"""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_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						"DiagonalGaussianDistribution"

class    a_   (  __UpperCamelCase			,		__UpperCamelCase      ):
					UpperCamelCase_			:						Union[str, Any]      =     True




					@register_to_config
					def __init__(						self    :  int	,    snake_case__    :  int = 3	,    snake_case__    :  int = 3	,    snake_case__    :  Tuple[str] = ("DownEncoderBlock2D",)	,    snake_case__    :  Tuple[str] = ("UpDecoderBlock2D",)	,    snake_case__    :  Tuple[int] = (64,)	,    snake_case__    :  int = 1	,    snake_case__    :  str = "silu"	,    snake_case__    :  int = 4	,    snake_case__    :  int = 32	,    snake_case__    :  int = 32	,    snake_case__    :  float = 0.1_8215	,    ):
							super().__init__()

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

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

							lowerCAmelCase__   = nn.Convad(2 * latent_channels	,    2 * latent_channels	,    1  )
							lowerCAmelCase__   = nn.Convad(snake_case__	,    snake_case__	,    1  )

							lowerCAmelCase__   = False
							lowerCAmelCase__   = False

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  int	,    snake_case__    :  Tuple=False  ):
							if isinstance(snake_case__	,    (Encoder, Decoder)  ):
									lowerCAmelCase__   = value




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  bool = True  ):
							lowerCAmelCase__   = use_tiling




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							self.enable_tiling(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = True




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							lowerCAmelCase__   = False




					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = {}

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

									for sub_name, child in module.named_children():
											fn_recursive_add_processors(F"""{name}.{sub_name}"""	,    snake_case__	,    snake_case__  )

									return processors

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

							return processors




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]	,    snake_case__    :  Union[AttentionProcessor, Dict[str, AttentionProcessor]]  ):
							lowerCAmelCase__   = len(self.attn_processors.keys()  )

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

							def fn_recursive_attn_processor(snake_case__    :  str	,    snake_case__    :  torch.nn.Module	,    snake_case__    :  Union[str, Any]  ):
									if hasattr(snake_case__	,    """set_processor"""  ):
											if not isinstance(snake_case__	,    snake_case__  ):
													module.set_processor(snake_case__  )
											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}"""	,    snake_case__	,    snake_case__  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							self.set_attn_processor(AttnProcessor()  )




					@apply_forward_hook
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]	,    snake_case__    :  torch.FloatTensor	,    snake_case__    :  bool = True  ):
							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(snake_case__	,    return_dict=snake_case__  )

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

							lowerCAmelCase__   = self.quant_conv(snake_case__  )
							lowerCAmelCase__   = DiagonalGaussianDistribution(snake_case__  )

							if not return_dict:
									return (posterior,)

							return AutoencoderKLOutput(latent_dist=snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  torch.FloatTensor	,    snake_case__    :  bool = True  ):
							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(snake_case__	,    return_dict=snake_case__  )

							lowerCAmelCase__   = self.post_quant_conv(snake_case__  )
							lowerCAmelCase__   = self.decoder(snake_case__  )

							if not return_dict:
									return (dec,)

							return DecoderOutput(sample=snake_case__  )




					@apply_forward_hook
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any	,    snake_case__    :  torch.FloatTensor	,    snake_case__    :  bool = True  ):
							if self.use_slicing and z.shape[0] > 1:
									lowerCAmelCase__   = [self._decode(snake_case__  ).sample for z_slice in z.split(1  )]
									lowerCAmelCase__   = torch.cat(snake_case__  )
							else:
									lowerCAmelCase__   = self._decode(snake_case__  ).sample

							if not return_dict:
									return (decoded,)

							return DecoderOutput(sample=snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any	,    snake_case__    :  int	,    snake_case__    :  Optional[int]	,    snake_case__    :  str  ):
							lowerCAmelCase__   = min(a.shape[2]	,    b.shape[2]	,    snake_case__  )
							for y in range(snake_case__  ):
									lowerCAmelCase__   = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
							return b




