic_name__            =     False

										# only relevant if vae tiling is enabled
										__magic_name__            =     self.config.sample_size
										__magic_name__            =     (
										    self.config.sample_size[0]
										    if isinstance(self.config.sample_size						,    (list, tuple)	)
										    else self.config.sample_size
										)
										__magic_name__            =     int(sample_size / (2 ** (len(self.config.block_out_channels	) - 1))	)
										__magic_name__            =     0.25







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict=False	)				->					Optional[int]:
										if isinstance(__lowerCamelCase						,    (Encoder, Decoder)	):
																__magic_name__            =     value







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						bool = True	)				->					int:
										__magic_name__            =     use_tiling







				def     _snake_case					(							self							:						Dict	)				->					Optional[int]:
										self.enable_tiling(__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					str:
										__magic_name__            =     True







				def     _snake_case					(							self							:						Optional[Any]	)				->					Tuple:
										__magic_name__            =     False







				@property
				# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
				def     _snake_case					(							self							:						Optional[int]	)				->					Dict[str, AttentionProcessor]:
										__magic_name__            =     {}

										def fn_recursive_add_processors(__lowerCamelCase							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						Dict[str, AttentionProcessor]	):
																if hasattr(__lowerCamelCase						,    "set_processor"	):
																						__magic_name__            =     module.processor

																for sub_name, child in module.named_children():
																						fn_recursive_add_processors(f'''{name}.{sub_name}'''						,    __lowerCamelCase						,    __lowerCamelCase	)

																return processors

										for name, module in self.named_children():
																fn_recursive_add_processors(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)

										return processors







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Union[AttentionProcessor, Dict[str, AttentionProcessor]]	)				->					int:
										__magic_name__            =     len(self.attn_processors.keys()	)

										if isinstance(__lowerCamelCase						,    __lowerCamelCase	) and len(__lowerCamelCase	) != count:
																raise ValueError(
																    f'''A dict of processors was passed, but the number of processors {len(__lowerCamelCase	)} does not match the'''
																    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''	)

										def fn_recursive_attn_processor(__lowerCamelCase							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						List[str]	):
																if hasattr(__lowerCamelCase						,    "set_processor"	):
																						if not isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																												module.set_processor(__lowerCamelCase	)
																						else:
																												module.set_processor(processor.pop(f'''{name}.processor'''	)	)

																for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(f'''{name}.{sub_name}'''						,    __lowerCamelCase						,    __lowerCamelCase	)

										for name, module in self.named_children():
																fn_recursive_attn_processor(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										self.set_attn_processor(AttnProcessor()	)







				@apply_forward_hook
				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					AutoencoderKLOutput:
										if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
																return self.tiled_encode(__lowerCamelCase						,    return_dict=__lowerCamelCase	)

										if self.use_slicing and x.shape[0] > 1:
																__magic_name__            =     [self.encoder(__lowerCamelCase	) for x_slice in x.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self.encoder(__lowerCamelCase	)

										__magic_name__            =     self.quant_conv(__lowerCamelCase	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
																return self.tiled_decode(__lowerCamelCase						,    return_dict=__lowerCamelCase	)

										__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
										__magic_name__            =     self.decoder(__lowerCamelCase	)

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				@apply_forward_hook
				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										if self.use_slicing and z.shape[0] > 1:
																__magic_name__            =     [self._decode(__lowerCamelCase	).sample for z_slice in z.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self._decode(__lowerCamelCase	).sample

										if not return_dict:
																return (decoded,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Dict	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[2]						,    b.shape[2]						,    __lowerCamelCase	)
										for y in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
										return b







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Tuple	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[3]						,    b.shape[3]						,    __lowerCamelCase	)
										for x in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
										return b







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					AutoencoderKLOutput:
										__magic_name__            =     int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_latent_min_size * self.tile_overlap_factor	)
										__magic_name__            =     self.tile_latent_min_size - blend_extent

										# Split the image into 512x512 tiles and encode them separately.
										__magic_name__            =     []
										for i in range(0						,    x.shape[2]						,    __lowerCamelCase	):
																__magic_name__            =     []
																for j in range(0						,    x.shape[3]						,    __lowerCamelCase	):
																						__magic_name__            =     x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																						__magic_name__            =     self.encoder(__lowerCamelCase	)
																						__magic_name__            =     self.quant_conv(__lowerCamelCase	)
																						row.append(__lowerCamelCase	)
																rows.append(__lowerCamelCase	)
										__magic_name__            =     []
										for i, row in enumerate(__lowerCamelCase	):
																__magic_name__            =     []
																for j, tile in enumerate(__lowerCamelCase	):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																												__magic_name__            =     self.blend_v(rows[i - 1][j]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						if j > 0:
																												__magic_name__            =     self.blend_h(row[j - 1]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						result_row.append(tile[:, :, :row_limit, :row_limit]	)
																result_rows.append(torch.cat(__lowerCamelCase						,    dim=3	)	)

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_sample_min_size * self.tile_overlap_factor	)
										__magic_name__            =     self.tile_sample_min_size - blend_extent

										# Split z into overlapping 64x64 tiles and decode them separately.
										# The tiles have an overlap to avoid seams between tiles.
										__magic_name__            =     []
										for i in range(0						,    z.shape[2]						,    __lowerCamelCase	):
																__magic_name__            =     []
																for j in range(0						,    z.shape[3]						,    __lowerCamelCase	):
																						__magic_name__            =     z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																						__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
																						__magic_name__            =     self.decoder(__lowerCamelCase	)
																						row.append(__lowerCamelCase	)
																rows.append(__lowerCamelCase	)
										__magic_name__            =     []
										for i, row in enumerate(__lowerCamelCase	):
																__magic_name__            =     []
																for j, tile in enumerate(__lowerCamelCase	):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																												__magic_name__            =     self.blend_v(rows[i - 1][j]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						if j > 0:
																												__magic_name__            =     self.blend_h(row[j - 1]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						result_row.append(tile[:, :, :row_limit, :row_limit]	)
																result_rows.append(torch.cat(__lowerCamelCase						,    dim=3	)	)

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[torch.Generator] = None						,    )				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     sample
										__magic_name__            =     self.encode(__lowerCamelCase	).latent_dist
										if sample_posterior:
																__magic_name__            =     posterior.sample(generator=__lowerCamelCase	)
										else:
																__magic_name__            =     posterior.mode()
										__magic_name__            =     self.decode(__lowerCamelCase	).sample

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)