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]	,    snake_case__    :  List[Any]	,    snake_case__    :  Tuple	,    snake_case__    :  Optional[int]  ):
							lowerCAmelCase__   = min(a.shape[3]	,    b.shape[3]	,    snake_case__  )
							for x in range(snake_case__  ):
									lowerCAmelCase__   = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
							return b




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  torch.FloatTensor	,    snake_case__    :  bool = True  ):
							lowerCAmelCase__   = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)  )
							lowerCAmelCase__   = int(self.tile_latent_min_size * self.tile_overlap_factor  )
							lowerCAmelCase__   = self.tile_latent_min_size - blend_extent

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

							lowerCAmelCase__   = torch.cat(snake_case__	,    dim=2  )
							lowerCAmelCase__   = DiagonalGaussianDistribution(snake_case__  )

							if not return_dict:
									return (posterior,)

							return AutoencoderKLOutput(latent_dist=snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  torch.FloatTensor	,    snake_case__    :  bool = True  ):
							lowerCAmelCase__   = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)  )
							lowerCAmelCase__   = int(self.tile_sample_min_size * self.tile_overlap_factor  )
							lowerCAmelCase__   = 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.
							lowerCAmelCase__   = []
							for i in range(0	,    z.shape[2]	,    snake_case__  ):
									lowerCAmelCase__   = []
									for j in range(0	,    z.shape[3]	,    snake_case__  ):
											lowerCAmelCase__   = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
											lowerCAmelCase__   = self.post_quant_conv(snake_case__  )
											lowerCAmelCase__   = self.decoder(snake_case__  )
											row.append(snake_case__  )
									rows.append(snake_case__  )
							lowerCAmelCase__   = []
							for i, row in enumerate(snake_case__  ):
									lowerCAmelCase__   = []
									for j, tile in enumerate(snake_case__  ):
											# blend the above tile and the left tile
											# to the current tile and add the current tile to the result row
											if i > 0:
													lowerCAmelCase__   = self.blend_v(rows[i - 1][j]	,    snake_case__	,    snake_case__  )
											if j > 0:
													lowerCAmelCase__   = self.blend_h(row[j - 1]	,    snake_case__	,    snake_case__  )
											result_row.append(tile[:, :, :row_limit, :row_limit]  )
									result_rows.append(torch.cat(snake_case__	,    dim=3  )  )

							lowerCAmelCase__   = torch.cat(snake_case__	,    dim=2  )
							if not return_dict:
									return (dec,)

							return DecoderOutput(sample=snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  torch.FloatTensor	,    snake_case__    :  bool = False	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[torch.Generator] = None	,    ):
							lowerCAmelCase__   = sample
							lowerCAmelCase__   = self.encode(snake_case__  ).latent_dist
							if sample_posterior:
									lowerCAmelCase__   = posterior.sample(generator=snake_case__  )
							else:
									lowerCAmelCase__   = posterior.mode()
							lowerCAmelCase__   = self.decode(snake_case__  ).sample

							if not return_dict:
									return (dec,)

							return DecoderOutput(sample=snake_case__  )





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		if len(lowerCamelCase__  ) <= 1:
				return lst

		lowerCAmelCase__   = 1

		while i < len(lowerCamelCase__  ):
				if lst[i - 1] <= lst[i]:
						i += 1
				else:
						lowerCAmelCase__ ,       lowerCAmelCase__   = lst[i], lst[i - 1]
						i -= 1
						if i == 0:
								lowerCAmelCase__   = 1

		return lst


if __name__ == "__main__":
			__lowerCAmelCase     :				List[Any]    				=    input("Enter numbers separated by a comma:\n").strip()
			__lowerCAmelCase     :				List[str]    				=    [int(item) for item in user_input.split(",")]
			print(gnome_sort(unsorted))




